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 link_prompts_to_trace(self, prompt_versions: list[PromptVersion], trace_id: str) -> None:
"""
Link multiple prompt versions to a trace.
Default implementation sets a tag on the trace. Stores can override with custom behavior.
Args:
prompt_versions: List of PromptVersion... |
Link multiple prompt versions to a trace.
Default implementation sets a tag on the trace. Stores can override with custom behavior.
Args:
prompt_versions: List of PromptVersion objects to link.
trace_id: Trace ID to link to each prompt version.
| link_prompts_to_trace | python | mlflow/mlflow | mlflow/store/model_registry/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/abstract_store.py | Apache-2.0 |
def set_prompt_version_tag(
self, name: str, version: Union[str, int], key: str, value: str
) -> None:
"""
Set a tag on a prompt version.
Default implementation: uses set_model_version_tag on the underlying ModelVersion.
Unity Catalog store implementations may override this ... |
Set a tag on a prompt version.
Default implementation: uses set_model_version_tag on the underlying ModelVersion.
Unity Catalog store implementations may override this method.
Args:
name: Name of the prompt.
version: Version number of the prompt.
ke... | set_prompt_version_tag | python | mlflow/mlflow | mlflow/store/model_registry/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/abstract_store.py | Apache-2.0 |
def delete_prompt_version_tag(self, name: str, version: Union[str, int], key: str) -> None:
"""
Delete a tag from a prompt version.
Default implementation: uses delete_model_version_tag on the underlying ModelVersion.
Unity Catalog store implementations may override this method.
... |
Delete a tag from a prompt version.
Default implementation: uses delete_model_version_tag on the underlying ModelVersion.
Unity Catalog store implementations may override this method.
Args:
name: Name of the prompt.
version: Version number of the prompt.
... | delete_prompt_version_tag | python | mlflow/mlflow | mlflow/store/model_registry/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/abstract_store.py | Apache-2.0 |
def link_prompt_version_to_model(self, name: str, version: str, model_id: str) -> None:
"""
Link a prompt version to a model.
Default implementation sets a tag. Stores can override with custom behavior.
Args:
name: Name of the prompt.
version: Version of the pro... |
Link a prompt version to a model.
Default implementation sets a tag. Stores can override with custom behavior.
Args:
name: Name of the prompt.
version: Version of the prompt to link.
model_id: ID of the model to link to.
| link_prompt_version_to_model | python | mlflow/mlflow | mlflow/store/model_registry/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/abstract_store.py | Apache-2.0 |
def link_prompt_version_to_run(self, name: str, version: str, run_id: str) -> None:
"""
Link a prompt version to a run.
Default implementation sets a tag. Stores can override with custom behavior.
Args:
name: Name of the prompt.
version: Version of the prompt to... |
Link a prompt version to a run.
Default implementation sets a tag. Stores can override with custom behavior.
Args:
name: Name of the prompt.
version: Version of the prompt to link.
run_id: ID of the run to link to.
| link_prompt_version_to_run | python | mlflow/mlflow | mlflow/store/model_registry/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/abstract_store.py | Apache-2.0 |
def _update_linked_prompts_tag(
self, current_tag_value: str, new_prompt_entries: list[dict[str, Any]]
) -> str:
"""
Utility method to update linked prompts tag value with new entries.
Args:
current_tag_value: Current JSON string value of the linked prompts tag
... |
Utility method to update linked prompts tag value with new entries.
Args:
current_tag_value: Current JSON string value of the linked prompts tag
new_prompt_entries: List of prompt entry dicts to add
Returns:
Updated JSON string with new entries added (avoid... | _update_linked_prompts_tag | python | mlflow/mlflow | mlflow/store/model_registry/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/abstract_store.py | Apache-2.0 |
def __init__(self, root_directory=None):
"""
Create a new FileStore with the given root directory.
"""
super().__init__()
self.root_directory = local_file_uri_to_path(root_directory or _default_root_dir())
# Create models directory if needed
if not exists(self.mo... |
Create a new FileStore with the given root directory.
| __init__ | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def _check_root_dir(self):
"""
Run checks before running directory operations.
"""
if not exists(self.root_directory):
raise Exception(f"'{self.root_directory}' does not exist.")
if not is_directory(self.root_directory):
raise Exception(f"'{self.root_direc... |
Run checks before running directory operations.
| _check_root_dir | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def update_registered_model(self, name, description, deployment_job_id=None):
"""
Update description of the registered model.
Args:
name: Registered model name.
description: New description.
deployment_job_id: Optional deployment job ID.
Returns:
... |
Update description of the registered model.
