partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | extract | Select the best match in a list or dictionary of choices.
Find best matches in a list or dictionary of choices, return a
list of tuples containing the match and its score. If a dictionary
is used, also returns the key for each match.
Arguments:
query: An object representing the thing we want t... | fuzzywuzzy/process.py | def extract(query, choices, processor=default_processor, scorer=default_scorer, limit=5):
"""Select the best match in a list or dictionary of choices.
Find best matches in a list or dictionary of choices, return a
list of tuples containing the match and its score. If a dictionary
is used, also returns ... | def extract(query, choices, processor=default_processor, scorer=default_scorer, limit=5):
"""Select the best match in a list or dictionary of choices.
Find best matches in a list or dictionary of choices, return a
list of tuples containing the match and its score. If a dictionary
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train | extractBests | Get a list of the best matches to a collection of choices.
Convenience function for getting the choices with best scores.
Args:
query: A string to match against
choices: A list or dictionary of choices, suitable for use with
extract().
processor: Optional function for trans... | fuzzywuzzy/process.py | def extractBests(query, choices, processor=default_processor, scorer=default_scorer, score_cutoff=0, limit=5):
"""Get a list of the best matches to a collection of choices.
Convenience function for getting the choices with best scores.
Args:
query: A string to match against
choices: A list... | def extractBests(query, choices, processor=default_processor, scorer=default_scorer, score_cutoff=0, limit=5):
"""Get a list of the best matches to a collection of choices.
Convenience function for getting the choices with best scores.
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query: A string to match against
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train | extractOne | Find the single best match above a score in a list of choices.
This is a convenience method which returns the single best choice.
See extract() for the full arguments list.
Args:
query: A string to match against
choices: A list or dictionary of choices, suitable for use with
ex... | fuzzywuzzy/process.py | def extractOne(query, choices, processor=default_processor, scorer=default_scorer, score_cutoff=0):
"""Find the single best match above a score in a list of choices.
This is a convenience method which returns the single best choice.
See extract() for the full arguments list.
Args:
query: A str... | def extractOne(query, choices, processor=default_processor, scorer=default_scorer, score_cutoff=0):
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This is a convenience method which returns the single best choice.
See extract() for the full arguments list.
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train | dedupe | This convenience function takes a list of strings containing duplicates and uses fuzzy matching to identify
and remove duplicates. Specifically, it uses the process.extract to identify duplicates that
score greater than a user defined threshold. Then, it looks for the longest item in the duplicate list
sinc... | fuzzywuzzy/process.py | def dedupe(contains_dupes, threshold=70, scorer=fuzz.token_set_ratio):
"""This convenience function takes a list of strings containing duplicates and uses fuzzy matching to identify
and remove duplicates. Specifically, it uses the process.extract to identify duplicates that
score greater than a user defined... | def dedupe(contains_dupes, threshold=70, scorer=fuzz.token_set_ratio):
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train | make_type_consistent | If both objects aren't either both string or unicode instances force them to unicode | fuzzywuzzy/utils.py | def make_type_consistent(s1, s2):
"""If both objects aren't either both string or unicode instances force them to unicode"""
if isinstance(s1, str) and isinstance(s2, str):
return s1, s2
elif isinstance(s1, unicode) and isinstance(s2, unicode):
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"""If both objects aren't either both string or unicode instances force them to unicode"""
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train | full_process | Process string by
-- removing all but letters and numbers
-- trim whitespace
-- force to lower case
if force_ascii == True, force convert to ascii | fuzzywuzzy/utils.py | def full_process(s, force_ascii=False):
"""Process string by
-- removing all but letters and numbers
-- trim whitespace
-- force to lower case
if force_ascii == True, force convert to ascii"""
if force_ascii:
s = asciidammit(s)
# Keep only Letters and Numbers (see Un... | def full_process(s, force_ascii=False):
"""Process string by
-- removing all but letters and numbers
-- trim whitespace
-- force to lower case
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train | partial_ratio | Return the ratio of the most similar substring
as a number between 0 and 100. | fuzzywuzzy/fuzz.py | def partial_ratio(s1, s2):
""""Return the ratio of the most similar substring
as a number between 0 and 100."""
s1, s2 = utils.make_type_consistent(s1, s2)
if len(s1) <= len(s2):
shorter = s1
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m = SequenceMatcher(None, s... | def partial_ratio(s1, s2):
""""Return the ratio of the most similar substring
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s1, s2 = utils.make_type_consistent(s1, s2)
if len(s1) <= len(s2):
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train | _process_and_sort | Return a cleaned string with token sorted. | fuzzywuzzy/fuzz.py | def _process_and_sort(s, force_ascii, full_process=True):
"""Return a cleaned string with token sorted."""
