code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def _generate_random_name(sep="-", integer_scale=3, max_length=20):
"""Helper function for generating a random predicate, noun, and integer combination
Args:
sep: String separator for word spacing.
integer_scale: Dictates the maximum scale range for random integer sampling (power of 10).
... | Helper function for generating a random predicate, noun, and integer combination
Args:
sep: String separator for word spacing.
integer_scale: Dictates the maximum scale range for random integer sampling (power of 10).
max_length: Maximum allowable string length.
Returns:
A rand... | _generate_random_name | python | mlflow/mlflow | mlflow/utils/name_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/name_utils.py | Apache-2.0 |
def refresh(self, logger=None):
"""Validates the token or API key configured for accessing the OpenAI resource."""
if self._api_token_env is not None:
return
if self._is_azure_ad:
if not self._azure_ad_token or self._azure_ad_token.expires_on < time.time() + 60:
... | Validates the token or API key configured for accessing the OpenAI resource. | refresh | python | mlflow/mlflow | mlflow/utils/openai_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/openai_utils.py | Apache-2.0 |
def get_oss_host_creds(server_uri=None):
"""
Retrieve the host credentials for the OSS server.
Args:
server_uri (str): The URI of the server.
Returns:
MlflowHostCreds: The host credentials for the OSS server.
"""
parsed_uri = urllib.parse.urlparse(server_uri)
if parsed_uri... |
Retrieve the host credentials for the OSS server.
Args:
server_uri (str): The URI of the server.
Returns:
MlflowHostCreds: The host credentials for the OSS server.
| get_oss_host_creds | python | mlflow/mlflow | mlflow/utils/oss_registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/oss_registry_utils.py | Apache-2.0 |
def _remove_inaccessible_python_path(env):
"""
Remove inaccessible path from PYTHONPATH environment variable.
"""
if python_path := env.get("PYTHONPATH"):
paths = [p for p in python_path.split(":") if os.access(p, os.R_OK)]
env["PYTHONPATH"] = ":".join(paths)
return env |
Remove inaccessible path from PYTHONPATH environment variable.
| _remove_inaccessible_python_path | python | mlflow/mlflow | mlflow/utils/process.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/process.py | Apache-2.0 |
def _exec_cmd(
cmd,
*,
throw_on_error=True,
extra_env=None,
capture_output=True,
synchronous=True,
stream_output=False,
**kwargs,
):
"""A convenience wrapper of `subprocess.Popen` for running a command from a Python script.
Args:
cmd: The command to run, as a string or a... | A convenience wrapper of `subprocess.Popen` for running a command from a Python script.
Args:
cmd: The command to run, as a string or a list of strings.
throw_on_error: If True, raises an Exception if the exit code of the program is nonzero.
extra_env: Extra environment variables to be defi... | _exec_cmd | python | mlflow/mlflow | mlflow/utils/process.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/process.py | Apache-2.0 |
def cache_return_value_per_process(fn):
"""
A decorator which globally caches the return value of the decorated function.
But if current process forked out a new child process, in child process,
old cache values are invalidated.
Restrictions: The decorated function must be called with only position... |
A decorator which globally caches the return value of the decorated function.
But if current process forked out a new child process, in child process,
old cache values are invalidated.
Restrictions: The decorated function must be called with only positional arguments,
and all the argument values m... | cache_return_value_per_process | python | mlflow/mlflow | mlflow/utils/process.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/process.py | Apache-2.0 |
def _mark_int64_fields_for_proto_maps(proto_map, value_field_type):
"""Converts a proto map to JSON, preserving only int64-related fields."""
json_dict = {}
for key, value in proto_map.items():
# The value of a protobuf map can only be a scalar or a message (not a map or repeated
# field).
... | Converts a proto map to JSON, preserving only int64-related fields. | _mark_int64_fields_for_proto_maps | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def _mark_int64_fields(proto_message):
"""Converts a proto message to JSON, preserving only int64-related fields."""
json_dict = {}
for field, value in proto_message.ListFields():
if (
# These three conditions check if this field is a protobuf map.
# See the official implemen... | Converts a proto message to JSON, preserving only int64-related fields. | _mark_int64_fields | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def _merge_json_dicts(from_dict, to_dict):
"""Merges the json elements of from_dict into to_dict. Only works for json dicts
converted from proto messages
"""
for key, value in from_dict.items():
if isinstance(key, int) and str(key) in to_dict:
# When the key (i.e. the proto field nam... | Merges the json elements of from_dict into to_dict. Only works for json dicts
converted from proto messages
| _merge_json_dicts | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def message_to_json(message):
"""Converts a message to JSON, using snake_case for field names."""
# Google's MessageToJson API converts int64 proto fields to JSON strings.
# For more info, see https://github.com/protocolbuffers/protobuf/issues/2954
json_dict_with_int64_as_str = json.loads(
Mess... | Converts a message to JSON, using snake_case for field names. | message_to_json | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def _stringify_all_experiment_ids(x):
"""Converts experiment_id fields which are defined as ints into strings in the given json.
This is necessary for backwards- and forwards-compatibility with MLflow clients/servers
running MLflow 0.9.0 and below, as experiment_id was changed from an int to a string.
T... | Converts experiment_id fields which are defined as ints into strings in the given json.
This is necessary for backwards- and forwards-compatibility with MLflow clients/servers
running MLflow 0.9.0 and below, as experiment_id was changed from an int to a string.
To note, the Python JSON serializer is happy t... | _stringify_all_experiment_ids | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def parse_dict(js_dict, message):
"""Parses a JSON dictionary into a message proto, ignoring unknown fields in the JSON."""
_stringify_all_experiment_ids(js_dict)
ParseDict(js_dict=js_dict, message=message, ignore_unknown_fields=True) | Parses a JSON dictionary into a message proto, ignoring unknown fields in the JSON. | parse_dict | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def set_pb_value(proto: Value, value: Any):
"""
DO NOT USE THIS FUNCTION. Preserved for backwards compatibility.
Set a value to the google.protobuf.Value object.