Args:
name: Registered model name.
description: New description.
deployment_job_id: Optional deployment job ID.
Returns:
A single updated :py:class:`mlflow.entities.model_registry.RegisteredModel` obje... | update_registered_model | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def rename_registered_model(self, name, new_name):
"""
Rename the registered model.
Args:
name: Registered model name.
new_name: New proposed name.
Returns:
A single updated :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
... |
Rename the registered model.
Args:
name: Registered model name.
new_name: New proposed name.
Returns:
A single updated :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
| rename_registered_model | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def create_model_version(
self,
name,
source,
run_id=None,
tags=None,
run_link=None,
description=None,
local_model_path=None,
model_id: Optional[str] = None,
) -> ModelVersion:
"""
Create a new model version from given source an... |
Create a new model version from given source and run ID.
Args:
name: Registered model name.
source: URI indicating the location of the model artifacts.
run_id: Run ID from MLflow tracking server that generated the model.
tags: A list of :py:class:`mlflow... | create_model_version | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def update_model_version(self, name, version, description) -> ModelVersion:
"""
Update metadata associated with a model version in backend.
Args:
name: Registered model name.
version: Registered model version.
description: New model description.
Retu... |
Update metadata associated with a model version in backend.
Args:
name: Registered model name.
version: Registered model version.
description: New model description.
Returns:
A single :py:class:`mlflow.entities.model_registry.ModelVersion` objec... | update_model_version | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def transition_model_version_stage(
self, name, version, stage, archive_existing_versions
) -> ModelVersion:
"""
Update model version stage.
Args:
name: Registered model name.
version: Registered model version.
stage: New desired stage for this mo... |
Update model version stage.
Args:
name: Registered model name.
version: Registered model version.
stage: New desired stage for this model version.
archive_existing_versions: If this flag is set to ``True``, all existing model
versions in ... | transition_model_version_stage | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def delete_model_version(self, name, version):
"""
Delete model version in backend.
Args:
name: Registered model name.
version: Registered model version.
Returns:
None
"""
model_version = self._fetch_file_model_version_if_exists(name=... |
Delete model version in backend.
Args:
name: Registered model name.
version: Registered model version.
Returns:
None
| delete_model_version | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def _read_yaml(root, file_name, retries=2):
"""
Read data from yaml file and return as dictionary, retrying up to
a specified number of times if the file contents are unexpectedly
empty due to a concurrent write.
Args:
root: Directory name.
file_name: Fil... |
Read data from yaml file and return as dictionary, retrying up to
a specified number of times if the file contents are unexpectedly
empty due to a concurrent write.
Args:
root: Directory name.
file_name: File name. Expects to have '.yaml' extension.
... | _read_yaml | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def _await_model_version_creation(self, mv, await_creation_for):
"""
Does not wait for the model version to become READY as a successful creation will
immediately place the model version in a READY state.
""" |
Does not wait for the model version to become READY as a successful creation will
immediately place the model version in a READY state.
| _await_model_version_creation | python | mlflow/mlflow | mlflow/store/model_registry/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/file_store.py | Apache-2.0 |
def create_registered_model(self, name, tags=None, description=None, deployment_job_id=None):
"""
Create a new registered model in backend store.
Args:
name: Name of the new model. This is expected to be unique in the backend store.
tags: A list of :py:class:`mlflow.enti... |
Create a new registered model in backend store.
Args:
name: Name of the new model. This is expected to be unique in the backend store.
tags: A list of :py:class:`mlflow.entities.model_registry.RegisteredModelTag`
instances associated with this registered model.
... | create_registered_model | python | mlflow/mlflow | mlflow/store/model_registry/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/rest_store.py | Apache-2.0 |
def search_registered_models(
self, filter_string=None, max_results=None, order_by=None, page_token=None
):
"""
Search for registered models in backend that satisfy the filter criteria.
Args:
filter_string: Filter query string, defaults to searching all registered models... |
Search for registered models in backend that satisfy the filter criteria.
Args:
filter_string: Filter query string, defaults to searching all registered models.
max_results: Maximum number of registered models desired.
order_by: List of column names with ASC|DESC an... | search_registered_models | python | mlflow/mlflow | mlflow/store/model_registry/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/rest_store.py | Apache-2.0 |
def __init__(self, db_uri):
"""
Create a database backed store.
Args:
db_uri: The SQLAlchemy database URI string to connect to the database. See
the `SQLAlchemy docs
<https://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls>`_
... |
Create a database backed store.