# pull tokens
ts = utils.full_process(s, force_ascii=force_ascii) if full_process else s
tokens = ts.split()
# sort tokens and join
sorted_string = u" ".join(sorted(tokens))
return sor... | def _process_and_sort(s, force_ascii, full_process=True):
"""Return a cleaned string with token sorted."""
# pull tokens
ts = utils.full_process(s, force_ascii=force_ascii) if full_process else s
tokens = ts.split()
# sort tokens and join
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train | token_sort_ratio | Return a measure of the sequences' similarity between 0 and 100
but sorting the token before comparing. | fuzzywuzzy/fuzz.py | def token_sort_ratio(s1, s2, force_ascii=True, full_process=True):
"""Return a measure of the sequences' similarity between 0 and 100
but sorting the token before comparing.
"""
return _token_sort(s1, s2, partial=False, force_ascii=force_ascii, full_process=full_process) | def token_sort_ratio(s1, s2, force_ascii=True, full_process=True):
"""Return a measure of the sequences' similarity between 0 and 100
but sorting the token before comparing.
"""
return _token_sort(s1, s2, partial=False, force_ascii=force_ascii, full_process=full_process) | [
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train | partial_token_sort_ratio | Return the ratio of the most similar substring as a number between
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"""Return the ratio of the most similar substring as a number between
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"""
return _token_sort(s1, s2, partial=True, force_ascii=force_ascii, full_process=full_process) | def partial_token_sort_ratio(s1, s2, force_ascii=True, full_process=True):
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train | _token_set | Find all alphanumeric tokens in each string...
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- take ratios of those two strings
- controls for unordered partial matches | fuzzywuzzy/fuzz.py | def _token_set(s1, s2, partial=True, force_ascii=True, full_process=True):
"""Find all alphanumeric tokens in each string...
- treat them as a set
- construct two strings of the form:
<sorted_intersection><sorted_remainder>
- take ratios of those two strings
- controls fo... | def _token_set(s1, s2, partial=True, force_ascii=True, full_process=True):
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train | QRatio | Quick ratio comparison between two strings.
Runs full_process from utils on both strings
Short circuits if either of the strings is empty after processing.
:param s1:
:param s2:
:param force_ascii: Allow only ASCII characters (Default: True)
:full_process: Process inputs, used here to avoid do... | fuzzywuzzy/fuzz.py | def QRatio(s1, s2, force_ascii=True, full_process=True):
"""
Quick ratio comparison between two strings.
Runs full_process from utils on both strings
Short circuits if either of the strings is empty after processing.
:param s1:
:param s2:
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Quick ratio comparison between two strings.
Runs full_process from utils on both strings
Short circuits if either of the strings is empty after processing.
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train | UQRatio | Unicode quick ratio
Calls QRatio with force_ascii set to False
:param s1:
:param s2:
:return: similarity ratio | fuzzywuzzy/fuzz.py | def UQRatio(s1, s2, full_process=True):
"""
Unicode quick ratio
Calls QRatio with force_ascii set to False
:param s1:
:param s2:
:return: similarity ratio
"""
return QRatio(s1, s2, force_ascii=False, full_process=full_process) | def UQRatio(s1, s2, full_process=True):
"""
Unicode quick ratio
Calls QRatio with force_ascii set to False
:param s1:
:param s2:
:return: similarity ratio
"""
return QRatio(s1, s2, force_ascii=False, full_process=full_process) | [
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train | WRatio | Return a measure of the sequences' similarity between 0 and 100, using different algorithms.
**Steps in the order they occur**
#. Run full_process from utils on both strings
#. Short circuit if this makes either string empty
#. Take the ratio of the two processed strings (fuzz.ratio)
#. Run checks... | fuzzywuzzy/fuzz.py | def WRatio(s1, s2, force_ascii=True, full_process=True):
"""
Return a measure of the sequences' similarity between 0 and 100, using different algorithms.
**Steps in the order they occur**
#. Run full_process from utils on both strings
#. Short circuit if this makes either string empty
#. Take ... | def WRatio(s1, s2, force_ascii=True, full_process=True):
"""
Return a measure of the sequences' similarity between 0 and 100, using different algorithms.
**Steps in the order they occur**
#. Run full_process from utils on both strings
#. Short circuit if this makes either string empty
#. Take ... | [
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train | UWRatio | Return a measure of the sequences' similarity between 0 and 100,
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"""Return a measure of the sequences' similarity between 0 and 100,
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"""
return WRatio(s1, s2, force_ascii=False, full_process=full_process) | def UWRatio(s1, s2, full_process=True):
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train | PipelineModel.deploy | Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
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train | PipelineModel._create_sagemaker_pipeline_model | Create a SageMaker Model Entity
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train | PipelineModel.delete_model | Delete the SageMaker model backing this pipeline model. This does not delete the list of SageMaker models used
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"""Delete the SageMaker model backing this pipeline model. This does not delete the list of SageMaker models used
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train | _stream_output | Stream the output of a process to stdout
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process(subprocess.Popen): a process that has been started with
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train | _SageMakerContainer.train | Run a training job locally using docker-compose.