"""
if isinstance(value, dict):
for key, val in value.items():
set_pb_value(proto.struct_value.fields[key], val)
... |
DO NOT USE THIS FUNCTION. Preserved for backwards compatibility.
Set a value to the google.protobuf.Value object.
| set_pb_value | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def parse_pb_value(proto: Value) -> Optional[Any]:
"""
DO NOT USE THIS FUNCTION. Preserved for backwards compatibility.
Extract a value from the google.protobuf.Value object.
"""
if proto.HasField("struct_value"):
return {key: parse_pb_value(val) for key, val in proto.struct_value.fields.it... |
DO NOT USE THIS FUNCTION. Preserved for backwards compatibility.
Extract a value from the google.protobuf.Value object.
| parse_pb_value | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def dataframe_from_parsed_json(decoded_input, pandas_orient, schema=None):
"""Convert parsed json into pandas.DataFrame. If schema is provided this methods will attempt to
cast data types according to the schema. This include base64 decoding for binary columns.
Args:
decoded_input: Parsed json - ei... | Convert parsed json into pandas.DataFrame. If schema is provided this methods will attempt to
cast data types according to the schema. This include base64 decoding for binary columns.
Args:
decoded_input: Parsed json - either a list or a dictionary.
pandas_orient: pandas data frame convention u... | dataframe_from_parsed_json | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def dataframe_from_raw_json(path_or_str, schema=None, pandas_orient: str = "split"):
"""Parse raw json into a pandas.Dataframe.
If schema is provided this methods will attempt to cast data types according to the schema. This
include base64 decoding for binary columns.
Args:
path_or_str: Path t... | Parse raw json into a pandas.Dataframe.
If schema is provided this methods will attempt to cast data types according to the schema. This
include base64 decoding for binary columns.
Args:
path_or_str: Path to a json file or a json string.
schema: MLflow schema used when parsing the data.
... | dataframe_from_raw_json | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def _get_jsonable_obj(data, pandas_orient="records"):
"""Attempt to make the data json-able via standard library.
Look for some commonly used types that are not jsonable and convert them into json-able ones.
Unknown data types are returned as is.
Args:
data: Data to be converted, works with pa... | Attempt to make the data json-able via standard library.
Look for some commonly used types that are not jsonable and convert them into json-able ones.
Unknown data types are returned as is.
Args:
data: Data to be converted, works with pandas and numpy, rest will be returned as is.
pandas_o... | _get_jsonable_obj | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def convert_data_type(data, spec):
"""
Convert input data to the type specified in the spec.
Args:
data: Input data.
spec: ColSpec or TensorSpec.
"""
import numpy as np
from mlflow.models.utils import _enforce_array, _enforce_map, _enforce_object
from mlflow.types.schema im... |
Convert input data to the type specified in the spec.
Args:
data: Input data.
spec: ColSpec or TensorSpec.
| convert_data_type | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def parse_inputs_data(inputs_data_or_path, schema=None):
"""
Helper function to cast inputs_data based on the schema.
Inputs data must be able to pass to the model for pyfunc predict directly.
Args:
inputs_data_or_path: A json-serializable object or path to a json file
schema: data sche... |
Helper function to cast inputs_data based on the schema.
Inputs data must be able to pass to the model for pyfunc predict directly.
Args:
inputs_data_or_path: A json-serializable object or path to a json file
schema: data schema to cast to. Be of type `mlflow.types.Schema`.
| parse_inputs_data | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def parse_tf_serving_input(inp_dict, schema=None):
"""
Args:
inp_dict: A dict deserialized from a JSON string formatted as described in TF's
serving API doc
(https://www.tensorflow.org/tfx/serving/api_rest#request_format_2)
schema: MLflow schema used when parsing the data... |
Args:
inp_dict: A dict deserialized from a JSON string formatted as described in TF's
serving API doc
(https://www.tensorflow.org/tfx/serving/api_rest#request_format_2)
schema: MLflow schema used when parsing the data.
| parse_tf_serving_input | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def dump_input_data(data, inputs_key="inputs", params: Optional[dict[str, Any]] = None):
"""
Args:
data: Input data.
inputs_key: Key to represent data in the request payload.
params: Additional parameters to pass to the model for inference.
"""
import numpy as np
import panda... |
Args:
data: Input data.
inputs_key: Key to represent data in the request payload.
params: Additional parameters to pass to the model for inference.
| dump_input_data | python | mlflow/mlflow | mlflow/utils/proto_json_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/proto_json_utils.py | Apache-2.0 |
def model_validator(mode: str, skip_on_failure: bool = False):
"""A wrapper for Pydantic model validator that is compatible with Pydantic v1 and v2.
Note that the `skip_on_failure` argument is only available in Pydantic v1.
"""
def decorator(func: Callable) -> Callable:
if IS_PYDANTIC_V2_OR_NEW... | A wrapper for Pydantic model validator that is compatible with Pydantic v1 and v2.
Note that the `skip_on_failure` argument is only available in Pydantic v1.
| model_validator | python | mlflow/mlflow | mlflow/utils/pydantic_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/pydantic_utils.py | Apache-2.0 |
def model_dump_compat(pydantic_model: BaseModel, **kwargs: Any) -> dict[str, Any]:
"""
Dump the Pydantic model to dictionary, in a compatible way for Pydantic v1 and v2.
Args:
pydantic_model: The Pydantic model to dump.
kwargs: Additional arguments to pass to the dump method.
"""
re... |
Dump the Pydantic model to dictionary, in a compatible way for Pydantic v1 and v2.