Args:
db_uri: The SQLAlchemy database URI string to connect to the database. See
the `SQLAlchemy docs
<https://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls>`_
for format specifications. MLflow ... | __init__ | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def _get_eager_registered_model_query_options():
"""
A list of SQLAlchemy query options that can be used to eagerly
load the following registered model attributes
when fetching a registered model: ``registered_model_tags``.
"""
# Use a subquery load rather than a joined l... |
A list of SQLAlchemy query options that can be used to eagerly
load the following registered model attributes
when fetching a registered model: ``registered_model_tags``.
| _get_eager_registered_model_query_options | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def _get_eager_model_version_query_options():
"""
A list of SQLAlchemy query options that can be used to eagerly
load the following model version attributes
when fetching a model version: ``model_version_tags``.
"""
# Use a subquery load rather than a joined load in order... |
A list of SQLAlchemy query options that can be used to eagerly
load the following model version attributes
when fetching a model version: ``model_version_tags``.
| _get_eager_model_version_query_options | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def create_registered_model(self, name, tags=None, description=None, deployment_job_id=None):
"""
Create a new registered model in backend store.
Args:
name: Name of the new model. This is expected to be unique in the backend store.
tags: A list of :py:class:`mlflow.enti... |
Create a new registered model in backend store.
Args:
name: Name of the new model. This is expected to be unique in the backend store.
tags: A list of :py:class:`mlflow.entities.model_registry.RegisteredModelTag`
instances associated with this registered model.
... | create_registered_model | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def _get_registered_model(cls, session, name, eager=False): # noqa: D417
"""
Args:
eager: If ``True``, eagerly loads the registered model's tags. If ``False``, these
attributes are not eagerly loaded and will be loaded when their corresponding object
properti... |
Args:
eager: If ``True``, eagerly loads the registered model's tags. If ``False``, these
attributes are not eagerly loaded and will be loaded when their corresponding object
properties are accessed from the resulting ``SqlRegisteredModel`` object.
| _get_registered_model | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def _update_query_to_exclude_prompts(
cls,
query: Any,
tag_filters: dict[str, list[Any]],
dialect: str,
main_db_model: Union[SqlModelVersion, SqlRegisteredModel],
tag_db_model: Union[SqlModelVersionTag, SqlRegisteredModelTag],
):
"""
Update query to ex... |
Update query to exclude all prompt rows and return only normal model or model versions.
Prompts and normal models are distinguished by the `mlflow.prompt.is_prompt` tag.
The search API should only return normal models by default. However, simply filtering
rows using the tag like this d... | _update_query_to_exclude_prompts | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def _parse_search_registered_models_order_by(cls, order_by_list):
"""Sorts a set of registered models based on their natural ordering and an overriding set
of order_bys. Registered models are naturally ordered first by name ascending.
"""
clauses = []
observed_order_by_clauses = ... | Sorts a set of registered models based on their natural ordering and an overriding set
of order_bys. Registered models are naturally ordered first by name ascending.
| _parse_search_registered_models_order_by | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def get_latest_versions(self, name, stages=None):
"""
Latest version models for each requested stage. If no ``stages`` argument is provided,
returns the latest version for each stage.
Args:
name: Registered model name.
stages: List of desired stages. If input lis... |
Latest version models for each requested stage. If no ``stages`` argument is provided,
returns the latest version for each stage.
Args:
name: Registered model name.
stages: List of desired stages. If input list is None, return latest versions for
each st... | get_latest_versions | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def _get_sql_model_version(cls, session, name, version, eager=False): # noqa: D417
"""
Args:
eager: If ``True``, eagerly loads the model version's tags.
If ``False``, these attributes are not eagerly loaded and
will be loaded when their corresponding object p... |
Args:
eager: If ``True``, eagerly loads the model version's tags.
If ``False``, these attributes are not eagerly loaded and
will be loaded when their corresponding object properties
are accessed from the resulting ``SqlModelVersion`` object.
| _get_sql_model_version | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def _get_sql_model_version_including_deleted(self, name, version):
"""
Private method to retrieve model versions including those that are internally deleted.
Used in tests to verify redaction behavior on deletion.
Args:
name: Registered model name.
version: Regis... |
Private method to retrieve model versions including those that are internally deleted.
Used in tests to verify redaction behavior on deletion.
Args:
name: Registered model name.
version: Registered model version.
Returns:
A single :py:class:`mlflow.... | _get_sql_model_version_including_deleted | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def _parse_search_model_versions_order_by(cls, order_by_list):
"""Sorts a set of model versions based on their natural ordering and an overriding set
of order_bys. Model versions are naturally ordered first by name ascending, then by
version ascending.