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input_data_config (dict): The Input Data Configuration, this contains data such as the
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hyperparameters (dict): The HyperParameters for the training job.
job_name (str): Na... | src/sagemaker/local/image.py | def train(self, input_data_config, output_data_config, hyperparameters, job_name):
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Args:
input_data_config (dict): The Input Data Configuration, this contains data such as the
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hy... | def train(self, input_data_config, output_data_config, hyperparameters, job_name):
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train | _SageMakerContainer.serve | Host a local endpoint using docker-compose.
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- 'ModelDataUrl' pointing to a file or s3:// location.
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primary_container (dict): dictionary containing the container runtime settings
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- 'ModelDataUrl' pointing to a file or s3:// location.
... | def serve(self, model_dir, environment):
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primary_container (dict): dictionary containing the container runtime settings
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train | _SageMakerContainer.stop_serving | Stop the serving container.
The serving container runs in async mode to allow the SDK to do other tasks. | src/sagemaker/local/image.py | def stop_serving(self):
"""Stop the serving container.
The serving container runs in async mode to allow the SDK to do other tasks.
"""
if self.container:
self.container.down()
self.container.join()
self._cleanup()
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"""Stop the serving container.
The serving container runs in async mode to allow the SDK to do other tasks.
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train | _SageMakerContainer.retrieve_artifacts | Get the model artifacts from all the container nodes.
Used after training completes to gather the data from all the individual containers. As the
official SageMaker Training Service, it will override duplicate files if multiple containers have
the same file names.
Args:
com... | src/sagemaker/local/image.py | def retrieve_artifacts(self, compose_data, output_data_config, job_name):
"""Get the model artifacts from all the container nodes.
Used after training completes to gather the data from all the individual containers. As the
official SageMaker Training Service, it will override duplicate files if... | def retrieve_artifacts(self, compose_data, output_data_config, job_name):
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train | _SageMakerContainer.write_config_files | Write the config files for the training containers.
This method writes the hyperparameters, resources and input data configuration files.
Args:
host (str): Host to write the configuration for
hyperparameters (dict): Hyperparameters for training.
input_data_config (d... | src/sagemaker/local/image.py | def write_config_files(self, host, hyperparameters, input_data_config):
"""Write the config files for the training containers.
This method writes the hyperparameters, resources and input data configuration files.
Args:
host (str): Host to write the configuration for
hyp... | def write_config_files(self, host, hyperparameters, input_data_config):
"""Write the config files for the training containers.
This method writes the hyperparameters, resources and input data configuration files.
Args:
host (str): Host to write the configuration for
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train | _SageMakerContainer._generate_compose_file | Writes a config file describing a training/hosting environment.
This method generates a docker compose configuration file, it has an entry for each container
that will be created (based on self.hosts). it calls
:meth:~sagemaker.local_session.SageMakerContainer._create_docker_host to generate t... | src/sagemaker/local/image.py | def _generate_compose_file(self, command, additional_volumes=None, additional_env_vars=None):
"""Writes a config file describing a training/hosting environment.
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that will be created (based on self.hos... | def _generate_compose_file(self, command, additional_volumes=None, additional_env_vars=None):
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train | _SageMakerContainer._build_optml_volumes | Generate a list of :class:`~sagemaker.local_session.Volume` required for the container to start.
It takes a folder with the necessary files for training and creates a list of opt volumes that
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Args:
host (str): container for which the volumes will be ge... | src/sagemaker/local/image.py | def _build_optml_volumes(self, host, subdirs):
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It takes a folder with the necessary files for training and creates a list of opt volumes that
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Args:
... | def _build_optml_volumes(self, host, subdirs):
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train | model_fn | Load the gluon model. Called once when hosting service starts.
:param: model_dir The directory where model files are stored.
:return: a model (in this case a Gluon network) | examples/cli/host/script.py | def model_fn(model_dir):
"""
Load the gluon model. Called once when hosting service starts.
:param: model_dir The directory where model files are stored.
:return: a model (in this case a Gluon network)
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symbol = mx.sym.load('%s/model.json' % model_dir)
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Load the gluon model. Called once when hosting service starts.
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train | transform_fn | Transform a request using the Gluon model. Called once per request.
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:param data: The request payload.
:param input_content_type: The request content type.
:param output_content_type: The (desired) response content type.
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"""
Transform a request using the Gluon model. Called once per request.
:param net: The Gluon model.
:param data: The request payload.
:param input_content_type: The request content type.
:param output_content_type: The (desi... | def transform_fn(net, data, input_content_type, output_content_type):
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Transform a request using the Gluon model. Called once per request.