Args:
pydantic_model: The Pydantic model to dump.
kwargs: Additional arguments to pass to the dump method.
| model_dump_compat | python | mlflow/mlflow | mlflow/utils/pydantic_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/pydantic_utils.py | Apache-2.0 |
def augmented_raise_for_status(response):
"""Wrap the standard `requests.response.raise_for_status()` method and return reason"""
try:
response.raise_for_status()
except HTTPError as e:
if response.text:
raise HTTPError(
f"{e}. Response text: {response.text}", req... | Wrap the standard `requests.response.raise_for_status()` method and return reason | augmented_raise_for_status | python | mlflow/mlflow | mlflow/utils/request_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/request_utils.py | Apache-2.0 |
def _cached_get_request_session(
max_retries,
backoff_factor,
backoff_jitter,
retry_codes,
raise_on_status,
# To create a new Session object for each process, we use the process id as the cache key.
# This is to avoid sharing the same Session object across processes, which can lead to issues... |
This function should not be called directly. Instead, use `_get_request_session` below.
| _cached_get_request_session | python | mlflow/mlflow | mlflow/utils/request_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/request_utils.py | Apache-2.0 |
def _get_request_session(
max_retries,
backoff_factor,
backoff_jitter,
retry_codes,
raise_on_status,
respect_retry_after_header,
):
"""Returns a `Requests.Session` object for making an HTTP request.
Args:
max_retries: Maximum total number of retries.
backoff_factor: A ti... | Returns a `Requests.Session` object for making an HTTP request.
Args:
max_retries: Maximum total number of retries.
backoff_factor: A time factor for exponential backoff. e.g. value 5 means the HTTP
request will be retried with interval 5, 10, 20... seconds. A value of 0 turns off the
... | _get_request_session | python | mlflow/mlflow | mlflow/utils/request_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/request_utils.py | Apache-2.0 |
def _get_http_response_with_retries( # noqa: D417
method,
url,
max_retries,
backoff_factor,
backoff_jitter,
retry_codes,
raise_on_status=True,
allow_redirects=None,
respect_retry_after_header=True,
**kwargs,
):
"""Performs an HTTP request using Python's `requests` module wit... | Performs an HTTP request using Python's `requests` module with an automatic retry policy.
Args:
method: A string indicating the method to use, e.g. "GET", "POST", "PUT".
url: The target URL address for the HTTP request.
max_retries: Maximum total number of retries.
backoff_factor: A... | _get_http_response_with_retries | python | mlflow/mlflow | mlflow/utils/request_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/request_utils.py | Apache-2.0 |
def cloud_storage_http_request(
method,
url,
max_retries=5,
backoff_factor=2,
backoff_jitter=1.0,
retry_codes=_TRANSIENT_FAILURE_RESPONSE_CODES,
timeout=None,
**kwargs,
):
"""Performs an HTTP PUT/GET/PATCH request using Python's `requests` module with automatic retry.
Args:
... | Performs an HTTP PUT/GET/PATCH request using Python's `requests` module with automatic retry.
Args:
method: string of 'PUT' or 'GET' or 'PATCH', specify to do http PUT or GET or PATCH.
url: the target URL address for the HTTP request.
max_retries: maximum number of retries before throwing a... | cloud_storage_http_request | python | mlflow/mlflow | mlflow/utils/request_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/request_utils.py | Apache-2.0 |
def _parse_requirements(requirements, is_constraint, base_dir=None):
"""A simplified version of `pip._internal.req.parse_requirements` which performs the following
operations on the given requirements file and yields the parsed requirements.
- Remove comments and blank lines
- Join continued lines
... | A simplified version of `pip._internal.req.parse_requirements` which performs the following
operations on the given requirements file and yields the parsed requirements.
- Remove comments and blank lines
- Join continued lines
- Resolve requirements file references (e.g. '-r requirements.txt')
- Re... | _parse_requirements | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _iter_requires(name: str):
"""
Iterates over the requirements of the specified package.
Args:
name: The name of the package.
Yields:
The names of the required packages.
"""
try:
reqs = importlib.metadata.requires(name)
except importlib.metadata.PackageNotFoundEr... |
Iterates over the requirements of the specified package.
Args:
name: The name of the package.
Yields:
The names of the required packages.
| _iter_requires | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _get_requires_recursive(pkg_name, seen_before=None):
"""
Recursively yields both direct and transitive dependencies of the specified
package.
"""
norm_pkg_name = _normalize_package_name(pkg_name)
seen_before = seen_before or {norm_pkg_name}
for req in _get_requires(pkg_name):
# P... |
Recursively yields both direct and transitive dependencies of the specified
package.
| _get_requires_recursive | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _prune_packages(packages):
"""
Prunes packages required by other packages. For example, `["scikit-learn", "numpy"]` is pruned
to `["scikit-learn"]`.
"""
packages = set(packages)
requires = set(_flatten(map(_get_requires_recursive, packages)))
# LlamaIndex have one root "llama-index" pac... |
Prunes packages required by other packages. For example, `["scikit-learn", "numpy"]` is pruned
to `["scikit-learn"]`.
| _prune_packages | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _run_command(cmd, timeout_seconds, env=None):
"""
Runs the specified command. If it exits with non-zero status, `MlflowException` is raised.
"""
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env)
timer = Timer(timeout_seconds, proc.kill)
try:
timer.... |
Runs the specified command. If it exits with non-zero status, `MlflowException` is raised.
| _run_command | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _get_installed_version(package: str, module: Optional[str] = None) -> str:
"""
Obtains the installed package version using `importlib_metadata.version`. If it fails, use
`__import__(module or package).__version__`.
"""
if package == "mlflow":
# `importlib.metadata.version` may return an ... |
Obtains the installed package version using `importlib_metadata.version`. If it fails, use
`__import__(module or package).__version__`.
| _get_installed_version | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _capture_imported_modules(model_uri, flavor, record_full_module=False, extra_env_vars=None):
"""Runs `_capture_modules.py` in a subprocess and captures modules imported during the model
loading procedure.
If flavor is `transformers`, `_capture_transformers_modules.py` is run instead.
Args:
... | Runs `_capture_modules.py` in a subprocess and captures modules imported during the model
loading procedure.
If flavor is `transformers`, `_capture_transformers_modules.py` is run instead.
Args:
model_uri: The URI of the model.
flavor: The flavor name of the model.
record_full_modul... | _capture_imported_modules | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _infer_requirements(model_uri, flavor, raise_on_error=False, extra_env_vars=None):
"""Infers the pip requirements of the specified model by creating a subprocess and loading
the model in it to determine which packages are imported.