"""
clauses = []
ob... | Sorts a set of model versions based on their natural ordering and an overriding set
of order_bys. Model versions are naturally ordered first by name ascending, then by
version ascending.
| _parse_search_model_versions_order_by | python | mlflow/mlflow | mlflow/store/model_registry/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/model_registry/sqlalchemy_store.py | Apache-2.0 |
def create_experiment(self, name, artifact_location, tags):
"""
Create a new experiment.
If an experiment with the given name already exists, throws exception.
Args:
name: Desired name for an experiment.
artifact_location: Base location for artifacts in runs. May... |
Create a new experiment.
If an experiment with the given name already exists, throws exception.
Args:
name: Desired name for an experiment.
artifact_location: Base location for artifacts in runs. May be None.
tags: Experiment tags to set upon experiment crea... | create_experiment | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def get_experiment(self, experiment_id):
"""
Fetch the experiment by ID from the backend store.
Args:
experiment_id: String id for the experiment
Returns:
A single :py:class:`mlflow.entities.Experiment` object if it exists,
otherwise raises an except... |
Fetch the experiment by ID from the backend store.
Args:
experiment_id: String id for the experiment
Returns:
A single :py:class:`mlflow.entities.Experiment` object if it exists,
otherwise raises an exception.
| get_experiment | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def get_experiment_by_name(self, experiment_name):
"""
Fetch the experiment by name from the backend store.
Args:
experiment_name: Name of experiment
Returns:
A single :py:class:`mlflow.entities.Experiment` object if it exists.
""" |
Fetch the experiment by name from the backend store.
Args:
experiment_name: Name of experiment
Returns:
A single :py:class:`mlflow.entities.Experiment` object if it exists.
| get_experiment_by_name | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def delete_experiment(self, experiment_id):
"""
Delete the experiment from the backend store. Deleted experiments can be restored until
permanently deleted.
Args:
experiment_id: String id for the experiment.
""" |
Delete the experiment from the backend store. Deleted experiments can be restored until
permanently deleted.
Args:
experiment_id: String id for the experiment.
| delete_experiment | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def restore_experiment(self, experiment_id):
"""
Restore deleted experiment unless it is permanently deleted.
Args:
experiment_id: String id for the experiment.
""" |
Restore deleted experiment unless it is permanently deleted.
Args:
experiment_id: String id for the experiment.
| restore_experiment | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def rename_experiment(self, experiment_id, new_name):
"""
Update an experiment's name. The new name must be unique.
Args:
experiment_id: String id for the experiment.
new_name: New name for the experiment.
""" |
Update an experiment's name. The new name must be unique.
Args:
experiment_id: String id for the experiment.
new_name: New name for the experiment.
| rename_experiment | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def get_run(self, run_id):
"""
Fetch the run from backend store. The resulting :py:class:`Run <mlflow.entities.Run>`
contains a collection of run metadata - :py:class:`RunInfo <mlflow.entities.RunInfo>`,
as well as a collection of run parameters, tags, and metrics -
:py:class:`Ru... |
Fetch the run from backend store. The resulting :py:class:`Run <mlflow.entities.Run>`
contains a collection of run metadata - :py:class:`RunInfo <mlflow.entities.RunInfo>`,
as well as a collection of run parameters, tags, and metrics -
:py:class:`RunData <mlflow.entities.RunData>`. In t... | get_run | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def update_run_info(self, run_id, run_status, end_time, run_name):
"""
Update the metadata of the specified run.
Returns:
mlflow.entities.RunInfo: Describing the updated run.
""" |
Update the metadata of the specified run.
Returns:
mlflow.entities.RunInfo: Describing the updated run.
| update_run_info | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def create_run(self, experiment_id, user_id, start_time, tags, run_name):
"""
Create a run under the specified experiment ID, setting the run's status to "RUNNING"
and the start time to the current time.
Args:
experiment_id: String id of the experiment for this run.
... |
Create a run under the specified experiment ID, setting the run's status to "RUNNING"
and the start time to the current time.
Args:
experiment_id: String id of the experiment for this run.
user_id: ID of the user launching this run.
start_time: Start time of... | create_run | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def delete_run(self, run_id):
"""
Delete a run.
Args:
run_id: The ID of the run to delete.
""" |
Delete a run.
Args:
run_id: The ID of the run to delete.
| delete_run | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def restore_run(self, run_id):
"""
Restore a run.
Args:
run_id: The ID of the run to restore.
""" |
Restore a run.
