Add files using upload-large-folder tool

testbed/aws__sagemaker-python-sdk/.codecov.yml

@@ -0,0 +1,2 @@
+ignore:
+  - "**/tensorflow/tensorflow_serving/.*"

testbed/aws__sagemaker-python-sdk/.coveragerc

@@ -0,0 +1,4 @@
+[run]
+concurrency = threading
+omit = sagemaker/tests/*
+timid = True

testbed/aws__sagemaker-python-sdk/.dictionary

@@ -0,0 +1,38 @@
+args
+arn
+autoscaling
+aws
+bool
+boolean
+boto
+botocore
+clienterror
+cloudwatch
+cron
+config
+dataset
+datasets
+datetime
+desc
+docstring
+entrypoint
+env
+iam
+hyperparameter
+hyperparameters
+jupyter
+kms
+kwargs
+neo
+noqa
+rc
+runtime
+sagemaker
+stdout
+str
+subdirectories
+subnet
+subnets
+unexpectedstatusexception
+uri
+vpc

testbed/aws__sagemaker-python-sdk/.flake8

@@ -0,0 +1,5 @@
+[flake8]
+application_import_names = sagemaker, tests
+import-order-style = google
+per-file-ignores =
+    tests/unit/test_tuner.py: F405

testbed/aws__sagemaker-python-sdk/.gitignore

@@ -0,0 +1,34 @@
+.idea
+build
+src/*.egg-info
+.cache
+.coverage
+sagemaker_venv*
+*.egg-info
+.tox
+**/__pycache__
+**/.ipynb_checkpoints
+dist/
+**/tensorflow-examples.tar.gz
+**/*.pyc
+**.pyc
+scratch*.py
+.eggs
+*.egg
+examples/tensorflow/distributed_mnist/data
+*.iml
+doc/_build
+doc/_static
+doc/_templates
+**/.DS_Store
+venv/
+*~
+.pytest_cache/
+*.swp
+.docker/
+env/
+.vscode/
+**/tmp
+.python-version
+**/_repack_model.py
+**/_repack_script_launcher.sh

testbed/aws__sagemaker-python-sdk/.pydocstylerc

@@ -0,0 +1,4 @@
+[pydocstyle]
+inherit = false
+ignore = D104,D107,D202,D203,D213,D214,D400,D401,D404,D406,D407,D411,D413,D414,D415,D417
+match = (?!record_pb2).*\.py

testbed/aws__sagemaker-python-sdk/.pylintrc

@@ -0,0 +1,438 @@
+# This config based on a few of Google's pylint configurations:
+# * https://chromium.googlesource.com/chromium/tools/depot_tools.git/+/refs/heads/master/pylintrc
+# * https://github.com/google/seq2seq/blob/master/pylintrc
+# * https://github.com/google/pygtrie/blob/master/.pylintrc
+
+[MASTER]
+
+# Specify a configuration file.
+#rcfile=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Profiled execution.
+profile=no
+
+# Add files or directories to the blacklist. They should be base names, not
+# paths.
+ignore=CVS,tensorflow_serving
+
+# Add files or directories matching the regex patterns to the blacklist.
+# The regex matches against base names, not paths.
+# Regex patterns can be comma(and newline)-separated
+ignore-patterns=
+    .*_pb2.py, # Ignore all files generated by the protocol buffer compiler
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Use multiple processes to speed up Pylint.
+jobs=1
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code
+extension-pkg-whitelist=numpy
+
+# Allow optimization of some AST trees. This will activate a peephole AST
+# optimizer, which will apply various small optimizations. For instance, it can
+# be used to obtain the result of joining multiple strings with the addition
+# operator. Joining a lot of strings can lead to a maximum recursion error in
+# Pylint and this flag can prevent that. It has one side effect, the resulting
+# AST will be different than the one from reality. This option is deprecated
+# and it will be removed in Pylint 2.0.
+optimize-ast=no
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
+confidence=
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+#enable=
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once).You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use"--disable=all --enable=classes
+# --disable=W"
+disable=
+    C0330, # Black disagrees with and explicitly violates this: https://github.com/python/black/issues/48
+    abstract-method, # TODO: Fix abstract methods
+    arguments-differ,
+    cyclic-import, # TODO: Resolve cyclic imports
+    fixme,
+    invalid-name,
+    import-error, # Since we run Pylint before any of our builds in tox, this will always fail
+    import-outside-toplevel,
+    no-self-use, # TODO: Convert methods to functions where appropriate
+    protected-access, # TODO: Fix access
+    signature-differs,  # TODO: fix kwargs
+    too-many-arguments,
+    too-many-branches, # TODO: Simplify or ignore as appropriate
+    too-many-instance-attributes,
+    too-many-lines,
+    too-many-locals,
+    useless-object-inheritance, # TODO: Enable this check and fix code once Python 2 is no longer supported.
+    super-with-arguments,
+    raise-missing-from,
+
+[REPORTS]
+# Set the output format. Available formats are text, parseable, colorized, msvs
+# (visual studio) and html. You can also give a reporter class, eg
+# mypackage.mymodule.MyReporterClass.
+output-format=colorized
+
+# Put messages in a separate file for each module / package specified on the
+# command line instead of printing them on stdout. Reports (if any) will be
+# written in a file name "pylint_global.[txt|html]". This option is deprecated
+# and it will be removed in Pylint 2.0.
+files-output=no
+
+# Tells whether to display a full report or only the messages
+reports=no
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Add a comment according to your evaluation note. This is used by the global
+# evaluation report (RP0004).
+comment=no
+
+[BASIC]
+
+# Good variable names which should always be accepted, separated by a comma
+good-names=i,j,k,ex,Run,_
+
+# Bad variable names which should always be refused, separated by a comma
+bad-names=foo,bar,baz,toto,tutu,tata
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Include a hint for the correct naming format with invalid-name
+include-naming-hint=no
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties.
+property-classes=abc.abstractproperty
+
+# Regular expression matching correct variable names
+variable-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for variable names
+variable-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Required attributes for module, separated by a comma
+#required-attributes=
+
+# Regular expression matching correct class attribute names
+class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
+
+# Naming hint for class attribute names
+class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
+
+# Regular expression matching correct argument names
+argument-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for argument names
+argument-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct module names
+module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Naming hint for module names
+module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Regular expression matching correct constant names
+const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$
+
+# Naming hint for constant names
+const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$
+
+# Regular expression matching correct inline iteration names
+inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$
+
+# Naming hint for inline iteration names
+inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$
+
+# Regular expression matching correct method names
+method-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for method names
+method-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct function names
+function-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for function names
+function-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct attribute names
+attr-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for attribute names
+attr-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct class names
+class-rgx=[A-Z_][a-zA-Z0-9]+$
+
+# Naming hint for class names
+class-name-hint=[A-Z_][a-zA-Z0-9]+$
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=^test_
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+# List of builtins function names that should not be used, separated by a comma.
+# XXX: Should we ban map() & filter() for list comprehensions?
+# exit & quit are for the interactive interpreter shell only.
+# https://docs.python.org/3/library/constants.html#constants-added-by-the-site-module
+bad-functions=
+    apply,
+    exit,
+    input,
+    quit,
+
+[ELIF]
+
+# Maximum number of nested blocks for function / method body
+max-nested-blocks=5
+
+[FORMAT]
+# Maximum number of characters on a single line.
+max-line-length=100
+
+# Regexp for a line that is allowed to be longer than the limit. Can only be a single regex.
+# The following matches any semblance of a url of any sort.
+ignore-long-lines=^\s*.*(# )?(<?https?:\/\/)?[-a-zA-Z0-9@:%%._\+~#=><]{1,256}\.[a-zA-Z0-9()]{1,6}\b[-a-zA-Z0-9()@:%%_\+.~#?&\/\/=<>]*\S*>?$
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=n
+
+# List of optional constructs for which whitespace checking is disabled. `dict-
+# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
+# `trailing-comma` allows a space between comma and closing bracket: (a, ).
+# `empty-line` allows space-only lines.
+no-space-check=trailing-comma,dict-separator
+
+# Maximum number of lines in a module
+max-module-lines=1000
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Number of spaces of indent required inside a hanging  or continued line.
+indent-after-paren=4
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=LF
+
+[LOGGING]
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format
+logging-modules=logging
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,XXX,TODO
+
+[SIMILARITIES]
+
+# Minimum lines number of a similarity.
+min-similarity-lines=20
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+[SPELLING]
+
+# Spelling dictionary name. Available dictionaries: none. To make it working
+# install python-enchant package.
+spelling-dict=
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains private dictionary; one word per line.
+spelling-private-dict-file=.dictionary
+
+# Tells whether to store unknown words to indicated private dictionary in
+# --spelling-private-dict-file option instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[TYPECHECK]
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis. It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=distutils
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes=optparse.Values,thread._local,_thread._local,matplotlib.cm,tensorflow.python,tensorflow,tensorflow.train.Example,RunOptions,sagemaker.workflow.properties.Properties
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=set_shape,np.float32
+
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators=contextlib.contextmanager
+
+[VARIABLES]
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# A regular expression matching the name of dummy variables (i.e. expectedly
+# not used).
+dummy-variables-rgx=_|unused_
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid to define new builtins when possible.
+additional-builtins=
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,_cb
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules=six.moves,future.builtins
+
+
+[CLASSES]
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,__new__,setUp
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=mcs
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,_fields,_replace,_source,_make
+
+
+[DESIGN]
+
+# Maximum number of arguments for function / method
+max-args=10
+
+# Argument names that match this expression will be ignored. Default to name
+# with leading underscore
+ignored-argument-names=_.*
+
+# Maximum number of locals for function / method body
+max-locals=30
+
+# Maximum number of return / yield for function / method body
+max-returns=6
+
+# Maximum number of branch for function / method body
+max-branches=12
+
+# Maximum number of statements in function / method body
+max-statements=100
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=10
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=0
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+# Maximum number of boolean expressions in a if statement
+max-bool-expr=5
+
+
+[IMPORTS]
+
+# Deprecated modules which should not be used, separated by a comma
+deprecated-modules=optparse
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled)
+import-graph=
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled)
+ext-import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled)
+int-import-graph=
+
+# Force import order to recognize a module as part of the standard
+# compatibility libraries.
+known-standard-library=
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party=boto3,botocore,enchant
+
+# Analyse import fallback blocks. This can be used to support both Python 2 and
+# 3 compatible code, which means that the block might have code that exists
+# only in one or another interpreter, leading to false positives when analysed.
+analyse-fallback-blocks=no
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "Exception"
+overgeneral-exceptions=Exception

testbed/aws__sagemaker-python-sdk/.readthedocs.yaml

@@ -0,0 +1,22 @@
+# ReadTheDocs environment customization to allow us to use conda to install
+# libraries which have C dependencies for the doc build. See:
+# https://docs.readthedocs.io/en/latest/config-file/v2.html
+
+version: 2
+
+build:
+  os: ubuntu-20.04
+  tools:
+    python: "3.9"
+
+
+python:
+  install:
+    - method: pip
+      path: .
+    - requirements: doc/requirements.txt
+
+
+sphinx:
+  configuration: doc/conf.py
+  fail_on_warning: true # http://www.sphinx-doc.org/en/master/man/sphinx-build.html#id6

testbed/aws__sagemaker-python-sdk/CODE_OF_CONDUCT.md

@@ -0,0 +1,4 @@
+## Code of Conduct
+This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 
+For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 
+opensource-codeofconduct@amazon.com with any additional questions or comments.

testbed/aws__sagemaker-python-sdk/CONTRIBUTING.md

@@ -0,0 +1,264 @@
+# Contributing Guidelines
+
+Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
+documentation, we greatly value feedback and contributions from our community.
+
+Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
+information to effectively respond to your bug report or contribution.
+
+
+## Table of Contents
+
+* [Report Bugs/Feature Requests](#report-bugsfeature-requests)
+* [Contribute via Pull Requests (PRs)](#contribute-via-pull-requests-prs)
+  * [Set up Your Development Environment *[Optional, but Recommended]*](#set-up-your-development-environment-optional-but-recommended)
+  * [Pull Down the Code](#pull-down-the-code)
+  * [Run the Unit Tests](#run-the-unit-tests)
+  * [Run the Integration Tests](#run-the-integration-tests)
+  * [Make and Test Your Change](#make-and-test-your-change)
+  * [Commit Your Change](#commit-your-change)
+  * [Send a Pull Request](#send-a-pull-request)
+* [Documentation Guidelines](#documentation-guidelines)
+  * [Overviews](#overviews)
+  * [API References (docstrings)](#api-references-docstrings)
+  * [Build and Test Documentation](#build-and-test-documentation)
+* [Find Contributions to Work On](#find-contributions-to-work-on)
+* [Code of Conduct](#code-of-conduct)
+* [Security Issue Notifications](#security-issue-notifications)
+* [Licensing](#licensing)
+
+## Report Bugs/Feature Requests
+
+We welcome you to use the GitHub issue tracker to report bugs or suggest features.
+
+When filing an issue, please check [existing open](https://github.com/aws/sagemaker-python-sdk/issues) and [recently closed](https://github.com/aws/sagemaker-python-sdk/issues?utf8=%E2%9C%93&q=is%3Aissue%20is%3Aclosed%20) issues to make sure somebody else hasn't already
+reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:
+
+* A reproducible test case or series of steps.
+* The version of our code being used.
+* Any modifications you've made relevant to the bug.
+* A description of your environment or deployment.
+
+
+## Contribute via Pull Requests (PRs)
+
+Contributions via pull requests are much appreciated.
+
+Before sending us a pull request, please ensure that:
+
+* You are working against the latest source on the *master* branch.
+* You check the existing open and recently merged pull requests to make sure someone else hasn't already addressed the problem.
+* You open an issue to discuss any significant work - we would hate for your time to be wasted.
+
+
+### Set up Your Development Environment *[Optional, but Recommended]*
+
+1. Set up the Cloud9 environment:
+   1. Instance type: You'll need at least 4 GB of RAM to avoid running into memory issues. We recommend at least a t3.medium to run the unit tests. A larger host will reduce the chance of encountering resource limits.
+   1. Follow the instructions at [Creating a Cloud9 EC2 Environment](https://docs.aws.amazon.com/cloud9/latest/user-guide/create-environment.html#create-environment-main) to set up a Cloud9 EC2 environment.
+1. Expand the storage of the EC2 instance from 10GB to 20GB:
+   1. Because you'll need a minimum of 11GB of disk storage on the EC2 instance to run the repository's unit tests, you'll need to expand your EC2 volume size. We recommend at least 20GB. A larger volume will reduce the chance of encountering resource limits.
+   1. Follow the instructions at [Modifying an EBS Volume Using Elastic Volumes (Console)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/requesting-ebs-volume-modifications.html#modify-ebs-volume) to increase the EBS volume size associated with the newly created EC2 instance.
+   1. Wait 5-10min for the new EBS volume increase to finalize.
+   1. Allow EC2 to claim the additional space by stopping and then starting your EC2 host.
+
+
+### Pull Down the Code
+
+1. If you do not already have one, create a GitHub account by following the prompts at [Join Github](https://github.com/join).
+1. Create a fork of this repository on GitHub. You should end up with a fork at `https://github.com/<username>/sagemaker-python-sdk`.
+   1. Follow the instructions at [Fork a Repo](https://help.github.com/en/articles/fork-a-repo) to fork a GitHub repository.
+1. Clone your fork of the repository: `git clone https://github.com/<username>/sagemaker-python-sdk` where `<username>` is your github username.
+
+
+### Run the Unit Tests
+
+1. Install tox using `pip install tox`
+1. Install coverage using `pip install .[test]`
+1. cd into the sagemaker-python-sdk folder: `cd sagemaker-python-sdk` or `cd /environment/sagemaker-python-sdk`
+1. Run the following tox command and verify that all code checks and unit tests pass: `tox tests/unit`
+
+You can also run a single test with the following command: `tox -e py310 -- -s -vv <path_to_file><file_name>::<test_function_name>`
+  * Note that the coverage test will fail if you only run a single test, so make sure to surround the command with `export IGNORE_COVERAGE=-` and `unset IGNORE_COVERAGE`
+  * Example: `export IGNORE_COVERAGE=- ; tox -e py310 -- -s -vv tests/unit/test_estimator.py::test_sagemaker_model_s3_uri_invalid ; unset IGNORE_COVERAGE`
+
+
+### Run the Integration Tests
+
+Our CI system runs integration tests (the ones in the `tests/integ` directory), in parallel, for every Pull Request.
+You should only worry about manually running any new integration tests that you write, or integration tests that test an area of code that you've modified.
+
+1. Follow the instructions at [Set Up the AWS Command Line Interface (AWS CLI)](https://docs.aws.amazon.com/polly/latest/dg/setup-aws-cli.html).
+1. To run a test, specify the test file and method you want to run per the following command: `tox -e py310 -- -s -vv <path_to_file><file_name>::<test_function_name>`
+   * Note that the coverage test will fail if you only run a single test, so make sure to surround the command with `export IGNORE_COVERAGE=-` and `unset IGNORE_COVERAGE`
+   * Example: `export IGNORE_COVERAGE=- ; tox -e py310 -- -s -vv tests/integ/test_tf_script_mode.py::test_mnist ; unset IGNORE_COVERAGE`
+
+If you are writing or modifying a test that creates a SageMaker job (training, tuner, or transform) or endpoint, it's important to assign a concurrency-friendly `job_name` (or `endpoint_name`), or your tests may fail randomly due to name collisions. We have a helper method `sagemaker.utils.unique_name_from_base(base, max_length)` that makes test-friendly names. You can find examples of how to use it [here](https://github.com/aws/sagemaker-python-sdk/blob/3816a5658d3737c9767e01bc8d37fc3ed5551593/tests/integ/test_tfs.py#L37) and
+[here](https://github.com/aws/sagemaker-python-sdk/blob/3816a5658d3737c9767e01bc8d37fc3ed5551593/tests/integ/test_tuner.py#L616), or by searching for "unique\_name\_from\_base" in our test code.
+
+
+### Make and Test Your Change
+
+1. Create a new git branch:
+     ```shell
+     git checkout -b my-fix-branch master
+     ```
+1. Make your changes, **including unit tests** and, if appropriate, integration tests.
+   1. Include unit tests when you contribute new features or make bug fixes, as they help to:
+      1. Prove that your code works correctly.
+      1. Guard against future breaking changes to lower the maintenance cost.
+   1. Please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.
+1. Run all the unit tests as per [Run the Unit Tests](#run-the-unit-tests), and verify that all checks and tests pass.
+   1. Note that this also runs tools that may be necessary for the automated build to pass (ex: code reformatting by 'black').
+1. If your changes include documentation changes, please see the [Documentation Guidelines](#documentation-guidelines).
+1. If you include integration tests, do not mark them as canaries if they will not run in all regions.
+
+
+### Commit Your Change
+
+We use commit messages to update the project version number and generate changelog entries, so it's important for them to follow the right format. Valid commit messages include a prefix, separated from the rest of the message by a colon and a space. Here are a few examples:
+
+```
+feature: support VPC config for hyperparameter tuning
+fix: fix flake8 errors
+documentation: add MXNet documentation
+```
+
+Valid prefixes are listed in the table below.
+
+| Prefix          | Use for...                                                                                     |
+|----------------:|:-----------------------------------------------------------------------------------------------|
+| `breaking`      | Incompatible API changes.                                                                      |
+| `deprecation`   | Deprecating an existing API or feature, or removing something that was previously deprecated.  |
+| `feature`       | Adding a new feature.                                                                          |
+| `fix`           | Bug fixes.                                                                                     |
+| `change`        | Any other code change.                                                                         |
+| `documentation` | Documentation changes.                                                                         |
+
+Some of the prefixes allow abbreviation ; `break`, `feat`, `depr`, and `doc` are all valid. If you omit a prefix, the commit will be treated as a `change`.
+
+For the rest of the message, use imperative style and keep things concise but informative. See [How to Write a Git Commit Message](https://chris.beams.io/posts/git-commit/) for guidance.
+
+
+### Send a Pull Request
+
+GitHub provides additional document on [Creating a Pull Request](https://help.github.com/articles/creating-a-pull-request/).
+
+Please remember to:
+* Use commit messages (and PR titles) that follow the guidelines under [Commit Your Change](#commit-your-change).
+* Send us a pull request, answering any default questions in the pull request interface.
+* Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.
+
+
+## Documentation Guidelines
+
+We use reStructuredText (RST) for most of our documentation. For a quick primer on the syntax,
+see [the Sphinx documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html).
+
+In this repository, we have two main categories of documentation: overviews and API references.
+"How to" tutorials are housed in the [Amazon SageMaker Examples repository](https://github.com/awslabs/amazon-sagemaker-examples).
+Overviews and API references are discussed in more detail below.
+
+Here are some general guidelines to follow when writing either kind of documentation:
+* Use present tense.
+  * 👍 "The estimator fits a model."
+  * 👎 "The estimator will fit a model."
+* When referring to an AWS product, use its full name in the first invocation.
+  (This applies only to prose; use what makes sense when it comes to writing code, etc.)
+  * 👍 "Amazon S3"
+  * 👎 "s3"
+* Provide links to other ReadTheDocs pages, AWS documentation, etc. when helpful.
+  Try to not duplicate documentation when you can reference it instead.
+  * Use meaningful text in a link.
+    * 👍 You can learn more about [hyperparameter tuning with SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html) in the SageMaker docs.
+    * 👎 Read more about it [here](#).
+
+
+### Overviews
+
+This section refers to documentation that discusses a specific topic or feature to
+help the reader deepen their understanding, and may include short snippets of how to do specific tasks.
+Examples include "[Amazon SageMaker Debugger](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html)"
+and "[Use MXNet with the SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/using_mxnet.html)."
+
+The goal of these documents is to explain basic usage.
+This includes the general purpose of the topic or feature,
+and common ways to use the SageMaker Python SDK in that context.
+
+This type of documentation should not be a step-by-step tutorial.
+That is better suited for the [example notebooks](https://github.com/awslabs/amazon-sagemaker-examples).
+Instead, keep the content focused on the unique aspects of the feature.
+For example, if one is writing specifically about deploying models,
+there is no need to also include instructions on how to train a model first.
+In this case, consider linking to existing documentation about training models and any other prerequisites.
+
+Lastly, in addition to the general guidelines listed above:
+* Use the imperative mood for headings.
+  * 👍 "Prepare a Training Script"
+  * 👎 "Preparing a Training Script"
+* Don’t refer to features as "new" - they might be at the time of writing, but they won’t always be!
+
+### API References (docstrings)
+
+The API references are generated from docstrings.
+A docstring is the comment in the source code that describes a module, class, function, or variable.
+
+```python
+def foo():
+    """This comment is a docstring for the function foo."""
+```
+
+We use [Google-style docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html).
+There should be a docstring for every public module, class, and function.
+For functions, make sure your docstring covers all of the arguments, exceptions, and any other relevant information.
+When possible, link to classes and functions, e.g. use ":class:~\`sagemaker.session.Session\`" over just "Session."
+
+If a parameter of a function has a default value, please note what the default is.
+If that default value is `None`, it can also be helpful to explain what happens when the parameter is `None`.
+If `**kwargs` is part of the function signature, link to the parent class(es) or method(s) so that the reader knows where to find the available parameters.
+
+For an example file with docstrings, see [the `processing` module](https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py).
+
+To have a class's docstrings included in the API reference, it needs to be included in one of the files in the `doc/` folder.
+For example, see the [Processing API reference](https://github.com/aws/sagemaker-python-sdk/blob/master/doc/processing.rst).
+
+
+### Build and Test Documentation
+
+To build the Sphinx docs, run the following command in the `doc/` directory:
+
+```shell
+make html
+```
+
+You can then find the generated HTML files in `doc/_build/html/`.
+
+To check both the README and API documentation for build errors, you can run the following:
+
+```shell
+tox -e twine,sphinx
+```
+
+
+## Find Contributions to Work On
+
+Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels ((enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any ['help wanted'](https://github.com/aws/sagemaker-python-sdk/labels/help%20wanted) issues is a great place to start.
+
+
+## Code of Conduct
+
+This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
+For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
+opensource-codeofconduct@amazon.com with any additional questions or comments.
+
+
+## Security Issue Notifications
+
+If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
+
+
+## Licensing
+
+See the [LICENSE](https://github.com/aws/sagemaker-python-sdk/blob/master/LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.
+
+We may ask you to sign a [Contributor License Agreement (CLA)](http://en.wikipedia.org/wiki/Contributor_License_Agreement) for larger changes.

testbed/aws__sagemaker-python-sdk/LICENSE.txt

@@ -0,0 +1,193 @@
+                                  Apache License
+                           Version 2.0, January 2004
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
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+
+      "Legal Entity" shall mean the union of the acting entity and all
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+
+      "Work" shall mean the work of authorship, whether in Source or
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+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   ======================================================================================
+    Amazon SageMaker Examples Subcomponents:
+
+    The Amazon SageMaker Examples project contains subcomponents with separate
+    copyright notices and license terms. Your use of the source code for the
+    these subcomponents is subject to the terms and conditions of the following
+    licenses. See licenses/ for text of these licenses.
+
+    If a folder hierarchy is listed as subcomponent, separate listings of
+    further subcomponents (files or folder hierarchies) part of the hierarchy
+    take precedence.
+
+    =======================================================================================
+    2-clause BSD license
+    =======================================================================================
+    _static/kendrasearchtools.js
+    _templates/search.html

testbed/aws__sagemaker-python-sdk/MANIFEST.in

@@ -0,0 +1,13 @@
+recursive-include src/sagemaker *.py
+
+include src/sagemaker/image_uri_config/*.json
+recursive-include requirements *
+
+include VERSION
+include LICENSE.txt
+include README.rst
+
+prune tests
+
+recursive-exclude * __pycache__
+recursive-exclude * *.py[co]

testbed/aws__sagemaker-python-sdk/NOTICE.txt

@@ -0,0 +1,2 @@
+Amazon SageMaker Python SDK
+Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

testbed/aws__sagemaker-python-sdk/README.rst

@@ -0,0 +1,240 @@
+.. image:: https://github.com/aws/sagemaker-python-sdk/raw/master/branding/icon/sagemaker-banner.png
+    :height: 100px
+    :alt: SageMaker
+
+====================
+SageMaker Python SDK
+====================
+
+.. image:: https://img.shields.io/pypi/v/sagemaker.svg
+   :target: https://pypi.python.org/pypi/sagemaker
+   :alt: Latest Version
+
+.. image:: https://img.shields.io/pypi/pyversions/sagemaker.svg
+   :target: https://pypi.python.org/pypi/sagemaker
+   :alt: Supported Python Versions
+
+.. image:: https://img.shields.io/badge/code_style-black-000000.svg
+   :target: https://github.com/python/black
+   :alt: Code style: black
+
+.. image:: https://readthedocs.org/projects/sagemaker/badge/?version=stable
+   :target: https://sagemaker.readthedocs.io/en/stable/
+   :alt: Documentation Status
+
+SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.
+
+With the SDK, you can train and deploy models using popular deep learning frameworks **Apache MXNet** and **TensorFlow**.
+You can also train and deploy models with **Amazon algorithms**,
+which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training.
+If you have **your own algorithms** built into SageMaker compatible Docker containers, you can train and host models using these as well.
+
+For detailed documentation, including the API reference, see `Read the Docs <https://sagemaker.readthedocs.io>`_.
+
+Table of Contents
+-----------------
+
+#. `Installing SageMaker Python SDK <#installing-the-sagemaker-python-sdk>`__
+#. `Using the SageMaker Python SDK <https://sagemaker.readthedocs.io/en/stable/overview.html>`__
+#. `Using MXNet <https://sagemaker.readthedocs.io/en/stable/using_mxnet.html>`__
+#. `Using TensorFlow <https://sagemaker.readthedocs.io/en/stable/using_tf.html>`__
+#. `Using Chainer <https://sagemaker.readthedocs.io/en/stable/using_chainer.html>`__
+#. `Using PyTorch <https://sagemaker.readthedocs.io/en/stable/using_pytorch.html>`__
+#. `Using Scikit-learn <https://sagemaker.readthedocs.io/en/stable/using_sklearn.html>`__
+#. `Using XGBoost <https://sagemaker.readthedocs.io/en/stable/using_xgboost.html>`__
+#. `SageMaker Reinforcement Learning Estimators <https://sagemaker.readthedocs.io/en/stable/using_rl.html>`__
+#. `SageMaker SparkML Serving <#sagemaker-sparkml-serving>`__
+#. `Amazon SageMaker Built-in Algorithm Estimators <src/sagemaker/amazon/README.rst>`__
+#. `Using SageMaker AlgorithmEstimators <https://sagemaker.readthedocs.io/en/stable/overview.html#using-sagemaker-algorithmestimators>`__
+#. `Consuming SageMaker Model Packages <https://sagemaker.readthedocs.io/en/stable/overview.html#consuming-sagemaker-model-packages>`__
+#. `BYO Docker Containers with SageMaker Estimators <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-docker-containers-with-sagemaker-estimators>`__
+#. `SageMaker Automatic Model Tuning <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-automatic-model-tuning>`__
+#. `SageMaker Batch Transform <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-batch-transform>`__
+#. `Secure Training and Inference with VPC <https://sagemaker.readthedocs.io/en/stable/overview.html#secure-training-and-inference-with-vpc>`__
+#. `BYO Model <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-model>`__
+#. `Inference Pipelines <https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines>`__
+#. `Amazon SageMaker Operators in Apache Airflow <https://sagemaker.readthedocs.io/en/stable/using_workflow.html>`__
+#. `SageMaker Autopilot <src/sagemaker/automl/README.rst>`__
+#. `Model Monitoring <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html>`__
+#. `SageMaker Debugger <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html>`__
+#. `SageMaker Processing <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html>`__
+
+
+Installing the SageMaker Python SDK
+-----------------------------------
+
+The SageMaker Python SDK is built to PyPI and can be installed with pip as follows:
+
+::
+
+    pip install sagemaker
+
+You can install from source by cloning this repository and running a pip install command in the root directory of the repository:
+
+::
+
+    git clone https://github.com/aws/sagemaker-python-sdk.git
+    cd sagemaker-python-sdk
+    pip install .
+
+Supported Operating Systems
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+SageMaker Python SDK supports Unix/Linux and Mac.
+
+Supported Python Versions
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+SageMaker Python SDK is tested on:
+
+- Python 3.7
+- Python 3.8
+- Python 3.9
+- Python 3.10
+
+AWS Permissions
+~~~~~~~~~~~~~~~
+
+As a managed service, Amazon SageMaker performs operations on your behalf on the AWS hardware that is managed by Amazon SageMaker.
+Amazon SageMaker can perform only operations that the user permits.
+You can read more about which permissions are necessary in the `AWS Documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html>`__.
+
+The SageMaker Python SDK should not require any additional permissions aside from what is required for using SageMaker.
+However, if you are using an IAM role with a path in it, you should grant permission for ``iam:GetRole``.
+
+Licensing
+~~~~~~~~~
+SageMaker Python SDK is licensed under the Apache 2.0 License. It is copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at:
+http://aws.amazon.com/apache2.0/
+
+Running tests
+~~~~~~~~~~~~~
+
+SageMaker Python SDK has unit tests and integration tests.
+
+You can install the libraries needed to run the tests by running :code:`pip install --upgrade .[test]` or, for Zsh users: :code:`pip install --upgrade .\[test\]`
+
+**Unit tests**
+
+We run unit tests with tox, which is a program that lets you run unit tests for multiple Python versions, and also make sure the
+code fits our style guidelines. We run tox with `all of our supported Python versions <#supported-python-versions>`_, so to run unit tests
+with the same configuration we do, you need to have interpreters for those Python versions installed.
+
+To run the unit tests with tox, run:
+
+::
+
+    tox tests/unit
+
+**Integrations tests**
+
+To run the integration tests, the following prerequisites must be met
+
+1. AWS account credentials are available in the environment for the boto3 client to use.
+2. The AWS account has an IAM role named :code:`SageMakerRole`.
+   It should have the AmazonSageMakerFullAccess policy attached as well as a policy with `the necessary permissions to use Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei-setup.html>`__.
+
+We recommend selectively running just those integration tests you'd like to run. You can filter by individual test function names with:
+
+::
+
+    tox -- -k 'test_i_care_about'
+
+
+You can also run all of the integration tests by running the following command, which runs them in sequence, which may take a while:
+
+::
+
+    tox -- tests/integ
+
+
+You can also run them in parallel:
+
+::
+
+    tox -- -n auto tests/integ
+
+
+Git Hooks
+~~~~~~~~~
+
+to enable all git hooks in the .githooks directory, run these commands in the repository directory:
+
+::
+
+    find .git/hooks -type l -exec rm {} \;
+    find .githooks -type f -exec ln -sf ../../{} .git/hooks/ \;
+
+To enable an individual git hook, simply move it from the .githooks/ directory to the .git/hooks/ directory.
+
+Building Sphinx docs
+~~~~~~~~~~~~~~~~~~~~
+
+Setup a Python environment, and install the dependencies listed in ``doc/requirements.txt``:
+
+::
+
+    # conda
+    conda create -n sagemaker python=3.7
+    conda activate sagemaker
+    conda install sphinx=3.1.1 sphinx_rtd_theme=0.5.0
+
+    # pip
+    pip install -r doc/requirements.txt
+
+
+Clone/fork the repo, and install your local version:
+
+::
+
+    pip install --upgrade .
+
+Then ``cd`` into the ``sagemaker-python-sdk/doc`` directory and run:
+
+::
+
+    make html
+
+You can edit the templates for any of the pages in the docs by editing the .rst files in the ``doc`` directory and then running ``make html`` again.
+
+Preview the site with a Python web server:
+
+::
+
+    cd _build/html
+    python -m http.server 8000
+
+View the website by visiting http://localhost:8000
+
+SageMaker SparkML Serving
+-------------------------
+
+With SageMaker SparkML Serving, you can now perform predictions against a SparkML Model in SageMaker.
+In order to host a SparkML model in SageMaker, it should be serialized with ``MLeap`` library.
+
+For more information on MLeap, see https://github.com/combust/mleap .
+
+Supported major version of Spark: 2.4 (MLeap version - 0.9.6)
+
+Here is an example on how to create an instance of  ``SparkMLModel`` class and use ``deploy()`` method to create an
+endpoint which can be used to perform prediction against your trained SparkML Model.
+
+.. code:: python
+
+    sparkml_model = SparkMLModel(model_data='s3://path/to/model.tar.gz', env={'SAGEMAKER_SPARKML_SCHEMA': schema})
+    model_name = 'sparkml-model'
+    endpoint_name = 'sparkml-endpoint'
+    predictor = sparkml_model.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge', endpoint_name=endpoint_name)
+
+Once the model is deployed, we can invoke the endpoint with a ``CSV`` payload like this:
+
+.. code:: python
+
+    payload = 'field_1,field_2,field_3,field_4,field_5'
+    predictor.predict(payload)
+
+
+For more information about the different ``content-type`` and ``Accept`` formats as well as the structure of the
+``schema`` that SageMaker SparkML Serving recognizes, please see `SageMaker SparkML Serving Container`_.
+
+.. _SageMaker SparkML Serving Container: https://github.com/aws/sagemaker-sparkml-serving-container

testbed/aws__sagemaker-python-sdk/VERSION

@@ -0,0 +1 @@
+2.113.1.dev0

testbed/aws__sagemaker-python-sdk/mypy.ini

@@ -0,0 +1,2 @@
+[mypy]
+ignore_missing_imports = True

testbed/aws__sagemaker-python-sdk/setup.cfg

@@ -0,0 +1,15 @@
+# Test args for pytest; disable stdout capturing by default.
+[tool:pytest]
+addopts =
+    -vv
+testpaths = tests
+
+[aliases]
+test=pytest
+
+[metadata]
+description-file = README.rst
+license_file = LICENSE.txt
+
+[wheel]
+universal = 1

testbed/aws__sagemaker-python-sdk/setup.py

@@ -0,0 +1,107 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Placeholder docstring"""
+from __future__ import absolute_import
+
+import os
+from glob import glob
+
+from setuptools import find_packages, setup
+
+
+def read(fname):
+    """
+    Args:
+        fname:
+    """
+    return open(os.path.join(os.path.dirname(__file__), fname)).read()
+
+
+def read_version():
+    return read("VERSION").strip()
+
+
+def read_requirements(filename):
+    """Reads requirements file which lists package dependencies.
+
+    Args:
+        filename: type(str) Relative file path of requirements.txt file
+
+    Returns:
+        list of dependencies extracted from file
+    """
+    with open(os.path.abspath(filename)) as fp:
+        deps = [line.strip() for line in fp.readlines()]
+    return deps
+
+
+# Declare minimal set for installation
+required_packages = [
+    "attrs>=20.3.0,<23",
+    "boto3>=1.20.21,<2.0",
+    "google-pasta",
+    "numpy>=1.9.0,<2.0",
+    "protobuf>=3.1,<4.0",
+    "protobuf3-to-dict>=0.1.5,<1.0",
+    "smdebug_rulesconfig==1.0.1",
+    "importlib-metadata>=1.4.0,<5.0",
+    "packaging>=20.0",
+    "pandas",
+    "pathos",
+    "schema",
+]
+
+# Specific use case dependencies
+# Keep format of *_requirements.txt to be tracked by dependabot
+extras = {
+    "local": read_requirements("requirements/extras/local_requirements.txt"),
+    "scipy": read_requirements("requirements/extras/scipy_requirements.txt"),
+}
+# Meta dependency groups
+extras["all"] = [item for group in extras.values() for item in group]
+# Tests specific dependencies (do not need to be included in 'all')
+extras["test"] = (extras["all"] + read_requirements("requirements/extras/test_requirements.txt"),)
+
+setup(
+    name="sagemaker",
+    version=read_version(),
+    description="Open source library for training and deploying models on Amazon SageMaker.",
+    packages=find_packages("src"),
+    package_dir={"": "src"},
+    py_modules=[os.path.splitext(os.path.basename(path))[0] for path in glob("src/*.py")],
+    include_package_data=True,
+    long_description=read("README.rst"),
+    author="Amazon Web Services",
+    url="https://github.com/aws/sagemaker-python-sdk/",
+    license="Apache License 2.0",
+    keywords="ML Amazon AWS AI Tensorflow MXNet",
+    python_requires=">= 3.6",
+    classifiers=[
+        "Development Status :: 5 - Production/Stable",
+        "Intended Audience :: Developers",
+        "Natural Language :: English",
+        "License :: OSI Approved :: Apache Software License",
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+    ],
+    install_requires=required_packages,
+    extras_require=extras,
+    entry_points={
+        "console_scripts": [
+            "sagemaker-upgrade-v2=sagemaker.cli.compatibility.v2.sagemaker_upgrade_v2:main",
+        ]
+    },
+)