Args:
model_uri: The URI of the model.
flavor: The flav... | Infers the pip requirements of the specified model by creating a subprocess and loading
the model in it to determine which packages are imported.
Args:
model_uri: The URI of the model.
flavor: The flavor name of the model.
raise_on_error: If True, raise an exception if an unrecognized p... | _infer_requirements | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _get_local_version_label(version):
"""Extracts a local version label from `version`.
Args:
version: A version string.
"""
try:
return Version(version).local
except InvalidVersion:
return None | Extracts a local version label from `version`.
Args:
version: A version string.
| _get_local_version_label | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _strip_local_version_label(version):
"""Strips a local version label in `version`.
Local version identifiers:
https://www.python.org/dev/peps/pep-0440/#local-version-identifiers
Args:
version: A version string to strip.
"""
class IgnoreLocal(Version):
@property
def... | Strips a local version label in `version`.
Local version identifiers:
https://www.python.org/dev/peps/pep-0440/#local-version-identifiers
Args:
version: A version string to strip.
| _strip_local_version_label | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _get_pinned_requirement(req_str, version=None, module=None):
"""Returns a string representing a pinned pip requirement to install the specified package and
version (e.g. 'mlflow==1.2.3').
Args:
req_str: The package requirement string (e.g. "mlflow" or "mlflow[gateway]").
version: The ve... | Returns a string representing a pinned pip requirement to install the specified package and
version (e.g. 'mlflow==1.2.3').
Args:
req_str: The package requirement string (e.g. "mlflow" or "mlflow[gateway]").
version: The version of the package. If None, defaults to the installed version.
... | _get_pinned_requirement | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def _check_requirement_satisfied(requirement_str):
"""
Checks whether the current python environment satisfies the given requirement if it is parsable
as a package name and a set of version specifiers, and returns a `_MismatchedPackageInfo`
object containing the mismatched package name, installed versio... |
Checks whether the current python environment satisfies the given requirement if it is parsable
as a package name and a set of version specifiers, and returns a `_MismatchedPackageInfo`
object containing the mismatched package name, installed version, and requirement if the
requirement is not satisfied... | _check_requirement_satisfied | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def warn_dependency_requirement_mismatches(model_requirements: list[str]):
"""
Inspects the model's dependencies and prints a warning if the current Python environment
doesn't satisfy them.
"""
# Suppress databricks-feature-lookup warning for feature store cases
# Suppress databricks-chains, dat... |
Inspects the model's dependencies and prints a warning if the current Python environment
doesn't satisfy them.
| warn_dependency_requirement_mismatches | python | mlflow/mlflow | mlflow/utils/requirements_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/requirements_utils.py | Apache-2.0 |
def http_request_safe(host_creds, endpoint, method, **kwargs):
"""
Wrapper around ``http_request`` that also verifies that the request succeeds with code 200.
"""
response = http_request(host_creds=host_creds, endpoint=endpoint, method=method, **kwargs)
return verify_rest_response(response, endpoint... |
Wrapper around ``http_request`` that also verifies that the request succeeds with code 200.
| http_request_safe | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def verify_rest_response(response, endpoint):
"""Verify the return code and format, raise exception if the request was not successful."""
# Handle Armeria-specific response case where response text is "200 OK"
if response.status_code == 200 and response.text.strip() == _ARMERIA_OK:
response._content... | Verify the return code and format, raise exception if the request was not successful. | verify_rest_response | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def _retry_databricks_sdk_call_with_exponential_backoff(
*,
call_func: Callable,
retry_codes: list[int],
retry_timeout_seconds: int,
backoff_factor: int,
backoff_jitter: float,
max_retries: int,
):
"""
Retry a Databricks SDK call with exponential backoff until timeout or max retries ... |
Retry a Databricks SDK call with exponential backoff until timeout or max retries reached.
Args:
call_func: Function to call that may raise DatabricksError
retry_codes: Set of HTTP status codes that should trigger retries
retry_timeout_seconds: Maximum time to spend retrying in seconds... | _retry_databricks_sdk_call_with_exponential_backoff | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def extract_api_info_for_service(service, path_prefix):
"""Return a dictionary mapping each API method to a tuple (path, HTTP method)"""
service_methods = service.DESCRIPTOR.methods
res = {}
for service_method in service_methods:
endpoints = service_method.GetOptions().Extensions[databricks_pb2.... | Return a dictionary mapping each API method to a tuple (path, HTTP method) | extract_api_info_for_service | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def extract_all_api_info_for_service(service, path_prefix):
"""Return a dictionary mapping each API method to a list of tuples [(path, HTTP method)]"""
service_methods = service.DESCRIPTOR.methods
res = {}
for service_method in service_methods:
endpoints = service_method.GetOptions().Extensions[... | Return a dictionary mapping each API method to a list of tuples [(path, HTTP method)] | extract_all_api_info_for_service | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def get_single_trace_endpoint(request_id, use_v3=False):
"""
Get the endpoint for a single trace.
For Databricks tracking URIs, use the V3 API.
For all other tracking URIs, use the V2 API.
Args:
request_id: The trace ID.
use_v3: Whether to use the V3 API. If True, use the V3 API. If... |
Get the endpoint for a single trace.
For Databricks tracking URIs, use the V3 API.
For all other tracking URIs, use the V2 API.
Args:
request_id: The trace ID.
use_v3: Whether to use the V3 API. If True, use the V3 API. If False, use the V2 API.
| get_single_trace_endpoint | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def get_create_assessment_endpoint(trace_id: str, is_databricks=False):
"""
Get the endpoint for creating an assessment.
Args:
trace_id: The trace ID.
is_databricks: Whether the tracking URI is a Databricks URI.
"""
if is_databricks:
return f"{_V3_TRACE_REST_API_PATH_PREFIX}... |
Get the endpoint for creating an assessment.