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train | PCA._prepare_for_training | Set hyperparameters needed for training.
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train | create_image_uri | Return the ECR URI of an image.
Args:
region (str): AWS region where the image is uploaded.
framework (str): framework used by the image.
instance_type (str): SageMaker instance type. Used to determine device type (cpu/gpu/family-specific optimized).
framework_version (str): The ver... | src/sagemaker/fw_utils.py | def create_image_uri(region, framework, instance_type, framework_version, py_version=None,
account='520713654638', accelerator_type=None, optimized_families=None):
"""Return the ECR URI of an image.
Args:
region (str): AWS region where the image is uploaded.
framework (str)... | def create_image_uri(region, framework, instance_type, framework_version, py_version=None,
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train | validate_source_dir | Validate that the source directory exists and it contains the user script
Args:
script (str): Script filename.
directory (str): Directory containing the source file.
Raises:
ValueError: If ``directory`` does not exist, is not a directory, or does not contain ``script``. | src/sagemaker/fw_utils.py | def validate_source_dir(script, directory):
"""Validate that the source directory exists and it contains the user script
Args:
script (str): Script filename.
directory (str): Directory containing the source file.
Raises:
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"""Validate that the source directory exists and it contains the user script
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script (str): Script filename.
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train | tar_and_upload_dir | Package source files and upload a compress tar file to S3. The S3 location will be
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If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
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"""Package source files and upload a compress tar file to S3. The S3 location will be
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train | framework_name_from_image | Extract the framework and Python version from the image name.
Args:
image_name (str): Image URI, which should be one of the following forms:
legacy:
'<account>.dkr.ecr.<region>.amazonaws.com/sagemaker-<fw>-<py_ver>-<device>:<container_version>'
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"""Extract the framework and Python version from the image name.
Args:
image_name (str): Image URI, which should be one of the following forms:
legacy:
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"""Extract the framework and Python version from the image name.
Args:
image_name (str): Image URI, which should be one of the following forms:
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train | framework_version_from_tag | Extract the framework version from the image tag.
Args:
image_tag (str): Image tag, which should take the form '<framework_version>-<device>-<py_version>'
Returns:
str: The framework version. | src/sagemaker/fw_utils.py | def framework_version_from_tag(image_tag):
"""Extract the framework version from the image tag.
Args:
image_tag (str): Image tag, which should take the form '<framework_version>-<device>-<py_version>'
Returns:
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tag_pattern = re.compile('^(.*)-(cpu|gpu... | def framework_version_from_tag(image_tag):
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Args:
image_tag (str): Image tag, which should take the form '<framework_version>-<device>-<py_version>'
Returns:
str: The framework version.
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train | parse_s3_url | Returns an (s3 bucket, key name/prefix) tuple from a url with an s3 scheme
Args:
url (str):
Returns:
tuple: A tuple containing:
str: S3 bucket name
str: S3 key | src/sagemaker/fw_utils.py | def parse_s3_url(url):
"""Returns an (s3 bucket, key name/prefix) tuple from a url with an s3 scheme
Args:
url (str):
Returns:
tuple: A tuple containing:
str: S3 bucket name
str: S3 key
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... | def parse_s3_url(url):
"""Returns an (s3 bucket, key name/prefix) tuple from a url with an s3 scheme
Args:
url (str):
Returns:
tuple: A tuple containing:
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str: S3 key
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train | model_code_key_prefix | Returns the s3 key prefix for uploading code during model deployment
The location returned is a potential concatenation of 2 parts
1. code_location_key_prefix if it exists
2. model_name or a name derived from the image
Args:
code_location_key_prefix (str): the s3 key prefix from code_l... | src/sagemaker/fw_utils.py | def model_code_key_prefix(code_location_key_prefix, model_name, image):
"""Returns the s3 key prefix for uploading code during model deployment
The location returned is a potential concatenation of 2 parts
1. code_location_key_prefix if it exists
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train | LocalSagemakerClient.create_training_job | Create a training job in Local Mode
Args:
TrainingJobName (str): local training job name.
AlgorithmSpecification (dict): Identifies the training algorithm to use.
InputDataConfig (dict): Describes the training dataset and the location where it is stored.
OutputDat... | src/sagemaker/local/local_session.py | def create_training_job(self, TrainingJobName, AlgorithmSpecification, OutputDataConfig,
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"""
Create a training job in Local Mode
Args:
TrainingJobName (str): local training job name.
AlgorithmSpe... | def create_training_job(self, TrainingJobName, AlgorithmSpecification, OutputDataConfig,
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Create a training job in Local Mode
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TrainingJobName (str): local training job name.
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train | LocalSagemakerClient.describe_training_job | Describe a local training job.
Args:
TrainingJobName (str): Training job name to describe.
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"""Describe a local training job.
Args:
TrainingJobName (str): Training job name to describe.