testbed/aws__sagemaker-python-sdk/src/sagemaker/__init__.py

@@ -0,0 +1,65 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Placeholder docstring"""
+from __future__ import absolute_import
+
+import importlib_metadata
+
+from sagemaker import estimator, parameter, tuner  # noqa: F401
+from sagemaker.amazon.kmeans import KMeans, KMeansModel, KMeansPredictor  # noqa: F401
+from sagemaker.amazon.pca import PCA, PCAModel, PCAPredictor  # noqa: F401
+from sagemaker.amazon.lda import LDA, LDAModel, LDAPredictor  # noqa: F401
+from sagemaker.amazon.linear_learner import (  # noqa: F401
+    LinearLearner,
+    LinearLearnerModel,
+    LinearLearnerPredictor,
+)
+from sagemaker.amazon.factorization_machines import (  # noqa: F401
+    FactorizationMachines,
+    FactorizationMachinesModel,
+)
+from sagemaker.amazon.factorization_machines import FactorizationMachinesPredictor  # noqa: F401
+from sagemaker.inputs import TrainingInput  # noqa: F401
+from sagemaker.amazon.ntm import NTM, NTMModel, NTMPredictor  # noqa: F401
+from sagemaker.amazon.randomcutforest import (  # noqa: F401
+    RandomCutForest,
+    RandomCutForestModel,
+    RandomCutForestPredictor,
+)
+from sagemaker.amazon.knn import KNN, KNNModel, KNNPredictor  # noqa: F401
+from sagemaker.amazon.object2vec import Object2Vec, Object2VecModel  # noqa: F401
+from sagemaker.amazon.ipinsights import (  # noqa: F401
+    IPInsights,
+    IPInsightsModel,
+    IPInsightsPredictor,
+)
+
+from sagemaker.algorithm import AlgorithmEstimator  # noqa: F401
+from sagemaker.analytics import TrainingJobAnalytics, HyperparameterTuningJobAnalytics  # noqa: F401
+from sagemaker.local.local_session import LocalSession  # noqa: F401
+
+from sagemaker.model import Model, ModelPackage  # noqa: F401
+from sagemaker.model_metrics import ModelMetrics, MetricsSource, FileSource  # noqa: F401
+from sagemaker.pipeline import PipelineModel  # noqa: F401
+from sagemaker.predictor import Predictor  # noqa: F401
+from sagemaker.processing import Processor, ScriptProcessor  # noqa: F401
+from sagemaker.session import Session  # noqa: F401
+from sagemaker.session import container_def, pipeline_container_def  # noqa: F401
+from sagemaker.session import get_model_package_args  # noqa: F401
+from sagemaker.session import production_variant  # noqa: F401
+from sagemaker.session import get_execution_role  # noqa: F401
+
+from sagemaker.automl.automl import AutoML, AutoMLJob, AutoMLInput  # noqa: F401
+from sagemaker.automl.candidate_estimator import CandidateEstimator, CandidateStep  # noqa: F401
+
+__version__ = importlib_metadata.version("sagemaker")

testbed/aws__sagemaker-python-sdk/src/sagemaker/_studio.py

@@ -0,0 +1,113 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Provides internal tooling for studio environments."""
+from __future__ import absolute_import
+
+import json
+import logging
+
+from pathlib import Path
+
+STUDIO_PROJECT_CONFIG = ".sagemaker-code-config"
+
+logger = logging.getLogger(__name__)
+
+
+def _append_project_tags(tags=None, working_dir=None):
+    """Appends the project tag to the list of tags, if it exists.
+
+    Args:
+        working_dir: the working directory to start looking.
+        tags: the list of tags to append to.
+
+    Returns:
+        A possibly extended list of tags that includes the project id.
+    """
+    path = _find_config(working_dir)
+    if path is None:
+        return tags
+
+    config = _load_config(path)
+    if config is None:
+        return tags
+
+    additional_tags = _parse_tags(config)
+    if additional_tags is None:
+        return tags
+
+    all_tags = tags or []
+    additional_tags = [tag for tag in additional_tags if tag not in all_tags]
+    all_tags.extend(additional_tags)
+
+    return all_tags
+
+
+def _find_config(working_dir=None):
+    """Gets project config on SageMaker Studio platforms, if it exists.
+
+    Args:
+        working_dir: the working directory to start looking.
+
+    Returns:
+        The project config path, if it exists. Otherwise None.
+    """
+    try:
+        wd = Path(working_dir) if working_dir else Path.cwd()
+
+        path = None
+        while path is None and not wd.match("/"):
+            candidate = wd / STUDIO_PROJECT_CONFIG
+            if Path.exists(candidate):
+                path = candidate
+            wd = wd.parent
+
+        return path
+    except Exception as e:  # pylint: disable=W0703
+        logger.debug("Could not find the studio project config. %s", e)
+
+
+def _load_config(path):
+    """Parse out the projectId attribute if it exists at path.
+
+    Args:
+        path: path to project config
+
+    Returns:
+        Project config Json, or None if it does not exist.
+    """
+    try:
+        with open(path, "r") as f:
+            content = f.read().strip()
+        config = json.loads(content)
+
+        return config
+    except Exception as e:  # pylint: disable=W0703
+        logger.debug("Could not load project config. %s", e)
+
+
+def _parse_tags(config):
+    """Parse out appropriate attributes and formats as tags.
+
+    Args:
+        config: project config dict
+
+    Returns:
+        List of tags
+    """
+    try:
+        return [
+            {"Key": "sagemaker:project-id", "Value": config["sagemakerProjectId"]},
+            {"Key": "sagemaker:project-name", "Value": config["sagemakerProjectName"]},
+        ]
+    except Exception as e:  # pylint: disable=W0703
+        logger.debug("Could not parse project config. %s", e)

testbed/aws__sagemaker-python-sdk/src/sagemaker/algorithm.py

@@ -0,0 +1,589 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Test docstring"""
+from __future__ import absolute_import
+
+from typing import Optional, Union, Dict, List
+
+import sagemaker
+import sagemaker.parameter
+from sagemaker import vpc_utils
+from sagemaker.deserializers import BytesDeserializer
+from sagemaker.deprecations import removed_kwargs
+from sagemaker.estimator import EstimatorBase
+from sagemaker.inputs import TrainingInput, FileSystemInput
+from sagemaker.serializers import IdentitySerializer
+from sagemaker.transformer import Transformer
+from sagemaker.predictor import Predictor
+from sagemaker.session import Session
+from sagemaker.workflow.entities import PipelineVariable
+
+from sagemaker.workflow import is_pipeline_variable
+
+
+class AlgorithmEstimator(EstimatorBase):
+    """A generic Estimator to train using any algorithm object (with an ``algorithm_arn``).
+
+    The Algorithm can be your own, or any Algorithm from AWS
+    Marketplace that you have a valid subscription for. This class will perform
+    client-side validation on all the inputs.
+    """
+
+    # These Hyperparameter Types have a range definition.
+    _hyperpameters_with_range = ("Integer", "Continuous", "Categorical")
+
+    def __init__(
+        self,
+        algorithm_arn: str,
+        role: str,
+        instance_count: Optional[Union[int, PipelineVariable]] = None,
+        instance_type: Optional[Union[str, PipelineVariable]] = None,
+        volume_size: Union[int, PipelineVariable] = 30,
+        volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
+        max_run: Union[int, PipelineVariable] = 24 * 60 * 60,
+        input_mode: Union[str, PipelineVariable] = "File",
+        output_path: Optional[Union[str, PipelineVariable]] = None,
+        output_kms_key: Optional[Union[str, PipelineVariable]] = None,
+        base_job_name: Optional[str] = None,
+        sagemaker_session: Optional[Session] = None,
+        hyperparameters: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        tags: Optional[List[Dict[str, Union[str, PipelineVariable]]]] = None,
+        subnets: Optional[List[Union[str, PipelineVariable]]] = None,
+        security_group_ids: Optional[List[Union[str, PipelineVariable]]] = None,
+        model_uri: Optional[str] = None,
+        model_channel_name: Union[str, PipelineVariable] = "model",
+        metric_definitions: Optional[List[Dict[str, Union[str, PipelineVariable]]]] = None,
+        encrypt_inter_container_traffic: Union[bool, PipelineVariable] = False,
+        use_spot_instances: Union[bool, PipelineVariable] = False,
+        max_wait: Optional[Union[int, PipelineVariable]] = None,
+        **kwargs  # pylint: disable=W0613
+    ):
+        """Initialize an ``AlgorithmEstimator`` instance.
+
+        Args:
+            algorithm_arn (str): algorithm arn used for training. Can be just the name if your
+                account owns the algorithm.
+            role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker
+                training jobs and APIsthat create Amazon SageMaker endpoints use this role to
+                access training data and model artifacts. After the endpoint
+                is created, the inference code might use the IAM role, if it
+                needs to access an AWS resource.
+            instance_count (int or PipelineVariable): Number of Amazon EC2 instances to use
+                for training.
+            instance_type (str or PipelineVariable): Type of EC2 instance to use for training,
+                for example, 'ml.c4.xlarge'.
+            volume_size (int or PipelineVariable): Size in GB of the EBS volume to use for
+                storing input data during training (default: 30). Must be large enough to store
+                training data if File Mode is used (which is the default).
+            volume_kms_key (str or PipelineVariable): Optional. KMS key ID for encrypting
+                EBS volume attached to the training instance (default: None).
+            max_run (int or PipelineVariable): Timeout in seconds for training
+                (default: 24 * 60 * 60).
+                After this amount of time Amazon SageMaker terminates the
+                job regardless of its current status.
+            input_mode (str or PipelineVariable): The input mode that the algorithm supports
+                (default: 'File'). Valid modes:
+
+                * 'File' - Amazon SageMaker copies the training dataset from
+                  the S3 location to a local directory.
+                * 'Pipe' - Amazon SageMaker streams data directly from S3 to
+                  the container via a Unix-named pipe.
+
+                This argument can be overriden on a per-channel basis using
+                ``sagemaker.inputs.TrainingInput.input_mode``.
+
+            output_path (str or PipelineVariable): S3 location for saving the training result
+                (model artifacts and output files). If not specified,
+                results are stored to a default bucket. If
+                the bucket with the specific name does not exist, the
+                estimator creates the bucket during the
+                :meth:`~sagemaker.estimator.EstimatorBase.fit` method
+                execution.
+            output_kms_key (str or PipelineVariable): Optional. KMS key ID for encrypting the
+                training output (default: None). base_job_name (str): Prefix for
+                training job name when the
+                :meth:`~sagemaker.estimator.EstimatorBase.fit`
+                method launches. If not specified, the estimator generates a
+                default job name, based on the training image name and
+                current timestamp.
+            sagemaker_session (sagemaker.session.Session): Session object which manages
+                interactions with Amazon SageMaker APIs and any other AWS services needed. If
+                not specified, the estimator creates one using the default
+                AWS configuration chain.
+            tags (list[dict[str, str] or list[dict[str, PipelineVariable]]): List of tags for
+                labeling a training job. For more, see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
+            subnets (list[str] or list[PipelineVariable]): List of subnet ids. If not specified
+                training job will be created without VPC config.
+                security_group_ids (list[str]): List of security group ids. If
+                not specified training job will be created without VPC config.
+            model_uri (str): URI where a pre-trained model is stored, either locally or in S3
+                (default: None). If specified, the estimator will create a channel pointing to
+                the model so the training job can download it. This model
+                can be a 'model.tar.gz' from a previous training job, or
+                other artifacts coming from a different source.
+                More information:
+                https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html#td-deserialization
+            model_channel_name (str or PipelineVariable): Name of the channel where 'model_uri'
+                will be downloaded (default: 'model'). metric_definitions
+                (list[dict]): A list of dictionaries that defines the metric(s)
+                used to evaluate the training jobs. Each dictionary contains two keys: 'Name' for
+                the name of the metric, and 'Regex' for the regular
+                expression used to extract the metric from the logs.
+            encrypt_inter_container_traffic (bool or PipelineVariable): Specifies whether traffic
+                between training containers is encrypted for the training job (default: ``False``).
+            use_spot_instances (bool or PipelineVariable): Specifies whether to use SageMaker
+                Managed Spot instances for training. If enabled then the
+                `max_wait` arg should also be set.
+
+                More information:
+                https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html
+                (default: ``False``).
+            max_wait (int or PipelineVariable): Timeout in seconds waiting for spot training
+                instances (default: None). After this amount of time Amazon
+                SageMaker will stop waiting for Spot instances to become
+                available (default: ``None``).
+            **kwargs: Additional kwargs. This is unused. It's only added for AlgorithmEstimator
+                to ignore the irrelevant arguments.
+        """
+        self.algorithm_arn = algorithm_arn
+        super(AlgorithmEstimator, self).__init__(
+            role,
+            instance_count=instance_count,
+            instance_type=instance_type,
+            volume_size=volume_size,
+            volume_kms_key=volume_kms_key,
+            max_run=max_run,
+            input_mode=input_mode,
+            output_path=output_path,
+            output_kms_key=output_kms_key,
+            base_job_name=base_job_name,
+            sagemaker_session=sagemaker_session,
+            tags=tags,
+            subnets=subnets,
+            security_group_ids=security_group_ids,
+            model_uri=model_uri,
+            model_channel_name=model_channel_name,
+            metric_definitions=metric_definitions,
+            encrypt_inter_container_traffic=encrypt_inter_container_traffic,
+            use_spot_instances=use_spot_instances,
+            max_wait=max_wait,
+        )
+
+        self.algorithm_spec = self.sagemaker_session.sagemaker_client.describe_algorithm(
+            AlgorithmName=algorithm_arn
+        )
+        self.validate_train_spec()
+        self.hyperparameter_definitions = self._parse_hyperparameters()
+
+        self._hyperparameters = {}
+        if hyperparameters:
+            self.set_hyperparameters(**hyperparameters)
+
+    def validate_train_spec(self):
+        """Placeholder docstring"""
+        train_spec = self.algorithm_spec["TrainingSpecification"]
+        algorithm_name = self.algorithm_spec["AlgorithmName"]
+
+        # Check that the input mode provided is compatible with the training input modes for the
+        # algorithm.
+        input_modes = self._algorithm_training_input_modes(train_spec["TrainingChannels"])
+        if not is_pipeline_variable(self.input_mode) and self.input_mode not in input_modes:
+            raise ValueError(
+                "Invalid input mode: %s. %s only supports: %s"
+                % (self.input_mode, algorithm_name, input_modes)
+            )
+
+        # Check that the training instance type is compatible with the algorithm.
+        supported_instances = train_spec["SupportedTrainingInstanceTypes"]
+        if (
+            not is_pipeline_variable(self.instance_type)
+            and self.instance_type not in supported_instances
+        ):
+            raise ValueError(
+                "Invalid instance_type: %s. %s supports the following instance types: %s"
+                % (self.instance_type, algorithm_name, supported_instances)
+            )
+
+        # Verify if distributed training is supported by the algorithm
+        if not is_pipeline_variable(self.instance_count) and (
+            self.instance_count > 1
+            and "SupportsDistributedTraining" in train_spec
+            and not train_spec["SupportsDistributedTraining"]
+        ):
+            raise ValueError(
+                "Distributed training is not supported by %s. "
+                "Please set instance_count=1" % algorithm_name
+            )
+
+    def set_hyperparameters(self, **kwargs):
+        """Placeholder docstring"""
+        for k, v in kwargs.items():
+            value = self._validate_and_cast_hyperparameter(k, v)
+            self._hyperparameters[k] = value
+
+        self._validate_and_set_default_hyperparameters()
+
+    def hyperparameters(self):
+        """Returns the hyperparameters as a dictionary to use for training.
+
+        The fit() method, that does the model training, calls this method to
+        find the hyperparameters you specified.
+        """
+        return self._hyperparameters
+
+    def training_image_uri(self):
+        """Returns the docker image to use for training.
+
+        The fit() method, that does the model training, calls this method to
+        find the image to use for model training.
+        """
+        raise RuntimeError("training_image_uri is never meant to be called on Algorithm Estimators")
+
+    def enable_network_isolation(self):
+        """Return True if this Estimator will need network isolation to run.
+
+        On Algorithm Estimators this depends on the algorithm being used. If
+        this is algorithm owned by your account it will be False. If this is an
+        an algorithm consumed from Marketplace it will be True.
+
+        Returns:
+            bool: Whether this Estimator needs network isolation or not.
+        """
+        return self._is_marketplace()
+
+    def create_model(
+        self,
+        role=None,
+        predictor_cls=None,
+        serializer=IdentitySerializer(),
+        deserializer=BytesDeserializer(),
+        vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
+        **kwargs
+    ):
+        """Create a model to deploy.
+
+        The serializer and deserializer are only used to define a default
+        Predictor. They are ignored if an explicit predictor class is passed in.
+        Other arguments are passed through to the Model class.
+
+        Args:
+            role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
+                which is also used during transform jobs. If not specified, the
+                role from the Estimator will be used.
+            predictor_cls (Predictor): The predictor class to use when
+                deploying the model.
+            serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
+                serializer object, used to encode data for an inference endpoint
+                (default: :class:`~sagemaker.serializers.IdentitySerializer`).
+            deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
+                deserializer object, used to decode data from an inference
+                endpoint (default: :class:`~sagemaker.deserializers.BytesDeserializer`).
+            vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
+                the model. Default: use subnets and security groups from this Estimator.
+                * 'Subnets' (list[str]): List of subnet ids.
+                * 'SecurityGroupIds' (list[str]): List of security group ids.
+            **kwargs: Additional arguments for creating a :class:`~sagemaker.model.ModelPackage`.
+
+        .. tip::
+
+            You can find additional parameters for using this method at
+            :class:`~sagemaker.model.ModelPackage` and
+            :class:`~sagemaker.model.Model`.
+
+        Returns:
+            a Model ready for deployment.
+        """
+        removed_kwargs("content_type", kwargs)
+        removed_kwargs("accept", kwargs)
+
+        if predictor_cls is None:
+
+            def predict_wrapper(endpoint, session):
+                return Predictor(endpoint, session, serializer, deserializer)
+
+            predictor_cls = predict_wrapper
+
+        role = role or self.role
+
+        return sagemaker.ModelPackage(
+            role,
+            algorithm_arn=self.algorithm_arn,
+            model_data=self.model_data,
+            vpc_config=self.get_vpc_config(vpc_config_override),
+            sagemaker_session=self.sagemaker_session,
+            predictor_cls=predictor_cls,
+            **kwargs
+        )
+
+    def transformer(
+        self,
+        instance_count,
+        instance_type,
+        strategy=None,
+        assemble_with=None,
+        output_path=None,
+        output_kms_key=None,
+        accept=None,
+        env=None,
+        max_concurrent_transforms=None,
+        max_payload=None,
+        tags=None,
+        role=None,
+        volume_kms_key=None,
+    ):
+        """Return a ``Transformer`` that uses a SageMaker Model based on the  training job.
+
+        It reuses the SageMaker Session and base job name used by the Estimator.
+
+        Args:
+            instance_count (int): Number of EC2 instances to use.
+            instance_type (str): Type of EC2 instance to use, for example,
+                'ml.c4.xlarge'.
+            strategy (str): The strategy used to decide how to batch records in
+                a single request (default: None). Valid values: 'MultiRecord'
+                and 'SingleRecord'.
+            assemble_with (str): How the output is assembled (default: None).
+                Valid values: 'Line' or 'None'.
+            output_path (str): S3 location for saving the transform result. If
+                not specified, results are stored to a default bucket.
+            output_kms_key (str): Optional. KMS key ID for encrypting the
+                transform output (default: None).
+            accept (str): The accept header passed by the client to
+                the inference endpoint. If it is supported by the endpoint,
+                it will be the format of the batch transform output.
+            env (dict): Environment variables to be set for use during the
+                transform job (default: None).
+            max_concurrent_transforms (int): The maximum number of HTTP requests
+                to be made to each individual transform container at one time.
+            max_payload (int): Maximum size of the payload in a single HTTP
+                request to the container in MB.
+            tags (list[dict]): List of tags for labeling a transform job. If
+                none specified, then the tags used for the training job are used
+                for the transform job.
+            role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
+                which is also used during transform jobs. If not specified, the
+                role from the Estimator will be used.
+            volume_kms_key (str): Optional. KMS key ID for encrypting the volume
+                attached to the ML compute instance (default: None).
+        """
+        role = role or self.role
+
+        if self.latest_training_job is not None:
+            model = self.create_model(role=role)
+            model._create_sagemaker_model()
+            model_name = model.name
+            transform_env = {}
+            if env is not None:
+                transform_env = model.env.copy()
+                transform_env.update(env)
+            if self._is_marketplace():
+                transform_env = None
+
+            tags = tags or self.tags
+        else:
+            raise RuntimeError("No finished training job found associated with this estimator")
+
+        return Transformer(
+            model_name,
+            instance_count,
+            instance_type,
+            strategy=strategy,
+            assemble_with=assemble_with,
+            output_path=output_path,
+            output_kms_key=output_kms_key,
+            accept=accept,
+            max_concurrent_transforms=max_concurrent_transforms,
+            max_payload=max_payload,
+            env=transform_env,
+            tags=tags,
+            base_transform_job_name=self.base_job_name,
+            volume_kms_key=volume_kms_key,
+            sagemaker_session=self.sagemaker_session,
+        )
+
+    def _is_marketplace(self):
+        """Placeholder docstring"""
+        return "ProductId" in self.algorithm_spec
+
+    def _ensure_base_job_name(self):
+        """Set ``self.base_job_name`` if it is not set already."""
+        if self.base_job_name is None:
+            self.base_job_name = self.algorithm_arn.split("/")[-1]
+
+    def _prepare_for_training(self, job_name=None):
+        # Validate hyperparameters
+        # an explicit call to set_hyperparameters() will also validate the hyperparameters
+        # but it is possible that the user never called it.
+        self._validate_and_set_default_hyperparameters()
+
+        super(AlgorithmEstimator, self)._prepare_for_training(job_name)
+
+    def fit(
+        self,
+        inputs: Optional[Union[str, Dict, TrainingInput, FileSystemInput]] = None,
+        wait: bool = True,
+        logs: bool = True,
+        job_name: Optional[str] = None,
+    ):
+        """Placeholder docstring"""
+        if inputs:
+            self._validate_input_channels(inputs)
+
+        return super(AlgorithmEstimator, self).fit(inputs, wait, logs, job_name)
+
+    def _validate_input_channels(self, channels):
+        """Placeholder docstring"""
+        train_spec = self.algorithm_spec["TrainingSpecification"]
+        algorithm_name = self.algorithm_spec["AlgorithmName"]
+        training_channels = {c["Name"]: c for c in train_spec["TrainingChannels"]}
+
+        # check for unknown channels that the algorithm does not support
+        for c in channels:
+            if c not in training_channels:
+                raise ValueError(
+                    "Unknown input channel: %s is not supported by: %s" % (c, algorithm_name)
+                )
+
+        # check for required channels that were not provided
+        for name, channel in training_channels.items():
+            if name not in channels and "IsRequired" in channel and channel["IsRequired"]:
+                raise ValueError("Required input channel: %s Was not provided." % (name))
+
+    def _validate_and_cast_hyperparameter(self, name, v):
+        """Placeholder docstring"""
+        algorithm_name = self.algorithm_spec["AlgorithmName"]
+
+        if name not in self.hyperparameter_definitions:
+            raise ValueError(
+                "Invalid hyperparameter: %s is not supported by %s" % (name, algorithm_name)
+            )
+
+        definition = self.hyperparameter_definitions[name]
+        if "class" in definition:
+            value = definition["class"].cast_to_type(v)
+        else:
+            value = v
+
+        if "range" in definition and not definition["range"].is_valid(value):
+            valid_range = definition["range"].as_tuning_range(name)
+            raise ValueError("Invalid value: %s Supported range: %s" % (value, valid_range))
+        return value
+
+    def _validate_and_set_default_hyperparameters(self):
+        """Placeholder docstring"""
+        # Check if all the required hyperparameters are set. If there is a default value
+        # for one, set it.
+        for name, definition in self.hyperparameter_definitions.items():
+            if name not in self._hyperparameters:
+                spec = definition["spec"]
+                if "DefaultValue" in spec:
+                    self._hyperparameters[name] = spec["DefaultValue"]
+                elif "IsRequired" in spec and spec["IsRequired"]:
+                    raise ValueError("Required hyperparameter: %s is not set" % name)
+
+    def _parse_hyperparameters(self):
+        """Placeholder docstring"""
+        definitions = {}
+
+        training_spec = self.algorithm_spec["TrainingSpecification"]
+        if "SupportedHyperParameters" in training_spec:
+            hyperparameters = training_spec["SupportedHyperParameters"]
+            for h in hyperparameters:
+                parameter_type = h["Type"]
+                name = h["Name"]
+                parameter_class, parameter_range = self._hyperparameter_range_and_class(
+                    parameter_type, h
+                )
+
+                definitions[name] = {"spec": h}
+                if parameter_range:
+                    definitions[name]["range"] = parameter_range
+                if parameter_class:
+                    definitions[name]["class"] = parameter_class
+
+        return definitions
+
+    def _hyperparameter_range_and_class(self, parameter_type, hyperparameter):
+        """Placeholder docstring."""
+        if parameter_type in self._hyperpameters_with_range:
+            range_name = parameter_type + "ParameterRangeSpecification"
+
+        parameter_class = None
+        parameter_range = None
+
+        if parameter_type in ("Integer", "Continuous"):
+            # Integer and Continuous are handled the same way. We get the min and max values
+            # and just create an Instance of Parameter. Note that the range is optional for all
+            # the Parameter Types.
+            if parameter_type == "Integer":
+                parameter_class = sagemaker.parameter.IntegerParameter
+            else:
+                parameter_class = sagemaker.parameter.ContinuousParameter
+
+            if "Range" in hyperparameter:
+                min_value = parameter_class.cast_to_type(
+                    hyperparameter["Range"][range_name]["MinValue"]
+                )
+                max_value = parameter_class.cast_to_type(
+                    hyperparameter["Range"][range_name]["MaxValue"]
+                )
+                parameter_range = parameter_class(min_value, max_value)
+
+        elif parameter_type == "Categorical":
+            parameter_class = sagemaker.parameter.CategoricalParameter
+            if "Range" in hyperparameter:
+                values = hyperparameter["Range"][range_name]["Values"]
+                parameter_range = sagemaker.parameter.CategoricalParameter(values)
+        elif parameter_type == "FreeText":
+            pass
+        else:
+            raise ValueError(
+                "Invalid Hyperparameter type: %s. Valid ones are:"
+                "(Integer, Continuous, Categorical, FreeText)" % parameter_type
+            )
+
+        return parameter_class, parameter_range
+
+    def _algorithm_training_input_modes(self, training_channels):
+        """Placeholder docstring"""
+        current_input_modes = {"File", "Pipe"}
+        for channel in training_channels:
+            supported_input_modes = set(channel["SupportedInputModes"])
+            current_input_modes = current_input_modes & supported_input_modes
+
+        return current_input_modes
+
+    @classmethod
+    def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
+        """Convert the job description to init params that can be handled by the class constructor.
+
+        Args:
+            job_details (dict): the returned job details from a DescribeTrainingJob
+                API call.
+            model_channel_name (str): Name of the channel where pre-trained
+                model data will be downloaded.
+
+        Returns:
+            dict: The transformed init_params
+        """
+        init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
+            job_details, model_channel_name
+        )
+
+        # This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
+        # It cannot be set through instantiating an estimator.
+        if "_tuning_objective_metric" in init_params["hyperparameters"]:
+            del init_params["hyperparameters"]["_tuning_objective_metric"]
+
+        return init_params