Args:
run_id: The ID of the run to restore.
| restore_run | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def start_trace(
self,
experiment_id: str,
timestamp_ms: int,
request_metadata: dict[str, str],
tags: dict[str, str],
) -> TraceInfoV2:
"""
Start an initial TraceInfo object in the backend store.
Args:
experiment_id: String id of the exper... |
Start an initial TraceInfo object in the backend store.
Args:
experiment_id: String id of the experiment for this run.
timestamp_ms: Start time of the trace, in milliseconds since the UNIX epoch.
request_metadata: Metadata of the trace.
tags: Tags of the... | start_trace | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def end_trace(
self,
request_id: str,
timestamp_ms: int,
status: TraceStatus,
request_metadata: dict[str, str],
tags: dict[str, str],
) -> TraceInfoV2:
"""
Update the TraceInfo object in the backend store with the completed trace info.
Args:
... |
Update the TraceInfo object in the backend store with the completed trace info.
Args:
request_id : Unique string identifier of the trace.
timestamp_ms: End time of the trace, in milliseconds. The execution time field
in the TraceInfo will be calculated by subtra... | end_trace | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def delete_traces(
self,
experiment_id: str,
max_timestamp_millis: Optional[int] = None,
max_traces: Optional[int] = None,
request_ids: Optional[list[str]] = None,
) -> int:
"""
Delete traces based on the specified criteria.
- Either `max_timestamp_mi... |
Delete traces based on the specified criteria.
- Either `max_timestamp_millis` or `request_ids` must be specified, but not both.
- `max_traces` can't be specified if `request_ids` is specified.
Args:
experiment_id: ID of the associated experiment.
max_timestamp... | delete_traces | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def search_traces(
self,
experiment_ids: list[str],
filter_string: Optional[str] = None,
max_results: int = SEARCH_TRACES_DEFAULT_MAX_RESULTS,
order_by: Optional[list[str]] = None,
page_token: Optional[str] = None,
model_id: Optional[str] = None,
sql_wareh... |
Return traces that match the given list of search expressions within the experiments.
Args:
experiment_ids: List of experiment ids to scope the search.
filter_string: A search filter string.
max_results: Maximum number of traces desired.
order_by: List o... | search_traces | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def set_experiment_tag(self, experiment_id, tag):
"""
Set a tag for the specified experiment
Args:
experiment_id: String id for the experiment.
tag: :py:class:`mlflow.entities.ExperimentTag` instance to set.
""" |
Set a tag for the specified experiment
Args:
experiment_id: String id for the experiment.
tag: :py:class:`mlflow.entities.ExperimentTag` instance to set.
| set_experiment_tag | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def get_metric_history(self, run_id, metric_key, max_results=None, page_token=None):
"""
Return a list of metric objects corresponding to all values logged for a given metric
within a run.
Args:
run_id: Unique identifier for run.
metric_key: Metric name within th... |
Return a list of metric objects corresponding to all values logged for a given metric
within a run.
Args:
run_id: Unique identifier for run.
metric_key: Metric name within the run.
max_results: Maximum number of metric history events (steps) to return per pa... | get_metric_history | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def get_metric_history_bulk_interval_from_steps(self, run_id, metric_key, steps, max_results):
"""
Return a list of metric objects corresponding to all values logged
for a given metric within a run for the specified steps.
Args:
run_id: Unique identifier for run.
... |
Return a list of metric objects corresponding to all values logged
for a given metric within a run for the specified steps.
Args:
run_id: Unique identifier for run.
metric_key: Metric name within the run.
steps: List of steps for which to return metrics.
... | get_metric_history_bulk_interval_from_steps | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def search_runs(
self,
experiment_ids,
filter_string,
run_view_type,
max_results=SEARCH_MAX_RESULTS_DEFAULT,
order_by=None,
page_token=None,
):
"""
Return runs that match the given list of search expressions within the experiments.
Arg... |
Return runs that match the given list of search expressions within the experiments.
Args:
experiment_ids: List of experiment ids to scope the search.
filter_string: A search filter string.
run_view_type: ACTIVE_ONLY, DELETED_ONLY, or ALL runs.
max_result... | search_runs | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def _search_runs(
self,
experiment_ids,
filter_string,
run_view_type,
max_results,
order_by,
page_token,
):
"""
Return runs that match the given list of search expressions within the experiments, as
well as a pagination token (indicatin... |
Return runs that match the given list of search expressions within the experiments, as
well as a pagination token (indicating where the next page should start). Subclasses of
``AbstractStore`` should implement this method to support pagination instead of
``search_runs``.