Returns: (dict) DescribeTrainingJob Response.
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"""Describe a local training job.
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TrainingJobName (str): Training job name to describe.
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train | LocalSagemakerClient.create_model | Create a Local Model Object
Args:
ModelName (str): the Model Name
PrimaryContainer (dict): a SageMaker primary container definition | src/sagemaker/local/local_session.py | def create_model(self, ModelName, PrimaryContainer, *args, **kwargs): # pylint: disable=unused-argument
"""Create a Local Model Object
Args:
ModelName (str): the Model Name
PrimaryContainer (dict): a SageMaker primary container definition
"""
LocalSagemakerClien... | def create_model(self, ModelName, PrimaryContainer, *args, **kwargs): # pylint: disable=unused-argument
"""Create a Local Model Object
Args:
ModelName (str): the Model Name
PrimaryContainer (dict): a SageMaker primary container definition
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train | LocalSession._initialize | Initialize this Local SageMaker Session. | src/sagemaker/local/local_session.py | def _initialize(self, boto_session, sagemaker_client, sagemaker_runtime_client):
"""Initialize this Local SageMaker Session."""
self.boto_session = boto_session or boto3.Session()
self._region_name = self.boto_session.region_name
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"""Initialize this Local SageMaker Session."""
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train | SKLearnModel.prepare_container_def | Return a container definition with framework configuration set in model environment variables.
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 ... | src/sagemaker/sklearn/model.py | def prepare_container_def(self, instance_type, accelerator_type=None):
"""Return a container definition with framework configuration set in model environment variables.
Args:
instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
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instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
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train | upload_numpy_to_s3_shards | Upload the training ``array`` and ``labels`` arrays to ``num_shards`` s3 objects,
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train | registry | Return docker registry for the given AWS region
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"""Return docker registry for the given AWS region
Note: Not all the algorithms listed below have an Amazon Estimator implemented. For full list of
pre-implemented Estimators, look at:
https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/amaz... | def registry(region_name, algorithm=None):
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train | get_image_uri | Return algorithm image URI for the given AWS region, repository name, and repository version | src/sagemaker/amazon/amazon_estimator.py | def get_image_uri(region_name, repo_name, repo_version=1):
"""Return algorithm image URI for the given AWS region, repository name, and repository version"""
repo = '{}:{}'.format(repo_name, repo_version)
return '{}/{}'.format(registry(region_name, repo_name), repo) | def get_image_uri(region_name, repo_name, repo_version=1):
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train | AmazonAlgorithmEstimatorBase._prepare_init_params_from_job_description | Convert the job description to init params that can be handled by the class constructor
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job_details: the returned job details from a describe_training_job API call.
model_channel_name (str): Name of the channel where pre-trained model data will be downloaded.
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"""Convert the job description to init params that can be handled by the class constructor
Args:
job_details: the returned job details from a describe_training_job API call.
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train | AmazonAlgorithmEstimatorBase._prepare_for_training | Set hyperparameters needed for training.
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train | AmazonAlgorithmEstimatorBase.fit | Fit this Estimator on serialized Record objects, stored in S3.
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``records`` should be an instance of :class:`~RecordSet`. This defines a collection of
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train | AmazonAlgorithmEstimatorBase.record_set | Build a :class:`~RecordSet` from a numpy :class:`~ndarray` matrix and label vector.
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train | get_data_source_instance | Return an Instance of :class:`sagemaker.local.data.DataSource` that can handle
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data_source can be either file:// or s3://
Args:
data_source (str): a valid URI that points to a data source.
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"""Return an Instance of :class:`sagemaker.local.data.DataSource` that can handle
the provided data_source URI.
data_source can be either file:// or s3://
Args:
data_source (str): a valid URI that points to a data source.
sa... | def get_data_source_instance(data_source, sagemaker_session):
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data_source can be either file:// or s3://
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train | get_splitter_instance | Return an Instance of :class:`sagemaker.local.data.Splitter` according to
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split_type (str): either 'Line' or 'RecordIO'. Can be left as None to signal no data split
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Args:
split_type (str): either 'Line' or 'RecordIO'. Can be left as None to signal no data split
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train | get_batch_strategy_instance | Return an Instance of :class:`sagemaker.local.data.BatchStrategy` according to `strategy`
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strategy (str): Either 'SingleRecord' or 'MultiRecord'
splitter (:class:`sagemaker.local.data.Splitter): splitter to get the data from.
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Args:
strategy (str): Either 'SingleRecord' or 'MultiRecord'
splitter (:class:`sagemaker.local.data.Splitter): splitter to get the data from.
Re... | def get_batch_strategy_instance(strategy, splitter):
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strategy (str): Either 'SingleRecord' or 'MultiRecord'
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train | LocalFileDataSource.get_file_list | Retrieve the list of absolute paths to all the files in this data source.