testbed/aws__sagemaker-python-sdk/src/sagemaker/analytics.py

@@ -0,0 +1,744 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Placeholder docstring"""
+from __future__ import print_function, absolute_import
+
+from abc import ABCMeta, abstractmethod
+from collections import defaultdict, OrderedDict
+import datetime
+import logging
+
+from six import with_metaclass
+
+from sagemaker.session import Session
+from sagemaker.utils import DeferredError
+from sagemaker.lineage import artifact
+
+logger = logging.getLogger(__name__)
+
+try:
+    import pandas as pd
+except ImportError as e:
+    logger.warning("pandas failed to import. Analytics features will be impaired or broken.")
+    # Any subsequent attempt to use pandas will raise the ImportError
+    pd = DeferredError(e)
+
+METRICS_PERIOD_DEFAULT = 60  # seconds
+
+
+class AnalyticsMetricsBase(with_metaclass(ABCMeta, object)):
+    """Base class for tuning job or training job analytics classes.
+
+    Understands common functionality like persistence and caching.
+    """
+
+    def __init__(self):
+        """Initializes ``AnalyticsMetricsBase`` instance."""
+        self._dataframe = None
+
+    def export_csv(self, filename):
+        """Persists the analytics dataframe to a file.
+
+        Args:
+            filename (str): The name of the file to save to.
+        """
+        self.dataframe().to_csv(filename)
+
+    def dataframe(self, force_refresh=False):
+        """A pandas dataframe with lots of interesting results about this object.
+
+        Created by calling SageMaker List and Describe APIs and converting them into a
+        convenient tabular summary.
+
+        Args:
+            force_refresh (bool): Set to True to fetch the latest data from
+                SageMaker API.
+        """
+        if force_refresh:
+            self.clear_cache()
+        if self._dataframe is None:
+            self._dataframe = self._fetch_dataframe()
+        return self._dataframe
+
+    @abstractmethod
+    def _fetch_dataframe(self):
+        """Sub-class must calculate the dataframe and return it."""
+
+    def clear_cache(self):
+        """Clear the object of all local caches of API methods.
+
+        So that the next time any properties are accessed they will be refreshed from the service.
+        """
+        self._dataframe = None
+
+
+class HyperparameterTuningJobAnalytics(AnalyticsMetricsBase):
+    """Fetch results about a hyperparameter tuning job and make them accessible for analytics."""
+
+    def __init__(self, hyperparameter_tuning_job_name, sagemaker_session=None):
+        """Initialize a ``HyperparameterTuningJobAnalytics`` instance.
+
+        Args:
+            hyperparameter_tuning_job_name (str): name of the
+                HyperparameterTuningJob to analyze.
+            sagemaker_session (sagemaker.session.Session): Session object which
+                manages interactions with Amazon SageMaker APIs and any other
+                AWS services needed. If not specified, one is created using the
+                default AWS configuration chain.
+        """
+        sagemaker_session = sagemaker_session or Session()
+        self._sage_client = sagemaker_session.sagemaker_client
+        self._tuning_job_name = hyperparameter_tuning_job_name
+        self._tuning_job_describe_result = None
+        self._training_job_summaries = None
+        super(HyperparameterTuningJobAnalytics, self).__init__()
+        self.clear_cache()
+
+    @property
+    def name(self):
+        """Name of the HyperparameterTuningJob being analyzed"""
+        return self._tuning_job_name
+
+    def __repr__(self):
+        """Human-readable representation override."""
+        return "<sagemaker.HyperparameterTuningJobAnalytics for %s>" % self.name
+
+    def clear_cache(self):
+        """Clear the object of all local caches of API methods."""
+        super(HyperparameterTuningJobAnalytics, self).clear_cache()
+        self._tuning_job_describe_result = None
+        self._training_job_summaries = None
+
+    def _fetch_dataframe(self):
+        """Return a pandas dataframe with all the training jobs.
+
+        This includes their hyperparameters, results, and metadata, as well as
+        a column to indicate if a training job was the best seen so far.
+        """
+
+        def reshape(training_summary):
+            # Helper method to reshape a single training job summary into a dataframe record
+            out = {}
+            for k, v in training_summary["TunedHyperParameters"].items():
+                # Something (bokeh?) gets confused with ints so convert to float
+                try:
+                    v = float(v)
+                except (TypeError, ValueError):
+                    pass
+                out[k] = v
+            out["TrainingJobName"] = training_summary["TrainingJobName"]
+            out["TrainingJobStatus"] = training_summary["TrainingJobStatus"]
+            out["FinalObjectiveValue"] = training_summary.get(
+                "FinalHyperParameterTuningJobObjectiveMetric", {}
+            ).get("Value")
+
+            start_time = training_summary.get("TrainingStartTime", None)
+            end_time = training_summary.get("TrainingEndTime", None)
+            out["TrainingStartTime"] = start_time
+            out["TrainingEndTime"] = end_time
+            if start_time and end_time:
+                out["TrainingElapsedTimeSeconds"] = (end_time - start_time).total_seconds()
+            if "TrainingJobDefinitionName" in training_summary:
+                out["TrainingJobDefinitionName"] = training_summary["TrainingJobDefinitionName"]
+            return out
+
+        # Run that helper over all the summaries.
+        df = pd.DataFrame([reshape(tjs) for tjs in self.training_job_summaries()])
+        return df
+
+    @property
+    def tuning_ranges(self):
+        """A dictionary describing the ranges of all tuned hyperparameters.
+
+        The keys are the names of the hyperparameter, and the values are the ranges.
+
+        The output can take one of two forms:
+
+            * If the 'TrainingJobDefinition' field is present in the job description, the output
+                is a dictionary constructed from 'ParameterRanges' in
+                'HyperParameterTuningJobConfig' of the job description. The keys are the
+                parameter names, while the values are the parameter ranges.
+                Example:
+                >>> {
+                >>>     "eta": {"MaxValue": "1", "MinValue": "0", "Name": "eta"},
+                >>>     "gamma": {"MaxValue": "10", "MinValue": "0", "Name": "gamma"},
+                >>>     "iterations": {"MaxValue": "100", "MinValue": "50", "Name": "iterations"},
+                >>>     "num_layers": {"MaxValue": "30", "MinValue": "5", "Name": "num_layers"},
+                >>> }
+            * If the 'TrainingJobDefinitions' field (list) is present in the job description,
+                the output is a dictionary with keys as the 'DefinitionName' values from
+                all items in 'TrainingJobDefinitions', and each value would be a dictionary
+                constructed from 'HyperParameterRanges' in each item in 'TrainingJobDefinitions'
+                in the same format as above
+                Example:
+                >>> {
+                >>>     "estimator_1": {
+                >>>         "eta": {"MaxValue": "1", "MinValue": "0", "Name": "eta"},
+                >>>         "gamma": {"MaxValue": "10", "MinValue": "0", "Name": "gamma"},
+                >>>     },
+                >>>     "estimator_2": {
+                >>>         "framework": {"Values": ["TF", "MXNet"], "Name": "framework"},
+                >>>         "gamma": {"MaxValue": "1.0", "MinValue": "0.2", "Name": "gamma"}
+                >>>     }
+                >>> }
+
+        For more details about the 'TrainingJobDefinition' and 'TrainingJobDefinitions' fields
+        in job description, see
+        https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_hyper_parameter_tuning_job
+        """
+        description = self.description()
+
+        if "TrainingJobDefinition" in description:
+            return self._prepare_parameter_ranges(
+                description["HyperParameterTuningJobConfig"]["ParameterRanges"]
+            )
+
+        return {
+            training_job_definition["DefinitionName"]: self._prepare_parameter_ranges(
+                training_job_definition["HyperParameterRanges"]
+            )
+            for training_job_definition in description["TrainingJobDefinitions"]
+        }
+
+    def _prepare_parameter_ranges(self, parameter_ranges):
+        """Convert parameter ranges a dictionary using the parameter range names as the keys"""
+        out = {}
+        for _, ranges in parameter_ranges.items():
+            for param in ranges:
+                out[param["Name"]] = param
+        return out
+
+    def description(self, force_refresh=False):
+        """Call ``DescribeHyperParameterTuningJob`` for the hyperparameter tuning job.
+
+        Args:
+            force_refresh (bool): Set to True to fetch the latest data from
+                SageMaker API.
+
+        Returns:
+            dict: The Amazon SageMaker response for
+            ``DescribeHyperParameterTuningJob``.
+        """
+        if force_refresh:
+            self.clear_cache()
+        if not self._tuning_job_describe_result:
+            self._tuning_job_describe_result = self._sage_client.describe_hyper_parameter_tuning_job(  # noqa: E501 # pylint: disable=line-too-long
+                HyperParameterTuningJobName=self.name
+            )
+        return self._tuning_job_describe_result
+
+    def training_job_summaries(self, force_refresh=False):
+        """A (paginated) list of everything from ``ListTrainingJobsForTuningJob``.
+
+        Args:
+            force_refresh (bool): Set to True to fetch the latest data from
+                SageMaker API.
+
+        Returns:
+            dict: The Amazon SageMaker response for
+            ``ListTrainingJobsForTuningJob``.
+        """
+        if force_refresh:
+            self.clear_cache()
+        if self._training_job_summaries is not None:
+            return self._training_job_summaries
+        output = []
+        next_args = {}
+        for count in range(100):
+            logger.debug("Calling list_training_jobs_for_hyper_parameter_tuning_job %d", count)
+            raw_result = self._sage_client.list_training_jobs_for_hyper_parameter_tuning_job(
+                HyperParameterTuningJobName=self.name, MaxResults=100, **next_args
+            )
+            new_output = raw_result["TrainingJobSummaries"]
+            output.extend(new_output)
+            logger.debug(
+                "Got %d more TrainingJobs. Total so far: %d",
+                len(new_output),
+                len(output),
+            )
+            if ("NextToken" in raw_result) and (len(new_output) > 0):
+                next_args["NextToken"] = raw_result["NextToken"]
+            else:
+                break
+        self._training_job_summaries = output
+        return output
+
+
+class TrainingJobAnalytics(AnalyticsMetricsBase):
+    """Fetch training curve data from CloudWatch Metrics for a specific training job."""
+
+    CLOUDWATCH_NAMESPACE = "/aws/sagemaker/TrainingJobs"
+
+    def __init__(
+        self,
+        training_job_name,
+        metric_names=None,
+        sagemaker_session=None,
+        start_time=None,
+        end_time=None,
+        period=None,
+    ):
+        """Initialize a ``TrainingJobAnalytics`` instance.
+
+        Args:
+            training_job_name (str): name of the TrainingJob to analyze.
+            metric_names (list, optional): string names of all the metrics to
+                collect for this training job. If not specified, then it will
+                use all metric names configured for this job.
+            sagemaker_session (sagemaker.session.Session): Session object which
+                manages interactions with Amazon SageMaker APIs and any other
+                AWS services needed. If not specified, one is specified using
+                the default AWS configuration chain.
+            start_time:
+            end_time:
+            period:
+        """
+        sagemaker_session = sagemaker_session or Session()
+        self._sage_client = sagemaker_session.sagemaker_client
+        self._cloudwatch = sagemaker_session.boto_session.client("cloudwatch")
+        self._training_job_name = training_job_name
+        self._start_time = start_time
+        self._end_time = end_time
+        self._period = period or METRICS_PERIOD_DEFAULT
+
+        if metric_names:
+            self._metric_names = metric_names
+        else:
+            self._metric_names = self._metric_names_for_training_job()
+
+        super(TrainingJobAnalytics, self).__init__()
+        self.clear_cache()
+
+    @property
+    def name(self):
+        """Name of the TrainingJob being analyzed"""
+        return self._training_job_name
+
+    def __repr__(self):
+        """The human-readable representation override."""
+        return "<sagemaker.TrainingJobAnalytics for %s>" % self.name
+
+    def clear_cache(self):
+        """Clear the object of all local caches of API methods.
+
+        This is so that the next time any properties are accessed they will be
+        refreshed from the service.
+        """
+        super(TrainingJobAnalytics, self).clear_cache()
+        self._data = defaultdict(list)
+        self._time_interval = self._determine_timeinterval()
+
+    def _determine_timeinterval(self):
+        """Return a dict with two datetime objects.
+
+        The dict includes the `start_time` and `end_time`, covering the interval
+        of the training job.
+
+        Returns:
+            a dict with the `start_time` and `end_time`.
+        """
+        description = self._sage_client.describe_training_job(TrainingJobName=self.name)
+        start_time = self._start_time or description["TrainingStartTime"]  # datetime object
+        # Incrementing end time by 1 min since CloudWatch drops seconds before finding the logs.
+        # This results in logs being searched in the time range in which the correct log line was
+        # not present.
+        # Example - Log time - 2018-10-22 08:25:55
+        #       Here calculated end time would also be 2018-10-22 08:25:55 (without 1 min addition)
+        #       CW will consider end time as 2018-10-22 08:25 and will not be able to search the
+        #           correct log.
+        end_time = self._end_time or description.get(
+            "TrainingEndTime", datetime.datetime.utcnow()
+        ) + datetime.timedelta(minutes=1)
+
+        return {"start_time": start_time, "end_time": end_time}
+
+    def _fetch_dataframe(self):
+        for metric_name in self._metric_names:
+            self._fetch_metric(metric_name)
+        return pd.DataFrame(self._data)
+
+    def _fetch_metric(self, metric_name):
+        """Fetch all the values of a named metric, and add them to _data
+
+        Args:
+            metric_name: The metric name to fetch.
+        """
+        request = {
+            "Namespace": self.CLOUDWATCH_NAMESPACE,
+            "MetricName": metric_name,
+            "Dimensions": [{"Name": "TrainingJobName", "Value": self.name}],
+            "StartTime": self._time_interval["start_time"],
+            "EndTime": self._time_interval["end_time"],
+            "Period": self._period,
+            "Statistics": ["Average"],
+        }
+        raw_cwm_data = self._cloudwatch.get_metric_statistics(**request)["Datapoints"]
+        if len(raw_cwm_data) == 0:
+            logger.warning("Warning: No metrics called %s found", metric_name)
+            return
+
+        # Process data: normalize to starting time, and sort.
+        base_time = min(raw_cwm_data, key=lambda pt: pt["Timestamp"])["Timestamp"]
+        all_xy = []
+        for pt in raw_cwm_data:
+            y = pt["Average"]
+            x = (pt["Timestamp"] - base_time).total_seconds()
+            all_xy.append([x, y])
+        all_xy = sorted(all_xy, key=lambda x: x[0])
+
+        # Store everything in _data to make a dataframe from
+        for elapsed_seconds, value in all_xy:
+            self._add_single_metric(elapsed_seconds, metric_name, value)
+
+    def _add_single_metric(self, timestamp, metric_name, value):
+        """Store a single metric in the _data dict.
+
+        This can be converted to a dataframe.
+
+        Args:
+            timestamp: The timestamp of the metric.
+            metric_name: The name of the metric.
+            value: The value of the metric.
+        """
+        # note that this method is built this way to make it possible to
+        # support live-refreshing charts in Bokeh at some point in the future.
+        self._data["timestamp"].append(timestamp)
+        self._data["metric_name"].append(metric_name)
+        self._data["value"].append(value)
+
+    def _metric_names_for_training_job(self):
+        """Helper method to discover the metrics defined for a training job."""
+        training_description = self._sage_client.describe_training_job(
+            TrainingJobName=self._training_job_name
+        )
+
+        metric_definitions = training_description["AlgorithmSpecification"]["MetricDefinitions"]
+        metric_names = [md["Name"] for md in metric_definitions]
+
+        return metric_names
+
+
+class ArtifactAnalytics(AnalyticsMetricsBase):
+    """Fetch artifact data and make them accessible for analytics."""
+
+    def __init__(
+        self,
+        sort_by=None,
+        sort_order=None,
+        source_uri=None,
+        artifact_type=None,
+        sagemaker_session=None,
+    ):
+        """Initialize a ``ArtifactAnalytics`` instance.
+
+        Args:
+            sort_by (str, optional): The name of the resource property used to sort
+                the set of artifacts. Currently only support for sort by Name
+            sort_order(str optional): How trial components are ordered, valid values are Ascending
+                and Descending. The default is Descending.
+            source_uri(dict optional): The artifact source uri for filtering.
+            artifact_type(dict optional): The artifact type for filtering.
+            sagemaker_session (obj, optional): Sagemaker session. Defaults to None.
+        """
+        self._sort_by = sort_by if sort_by == "Name" else None
+        self._sort_order = sort_order
+        self._source_uri = source_uri
+        self._artifact_type = artifact_type
+        self._sagemaker_session = sagemaker_session
+        super(ArtifactAnalytics, self).__init__()
+        self.clear_cache()
+
+    def __repr__(self):
+        """Human-readable representation override."""
+        return "<sagemaker.ArtifactAnalytics>"
+
+    def _reshape_source_type(self, artifact_source_types):
+        """Reshape artifact source type."""
+        out = OrderedDict()
+        for artifact_source_type in artifact_source_types:
+            out["ArtifactSourceType"] = artifact_source_type
+        return out
+
+    def _reshape(self, artifact_summary):
+        """Reshape artifact summary."""
+        out = OrderedDict()
+        out["ArtifactName"] = artifact_summary.artifact_name
+        out["ArtifactArn"] = artifact_summary.artifact_arn
+        out["ArtifactType"] = artifact_summary.artifact_type
+        out["ArtifactSourceUri"] = artifact_summary.source.source_uri
+        out["CreationTime"] = artifact_summary.creation_time
+        out["LastModifiedTime"] = artifact_summary.last_modified_time
+        return out
+
+    def _fetch_dataframe(self):
+        """Return a pandas dataframe with all artifacts."""
+        df = pd.DataFrame([self._reshape(artifact) for artifact in self._get_list_artifacts()])
+        return df
+
+    def _get_list_artifacts(self):
+        """List artifacts."""
+        artifacts = artifact.Artifact.list(
+            source_uri=self._source_uri,
+            artifact_type=self._artifact_type,
+            sort_by=self._sort_by,
+            sort_order=self._sort_order,
+            sagemaker_session=self._sagemaker_session,
+        )
+        return artifacts
+
+
+class ExperimentAnalytics(AnalyticsMetricsBase):
+    """Fetch trial component data and make them accessible for analytics."""
+
+    MAX_TRIAL_COMPONENTS = 10000
+
+    def __init__(
+        self,
+        experiment_name=None,
+        search_expression=None,
+        sort_by=None,
+        sort_order=None,
+        metric_names=None,
+        parameter_names=None,
+        sagemaker_session=None,
+        input_artifact_names=None,
+        output_artifact_names=None,
+    ):
+        """Initialize a ``ExperimentAnalytics`` instance.
+
+        Args:
+            experiment_name (str, optional): Name of the experiment if you want to constrain the
+                search to only trial components belonging to an experiment.
+            search_expression (dict, optional): The search query to find the set of trial components
+                to use to populate the data frame.
+            sort_by (str, optional): The name of the resource property used to sort
+                the set of trial components.
+            sort_order(str optional): How trial components are ordered, valid values are Ascending
+                and Descending. The default is Descending.
+            metric_names (list, optional): string names of all the metrics to be shown in the
+                data frame. If not specified, all metrics will be shown of all trials.
+            parameter_names (list, optional): string names of the parameters to be shown in the
+                data frame. If not specified, all parameters will be shown of all trials.
+            sagemaker_session (sagemaker.session.Session): Session object which manages interactions
+                with Amazon SageMaker APIs and any other AWS services needed. If not specified,
+                one is created using the default AWS configuration chain.
+            input_artifact_names(dict optional):The input artifacts for the experiment. Examples of
+                input artifacts are datasets, algorithms, hyperparameters, source code, and instance
+                types.
+            output_artifact_names(dict optional): The output artifacts for the experiment. Examples
+                of output artifacts are metrics, snapshots, logs, and images.
+        """
+        sagemaker_session = sagemaker_session or Session()
+        self._sage_client = sagemaker_session.sagemaker_client
+
+        if not experiment_name and not search_expression:
+            raise ValueError("Either experiment_name or search_expression must be supplied.")
+
+        self._experiment_name = experiment_name
+        self._search_expression = search_expression
+        self._sort_by = sort_by
+        self._sort_order = sort_order
+        self._metric_names = metric_names
+        self._parameter_names = parameter_names
+        self._input_artifact_names = input_artifact_names
+        self._output_artifact_names = output_artifact_names
+        self._trial_components = None
+        super(ExperimentAnalytics, self).__init__()
+        self.clear_cache()
+
+    @property
+    def name(self):
+        """Name of the Experiment being analyzed."""
+        return self._experiment_name
+
+    def __repr__(self):
+        """The human-readable representation override."""
+        return "<sagemaker.ExperimentAnalytics for %s>" % self.name
+
+    def clear_cache(self):
+        """Clear the object of all local caches of API methods."""
+        super(ExperimentAnalytics, self).clear_cache()
+        self._trial_components = None
+
+    def _reshape_parameters(self, parameters):
+        """Reshape trial component parameters to a pandas column.
+
+        Args:
+            parameters: trial component parameters
+        Returns:
+            dict: Key: Parameter name, Value: Parameter value
+        """
+        out = OrderedDict()
+        for name, value in sorted(parameters.items()):
+            if self._parameter_names and name not in self._parameter_names:
+                continue
+            out[name] = value.get("NumberValue", value.get("StringValue"))
+        return out
+
+    def _reshape_metrics(self, metrics):
+        """Reshape trial component metrics to a pandas column.
+
+        Args:
+            metrics: trial component metrics
+        Returns:
+            dict: Key: Metric name, Value: Metric value
+        """
+        statistic_types = ["Min", "Max", "Avg", "StdDev", "Last", "Count"]
+        out = OrderedDict()
+        for metric_summary in metrics:
+            metric_name = metric_summary["MetricName"]
+            if self._metric_names and metric_name not in self._metric_names:
+                continue
+
+            for stat_type in statistic_types:
+                stat_value = metric_summary.get(stat_type)
+                if stat_value is not None:
+                    out["{} - {}".format(metric_name, stat_type)] = stat_value
+        return out
+
+    def _reshape_artifacts(self, artifacts, _artifact_names):
+        """Reshape trial component input/output artifacts to a pandas column.
+
+        Args:
+            artifacts: trial component input/output artifacts
+        Returns:
+            dict: Key: artifacts name, Value: artifacts value
+        """
+        out = OrderedDict()
+        for name, value in sorted(artifacts.items()):
+            if _artifact_names and (name not in _artifact_names):
+                continue
+            out["{} - {}".format(name, "MediaType")] = value.get("MediaType")
+            out["{} - {}".format(name, "Value")] = value.get("Value")
+        return out
+
+    def _reshape_parents(self, parents):
+        """Reshape trial component parents to a pandas column.
+
+        Args:
+            parents: trial component parents (trials and experiments)
+        Returns:
+            dict: Key: artifacts name, Value: artifacts value
+        """
+        out = OrderedDict()
+        trials = []
+        experiments = []
+        for parent in parents:
+            trials.append(parent["TrialName"])
+            experiments.append(parent["ExperimentName"])
+        out["Trials"] = trials
+        out["Experiments"] = experiments
+        return out
+
+    def _reshape(self, trial_component):
+        """Reshape trial component data to pandas columns.
+
+        Args:
+            trial_component: dict representing a trial component
+        Returns:
+            dict: Key-Value pair representing the data in the pandas dataframe
+        """
+        out = OrderedDict()
+        for attribute in ["TrialComponentName", "DisplayName"]:
+            out[attribute] = trial_component.get(attribute, "")
+
+        source = trial_component.get("Source", "")
+        if source:
+            out["SourceArn"] = source["SourceArn"]
+
+        out.update(self._reshape_parameters(trial_component.get("Parameters", [])))
+        out.update(self._reshape_metrics(trial_component.get("Metrics", [])))
+        out.update(
+            self._reshape_artifacts(
+                trial_component.get("InputArtifacts", []), self._input_artifact_names
+            )
+        )
+        out.update(
+            self._reshape_artifacts(
+                trial_component.get("OutputArtifacts", []), self._output_artifact_names
+            )
+        )
+        out.update(self._reshape_parents(trial_component.get("Parents", [])))
+        return out
+
+    def _fetch_dataframe(self):
+        """Return a pandas dataframe includes all the trial_components."""
+
+        df = pd.DataFrame([self._reshape(component) for component in self._get_trial_components()])
+        return df
+
+    def _get_trial_components(self, force_refresh=False):
+        """Get all trial components matching the given search query expression.
+
+        Args:
+            force_refresh (bool): Set to True to fetch the latest data from SageMaker API.
+
+        Returns:
+            list: List of dicts representing the trial components
+        """
+        if force_refresh:
+            self.clear_cache()
+        if self._trial_components is not None:
+            return self._trial_components
+
+        if not self._search_expression:
+            self._search_expression = {}
+
+        if self._experiment_name:
+            if not self._search_expression.get("Filters"):
+                self._search_expression["Filters"] = []
+
+            self._search_expression["Filters"].append(
+                {
+                    "Name": "Parents.ExperimentName",
+                    "Operator": "Equals",
+                    "Value": self._experiment_name,
+                }
+            )
+
+        return self._search(self._search_expression, self._sort_by, self._sort_order)
+
+    def _search(self, search_expression, sort_by, sort_order):
+        """Perform a search query using SageMaker Search and return the matching trial components.
+
+        Args:
+            search_expression: Search expression to filter trial components.
+            sort_by: The name of the resource property used to sort the trial components.
+            sort_order: How trial components are ordered, valid values are Ascending
+                and Descending. The default is Descending.
+        Returns:
+            list: List of dict representing trial components.
+        """
+        trial_components = []
+
+        search_args = {
+            "Resource": "ExperimentTrialComponent",
+            "SearchExpression": search_expression,
+        }
+
+        if sort_by:
+            search_args["SortBy"] = sort_by
+
+        if sort_order:
+            search_args["SortOrder"] = sort_order
+
+        while len(trial_components) < self.MAX_TRIAL_COMPONENTS:
+            search_response = self._sage_client.search(**search_args)
+            components = [result["TrialComponent"] for result in search_response["Results"]]
+            trial_components.extend(components)
+            if "NextToken" in search_response and len(components) > 0:
+                search_args["NextToken"] = search_response["NextToken"]
+            else:
+                break
+
+        return trial_components

testbed/aws__sagemaker-python-sdk/src/sagemaker/content_types.py

@@ -0,0 +1,23 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Deprecated content type constants. Just use the mime type strings."""
+from __future__ import absolute_import
+
+import deprecations
+
+deprecations.removed_warning("The sagemaker.content_types module")
+
+CONTENT_TYPE_JSON = "application/json"
+CONTENT_TYPE_CSV = "text/csv"
+CONTENT_TYPE_OCTET_STREAM = "application/octet-stream"
+CONTENT_TYPE_NPY = "application/x-npy"

testbed/aws__sagemaker-python-sdk/src/sagemaker/deprecations.py

@@ -0,0 +1,245 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Module for deprecation abstractions."""
+from __future__ import absolute_import
+
+import logging
+import warnings
+
+logger = logging.getLogger(__name__)
+
+V2_URL = "https://sagemaker.readthedocs.io/en/stable/v2.html"
+
+
+def _warn(msg, sdk_version=None):
+    """Generic warning raiser referencing V2
+
+    Args:
+        phrase: The phrase to include in the warning.
+        sdk_version: the sdk version of removal of support.
+    """
+    _sdk_version = sdk_version if sdk_version is not None else "2"
+    full_msg = f"{msg} in sagemaker>={_sdk_version}.\nSee: {V2_URL} for details."
+    warnings.warn(full_msg, DeprecationWarning, stacklevel=2)
+    logger.warning(full_msg)
+
+
+def removed_warning(phrase, sdk_version=None):
+    """Raise a warning for a no-op in sagemaker>=2
+
+    Args:
+        phrase: the prefix phrase of the warning message.
+        sdk_version: the sdk version of removal of support.
+    """
+    _warn(f"{phrase} is a no-op", sdk_version)
+
+
+def renamed_warning(phrase):
+    """Raise a warning for a rename in sagemaker>=2
+
+    Args:
+        phrase: the prefix phrase of the warning message.
+    """
+    _warn(f"{phrase} has been renamed")
+
+
+def deprecation_warn(name, date, msg=None):
+    """Raise a warning for soon to be deprecated feature in sagemaker>=2
+
+    Args:
+        name (str): Name of the feature
+        date (str): the date when the feature will be deprecated
+        msg (str): the prefix phrase of the warning message.
+    """
+    _warn(f"{name} will be deprecated on {date}.{msg}")
+
+
+def deprecation_warning(date, msg=None):
+    """Decorator for raising deprecation warning for a feature in sagemaker>=2
+
+    Args:
+        date (str): the date when the feature will be deprecated
+        msg (str): the prefix phrase of the warning message.
+
+    Usage:
+        @deprecation_warning(msg="message", date="date")
+        def sample_function():
+            print("xxxx....")
+
+        @deprecation_warning(msg="message", date="date")
+        class SampleClass():
+            def __init__(self):
+                print("xxxx....")
+
+    """
+
+    def deprecate(obj):
+        def wrapper(*args, **kwargs):
+            deprecation_warn(obj.__name__, date, msg)
+            return obj(*args, **kwargs)
+
+        return wrapper
+
+    return deprecate
+
+
+def renamed_kwargs(old_name, new_name, value, kwargs):
+    """Checks if the deprecated argument is in kwargs
+
+    Raises warning, if present.
+
+    Args:
+        old_name: name of deprecated argument
+        new_name: name of the new argument
+        value: value associated with new name, if supplied
+        kwargs: keyword arguments dict
+
+    Returns:
+        value of the keyword argument, if present
+    """
+    if old_name in kwargs:
+        value = kwargs.get(old_name, value)
+        kwargs[new_name] = value
+        renamed_warning(old_name)
+    return value
+
+
+def removed_arg(name, arg):
+    """Checks if the deprecated argument is populated.
+
+    Raises warning, if not None.
+
+    Args:
+        name: name of deprecated argument
+        arg: the argument to check
+    """
+    if arg is not None:
+        removed_warning(name)
+
+
+def removed_kwargs(name, kwargs):
+    """Checks if the deprecated argument is in kwargs
+
+    Raises warning, if present.
+
+    Args:
+        name: name of deprecated argument
+        kwargs: keyword arguments dict
+    """
+    if name in kwargs:
+        removed_warning(name)
+
+
+def removed_function(name):
+    """A no-op deprecated function factory."""
+
+    def func(*args, **kwargs):  # pylint: disable=W0613
+        removed_warning(f"The function {name}")
+
+    return func
+
+
+def deprecated(sdk_version=None):
+    """Decorator for raising deprecated warning for a feature in sagemaker>=2
+
+    Args:
+        sdk_version (str): the sdk version of removal of support.
+
+    Usage:
+        @deprecated()
+        def sample_function():
+            print("xxxx....")
+
+        @deprecated(sdk_version="2.66")
+        class SampleClass():
+            def __init__(self):
+                print("xxxx....")
+
+    """
+
+    def deprecate(obj):
+        def wrapper(*args, **kwargs):
+            removed_warning(obj.__name__, sdk_version)
+            return obj(*args, **kwargs)
+
+        return wrapper
+
+    return deprecate
+
+
+def deprecated_function(func, name):
+    """Wrap a function with a deprecation warning.
+
+    Args:
+        func: Function to wrap in a deprecation warning.
+        name: The name that has been deprecated.
+
+    Returns:
+        The modified function
+    """
+
+    def deprecate(*args, **kwargs):
+        renamed_warning(f"The {name}")
+        return func(*args, **kwargs)
+
+    return deprecate
+
+
+def deprecated_serialize(instance, name):
+    """Modifies a serializer instance serialize method.
+
+    Args:
+        instance: Instance to modify serialize method.
+        name: The name that has been deprecated.
+
+    Returns:
+        The modified instance
+    """
+    instance.serialize = deprecated_function(instance.serialize, name)
+    return instance
+
+
+def deprecated_deserialize(instance, name):
+    """Modifies a deserializer instance deserialize method.
+
+    Args:
+        instance: Instance to modify deserialize method.
+        name: The name that has been deprecated.
+
+    Returns:
+        The modified instance
+    """
+    instance.deserialize = deprecated_function(instance.deserialize, name)
+    return instance
+
+
+def deprecated_class(cls, name):
+    """Returns a class based on super class with a deprecation warning.
+
+    Args:
+        cls: The class to derive with a deprecation warning on __init__
+        name: The name of the class.
+
+    Returns:
+        The modified class.
+    """
+
+    class DeprecatedClass(cls):
+        """Provides a warning for the class name."""
+
+        def __init__(self, *args, **kwargs):
+            """Provides a warning for the class name."""
+            renamed_warning(f"The class {name}")
+            super(DeprecatedClass, self).__init__(*args, **kwargs)
+
+    return DeprecatedClass

testbed/aws__sagemaker-python-sdk/src/sagemaker/deserializers.py

@@ -0,0 +1,324 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Implements methods for deserializing data returned from an inference endpoint."""
+from __future__ import absolute_import
+
+import csv
+
+import abc
+import codecs
+import io
+import json
+
+import numpy as np
+from six import with_metaclass
+
+from sagemaker.utils import DeferredError
+
+try:
+    import pandas
+except ImportError as e:
+    pandas = DeferredError(e)
+
+
+class BaseDeserializer(abc.ABC):
+    """Abstract base class for creation of new deserializers.
+
+    Provides a skeleton for customization requiring the overriding of the method
+    deserialize and the class attribute ACCEPT.
+    """
+
+    @abc.abstractmethod
+    def deserialize(self, stream, content_type):
+        """Deserialize data received from an inference endpoint.
+
+        Args:
+            stream (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            object: The data deserialized into an object.
+        """
+
+    @property
+    @abc.abstractmethod
+    def ACCEPT(self):
+        """The content types that are expected from the inference endpoint."""
+
+
+class SimpleBaseDeserializer(with_metaclass(abc.ABCMeta, BaseDeserializer)):
+    """Abstract base class for creation of new deserializers.
+
+    This class extends the API of :class:~`sagemaker.deserializers.BaseDeserializer` with more
+    user-friendly options for setting the ACCEPT content type header, in situations where it can be
+    provided at init and freely updated.
+    """
+
+    def __init__(self, accept="*/*"):
+        """Initialize a ``SimpleBaseDeserializer`` instance.
+
+        Args:
+            accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
+                is expected from the inference endpoint (default: "*/*").
+        """
+        super(SimpleBaseDeserializer, self).__init__()
+        self.accept = accept
+
+    @property
+    def ACCEPT(self):
+        """The tuple of possible content types that are expected from the inference endpoint."""
+        if isinstance(self.accept, str):
+            return (self.accept,)
+        return self.accept
+
+
+class StringDeserializer(SimpleBaseDeserializer):
+    """Deserialize data from an inference endpoint into a decoded string."""
+
+    def __init__(self, encoding="UTF-8", accept="application/json"):
+        """Initialize a ``StringDeserializer`` instance.
+
+        Args:
+            encoding (str): The string encoding to use (default: UTF-8).
+            accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
+                is expected from the inference endpoint (default: "application/json").
+        """
+        super(StringDeserializer, self).__init__(accept=accept)
+        self.encoding = encoding
+
+    def deserialize(self, stream, content_type):
+        """Deserialize data from an inference endpoint into a decoded string.
+
+        Args:
+            stream (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            str: The data deserialized into a decoded string.
+        """
+        try:
+            return stream.read().decode(self.encoding)
+        finally:
+            stream.close()
+
+
+class BytesDeserializer(SimpleBaseDeserializer):
+    """Deserialize a stream of bytes into a bytes object."""
+
+    def deserialize(self, stream, content_type):
+        """Read a stream of bytes returned from an inference endpoint.
+
+        Args:
+            stream (botocore.response.StreamingBody): A stream of bytes.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            bytes: The bytes object read from the stream.
+        """
+        try:
+            return stream.read()
+        finally:
+            stream.close()
+
+
+class CSVDeserializer(SimpleBaseDeserializer):
+    """Deserialize a stream of bytes into a list of lists.
+
+    Consider using :class:~`sagemaker.deserializers.NumpyDeserializer` or
+    :class:~`sagemaker.deserializers.PandasDeserializer` instead, if you'd like to convert text/csv
+    responses directly into other data types.
+    """
+
+    def __init__(self, encoding="utf-8", accept="text/csv"):
+        """Initialize a ``CSVDeserializer`` instance.
+
+        Args:
+            encoding (str): The string encoding to use (default: "utf-8").
+            accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
+                is expected from the inference endpoint (default: "text/csv").
+        """
+        super(CSVDeserializer, self).__init__(accept=accept)
+        self.encoding = encoding
+
+    def deserialize(self, stream, content_type):
+        """Deserialize data from an inference endpoint into a list of lists.
+
+        Args:
+            stream (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            list: The data deserialized into a list of lists representing the
+                contents of a CSV file.
+        """
+        try:
+            decoded_string = stream.read().decode(self.encoding)
+            return list(csv.reader(decoded_string.splitlines()))
+        finally:
+            stream.close()
+
+
+class StreamDeserializer(SimpleBaseDeserializer):
+    """Directly return the data and content-type received from an inference endpoint.
+
+    It is the user's responsibility to close the data stream once they're done
+    reading it.
+    """
+
+    def deserialize(self, stream, content_type):
+        """Returns a stream of the response body and the MIME type of the data.
+
+        Args:
+            stream (botocore.response.StreamingBody): A stream of bytes.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            tuple: A two-tuple containing the stream and content-type.
+        """
+        return stream, content_type
+
+
+class NumpyDeserializer(SimpleBaseDeserializer):
+    """Deserialize a stream of data in .npy or UTF-8 CSV/JSON format to a numpy array."""
+
+    def __init__(self, dtype=None, accept="application/x-npy", allow_pickle=True):
+        """Initialize a ``NumpyDeserializer`` instance.
+
+        Args:
+            dtype (str): The dtype of the data (default: None).
+            accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
+                is expected from the inference endpoint (default: "application/x-npy").
+            allow_pickle (bool): Allow loading pickled object arrays (default: True).
+        """
+        super(NumpyDeserializer, self).__init__(accept=accept)
+        self.dtype = dtype
+        self.allow_pickle = allow_pickle
+
+    def deserialize(self, stream, content_type):
+        """Deserialize data from an inference endpoint into a NumPy array.
+
+        Args:
+            stream (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            numpy.ndarray: The data deserialized into a NumPy array.
+        """
+        try:
+            if content_type == "text/csv":
+                return np.genfromtxt(
+                    codecs.getreader("utf-8")(stream), delimiter=",", dtype=self.dtype
+                )
+            if content_type == "application/json":
+                return np.array(json.load(codecs.getreader("utf-8")(stream)), dtype=self.dtype)
+            if content_type == "application/x-npy":
+                return np.load(io.BytesIO(stream.read()), allow_pickle=self.allow_pickle)
+        finally:
+            stream.close()
+
+        raise ValueError("%s cannot read content type %s." % (__class__.__name__, content_type))
+
+
+class JSONDeserializer(SimpleBaseDeserializer):
+    """Deserialize JSON data from an inference endpoint into a Python object."""
+
+    def __init__(self, accept="application/json"):
+        """Initialize a ``JSONDeserializer`` instance.
+
+        Args:
+            accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
+                is expected from the inference endpoint (default: "application/json").
+        """
+        super(JSONDeserializer, self).__init__(accept=accept)
+
+    def deserialize(self, stream, content_type):
+        """Deserialize JSON data from an inference endpoint into a Python object.
+
+        Args:
+            stream (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            object: The JSON-formatted data deserialized into a Python object.
+        """
+        try:
+            return json.load(codecs.getreader("utf-8")(stream))
+        finally:
+            stream.close()
+
+
+class PandasDeserializer(SimpleBaseDeserializer):
+    """Deserialize CSV or JSON data from an inference endpoint into a pandas dataframe."""
+
+    def __init__(self, accept=("text/csv", "application/json")):
+        """Initialize a ``PandasDeserializer`` instance.
+
+        Args:
+            accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
+                is expected from the inference endpoint (default: ("text/csv","application/json")).
+        """
+        super(PandasDeserializer, self).__init__(accept=accept)
+
+    def deserialize(self, stream, content_type):
+        """Deserialize CSV or JSON data from an inference endpoint into a pandas dataframe.
+
+        If the data is JSON, the data should be formatted in the 'columns' orient.
+        See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html
+
+        Args:
+            stream (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            pandas.DataFrame: The data deserialized into a pandas DataFrame.
+        """
+        if content_type == "text/csv":
+            return pandas.read_csv(stream)
+
+        if content_type == "application/json":
+            return pandas.read_json(stream)
+
+        raise ValueError("%s cannot read content type %s." % (__class__.__name__, content_type))
+
+
+class JSONLinesDeserializer(SimpleBaseDeserializer):
+    """Deserialize JSON lines data from an inference endpoint."""
+
+    def __init__(self, accept="application/jsonlines"):
+        """Initialize a ``JSONLinesDeserializer`` instance.
+
+        Args:
+            accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
+                is expected from the inference endpoint (default: ("text/csv","application/json")).
+        """
+        super(JSONLinesDeserializer, self).__init__(accept=accept)
+
+    def deserialize(self, stream, content_type):
+        """Deserialize JSON lines data from an inference endpoint.
+
+        See https://docs.python.org/3/library/json.html#py-to-json-table to
+        understand how JSON values are converted to Python objects.
+
+        Args:
+            stream (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            list: A list of JSON serializable objects.
+        """
+        try:
+            body = stream.read().decode("utf-8")
+            lines = body.rstrip().split("\n")
+            return [json.loads(line) for line in lines]
+        finally:
+            stream.close()

testbed/aws__sagemaker-python-sdk/src/sagemaker/drift_check_baselines.py

@@ -0,0 +1,106 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""This file contains code related to drift check baselines"""
+from __future__ import absolute_import
+
+from typing import Optional
+
+from sagemaker.model_metrics import MetricsSource, FileSource
+
+
+class DriftCheckBaselines(object):
+    """Accepts drift check baselines parameters for conversion to request dict."""
+
+    def __init__(
+        self,
+        model_statistics: Optional[MetricsSource] = None,
+        model_constraints: Optional[MetricsSource] = None,
+        model_data_statistics: Optional[MetricsSource] = None,
+        model_data_constraints: Optional[MetricsSource] = None,
+        bias_config_file: Optional[FileSource] = None,
+        bias_pre_training_constraints: Optional[MetricsSource] = None,
+        bias_post_training_constraints: Optional[MetricsSource] = None,
+        explainability_constraints: Optional[MetricsSource] = None,
+        explainability_config_file: Optional[FileSource] = None,
+    ):
+        """Initialize a ``DriftCheckBaselines`` instance and turn parameters into dict.
+
+        Args:
+            model_statistics (MetricsSource): A metric source object that represents
+                model statistics (default: None).
+            model_constraints (MetricsSource): A metric source object that represents
+                model constraints (default: None).
+            model_data_statistics (MetricsSource): A metric source object that represents
+                model data statistics (default: None).
+            model_data_constraints (MetricsSource): A metric source object that represents
+                model data constraints (default: None).
+            bias_config_file (FileSource): A file source object that represents bias config
+                (default: None).
+            bias_pre_training_constraints (MetricsSource):
+                A metric source object that represents Pre-training constraints (default: None).
+            bias_post_training_constraints (MetricsSource):
+                A metric source object that represents Post-training constraits (default: None).
+            explainability_constraints (MetricsSource):
+                A metric source object that represents explainability constraints (default: None).
+            explainability_config_file (FileSource): A file source object that represents
+                explainability config (default: None).
+        """
+        self.model_statistics = model_statistics
+        self.model_constraints = model_constraints
+        self.model_data_statistics = model_data_statistics
+        self.model_data_constraints = model_data_constraints
+        self.bias_config_file = bias_config_file
+        self.bias_pre_training_constraints = bias_pre_training_constraints
+        self.bias_post_training_constraints = bias_post_training_constraints
+        self.explainability_constraints = explainability_constraints
+        self.explainability_config_file = explainability_config_file
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        drift_check_baselines_request = {}
+
+        model_quality = {}
+        if self.model_statistics is not None:
+            model_quality["Statistics"] = self.model_statistics._to_request_dict()
+        if self.model_constraints is not None:
+            model_quality["Constraints"] = self.model_constraints._to_request_dict()
+        if model_quality:
+            drift_check_baselines_request["ModelQuality"] = model_quality
+
+        model_data_quality = {}
+        if self.model_data_statistics is not None:
+            model_data_quality["Statistics"] = self.model_data_statistics._to_request_dict()
+        if self.model_data_constraints is not None:
+            model_data_quality["Constraints"] = self.model_data_constraints._to_request_dict()
+        if model_data_quality:
+            drift_check_baselines_request["ModelDataQuality"] = model_data_quality
+
+        bias = {}
+        if self.bias_config_file is not None:
+            bias["ConfigFile"] = self.bias_config_file._to_request_dict()
+        if self.bias_pre_training_constraints is not None:
+            bias["PreTrainingConstraints"] = self.bias_pre_training_constraints._to_request_dict()
+        if self.bias_post_training_constraints is not None:
+            bias["PostTrainingConstraints"] = self.bias_post_training_constraints._to_request_dict()
+        if bias:
+            drift_check_baselines_request["Bias"] = bias
+
+        explainability = {}
+        if self.explainability_constraints is not None:
+            explainability["Constraints"] = self.explainability_constraints._to_request_dict()
+        if self.explainability_config_file is not None:
+            explainability["ConfigFile"] = self.explainability_config_file._to_request_dict()
+        if explainability:
+            drift_check_baselines_request["Explainability"] = explainability
+
+        return drift_check_baselines_request

testbed/aws__sagemaker-python-sdk/src/sagemaker/environment_variables.py

@@ -0,0 +1,51 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Accessors to retrieve environment variables for hosting containers."""
+
+from __future__ import absolute_import
+
+import logging
+from typing import Dict
+
+from sagemaker.jumpstart import utils as jumpstart_utils
+from sagemaker.jumpstart import artifacts
+
+logger = logging.getLogger(__name__)
+
+
+def retrieve_default(
+    region=None,
+    model_id=None,
+    model_version=None,
+) -> Dict[str, str]:
+    """Retrieves the default container environment variables for the model matching the arguments.
+
+    Args:
+        region (str): Optional. The AWS Region for which to retrieve the default environment
+             variables. (Default: None).
+        model_id (str): Optional. The model ID of the model for which to
+            retrieve the default environment variables. (Default: None).
+        model_version (str): Optional. The version of the model for which to retrieve the
+            default environment variables. (Default: None).
+    Returns:
+        dict: The variables to use for the model.
+
+    Raises:
+        ValueError: If the combination of arguments specified is not supported.
+    """
+    if not jumpstart_utils.is_jumpstart_model_input(model_id, model_version):
+        raise ValueError(
+            "Must specify `model_id` and `model_version` when retrieving environment variables."
+        )
+
+    return artifacts._retrieve_default_environment_variables(model_id, model_version, region)

testbed/aws__sagemaker-python-sdk/src/sagemaker/exceptions.py

@@ -0,0 +1,65 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Custom exception classes for Sagemaker SDK"""
+from __future__ import absolute_import
+
+
+class UnexpectedStatusException(ValueError):
+    """Raised when resource status is not expected and thus not allowed for further execution"""
+
+    def __init__(self, message, allowed_statuses, actual_status):
+        self.allowed_statuses = allowed_statuses
+        self.actual_status = actual_status
+        super(UnexpectedStatusException, self).__init__(message)
+
+
+class CapacityError(UnexpectedStatusException):
+    """Raised when resource status is not expected and fails with a reason of CapacityError"""
+
+
+class AsyncInferenceError(Exception):
+    """The base exception class for Async Inference exceptions."""
+
+    fmt = "An unspecified error occurred"
+
+    def __init__(self, **kwargs):
+        msg = self.fmt.format(**kwargs)
+        Exception.__init__(self, msg)
+        self.kwargs = kwargs
+
+
+class ObjectNotExistedError(AsyncInferenceError):
+    """Raised when Amazon S3 object not exist in the given path"""
+
+    fmt = "Object not exist at {output_path}. {message}"
+
+    def __init__(self, message, output_path):
+        super().__init__(message=message, output_path=output_path)
+
+
+class PollingTimeoutError(AsyncInferenceError):
+    """Raised when wait longer than expected and no result object in Amazon S3 bucket yet"""
+
+    fmt = "No result at {output_path} after polling for {seconds} seconds. {message}"
+
+    def __init__(self, message, output_path, seconds):
+        super().__init__(message=message, output_path=output_path, seconds=seconds)
+
+
+class UnexpectedClientError(AsyncInferenceError):
+    """Raised when ClientError's error code is not expected"""
+
+    fmt = "Encountered unexpected client error: {message}"
+
+    def __init__(self, message):
+        super().__init__(message=message)