See ``... | _search_runs | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def log_batch(self, run_id, metrics, params, tags):
"""
Log multiple metrics, params, and tags for the specified run
Args:
run_id: String id for the run
metrics: List of :py:class:`mlflow.entities.Metric` instances to log
params: List of :py:class:`mlflow.ent... |
Log multiple metrics, params, and tags for the specified run
Args:
run_id: String id for the run
metrics: List of :py:class:`mlflow.entities.Metric` instances to log
params: List of :py:class:`mlflow.entities.Param` instances to log
tags: List of :py:cla... | log_batch | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def log_batch_async(self, run_id, metrics, params, tags) -> RunOperations:
"""
Log multiple metrics, params, and tags for the specified run in async fashion.
This API does not offer immediate consistency of the data. When API returns,
data is accepted but not persisted/processed by back ... |
Log multiple metrics, params, and tags for the specified run in async fashion.
This API does not offer immediate consistency of the data. When API returns,
data is accepted but not persisted/processed by back end. Data would be processed
in near real time fashion.
Args:
... | log_batch_async | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def log_inputs(
self,
run_id: str,
datasets: Optional[list[DatasetInput]] = None,
models: Optional[list[LoggedModelInput]] = None,
):
"""
Log inputs, such as datasets, to the specified run.
Args:
run_id: String id for the run
datasets:... |
Log inputs, such as datasets, to the specified run.
Args:
run_id: String id for the run
datasets: List of :py:class:`mlflow.entities.DatasetInput` instances to log
as inputs to the run.
models: List of :py:class:`mlflow.entities.LoggedModelInput` ins... | log_inputs | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def create_logged_model(
self,
experiment_id: str,
name: Optional[str] = None,
source_run_id: Optional[str] = None,
tags: Optional[list[LoggedModelTag]] = None,
params: Optional[list[LoggedModelParameter]] = None,
model_type: Optional[str] = None,
) -> LoggedM... |
Create a new logged model.
Args:
experiment_id: ID of the experiment to which the model belongs.
name: Name of the model. If not specified, a random name will be generated.
source_run_id: ID of the run that produced the model.
tags: Tags to set on the mo... | create_logged_model | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def search_logged_models(
self,
experiment_ids: list[str],
filter_string: Optional[str] = None,
datasets: Optional[list[dict[str, Any]]] = None,
max_results: Optional[int] = None,
order_by: Optional[list[dict[str, Any]]] = None,
page_token: Optional[str] = None,
... |
Search for logged models that match the specified search criteria.
Args:
experiment_ids: List of experiment ids to scope the search.
filter_string: A search filter string.
datasets: List of dictionaries to specify datasets on which to apply metrics filters.
... | search_logged_models | python | mlflow/mlflow | mlflow/store/tracking/abstract_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/abstract_store.py | Apache-2.0 |
def __init__(self, root_directory=None, artifact_root_uri=None):
"""
Create a new FileStore with the given root directory and a given default artifact root URI.
"""
super().__init__()
self.root_directory = local_file_uri_to_path(root_directory or _default_root_dir())
if n... |
Create a new FileStore with the given root directory and a given default artifact root URI.
| __init__ | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def get_experiment(self, experiment_id):
"""
Fetch the experiment.
Note: This API will search for active as well as deleted experiments.
Args:
experiment_id: Integer id for the experiment
Returns:
A single Experiment object if it exists, otherwise raises... |
Fetch the experiment.
Note: This API will search for active as well as deleted experiments.
Args:
experiment_id: Integer id for the experiment
Returns:
A single Experiment object if it exists, otherwise raises an Exception.
| get_experiment | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def _get_deleted_runs(self, older_than=0):
"""
Get all deleted run ids.
Args:
older_than: get runs that is older than this variable in number of milliseconds.
defaults to 0 ms to get all deleted runs.
"""
current_time = get_current_time_millis... |
Get all deleted run ids.
Args:
older_than: get runs that is older than this variable in number of milliseconds.
defaults to 0 ms to get all deleted runs.
| _get_deleted_runs | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def _find_experiment_folder(self, run_path):
"""
Given a run path, return the parent directory for its experiment.
"""
parent = get_parent_dir(run_path)
if os.path.basename(parent) == FileStore.TRASH_FOLDER_NAME:
return get_parent_dir(parent)
return parent |
Given a run path, return the parent directory for its experiment.
| _find_experiment_folder | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def create_run(self, experiment_id, user_id, start_time, tags, run_name):
"""
Creates a run with the specified attributes.
"""
experiment_id = FileStore.DEFAULT_EXPERIMENT_ID if experiment_id is None else experiment_id
experiment = self.get_experiment(experiment_id)
if ex... |
Creates a run with the specified attributes.
| create_run | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def get_run(self, run_id):
"""
Note: Will get both active and deleted runs.