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"""Retrieve the list of absolute paths to all the files in this data source.
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train | LocalFileDataSource.get_root_dir | Retrieve the absolute path to the root directory of this data source.
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train | RecordIOSplitter.split | Split a file into records using a specific strategy
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Args:
file (str): path to the file to split
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Args:
file (str): path to the file to split
Returns: generator for the individual records that were split from the file
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file (str): path to the file to split
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train | MultiRecordStrategy.pad | Group together as many records as possible to fit in the specified size
Args:
file (str): file path to read the records from.
size (int): maximum size in MB that each group of records will be fitted to.
passing 0 means unlimited size.
Returns:
genera... | src/sagemaker/local/data.py | def pad(self, file, size=6):
"""Group together as many records as possible to fit in the specified size
Args:
file (str): file path to read the records from.
size (int): maximum size in MB that each group of records will be fitted to.
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train | SingleRecordStrategy.pad | Group together as many records as possible to fit in the specified size
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train | Chainer.hyperparameters | Return hyperparameters used by your custom Chainer code during training. | src/sagemaker/chainer/estimator.py | def hyperparameters(self):
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train | Chainer.create_model | Create a SageMaker ``ChainerModel`` object that can be deployed to an ``Endpoint``.
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role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used during
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train | Chainer._prepare_init_params_from_job_description | Convert the job description to init params that can be handled by the class constructor
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job_details: the returned job details from a describe_training_job API call.
model_channel_name (str): Name of the channel where pre-trained model data will be downloaded.
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job_details: the returned job details from a describe_training_job API call.
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train | write_numpy_to_dense_tensor | Writes a numpy array to a dense tensor | src/sagemaker/amazon/common.py | def write_numpy_to_dense_tensor(file, array, labels=None):
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# Validate shape of array and labels, resolve array and label types
if not len(array.shape) == 2:
raise ValueError("Array must be a Matrix")
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if not len(labels.s... | def write_numpy_to_dense_tensor(file, array, labels=None):
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train | write_spmatrix_to_sparse_tensor | Writes a scipy sparse matrix to a sparse tensor | src/sagemaker/amazon/common.py | def write_spmatrix_to_sparse_tensor(file, array, labels=None):
"""Writes a scipy sparse matrix to a sparse tensor"""
if not issparse(array):
raise TypeError("Array must be sparse")
# Validate shape of array and labels, resolve array and label types
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train | read_records | Eagerly read a collection of amazon Record protobuf objects from file. | src/sagemaker/amazon/common.py | def read_records(file):
"""Eagerly read a collection of amazon Record protobuf objects from file."""
records = []
for record_data in read_recordio(file):
record = Record()
record.ParseFromString(record_data)
records.append(record)
return records | def read_records(file):
"""Eagerly read a collection of amazon Record protobuf objects from file."""
records = []
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record.ParseFromString(record_data)
records.append(record)
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train | _write_recordio | Writes a single data point as a RecordIO record to the given file. | src/sagemaker/amazon/common.py | def _write_recordio(f, data):
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train | Model.prepare_container_def | Return a dict created by ``sagemaker.container_def()`` 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 ... | src/sagemaker/model.py | def prepare_container_def(self, instance_type, accelerator_type=None): # pylint: disable=unused-argument
"""Return a dict created by ``sagemaker.container_def()`` for deploying this model to a specified instance type.
Subclasses can override this to provide custom container definitions for
dep... | def prepare_container_def(self, instance_type, accelerator_type=None): # pylint: disable=unused-argument
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train | Model._create_sagemaker_model | Create a SageMaker Model Entity
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instance_type (str): The EC2 instance type that this Model will be used for, this is only
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accelerator_type (str): Type of Elastic Inference accelerator to attach to an endpoint... | src/sagemaker/model.py | def _create_sagemaker_model(self, instance_type, accelerator_type=None, tags=None):
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Args:
instance_type (str): The EC2 instance type that this Model will be used for, this is only
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... | def _create_sagemaker_model(self, instance_type, accelerator_type=None, tags=None):
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Args:
instance_type (str): The EC2 instance type that this Model will be used for, this is only
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train | Model.compile | 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. Allowed strings are: ml_c5, ml_m5, ml_c4, ml_m4, jetsontx1, jetsontx2, ml_p2, ml_p3,
deeplens, ... | src/sagemaker/model.py | def compile(self, target_instance_family, input_shape, output_path, role,
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"""Compile this ``Model`` with SageMaker Neo.
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train | Model.deploy | 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
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"""Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
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train | Model.transformer | 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 (def... | src/sagemaker/model.py | def transformer(self, instance_count, instance_type, strategy=None, assemble_with=None, output_path=None,
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train | FrameworkModel.prepare_container_def | Return a container definition with framework configuration set in model environment variables.
This also uploads user-supplied code to S3.