testbed/aws__sagemaker-python-sdk/src/sagemaker/git_utils.py

@@ -0,0 +1,343 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Placeholder docstring"""
+from __future__ import absolute_import
+
+import os
+import subprocess
+import tempfile
+import warnings
+import six
+from six.moves import urllib
+
+
+def git_clone_repo(git_config, entry_point, source_dir=None, dependencies=None):
+    """Git clone repo containing the training code and serving code.
+
+    This method also validate ``git_config``, and set ``entry_point``,
+    ``source_dir`` and ``dependencies`` to the right file or directory in the
+    repo cloned.
+
+    Args:
+        git_config (dict[str, str]): Git configurations used for cloning files,
+            including ``repo``, ``branch``, ``commit``, ``2FA_enabled``,
+            ``username``, ``password`` and ``token``. The ``repo`` field is
+            required. All other fields are optional. ``repo`` specifies the Git
+            repository where your training script is stored. If you don't
+            provide ``branch``, the default value 'master' is used. If you don't
+            provide ``commit``, the latest commit in the specified branch is
+            used. ``2FA_enabled``, ``username``, ``password`` and ``token`` are
+            for authentication purpose. If ``2FA_enabled`` is not provided, we
+            consider 2FA as disabled.
+
+            For GitHub and GitHub-like repos, when SSH URLs are provided, it
+            doesn't matter whether 2FA is enabled or disabled; you should either
+            have no passphrase for the SSH key pairs, or have the ssh-agent
+            configured so that you will not be prompted for SSH passphrase when
+            you do 'git clone' command with SSH URLs. When https URLs are
+            provided: if 2FA is disabled, then either token or username+password
+            will be used for authentication if provided (token prioritized); if
+            2FA is enabled, only token will be used for authentication if
+            provided. If required authentication info is not provided, python
+            SDK will try to use local credentials storage to authenticate. If
+            that fails either, an error message will be thrown.
+
+            For CodeCommit repos, 2FA is not supported, so '2FA_enabled' should
+            not be provided. There is no token in CodeCommit, so 'token' should
+            not be provided too. When 'repo' is an SSH URL, the requirements are
+            the same as GitHub-like repos. When 'repo' is an https URL,
+            username+password will be used for authentication if they are
+            provided; otherwise, python SDK will try to use either CodeCommit
+            credential helper or local credential storage for authentication.
+        entry_point (str): A relative location to the Python source file which
+            should be executed as the entry point to training or model hosting
+            in the Git repo.
+        source_dir (str): A relative location to a directory with other training
+            or model hosting source code dependencies aside from the entry point
+            file in the Git repo (default: None). Structure within this
+            directory are preserved when training on Amazon SageMaker.
+        dependencies (list[str]): A list of relative locations to directories
+            with any additional libraries that will be exported to the container
+            in the Git repo (default: []).
+
+    Returns:
+        dict: A dict that contains the updated values of entry_point, source_dir
+        and dependencies.
+
+    Raises:
+        CalledProcessError: If 1. failed to clone git repo
+                               2. failed to checkout the required branch
+                               3. failed to checkout the required commit
+        ValueError: If 1. entry point specified does not exist in the repo
+                       2. source dir specified does not exist in the repo
+                       3. dependencies specified do not exist in the repo
+                       4. wrong format is provided for git_config
+    """
+    if entry_point is None:
+        raise ValueError("Please provide an entry point.")
+    _validate_git_config(git_config)
+    dest_dir = tempfile.mkdtemp()
+    _generate_and_run_clone_command(git_config, dest_dir)
+
+    _checkout_branch_and_commit(git_config, dest_dir)
+
+    updated_paths = {
+        "entry_point": entry_point,
+        "source_dir": source_dir,
+        "dependencies": dependencies,
+    }
+
+    # check if the cloned repo contains entry point, source directory and dependencies
+    if source_dir:
+        if not os.path.isdir(os.path.join(dest_dir, source_dir)):
+            raise ValueError("Source directory does not exist in the repo.")
+        if not os.path.isfile(os.path.join(dest_dir, source_dir, entry_point)):
+            raise ValueError("Entry point does not exist in the repo.")
+        updated_paths["source_dir"] = os.path.join(dest_dir, source_dir)
+    else:
+        if os.path.isfile(os.path.join(dest_dir, entry_point)):
+            updated_paths["entry_point"] = os.path.join(dest_dir, entry_point)
+        else:
+            raise ValueError("Entry point does not exist in the repo.")
+    if dependencies is not None:
+        updated_paths["dependencies"] = []
+        for path in dependencies:
+            if os.path.exists(os.path.join(dest_dir, path)):
+                updated_paths["dependencies"].append(os.path.join(dest_dir, path))
+            else:
+                raise ValueError("Dependency {} does not exist in the repo.".format(path))
+    return updated_paths
+
+
+def _validate_git_config(git_config):
+    """Validates the git configuration.
+
+    Checks all configuration values except 2FA_enabled are string types. The
+    2FA_enabled configuration should be a boolean.
+
+    Args:
+        git_config: The configuration to validate.
+    """
+    if "repo" not in git_config:
+        raise ValueError("Please provide a repo for git_config.")
+    for key in git_config:
+        if key == "2FA_enabled":
+            if not isinstance(git_config["2FA_enabled"], bool):
+                raise ValueError("Please enter a bool type for 2FA_enabled'.")
+        elif not isinstance(git_config[key], six.string_types):
+            raise ValueError("'{}' must be a string.".format(key))
+
+
+def _generate_and_run_clone_command(git_config, dest_dir):
+    """Check if a git_config param is valid.
+
+    If it is valid, create the command to git, clone the repo, and run it.
+
+    Args:
+        git_config ((dict[str, str]): Git configurations used for cloning files,
+            including ``repo``, ``branch`` and ``commit``.
+        dest_dir (str): The local directory to clone the Git repo into.
+
+    Raises:
+        CalledProcessError: If failed to clone git repo.
+    """
+    if git_config["repo"].startswith("https://git-codecommit") or git_config["repo"].startswith(
+        "ssh://git-codecommit"
+    ):
+        _clone_command_for_codecommit(git_config, dest_dir)
+    else:
+        _clone_command_for_github_like(git_config, dest_dir)
+
+
+def _clone_command_for_github_like(git_config, dest_dir):
+    """Check if a git_config param representing a GitHub (or like) repo is valid.
+
+    If it is valid, create the command to git clone the repo, and run it.
+
+    Args:
+        git_config ((dict[str, str]): Git configurations used for cloning files,
+            including ``repo``, ``branch`` and ``commit``.
+        dest_dir (str): The local directory to clone the Git repo into.
+
+    Raises:
+        ValueError: If git_config['repo'] is in the wrong format.
+        CalledProcessError: If failed to clone git repo.
+    """
+    is_https = git_config["repo"].startswith("https://")
+    is_ssh = git_config["repo"].startswith("git@")
+    if not is_https and not is_ssh:
+        raise ValueError("Invalid Git url provided.")
+    if is_ssh:
+        _clone_command_for_ssh(git_config, dest_dir)
+    elif "2FA_enabled" in git_config and git_config["2FA_enabled"] is True:
+        _clone_command_for_github_like_https_2fa_enabled(git_config, dest_dir)
+    else:
+        _clone_command_for_github_like_https_2fa_disabled(git_config, dest_dir)
+
+
+def _clone_command_for_ssh(git_config, dest_dir):
+    """Placeholder docstring"""
+    if "username" in git_config or "password" in git_config or "token" in git_config:
+        warnings.warn("SSH cloning, authentication information in git config will be ignored.")
+    _run_clone_command(git_config["repo"], dest_dir)
+
+
+def _clone_command_for_github_like_https_2fa_disabled(git_config, dest_dir):
+    """Placeholder docstring"""
+    updated_url = git_config["repo"]
+    if "token" in git_config:
+        if "username" in git_config or "password" in git_config:
+            warnings.warn("Using token for authentication, " "other credentials will be ignored.")
+        updated_url = _insert_token_to_repo_url(url=git_config["repo"], token=git_config["token"])
+    elif "username" in git_config and "password" in git_config:
+        updated_url = _insert_username_and_password_to_repo_url(
+            url=git_config["repo"], username=git_config["username"], password=git_config["password"]
+        )
+    elif "username" in git_config or "password" in git_config:
+        warnings.warn("Credentials provided in git config will be ignored.")
+    _run_clone_command(updated_url, dest_dir)
+
+
+def _clone_command_for_github_like_https_2fa_enabled(git_config, dest_dir):
+    """Placeholder docstring"""
+    updated_url = git_config["repo"]
+    if "token" in git_config:
+        if "username" in git_config or "password" in git_config:
+            warnings.warn("Using token for authentication, " "other credentials will be ignored.")
+        updated_url = _insert_token_to_repo_url(url=git_config["repo"], token=git_config["token"])
+    _run_clone_command(updated_url, dest_dir)
+
+
+def _clone_command_for_codecommit(git_config, dest_dir):
+    """Check if a git_config param representing a CodeCommit repo is valid.
+
+    If it is, create the command to git clone the repo, and run it.
+
+    Args:
+        git_config ((dict[str, str]): Git configurations used for cloning files,
+            including ``repo``, ``branch`` and ``commit``.
+        dest_dir (str): The local directory to clone the Git repo into.
+
+    Raises:
+        ValueError: If git_config['repo'] is in the wrong format.
+        CalledProcessError: If failed to clone git repo.
+    """
+    is_https = git_config["repo"].startswith("https://git-codecommit")
+    is_ssh = git_config["repo"].startswith("ssh://git-codecommit")
+    if not is_https and not is_ssh:
+        raise ValueError("Invalid Git url provided.")
+    if "2FA_enabled" in git_config:
+        warnings.warn("CodeCommit does not support 2FA, '2FA_enabled' will be ignored.")
+    if "token" in git_config:
+        warnings.warn("There are no tokens in CodeCommit, the token provided will be ignored.")
+    if is_ssh:
+        _clone_command_for_ssh(git_config, dest_dir)
+    else:
+        _clone_command_for_codecommit_https(git_config, dest_dir)
+
+
+def _clone_command_for_codecommit_https(git_config, dest_dir):
+    """Invoke the clone command for codecommit.
+
+    Args:
+        git_config: The git configuration.
+        dest_dir: The destination directory for the clone.
+    """
+    updated_url = git_config["repo"]
+    if "username" in git_config and "password" in git_config:
+        updated_url = _insert_username_and_password_to_repo_url(
+            url=git_config["repo"], username=git_config["username"], password=git_config["password"]
+        )
+    elif "username" in git_config or "password" in git_config:
+        warnings.warn("Credentials provided in git config will be ignored.")
+    _run_clone_command(updated_url, dest_dir)
+
+
+def _run_clone_command(repo_url, dest_dir):
+    """Run the 'git clone' command with the repo url and the directory to clone the repo into.
+
+    Args:
+        repo_url (str): Git repo url to be cloned.
+        dest_dir: (str): Local path where the repo should be cloned into.
+
+    Raises:
+        CalledProcessError: If failed to clone git repo.
+    """
+    my_env = os.environ.copy()
+    if repo_url.startswith("https://"):
+        my_env["GIT_TERMINAL_PROMPT"] = "0"
+        subprocess.check_call(["git", "clone", repo_url, dest_dir], env=my_env)
+    elif repo_url.startswith("git@"):
+        with tempfile.NamedTemporaryFile() as sshnoprompt:
+            write_pipe = open(sshnoprompt.name, "w")
+            write_pipe.write("ssh -oBatchMode=yes $@")
+            write_pipe.close()
+            os.chmod(sshnoprompt.name, 0o511)
+            my_env["GIT_SSH"] = sshnoprompt.name
+            subprocess.check_call(["git", "clone", repo_url, dest_dir], env=my_env)
+
+
+def _insert_token_to_repo_url(url, token):
+    """Insert the token to the Git repo url, to make a component of the git clone command.
+
+    This method can only be called when repo_url is an https url.
+
+    Args:
+        url (str): Git repo url where the token should be inserted into.
+        token (str): Token to be inserted.
+
+    Returns:
+        str: the component needed fot the git clone command.
+    """
+    index = len("https://")
+    if url.find(token) == index:
+        return url
+    return url.replace("https://", "https://" + token + "@")
+
+
+def _insert_username_and_password_to_repo_url(url, username, password):
+    """Insert username and password to the Git repo url to make a component of git clone command.
+
+    This method can only be called when repo_url is an https url.
+
+    Args:
+        url (str): Git repo url where the token should be inserted into.
+        username (str): Username to be inserted.
+        password (str): Password to be inserted.
+
+    Returns:
+        str: the component needed for the git clone command.
+    """
+    password = urllib.parse.quote_plus(password)
+    # urllib parses ' ' as '+', but what we need is '%20' here
+    password = password.replace("+", "%20")
+    index = len("https://")
+    return url[:index] + username + ":" + password + "@" + url[index:]
+
+
+def _checkout_branch_and_commit(git_config, dest_dir):
+    """Checkout the required branch and commit.
+
+    Args:
+        git_config (dict[str, str]): Git configurations used for cloning files,
+            including ``repo``, ``branch`` and ``commit``.
+        dest_dir (str): the directory where the repo is cloned
+
+    Raises:
+        CalledProcessError: If 1. failed to checkout the required branch 2.
+            failed to checkout the required commit
+    """
+    if "branch" in git_config:
+        subprocess.check_call(args=["git", "checkout", git_config["branch"]], cwd=str(dest_dir))
+    if "commit" in git_config:
+        subprocess.check_call(args=["git", "checkout", git_config["commit"]], cwd=str(dest_dir))

testbed/aws__sagemaker-python-sdk/src/sagemaker/hyperparameters.py

@@ -0,0 +1,111 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Accessors to retrieve hyperparameters for training jobs."""
+
+from __future__ import absolute_import
+
+import logging
+from typing import Dict, Optional
+
+from sagemaker.jumpstart import utils as jumpstart_utils
+from sagemaker.jumpstart import artifacts
+from sagemaker.jumpstart.enums import HyperparameterValidationMode
+from sagemaker.jumpstart.validators import validate_hyperparameters
+
+logger = logging.getLogger(__name__)
+
+
+def retrieve_default(
+    region=None,
+    model_id=None,
+    model_version=None,
+    include_container_hyperparameters=False,
+) -> Dict[str, str]:
+    """Retrieves the default training hyperparameters for the model matching the given arguments.
+
+    Args:
+        region (str): The AWS Region for which to retrieve the default hyperparameters.
+            Defaults to ``None``.
+        model_id (str): The model ID of the model for which to
+            retrieve the default hyperparameters. (Default: None).
+        model_version (str): The version of the model for which to retrieve the
+            default hyperparameters. (Default: None).
+        include_container_hyperparameters (bool): ``True`` if the container hyperparameters
+            should be returned. Container hyperparameters are not used to tune
+            the specific algorithm. They are used by SageMaker Training jobs to set up
+            the training container environment. For example, there is a container hyperparameter
+            that indicates the entrypoint script to use. These hyperparameters may be required
+            when creating a training job with boto3, however the ``Estimator`` classes
+            add required container hyperparameters to the job. (Default: False).
+    Returns:
+        dict: The hyperparameters to use for the model.
+
+    Raises:
+        ValueError: If the combination of arguments specified is not supported.
+    """
+    if not jumpstart_utils.is_jumpstart_model_input(model_id, model_version):
+        raise ValueError(
+            "Must specify `model_id` and `model_version` when retrieving hyperparameters."
+        )
+
+    return artifacts._retrieve_default_hyperparameters(
+        model_id, model_version, region, include_container_hyperparameters
+    )
+
+
+def validate(
+    region: Optional[str] = None,
+    model_id: Optional[str] = None,
+    model_version: Optional[str] = None,
+    hyperparameters: Optional[dict] = None,
+    validation_mode: Optional[HyperparameterValidationMode] = None,
+) -> None:
+    """Validates hyperparameters for models.
+
+    Args:
+        region (str): The AWS Region for which to validate hyperparameters. (Default: None).
+        model_id (str): The model ID of the model for which to validate hyperparameters.
+            (Default: None).
+        model_version (str): The version of the model for which to validate hyperparameters.
+            (Default: None).
+        hyperparameters (dict): Hyperparameters to validate.
+            (Default: None).
+        validation_mode (HyperparameterValidationMode): Method of validation to use with
+          hyperparameters. If set to ``VALIDATE_PROVIDED``, only hyperparameters provided
+          to this function will be validated, the missing hyperparameters will be ignored.
+          If set to``VALIDATE_ALGORITHM``, all algorithm hyperparameters will be validated.
+          If set to ``VALIDATE_ALL``, all hyperparameters for the model will be validated.
+          (Default: None).
+
+    Raises:
+        JumpStartHyperparametersError: If the hyperparameter is not formatted correctly,
+            according to its specs in the model metadata.
+        ValueError: If the combination of arguments specified is not supported.
+
+    """
+
+    if not jumpstart_utils.is_jumpstart_model_input(model_id, model_version):
+        raise ValueError(
+            "Must specify `model_id` and `model_version` when validating hyperparameters."
+        )
+
+    if hyperparameters is None:
+        raise ValueError("Must specify hyperparameters.")
+
+    return validate_hyperparameters(
+        model_id=model_id,
+        model_version=model_version,
+        hyperparameters=hyperparameters,
+        validation_mode=validation_mode,
+        region=region,
+    )

testbed/aws__sagemaker-python-sdk/src/sagemaker/inputs.py

@@ -0,0 +1,277 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Amazon SageMaker channel configurations for S3 data sources and file system data sources"""
+from __future__ import absolute_import, print_function
+
+from typing import Union, Optional, List
+import attr
+
+from sagemaker.workflow.entities import PipelineVariable
+
+FILE_SYSTEM_TYPES = ["FSxLustre", "EFS"]
+FILE_SYSTEM_ACCESS_MODES = ["ro", "rw"]
+
+
+class TrainingInput(object):
+    """Amazon SageMaker channel configurations for S3 data sources.
+
+    Attributes:
+        config (dict[str, dict]): A SageMaker ``DataSource`` referencing
+            a SageMaker ``S3DataSource``.
+    """
+
+    def __init__(
+        self,
+        s3_data: Union[str, PipelineVariable],
+        distribution: Optional[Union[str, PipelineVariable]] = None,
+        compression: Optional[Union[str, PipelineVariable]] = None,
+        content_type: Optional[Union[str, PipelineVariable]] = None,
+        record_wrapping: Optional[Union[str, PipelineVariable]] = None,
+        s3_data_type: Union[str, PipelineVariable] = "S3Prefix",
+        instance_groups: Optional[List[Union[str, PipelineVariable]]] = None,
+        input_mode: Optional[Union[str, PipelineVariable]] = None,
+        attribute_names: Optional[List[Union[str, PipelineVariable]]] = None,
+        target_attribute_name: Optional[Union[str, PipelineVariable]] = None,
+        shuffle_config: Optional["ShuffleConfig"] = None,
+    ):
+        r"""Create a definition for input data used by an SageMaker training job.
+
+        See AWS documentation on the ``CreateTrainingJob`` API for more details
+        on the parameters.
+
+        Args:
+            s3_data (str or PipelineVariable): Defines the location of S3 data to train on.
+            distribution (str or PipelineVariable): Valid values: ``'FullyReplicated'``,
+                ``'ShardedByS3Key'`` (default: ``'FullyReplicated'``).
+            compression (str or PipelineVariable): Valid values: ``'Gzip'``, ``None``
+                (default: None). This is used only in Pipe input mode.
+            content_type (str or PipelineVariable): MIME type of the input data
+                (default: None).
+            record_wrapping (str or PipelineVariable): Valid values: 'RecordIO'
+                (default: None).
+            s3_data_type (str or PipelineVariable): Valid values: ``'S3Prefix'``,
+                ``'ManifestFile'``, ``'AugmentedManifestFile'``.
+                If ``'S3Prefix'``, ``s3_data`` defines a prefix of s3 objects to train on.
+                All objects with s3 keys beginning with ``s3_data`` will be used to train.
+                If ``'ManifestFile'`` or ``'AugmentedManifestFile'``,
+                then ``s3_data`` defines a
+                single S3 manifest file or augmented manifest file respectively,
+                listing the S3 data to train on. Both the ManifestFile and
+                AugmentedManifestFile formats are described at `S3DataSource
+                <https://docs.aws.amazon.com/sagemaker/latest/dg/API_S3DataSource.html>`_
+                in the `Amazon SageMaker API reference`.
+            instance_groups (list[str] or list[PipelineVariable]): Optional. A list of
+                instance group names in string format that you specified while configuring
+                a heterogeneous cluster using the :class:`sagemaker.instance_group.InstanceGroup`.
+                S3 data will be sent to all instance groups in the specified list.
+                For instructions on how to use InstanceGroup objects
+                to configure a heterogeneous cluster
+                through the SageMaker generic and framework estimator classes, see
+                `Train Using a Heterogeneous Cluster
+                <https://docs.aws.amazon.com/sagemaker/latest/dg/train-heterogeneous-cluster.html>`_
+                in the *Amazon SageMaker developer guide*.
+                (default: None)
+            input_mode (str or PipelineVariable): Optional override for this channel's input mode
+                (default: None). By default, channels will use the input mode defined on
+                ``sagemaker.estimator.EstimatorBase.input_mode``, but they will ignore
+                that setting if this parameter is set.
+
+                    * None - Amazon SageMaker will use the input mode specified in the ``Estimator``
+                    * 'File' - Amazon SageMaker copies the training dataset from the S3 location to
+                        a local directory.
+                    * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via
+                        a Unix-named pipe.
+                    * 'FastFile' - Amazon SageMaker streams data from S3 on demand instead of
+                        downloading the entire dataset before training begins.
+
+            attribute_names (list[str] or list[PipelineVariable]): A list of one or more attribute
+                names to use that are found in a specified AugmentedManifestFile.
+            target_attribute_name (str or PipelineVariable): The name of the attribute will be
+                predicted (classified) in a SageMaker AutoML job. It is required if the input is
+                for SageMaker AutoML job.
+            shuffle_config (sagemaker.inputs.ShuffleConfig): If specified this configuration enables
+                shuffling on this channel. See the SageMaker API documentation for more info:
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_ShuffleConfig.html
+        """
+        self.config = {
+            "DataSource": {"S3DataSource": {"S3DataType": s3_data_type, "S3Uri": s3_data}}
+        }
+
+        if not (target_attribute_name or distribution):
+            distribution = "FullyReplicated"
+
+        if distribution is not None:
+            self.config["DataSource"]["S3DataSource"]["S3DataDistributionType"] = distribution
+
+        if compression is not None:
+            self.config["CompressionType"] = compression
+        if content_type is not None:
+            self.config["ContentType"] = content_type
+        if record_wrapping is not None:
+            self.config["RecordWrapperType"] = record_wrapping
+        if instance_groups is not None:
+            self.config["DataSource"]["S3DataSource"]["InstanceGroupNames"] = instance_groups
+        if input_mode is not None:
+            self.config["InputMode"] = input_mode
+        if attribute_names is not None:
+            self.config["DataSource"]["S3DataSource"]["AttributeNames"] = attribute_names
+        if target_attribute_name is not None:
+            self.config["TargetAttributeName"] = target_attribute_name
+        if shuffle_config is not None:
+            self.config["ShuffleConfig"] = {"Seed": shuffle_config.seed}
+
+
+class ShuffleConfig(object):
+    """For configuring channel shuffling using a seed.
+
+    For more detail, see the AWS documentation:
+    https://docs.aws.amazon.com/sagemaker/latest/dg/API_ShuffleConfig.html
+    """
+
+    def __init__(self, seed):
+        """Create a ShuffleConfig.
+
+        Args:
+            seed (long): the long value used to seed the shuffled sequence.
+        """
+        self.seed = seed
+
+
+@attr.s
+class CreateModelInput(object):
+    """A class containing parameters which can be used to create a SageMaker Model
+
+    Parameters:
+        instance_type (str): type or EC2 instance will be used for model deployment.
+        accelerator_type (str): elastic inference accelerator type.
+    """
+
+    instance_type: str = attr.ib(default=None)
+    accelerator_type: str = attr.ib(default=None)
+
+
+@attr.s
+class TransformInput(object):
+    """Create a class containing all the parameters.
+
+    It can be used when calling ``sagemaker.transformer.Transformer.transform()``
+    """
+
+    data: str = attr.ib()
+    data_type: str = attr.ib(default="S3Prefix")
+    content_type: str = attr.ib(default=None)
+    compression_type: str = attr.ib(default=None)
+    split_type: str = attr.ib(default=None)
+    input_filter: str = attr.ib(default=None)
+    output_filter: str = attr.ib(default=None)
+    join_source: str = attr.ib(default=None)
+    model_client_config: dict = attr.ib(default=None)
+    batch_data_capture_config: dict = attr.ib(default=None)
+
+
+class FileSystemInput(object):
+    """Amazon SageMaker channel configurations for file system data sources.
+
+    Attributes:
+        config (dict[str, dict]): A Sagemaker File System ``DataSource``.
+    """
+
+    def __init__(
+        self,
+        file_system_id,
+        file_system_type,
+        directory_path,
+        file_system_access_mode="ro",
+        content_type=None,
+    ):
+        """Create a new file system input used by an SageMaker training job.
+
+        Args:
+            file_system_id (str): An Amazon file system ID starting with 'fs-'.
+            file_system_type (str): The type of file system used for the input.
+                Valid values: 'EFS', 'FSxLustre'.
+            directory_path (str): Absolute or normalized path to the root directory (mount point) in
+                the file system.
+                Reference: https://docs.aws.amazon.com/efs/latest/ug/mounting-fs.html and
+                https://docs.aws.amazon.com/fsx/latest/LustreGuide/mount-fs-auto-mount-onreboot.html
+            file_system_access_mode (str): Permissions for read and write.
+                Valid values: 'ro' or 'rw'. Defaults to 'ro'.
+        """
+
+        if file_system_type not in FILE_SYSTEM_TYPES:
+            raise ValueError(
+                "Unrecognized file system type: %s. Valid values: %s."
+                % (file_system_type, ", ".join(FILE_SYSTEM_TYPES))
+            )
+
+        if file_system_access_mode not in FILE_SYSTEM_ACCESS_MODES:
+            raise ValueError(
+                "Unrecognized file system access mode: %s. Valid values: %s."
+                % (file_system_access_mode, ", ".join(FILE_SYSTEM_ACCESS_MODES))
+            )
+
+        self.config = {
+            "DataSource": {
+                "FileSystemDataSource": {
+                    "FileSystemId": file_system_id,
+                    "FileSystemType": file_system_type,
+                    "DirectoryPath": directory_path,
+                    "FileSystemAccessMode": file_system_access_mode,
+                }
+            }
+        }
+
+        if content_type:
+            self.config["ContentType"] = content_type
+
+
+class BatchDataCaptureConfig(object):
+    """Configuration object passed in when create a batch transform job.
+
+    Specifies configuration related to batch transform job data capture for use with
+    Amazon SageMaker Model Monitoring
+    """
+
+    def __init__(
+        self,
+        destination_s3_uri: str,
+        kms_key_id: str = None,
+        generate_inference_id: bool = None,
+    ):
+        """Create new BatchDataCaptureConfig
+
+        Args:
+            destination_s3_uri (str): S3 Location to store the captured data
+            kms_key_id (str): The KMS key to use when writing to S3.
+                KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key,
+                or alias of a KMS key. The KmsKeyId is applied to all outputs.
+                (default: None)
+            generate_inference_id (bool): Flag to generate an inference id
+                (default: None)
+        """
+        self.destination_s3_uri = destination_s3_uri
+        self.kms_key_id = kms_key_id
+        self.generate_inference_id = generate_inference_id
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        batch_data_capture_config = {
+            "DestinationS3Uri": self.destination_s3_uri,
+        }
+
+        if self.kms_key_id is not None:
+            batch_data_capture_config["KmsKeyId"] = self.kms_key_id
+        if self.generate_inference_id is not None:
+            batch_data_capture_config["GenerateInferenceId"] = self.generate_inference_id
+
+        return batch_data_capture_config

testbed/aws__sagemaker-python-sdk/src/sagemaker/instance_group.py

@@ -0,0 +1,61 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Defines the InstanceGroup class that configures a heterogeneous cluster."""
+from __future__ import absolute_import
+
+
+class InstanceGroup(object):
+    """The class to create instance groups for a heterogeneous cluster."""
+
+    def __init__(
+        self,
+        instance_group_name=None,
+        instance_type=None,
+        instance_count=None,
+    ):
+        """It initializes an ``InstanceGroup`` instance.
+
+        You can create instance group object of the ``InstanceGroup`` class
+        by specifying the instance group configuration arguments.
+
+        For instructions on how to use InstanceGroup objects
+        to configure a heterogeneous cluster
+        through the SageMaker generic and framework estimator classes, see
+        `Train Using a Heterogeneous Cluster
+        <https://docs.aws.amazon.com/sagemaker/latest/dg/train-heterogeneous-cluster.html>`_
+        in the *Amazon SageMaker developer guide*.
+
+        Args:
+            instance_group_name (str): The name of the instance group.
+            instance_type (str): The instance type to use in the instance group.
+            instance_count (int): The number of instances to use in the instance group.
+
+                .. tip::
+
+                    For more information about available values for the arguments,
+                    see `InstanceGroup
+                    <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_InstanceGroup.html>`_
+                    API in the `Amazon SageMaker API reference`.
+
+        """
+        self.instance_group_name = instance_group_name
+        self.instance_type = instance_type
+        self.instance_count = instance_count
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        return {
+            "InstanceGroupName": self.instance_group_name,
+            "InstanceType": self.instance_type,
+            "InstanceCount": self.instance_count,
+        }

testbed/aws__sagemaker-python-sdk/src/sagemaker/job.py

@@ -0,0 +1,329 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Placeholder docstring"""
+from __future__ import absolute_import
+
+from abc import abstractmethod
+from six import string_types
+
+from sagemaker.inputs import FileSystemInput, TrainingInput
+from sagemaker.local import file_input
+from sagemaker.workflow import is_pipeline_variable
+
+
+class _Job(object):
+    """Handle creating, starting and waiting for Amazon SageMaker jobs to finish.
+
+    This class shouldn't be directly instantiated.
+
+    Subclasses must define a way to create, start and wait for an Amazon
+    SageMaker job.
+    """
+
+    def __init__(self, sagemaker_session, job_name):
+        """Placeholder docstring"""
+        self.sagemaker_session = sagemaker_session
+        self.job_name = job_name
+
+    @abstractmethod
+    def start_new(self, estimator, inputs):
+        """Create a new Amazon SageMaker job from the estimator.
+
+        Args:
+            estimator (sagemaker.estimator.EstimatorBase): Estimator object
+                created by the user.
+            inputs (str): Parameters used when called
+                :meth:`~sagemaker.estimator.EstimatorBase.fit`.
+
+        Returns:
+            sagemaker.job: Constructed object that captures all information
+            about the started job.
+        """
+
+    @abstractmethod
+    def wait(self):
+        """Wait for the Amazon SageMaker job to finish."""
+
+    @abstractmethod
+    def describe(self):
+        """Describe the job."""
+
+    @abstractmethod
+    def stop(self):
+        """Stop the job."""
+
+    @staticmethod
+    def _load_config(inputs, estimator, expand_role=True, validate_uri=True):
+        """Placeholder docstring"""
+        input_config = _Job._format_inputs_to_input_config(inputs, validate_uri)
+        role = (
+            estimator.sagemaker_session.expand_role(estimator.role)
+            if (expand_role and not is_pipeline_variable(estimator.role))
+            else estimator.role
+        )
+        output_config = _Job._prepare_output_config(estimator.output_path, estimator.output_kms_key)
+        resource_config = _Job._prepare_resource_config(
+            estimator.instance_count,
+            estimator.instance_type,
+            estimator.instance_groups,
+            estimator.volume_size,
+            estimator.volume_kms_key,
+            estimator.keep_alive_period_in_seconds,
+        )
+        stop_condition = _Job._prepare_stop_condition(estimator.max_run, estimator.max_wait)
+        vpc_config = estimator.get_vpc_config()
+
+        model_channel = _Job._prepare_channel(
+            input_config,
+            estimator.model_uri,
+            estimator.model_channel_name,
+            validate_uri,
+            content_type="application/x-sagemaker-model",
+            input_mode="File",
+        )
+        if model_channel:
+            input_config = [] if input_config is None else input_config
+            input_config.append(model_channel)
+
+        if estimator.enable_network_isolation():
+            code_channel = _Job._prepare_channel(
+                input_config, estimator.code_uri, estimator.code_channel_name, validate_uri
+            )
+
+            if code_channel:
+                input_config = [] if input_config is None else input_config
+                input_config.append(code_channel)
+
+        return {
+            "input_config": input_config,
+            "role": role,
+            "output_config": output_config,
+            "resource_config": resource_config,
+            "stop_condition": stop_condition,
+            "vpc_config": vpc_config,
+        }
+
+    @staticmethod
+    def _format_inputs_to_input_config(inputs, validate_uri=True):
+        """Placeholder docstring"""
+        if inputs is None:
+            return None
+
+        # Deferred import due to circular dependency
+        from sagemaker.amazon.amazon_estimator import RecordSet
+        from sagemaker.amazon.amazon_estimator import FileSystemRecordSet
+
+        if isinstance(inputs, (RecordSet, FileSystemRecordSet)):
+            inputs = inputs.data_channel()
+
+        input_dict = {}
+        if isinstance(inputs, string_types):
+            input_dict["training"] = _Job._format_string_uri_input(inputs, validate_uri)
+        elif isinstance(inputs, TrainingInput):
+            input_dict["training"] = inputs
+        elif isinstance(inputs, file_input):
+            input_dict["training"] = inputs
+        elif isinstance(inputs, dict):
+            for k, v in inputs.items():
+                input_dict[k] = _Job._format_string_uri_input(v, validate_uri)
+        elif isinstance(inputs, list):
+            input_dict = _Job._format_record_set_list_input(inputs)
+        elif isinstance(inputs, FileSystemInput):
+            input_dict["training"] = inputs
+        else:
+            msg = (
+                "Cannot format input {}. Expecting one of str, dict, TrainingInput or "
+                "FileSystemInput"
+            )
+            raise ValueError(msg.format(inputs))
+
+        channels = [
+            _Job._convert_input_to_channel(name, input) for name, input in input_dict.items()
+        ]
+
+        return channels
+
+    @staticmethod
+    def _convert_input_to_channel(channel_name, channel_s3_input):
+        """Placeholder docstring"""
+        channel_config = channel_s3_input.config.copy()
+        channel_config["ChannelName"] = channel_name
+        return channel_config
+
+    @staticmethod
+    def _format_string_uri_input(
+        uri_input,
+        validate_uri=True,
+        content_type=None,
+        input_mode=None,
+        compression=None,
+        target_attribute_name=None,
+    ):
+        """Placeholder docstring"""
+        s3_input_result = TrainingInput(
+            uri_input,
+            content_type=content_type,
+            input_mode=input_mode,
+            compression=compression,
+            target_attribute_name=target_attribute_name,
+        )
+        if isinstance(uri_input, str) and validate_uri and uri_input.startswith("s3://"):
+            return s3_input_result
+        if isinstance(uri_input, str) and validate_uri and uri_input.startswith("file://"):
+            return file_input(uri_input)
+        if isinstance(uri_input, str) and validate_uri:
+            raise ValueError(
+                'URI input {} must be a valid S3 or FILE URI: must start with "s3://" or '
+                '"file://"'.format(uri_input)
+            )
+        if isinstance(uri_input, str):
+            return s3_input_result
+        if isinstance(uri_input, (TrainingInput, file_input, FileSystemInput)):
+            return uri_input
+        if is_pipeline_variable(uri_input):
+            return s3_input_result
+
+        raise ValueError(
+            "Cannot format input {}. Expecting one of str, TrainingInput, file_input or "
+            "FileSystemInput".format(uri_input)
+        )
+
+    @staticmethod
+    def _prepare_channel(
+        input_config,
+        channel_uri=None,
+        channel_name=None,
+        validate_uri=True,
+        content_type=None,
+        input_mode=None,
+    ):
+        """Placeholder docstring"""
+        if not channel_uri:
+            return None
+        if not channel_name:
+            raise ValueError(
+                "Expected a channel name if a channel URI {} is specified".format(channel_uri)
+            )
+
+        if input_config:
+            for existing_channel in input_config:
+                if existing_channel["ChannelName"] == channel_name:
+                    raise ValueError("Duplicate channel {} not allowed.".format(channel_name))
+
+        channel_input = _Job._format_string_uri_input(
+            channel_uri, validate_uri, content_type, input_mode
+        )
+        channel = _Job._convert_input_to_channel(channel_name, channel_input)
+
+        return channel
+
+    @staticmethod
+    def _format_model_uri_input(model_uri, validate_uri=True):
+        """Placeholder docstring"""
+        if isinstance(model_uri, string_types) and validate_uri and model_uri.startswith("s3://"):
+            return TrainingInput(
+                model_uri,
+                input_mode="File",
+                distribution="FullyReplicated",
+                content_type="application/x-sagemaker-model",
+            )
+        if isinstance(model_uri, string_types) and validate_uri and model_uri.startswith("file://"):
+            return file_input(model_uri)
+        if isinstance(model_uri, string_types) and validate_uri:
+            raise ValueError(
+                'Model URI must be a valid S3 or FILE URI: must start with "s3://" or ' '"file://'
+            )
+        if isinstance(model_uri, string_types):
+            return TrainingInput(
+                model_uri,
+                input_mode="File",
+                distribution="FullyReplicated",
+                content_type="application/x-sagemaker-model",
+            )
+        raise ValueError("Cannot format model URI {}. Expecting str".format(model_uri))
+
+    @staticmethod
+    def _format_record_set_list_input(inputs):
+        """Placeholder docstring"""
+        # Deferred import due to circular dependency
+        from sagemaker.amazon.amazon_estimator import FileSystemRecordSet, RecordSet
+
+        input_dict = {}
+        for record in inputs:
+            if not isinstance(record, (RecordSet, FileSystemRecordSet)):
+                raise ValueError("List compatible only with RecordSets or FileSystemRecordSets.")
+
+            if record.channel in input_dict:
+                raise ValueError("Duplicate channels not allowed.")
+            if isinstance(record, RecordSet):
+                input_dict[record.channel] = record.records_s3_input()
+            if isinstance(record, FileSystemRecordSet):
+                input_dict[record.channel] = record.file_system_input
+
+        return input_dict
+
+    @staticmethod
+    def _prepare_output_config(s3_path, kms_key_id):
+        """Placeholder docstring"""
+        config = {"S3OutputPath": s3_path}
+        if kms_key_id is not None:
+            config["KmsKeyId"] = kms_key_id
+        return config
+
+    @staticmethod
+    def _prepare_resource_config(
+        instance_count,
+        instance_type,
+        instance_groups,
+        volume_size,
+        volume_kms_key,
+        keep_alive_period_in_seconds,
+    ):
+        """Placeholder docstring"""
+        resource_config = {
+            "VolumeSizeInGB": volume_size,
+        }
+        if volume_kms_key is not None:
+            resource_config["VolumeKmsKeyId"] = volume_kms_key
+        if keep_alive_period_in_seconds is not None:
+            resource_config["KeepAlivePeriodInSeconds"] = keep_alive_period_in_seconds
+        if instance_groups is not None:
+            if instance_count is not None or instance_type is not None:
+                raise ValueError(
+                    "instance_count and instance_type cannot be set when instance_groups is set"
+                )
+
+            resource_config["InstanceGroups"] = [
+                group._to_request_dict() for group in instance_groups
+            ]
+        else:
+            if instance_count is None or instance_type is None:
+                raise ValueError(
+                    "instance_count and instance_type must be set if instance_groups is not set"
+                )
+            resource_config["InstanceCount"] = instance_count
+            resource_config["InstanceType"] = instance_type
+
+        return resource_config
+
+    @staticmethod
+    def _prepare_stop_condition(max_run, max_wait):
+        """Placeholder docstring"""
+        if max_wait:
+            return {"MaxRuntimeInSeconds": max_run, "MaxWaitTimeInSeconds": max_wait}
+        return {"MaxRuntimeInSeconds": max_run}
+
+    @property
+    def name(self):
+        """Placeholder docstring"""
+        return self.job_name