Args:
trace_id: The trace ID.
is_databricks: Whether the tracking URI is a Databricks URI.
| get_create_assessment_endpoint | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def get_single_assessment_endpoint(trace_id: str, assessment_id: str, is_databricks=False):
"""
Get the endpoint for a single assessment.
Args:
trace_id: The trace ID.
assessment_id: The assessment ID.
is_databricks: Whether the tracking URI is a Databricks URI.
"""
if is_da... |
Get the endpoint for a single assessment.
Args:
trace_id: The trace ID.
assessment_id: The assessment ID.
is_databricks: Whether the tracking URI is a Databricks URI.
| get_single_assessment_endpoint | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def get_search_traces_v3_endpoint(is_databricks=False):
"""
Return the endpoint for the SearchTraces API.
Args:
is_databricks: Whether the tracking URI is a Databricks URI. If True,
returns the v3 endpoint, otherwise returns the v2 endpoint.
"""
if is_databricks:
... |
Return the endpoint for the SearchTraces API.
Args:
is_databricks: Whether the tracking URI is a Databricks URI. If True,
returns the v3 endpoint, otherwise returns the v2 endpoint.
| get_search_traces_v3_endpoint | python | mlflow/mlflow | mlflow/utils/rest_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/rest_utils.py | Apache-2.0 |
def is_numeric(self) -> bool:
"""
Does this entity represent a numeric column?
"""
return self.type == EntityType.METRIC or (
self.type == EntityType.ATTRIBUTE and SqlLoggedModel.is_numeric(self.key)
) |
Does this entity represent a numeric column?
| is_numeric | python | mlflow/mlflow | mlflow/utils/search_logged_model_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_logged_model_utils.py | Apache-2.0 |
def _join_in_comparison_tokens(tokens, search_traces=False):
"""
Find a sequence of tokens that matches the pattern of an IN comparison or a NOT IN comparison,
join the tokens into a single Comparison token. Otherwise, return the original list of tokens.
"""
if Version(sqlparse.__version__) < Versio... |
Find a sequence of tokens that matches the pattern of an IN comparison or a NOT IN comparison,
join the tokens into a single Comparison token. Otherwise, return the original list of tokens.
| _join_in_comparison_tokens | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def _trim_backticks(cls, entity_type):
"""Remove backticks from identifier like `param`, if they exist."""
if cls._is_quoted(entity_type, "`"):
return cls._trim_ends(entity_type)
return entity_type | Remove backticks from identifier like `param`, if they exist. | _trim_backticks | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def _strip_quotes(cls, value, expect_quoted_value=False):
"""
Remove quotes for input string.
Values of type strings are expected to have quotes.
Keys containing special characters are also expected to be enclose in quotes.
"""
if cls._is_quoted(value, "'") or cls._is_quo... |
Remove quotes for input string.
Values of type strings are expected to have quotes.
Keys containing special characters are also expected to be enclose in quotes.
| _strip_quotes | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def filter(cls, runs, filter_string):
"""Filters a set of runs based on a search filter string."""
if not filter_string:
return runs
parsed = cls.parse_search_filter(filter_string)
def run_matches(run):
return all(cls._does_run_match_clause(run, s) for s in parse... | Filters a set of runs based on a search filter string. | filter | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def _get_value_for_sort(cls, run, key_type, key, ascending):
"""Returns a tuple suitable to be used as a sort key for runs."""
sort_value = None
key = SearchUtils.translate_key_alias(key)
if key_type == cls._METRIC_IDENTIFIER:
sort_value = run.data.metrics.get(key)
el... | Returns a tuple suitable to be used as a sort key for runs. | _get_value_for_sort | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def _get_model_value_for_sort(cls, model, key_type, key, ascending):
"""Returns a tuple suitable to be used as a sort key for models."""
sort_value = None
key = SearchUtils.translate_key_alias(key)
if key_type == cls._METRIC_IDENTIFIER:
matching_metrics = [metric for metric i... | Returns a tuple suitable to be used as a sort key for models. | _get_model_value_for_sort | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def sort(cls, runs, order_by_list):
"""Sorts a set of runs based on their natural ordering and an overriding set of order_bys.
Runs are naturally ordered first by start time descending, then by run id for tie-breaking.
"""
runs = sorted(runs, key=lambda run: (-run.info.start_time, run.in... | Sorts a set of runs based on their natural ordering and an overriding set of order_bys.
Runs are naturally ordered first by start time descending, then by run id for tie-breaking.
| sort | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def _check_valid_identifier_list(cls, tup: tuple[Any, ...]) -> None:
"""
Validate that `tup` is a non-empty tuple of strings.
"""
if len(tup) == 0:
raise MlflowException(
"While parsing a list in the query,"
" expected a non-empty list of strin... |
Validate that `tup` is a non-empty tuple of strings.
| _check_valid_identifier_list | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def filter(cls, registered_models, filter_string):
"""Filters a set of registered models based on a search filter string."""
if not filter_string:
return registered_models
parsed = cls.parse_search_filter(filter_string)
def registered_model_matches(model):
return... | Filters a set of registered models based on a search filter string. | filter | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def filter(cls, model_versions, filter_string):
"""Filters a set of model versions based on a search filter string."""
model_versions = [mv for mv in model_versions if mv.current_stage != STAGE_DELETED_INTERNAL]
if not filter_string:
return model_versions
parsed = cls.parse_s... | Filters a set of model versions based on a search filter string. | filter | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def filter(cls, traces, filter_string):
"""Filters a set of traces based on a search filter string."""
if not filter_string:
return traces
parsed = cls.parse_search_filter_for_search_traces(filter_string)
def trace_matches(trace):
return all(cls._does_trace_match... | Filters a set of traces based on a search filter string. | filter | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def _replace_key_to_tag_or_metadata(cls, parsed: dict[str, Any]):
"""
Replace search key to tag or metadata key if it is in the mapping.
"""
key = parsed.get("key").lower()
if key in cls.SEARCH_KEY_TO_TAG:
parsed["type"] = cls._TAG_IDENTIFIER
parsed["key"]... |
Replace search key to tag or metadata key if it is in the mapping.
| _replace_key_to_tag_or_metadata | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def validate_list_supported(cls, key: str) -> None:
"""
Override to allow logged model attributes to be used with IN/NOT IN.