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instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
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train | RealTimePredictor.delete_model | Deletes the Amazon SageMaker models backing this predictor. | src/sagemaker/predictor.py | def delete_model(self):
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train | AnalyticsMetricsBase.dataframe | A pandas dataframe with lots of interesting results about this object.
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train | HyperparameterTuningJobAnalytics.clear_cache | Clear the object of all local caches of API methods. | src/sagemaker/analytics.py | def clear_cache(self):
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train | HyperparameterTuningJobAnalytics._fetch_dataframe | Return a pandas dataframe with all the training jobs, along with their
hyperparameters, results, and metadata. This also includes a column to indicate
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"""
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# Helpe... | def _fetch_dataframe(self):
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train | HyperparameterTuningJobAnalytics.description | Call ``DescribeHyperParameterTuningJob`` for the hyperparameter tuning job.
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force_refresh (bool): Set to True to fetch the latest data from SageMaker API.
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dict: The Amazon SageMaker response for ``DescribeHyperParameterTuningJob``. | src/sagemaker/analytics.py | def description(self, force_refresh=False):
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train | HyperparameterTuningJobAnalytics.training_job_summaries | A (paginated) list of everything from ``ListTrainingJobsForTuningJob``.
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force_refresh (bool): Set to True to fetch the latest data from SageMaker API.
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train | TrainingJobAnalytics.clear_cache | Clear the object of all local caches of API methods, so
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train | TrainingJobAnalytics._determine_timeinterval | Return a dictionary with two datetime objects, start_time and end_time,
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description = self._sage_client.describe_training_job(TrainingJobName=self.name)
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train | TrainingJobAnalytics._fetch_metric | Fetch all the values of a named metric, and add them to _data | src/sagemaker/analytics.py | def _fetch_metric(self, metric_name):
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request = {
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train | TrainingJobAnalytics._add_single_metric | Store a single metric in the _data dict which can be
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"""Store a single metric in the _data dict which can be
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"""
# note that this method is built this way to make it possible to
# support live-refreshing charts in Bokeh at some point in the futur... | def _add_single_metric(self, timestamp, metric_name, value):
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train | TrainingJobAnalytics._metric_names_for_training_job | Helper method to discover the metrics defined for a training job. | src/sagemaker/analytics.py | def _metric_names_for_training_job(self):
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train | name_from_base | Append a timestamp to the provided string.
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Args:
base (str): String used as prefix to generate the unique name.
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train | base_name_from_image | Extract the base name of the image to use as the 'algorithm name' for the job.
Args:
image (str): Image name.
Returns:
str: Algorithm name, as extracted from the image name. | src/sagemaker/utils.py | def base_name_from_image(image):
"""Extract the base name of the image to use as the 'algorithm name' for the job.
Args:
image (str): Image name.
Returns:
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image (str): Image name.
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train | sagemaker_timestamp | Return a timestamp with millisecond precision. | src/sagemaker/utils.py | def sagemaker_timestamp():
"""Return a timestamp with millisecond precision."""
moment = time.time()
moment_ms = repr(moment).split('.')[1][:3]
return time.strftime("%Y-%m-%d-%H-%M-%S-{}".format(moment_ms), time.gmtime(moment)) | def sagemaker_timestamp():
"""Return a timestamp with millisecond precision."""
moment = time.time()
moment_ms = repr(moment).split('.')[1][:3]
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train | debug | Print the function name and arguments for debugging. | src/sagemaker/utils.py | def debug(func):
"""Print the function name and arguments for debugging."""
@wraps(func)
def wrapper(*args, **kwargs):
print("{} args: {} kwargs: {}".format(func.__name__, args, kwargs))
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"""Print the function name and arguments for debugging."""
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train | to_str | Convert the input to a string, unless it is a unicode string in Python 2.
Unicode strings are supported as native strings in Python 3, but ``str()`` cannot be
invoked on unicode strings in Python 2, so we need to check for that case when
converting user-specified values to strings.
Args:
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"""Convert the input to a string, unless it is a unicode string in Python 2.
Unicode strings are supported as native strings in Python 3, but ``str()`` cannot be
invoked on unicode strings in Python 2, so we need to check for that case when
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"""Convert the input to a string, unless it is a unicode string in Python 2.
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train | extract_name_from_job_arn | Returns the name used in the API given a full ARN for a training job
or hyperparameter tuning job. | src/sagemaker/utils.py | def extract_name_from_job_arn(arn):
"""Returns the name used in the API given a full ARN for a training job
or hyperparameter tuning job.
"""
slash_pos = arn.find('/')
if slash_pos == -1:
raise ValueError("Cannot parse invalid ARN: %s" % arn)
return arn[(slash_pos + 1):] | def extract_name_from_job_arn(arn):
"""Returns the name used in the API given a full ARN for a training job
or hyperparameter tuning job.