testbed/aws__sagemaker-python-sdk/src/sagemaker/metadata_properties.py

@@ -0,0 +1,56 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""This file contains code related to metadata properties."""
+from __future__ import absolute_import
+
+from typing import Optional, Union
+
+from sagemaker.workflow.entities import PipelineVariable
+
+
+class MetadataProperties(object):
+    """Accepts metadata properties parameters for conversion to request dict."""
+
+    def __init__(
+        self,
+        commit_id: Optional[Union[str, PipelineVariable]] = None,
+        repository: Optional[Union[str, PipelineVariable]] = None,
+        generated_by: Optional[Union[str, PipelineVariable]] = None,
+        project_id: Optional[Union[str, PipelineVariable]] = None,
+    ):
+        """Initialize a ``MetadataProperties`` instance and turn parameters into dict.
+
+        # TODO: flesh out docstrings
+        Args:
+            commit_id (str or PipelineVariable):
+            repository (str or PipelineVariable):
+            generated_by (str or PipelineVariable):
+            project_id (str or PipelineVariable):
+        """
+        self.commit_id = commit_id
+        self.repository = repository
+        self.generated_by = generated_by
+        self.project_id = project_id
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        metadata_properties_request = dict()
+        if self.commit_id:
+            metadata_properties_request["CommitId"] = self.commit_id
+        if self.repository:
+            metadata_properties_request["Repository"] = self.repository
+        if self.generated_by:
+            metadata_properties_request["GeneratedBy"] = self.generated_by
+        if self.project_id:
+            metadata_properties_request["ProjectId"] = self.project_id
+        return metadata_properties_request

testbed/aws__sagemaker-python-sdk/src/sagemaker/model.py

@@ -0,0 +1,1604 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Placeholder docstring"""
+from __future__ import absolute_import
+
+import abc
+import json
+import logging
+import os
+import copy
+from typing import List, Dict, Optional, Union
+
+import sagemaker
+from sagemaker import (
+    fw_utils,
+    local,
+    s3,
+    session,
+    utils,
+    git_utils,
+)
+from sagemaker.session import Session
+from sagemaker.model_metrics import ModelMetrics
+from sagemaker.deprecations import removed_kwargs
+from sagemaker.drift_check_baselines import DriftCheckBaselines
+from sagemaker.metadata_properties import MetadataProperties
+from sagemaker.predictor import PredictorBase
+from sagemaker.serverless import ServerlessInferenceConfig
+from sagemaker.transformer import Transformer
+from sagemaker.jumpstart.utils import add_jumpstart_tags, get_jumpstart_base_name_if_jumpstart_model
+from sagemaker.utils import (
+    unique_name_from_base,
+    update_container_with_inference_params,
+    to_string,
+)
+from sagemaker.async_inference import AsyncInferenceConfig
+from sagemaker.predictor_async import AsyncPredictor
+from sagemaker.workflow import is_pipeline_variable
+from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.workflow.pipeline_context import runnable_by_pipeline, PipelineSession
+
+LOGGER = logging.getLogger("sagemaker")
+
+NEO_ALLOWED_FRAMEWORKS = set(
+    ["mxnet", "tensorflow", "keras", "pytorch", "onnx", "xgboost", "tflite"]
+)
+
+NEO_IOC_TARGET_DEVICES = ["ml_c4", "ml_c5", "ml_m4", "ml_m5", "ml_p2", "ml_p3", "ml_g4dn"]
+
+
+class ModelBase(abc.ABC):
+    """An object that encapsulates a trained model.
+
+    Models can be deployed to compute services like a SageMaker ``Endpoint``
+    or Lambda. Deployed models can be used to perform real-time inference.
+    """
+
+    @abc.abstractmethod
+    def deploy(self, *args, **kwargs) -> PredictorBase:
+        """Deploy this model to a compute service."""
+
+    @abc.abstractmethod
+    def delete_model(self, *args, **kwargs) -> None:
+        """Destroy resources associated with this model."""
+
+
+SCRIPT_PARAM_NAME = "sagemaker_program"
+DIR_PARAM_NAME = "sagemaker_submit_directory"
+CONTAINER_LOG_LEVEL_PARAM_NAME = "sagemaker_container_log_level"
+JOB_NAME_PARAM_NAME = "sagemaker_job_name"
+MODEL_SERVER_WORKERS_PARAM_NAME = "sagemaker_model_server_workers"
+SAGEMAKER_REGION_PARAM_NAME = "sagemaker_region"
+SAGEMAKER_OUTPUT_LOCATION = "sagemaker_s3_output"
+
+
+class Model(ModelBase):
+    """A SageMaker ``Model`` that can be deployed to an ``Endpoint``."""
+
+    def __init__(
+        self,
+        image_uri: Union[str, PipelineVariable],
+        model_data: Optional[Union[str, PipelineVariable]] = None,
+        role: Optional[str] = None,
+        predictor_cls: Optional[callable] = None,
+        env: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        name: Optional[str] = None,
+        vpc_config: Optional[Dict[str, List[Union[str, PipelineVariable]]]] = None,
+        sagemaker_session: Optional[Session] = None,
+        enable_network_isolation: Union[bool, PipelineVariable] = False,
+        model_kms_key: Optional[str] = None,
+        image_config: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        source_dir: Optional[str] = None,
+        code_location: Optional[str] = None,
+        entry_point: Optional[str] = None,
+        container_log_level: Union[int, PipelineVariable] = logging.INFO,
+        dependencies: Optional[List[str]] = None,
+        git_config: Optional[Dict[str, str]] = None,
+    ):
+        """Initialize an SageMaker ``Model``.
+
+        Args:
+            image_uri (str or PipelineVariable): A Docker image URI.
+            model_data (str or PipelineVariable): The S3 location of a SageMaker
+                model data ``.tar.gz`` file (default: None).
+            role (str): An AWS IAM role (either name or full ARN). The Amazon
+                SageMaker training jobs and APIs that create Amazon SageMaker
+                endpoints use this role to access training data and model
+                artifacts. After the endpoint is created, the inference code
+                might use the IAM role if it needs to access some AWS resources.
+                It can be null if this is being used to create a Model to pass
+                to a ``PipelineModel`` which has its own Role field. (default:
+                None)
+            predictor_cls (callable[string, sagemaker.session.Session]): A
+                function to call to create a predictor (default: None). If not
+                None, ``deploy`` will return the result of invoking this
+                function on the created endpoint name.
+            env (dict[str, str] or dict[str, PipelineVariable]): Environment variables
+                to run with ``image_uri`` when hosted in SageMaker (default: None).
+            name (str): The model name. If None, a default model name will be
+                selected on each ``deploy``.
+            vpc_config (dict[str, list[str]] or dict[str, list[PipelineVariable]]):
+                The VpcConfig set on the model (default: None)
+                * 'Subnets' (list[str]): List of subnet ids.
+                * 'SecurityGroupIds' (list[str]): List of security group ids.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
+            enable_network_isolation (Boolean or PipelineVariable): Default False.
+                if True, enables network isolation in the endpoint, isolating the model
+                container. No inbound or outbound network calls can be made to
+                or from the model container.
+            model_kms_key (str): KMS key ARN used to encrypt the repacked
+                model archive file if the model is repacked
+            image_config (dict[str, str] or dict[str, PipelineVariable]): Specifies
+                whether the image of model container is pulled from ECR, or private
+                registry in your VPC. By default it is set to pull model container
+                image from ECR. (default: None).
+            source_dir (str): The absolute, relative, or S3 URI Path to a directory
+                with any other training source code dependencies aside from the entry
+                point file (default: None). If ``source_dir`` is an S3 URI, it must
+                point to a tar.gz file. Structure within this directory is preserved
+                when training on Amazon SageMaker. If 'git_config' is provided,
+                'source_dir' should be a relative location to a directory in the Git repo.
+                If the directory points to S3, no code is uploaded and the S3 location
+                is used instead.
+
+                .. admonition:: Example
+
+                    With the following GitHub repo directory structure:
+
+                    >>> |----- README.md
+                    >>> |----- src
+                    >>>         |----- inference.py
+                    >>>         |----- test.py
+
+                    You can assign entry_point='inference.py', source_dir='src'.
+            code_location (str): Name of the S3 bucket where custom code is
+                uploaded (default: None). If not specified, the default bucket
+                created by ``sagemaker.session.Session`` is used.
+            entry_point (str): The absolute or relative path to the local Python
+                source file that should be executed as the entry point to
+                model hosting. (Default: None). If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
+                If 'git_config' is provided, 'entry_point' should be
+                a relative location to the Python source file in the Git repo.
+
+                Example:
+                    With the following GitHub repo directory structure:
+
+                    >>> |----- README.md
+                    >>> |----- src
+                    >>>         |----- inference.py
+                    >>>         |----- test.py
+
+                    You can assign entry_point='src/inference.py'.
+            container_log_level (int or PipelineVariable): Log level to use within the
+                container (default: logging.INFO). Valid values are defined in the Python
+                logging module.
+            dependencies (list[str]): A list of absolute or relative paths to directories
+                with any additional libraries that should be exported
+                to the container (default: []). The library folders are
+                copied to SageMaker in the same folder where the entrypoint is
+                copied. If 'git_config' is provided, 'dependencies' should be a
+                list of relative locations to directories with any additional
+                libraries needed in the Git repo. If the ```source_dir``` points
+                to S3, code will be uploaded and the S3 location will be used
+                instead.
+
+                .. admonition:: Example
+
+                    The following call
+
+                    >>> Model(entry_point='inference.py',
+                    ...       dependencies=['my/libs/common', 'virtual-env'])
+
+                    results in the following structure inside the container:
+
+                    >>> $ ls
+
+                    >>> opt/ml/code
+                    >>>     |------ inference.py
+                    >>>     |------ common
+                    >>>     |------ virtual-env
+
+                This is not supported with "local code" in Local Mode.
+            git_config (dict[str, str]): Git configurations used for cloning
+                files, including ``repo``, ``branch``, ``commit``,
+                ``2FA_enabled``, ``username``, ``password`` and ``token``. The
+                ``repo`` field is required. All other fields are optional.
+                ``repo`` specifies the Git repository where your training script
+                is stored. If you don't provide ``branch``, the default value
+                'master' is used. If you don't provide ``commit``, the latest
+                commit in the specified branch is used.
+
+                .. admonition:: Example
+
+                    The following config:
+
+                    >>> git_config = {'repo': 'https://github.com/aws/sagemaker-python-sdk.git',
+                    >>>               'branch': 'test-branch-git-config',
+                    >>>               'commit': '329bfcf884482002c05ff7f44f62599ebc9f445a'}
+
+                    results in cloning the repo specified in 'repo', then
+                    checking out the 'master' branch, and checking out the specified
+                    commit.
+
+                ``2FA_enabled``, ``username``, ``password`` and ``token`` are
+                used for authentication. For GitHub (or other Git) accounts, set
+                ``2FA_enabled`` to 'True' if two-factor authentication is
+                enabled for the account, otherwise set it to 'False'. If you do
+                not provide a value for ``2FA_enabled``, a default value of
+                'False' is used. CodeCommit does not support two-factor
+                authentication, so do not provide "2FA_enabled" with CodeCommit
+                repositories.
+
+                For GitHub and other Git repos, when SSH URLs are provided, it
+                doesn't matter whether 2FA is enabled or disabled. You should
+                either have no passphrase for the SSH key pairs or have the
+                ssh-agent configured so that you will not be prompted for the SSH
+                passphrase when you run the 'git clone' command with SSH URLs. When
+                HTTPS URLs are provided, if 2FA is disabled, then either ``token``
+                or ``username`` and ``password`` are be used for authentication if provided.
+                ``Token`` is prioritized. If 2FA is enabled, only ``token`` is used
+                for authentication if provided. If required authentication info
+                is not provided, the SageMaker Python SDK attempts to use local credentials
+                to authenticate. If that fails, an error message is thrown.
+
+                For CodeCommit repos, 2FA is not supported, so ``2FA_enabled``
+                should not be provided. There is no token in CodeCommit, so
+                ``token`` should also not be provided. When ``repo`` is an SSH URL,
+                the requirements are the same as GitHub  repos. When ``repo``
+                is an HTTPS URL, ``username`` and ``password`` are used for
+                authentication if they are provided. If they are not provided,
+                the SageMaker Python SDK attempts to use either the CodeCommit
+                credential helper or local credential storage for authentication.
+
+        """
+        self.model_data = model_data
+        self.image_uri = image_uri
+        self.role = role
+        self.predictor_cls = predictor_cls
+        self.env = env or {}
+        self.name = name
+        self._base_name = None
+        self.vpc_config = vpc_config
+        self.sagemaker_session = sagemaker_session
+        self.endpoint_name = None
+        self._is_compiled_model = False
+        self._compilation_job_name = None
+        self._is_edge_packaged_model = False
+        self._enable_network_isolation = enable_network_isolation
+        self.model_kms_key = model_kms_key
+        self.image_config = image_config
+        self.entry_point = entry_point
+        self.source_dir = source_dir
+        self.dependencies = dependencies or []
+        self.git_config = git_config
+        self.container_log_level = container_log_level
+        if code_location:
+            self.bucket, self.key_prefix = s3.parse_s3_url(code_location)
+        else:
+            self.bucket, self.key_prefix = None, None
+        if self.git_config:
+            updates = git_utils.git_clone_repo(
+                self.git_config, self.entry_point, self.source_dir, self.dependencies
+            )
+            self.entry_point = updates["entry_point"]
+            self.source_dir = updates["source_dir"]
+            self.dependencies = updates["dependencies"]
+        self.uploaded_code = None
+        self.repacked_model_data = None
+
+    @runnable_by_pipeline
+    def register(
+        self,
+        content_types: List[Union[str, PipelineVariable]],
+        response_types: List[Union[str, PipelineVariable]],
+        inference_instances: Optional[List[Union[str, PipelineVariable]]] = None,
+        transform_instances: Optional[List[Union[str, PipelineVariable]]] = None,
+        model_package_name: Optional[Union[str, PipelineVariable]] = None,
+        model_package_group_name: Optional[Union[str, PipelineVariable]] = None,
+        image_uri: Optional[Union[str, PipelineVariable]] = None,
+        model_metrics: Optional[ModelMetrics] = None,
+        metadata_properties: Optional[MetadataProperties] = None,
+        marketplace_cert: bool = False,
+        approval_status: Optional[Union[str, PipelineVariable]] = None,
+        description: Optional[str] = None,
+        drift_check_baselines: Optional[DriftCheckBaselines] = None,
+        customer_metadata_properties: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        validation_specification: Optional[Union[str, PipelineVariable]] = None,
+        domain: Optional[Union[str, PipelineVariable]] = None,
+        task: Optional[Union[str, PipelineVariable]] = None,
+        sample_payload_url: Optional[Union[str, PipelineVariable]] = None,
+        framework: Optional[Union[str, PipelineVariable]] = None,
+        framework_version: Optional[Union[str, PipelineVariable]] = None,
+        nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
+        data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+    ):
+        """Creates a model package for creating SageMaker models or listing on Marketplace.
+
+        Args:
+            content_types (list[str] or list[PipelineVariable]): The supported MIME types
+                for the input data.
+            response_types (list[str] or list[PipelineVariable]): The supported MIME types
+                for the output data.
+            inference_instances (list[str] or list[PipelineVariable]): A list of the instance
+                types that are used to generate inferences in real-time (default: None).
+            transform_instances (list[str] or list[PipelineVariable]): A list of the instance
+                types on which a transformation job can be run or on which an endpoint can be
+                deployed (default: None).
+            model_package_name (str or PipelineVariable): Model Package name, exclusive to
+                `model_package_group_name`, using `model_package_name` makes the Model Package
+                un-versioned (default: None).
+            model_package_group_name (str or PipelineVariable): Model Package Group name,
+                exclusive to `model_package_name`, using `model_package_group_name` makes
+                the Model Package versioned (default: None).
+            image_uri (str or PipelineVariable): Inference image uri for the container.
+                Model class' self.image will be used if it is None (default: None).
+            model_metrics (ModelMetrics): ModelMetrics object (default: None).
+            metadata_properties (MetadataProperties): MetadataProperties object (default: None).
+            marketplace_cert (bool): A boolean value indicating if the Model Package is certified
+                for AWS Marketplace (default: False).
+            approval_status (str or PipelineVariable): Model Approval Status, values can be
+                "Approved", "Rejected", or "PendingManualApproval"
+                (default: "PendingManualApproval").
+            description (str): Model Package description (default: None).
+            drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
+            customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]):
+                A dictionary of key-value paired metadata properties (default: None).
+            domain (str or PipelineVariable): Domain values can be "COMPUTER_VISION",
+                "NATURAL_LANGUAGE_PROCESSING", "MACHINE_LEARNING" (default: None).
+            task (str or PipelineVariable): Task values which are supported by Inference Recommender
+                are "FILL_MASK", "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION",
+                "IMAGE_SEGMENTATION", "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+            sample_payload_url (str or PipelineVariable): The S3 path where the sample
+                payload is stored (default: None).
+            framework (str or PipelineVariable): Machine learning framework of the model package
+                container image (default: None).
+            framework_version (str or PipelineVariable): Framework version of the Model Package
+                Container Image (default: None).
+            nearest_model_name (str or PipelineVariable): Name of a pre-trained machine learning
+                benchmarked by Amazon SageMaker Inference Recommender (default: None).
+            data_input_configuration (str or PipelineVariable): Input object for the model
+                (default: None).
+
+        Returns:
+            A `sagemaker.model.ModelPackage` instance or pipeline step arguments
+            in case the Model instance is built with
+            :class:`~sagemaker.workflow.pipeline_context.PipelineSession`
+        """
+        if self.model_data is None:
+            raise ValueError("SageMaker Model Package cannot be created without model data.")
+        if image_uri is not None:
+            self.image_uri = image_uri
+
+        if model_package_group_name is not None:
+            container_def = self.prepare_container_def()
+            container_def = update_container_with_inference_params(
+                framework=framework,
+                framework_version=framework_version,
+                nearest_model_name=nearest_model_name,
+                data_input_configuration=data_input_configuration,
+                container_def=container_def,
+            )
+        else:
+            container_def = {
+                "Image": self.image_uri,
+                "ModelDataUrl": self.model_data,
+            }
+
+        model_pkg_args = sagemaker.get_model_package_args(
+            content_types,
+            response_types,
+            inference_instances=inference_instances,
+            transform_instances=transform_instances,
+            model_package_name=model_package_name,
+            model_package_group_name=model_package_group_name,
+            model_metrics=model_metrics,
+            metadata_properties=metadata_properties,
+            marketplace_cert=marketplace_cert,
+            approval_status=approval_status,
+            description=description,
+            container_def_list=[container_def],
+            drift_check_baselines=drift_check_baselines,
+            customer_metadata_properties=customer_metadata_properties,
+            validation_specification=validation_specification,
+            domain=domain,
+            sample_payload_url=sample_payload_url,
+            task=task,
+        )
+        model_package = self.sagemaker_session.create_model_package_from_containers(
+            **model_pkg_args
+        )
+        if isinstance(self.sagemaker_session, PipelineSession):
+            return None
+        return ModelPackage(
+            role=self.role,
+            model_data=self.model_data,
+            model_package_arn=model_package.get("ModelPackageArn"),
+        )
+
+    @runnable_by_pipeline
+    def create(
+        self,
+        instance_type: Optional[str] = None,
+        accelerator_type: Optional[str] = None,
+        serverless_inference_config: Optional[ServerlessInferenceConfig] = None,
+        tags: Optional[List[Dict[str, Union[str, PipelineVariable]]]] = None,
+    ):
+        """Create a SageMaker Model Entity
+
+        Args:
+            instance_type (str): The EC2 instance type that this Model will be
+                used for, this is only used to determine if the image needs GPU
+                support or not (default: None).
+            accelerator_type (str): Type of Elastic Inference accelerator to
+                attach to an endpoint for model loading and inference, for
+                example, 'ml.eia1.medium'. If not specified, no Elastic
+                Inference accelerator will be attached to the endpoint (default: None).
+            serverless_inference_config (ServerlessInferenceConfig):
+                Specifies configuration related to serverless endpoint. Instance type is
+                not provided in serverless inference. So this is used to find image URIs
+                (default: None).
+            tags (list[dict[str, str] or list[dict[str, PipelineVariable]]): The list of
+                tags to add to the model (default: None). Example::
+
+                    tags = [{'Key': 'tagname', 'Value':'tagvalue'}]
+
+                For more information about tags, see
+                `boto3 documentation <https://boto3.amazonaws.com/v1/documentation/\
+api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags>`_
+
+        Returns:
+            None or pipeline step arguments in case the Model instance is built with
+            :class:`~sagemaker.workflow.pipeline_context.PipelineSession`
+        """
+        # TODO: we should replace _create_sagemaker_model() with create()
+        self._create_sagemaker_model(
+            instance_type=instance_type,
+            accelerator_type=accelerator_type,
+            tags=tags,
+            serverless_inference_config=serverless_inference_config,
+        )
+
+    def _init_sagemaker_session_if_does_not_exist(self, instance_type=None):
+        """Set ``self.sagemaker_session`` to ``LocalSession`` or ``Session`` if it's not already.
+
+        The type of session object is determined by the instance type.
+        """
+        if self.sagemaker_session:
+            return
+
+        if instance_type in ("local", "local_gpu"):
+            self.sagemaker_session = local.LocalSession()
+        else:
+            self.sagemaker_session = session.Session()
+
+    def prepare_container_def(
+        self,
+        instance_type=None,
+        accelerator_type=None,
+        serverless_inference_config=None,
+    ):  # pylint: disable=unused-argument
+        """Return a dict created by ``sagemaker.container_def()``.
+
+        It is used for deploying this model to a specified instance type.
+
+        Subclasses can override this to provide custom container definitions
+        for deployment to a specific instance type. Called by ``deploy()``.
+
+        Args:
+            instance_type (str): The EC2 instance type to deploy this Model to.
+                For example, 'ml.p2.xlarge'.
+            accelerator_type (str): The Elastic Inference accelerator type to
+                deploy to the instance for loading and making inferences to the
+                model. For example, 'ml.eia1.medium'.
+            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
+                Specifies configuration related to serverless endpoint. Instance type is
+                not provided in serverless inference. So this is used to find image URIs.
+
+        Returns:
+            dict: A container definition object usable with the CreateModel API.
+        """
+        deploy_key_prefix = fw_utils.model_code_key_prefix(
+            self.key_prefix, self.name, self.image_uri
+        )
+        deploy_env = copy.deepcopy(self.env)
+        if self.source_dir or self.dependencies or self.entry_point or self.git_config:
+            is_repack = (
+                self.source_dir and self.entry_point and not (self.key_prefix or self.git_config)
+            )
+            self._upload_code(deploy_key_prefix, repack=is_repack)
+            deploy_env.update(self._script_mode_env_vars())
+        return sagemaker.container_def(
+            self.image_uri,
+            self.repacked_model_data or self.model_data,
+            deploy_env,
+            image_config=self.image_config,
+        )
+
+    def _upload_code(self, key_prefix: str, repack: bool = False) -> None:
+        """Uploads code to S3 to be used with script mode with SageMaker inference.
+
+        Args:
+            key_prefix (str): The S3 key associated with the ``code_location`` parameter of the
+                ``Model`` class.
+            repack (bool): Optional. Set to ``True`` to indicate that the source code and model
+                artifact should be repackaged into a new S3 object. (default: False).
+        """
+        local_code = utils.get_config_value("local.local_code", self.sagemaker_session.config)
+        bucket = self.bucket or self.sagemaker_session.default_bucket()
+        if (self.sagemaker_session.local_mode and local_code) or self.entry_point is None:
+            self.uploaded_code = None
+        elif not repack:
+            self.uploaded_code = fw_utils.tar_and_upload_dir(
+                session=self.sagemaker_session.boto_session,
+                bucket=bucket,
+                s3_key_prefix=key_prefix,
+                script=self.entry_point,
+                directory=self.source_dir,
+                dependencies=self.dependencies,
+                settings=self.sagemaker_session.settings,
+            )
+
+        if repack and self.model_data is not None and self.entry_point is not None:
+            if is_pipeline_variable(self.model_data):
+                # model is not yet there, defer repacking to later during pipeline execution
+                if not isinstance(self.sagemaker_session, PipelineSession):
+                    logging.warning(
+                        "The model_data is a Pipeline variable of type %s, "
+                        "which should be used under `PipelineSession` and "
+                        "leverage `ModelStep` to create or register model. "
+                        "Otherwise some functionalities e.g. "
+                        "runtime repack may be missing. For more, see: "
+                        "https://sagemaker.readthedocs.io/en/stable/"
+                        "amazon_sagemaker_model_building_pipeline.html#model-step",
+                        type(self.model_data),
+                    )
+                    return
+                self.sagemaker_session.context.need_runtime_repack.add(id(self))
+                self.sagemaker_session.context.runtime_repack_output_prefix = "s3://{}/{}".format(
+                    bucket, key_prefix
+                )
+                # Add the uploaded_code and repacked_model_data to update the container env
+                self.repacked_model_data = self.model_data
+                self.uploaded_code = fw_utils.UploadedCode(
+                    s3_prefix=self.repacked_model_data,
+                    script_name=os.path.basename(self.entry_point),
+                )
+                return
+            if local_code and self.model_data.startswith("file://"):
+                repacked_model_data = self.model_data
+            else:
+                repacked_model_data = "s3://" + "/".join([bucket, key_prefix, "model.tar.gz"])
+                self.uploaded_code = fw_utils.UploadedCode(
+                    s3_prefix=repacked_model_data, script_name=os.path.basename(self.entry_point)
+                )
+
+            utils.repack_model(
+                inference_script=self.entry_point,
+                source_directory=self.source_dir,
+                dependencies=self.dependencies,
+                model_uri=self.model_data,
+                repacked_model_uri=repacked_model_data,
+                sagemaker_session=self.sagemaker_session,
+                kms_key=self.model_kms_key,
+            )
+
+            self.repacked_model_data = repacked_model_data
+
+    def _script_mode_env_vars(self):
+        """Returns a mapping of environment variables for script mode execution"""
+        script_name = None
+        dir_name = None
+        if self.uploaded_code:
+            script_name = self.uploaded_code.script_name
+            if self.repacked_model_data or self.enable_network_isolation():
+                dir_name = "/opt/ml/model/code"
+            else:
+                dir_name = self.uploaded_code.s3_prefix
+        elif self.entry_point is not None:
+            script_name = self.entry_point
+            if self.source_dir is not None:
+                dir_name = (
+                    self.source_dir
+                    if self.source_dir.startswith("s3://")
+                    else "file://" + self.source_dir
+                )
+        return {
+            SCRIPT_PARAM_NAME.upper(): script_name or str(),
+            DIR_PARAM_NAME.upper(): dir_name or str(),
+            CONTAINER_LOG_LEVEL_PARAM_NAME.upper(): to_string(self.container_log_level),
+            SAGEMAKER_REGION_PARAM_NAME.upper(): self.sagemaker_session.boto_region_name,
+        }
+
+    def enable_network_isolation(self):
+        """Whether to enable network isolation when creating this Model
+
+        Returns:
+            bool: If network isolation should be enabled or not.
+        """
+        return self._enable_network_isolation
+
+    def _create_sagemaker_model(
+        self, instance_type=None, accelerator_type=None, tags=None, serverless_inference_config=None
+    ):
+        """Create a SageMaker Model Entity
+
+        Args:
+            instance_type (str): The EC2 instance type that this Model will be
+                used for, this is only used to determine if the image needs GPU
+                support or not.
+            accelerator_type (str): Type of Elastic Inference accelerator to
+                attach to an endpoint for model loading and inference, for
+                example, 'ml.eia1.medium'. If not specified, no Elastic
+                Inference accelerator will be attached to the endpoint.
+            tags (List[dict[str, str]]): Optional. The list of tags to add to
+                the model. Example: >>> tags = [{'Key': 'tagname', 'Value':
+                'tagvalue'}] For more information about tags, see
+                https://boto3.amazonaws.com/v1/documentation
+                /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
+            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
+                Specifies configuration related to serverless endpoint. Instance type is
+                not provided in serverless inference. So this is used to find image URIs.
+        """
+        container_def = self.prepare_container_def(
+            instance_type,
+            accelerator_type=accelerator_type,
+            serverless_inference_config=serverless_inference_config,
+        )
+
+        if not isinstance(self.sagemaker_session, PipelineSession):
+            # _base_name, model_name are not needed under PipelineSession.
+            # the model_data may be Pipeline variable
+            # which may break the _base_name generation
+            self._ensure_base_name_if_needed(
+                image_uri=container_def["Image"],
+                script_uri=self.source_dir,
+                model_uri=self.model_data,
+            )
+            self._set_model_name_if_needed()
+
+        enable_network_isolation = self.enable_network_isolation()
+
+        self._init_sagemaker_session_if_does_not_exist(instance_type)
+        create_model_args = dict(
+            name=self.name,
+            role=self.role,
+            container_defs=container_def,
+            vpc_config=self.vpc_config,
+            enable_network_isolation=enable_network_isolation,
+            tags=tags,
+        )
+        self.sagemaker_session.create_model(**create_model_args)
+
+    def _ensure_base_name_if_needed(self, image_uri, script_uri, model_uri):
+        """Create a base name from the image URI if there is no model name provided.
+
+        If a JumpStart script or model uri is used, select the JumpStart base name.
+        """
+        if self.name is None:
+            self._base_name = (
+                self._base_name
+                or get_jumpstart_base_name_if_jumpstart_model(script_uri, model_uri)
+                or utils.base_name_from_image(image_uri, default_base_name=Model.__name__)
+            )
+
+    def _set_model_name_if_needed(self):
+        """Generate a new model name if ``self._base_name`` is present."""
+        if self._base_name:
+            self.name = utils.name_from_base(self._base_name)
+
+    def _framework(self):
+        """Placeholder docstring"""
+        return getattr(self, "_framework_name", None)
+
+    def _get_framework_version(self):
+        """Placeholder docstring"""
+        return getattr(self, "framework_version", None)
+
+    def _edge_packaging_job_config(
+        self,
+        output_path,
+        role,
+        model_name,
+        model_version,
+        packaging_job_name,
+        compilation_job_name,
+        resource_key,
+        s3_kms_key,
+        tags,
+    ):
+        """Creates a request object for a packaging job.
+
+        Args:
+            output_path (str): where in S3 to store the output of the job
+            role (str): what role to use when executing the job
+            packaging_job_name (str): what to name the packaging job
+            compilation_job_name (str): what compilation job to source the model from
+            resource_key (str): the kms key to encrypt the disk with
+            s3_kms_key (str): the kms key to encrypt the output with
+            tags (list[dict]): List of tags for labeling an edge packaging job. For
+                more, see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
+        Returns:
+            dict: the request object to use when creating a packaging job
+        """
+        output_model_config = {
+            "S3OutputLocation": output_path,
+        }
+        if s3_kms_key is not None:
+            output_model_config["KmsKeyId"] = s3_kms_key
+
+        return {
+            "output_model_config": output_model_config,
+            "role": role,
+            "tags": tags,
+            "model_name": model_name,
+            "model_version": model_version,
+            "job_name": packaging_job_name,
+            "compilation_job_name": compilation_job_name,
+            "resource_key": resource_key,
+        }
+
+    def _compilation_job_config(
+        self,
+        target_instance_type,
+        input_shape,
+        output_path,
+        role,
+        compile_max_run,
+        job_name,
+        framework,
+        tags,
+        target_platform_os=None,
+        target_platform_arch=None,
+        target_platform_accelerator=None,
+        compiler_options=None,
+        framework_version=None,
+    ):
+        """Placeholder Docstring"""
+        input_model_config = {
+            "S3Uri": self.model_data,
+            "DataInputConfig": json.dumps(input_shape)
+            if isinstance(input_shape, dict)
+            else input_shape,
+            "Framework": framework.upper(),
+        }
+
+        def multi_version_compilation_supported(
+            target_instance_type: str, framework: str, framework_version: str
+        ):
+            if target_instance_type and framework and framework_version:
+                framework = framework.lower()
+                multi_version_frameworks_support_mapping = {
+                    "inferentia": ["pytorch", "tensorflow", "mxnet"],
+                    "neo_ioc_targets": ["pytorch", "tensorflow"],
+                }
+                if target_instance_type in NEO_IOC_TARGET_DEVICES:
+                    return framework in multi_version_frameworks_support_mapping["neo_ioc_targets"]
+                if target_instance_type == "ml_inf":
+                    return framework in multi_version_frameworks_support_mapping["inferentia"]
+            return False
+
+        if multi_version_compilation_supported(target_instance_type, framework, framework_version):
+            input_model_config["FrameworkVersion"] = utils.get_short_version(framework_version)
+
+        role = self.sagemaker_session.expand_role(role)
+        output_model_config = {
+            "S3OutputLocation": output_path,
+        }
+
+        if target_instance_type is not None:
+            output_model_config["TargetDevice"] = target_instance_type
+        else:
+            if target_platform_os is None and target_platform_arch is None:
+                raise ValueError(
+                    "target_instance_type or (target_platform_os and target_platform_arch) "
+                    "should be provided"
+                )
+            target_platform = {
+                "Os": target_platform_os,
+                "Arch": target_platform_arch,
+            }
+            if target_platform_accelerator is not None:
+                target_platform["Accelerator"] = target_platform_accelerator
+            output_model_config["TargetPlatform"] = target_platform
+
+        if compiler_options is not None:
+            output_model_config["CompilerOptions"] = (
+                json.dumps(compiler_options)
+                if isinstance(compiler_options, dict)
+                else compiler_options
+            )
+
+        return {
+            "input_model_config": input_model_config,
+            "output_model_config": output_model_config,
+            "role": role,
+            "stop_condition": {"MaxRuntimeInSeconds": compile_max_run},
+            "tags": tags,
+            "job_name": job_name,
+        }
+
+    def package_for_edge(
+        self,
+        output_path,
+        model_name,
+        model_version,
+        role=None,
+        job_name=None,
+        resource_key=None,
+        s3_kms_key=None,
+        tags=None,
+    ):
+        """Package this ``Model`` with SageMaker Edge.
+
+        Creates a new EdgePackagingJob and wait for it to finish.
+        model_data will now point to the packaged artifacts.
+
+        Args:
+            output_path (str): Specifies where to store the packaged model
+            role (str): Execution role
+            model_name (str): the name to attach to the model metadata
+            model_version (str): the version to attach to the model metadata
+            job_name (str): The name of the edge packaging job
+            resource_key (str): the kms key to encrypt the disk with
+            s3_kms_key (str): the kms key to encrypt the output with
+            tags (list[dict]): List of tags for labeling an edge packaging job. For
+                more, see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
+
+        Returns:
+            sagemaker.model.Model: A SageMaker ``Model`` object. See
+            :func:`~sagemaker.model.Model` for full details.
+        """
+        if self._compilation_job_name is None:
+            raise ValueError("You must first compile this model")
+        if job_name is None:
+            job_name = f"packaging{self._compilation_job_name[11:]}"
+        if role is None:
+            role = self.sagemaker_session.expand_role(role)
+
+        self._init_sagemaker_session_if_does_not_exist(None)
+        config = self._edge_packaging_job_config(
+            output_path,
+            role,
+            model_name,
+            model_version,
+            job_name,
+            self._compilation_job_name,
+            resource_key,
+            s3_kms_key,
+            tags,
+        )
+        self.sagemaker_session.package_model_for_edge(**config)
+        job_status = self.sagemaker_session.wait_for_edge_packaging_job(job_name)
+        self.model_data = job_status["ModelArtifact"]
+        self._is_edge_packaged_model = True
+
+        return self
+
+    def compile(
+        self,
+        target_instance_family,
+        input_shape,
+        output_path,
+        role,
+        tags=None,
+        job_name=None,
+        compile_max_run=15 * 60,
+        framework=None,
+        framework_version=None,
+        target_platform_os=None,
+        target_platform_arch=None,
+        target_platform_accelerator=None,
+        compiler_options=None,
+    ):
+        """Compile this ``Model`` with SageMaker Neo.
+
+        Args:
+            target_instance_family (str): Identifies the device that you want to
+                run your model after compilation, for example: ml_c5. For allowed
+                strings see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
+                Alternatively, you can select an OS, Architecture and Accelerator using
+                ``target_platform_os``, ``target_platform_arch``,
+                and ``target_platform_accelerator``.
+            input_shape (dict): Specifies the name and shape of the expected
+                inputs for your trained model in json dictionary form, for
+                example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
+                'var2': [1,1,28,28]}
+            output_path (str): Specifies where to store the compiled model
+            role (str): Execution role
+            tags (list[dict]): List of tags for labeling a compilation job. For
+                more, see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
+            job_name (str): The name of the compilation job
+            compile_max_run (int): Timeout in seconds for compilation (default:
+                15 * 60). After this amount of time Amazon SageMaker Neo
+                terminates the compilation job regardless of its current status.
+            framework (str): The framework that is used to train the original
+                model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
+                'onnx', 'xgboost'
+            framework_version (str): The version of framework, for example:
+                '1.5' for PyTorch
+            target_platform_os (str): Target Platform OS, for example: 'LINUX'.
+                For allowed strings see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
+                It can be used instead of target_instance_family by setting target_instance
+                family to None.
+            target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
+                For allowed strings see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
+                It can be used instead of target_instance_family by setting target_instance
+                family to None.
+            target_platform_accelerator (str, optional): Target Platform Accelerator,
+                for example: 'NVIDIA'. For allowed strings see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
+                It can be used instead of target_instance_family by setting target_instance
+                family to None.
+            compiler_options (dict, optional): Additional parameters for compiler.
+                Compiler Options are TargetPlatform / target_instance_family specific. See
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
+
+        Returns:
+            sagemaker.model.Model: A SageMaker ``Model`` object. See
+            :func:`~sagemaker.model.Model` for full details.
+        """
+        framework = framework or self._framework()
+        if framework is None:
+            raise ValueError(
+                "You must specify framework, allowed values {}".format(NEO_ALLOWED_FRAMEWORKS)
+            )
+        if framework not in NEO_ALLOWED_FRAMEWORKS:
+            raise ValueError(
+                "You must provide valid framework, allowed values {}".format(NEO_ALLOWED_FRAMEWORKS)
+            )
+        if job_name is None:
+            raise ValueError("You must provide a compilation job name")
+        if self.model_data is None:
+            raise ValueError("You must provide an S3 path to the compressed model artifacts.")
+
+        framework_version = framework_version or self._get_framework_version()
+
+        self._init_sagemaker_session_if_does_not_exist(target_instance_family)
+        config = self._compilation_job_config(
+            target_instance_family,
+            input_shape,
+            output_path,
+            role,
+            compile_max_run,
+            job_name,
+            framework,
+            tags,
+            target_platform_os,
+            target_platform_arch,
+            target_platform_accelerator,
+            compiler_options,
+            framework_version,
+        )
+        self.sagemaker_session.compile_model(**config)
+        job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
+        self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
+        if target_instance_family is not None:
+            if target_instance_family == "ml_eia2":
+                pass
+            elif target_instance_family.startswith("ml_"):
+                self.image_uri = job_status.get("InferenceImage", None)
+                self._is_compiled_model = True
+            else:
+                LOGGER.warning(
+                    "The instance type %s is not supported for deployment via SageMaker."
+                    "Please deploy the model manually.",
+                    target_instance_family,
+                )
+        else:
+            LOGGER.warning(
+                "Devices described by Target Platform OS, Architecture and Accelerator are not"
+                "supported for deployment via SageMaker. Please deploy the model manually."
+            )
+
+        self._compilation_job_name = job_name
+
+        return self
+
+    def deploy(
+        self,
+        initial_instance_count=None,
+        instance_type=None,
+        serializer=None,
+        deserializer=None,
+        accelerator_type=None,
+        endpoint_name=None,
+        tags=None,
+        kms_key=None,
+        wait=True,
+        data_capture_config=None,
+        async_inference_config=None,
+        serverless_inference_config=None,
+        **kwargs,
+    ):
+        """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
+
+        Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an
+        ``Endpoint`` from this ``Model``. If ``self.predictor_cls`` is not None,
+        this method returns a the result of invoking ``self.predictor_cls`` on
+        the created endpoint name.
+
+        The name of the created model is accessible in the ``name`` field of
+        this ``Model`` after deploy returns
+
+        The name of the created endpoint is accessible in the
+        ``endpoint_name`` field of this ``Model`` after deploy returns.
+
+        Args:
+            initial_instance_count (int): The initial number of instances to run
+                in the ``Endpoint`` created from this ``Model``. If not using
+                serverless inference, then it need to be a number larger or equals
+                to 1 (default: None)
+            instance_type (str): The EC2 instance type to deploy this Model to.
+                For example, 'ml.p2.xlarge', or 'local' for local mode. If not using
+                serverless inference, then it is required to deploy a model.
+                (default: None)
+            serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
+                serializer object, used to encode data for an inference endpoint
+                (default: None). If ``serializer`` is not None, then
+                ``serializer`` will override the default serializer. The
+                default serializer is set by the ``predictor_cls``.
+            deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
+                deserializer object, used to decode data from an inference
+                endpoint (default: None). If ``deserializer`` is not None, then
+                ``deserializer`` will override the default deserializer. The
+                default deserializer is set by the ``predictor_cls``.
+            accelerator_type (str): Type of Elastic Inference accelerator to
+                deploy this model for model loading and inference, for example,
+                'ml.eia1.medium'. If not specified, no Elastic Inference
+                accelerator will be attached to the endpoint. For more
+                information:
+                https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
+            endpoint_name (str): The name of the endpoint to create (default:
+                None). If not specified, a unique endpoint name will be created.
+            tags (List[dict[str, str]]): The list of tags to attach to this
+                specific endpoint.
+            kms_key (str): The ARN of the KMS key that is used to encrypt the
+                data on the storage volume attached to the instance hosting the
+                endpoint.
+            wait (bool): Whether the call should wait until the deployment of
+                this model completes (default: True).
+            data_capture_config (sagemaker.model_monitor.DataCaptureConfig): Specifies
+                configuration related to Endpoint data capture for use with
+                Amazon SageMaker Model Monitoring. Default: None.
+            async_inference_config (sagemaker.model_monitor.AsyncInferenceConfig): Specifies
+                configuration related to async endpoint. Use this configuration when trying
+                to create async endpoint and make async inference. If empty config object
+                passed through, will use default config to deploy async endpoint. Deploy a
+                real-time endpoint if it's None. (default: None)
+            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
+                Specifies configuration related to serverless endpoint. Use this configuration
+                when trying to create serverless endpoint and make serverless inference. If
+                empty object passed through, will use pre-defined values in
+                ``ServerlessInferenceConfig`` class to deploy serverless endpoint. Deploy an
+                instance based endpoint if it's None. (default: None)
+        Raises:
+             ValueError: If arguments combination check failed in these circumstances:
+                - If no role is specified or
+                - If serverless inference config is not specified and instance type and instance
+                    count are also not specified or
+                - If a wrong type of object is provided as serverless inference config or async
+                    inference config
+        Returns:
+            callable[string, sagemaker.session.Session] or None: Invocation of
+                ``self.predictor_cls`` on the created endpoint name, if ``self.predictor_cls``
+                is not None. Otherwise, return None.
+        """
+        removed_kwargs("update_endpoint", kwargs)
+        self._init_sagemaker_session_if_does_not_exist(instance_type)
+
+        tags = add_jumpstart_tags(
+            tags=tags, inference_model_uri=self.model_data, inference_script_uri=self.source_dir
+        )
+
+        if self.role is None:
+            raise ValueError("Role can not be null for deploying a model")
+
+        is_async = async_inference_config is not None
+        if is_async and not isinstance(async_inference_config, AsyncInferenceConfig):
+            raise ValueError("async_inference_config needs to be a AsyncInferenceConfig object")
+
+        is_serverless = serverless_inference_config is not None
+        if not is_serverless and not (instance_type and initial_instance_count):
+            raise ValueError(
+                "Must specify instance type and instance count unless using serverless inference"
+            )
+
+        if is_serverless and not isinstance(serverless_inference_config, ServerlessInferenceConfig):
+            raise ValueError(
+                "serverless_inference_config needs to be a ServerlessInferenceConfig object"
+            )
+
+        if instance_type and instance_type.startswith("ml.inf") and not self._is_compiled_model:
+            LOGGER.warning(
+                "Your model is not compiled. Please compile your model before using Inferentia."
+            )
+
+        compiled_model_suffix = None if is_serverless else "-".join(instance_type.split(".")[:-1])
+        if self._is_compiled_model and not is_serverless:
+            self._ensure_base_name_if_needed(
+                image_uri=self.image_uri, script_uri=self.source_dir, model_uri=self.model_data
+            )
+            if self._base_name is not None:
+                self._base_name = "-".join((self._base_name, compiled_model_suffix))
+
+        self._create_sagemaker_model(
+            instance_type, accelerator_type, tags, serverless_inference_config
+        )
+
+        serverless_inference_config_dict = (
+            serverless_inference_config._to_request_dict() if is_serverless else None
+        )
+        production_variant = sagemaker.production_variant(
+            self.name,
+            instance_type,
+            initial_instance_count,
+            accelerator_type=accelerator_type,
+            serverless_inference_config=serverless_inference_config_dict,
+        )
+        if endpoint_name:
+            self.endpoint_name = endpoint_name
+        else:
+            base_endpoint_name = self._base_name or utils.base_from_name(self.name)
+            if self._is_compiled_model and not is_serverless:
+                if not base_endpoint_name.endswith(compiled_model_suffix):
+                    base_endpoint_name = "-".join((base_endpoint_name, compiled_model_suffix))
+            self.endpoint_name = utils.name_from_base(base_endpoint_name)
+
+        data_capture_config_dict = None
+        if data_capture_config is not None:
+            data_capture_config_dict = data_capture_config._to_request_dict()
+
+        async_inference_config_dict = None
+        if is_async:
+            if async_inference_config.output_path is None:
+                async_inference_config = self._build_default_async_inference_config(
+                    async_inference_config
+                )
+            async_inference_config_dict = async_inference_config._to_request_dict()
+
+        self.sagemaker_session.endpoint_from_production_variants(
+            name=self.endpoint_name,
+            production_variants=[production_variant],
+            tags=tags,
+            kms_key=kms_key,
+            wait=wait,
+            data_capture_config_dict=data_capture_config_dict,
+            async_inference_config_dict=async_inference_config_dict,
+        )
+
+        if self.predictor_cls:
+            predictor = self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+            if serializer:
+                predictor.serializer = serializer
+            if deserializer:
+                predictor.deserializer = deserializer
+            if is_async:
+                return AsyncPredictor(predictor, self.name)
+            return predictor
+        return None
+
+    def _build_default_async_inference_config(self, async_inference_config):
+        """Build default async inference config and return ``AsyncInferenceConfig``"""
+        async_output_folder = unique_name_from_base(self.name)
+        async_output_s3uri = "s3://{}/async-endpoint-outputs/{}".format(
+            self.sagemaker_session.default_bucket(), async_output_folder
+        )
+        async_inference_config.output_path = async_output_s3uri
+        return async_inference_config
+
+    def transformer(
+        self,
+        instance_count,
+        instance_type,
+        strategy=None,
+        assemble_with=None,
+        output_path=None,
+        output_kms_key=None,
+        accept=None,
+        env=None,
+        max_concurrent_transforms=None,
+        max_payload=None,
+        tags=None,
+        volume_kms_key=None,
+    ):
+        """Return a ``Transformer`` that uses this Model.
+
+        Args:
+            instance_count (int): Number of EC2 instances to use.
+            instance_type (str): Type of EC2 instance to use, for example,
+                'ml.c4.xlarge'.
+            strategy (str): The strategy used to decide how to batch records in
+                a single request (default: None). Valid values: 'MultiRecord'
+                and 'SingleRecord'.
+            assemble_with (str): How the output is assembled (default: None).
+                Valid values: 'Line' or 'None'.
+            output_path (str): S3 location for saving the transform result. If
+                not specified, results are stored to a default bucket.
+            output_kms_key (str): Optional. KMS key ID for encrypting the
+                transform output (default: None).
+            accept (str): The accept header passed by the client to
+                the inference endpoint. If it is supported by the endpoint,
+                it will be the format of the batch transform output.
+            env (dict): Environment variables to be set for use during the
+                transform job (default: None).
+            max_concurrent_transforms (int): The maximum number of HTTP requests
+                to be made to each individual transform container at one time.
+            max_payload (int): Maximum size of the payload in a single HTTP
+                request to the container in MB.
+            tags (list[dict]): List of tags for labeling a transform job. If
+                none specified, then the tags used for the training job are used
+                for the transform job.
+            volume_kms_key (str): Optional. KMS key ID for encrypting the volume
+                attached to the ML compute instance (default: None).
+        """
+        self._init_sagemaker_session_if_does_not_exist(instance_type)
+
+        self._create_sagemaker_model(instance_type, tags=tags)
+        if self.enable_network_isolation():
+            env = None
+
+        return Transformer(
+            self.name,
+            instance_count,
+            instance_type,
+            strategy=strategy,
+            assemble_with=assemble_with,
+            output_path=output_path,
+            output_kms_key=output_kms_key,
+            accept=accept,
+            max_concurrent_transforms=max_concurrent_transforms,
+            max_payload=max_payload,
+            env=env,
+            tags=tags,
+            base_transform_job_name=self._base_name or self.name,
+            volume_kms_key=volume_kms_key,
+            sagemaker_session=self.sagemaker_session,
+        )
+
+    def delete_model(self):
+        """Delete an Amazon SageMaker Model.
+
+        Raises:
+            ValueError: if the model is not created yet.
+        """
+        if self.name is None:
+            raise ValueError(
+                "The SageMaker model must be created first before attempting to delete."
+            )
+        self.sagemaker_session.delete_model(self.name)
+
+
+class FrameworkModel(Model):
+    """A Model for working with an SageMaker ``Framework``.
+
+    This class hosts user-defined code in S3 and sets code location and
+    configuration in model environment variables.
+    """
+
+    def __init__(
+        self,
+        model_data: Union[str, PipelineVariable],
+        image_uri: Union[str, PipelineVariable],
+        role: str,
+        entry_point: str,
+        source_dir: Optional[str] = None,
+        predictor_cls: Optional[callable] = None,
+        env: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        name: Optional[str] = None,
+        container_log_level: Union[int, PipelineVariable] = logging.INFO,
+        code_location: Optional[str] = None,
+        sagemaker_session: Optional[Session] = None,
+        dependencies: Optional[List[str]] = None,
+        git_config: Optional[Dict[str, str]] = None,
+        **kwargs,
+    ):
+        """Initialize a ``FrameworkModel``.
+
+        Args:
+            model_data (str or PipelineVariable): The S3 location of a SageMaker
+                model data ``.tar.gz`` file.
+            image_uri (str or PipelineVariable): A Docker image URI.
+            role (str): An IAM role name or ARN for SageMaker to access AWS
+                resources on your behalf.
+            entry_point (str): Path (absolute or relative) to the Python source
+                file which should be executed as the entry point to model
+                hosting. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
+                If 'git_config' is provided, 'entry_point' should be
+                a relative location to the Python source file in the Git repo.
+
+                Example:
+                    With the following GitHub repo directory structure:
+
+                    >>> |----- README.md
+                    >>> |----- src
+                    >>>         |----- inference.py
+                    >>>         |----- test.py
+
+                    You can assign entry_point='src/inference.py'.
+            source_dir (str): Path (absolute, relative or an S3 URI) to a directory
+                with any other training source code dependencies aside from the entry
+                point file (default: None). If ``source_dir`` is an S3 URI, it must
+                point to a tar.gz file. Structure within this directory are preserved
+                when training on Amazon SageMaker. If 'git_config' is provided,
+                'source_dir' should be a relative location to a directory in the Git repo.
+                If the directory points to S3, no code will be uploaded and the S3 location
+                will be used instead.
+
+                .. admonition:: Example
+
+                    With the following GitHub repo directory structure:
+
+                    >>> |----- README.md
+                    >>> |----- src
+                    >>>         |----- inference.py
+                    >>>         |----- test.py
+
+                    You can assign entry_point='inference.py', source_dir='src'.
+            predictor_cls (callable[string, sagemaker.session.Session]): A
+                function to call to create a predictor (default: None). If not
+                None, ``deploy`` will return the result of invoking this
+                function on the created endpoint name.
+            env (dict[str, str] or dict[str, PipelineVariable]): Environment variables to
+                run with ``image_uri`` when hosted in SageMaker (default: None).
+            name (str): The model name. If None, a default model name will be
+                selected on each ``deploy``.
+            container_log_level (int or PipelineVariable): Log level to use within
+                the container (default: logging.INFO). Valid values are defined
+                in the Python logging module.
+            code_location (str): Name of the S3 bucket where custom code is
+                uploaded (default: None). If not specified, default bucket
+                created by ``sagemaker.session.Session`` is used.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
+            dependencies (list[str]): A list of paths to directories (absolute
+                or relative) with any additional libraries that will be exported
+                to the container (default: []). The library folders will be
+                copied to SageMaker in the same folder where the entrypoint is
+                copied. If 'git_config' is provided, 'dependencies' should be a
+                list of relative locations to directories with any additional
+                libraries needed in the Git repo. If the ```source_dir``` points
+                to S3, code will be uploaded and the S3 location will be used
+                instead.
+
+                .. admonition:: Example
+
+                    The following call
+
+                    >>> Model(entry_point='inference.py',
+                    ...       dependencies=['my/libs/common', 'virtual-env'])
+
+                    results in the following inside the container:
+
+                    >>> $ ls
+
+                    >>> opt/ml/code
+                    >>>     |------ inference.py
+                    >>>     |------ common
+                    >>>     |------ virtual-env
+
+                This is not supported with "local code" in Local Mode.
+            git_config (dict[str, str]): Git configurations used for cloning
+                files, including ``repo``, ``branch``, ``commit``,
+                ``2FA_enabled``, ``username``, ``password`` and ``token``. The
+                ``repo`` field is required. All other fields are optional.
+                ``repo`` specifies the Git repository where your training script
+                is stored. If you don't provide ``branch``, the default value
+                'master' is used. If you don't provide ``commit``, the latest
+                commit in the specified branch is used.
+
+                .. admonition:: Example
+
+                    The following config:
+
+                    >>> git_config = {'repo': 'https://github.com/aws/sagemaker-python-sdk.git',
+                    >>>               'branch': 'test-branch-git-config',
+                    >>>               'commit': '329bfcf884482002c05ff7f44f62599ebc9f445a'}
+
+                    results in cloning the repo specified in 'repo', then
+                    checkout the 'master' branch, and checkout the specified
+                    commit.
+
+                ``2FA_enabled``, ``username``, ``password`` and ``token`` are
+                used for authentication. For GitHub (or other Git) accounts, set
+                ``2FA_enabled`` to 'True' if two-factor authentication is
+                enabled for the account, otherwise set it to 'False'. If you do
+                not provide a value for ``2FA_enabled``, a default value of
+                'False' is used. CodeCommit does not support two-factor
+                authentication, so do not provide "2FA_enabled" with CodeCommit
+                repositories.
+
+                For GitHub and other Git repos, when SSH URLs are provided, it
+                doesn't matter whether 2FA is enabled or disabled; you should
+                either have no passphrase for the SSH key pairs, or have the
+                ssh-agent configured so that you will not be prompted for SSH
+                passphrase when you do 'git clone' command with SSH URLs. When
+                HTTPS URLs are provided: if 2FA is disabled, then either token
+                or username+password will be used for authentication if provided
+                (token prioritized); if 2FA is enabled, only token will be used
+                for authentication if provided. If required authentication info
+                is not provided, python SDK will try to use local credentials
+                storage to authenticate. If that fails either, an error message
+                will be thrown.
+
+                For CodeCommit repos, 2FA is not supported, so '2FA_enabled'
+                should not be provided. There is no token in CodeCommit, so
+                'token' should not be provided too. When 'repo' is an SSH URL,
+                the requirements are the same as GitHub-like repos. When 'repo'
+                is an HTTPS URL, username+password will be used for
+                authentication if they are provided; otherwise, python SDK will
+                try to use either CodeCommit credential helper or local
+                credential storage for authentication.
+            **kwargs: Keyword arguments passed to the superclass
+                :class:`~sagemaker.model.Model`.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.model.Model`.
+        """
+        super(FrameworkModel, self).__init__(
+            image_uri,
+            model_data,
+            role,
+            predictor_cls=predictor_cls,
+            env=env,
+            name=name,
+            sagemaker_session=sagemaker_session,
+            source_dir=source_dir,
+            code_location=code_location,
+            entry_point=entry_point,
+            container_log_level=container_log_level,
+            dependencies=dependencies,
+            git_config=git_config,
+            **kwargs,
+        )
+
+
+class ModelPackage(Model):
+    """A SageMaker ``Model`` that can be deployed to an ``Endpoint``."""
+
+    def __init__(self, role, model_data=None, algorithm_arn=None, model_package_arn=None, **kwargs):
+        """Initialize a SageMaker ModelPackage.
+
+        Args:
+            role (str): An AWS IAM role (either name or full ARN). The Amazon
+                SageMaker training jobs and APIs that create Amazon SageMaker
+                endpoints use this role to access training data and model
+                artifacts. After the endpoint is created, the inference code
+                might use the IAM role, if it needs to access an AWS resource.
+            model_data (str): The S3 location of a SageMaker model data
+                ``.tar.gz`` file. Must be provided if algorithm_arn is provided.
+            algorithm_arn (str): algorithm arn used to train the model, can be
+                just the name if your account owns the algorithm. Must also
+                provide ``model_data``.
+            model_package_arn (str): An existing SageMaker Model Package arn,
+                can be just the name if your account owns the Model Package.
+                ``model_data`` is not required.
+            **kwargs: Additional kwargs passed to the Model constructor.
+        """
+        super(ModelPackage, self).__init__(
+            role=role, model_data=model_data, image_uri=None, **kwargs
+        )
+
+        if model_package_arn and algorithm_arn:
+            raise ValueError(
+                "model_package_arn and algorithm_arn are mutually exclusive."
+                "Both were provided: model_package_arn: %s algorithm_arn: %s"
+                % (model_package_arn, algorithm_arn)
+            )
+
+        if model_package_arn is None and algorithm_arn is None:
+            raise ValueError(
+                "either model_package_arn or algorithm_arn is required." " None was provided."
+            )
+
+        self.algorithm_arn = algorithm_arn
+        if self.algorithm_arn is not None:
+            if model_data is None:
+                raise ValueError("model_data must be provided with algorithm_arn")
+            self.model_data = model_data
+
+        self.model_package_arn = model_package_arn
+        self._created_model_package_name = None
+
+    def _create_sagemaker_model_package(self):
+        """Placeholder docstring"""
+        if self.algorithm_arn is None:
+            raise ValueError("No algorithm_arn was provided to create a SageMaker Model Pacakge")
+
+        name = self.name or utils.name_from_base(self.algorithm_arn.split("/")[-1])
+        description = "Model Package created from training with %s" % self.algorithm_arn
+        self.sagemaker_session.create_model_package_from_algorithm(
+            name, description, self.algorithm_arn, self.model_data
+        )
+        return name
+
+    def enable_network_isolation(self):
+        """Whether to enable network isolation when creating a model out of this ModelPackage
+
+        Returns:
+            bool: If network isolation should be enabled or not.
+        """
+        return self._is_marketplace()
+
+    def _is_marketplace(self):
+        """Placeholder docstring"""
+        model_package_name = self.model_package_arn or self._created_model_package_name
+        if model_package_name is None:
+            return True
+
+        # Models can lazy-init sagemaker_session until deploy() is called to support
+        # LocalMode so we must make sure we have an actual session to describe the model package.
+        sagemaker_session = self.sagemaker_session or sagemaker.Session()
+
+        model_package_desc = sagemaker_session.sagemaker_client.describe_model_package(
+            ModelPackageName=model_package_name
+        )
+        for container in model_package_desc["InferenceSpecification"]["Containers"]:
+            if "ProductId" in container:
+                return True
+        return False
+
+    def _create_sagemaker_model(self, *args, **kwargs):  # pylint: disable=unused-argument
+        """Create a SageMaker Model Entity
+
+        Args:
+            args: Positional arguments coming from the caller. This class does not require
+                any so they are ignored.
+
+            kwargs: Keyword arguments coming from the caller. This class does not require
+                any so they are ignored.
+        """
+        if self.algorithm_arn:
+            # When ModelPackage is created using an algorithm_arn we need to first
+            # create a ModelPackage. If we had already created one then its fine to re-use it.
+            if self._created_model_package_name is None:
+                model_package_name = self._create_sagemaker_model_package()
+                self.sagemaker_session.wait_for_model_package(model_package_name)
+                self._created_model_package_name = model_package_name
+            model_package_name = self._created_model_package_name
+        else:
+            # When a ModelPackageArn is provided we just create the Model
+            model_package_name = self.model_package_arn
+
+        container_def = {"ModelPackageName": model_package_name}
+
+        self._ensure_base_name_if_needed(model_package_name.split("/")[-1])
+        self._set_model_name_if_needed()
+
+        self.sagemaker_session.create_model(
+            self.name,
+            self.role,
+            container_def,
+            vpc_config=self.vpc_config,
+            enable_network_isolation=self.enable_network_isolation(),
+        )
+
+    def _ensure_base_name_if_needed(self, base_name):
+        """Set the base name if there is no model name provided."""
+        if self.name is None:
+            self._base_name = base_name