""" |
Override to allow logged model attributes to be used with IN/NOT IN.
| validate_list_supported | python | mlflow/mlflow | mlflow/utils/search_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/search_utils.py | Apache-2.0 |
def _create_table(
rows: list[list[str]], headers: list[str], column_sep: str = " " * 2, min_column_width: int = 4
) -> str:
"""
Creates a table from a list of rows and headers.
Example
=======
>>> print(_create_table([["a", "b", "c"], ["d", "e", "f"]], ["x", "y", "z"]))
x y z
-... |
Creates a table from a list of rows and headers.
Example
=======
>>> print(_create_table([["a", "b", "c"], ["d", "e", "f"]], ["x", "y", "z"]))
x y z
---- ---- ----
a b c
d e f
| _create_table | python | mlflow/mlflow | mlflow/utils/string_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/string_utils.py | Apache-2.0 |
def mslex_quote(s, for_cmd=True):
"""
Quote a string for use as a command line argument in DOS or Windows.
On windows, before a command line argument becomes a char* in a
program's argv, it must be parsed by both cmd.exe, and by
CommandLineToArgvW.
If for_cmd is true, then this will quote the ... |
Quote a string for use as a command line argument in DOS or Windows.
On windows, before a command line argument becomes a char* in a
program's argv, it must be parsed by both cmd.exe, and by
CommandLineToArgvW.
If for_cmd is true, then this will quote the string so it will
be parsed correctly... | mslex_quote | python | mlflow/mlflow | mlflow/utils/string_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/string_utils.py | Apache-2.0 |
def get(self):
"""
Get the thread-local variable value.
If the thread-local variable is not set, return the provided `init_value` value.
If `get` is called in a forked subprocess and `reset_in_subprocess` is True,
return the provided `init_value` value
"""
if hasa... |
Get the thread-local variable value.
If the thread-local variable is not set, return the provided `init_value` value.
If `get` is called in a forked subprocess and `reset_in_subprocess` is True,
return the provided `init_value` value
| get | python | mlflow/mlflow | mlflow/utils/thread_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/thread_utils.py | Apache-2.0 |
def set(self, value):
"""
Set a value for the thread-local variable.
"""
self.thread_local.value = (value, os.getpid())
self.__global_thread_values[threading.get_ident()] = value |
Set a value for the thread-local variable.
| set | python | mlflow/mlflow | mlflow/utils/thread_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/thread_utils.py | Apache-2.0 |
def run_with_timeout(seconds):
"""
Context manager to runs a block of code with a timeout. If the block of code takes longer
than `seconds` to execute, a `TimeoutError` is raised.
NB: This function uses Unix signals to implement the timeout, so it is not thread-safe.
Also it does not work on non-Uni... |
Context manager to runs a block of code with a timeout. If the block of code takes longer
than `seconds` to execute, a `TimeoutError` is raised.
NB: This function uses Unix signals to implement the timeout, so it is not thread-safe.
Also it does not work on non-Unix platforms such as Windows.
E.g.... | run_with_timeout | python | mlflow/mlflow | mlflow/utils/timeout.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/timeout.py | Apache-2.0 |
def is_local_uri(uri, is_tracking_or_registry_uri=True):
"""Returns true if the specified URI is a local file path (/foo or file:/foo).
Args:
uri: The URI.
is_tracking_or_registry_uri: Whether or not the specified URI is an MLflow Tracking or
MLflow Model Registry URI. Examples of o... | Returns true if the specified URI is a local file path (/foo or file:/foo).
Args:
uri: The URI.
is_tracking_or_registry_uri: Whether or not the specified URI is an MLflow Tracking or
MLflow Model Registry URI. Examples of other URIs are MLflow artifact URIs,
filesystem paths... | is_local_uri | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def is_databricks_uri(uri):
"""
Databricks URIs look like 'databricks' (default profile) or 'databricks://profile'
or 'databricks://secret_scope:secret_key_prefix'.
"""
scheme = urllib.parse.urlparse(uri).scheme
return scheme == "databricks" or uri == "databricks" |
Databricks URIs look like 'databricks' (default profile) or 'databricks://profile'
or 'databricks://secret_scope:secret_key_prefix'.
| is_databricks_uri | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def is_fuse_or_uc_volumes_uri(uri):
"""
Validates whether a provided URI is directed to a FUSE mount point or a UC volumes mount point.
Multiple directory paths are collapsed into a single designator for root path validation.
For example, "////Volumes/" will resolve to "/Volumes/" for validation purpose... |
Validates whether a provided URI is directed to a FUSE mount point or a UC volumes mount point.
Multiple directory paths are collapsed into a single designator for root path validation.
For example, "////Volumes/" will resolve to "/Volumes/" for validation purposes.
| is_fuse_or_uc_volumes_uri | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def construct_db_uc_uri_from_profile(profile):
"""
Construct a databricks-uc URI from a profile.
Args:
profile: The profile name, optionally with key_prefix (e.g., "profile" or "scope:key")
Returns:
A databricks-uc URI string, or the scheme alone if no profile is provided
"""
i... |
Construct a databricks-uc URI from a profile.
Args:
profile: The profile name, optionally with key_prefix (e.g., "profile" or "scope:key")
Returns:
A databricks-uc URI string, or the scheme alone if no profile is provided
| construct_db_uc_uri_from_profile | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def get_db_info_from_uri(uri):
"""
Get the Databricks profile specified by the tracking URI (if any), otherwise
returns None.
"""
parsed_uri = urllib.parse.urlparse(uri)
if parsed_uri.scheme == "databricks" or parsed_uri.scheme == _DATABRICKS_UNITY_CATALOG_SCHEME:
# netloc should not be ... |
Get the Databricks profile specified by the tracking URI (if any), otherwise
returns None.
| get_db_info_from_uri | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def get_databricks_profile_uri_from_artifact_uri(uri, result_scheme="databricks"):
"""
Retrieves the netloc portion of the URI as a ``databricks://`` or `databricks-uc://` URI,
if it is a proper Databricks profile specification, e.g.