"""
slash_pos = arn.find('/')
if slash_pos == -1:
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train | secondary_training_status_message | Returns a string contains last modified time and the secondary training job status message.
Args:
job_description: Returned response from DescribeTrainingJob call
prev_description: Previous job description from DescribeTrainingJob call
Returns:
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"""Returns a string contains last modified time and the secondary training job status message.
Args:
job_description: Returned response from DescribeTrainingJob call
prev_description: Previous job description from Describ... | def secondary_training_status_message(job_description, prev_description):
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job_description: Returned response from DescribeTrainingJob call
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train | download_folder | Download a folder from S3 to a local path
Args:
bucket_name (str): S3 bucket name
prefix (str): S3 prefix within the bucket that will be downloaded. Can be a single file.
target (str): destination path where the downloaded items will be placed
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"""Download a folder from S3 to a local path
Args:
bucket_name (str): S3 bucket name
prefix (str): S3 prefix within the bucket that will be downloaded. Can be a single file.
target (str): destination path where the dow... | def download_folder(bucket_name, prefix, target, sagemaker_session):
"""Download a folder from S3 to a local path
Args:
bucket_name (str): S3 bucket name
prefix (str): S3 prefix within the bucket that will be downloaded. Can be a single file.
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train | create_tar_file | Create a tar file containing all the source_files
Args:
source_files (List[str]): List of file paths that will be contained in the tar file
Returns:
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source_files (List[str]): List of file paths that will be contained in the tar file
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(str): path to created tar file
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(str): path to created tar file
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train | download_file | Download a Single File from S3 into a local path
Args:
bucket_name (str): S3 bucket name
path (str): file path within the bucket
target (str): destination directory for the downloaded file.
sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with ... | src/sagemaker/utils.py | def download_file(bucket_name, path, target, sagemaker_session):
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Args:
bucket_name (str): S3 bucket name
path (str): file path within the bucket
target (str): destination directory for the downloaded file.
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bucket_name (str): S3 bucket name
path (str): file path within the bucket
target (str): destination directory for the downloaded file.
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train | Tensorboard._sync_directories | Sync to_directory with from_directory by copying each file in
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overwritten by files of the same name in from_directory. We need to
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train | Tensorboard.create_tensorboard_process | Create a TensorBoard process.
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tuple: A tuple containing:
int: The port number.
process: The TensorBoard process.
Raises:
OSError: If no ports between 6006 and 6105 are available for starting TensorBoard. | src/sagemaker/tensorflow/estimator.py | def create_tensorboard_process(self):
"""Create a TensorBoard process.
Returns:
tuple: A tuple containing:
int: The port number.
process: The TensorBoard process.
Raises:
OSError: If no ports between 6006 and 6105 are available for starti... | def create_tensorboard_process(self):
"""Create a TensorBoard process.
Returns:
tuple: A tuple containing:
int: The port number.
process: The TensorBoard process.
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train | Tensorboard.run | Run TensorBoard process. | src/sagemaker/tensorflow/estimator.py | def run(self):
"""Run TensorBoard process."""
port, tensorboard_process = self.create_tensorboard_process()
LOGGER.info('TensorBoard 0.1.7 at http://localhost:{}'.format(port))
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"""Run TensorBoard process."""
port, tensorboard_process = self.create_tensorboard_process()
LOGGER.info('TensorBoard 0.1.7 at http://localhost:{}'.format(port))
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"... | a9e724c7d3f5572b68c3903548c792a59d99799a |
train | TensorFlow.fit | Train a model using the input training dataset.
See :func:`~sagemaker.estimator.EstimatorBase.fit` for more details.
Args:
inputs (str or dict or sagemaker.session.s3_input): Information about the training data.
This can be one of three types:
* (str) - the... | src/sagemaker/tensorflow/estimator.py | def fit(self, inputs=None, wait=True, logs=True, job_name=None, run_tensorboard_locally=False):
"""Train a model using the input training dataset.
See :func:`~sagemaker.estimator.EstimatorBase.fit` for more details.
Args:
inputs (str or dict or sagemaker.session.s3_input): Informat... | def fit(self, inputs=None, wait=True, logs=True, job_name=None, run_tensorboard_locally=False):
"""Train a model using the input training dataset.
See :func:`~sagemaker.estimator.EstimatorBase.fit` for more details.
Args:
inputs (str or dict or sagemaker.session.s3_input): Informat... | [
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train | TensorFlow._prepare_init_params_from_job_description | Convert the job description to init params that can be handled by the class constructor
Args:
job_details: the returned job details from a describe_training_job API call.
Returns:
dictionary: The transformed init_params | src/sagemaker/tensorflow/estimator.py | 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: the returned job details from a describe_training_job API call.
Returns:
... | 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: the returned job details from a describe_training_job API call.
Returns:
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