testbed/aws__sagemaker-python-sdk/src/sagemaker/model_metrics.py

@@ -0,0 +1,160 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""This file contains code related to model metrics, including metric source and file source."""
+from __future__ import absolute_import
+
+from typing import Optional, Union
+
+from sagemaker.workflow.entities import PipelineVariable
+
+
+class ModelMetrics(object):
+    """Accepts model metrics parameters for conversion to request dict."""
+
+    def __init__(
+        self,
+        model_statistics: Optional["MetricsSource"] = None,
+        model_constraints: Optional["MetricsSource"] = None,
+        model_data_statistics: Optional["MetricsSource"] = None,
+        model_data_constraints: Optional["MetricsSource"] = None,
+        bias: Optional["MetricsSource"] = None,
+        explainability: Optional["MetricsSource"] = None,
+        bias_pre_training: Optional["MetricsSource"] = None,
+        bias_post_training: Optional["MetricsSource"] = None,
+    ):
+        """Initialize a ``ModelMetrics`` instance and turn parameters into dict.
+
+        Args:
+            model_statistics (MetricsSource): A metric source object that represents
+                model statistics (default: None).
+            model_constraints (MetricsSource): A metric source object that represents
+                model constraints (default: None).
+            model_data_statistics (MetricsSource): A metric source object that represents
+                model data statistics (default: None).
+            model_data_constraints (MetricsSource): A metric source object that represents
+                model data constraints (default: None).
+            bias (MetricsSource): A metric source object that represents bias report
+                (default: None).
+            explainability (MetricsSource): A metric source object that represents
+                explainability report (default: None).
+            bias_pre_training (MetricsSource): A metric source object that represents
+                Pre-training report (default: None).
+            bias_post_training (MetricsSource): A metric source object that represents
+                Post-training report (default: None).
+        """
+        self.model_statistics = model_statistics
+        self.model_constraints = model_constraints
+        self.model_data_statistics = model_data_statistics
+        self.model_data_constraints = model_data_constraints
+        self.bias = bias
+        self.bias_pre_training = bias_pre_training
+        self.bias_post_training = bias_post_training
+        self.explainability = explainability
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        model_metrics_request = {}
+
+        model_quality = {}
+        if self.model_statistics is not None:
+            model_quality["Statistics"] = self.model_statistics._to_request_dict()
+        if self.model_constraints is not None:
+            model_quality["Constraints"] = self.model_constraints._to_request_dict()
+        if model_quality:
+            model_metrics_request["ModelQuality"] = model_quality
+
+        model_data_quality = {}
+        if self.model_data_statistics is not None:
+            model_data_quality["Statistics"] = self.model_data_statistics._to_request_dict()
+        if self.model_data_constraints is not None:
+            model_data_quality["Constraints"] = self.model_data_constraints._to_request_dict()
+        if model_data_quality:
+            model_metrics_request["ModelDataQuality"] = model_data_quality
+
+        bias = {}
+        if self.bias is not None:
+            bias["Report"] = self.bias._to_request_dict()
+        if self.bias_pre_training is not None:
+            bias["PreTrainingReport"] = self.bias_pre_training._to_request_dict()
+        if self.bias_post_training is not None:
+            bias["PostTrainingReport"] = self.bias_post_training._to_request_dict()
+        model_metrics_request["Bias"] = bias
+
+        explainability = {}
+        if self.explainability is not None:
+            explainability["Report"] = self.explainability._to_request_dict()
+        model_metrics_request["Explainability"] = explainability
+
+        return model_metrics_request
+
+
+class MetricsSource(object):
+    """Accepts metrics source parameters for conversion to request dict."""
+
+    def __init__(
+        self,
+        content_type: Union[str, PipelineVariable],
+        s3_uri: Union[str, PipelineVariable],
+        content_digest: Optional[Union[str, PipelineVariable]] = None,
+    ):
+        """Initialize a ``MetricsSource`` instance and turn parameters into dict.
+
+        Args:
+            content_type (str or PipelineVariable): Specifies the type of content
+                in S3 URI
+            s3_uri (str or PipelineVariable): The S3 URI of the metric
+            content_digest (str or PipelineVariable): The digest of the metric
+                (default: None)
+        """
+        self.content_type = content_type
+        self.s3_uri = s3_uri
+        self.content_digest = content_digest
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        metrics_source_request = {"ContentType": self.content_type, "S3Uri": self.s3_uri}
+        if self.content_digest is not None:
+            metrics_source_request["ContentDigest"] = self.content_digest
+        return metrics_source_request
+
+
+class FileSource(object):
+    """Accepts file source parameters for conversion to request dict."""
+
+    def __init__(
+        self,
+        s3_uri: Union[str, PipelineVariable],
+        content_digest: Optional[Union[str, PipelineVariable]] = None,
+        content_type: Optional[Union[str, PipelineVariable]] = None,
+    ):
+        """Initialize a ``FileSource`` instance and turn parameters into dict.
+
+        Args:
+            s3_uri (str or PipelineVariable): The S3 URI of the metric
+            content_digest (str or PipelineVariable): The digest of the metric
+                (default: None)
+            content_type (str or PipelineVariable): Specifies the type of content
+                in S3 URI (default: None)
+        """
+        self.content_type = content_type
+        self.s3_uri = s3_uri
+        self.content_digest = content_digest
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        file_source_request = {"S3Uri": self.s3_uri}
+        if self.content_digest is not None:
+            file_source_request["ContentDigest"] = self.content_digest
+        if self.content_type is not None:
+            file_source_request["ContentType"] = self.content_type
+        return file_source_request

testbed/aws__sagemaker-python-sdk/src/sagemaker/multidatamodel.py

@@ -0,0 +1,345 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""This module contains code to create and manage SageMaker ``MultiDataModel``"""
+from __future__ import absolute_import
+
+import os
+from typing import Union, Optional
+
+from six.moves.urllib.parse import urlparse
+
+import sagemaker
+from sagemaker import local, s3
+from sagemaker.deprecations import removed_kwargs
+from sagemaker.model import Model
+from sagemaker.session import Session
+from sagemaker.utils import pop_out_unused_kwarg
+from sagemaker.workflow.entities import PipelineVariable
+
+MULTI_MODEL_CONTAINER_MODE = "MultiModel"
+
+
+class MultiDataModel(Model):
+    """SageMaker ``MultiDataModel`` can be used to deploy multiple models to the same ``Endpoint``.
+
+    And also deploy additional models to an existing SageMaker multi-model ``Endpoint``
+    """
+
+    def __init__(
+        self,
+        name: str,
+        model_data_prefix: str,
+        model: Optional[Model] = None,
+        image_uri: Optional[Union[str, PipelineVariable]] = None,
+        role: Optional[str] = None,
+        sagemaker_session: Optional[Session] = None,
+        **kwargs,
+    ):
+        """Initialize a ``MultiDataModel``.
+
+        Addition to these arguments, it supports all arguments supported by ``Model`` constructor.
+
+        Args:
+            name (str): The model name.
+            model_data_prefix (str): The S3 prefix where all the models artifacts (.tar.gz)
+                in a Multi-Model endpoint are located
+            model (sagemaker.Model): The Model object that would define the
+                SageMaker model attributes like vpc_config, predictors, etc.
+                If this is present, the attributes from this model are used when
+                deploying the ``MultiDataModel``.  Parameters 'image_uri', 'role' and 'kwargs'
+                are not permitted when model parameter is set.
+            image_uri (str or PipelineVariable): A Docker image URI. It can be null if the 'model'
+                parameter is passed to during ``MultiDataModel`` initialization (default: None)
+            role (str): An AWS IAM role (either name or full ARN). The Amazon
+                SageMaker training jobs and APIs that create Amazon SageMaker
+                endpoints use this role to access training data and model
+                artifacts. After the endpoint is created, the inference code
+                might use the IAM role if it needs to access some AWS resources.
+                It can be null if this is being used to create a Model to pass
+                to a ``PipelineModel`` which has its own Role field or if the 'model' parameter
+                is passed to during ``MultiDataModel`` initialization (default: None)
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
+            **kwargs: Keyword arguments passed to the
+                :class:`~sagemaker.model.Model` initializer.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.model.Model`.
+        """
+        # Validate path
+        if not model_data_prefix.startswith("s3://"):
+            raise ValueError(
+                'Expecting S3 model prefix beginning with "s3://". Received: "{}"'.format(
+                    model_data_prefix
+                )
+            )
+
+        if model and (image_uri or role or kwargs):
+            raise ValueError(
+                "Parameters image_uri, role, and kwargs are not permitted when "
+                "model parameter is passed."
+            )
+
+        self.name = name
+        self.model_data_prefix = model_data_prefix
+        self.model = model
+        self.container_mode = MULTI_MODEL_CONTAINER_MODE
+        self.sagemaker_session = sagemaker_session or Session()
+
+        if self.sagemaker_session.s3_client is None:
+            self.s3_client = self.sagemaker_session.boto_session.client(
+                "s3", region_name=self.sagemaker_session.boto_session.region_name
+            )
+        else:
+            self.s3_client = self.sagemaker_session.s3_client
+
+        # Set the ``Model`` parameters if the model parameter is not specified
+        if not self.model:
+            pop_out_unused_kwarg("model_data", kwargs, self.model_data_prefix)
+            super(MultiDataModel, self).__init__(
+                image_uri,
+                self.model_data_prefix,
+                role,
+                name=self.name,
+                sagemaker_session=self.sagemaker_session,
+                **kwargs,
+            )
+
+    def prepare_container_def(
+        self, instance_type=None, accelerator_type=None, serverless_inference_config=None
+    ):
+        """Return a container definition set.
+
+        Definition set includes MultiModel mode, model data and other parameters
+        from the model (if available).
+
+        Subclasses can override this to provide custom container definitions
+        for deployment to a specific instance type. Called by ``deploy()``.
+
+        Returns:
+            dict[str, str]: A complete container definition object usable with the CreateModel API
+        """
+        # Copy the trained model's image URI and environment variables if they exist. Models trained
+        # with FrameworkEstimator set framework specific environment variables which need to be
+        # copied over
+        if self.model:
+            container_definition = self.model.prepare_container_def(instance_type, accelerator_type)
+            image_uri = container_definition["Image"]
+            environment = container_definition["Environment"]
+        else:
+            image_uri = self.image_uri
+            environment = self.env
+        return sagemaker.container_def(
+            image_uri,
+            env=environment,
+            model_data_url=self.model_data_prefix,
+            container_mode=self.container_mode,
+        )
+
+    def deploy(
+        self,
+        initial_instance_count,
+        instance_type,
+        serializer=None,
+        deserializer=None,
+        accelerator_type=None,
+        endpoint_name=None,
+        tags=None,
+        kms_key=None,
+        wait=True,
+        data_capture_config=None,
+        **kwargs,
+    ):
+        """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
+
+        Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an
+        ``Endpoint`` from this ``Model``. If self.model is not None, then the ``Endpoint``
+        will be deployed with parameters in self.model (like vpc_config,
+        enable_network_isolation, etc).  If self.model is None, then use the parameters
+        in ``MultiDataModel`` constructor will be used. If ``self.predictor_cls`` is not
+        None, this method returns a the result of invoking ``self.predictor_cls`` on
+        the created endpoint name.
+
+        The name of the created model is accessible in the ``name`` field of
+        this ``Model`` after deploy returns
+
+        The name of the created endpoint is accessible in the
+        ``endpoint_name`` field of this ``Model`` after deploy returns.
+
+        Args:
+            initial_instance_count (int): The initial number of instances to run
+                in the ``Endpoint`` created from this ``Model``.
+            instance_type (str): The EC2 instance type to deploy this Model to.
+                For example, 'ml.p2.xlarge', or 'local' for local mode.
+            serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
+                serializer object, used to encode data for an inference endpoint
+                (default: None). If ``serializer`` is not None, then
+                ``serializer`` will override the default serializer. The
+                default serializer is set by the ``predictor_cls``.
+            deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
+                deserializer object, used to decode data from an inference
+                endpoint (default: None). If ``deserializer`` is not None, then
+                ``deserializer`` will override the default deserializer. The
+                default deserializer is set by the ``predictor_cls``.
+            accelerator_type (str): Type of Elastic Inference accelerator to
+                deploy this model for model loading and inference, for example,
+                'ml.eia1.medium'. If not specified, no Elastic Inference
+                accelerator will be attached to the endpoint. For more
+                information:
+                https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
+            endpoint_name (str): The name of the endpoint to create (default:
+                None). If not specified, a unique endpoint name will be created.
+            tags (List[dict[str, str]]): The list of tags to attach to this
+                specific endpoint.
+            kms_key (str): The ARN of the KMS key that is used to encrypt the
+                data on the storage volume attached to the instance hosting the
+                endpoint.
+            wait (bool): Whether the call should wait until the deployment of
+                this model completes (default: True).
+            data_capture_config (sagemaker.model_monitor.DataCaptureConfig): Specifies
+                configuration related to Endpoint data capture for use with
+                Amazon SageMaker Model Monitoring. Default: None.
+
+        Returns:
+            callable[string, sagemaker.session.Session] or None: Invocation of
+                ``self.predictor_cls`` on the created endpoint name,
+                if ``self.predictor_cls``
+                is not None. Otherwise, return None.
+        """
+        removed_kwargs("update_endpoint", kwargs)
+        # Set model specific parameters
+        if self.model:
+            enable_network_isolation = self.model.enable_network_isolation()
+            role = self.model.role
+            vpc_config = self.model.vpc_config
+            predictor_cls = self.model.predictor_cls
+        else:
+            enable_network_isolation = self.enable_network_isolation()
+            role = self.role
+            vpc_config = self.vpc_config
+            predictor_cls = self.predictor_cls
+
+        if role is None:
+            raise ValueError("Role can not be null for deploying a model")
+
+        if instance_type == "local" and not isinstance(self.sagemaker_session, local.LocalSession):
+            self.sagemaker_session = local.LocalSession()
+
+        container_def = self.prepare_container_def(instance_type, accelerator_type=accelerator_type)
+        self.sagemaker_session.create_model(
+            self.name,
+            role,
+            container_def,
+            vpc_config=vpc_config,
+            enable_network_isolation=enable_network_isolation,
+            tags=tags,
+        )
+
+        production_variant = sagemaker.production_variant(
+            self.name, instance_type, initial_instance_count, accelerator_type=accelerator_type
+        )
+        if endpoint_name:
+            self.endpoint_name = endpoint_name
+        else:
+            self.endpoint_name = self.name
+
+        data_capture_config_dict = None
+        if data_capture_config is not None:
+            data_capture_config_dict = data_capture_config._to_request_dict()
+
+        self.sagemaker_session.endpoint_from_production_variants(
+            name=self.endpoint_name,
+            production_variants=[production_variant],
+            tags=tags,
+            kms_key=kms_key,
+            wait=wait,
+            data_capture_config_dict=data_capture_config_dict,
+        )
+
+        if predictor_cls:
+            predictor = predictor_cls(self.endpoint_name, self.sagemaker_session)
+            if serializer:
+                predictor.serializer = serializer
+            if deserializer:
+                predictor.deserializer = deserializer
+            return predictor
+        return None
+
+    def add_model(self, model_data_source, model_data_path=None):
+        """Adds a model to the ``MultiDataModel``.
+
+        It is done by uploading or copying the model_data_source artifact to the given
+        S3 path model_data_path relative to model_data_prefix
+
+        Args:
+            model_source: Valid local file path or S3 path of the trained model artifact
+            model_data_path: S3 path where the trained model artifact
+                should be uploaded relative to ``self.model_data_prefix`` path. (default: None).
+                If None, then the model artifact is uploaded to a path relative to model_data_prefix
+
+        Returns:
+            str: S3 uri to uploaded model artifact
+        """
+        parse_result = urlparse(model_data_source)
+
+        # If the model source is an S3 path, copy the model artifact to the destination S3 path
+        if parse_result.scheme == "s3":
+            source_bucket, source_model_data_path = s3.parse_s3_url(model_data_source)
+            copy_source = {"Bucket": source_bucket, "Key": source_model_data_path}
+
+            if not model_data_path:
+                model_data_path = source_model_data_path
+
+            # Construct the destination path
+            dst_url = s3.s3_path_join(self.model_data_prefix, model_data_path)
+            destination_bucket, destination_model_data_path = s3.parse_s3_url(dst_url)
+
+            # Copy the model artifact
+            self.s3_client.copy(copy_source, destination_bucket, destination_model_data_path)
+            return s3.s3_path_join("s3://", destination_bucket, destination_model_data_path)
+
+        # If the model source is a local path, upload the local model artifact to the destination
+        # S3 path
+        if os.path.exists(model_data_source):
+            destination_bucket, dst_prefix = s3.parse_s3_url(self.model_data_prefix)
+            if model_data_path:
+                dst_s3_uri = s3.s3_path_join(dst_prefix, model_data_path)
+            else:
+                dst_s3_uri = s3.s3_path_join(dst_prefix, os.path.basename(model_data_source))
+            self.s3_client.upload_file(model_data_source, destination_bucket, dst_s3_uri)
+            # return upload_path
+            return s3.s3_path_join("s3://", destination_bucket, dst_s3_uri)
+
+        # Raise error if the model source is of an unexpected type
+        raise ValueError(
+            "model_source must either be a valid local file path or s3 uri. Received: "
+            '"{}"'.format(model_data_source)
+        )
+
+    def list_models(self):
+        """Generates and returns relative paths to model archives.
+
+        Archives are stored at model_data_prefix S3 location.
+
+        Yields: Paths to model archives relative to model_data_prefix path.
+        """
+        bucket, url_prefix = s3.parse_s3_url(self.model_data_prefix)
+        file_keys = self.sagemaker_session.list_s3_files(bucket=bucket, key_prefix=url_prefix)
+        for file_key in file_keys:
+            # Return the model paths relative to the model_data_prefix
+            # Ex: "a/b/c.tar.gz" -> "b/c.tar.gz" where url_prefix = "a/"
+            yield file_key.replace(url_prefix, "")