"""
_validate_run_id(run_id)
run_info = self._get_run_info(run_id)
if run_info is None:
raise MlflowException(
f"Run '{run_id}' metadata is in invalid state.",
... |
Note: Will get both active and deleted runs.
| get_run | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def get_metric_history(self, run_id, metric_key, max_results=None, page_token=None):
"""
Return all logged values for a given metric.
Args:
run_id: Unique identifier for run.
metric_key: Metric name within the run.
max_results: An indicator for paginated resu... |
Return all logged values for a given metric.
Args:
run_id: Unique identifier for run.
metric_key: Metric name within the run.
max_results: An indicator for paginated results. This functionality is not
implemented for FileStore and is unused in this s... | get_metric_history | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def _validate_new_param_value(self, param_path, param_key, run_id, new_value):
"""
When logging a parameter with a key that already exists, this function is used to
enforce immutability by verifying that the specified parameter value matches the existing
value.
:raises: py:class:... |
When logging a parameter with a key that already exists, this function is used to
enforce immutability by verifying that the specified parameter value matches the existing
value.
:raises: py:class:`mlflow.exceptions.MlflowException` if the specified new parameter value
... | _validate_new_param_value | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def set_experiment_tag(self, experiment_id, tag):
"""
Set a tag for the specified experiment
Args:
experiment_id: String ID of the experiment
tag: ExperimentRunTag instance to log
"""
_validate_tag_name(tag.key)
experiment = self.get_experiment(ex... |
Set a tag for the specified experiment
Args:
experiment_id: String ID of the experiment
tag: ExperimentRunTag instance to log
| set_experiment_tag | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def delete_tag(self, run_id, key):
"""
Delete a tag from a run. This is irreversible.
Args:
run_id: String ID of the run.
key: Name of the tag.
"""
_validate_run_id(run_id)
run_info = self._get_run_info(run_id)
check_run_is_active(run_info... |
Delete a tag from a run. This is irreversible.
Args:
run_id: String ID of the run.
key: Name of the tag.
| delete_tag | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def log_inputs(
self,
run_id: str,
datasets: Optional[list[DatasetInput]] = None,
models: Optional[list[LoggedModelInput]] = None,
):
"""
Log inputs, such as datasets and models, to the specified run.
Args:
run_id: String id for the run
... |
Log inputs, such as datasets and models, to the specified run.
Args:
run_id: String id for the run
datasets: List of :py:class:`mlflow.entities.DatasetInput` instances to log
as inputs to the run.
models: List of :py:class:`mlflow.entities.LoggedMode... | log_inputs | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def log_outputs(self, run_id: str, models: list[LoggedModelOutput]):
"""
Log outputs, such as models, to the specified run.
Args:
run_id: String id for the run
models: List of :py:class:`mlflow.entities.LoggedModelOutput` instances to log
as outputs of th... |
Log outputs, such as models, to the specified run.
Args:
run_id: String id for the run
models: List of :py:class:`mlflow.entities.LoggedModelOutput` instances to log
as outputs of the run.
Returns:
None.
| log_outputs | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def _search_datasets(self, experiment_ids) -> list[_DatasetSummary]:
"""
Return all dataset summaries associated to the given experiments.
Args:
experiment_ids: List of experiment ids to scope the search
Returns:
A List of :py:class:`mlflow.entities.DatasetSumma... |
Return all dataset summaries associated to the given experiments.
Args:
experiment_ids: List of experiment ids to scope the search
Returns:
A List of :py:class:`mlflow.entities.DatasetSummary` entities.
| _search_datasets | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def _save_trace_info(self, trace_info: TraceInfoV2, trace_dir, overwrite=False):
"""
TraceInfo is saved into `traces` folder under the experiment, each trace
is saved in the folder named by its request_id.
`request_metadata` and `tags` folder store their key-value pairs such that each
... |
TraceInfo is saved into `traces` folder under the experiment, each trace
is saved in the folder named by its request_id.
`request_metadata` and `tags` folder store their key-value pairs such that each
key is the file name, and value is written as the string value.
Detailed direc... | _save_trace_info | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def _convert_trace_info_to_dict(self, trace_info: TraceInfoV2):
"""
Convert trace info to a dictionary for persistence.
Drop request_metadata and tags as they're saved into separate files.
"""
trace_info_dict = trace_info.to_dict()
trace_info_dict.pop("request_metadata", ... |
Convert trace info to a dictionary for persistence.