``profile@databricks`` or ``secret_scope:key_prefix@databricks``.
... |
Retrieves the netloc portion of the URI as a ``databricks://`` or `databricks-uc://` URI,
if it is a proper Databricks profile specification, e.g.
``profile@databricks`` or ``secret_scope:key_prefix@databricks``.
| get_databricks_profile_uri_from_artifact_uri | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def remove_databricks_profile_info_from_artifact_uri(artifact_uri):
"""
Only removes the netloc portion of the URI if it is a Databricks
profile specification, e.g.
``profile@databricks`` or ``secret_scope:key_prefix@databricks``.
"""
parsed = urllib.parse.urlparse(artifact_uri)
if not parse... |
Only removes the netloc portion of the URI if it is a Databricks
profile specification, e.g.
``profile@databricks`` or ``secret_scope:key_prefix@databricks``.
| remove_databricks_profile_info_from_artifact_uri | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def add_databricks_profile_info_to_artifact_uri(artifact_uri, databricks_profile_uri):
"""
Throws an exception if ``databricks_profile_uri`` is not valid.
"""
if not databricks_profile_uri or not is_databricks_uri(databricks_profile_uri):
return artifact_uri
artifact_uri_parsed = urllib.pars... |
Throws an exception if ``databricks_profile_uri`` is not valid.
| add_databricks_profile_info_to_artifact_uri | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def extract_db_type_from_uri(db_uri):
"""
Parse the specified DB URI to extract the database type. Confirm the database type is
supported. If a driver is specified, confirm it passes a plausible regex.
"""
scheme = urllib.parse.urlparse(db_uri).scheme
scheme_plus_count = scheme.count("+")
i... |
Parse the specified DB URI to extract the database type. Confirm the database type is
supported. If a driver is specified, confirm it passes a plausible regex.
| extract_db_type_from_uri | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def append_to_uri_path(uri, *paths):
"""Appends the specified POSIX `paths` to the path component of the specified `uri`.
Args:
uri: The input URI, represented as a string.
paths: The POSIX paths to append to the specified `uri`'s path component.
Returns:
A new URI with a path comp... | Appends the specified POSIX `paths` to the path component of the specified `uri`.
Args:
uri: The input URI, represented as a string.
paths: The POSIX paths to append to the specified `uri`'s path component.
Returns:
A new URI with a path component consisting of the specified `paths` ap... | append_to_uri_path | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def append_to_uri_query_params(uri, *query_params: tuple[str, Any]) -> str:
"""Appends the specified query parameters to an existing URI.
Args:
uri: The URI to which to append query parameters.
query_params: Query parameters to append. Each parameter should
be a 2-element tuple. For... | Appends the specified query parameters to an existing URI.
Args:
uri: The URI to which to append query parameters.
query_params: Query parameters to append. Each parameter should
be a 2-element tuple. For example, ``("key", "value")``.
| append_to_uri_query_params | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def _join_posixpaths_and_append_absolute_suffixes(prefix_path, suffix_path):
"""
Joins the POSIX path `prefix_path` with the POSIX path `suffix_path`. Unlike posixpath.join(),
if `suffix_path` is an absolute path, it is appended to prefix_path.
>>> result1 = _join_posixpaths_and_append_absolute_suffixe... |
Joins the POSIX path `prefix_path` with the POSIX path `suffix_path`. Unlike posixpath.join(),
if `suffix_path` is an absolute path, it is appended to prefix_path.
>>> result1 = _join_posixpaths_and_append_absolute_suffixes("relpath1", "relpath2")
>>> assert result1 == "relpath1/relpath2"
>>> resu... | _join_posixpaths_and_append_absolute_suffixes | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def dbfs_hdfs_uri_to_fuse_path(dbfs_uri):
"""Converts the provided DBFS URI into a DBFS FUSE path
Args:
dbfs_uri: A DBFS URI like "dbfs:/my-directory". Can also be a scheme-less URI like
"/my-directory" if running in an environment where the default HDFS filesystem
is "dbfs:/" (... | Converts the provided DBFS URI into a DBFS FUSE path
Args:
dbfs_uri: A DBFS URI like "dbfs:/my-directory". Can also be a scheme-less URI like
"/my-directory" if running in an environment where the default HDFS filesystem
is "dbfs:/" (e.g. Databricks)
Returns:
A DBFS FUS... | dbfs_hdfs_uri_to_fuse_path | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def resolve_uri_if_local(local_uri):
"""
if `local_uri` is passed in as a relative local path, this function
resolves it to absolute path relative to current working directory.
Args:
local_uri: Relative or absolute path or local file uri
Returns:
a fully-formed absolute uri path or... |
if `local_uri` is passed in as a relative local path, this function
resolves it to absolute path relative to current working directory.
Args:
local_uri: Relative or absolute path or local file uri
Returns:
a fully-formed absolute uri path or an absolute filesystem path
| resolve_uri_if_local | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def validate_path_is_safe(path):
"""
Validates that the specified path is safe to join with a trusted prefix. This is a security
measure to prevent path traversal attacks.
A valid path should:
not contain separators other than '/'
not contain .. to navigate to parent dir in path
... |
Validates that the specified path is safe to join with a trusted prefix. This is a security
measure to prevent path traversal attacks.
A valid path should:
not contain separators other than '/'
not contain .. to navigate to parent dir in path
not be an absolute path
| validate_path_is_safe | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def strip_scheme(uri: str) -> str:
"""
Strips the scheme from the specified URI.
Example:
>>> strip_scheme("http://example.com")
'//example.com'
"""
parsed = urllib.parse.urlparse(uri)
# `_replace` looks like a private method, but it's actually part of the public API:
# https://doc... |
Strips the scheme from the specified URI.