testbed/aws__sagemaker-python-sdk/src/sagemaker/network.py

@@ -0,0 +1,74 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""This file contains code related to network configuration.
+
+It also includes encryption, network isolation, and VPC configurations.
+"""
+from __future__ import absolute_import
+
+from typing import Union, Optional, List
+
+from sagemaker.workflow.entities import PipelineVariable
+
+
+class NetworkConfig(object):
+    """Accepts network configuration parameters for conversion to request dict.
+
+    The `_to_request_dict` provides a method to turn the parameters into a dict.
+    """
+
+    def __init__(
+        self,
+        enable_network_isolation: Union[bool, PipelineVariable] = False,
+        security_group_ids: Optional[List[Union[str, PipelineVariable]]] = None,
+        subnets: Optional[List[Union[str, PipelineVariable]]] = None,
+        encrypt_inter_container_traffic: Optional[Union[bool, PipelineVariable]] = None,
+    ):
+        """Initialize a ``NetworkConfig`` instance.
+
+        NetworkConfig accepts network configuration parameters and provides a method to turn
+        these parameters into a dictionary.
+
+        Args:
+            enable_network_isolation (bool or PipelineVariable): Boolean that determines
+                whether to enable network isolation.
+            security_group_ids (list[str] or list[PipelineVariable]): A list of strings representing
+                security group IDs.
+            subnets (list[str] or list[PipelineVariable]): A list of strings representing subnets.
+            encrypt_inter_container_traffic (bool or PipelineVariable): Boolean that determines
+                whether to encrypt inter-container traffic. Default value is None.
+        """
+        self.enable_network_isolation = enable_network_isolation
+        self.security_group_ids = security_group_ids
+        self.subnets = subnets
+        self.encrypt_inter_container_traffic = encrypt_inter_container_traffic
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        network_config_request = {"EnableNetworkIsolation": self.enable_network_isolation}
+
+        if self.encrypt_inter_container_traffic is not None:
+            network_config_request[
+                "EnableInterContainerTrafficEncryption"
+            ] = self.encrypt_inter_container_traffic
+
+        if self.security_group_ids is not None or self.subnets is not None:
+            network_config_request["VpcConfig"] = {}
+
+        if self.security_group_ids is not None:
+            network_config_request["VpcConfig"]["SecurityGroupIds"] = self.security_group_ids
+
+        if self.subnets is not None:
+            network_config_request["VpcConfig"]["Subnets"] = self.subnets
+
+        return network_config_request

testbed/aws__sagemaker-python-sdk/src/sagemaker/pipeline.py

@@ -0,0 +1,463 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Placeholder docstring"""
+from __future__ import absolute_import
+
+from typing import Optional, Dict, List, Union
+
+import sagemaker
+from sagemaker import ModelMetrics, Model
+from sagemaker.drift_check_baselines import DriftCheckBaselines
+from sagemaker.metadata_properties import MetadataProperties
+from sagemaker.session import Session
+from sagemaker.utils import (
+    name_from_image,
+    update_container_with_inference_params,
+)
+from sagemaker.transformer import Transformer
+from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.workflow.pipeline_context import runnable_by_pipeline
+
+
+class PipelineModel(object):
+    """A pipeline of SageMaker `Model` instances.
+
+    This pipeline can be deployed as an `Endpoint` on SageMaker.
+    """
+
+    def __init__(
+        self,
+        models: List[Model],
+        role: str,
+        predictor_cls: Optional[callable] = None,
+        name: Optional[str] = None,
+        vpc_config: Optional[Dict[str, List[Union[str, PipelineVariable]]]] = None,
+        sagemaker_session: Optional[Session] = None,
+        enable_network_isolation: Union[bool, PipelineVariable] = False,
+    ):
+        """Initialize a SageMaker `Model` instance.
+
+        The `Model` can be used to build an Inference Pipeline comprising of
+        multiple model containers.
+
+        Args:
+            models (list[sagemaker.Model]): For using multiple containers to
+                build an inference pipeline, you can pass a list of
+                ``sagemaker.Model`` objects in the order you want the inference
+                to happen.
+            role (str): An AWS IAM role (either name or full ARN). The Amazon
+                SageMaker training jobs and APIs that create Amazon SageMaker
+                endpoints use this role to access training data and model
+                artifacts. After the endpoint is created, the inference code
+                might use the IAM role, if it needs to access an AWS resource.
+            predictor_cls (callable[string, sagemaker.session.Session]): A
+                function to call to create a predictor (default: None). If not
+                None, ``deploy`` will return the result of invoking this
+                function on the created endpoint name.
+            name (str): The model name. If None, a default model name will be
+                selected on each ``deploy``.
+            vpc_config (dict[str, list[str]] or dict[str, list[PipelineVariable]]):
+                The VpcConfig set on the model (default: None)
+                * 'Subnets' (list[str]): List of subnet ids.
+                * 'SecurityGroupIds' (list[str]): List of security group ids.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
+            enable_network_isolation (bool or PipelineVariable): Default False. if True,
+                enables network isolation in the endpoint, isolating the model
+                container. No inbound or outbound network calls can be made to
+                or from the model container.Boolean
+        """
+        self.models = models
+        self.role = role
+        self.predictor_cls = predictor_cls
+        self.name = name
+        self.vpc_config = vpc_config
+        self.sagemaker_session = sagemaker_session
+        self.enable_network_isolation = enable_network_isolation
+        self.endpoint_name = None
+
+    def pipeline_container_def(self, instance_type=None):
+        """The pipeline definition for deploying this model.
+
+        This is the dict created by ``sagemaker.pipeline_container_def()``.
+
+        The instance type to be used may be specified.
+
+        Subclasses can override this to provide custom container definitions
+        for deployment to a specific instance type. Called by ``deploy()``.
+
+        Args:
+            instance_type (str): The EC2 instance type to deploy this Model to.
+                For example, 'ml.p2.xlarge'.
+
+        Returns:
+            list[dict[str, str]]: A list of container definition objects usable
+            with the CreateModel API in the scenario of multiple containers
+            (Inference Pipeline).
+        """
+
+        return sagemaker.pipeline_container_def(self.models, instance_type)
+
+    def deploy(
+        self,
+        initial_instance_count,
+        instance_type,
+        serializer=None,
+        deserializer=None,
+        endpoint_name=None,
+        tags=None,
+        wait=True,
+        update_endpoint=False,
+        data_capture_config=None,
+        kms_key=None,
+    ):
+        """Deploy the ``Model`` to an ``Endpoint``.
+
+        It optionally return a ``Predictor``.
+
+        Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an
+        ``Endpoint`` from this ``Model``. If ``self.predictor_cls`` is not None,
+        this method returns a the result of invoking ``self.predictor_cls`` on
+        the created endpoint name.
+
+        The name of the created model is accessible in the ``name`` field of
+        this ``Model`` after deploy returns
+
+        The name of the created endpoint is accessible in the
+        ``endpoint_name`` field of this ``Model`` after deploy returns.
+
+        Args:
+            initial_instance_count (int): The initial number of instances to run
+                in the ``Endpoint`` created from this ``Model``.
+            instance_type (str): The EC2 instance type to deploy this Model to.
+                For example, 'ml.p2.xlarge'.
+            serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
+                serializer object, used to encode data for an inference endpoint
+                (default: None). If ``serializer`` is not None, then
+                ``serializer`` will override the default serializer. The
+                default serializer is set by the ``predictor_cls``.
+            deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
+                deserializer object, used to decode data from an inference
+                endpoint (default: None). If ``deserializer`` is not None, then
+                ``deserializer`` will override the default deserializer. The
+                default deserializer is set by the ``predictor_cls``.
+            endpoint_name (str): The name of the endpoint to create (default:
+                None). If not specified, a unique endpoint name will be created.
+            tags (List[dict[str, str]]): The list of tags to attach to this
+                specific endpoint.
+            wait (bool): Whether the call should wait until the deployment of
+                model completes (default: True).
+            update_endpoint (bool): Flag to update the model in an existing
+                Amazon SageMaker endpoint. If True, this will deploy a new
+                EndpointConfig to an already existing endpoint and delete
+                resources corresponding to the previous EndpointConfig. If
+                False, a new endpoint will be created. Default: False
+            data_capture_config (sagemaker.model_monitor.DataCaptureConfig): Specifies
+                configuration related to Endpoint data capture for use with
+                Amazon SageMaker Model Monitoring. Default: None.
+            kms_key (str): The ARN, Key ID or Alias of the KMS key that is used to
+                encrypt the data on the storage volume attached to the instance hosting
+                the endpoint.
+
+        Returns:
+            callable[string, sagemaker.session.Session] or None: Invocation of
+            ``self.predictor_cls`` on the created endpoint name, if ``self.predictor_cls``
+            is not None. Otherwise, return None.
+        """
+        if not self.sagemaker_session:
+            self.sagemaker_session = Session()
+
+        containers = self.pipeline_container_def(instance_type)
+
+        self.name = self.name or name_from_image(containers[0]["Image"])
+        self.sagemaker_session.create_model(
+            self.name,
+            self.role,
+            containers,
+            vpc_config=self.vpc_config,
+            enable_network_isolation=self.enable_network_isolation,
+        )
+
+        production_variant = sagemaker.production_variant(
+            self.name, instance_type, initial_instance_count
+        )
+        self.endpoint_name = endpoint_name or self.name
+
+        data_capture_config_dict = None
+        if data_capture_config is not None:
+            data_capture_config_dict = data_capture_config._to_request_dict()
+
+        if update_endpoint:
+            endpoint_config_name = self.sagemaker_session.create_endpoint_config(
+                name=self.name,
+                model_name=self.name,
+                initial_instance_count=initial_instance_count,
+                instance_type=instance_type,
+                tags=tags,
+                kms_key=kms_key,
+                data_capture_config_dict=data_capture_config_dict,
+            )
+            self.sagemaker_session.update_endpoint(
+                self.endpoint_name, endpoint_config_name, wait=wait
+            )
+        else:
+            self.sagemaker_session.endpoint_from_production_variants(
+                name=self.endpoint_name,
+                production_variants=[production_variant],
+                tags=tags,
+                kms_key=kms_key,
+                wait=wait,
+                data_capture_config_dict=data_capture_config_dict,
+            )
+
+        if self.predictor_cls:
+            predictor = self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+            if serializer:
+                predictor.serializer = serializer
+            if deserializer:
+                predictor.deserializer = deserializer
+            return predictor
+        return None
+
+    @runnable_by_pipeline
+    def create(self, instance_type: str):
+        """Create a SageMaker Model Entity
+
+        Args:
+            instance_type (str): The EC2 instance type that this Model will be
+                used for, this is only used to determine if the image needs GPU
+                support or not.
+        """
+        self._create_sagemaker_pipeline_model(instance_type)
+
+    def _create_sagemaker_pipeline_model(self, instance_type):
+        """Create a SageMaker Model Entity
+
+        Args:
+            instance_type (str): The EC2 instance type that this Model will be
+                used for, this is only used to determine if the image needs GPU
+                support or not.
+        """
+        if not self.sagemaker_session:
+            self.sagemaker_session = Session()
+
+        containers = self.pipeline_container_def(instance_type)
+
+        self.name = self.name or name_from_image(containers[0]["Image"])
+        create_model_args = dict(
+            name=self.name,
+            role=self.role,
+            container_defs=containers,
+            vpc_config=self.vpc_config,
+            enable_network_isolation=self.enable_network_isolation,
+        )
+        self.sagemaker_session.create_model(**create_model_args)
+
+    @runnable_by_pipeline
+    def register(
+        self,
+        content_types: List[Union[str, PipelineVariable]],
+        response_types: List[Union[str, PipelineVariable]],
+        inference_instances: Optional[List[Union[str, PipelineVariable]]] = None,
+        transform_instances: Optional[List[Union[str, PipelineVariable]]] = None,
+        model_package_name: Optional[Union[str, PipelineVariable]] = None,
+        model_package_group_name: Optional[Union[str, PipelineVariable]] = None,
+        image_uri: Optional[Union[str, PipelineVariable]] = None,
+        model_metrics: Optional[ModelMetrics] = None,
+        metadata_properties: Optional[MetadataProperties] = None,
+        marketplace_cert: bool = False,
+        approval_status: Optional[Union[str, PipelineVariable]] = None,
+        description: Optional[str] = None,
+        drift_check_baselines: Optional[DriftCheckBaselines] = None,
+        customer_metadata_properties: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        domain: Optional[Union[str, PipelineVariable]] = None,
+        sample_payload_url: Optional[Union[str, PipelineVariable]] = None,
+        task: Optional[Union[str, PipelineVariable]] = None,
+        framework: Optional[Union[str, PipelineVariable]] = None,
+        framework_version: Optional[Union[str, PipelineVariable]] = None,
+        nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
+        data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+    ):
+        """Creates a model package for creating SageMaker models or listing on Marketplace.
+
+        Args:
+            content_types (list[str] or list[PipelineVariable]): The supported MIME types
+                for the input data.
+            response_types (list[str] or list[PipelineVariable]): The supported MIME types
+                for the output data.
+            inference_instances (list[str] or list[PipelineVariable]): A list of the instance
+                types that are used to generate inferences in real-time (default: None).
+            transform_instances (list[str] or list[PipelineVariable]): A list of the instance types
+                on which a transformation job can be run or on which an endpoint can be deployed
+                (default: None).
+            model_package_name (str or PipelineVariable): Model Package name, exclusive to
+                `model_package_group_name`, using `model_package_name` makes the Model Package
+                un-versioned (default: None).
+            model_package_group_name (str or PipelineVariable): Model Package Group name,
+                exclusive to `model_package_name`, using `model_package_group_name` makes
+                the Model Package versioned (default: None).
+            image_uri (str or PipelineVariable): Inference image uri for the container.
+                Model class' self.image will be used if it is None (default: None).
+            model_metrics (ModelMetrics): ModelMetrics object (default: None).
+            metadata_properties (MetadataProperties): MetadataProperties object (default: None).
+            marketplace_cert (bool): A boolean value indicating if the Model Package is certified
+                for AWS Marketplace (default: False).
+            approval_status (str or PipelineVariable): Model Approval Status, values can
+                be "Approved", "Rejected", or "PendingManualApproval"
+                (default: "PendingManualApproval").
+            description (str): Model Package description (default: None).
+            drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
+            customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]):
+                A dictionary of key-value paired metadata properties (default: None).
+            domain (str or PipelineVariable): Domain values can be "COMPUTER_VISION",
+                "NATURAL_LANGUAGE_PROCESSING", "MACHINE_LEARNING" (default: None).
+            sample_payload_url (str or PipelineVariable): The S3 path where the sample payload
+                is stored (default: None).
+            task (str or PipelineVariable): Task values which are supported by Inference Recommender
+                are "FILL_MASK", "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION",
+                "IMAGE_SEGMENTATION", "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+            framework (str or PipelineVariable): Machine learning framework of the model package
+                container image (default: None).
+            framework_version (str or PipelineVariable): Framework version of the Model Package
+                Container Image (default: None).
+            nearest_model_name (str or PipelineVariable): Name of a pre-trained machine learning
+                benchmarked by Amazon SageMaker Inference Recommender (default: None).
+            data_input_configuration (str or PipelineVariable): Input object for the model
+                (default: None).
+
+        Returns:
+            A `sagemaker.model.ModelPackage` instance.
+        """
+        for model in self.models:
+            if model.model_data is None:
+                raise ValueError("SageMaker Model Package cannot be created without model data.")
+        if model_package_group_name is not None:
+            container_def = self.pipeline_container_def(
+                inference_instances[0] if inference_instances else None
+            )
+            container_def = update_container_with_inference_params(
+                framework=framework,
+                framework_version=framework_version,
+                nearest_model_name=nearest_model_name,
+                data_input_configuration=data_input_configuration,
+                container_list=container_def,
+            )
+        else:
+            container_def = [
+                {
+                    "Image": image_uri or model.image_uri,
+                    "ModelDataUrl": model.model_data,
+                }
+                for model in self.models
+            ]
+
+        model_pkg_args = sagemaker.get_model_package_args(
+            content_types,
+            response_types,
+            inference_instances=inference_instances,
+            transform_instances=transform_instances,
+            model_package_name=model_package_name,
+            model_package_group_name=model_package_group_name,
+            model_metrics=model_metrics,
+            metadata_properties=metadata_properties,
+            marketplace_cert=marketplace_cert,
+            approval_status=approval_status,
+            description=description,
+            container_def_list=container_def,
+            drift_check_baselines=drift_check_baselines,
+            customer_metadata_properties=customer_metadata_properties,
+            domain=domain,
+            sample_payload_url=sample_payload_url,
+            task=task,
+        )
+
+        self.sagemaker_session.create_model_package_from_containers(**model_pkg_args)
+
+    def transformer(
+        self,
+        instance_count,
+        instance_type,
+        strategy=None,
+        assemble_with=None,
+        output_path=None,
+        output_kms_key=None,
+        accept=None,
+        env=None,
+        max_concurrent_transforms=None,
+        max_payload=None,
+        tags=None,
+        volume_kms_key=None,
+    ):
+        """Return a ``Transformer`` that uses this Model.
+
+        Args:
+            instance_count (int): Number of EC2 instances to use.
+            instance_type (str): Type of EC2 instance to use, for example,
+                'ml.c4.xlarge'.
+            strategy (str): The strategy used to decide how to batch records in
+                a single request (default: None). Valid values: 'MultiRecord'
+                and 'SingleRecord'.
+            assemble_with (str): How the output is assembled (default: None).
+                Valid values: 'Line' or 'None'.
+            output_path (str): S3 location for saving the transform result. If
+                not specified, results are stored to a default bucket.
+            output_kms_key (str): Optional. KMS key ID for encrypting the
+                transform output (default: None).
+            accept (str): The accept header passed by the client to
+                the inference endpoint. If it is supported by the endpoint,
+                it will be the format of the batch transform output.
+            env (dict): Environment variables to be set for use during the
+                transform job (default: None).
+            max_concurrent_transforms (int): The maximum number of HTTP requests
+                to be made to each individual transform container at one time.
+            max_payload (int): Maximum size of the payload in a single HTTP
+                request to the container in MB.
+            tags (list[dict]): List of tags for labeling a transform job. If
+                none specified, then the tags used for the training job are used
+                for the transform job.
+            volume_kms_key (str): Optional. KMS key ID for encrypting the volume
+                attached to the ML compute instance (default: None).
+        """
+        self._create_sagemaker_pipeline_model(instance_type)
+
+        return Transformer(
+            self.name,
+            instance_count,
+            instance_type,
+            strategy=strategy,
+            assemble_with=assemble_with,
+            output_path=output_path,
+            output_kms_key=output_kms_key,
+            accept=accept,
+            max_concurrent_transforms=max_concurrent_transforms,
+            max_payload=max_payload,
+            env=env,
+            tags=tags,
+            base_transform_job_name=self.name,
+            volume_kms_key=volume_kms_key,
+            sagemaker_session=self.sagemaker_session,
+        )
+
+    def delete_model(self):
+        """Delete the SageMaker model backing this pipeline model.
+
+        This does not delete the list of SageMaker models used in multiple containers to build
+        the inference pipeline.
+        """
+
+        if self.name is None:
+            raise ValueError("The SageMaker model must be created before attempting to delete.")
+
+        self.sagemaker_session.delete_model(self.name)

testbed/aws__sagemaker-python-sdk/src/sagemaker/predictor.py

@@ -0,0 +1,560 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Placeholder docstring"""
+from __future__ import print_function, absolute_import
+
+import abc
+from typing import Any, Tuple
+
+from sagemaker.deprecations import (
+    deprecated_class,
+    deprecated_deserialize,
+    deprecated_serialize,
+    removed_kwargs,
+    renamed_kwargs,
+    renamed_warning,
+)
+from sagemaker.deserializers import (  # noqa: F401 # pylint: disable=unused-import
+    BytesDeserializer,
+    CSVDeserializer,
+    JSONDeserializer,
+    NumpyDeserializer,
+    StreamDeserializer,
+    StringDeserializer,
+)
+from sagemaker.model_monitor import (
+    DataCaptureConfig,
+    DefaultModelMonitor,
+    ModelBiasMonitor,
+    ModelExplainabilityMonitor,
+    ModelMonitor,
+    ModelQualityMonitor,
+)
+from sagemaker.serializers import (
+    CSVSerializer,
+    IdentitySerializer,
+    JSONSerializer,
+    NumpySerializer,
+)
+from sagemaker.session import production_variant, Session
+from sagemaker.utils import name_from_base
+
+from sagemaker.model_monitor.model_monitoring import DEFAULT_REPOSITORY_NAME
+
+from sagemaker.lineage.context import EndpointContext
+
+
+class PredictorBase(abc.ABC):
+    """An object that encapsulates a deployed model."""
+
+    @abc.abstractmethod
+    def predict(self, *args, **kwargs) -> Any:
+        """Perform inference on the provided data and return a prediction."""
+
+    @abc.abstractmethod
+    def delete_predictor(self, *args, **kwargs) -> None:
+        """Destroy resources associated with this predictor."""
+
+    @property
+    @abc.abstractmethod
+    def content_type(self) -> str:
+        """The MIME type of the data sent to the inference server."""
+
+    @property
+    @abc.abstractmethod
+    def accept(self) -> Tuple[str]:
+        """The content type(s) that are expected from the inference server."""
+
+
+class Predictor(PredictorBase):
+    """Make prediction requests to an Amazon SageMaker endpoint."""
+
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=IdentitySerializer(),
+        deserializer=BytesDeserializer(),
+        **kwargs,
+    ):
+        """Initialize a ``Predictor``.
+
+        Behavior for serialization of input data and deserialization of
+        result data can be configured through initializer arguments. If not
+        specified, a sequence of bytes is expected and the API sends it in the
+        request body without modifications. In response, the API returns the
+        sequence of bytes from the prediction result without any modifications.
+
+        Args:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
+            serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
+                serializer object, used to encode data for an inference endpoint
+                (default: :class:`~sagemaker.serializers.IdentitySerializer`).
+            deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
+                deserializer object, used to decode data from an inference
+                endpoint (default: :class:`~sagemaker.deserializers.BytesDeserializer`).
+        """
+        removed_kwargs("content_type", kwargs)
+        removed_kwargs("accept", kwargs)
+        endpoint_name = renamed_kwargs("endpoint", "endpoint_name", endpoint_name, kwargs)
+        self.endpoint_name = endpoint_name
+        self.sagemaker_session = sagemaker_session or Session()
+        self.serializer = serializer
+        self.deserializer = deserializer
+        self._endpoint_config_name = None
+        self._model_names = None
+        self._context = None
+
+    def predict(
+        self,
+        data,
+        initial_args=None,
+        target_model=None,
+        target_variant=None,
+        inference_id=None,
+    ):
+        """Return the inference from the specified endpoint.
+
+        Args:
+            data (object): Input data for which you want the model to provide
+                inference. If a serializer was specified when creating the
+                Predictor, the result of the serializer is sent as input
+                data. Otherwise the data must be sequence of bytes, and the
+                predict method then sends the bytes in the request body as is.
+            initial_args (dict[str,str]): Optional. Default arguments for boto3
+                ``invoke_endpoint`` call. Default is None (no default
+                arguments).
+            target_model (str): S3 model artifact path to run an inference request on,
+                in case of a multi model endpoint. Does not apply to endpoints hosting
+                single model (Default: None)
+            target_variant (str): The name of the production variant to run an inference
+                request on (Default: None). Note that the ProductionVariant identifies the
+                model you want to host and the resources you want to deploy for hosting it.
+            inference_id (str): If you provide a value, it is added to the captured data
+                when you enable data capture on the endpoint (Default: None).
+
+        Returns:
+            object: Inference for the given input. If a deserializer was specified when creating
+                the Predictor, the result of the deserializer is
+                returned. Otherwise the response returns the sequence of bytes
+                as is.
+        """
+
+        request_args = self._create_request_args(
+            data, initial_args, target_model, target_variant, inference_id
+        )
+        response = self.sagemaker_session.sagemaker_runtime_client.invoke_endpoint(**request_args)
+        return self._handle_response(response)
+
+    def _handle_response(self, response):
+        """Placeholder docstring"""
+        response_body = response["Body"]
+        content_type = response.get("ContentType", "application/octet-stream")
+        return self.deserializer.deserialize(response_body, content_type)
+
+    def _create_request_args(
+        self,
+        data,
+        initial_args=None,
+        target_model=None,
+        target_variant=None,
+        inference_id=None,
+    ):
+        """Placeholder docstring"""
+        args = dict(initial_args) if initial_args else {}
+
+        if "EndpointName" not in args:
+            args["EndpointName"] = self.endpoint_name
+
+        if "ContentType" not in args:
+            args["ContentType"] = self.content_type
+
+        if "Accept" not in args:
+            args["Accept"] = ", ".join(self.accept)
+
+        if target_model:
+            args["TargetModel"] = target_model
+
+        if target_variant:
+            args["TargetVariant"] = target_variant
+
+        if inference_id:
+            args["InferenceId"] = inference_id
+
+        data = self.serializer.serialize(data)
+
+        args["Body"] = data
+        return args
+
+    def update_endpoint(
+        self,
+        initial_instance_count=None,
+        instance_type=None,
+        accelerator_type=None,
+        model_name=None,
+        tags=None,
+        kms_key=None,
+        data_capture_config_dict=None,
+        wait=True,
+    ):
+        """Update the existing endpoint with the provided attributes.
+
+        This creates a new EndpointConfig in the process. If ``initial_instance_count``,
+        ``instance_type``, ``accelerator_type``, or ``model_name`` is specified, then a new
+        ProductionVariant configuration is created; values from the existing configuration
+        are not preserved if any of those parameters are specified.
+
+        Args:
+            initial_instance_count (int): The initial number of instances to run in the endpoint.
+                This is required if ``instance_type``, ``accelerator_type``, or ``model_name`` is
+                specified. Otherwise, the values from the existing endpoint configuration's
+                ProductionVariants are used.
+            instance_type (str): The EC2 instance type to deploy the endpoint to.
+                This is required if ``initial_instance_count`` or ``accelerator_type`` is specified.
+                Otherwise, the values from the existing endpoint configuration's
+                ``ProductionVariants`` are used.
+            accelerator_type (str): The type of Elastic Inference accelerator to attach to
+                the endpoint, e.g. "ml.eia1.medium". If not specified, and
+                ``initial_instance_count``, ``instance_type``, and ``model_name`` are also ``None``,
+                the values from the existing endpoint configuration's ``ProductionVariants`` are
+                used. Otherwise, no Elastic Inference accelerator is attached to the endpoint.
+            model_name (str): The name of the model to be associated with the endpoint.
+                This is required if ``initial_instance_count``, ``instance_type``, or
+                ``accelerator_type`` is specified and if there is more than one model associated
+                with the endpoint. Otherwise, the existing model for the endpoint is used.
+            tags (list[dict[str, str]]): The list of tags to add to the endpoint
+                config. If not specified, the tags of the existing endpoint configuration are used.
+                If any of the existing tags are reserved AWS ones (i.e. begin with "aws"),
+                they are not carried over to the new endpoint configuration.
+            kms_key (str): The KMS key that is used to encrypt the data on the storage volume
+                attached to the instance hosting the endpoint If not specified,
+                the KMS key of the existing endpoint configuration is used.
+            data_capture_config_dict (dict): The endpoint data capture configuration
+                for use with Amazon SageMaker Model Monitoring. If not specified,
+                the data capture configuration of the existing endpoint configuration is used.
+
+        Raises:
+            ValueError: If there is not enough information to create a new ``ProductionVariant``:
+
+                - If ``initial_instance_count``, ``accelerator_type``, or ``model_name`` is
+                  specified, but ``instance_type`` is ``None``.
+                - If ``initial_instance_count``, ``instance_type``, or ``accelerator_type`` is
+                  specified and either ``model_name`` is ``None`` or there are multiple models
+                  associated with the endpoint.
+        """
+        production_variants = None
+        current_model_names = self._get_model_names()
+
+        if initial_instance_count or instance_type or accelerator_type or model_name:
+            if instance_type is None or initial_instance_count is None:
+                raise ValueError(
+                    "Missing initial_instance_count and/or instance_type. Provided values: "
+                    "initial_instance_count={}, instance_type={}, accelerator_type={}, "
+                    "model_name={}.".format(
+                        initial_instance_count,
+                        instance_type,
+                        accelerator_type,
+                        model_name,
+                    )
+                )
+
+            if model_name is None:
+                if len(current_model_names) > 1:
+                    raise ValueError(
+                        "Unable to choose a default model for a new EndpointConfig because "
+                        "the endpoint has multiple models: {}".format(
+                            ", ".join(current_model_names)
+                        )
+                    )
+                model_name = current_model_names[0]
+            else:
+                self._model_names = [model_name]
+
+            production_variant_config = production_variant(
+                model_name,
+                instance_type,
+                initial_instance_count=initial_instance_count,
+                accelerator_type=accelerator_type,
+            )
+            production_variants = [production_variant_config]
+
+        current_endpoint_config_name = self._get_endpoint_config_name()
+        new_endpoint_config_name = name_from_base(current_endpoint_config_name)
+        self.sagemaker_session.create_endpoint_config_from_existing(
+            current_endpoint_config_name,
+            new_endpoint_config_name,
+            new_tags=tags,
+            new_kms_key=kms_key,
+            new_data_capture_config_dict=data_capture_config_dict,
+            new_production_variants=production_variants,
+        )
+        self.sagemaker_session.update_endpoint(
+            self.endpoint_name, new_endpoint_config_name, wait=wait
+        )
+        self._endpoint_config_name = new_endpoint_config_name
+
+    def _delete_endpoint_config(self):
+        """Delete the Amazon SageMaker endpoint configuration"""
+        current_endpoint_config_name = self._get_endpoint_config_name()
+        self.sagemaker_session.delete_endpoint_config(current_endpoint_config_name)
+
+    def delete_endpoint(self, delete_endpoint_config=True):
+        """Delete the Amazon SageMaker endpoint backing this predictor.
+
+        This also delete the endpoint configuration attached to it if
+        delete_endpoint_config is True.
+
+        Args:
+            delete_endpoint_config (bool, optional): Flag to indicate whether to
+                delete endpoint configuration together with endpoint. Defaults
+                to True. If True, both endpoint and endpoint configuration will
+                be deleted. If False, only endpoint will be deleted.
+        """
+        if delete_endpoint_config:
+            self._delete_endpoint_config()
+
+        self.sagemaker_session.delete_endpoint(self.endpoint_name)
+
+    delete_predictor = delete_endpoint
+
+    def delete_model(self):
+        """Deletes the Amazon SageMaker models backing this predictor."""
+        request_failed = False
+        failed_models = []
+        current_model_names = self._get_model_names()
+        for model_name in current_model_names:
+            try:
+                self.sagemaker_session.delete_model(model_name)
+            except Exception:  # pylint: disable=broad-except
+                request_failed = True
+                failed_models.append(model_name)
+
+        if request_failed:
+            raise Exception(
+                "One or more models cannot be deleted, please retry. \n"
+                "Failed models: {}".format(", ".join(failed_models))
+            )
+
+    def enable_data_capture(self):
+        """Enables data capture by updating DataCaptureConfig.
+
+        This function updates the DataCaptureConfig for the Predictor's associated Amazon SageMaker
+        Endpoint to enable data capture. For a more customized experience, refer to
+        update_data_capture_config, instead.
+        """
+        self.update_data_capture_config(
+            data_capture_config=DataCaptureConfig(
+                enable_capture=True, sagemaker_session=self.sagemaker_session
+            )
+        )
+
+    def disable_data_capture(self):
+        """Disables data capture by updating DataCaptureConfig.
+
+        This function updates the DataCaptureConfig for the Predictor's associated Amazon SageMaker
+        Endpoint to disable data capture. For a more customized experience, refer to
+        update_data_capture_config, instead.
+        """
+        self.update_data_capture_config(
+            data_capture_config=DataCaptureConfig(
+                enable_capture=False, sagemaker_session=self.sagemaker_session
+            )
+        )
+
+    def update_data_capture_config(self, data_capture_config):
+        """Updates the DataCaptureConfig for the Predictor's associated Amazon SageMaker Endpoint.
+
+        Update is done using the provided DataCaptureConfig.
+
+        Args:
+            data_capture_config (sagemaker.model_monitor.DataCaptureConfig): The
+                DataCaptureConfig to update the predictor's endpoint to use.
+        """
+        endpoint_desc = self.sagemaker_session.sagemaker_client.describe_endpoint(
+            EndpointName=self.endpoint_name
+        )
+
+        new_config_name = name_from_base(base=self.endpoint_name)
+
+        data_capture_config_dict = None
+        if data_capture_config is not None:
+            data_capture_config_dict = data_capture_config._to_request_dict()
+
+        self.sagemaker_session.create_endpoint_config_from_existing(
+            existing_config_name=endpoint_desc["EndpointConfigName"],
+            new_config_name=new_config_name,
+            new_data_capture_config_dict=data_capture_config_dict,
+        )
+
+        self.sagemaker_session.update_endpoint(
+            endpoint_name=self.endpoint_name, endpoint_config_name=new_config_name
+        )
+
+    def list_monitors(self):
+        """Generates ModelMonitor objects (or DefaultModelMonitors).
+
+        Objects are generated based on the schedule(s) associated with the endpoint
+        that this predictor refers to.
+
+        Returns:
+            [sagemaker.model_monitor.model_monitoring.ModelMonitor]: A list of
+                ModelMonitor (or DefaultModelMonitor) objects.
+
+        """
+        monitoring_schedules_dict = self.sagemaker_session.list_monitoring_schedules(
+            endpoint_name=self.endpoint_name
+        )
+        if len(monitoring_schedules_dict["MonitoringScheduleSummaries"]) == 0:
+            print("No monitors found for endpoint. endpoint: {}".format(self.endpoint_name))
+            return []
+
+        monitors = []
+        for schedule_dict in monitoring_schedules_dict["MonitoringScheduleSummaries"]:
+            schedule_name = schedule_dict["MonitoringScheduleName"]
+            monitoring_type = schedule_dict.get("MonitoringType")
+            clazz = self._get_model_monitor_class(schedule_name, monitoring_type)
+            monitors.append(
+                clazz.attach(
+                    monitor_schedule_name=schedule_name,
+                    sagemaker_session=self.sagemaker_session,
+                )
+            )
+
+        return monitors
+
+    def _get_model_monitor_class(self, schedule_name, monitoring_type):
+        """Decide which ModelMonitor class the given schedule should attach to
+
+        Args:
+            schedule_name (str): The schedule to be attached.
+            monitoring_type (str): The monitoring type of the schedule
+
+        Returns:
+            sagemaker.model_monitor.ModelMonitor: ModelMonitor or a subclass of ModelMonitor.
+
+        Raises:
+            TypeError: If the class could not be decided (due to unknown monitoring type).
+        """
+        if monitoring_type == "ModelBias":
+            clazz = ModelBiasMonitor
+        elif monitoring_type == "ModelExplainability":
+            clazz = ModelExplainabilityMonitor
+        else:
+            schedule = self.sagemaker_session.describe_monitoring_schedule(
+                monitoring_schedule_name=schedule_name
+            )
+            embedded_job_definition = schedule["MonitoringScheduleConfig"].get(
+                "MonitoringJobDefinition"
+            )
+            if embedded_job_definition is not None:  # legacy v1 schedule
+                image_uri = embedded_job_definition["MonitoringAppSpecification"]["ImageUri"]
+                if image_uri.endswith(DEFAULT_REPOSITORY_NAME):
+                    clazz = DefaultModelMonitor
+                else:
+                    clazz = ModelMonitor
+            elif monitoring_type == "DataQuality":
+                clazz = DefaultModelMonitor
+            elif monitoring_type == "ModelQuality":
+                clazz = ModelQualityMonitor
+            else:
+                raise TypeError("Unknown monitoring type: {}".format(monitoring_type))
+        return clazz
+
+    def endpoint_context(self):
+        """Retrieves the lineage context object representing the endpoint.
+
+        Examples:
+            .. code-block:: python
+
+            predictor = Predictor()
+            ...
+            context = predictor.endpoint_context()
+            models = context.models()
+
+        Returns:
+            ContextEndpoint: The context for the endpoint.
+        """
+        if self._context:
+            return self._context
+
+        # retrieve endpoint by name to get arn
+        response = self.sagemaker_session.sagemaker_client.describe_endpoint(
+            EndpointName=self.endpoint_name
+        )
+        endpoint_arn = response["EndpointArn"]
+
+        # list context by source uri using arn
+        contexts = list(
+            EndpointContext.list(sagemaker_session=self.sagemaker_session, source_uri=endpoint_arn)
+        )
+
+        if len(contexts) != 0:
+            # create endpoint context object
+            self._context = EndpointContext.load(
+                sagemaker_session=self.sagemaker_session,
+                context_name=contexts[0].context_name,
+            )
+
+        return self._context
+
+    def _get_endpoint_config_name(self):
+        """Placeholder docstring"""
+        if self._endpoint_config_name is not None:
+            return self._endpoint_config_name
+        endpoint_desc = self.sagemaker_session.sagemaker_client.describe_endpoint(
+            EndpointName=self.endpoint_name
+        )
+        self._endpoint_config_name = endpoint_desc["EndpointConfigName"]
+        return self._endpoint_config_name
+
+    def _get_model_names(self):
+        """Placeholder docstring"""
+        if self._model_names is not None:
+            return self._model_names
+        current_endpoint_config_name = self._get_endpoint_config_name()
+        endpoint_config = self.sagemaker_session.sagemaker_client.describe_endpoint_config(
+            EndpointConfigName=current_endpoint_config_name
+        )
+        production_variants = endpoint_config["ProductionVariants"]
+        self._model_names = [d["ModelName"] for d in production_variants]
+        return self._model_names
+
+    @property
+    def content_type(self):
+        """The MIME type of the data sent to the inference endpoint."""
+        return self.serializer.CONTENT_TYPE
+
+    @property
+    def accept(self):
+        """The content type(s) that are expected from the inference endpoint."""
+        return self.deserializer.ACCEPT
+
+    @property
+    def endpoint(self):
+        """Deprecated attribute. Please use endpoint_name."""
+        renamed_warning("The endpoint attribute")
+        return self.endpoint_name
+
+
+csv_serializer = deprecated_serialize(CSVSerializer(), "csv_serializer")
+json_serializer = deprecated_serialize(JSONSerializer(), "json_serializer")
+npy_serializer = deprecated_serialize(NumpySerializer(), "npy_serializer")
+csv_deserializer = deprecated_deserialize(CSVDeserializer(), "csv_deserializer")
+json_deserializer = deprecated_deserialize(JSONDeserializer(), "json_deserializer")
+numpy_deserializer = deprecated_deserialize(NumpyDeserializer(), "numpy_deserializer")
+RealTimePredictor = deprecated_class(Predictor, "RealTimePredictor")

testbed/aws__sagemaker-python-sdk/src/sagemaker/tensorflow/model.py

@@ -0,0 +1,482 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Classes for using TensorFlow on Amazon SageMaker for inference."""
+from __future__ import absolute_import
+
+import logging
+from typing import Union, Optional, List, Dict
+
+import sagemaker
+from sagemaker import image_uris, s3, ModelMetrics
+from sagemaker.deserializers import JSONDeserializer
+from sagemaker.deprecations import removed_kwargs
+from sagemaker.drift_check_baselines import DriftCheckBaselines
+from sagemaker.metadata_properties import MetadataProperties
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import JSONSerializer
+from sagemaker.workflow import is_pipeline_variable
+from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.workflow.pipeline_context import PipelineSession
+
+logger = logging.getLogger(__name__)
+
+
+class TensorFlowPredictor(Predictor):
+    """A ``Predictor`` implementation for inference against TensorFlow Serving endpoints."""
+
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=JSONSerializer(),
+        deserializer=JSONDeserializer(),
+        model_name=None,
+        model_version=None,
+        **kwargs,
+    ):
+        """Initialize a ``TensorFlowPredictor``.
+
+        See :class:`~sagemaker.predictor.Predictor` for more info about parameters.
+
+        Args:
+            endpoint_name (str): The name of the endpoint to perform inference
+                on.
+            sagemaker_session (sagemaker.session.Session): Session object which
+                manages interactions with Amazon SageMaker APIs and any other
+                AWS services needed. If not specified, the estimator creates one
+                using the default AWS configuration chain.
+            serializer (callable): Optional. Default serializes input data to
+                json. Handles dicts, lists, and numpy arrays.
+            deserializer (callable): Optional. Default parses the response using
+                ``json.load(...)``.
+            model_name (str): Optional. The name of the SavedModel model that
+                should handle the request. If not specified, the endpoint's
+                default model will handle the request.
+            model_version (str): Optional. The version of the SavedModel model
+                that should handle the request. If not specified, the latest
+                version of the model will be used.
+        """
+        removed_kwargs("content_type", kwargs)
+        removed_kwargs("accept", kwargs)
+        super(TensorFlowPredictor, self).__init__(
+            endpoint_name,
+            sagemaker_session,
+            serializer,
+            deserializer,
+        )
+
+        attributes = []
+        if model_name:
+            attributes.append("tfs-model-name={}".format(model_name))
+        if model_version:
+            attributes.append("tfs-model-version={}".format(model_version))
+        self._model_attributes = ",".join(attributes) if attributes else None
+
+    def classify(self, data):
+        """Placeholder docstring."""
+        return self._classify_or_regress(data, "classify")
+
+    def regress(self, data):
+        """Placeholder docstring."""
+        return self._classify_or_regress(data, "regress")
+
+    def _classify_or_regress(self, data, method):
+        """Placeholder docstring."""
+        if method not in ["classify", "regress"]:
+            raise ValueError("invalid TensorFlow Serving method: {}".format(method))
+
+        if self.content_type != "application/json":
+            raise ValueError("The {} api requires json requests.".format(method))
+
+        args = {"CustomAttributes": "tfs-method={}".format(method)}
+
+        return self.predict(data, args)
+
+    def predict(self, data, initial_args=None):
+        """Placeholder docstring."""
+        args = dict(initial_args) if initial_args else {}
+        if self._model_attributes:
+            if "CustomAttributes" in args:
+                args["CustomAttributes"] += "," + self._model_attributes
+            else:
+                args["CustomAttributes"] = self._model_attributes
+
+        return super(TensorFlowPredictor, self).predict(data, args)
+
+
+class TensorFlowModel(sagemaker.model.FrameworkModel):
+    """A ``FrameworkModel`` implementation for inference with TensorFlow Serving."""
+
+    _framework_name = "tensorflow"
+    LOG_LEVEL_PARAM_NAME = "SAGEMAKER_TFS_NGINX_LOGLEVEL"
+    LOG_LEVEL_MAP = {
+        logging.DEBUG: "debug",
+        logging.INFO: "info",
+        logging.WARNING: "warn",
+        logging.ERROR: "error",
+        logging.CRITICAL: "crit",
+    }
+    LATEST_EIA_VERSION = [2, 3]
+
+    def __init__(
+        self,
+        model_data: Union[str, PipelineVariable],
+        role: str,
+        entry_point: Optional[str] = None,
+        image_uri: Optional[Union[str, PipelineVariable]] = None,
+        framework_version: Optional[str] = None,
+        container_log_level: Optional[int] = None,
+        predictor_cls: callable = TensorFlowPredictor,
+        **kwargs,
+    ):
+        """Initialize a Model.
+
+        Args:
+            model_data (str or PipelineVariable): The S3 location of a SageMaker model data
+                ``.tar.gz`` file.
+            role (str): An AWS IAM role (either name or full ARN). The Amazon
+                SageMaker training jobs and APIs that create Amazon SageMaker
+                endpoints use this role to access training data and model
+                artifacts. After the endpoint is created, the inference code
+                might use the IAM role, if it needs to access an AWS resource.
+            entry_point (str): Path (absolute or relative) to the Python source
+                file which should be executed as the entry point to model
+                hosting. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
+            image_uri (str or PipelineVariable): A Docker image URI (default: None).
+                If not specified, a default image for TensorFlow Serving will be used.
+                If ``framework_version`` is ``None``, then ``image_uri`` is required.
+                If ``image_uri`` is also ``None``, then a ``ValueError``
+                will be raised.
+            framework_version (str): Optional. TensorFlow Serving version you
+                want to use. Defaults to ``None``. Required unless ``image_uri`` is
+                provided.
+            container_log_level (int): Log level to use within the container
+                (default: logging.ERROR). Valid values are defined in the Python
+                logging module.
+            predictor_cls (callable[str, sagemaker.session.Session]): A function
+                to call to create a predictor with an endpoint name and
+                SageMaker ``Session``. If specified, ``deploy()`` returns the
+                result of invoking this function on the created endpoint name.
+            **kwargs: Keyword arguments passed to the superclass
+                :class:`~sagemaker.model.FrameworkModel` and, subsequently, its
+                superclass :class:`~sagemaker.model.Model`.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.model.FrameworkModel` and
+            :class:`~sagemaker.model.Model`.
+        """
+        if framework_version is None and image_uri is None:
+            raise ValueError(
+                "Both framework_version and image_uri were None. "
+                "Either specify framework_version or specify image_uri."
+            )
+        self.framework_version = framework_version
+
+        super(TensorFlowModel, self).__init__(
+            model_data=model_data,
+            role=role,
+            image_uri=image_uri,
+            predictor_cls=predictor_cls,
+            entry_point=entry_point,
+            **kwargs,
+        )
+        self._container_log_level = container_log_level
+
+    def register(
+        self,
+        content_types: List[Union[str, PipelineVariable]],
+        response_types: List[Union[str, PipelineVariable]],
+        inference_instances: Optional[List[Union[str, PipelineVariable]]] = None,
+        transform_instances: Optional[List[Union[str, PipelineVariable]]] = None,
+        model_package_name: Optional[Union[str, PipelineVariable]] = None,
+        model_package_group_name: Optional[Union[str, PipelineVariable]] = None,
+        image_uri: Optional[Union[str, PipelineVariable]] = None,
+        model_metrics: Optional[ModelMetrics] = None,
+        metadata_properties: Optional[MetadataProperties] = None,
+        marketplace_cert: bool = False,
+        approval_status: Optional[Union[str, PipelineVariable]] = None,
+        description: Optional[str] = None,
+        drift_check_baselines: Optional[DriftCheckBaselines] = None,
+        customer_metadata_properties: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
+        domain: Optional[Union[str, PipelineVariable]] = None,
+        sample_payload_url: Optional[Union[str, PipelineVariable]] = None,
+        task: Optional[Union[str, PipelineVariable]] = None,
+        framework: Optional[Union[str, PipelineVariable]] = None,
+        framework_version: Optional[Union[str, PipelineVariable]] = None,
+        nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
+        data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+    ):
+        """Creates a model package for creating SageMaker models or listing on Marketplace.
+
+        Args:
+            content_types (list[str] or list[PipelineVariable]): The supported MIME types
+                for the input data.
+            response_types (list[str] or list[PipelineVariable]): The supported MIME types
+                for the output data.
+            inference_instances (list[str] or list[PipelineVariable]): A list of the instance
+                types that are used to generate inferences in real-time (default: None).
+            transform_instances (list[str] or list[PipelineVariable]): A list of the instance
+                types on which a transformation job can be run or on which an endpoint can
+                be deployed (default: None).
+            model_package_name (str or PipelineVariable): Model Package name, exclusive to
+                `model_package_group_name`, using `model_package_name` makes the Model Package
+                un-versioned (default: None).
+            model_package_group_name (str or PipelineVariable): Model Package Group name,
+                exclusive to `model_package_name`, using `model_package_group_name` makes the
+                Model Package versioned (default: None).
+            image_uri (str or PipelineVariable): Inference image uri for the container. Model class'
+                self.image will be used if it is None (default: None).
+            model_metrics (ModelMetrics): ModelMetrics object (default: None).
+            metadata_properties (MetadataProperties): MetadataProperties object (default: None).
+            marketplace_cert (bool): A boolean value indicating if the Model Package is certified
+                for AWS Marketplace (default: False).
+            approval_status (str or PipelineVariable): Model Approval Status, values can be
+                "Approved", "Rejected", or "PendingManualApproval"
+                (default: "PendingManualApproval").
+            description (str): Model Package description (default: None).
+            drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
+            customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]):
+                A dictionary of key-value paired metadata properties (default: None).
+            domain (str or PipelineVariable): Domain values can be "COMPUTER_VISION",
+                "NATURAL_LANGUAGE_PROCESSING", "MACHINE_LEARNING" (default: None).
+            sample_payload_url (str or PipelineVariable): The S3 path where the sample payload
+                is stored (default: None).
+            task (str or PipelineVariable): Task values which are supported by Inference Recommender
+                are "FILL_MASK", "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION",
+                "IMAGE_SEGMENTATION", "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+            framework (str or PipelineVariable): Machine learning framework of the model package
+                container image (default: None).
+            framework_version (str or PipelineVariable): Framework version of the Model Package
+                Container Image (default: None).
+            nearest_model_name (str or PipelineVariable): Name of a pre-trained machine learning
+                benchmarked by Amazon SageMaker Inference Recommender (default: None).
+            data_input_configuration (str or PipelineVariable): Input object for the model
+                (default: None).
+
+        Returns:
+            A `sagemaker.model.ModelPackage` instance.
+        """
+        instance_type = inference_instances[0] if inference_instances else None
+        self._init_sagemaker_session_if_does_not_exist(instance_type)
+
+        if image_uri:
+            self.image_uri = image_uri
+        if not self.image_uri:
+            self.image_uri = self.serving_image_uri(
+                region_name=self.sagemaker_session.boto_session.region_name,
+                instance_type=instance_type,
+            )
+        if not is_pipeline_variable(framework):
+            framework = (framework or self._framework_name).upper()
+        return super(TensorFlowModel, self).register(
+            content_types,
+            response_types,
+            inference_instances,
+            transform_instances,
+            model_package_name,
+            model_package_group_name,
+            image_uri,
+            model_metrics,
+            metadata_properties,
+            marketplace_cert,
+            approval_status,
+            description,
+            drift_check_baselines=drift_check_baselines,
+            customer_metadata_properties=customer_metadata_properties,
+            domain=domain,
+            sample_payload_url=sample_payload_url,
+            task=task,
+            framework=framework,
+            framework_version=framework_version or self.framework_version,
+            nearest_model_name=nearest_model_name,
+            data_input_configuration=data_input_configuration,
+        )
+
+    def deploy(
+        self,
+        initial_instance_count=None,
+        instance_type=None,
+        serializer=None,
+        deserializer=None,
+        accelerator_type=None,
+        endpoint_name=None,
+        tags=None,
+        kms_key=None,
+        wait=True,
+        data_capture_config=None,
+        update_endpoint=None,
+        async_inference_config=None,
+        serverless_inference_config=None,
+    ):
+        """Deploy a Tensorflow ``Model`` to a SageMaker ``Endpoint``."""
+
+        if accelerator_type and not self._eia_supported():
+            msg = "The TensorFlow version %s doesn't support EIA." % self.framework_version
+            raise AttributeError(msg)
+
+        return super(TensorFlowModel, self).deploy(
+            initial_instance_count=initial_instance_count,
+            instance_type=instance_type,
+            serializer=serializer,
+            deserializer=deserializer,
+            accelerator_type=accelerator_type,
+            endpoint_name=endpoint_name,
+            tags=tags,
+            kms_key=kms_key,
+            wait=wait,
+            data_capture_config=data_capture_config,
+            async_inference_config=async_inference_config,
+            serverless_inference_config=serverless_inference_config,
+            update_endpoint=update_endpoint,
+        )
+
+    def _eia_supported(self):
+        """Return true if TF version is EIA enabled"""
+        framework_version = [int(s) for s in self.framework_version.split(".")][:2]
+        return (
+            framework_version != [2, 1]
+            and framework_version != [2, 2]
+            and framework_version <= self.LATEST_EIA_VERSION
+        )
+
+    def prepare_container_def(
+        self, instance_type=None, accelerator_type=None, serverless_inference_config=None
+    ):
+        """Prepare the container definition.
+
+        Args:
+            instance_type: Instance type of the container.
+            accelerator_type: Accelerator type, if applicable.
+            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
+                Specifies configuration related to serverless endpoint. Instance type is
+                not provided in serverless inference. So this is used to find image URIs.
+
+        Returns:
+            A container definition for deploying a ``Model`` to an ``Endpoint``.
+        """
+        if not self.image_uri:
+            if instance_type is None and serverless_inference_config is None:
+                raise ValueError(
+                    "Must supply either an instance type (for choosing CPU vs GPU) or an image URI."
+                )
+
+        image_uri = self._get_image_uri(
+            instance_type, accelerator_type, serverless_inference_config=serverless_inference_config
+        )
+        env = self._get_container_env()
+        key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image_uri)
+        bucket = self.bucket or self.sagemaker_session.default_bucket()
+
+        if self.entry_point and not is_pipeline_variable(self.model_data):
+            model_data = s3.s3_path_join("s3://", bucket, key_prefix, "model.tar.gz")
+
+            sagemaker.utils.repack_model(
+                self.entry_point,
+                self.source_dir,
+                self.dependencies,
+                self.model_data,
+                model_data,
+                self.sagemaker_session,
+                kms_key=self.model_kms_key,
+            )
+        elif self.entry_point and is_pipeline_variable(self.model_data):
+            # model is not yet there, defer repacking to later during pipeline execution
+            if isinstance(self.sagemaker_session, PipelineSession):
+                self.sagemaker_session.context.need_runtime_repack.add(id(self))
+                self.sagemaker_session.context.runtime_repack_output_prefix = "s3://{}/{}".format(
+                    bucket, key_prefix
+                )
+            else:
+                logging.warning(
+                    "The model_data is a Pipeline variable of type %s, "
+                    "which should be used under `PipelineSession` and "
+                    "leverage `ModelStep` to create or register model. "
+                    "Otherwise some functionalities e.g. "
+                    "runtime repack may be missing. For more, see: "
+                    "https://sagemaker.readthedocs.io/en/stable/"
+                    "amazon_sagemaker_model_building_pipeline.html#model-step",
+                    type(self.model_data),
+                )
+            model_data = self.model_data
+        else:
+            model_data = self.model_data
+
+        return sagemaker.container_def(image_uri, model_data, env)
+
+    def _get_container_env(self):
+        """Placeholder docstring."""
+        if not self._container_log_level:
+            return self.env
+
+        if self._container_log_level not in self.LOG_LEVEL_MAP:
+            logging.warning("ignoring invalid container log level: %s", self._container_log_level)
+            return self.env
+
+        env = dict(self.env)
+        env[self.LOG_LEVEL_PARAM_NAME] = self.LOG_LEVEL_MAP[self._container_log_level]
+        return env
+
+    def _get_image_uri(
+        self,
+        instance_type,
+        accelerator_type=None,
+        region_name=None,
+        serverless_inference_config=None,
+    ):
+        """Placeholder docstring."""
+        if self.image_uri:
+            return self.image_uri
+
+        logger.info(
+            "image_uri is not presented, retrieving image_uri based on instance_type, "
+            "framework etc."
+        )
+        return image_uris.retrieve(
+            self._framework_name,
+            region_name or self.sagemaker_session.boto_region_name,
+            version=self.framework_version,
+            instance_type=instance_type,
+            accelerator_type=accelerator_type,
+            image_scope="inference",
+            serverless_inference_config=serverless_inference_config,
+        )
+
+    def serving_image_uri(
+        self, region_name, instance_type, accelerator_type=None, serverless_inference_config=None
+    ):  # pylint: disable=unused-argument
+        """Create a URI for the serving image.
+
+        Args:
+            region_name (str): AWS region where the image is uploaded.
+            instance_type (str): SageMaker instance type. Used to determine device type
+                (cpu/gpu/family-specific optimized).
+            accelerator_type (str): The Elastic Inference accelerator type to
+                deploy to the instance for loading and making inferences to the
+                model (default: None). For example, 'ml.eia1.medium'.
+            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
+                Specifies configuration related to serverless endpoint. Instance type is
+                not provided in serverless inference. So this is used to determine device type.
+
+        Returns:
+            str: The appropriate image URI based on the given parameters.
+
+        """
+        return self._get_image_uri(
+            instance_type=instance_type,
+            accelerator_type=accelerator_type,
+            region_name=region_name,
+            serverless_inference_config=serverless_inference_config,
+        )

testbed/aws__sagemaker-python-sdk/src/sagemaker/tensorflow/training_compiler/config.py

@@ -0,0 +1,111 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Configuration for the SageMaker Training Compiler."""
+from __future__ import absolute_import
+import logging
+from packaging.specifiers import SpecifierSet
+from packaging.version import Version
+
+from sagemaker.training_compiler.config import TrainingCompilerConfig as BaseConfig
+
+logger = logging.getLogger(__name__)
+
+
+class TrainingCompilerConfig(BaseConfig):
+    """The SageMaker Training Compiler configuration class."""
+
+    SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "g4dn", "p4", "g5"]
+    MIN_SUPPORTED_VERSION = "2.9"
+
+    def __init__(
+        self,
+        enabled=True,
+        debug=False,
+    ):
+        """This class initializes a ``TrainingCompilerConfig`` instance.
+
+        `Amazon SageMaker Training Compiler
+        <https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_
+        is a feature of SageMaker Training
+        and speeds up training jobs by optimizing model execution graphs.
+
+        You can compile TensorFlow models
+        by passing the object of this configuration class to the ``compiler_config``
+        parameter of the :class:`~sagemaker.tensorflow.TensorFlow`
+        estimator.
+
+        Args:
+            enabled (bool): Optional. Switch to enable SageMaker Training Compiler.
+                The default is ``True``.
+            debug (bool): Optional. Whether to dump detailed logs for debugging.
+                This comes with a potential performance slowdown.
+                The default is ``False``.
+
+        **Example**: The following code shows the basic usage of the
+        :class:`sagemaker.tensorflow.TrainingCompilerConfig()` class
+        to run a TensorFlow training job with the compiler.
+
+        .. code-block:: python
+
+            from sagemaker.tensorflow import TensorFlow, TrainingCompilerConfig
+
+            tensorflow_estimator=TensorFlow(
+                ...
+                compiler_config=TrainingCompilerConfig()
+            )
+
+        .. seealso::
+
+            For more information about how to enable SageMaker Training Compiler
+            for various training settings such as using TensorFlow-based models,
+            PyTorch-based models, and distributed training,
+            see `Enable SageMaker Training Compiler
+            <https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler-enable.html>`_
+            in the `Amazon SageMaker Training Compiler developer guide
+            <https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_.
+
+        """
+
+        super(TrainingCompilerConfig, self).__init__(enabled=enabled, debug=debug)
+
+    @classmethod
+    def validate(
+        cls,
+        estimator,
+    ):
+        """Checks if SageMaker Training Compiler is configured correctly.
+
+        Args:
+            estimator (str): A estimator object
+                If SageMaker Training Compiler is enabled, it will validate whether
+                the estimator is configured to be compatible with Training Compiler.
+
+        Raises:
+            ValueError: Raised if the requested configuration is not compatible
+                        with SageMaker Training Compiler.
+        """
+
+        super(TrainingCompilerConfig, cls).validate(estimator)
+
+        if estimator.framework_version:
+            if Version(estimator.framework_version) in SpecifierSet(
+                f"< {cls.MIN_SUPPORTED_VERSION}"
+            ):
+                error_helper_string = (
+                    "SageMaker Training Compiler only supports TensorFlow version "
+                    ">= {} but received {}"
+                )
+                error_helper_string = error_helper_string.format(
+                    cls.MIN_SUPPORTED_VERSION, estimator.framework_version
+                )
+                raise ValueError(error_helper_string)

testbed/aws__sagemaker-python-sdk/src/sagemaker/workflow/__init__.py

@@ -0,0 +1,43 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Defines Types etc. used in workflow."""
+from __future__ import absolute_import
+
+from sagemaker.workflow.entities import Expression
+from sagemaker.workflow.parameters import ParameterString
+
+
+def is_pipeline_variable(var: object) -> bool:
+    """Check if the variable is a pipeline variable
+
+    Args:
+        var (object): The variable to be verified.
+    Returns:
+         bool: True if it is, False otherwise.
+    """
+
+    # Currently Expression is on top of all kinds of pipeline variables
+    # as well as PipelineExperimentConfigProperty and PropertyFile
+    # TODO: We should deprecate the Expression and replace it with PipelineVariable
+    return isinstance(var, Expression)
+
+
+def is_pipeline_parameter_string(var: object) -> bool:
+    """Check if the variable is a pipeline parameter string
+
+    Args:
+        var (object): The variable to be verified.
+    Returns:
+         bool: True if it is, False otherwise.
+    """
+    return isinstance(var, ParameterString)

testbed/aws__sagemaker-python-sdk/src/sagemaker/workflow/callback_step.py

@@ -0,0 +1,145 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""The step definitions for workflow."""
+from __future__ import absolute_import
+
+from typing import List, Dict, Union, Optional
+from enum import Enum
+
+import attr
+
+from sagemaker.workflow.entities import (
+    RequestType,
+)
+from sagemaker.workflow.properties import (
+    Properties,
+)
+from sagemaker.workflow.entities import (
+    DefaultEnumMeta,
+)
+from sagemaker.workflow.step_collections import StepCollection
+from sagemaker.workflow.steps import Step, StepTypeEnum, CacheConfig
+
+
+class CallbackOutputTypeEnum(Enum, metaclass=DefaultEnumMeta):
+    """CallbackOutput type enum."""
+
+    String = "String"
+    Integer = "Integer"
+    Boolean = "Boolean"
+    Float = "Float"
+
+
+@attr.s
+class CallbackOutput:
+    """Output for a callback step.
+
+    Attributes:
+        output_name (str): The output name
+        output_type (CallbackOutputTypeEnum): The output type
+    """
+
+    output_name: str = attr.ib(default=None)
+    output_type: CallbackOutputTypeEnum = attr.ib(default=CallbackOutputTypeEnum.String)
+
+    def to_request(self) -> RequestType:
+        """Get the request structure for workflow service calls."""
+        return {
+            "OutputName": self.output_name,
+            "OutputType": self.output_type.value,
+        }
+
+    def expr(self, step_name) -> Dict[str, str]:
+        """The 'Get' expression dict for a `CallbackOutput`."""
+        return CallbackOutput._expr(self.output_name, step_name)
+
+    @classmethod
+    def _expr(cls, name, step_name):
+        """An internal classmethod for the 'Get' expression dict for a `CallbackOutput`.
+
+        Args:
+            name (str): The name of the callback output.
+            step_name (str): The name of the step the callback step associated
+                with this output belongs to.
+        """
+        return {"Get": f"Steps.{step_name}.OutputParameters['{name}']"}
+
+
+class CallbackStep(Step):
+    """Callback step for workflow."""
+
+    def __init__(
+        self,
+        name: str,
+        sqs_queue_url: str,
+        inputs: dict,
+        outputs: List[CallbackOutput],
+        display_name: str = None,
+        description: str = None,
+        cache_config: CacheConfig = None,
+        depends_on: Optional[List[Union[str, Step, StepCollection]]] = None,
+    ):
+        """Constructs a CallbackStep.
+
+        Args:
+            name (str): The name of the callback step.
+            sqs_queue_url (str): An SQS queue URL for receiving callback messages.
+            inputs (dict): Input arguments that will be provided
+                in the SQS message body of callback messages.
+            outputs (List[CallbackOutput]): Outputs that can be provided when completing a callback.
+            display_name (str): The display name of the callback step.
+            description (str): The description of the callback step.
+            cache_config (CacheConfig):  A `sagemaker.workflow.steps.CacheConfig` instance.
+            depends_on (List[Union[str, Step, StepCollection]]): A list of `Step`/`StepCollection`
+                names or `Step` instances or `StepCollection` instances that this `CallbackStep`
+                depends on.
+        """
+        super(CallbackStep, self).__init__(
+            name, display_name, description, StepTypeEnum.CALLBACK, depends_on
+        )
+        self.sqs_queue_url = sqs_queue_url
+        self.outputs = outputs
+        self.cache_config = cache_config
+        self.inputs = inputs
+
+        root_prop = Properties(step_name=name)
+
+        property_dict = {}
+        for output in outputs:
+            property_dict[output.output_name] = Properties(
+                step_name=name, path=f"OutputParameters['{output.output_name}']"
+            )
+
+        root_prop.__dict__["Outputs"] = property_dict
+        self._properties = root_prop
+
+    @property
+    def arguments(self) -> RequestType:
+        """The arguments dict that is used to define the callback step."""
+        return self.inputs
+
+    @property
+    def properties(self):
+        """A Properties object representing the output parameters of the callback step."""
+        return self._properties
+
+    def to_request(self) -> RequestType:
+        """Updates the dictionary with cache configuration."""
+        request_dict = super().to_request()
+        if self.cache_config:
+            request_dict.update(self.cache_config.config)
+
+        request_dict["SqsQueueUrl"] = self.sqs_queue_url
+        request_dict["OutputParameters"] = list(map(lambda op: op.to_request(), self.outputs))
+
+        return request_dict

testbed/aws__sagemaker-python-sdk/src/sagemaker/workflow/entities.py

@@ -0,0 +1,114 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Defines the base entities used in workflow."""
+from __future__ import absolute_import
+
+import abc
+
+from enum import EnumMeta
+from typing import Any, Dict, List, Union
+
+PrimitiveType = Union[str, int, bool, float, None]
+RequestType = Union[Dict[str, Any], List[Dict[str, Any]]]
+
+
+class Entity(abc.ABC):
+    """Base object for workflow entities.
+
+    Entities must implement the to_request method.
+    """
+
+    @abc.abstractmethod
+    def to_request(self) -> RequestType:
+        """Get the request structure for workflow service calls."""
+
+
+class DefaultEnumMeta(EnumMeta):
+    """An EnumMeta which defaults to the first value in the Enum list."""
+
+    default = object()
+
+    def __call__(cls, *args, value=default, **kwargs):
+        """Defaults to the first value in the Enum list."""
+        if value is DefaultEnumMeta.default:
+            return next(iter(cls))
+        return super().__call__(value, *args, **kwargs)
+
+    factory = __call__
+
+
+class Expression(abc.ABC):
+    """Base object for expressions.
+
+    Expressions must implement the expr property.
+    """
+
+    @property
+    @abc.abstractmethod
+    def expr(self) -> RequestType:
+        """Get the expression structure for workflow service calls."""
+
+
+class PipelineVariable(Expression):
+    """Base object for pipeline variables
+
+    PipelineVariable subclasses must implement the expr property. Its subclasses include:
+    :class:`~sagemaker.workflow.parameters.Parameter`,
+    :class:`~sagemaker.workflow.properties.Properties`,
+    :class:`~sagemaker.workflow.functions.Join`,
+    :class:`~sagemaker.workflow.functions.JsonGet`,
+    :class:`~sagemaker.workflow.execution_variables.ExecutionVariable`.
+    """
+
+    def __add__(self, other: Union[Expression, PrimitiveType]):
+        """Add function for PipelineVariable
+
+        Args:
+            other (Union[Expression, PrimitiveType]): The other object to be concatenated.
+
+        Always raise an error since pipeline variables do not support concatenation
+        """
+
+        raise TypeError("Pipeline variables do not support concatenation.")
+
+    def __str__(self):
+        """Override built-in String function for PipelineVariable"""
+        raise TypeError(
+            "Pipeline variables do not support __str__ operation. "
+            "Please use `.to_string()` to convert it to string type in execution time"
+            "or use `.expr` to translate it to Json for display purpose in Python SDK."
+        )
+
+    def __int__(self):
+        """Override built-in Integer function for PipelineVariable"""
+        raise TypeError("Pipeline variables do not support __int__ operation.")
+
+    def __float__(self):
+        """Override built-in Float function for PipelineVariable"""
+        raise TypeError("Pipeline variables do not support __float__ operation.")
+
+    def to_string(self):
+        """Prompt the pipeline to convert the pipeline variable to String in runtime"""
+        from sagemaker.workflow.functions import Join
+
+        return Join(on="", values=[self])
+
+    @property
+    @abc.abstractmethod
+    def expr(self) -> RequestType:
+        """Get the expression structure for workflow service calls."""
+
+    @property
+    @abc.abstractmethod
+    def _referenced_steps(self) -> List[str]:
+        """List of step names that this function depends on."""

testbed/aws__sagemaker-python-sdk/src/sagemaker/workflow/fail_step.py

@@ -0,0 +1,73 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""The `Step` definitions for SageMaker Pipelines Workflows."""
+from __future__ import absolute_import
+
+from typing import List, Union, Optional
+
+from sagemaker.workflow.entities import (
+    RequestType,
+    PipelineVariable,
+)
+from sagemaker.workflow.step_collections import StepCollection
+from sagemaker.workflow.steps import Step, StepTypeEnum
+
+
+class FailStep(Step):
+    """`FailStep` for SageMaker Pipelines Workflows."""
+
+    def __init__(
+        self,
+        name: str,
+        error_message: Union[str, PipelineVariable] = None,
+        display_name: str = None,
+        description: str = None,
+        depends_on: Optional[List[Union[str, Step, StepCollection]]] = None,
+    ):
+        """Constructs a `FailStep`.
+
+        Args:
+            name (str): The name of the `FailStep`. A name is required and must be
+                unique within a pipeline.
+            error_message (str or PipelineVariable):
+                An error message defined by the user.
+                Once the `FailStep` is reached, the execution fails and the
+                error message is set as the failure reason (default: None).
+            display_name (str): The display name of the `FailStep`.
+                The display name provides better UI readability. (default: None).
+            description (str): The description of the `FailStep` (default: None).
+            depends_on (List[Union[str, Step, StepCollection]]): A list of `Step`/`StepCollection`
+                names or `Step` instances or `StepCollection` instances that this `FailStep`
+                depends on.
+                If a listed `Step` name does not exist, an error is returned (default: None).
+        """
+        super(FailStep, self).__init__(
+            name, display_name, description, StepTypeEnum.FAIL, depends_on
+        )
+        self.error_message = error_message if error_message is not None else ""
+
+    @property
+    def arguments(self) -> RequestType:
+        """The arguments dictionary that is used to define the `FailStep`."""
+        return dict(ErrorMessage=self.error_message)
+
+    @property
+    def properties(self):
+        """A `Properties` object is not available for the `FailStep`.
+
+        Executing a `FailStep` will terminate the pipeline.
+        `FailStep` properties should not be referenced.
+        """
+        raise RuntimeError(
+            "FailStep is a terminal step and the Properties object is not available for it."
+        )

testbed/aws__sagemaker-python-sdk/src/sagemaker/workflow/functions.py

@@ -0,0 +1,112 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""The step definitions for workflow."""
+from __future__ import absolute_import
+
+from typing import List, Union
+
+import attr
+
+from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.workflow.properties import PropertyFile
+
+
+@attr.s
+class Join(PipelineVariable):
+    """Join together properties.
+
+    Examples:
+    Build a Amazon S3 Uri with bucket name parameter and pipeline execution Id and use it
+    as training input::
+
+        bucket = ParameterString('bucket', default_value='my-bucket')
+
+        TrainingInput(
+            s3_data=Join(on='/', ['s3:/', bucket, ExecutionVariables.PIPELINE_EXECUTION_ID]),
+            content_type="text/csv")
+
+    Attributes:
+        values (List[Union[PrimitiveType, Parameter, Expression]]):
+            The primitive type values, parameters, step properties, expressions to join.
+        on (str): The string to join the values on (Defaults to "").
+    """
+
+    on: str = attr.ib(factory=str)
+    values: List = attr.ib(factory=list)
+
+    def to_string(self) -> PipelineVariable:
+        """Prompt the pipeline to convert the pipeline variable to String in runtime
+
+        As Join is treated as String in runtime, no extra actions are needed.
+        """
+        return self
+
+    @property
+    def expr(self):
+        """The expression dict for a `Join` function."""
+
+        return {
+            "Std:Join": {
+                "On": self.on,
+                "Values": [
+                    value.expr if hasattr(value, "expr") else value for value in self.values
+                ],
+            },
+        }
+
+    @property
+    def _referenced_steps(self) -> List[str]:
+        """List of step names that this function depends on."""
+        steps = []
+        for value in self.values:
+            if isinstance(value, PipelineVariable):
+                steps.extend(value._referenced_steps)
+        return steps
+
+
+@attr.s
+class JsonGet(PipelineVariable):
+    """Get JSON properties from PropertyFiles.
+
+    Attributes:
+        step_name (str): The step name from which to get the property file.
+        property_file (Union[PropertyFile, str]): Either a PropertyFile instance
+            or the name of a property file.
+        json_path (str): The JSON path expression to the requested value.
+    """
+
+    step_name: str = attr.ib()
+    property_file: Union[PropertyFile, str] = attr.ib()
+    json_path: str = attr.ib()
+
+    @property
+    def expr(self):
+        """The expression dict for a `JsonGet` function."""
+        if not isinstance(self.step_name, str) or not self.step_name:
+            raise ValueError("Please give a valid step name as a string")
+
+        if isinstance(self.property_file, PropertyFile):
+            name = self.property_file.name
+        else:
+            name = self.property_file
+        return {
+            "Std:JsonGet": {
+                "PropertyFile": {"Get": f"Steps.{self.step_name}.PropertyFiles.{name}"},
+                "Path": self.json_path,
+            }
+        }
+
+    @property
+    def _referenced_steps(self) -> List[str]:
+        """List of step names that this function depends on."""
+        return [self.step_name]