Example:
>>> strip_scheme("http://example.com")
'//example.com'
| strip_scheme | python | mlflow/mlflow | mlflow/utils/uri.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/uri.py | Apache-2.0 |
def invalid_value(path, value, message=None):
"""
Compose a standardized error message for invalid parameter values.
"""
formattedValue = json.dumps(value, sort_keys=True, separators=(",", ":"))
if message:
return f"Invalid value {formattedValue} for parameter '{path}' supplied: {message}"
... |
Compose a standardized error message for invalid parameter values.
| invalid_value | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_metric_name(name, path="name"):
"""Check that `name` is a valid metric name and raise an exception if it isn't."""
if name is None:
raise MlflowException(
invalid_value(path, name, f"Metric name cannot be None. {_MISSING_KEY_NAME_MESSAGE}"),
error_code=INVALID_PARAM... | Check that `name` is a valid metric name and raise an exception if it isn't. | _validate_metric_name | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _is_numeric(value):
"""
Returns True if the passed-in value is numeric.
"""
# Note that `isinstance(bool_value, numbers.Number)` returns `True` because `bool` is a
# subclass of `int`.
return not isinstance(value, bool) and isinstance(value, numbers.Number) |
Returns True if the passed-in value is numeric.
| _is_numeric | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_metric(key, value, timestamp, step, path=""):
"""
Check that a metric with the specified key, value, timestamp, and step is valid and raise an
exception if it isn't.
"""
_validate_metric_name(key, append_to_json_path(path, "name"))
# If invocated via log_metric, no prior validatio... |
Check that a metric with the specified key, value, timestamp, and step is valid and raise an
exception if it isn't.
| _validate_metric | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_param(key, value, path=""):
"""
Check that a param with the specified key & value is valid and raise an exception if it
isn't.
"""
_validate_param_name(key, append_to_json_path(path, "key"))
return Param(
_validate_length_limit("Param key", MAX_ENTITY_KEY_LENGTH, key),
... |
Check that a param with the specified key & value is valid and raise an exception if it
isn't.
| _validate_param | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_tag(key, value, path=""):
"""
Check that a tag with the specified key & value is valid and raise an exception if it isn't.
"""
_validate_tag_name(key, append_to_json_path(path, "key"))
return RunTag(
_validate_length_limit(append_to_json_path(path, "key"), MAX_ENTITY_KEY_LENGTH... |
Check that a tag with the specified key & value is valid and raise an exception if it isn't.
| _validate_tag | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_param_keys_unique(params):
"""Ensures that duplicate param keys are not present in the `log_batch()` params argument"""
unique_keys = []
dupe_keys = []
for param in params:
if param.key not in unique_keys:
unique_keys.append(param.key)
else:
dupe_key... | Ensures that duplicate param keys are not present in the `log_batch()` params argument | _validate_param_keys_unique | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_param_name(name, path="key"):
"""Check that `name` is a valid parameter name and raise an exception if it isn't."""
if name is None:
raise MlflowException(
invalid_value(path, "", _MISSING_KEY_NAME_MESSAGE),
error_code=INVALID_PARAMETER_VALUE,
)
if not v... | Check that `name` is a valid parameter name and raise an exception if it isn't. | _validate_param_name | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_tag_name(name, path="key"):
"""Check that `name` is a valid tag name and raise an exception if it isn't."""
# Reuse param & metric check.
if name is None:
raise MlflowException(
missing_value(path),
error_code=INVALID_PARAMETER_VALUE,
)
if not valida... | Check that `name` is a valid tag name and raise an exception if it isn't. | _validate_tag_name | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_run_id(run_id, path="run_id"):
"""Check that `run_id` is a valid run ID and raise an exception if it isn't."""
if _RUN_ID_REGEX.match(run_id) is None:
raise MlflowException(invalid_value(path, run_id), error_code=INVALID_PARAMETER_VALUE) | Check that `run_id` is a valid run ID and raise an exception if it isn't. | _validate_run_id | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_experiment_id(exp_id):
"""Check that `experiment_id`is a valid string or None, raise an exception if it isn't."""
if exp_id is not None and _EXPERIMENT_ID_REGEX.match(exp_id) is None:
raise MlflowException(
f"Invalid experiment ID: '{exp_id}'", error_code=INVALID_PARAMETER_VALU... | Check that `experiment_id`is a valid string or None, raise an exception if it isn't. | _validate_experiment_id | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_experiment_name(experiment_name):
"""Check that `experiment_name` is a valid string and raise an exception if it isn't."""
if experiment_name == "" or experiment_name is None:
raise MlflowException(
f"Invalid experiment name: '{experiment_name}'",
error_code=INVALID... | Check that `experiment_name` is a valid string and raise an exception if it isn't. | _validate_experiment_name | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_experiment_id_type(experiment_id):
"""
Check that a user-provided experiment_id is either a string, int, or None and raise an
exception if it isn't.
"""
if experiment_id is not None and not isinstance(experiment_id, (str, int)):
raise MlflowException(
f"Invalid expe... |
Check that a user-provided experiment_id is either a string, int, or None and raise an
exception if it isn't.
| _validate_experiment_id_type | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_db_type_string(db_type):
"""validates db_type parsed from DB URI is supported"""
from mlflow.store.db.db_types import DATABASE_ENGINES
if db_type not in DATABASE_ENGINES:
error_msg = (
f"Invalid database engine: '{db_type}'. "
f"Supported database engines are {... | validates db_type parsed from DB URI is supported | _validate_db_type_string | python | mlflow/mlflow | mlflow/utils/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/validation.py | Apache-2.0 |
def _validate_pyenv_is_available():
"""
Validates pyenv is available. If not, throws an `MlflowException` with a brief instruction on
how to install pyenv.
"""
url = (
"https://github.com/pyenv/pyenv#installation"
if not is_windows()
else "https://github.com/pyenv-win/pyenv-w... |
Validates pyenv is available. If not, throws an `MlflowException` with a brief instruction on
how to install pyenv.
| _validate_pyenv_is_available | python | mlflow/mlflow | mlflow/utils/virtualenv.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/virtualenv.py | Apache-2.0 |
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