Drop request_metadata and tags as they're saved into separate files.
| _convert_trace_info_to_dict | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def get_trace_info(self, request_id: str, should_query_v3: bool = False) -> TraceInfoV2:
"""
Get the trace matching the `request_id`.
Args:
request_id: String id of the trace to fetch.
should_query_v3: If True, the backend store will query the V3 API for the trace info.
... |
Get the trace matching the `request_id`.
Args:
request_id: String id of the trace to fetch.
should_query_v3: If True, the backend store will query the V3 API for the trace info.
TODO: Remove this flag once the V3 API is the default in OSS.
Returns:
... | get_trace_info | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def set_trace_tag(self, request_id: str, key: str, value: str):
"""
Set a tag on the trace with the given request_id.
Args:
request_id: The ID of the trace.
key: The string key of the tag.
value: The string value of the tag.
"""
trace_dir = se... |
Set a tag on the trace with the given request_id.
Args:
request_id: The ID of the trace.
key: The string key of the tag.
value: The string value of the tag.
| set_trace_tag | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def delete_trace_tag(self, request_id: str, key: str):
"""
Delete a tag on the trace with the given request_id.
Args:
request_id: The ID of the trace.
key: The string key of the tag.
"""
_validate_tag_name(key)
trace_dir = self._find_trace_dir(req... |
Delete a tag on the trace with the given request_id.
Args:
request_id: The ID of the trace.
key: The string key of the tag.
| delete_trace_tag | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def search_traces(
self,
experiment_ids: list[str],
filter_string: Optional[str] = None,
max_results: int = SEARCH_TRACES_DEFAULT_MAX_RESULTS,
order_by: Optional[list[str]] = None,
page_token: Optional[str] = None,
model_id: Optional[str] = None,
sql_wareh... |
Return traces that match the given list of search expressions within the experiments.
Args:
experiment_ids: List of experiment ids to scope the search.
filter_string: A search filter string. Supported filter keys are `name`,
`status`, `timestamp_ms` a... | search_traces | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def log_logged_model_params(self, model_id: str, params: list[LoggedModelParameter]):
"""
Set parameters on the specified logged model.
Args:
model_id: ID of the model.
params: Parameters to set on the model.
Returns:
None
"""
for par... |
Set parameters on the specified logged model.
Args:
model_id: ID of the model.
params: Parameters to set on the model.
Returns:
None
| log_logged_model_params | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def finalize_logged_model(self, model_id: str, status: LoggedModelStatus) -> LoggedModel:
"""
Finalize a model by updating its status.
Args:
model_id: ID of the model to finalize.
status: Final status to set on the model.
Returns:
The updated model.
... |
Finalize a model by updating its status.
Args:
model_id: ID of the model to finalize.
status: Final status to set on the model.
Returns:
The updated model.
| finalize_logged_model | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def set_logged_model_tags(self, model_id: str, tags: list[LoggedModelTag]) -> None:
"""
Set tags on the specified logged model.
Args:
model_id: ID of the model.
tags: Tags to set on the model.
Returns:
None
"""
model = self.get_logged... |
Set tags on the specified logged model.
Args:
model_id: ID of the model.
tags: Tags to set on the model.
Returns:
None
| set_logged_model_tags | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def delete_logged_model_tag(self, model_id: str, key: str) -> None:
"""
Delete a tag on the specified logged model.
Args:
model_id: ID of the model.
key: The string key of the tag.
Returns:
None
"""
_validate_tag_name(key)
mod... |
Delete a tag on the specified logged model.
Args:
model_id: ID of the model.
key: The string key of the tag.
Returns:
None
| delete_logged_model_tag | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def search_logged_models(
self,
experiment_ids: list[str],
filter_string: Optional[str] = None,
datasets: Optional[list[str]] = None,
max_results: Optional[int] = None,
order_by: Optional[list[dict[str, Any]]] = None,
page_token: Optional[str] = None,
) -> Pag... |
Search for logged models that match the specified search criteria.
Args:
experiment_ids: List of experiment ids to scope the search.
filter_string: A search filter string.
datasets: List of dictionaries to specify datasets on which to apply metrics filters.
... | search_logged_models | python | mlflow/mlflow | mlflow/store/tracking/file_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/file_store.py | Apache-2.0 |
def create_experiment(self, name, artifact_location=None, tags=None):
"""
Create a new experiment.
If an experiment with the given name already exists, throws exception.
Args:
name: Desired name for an experiment.
artifact_location: Location to store run artifact... |
Create a new experiment.
If an experiment with the given name already exists, throws exception.
Args:
name: Desired name for an experiment.
artifact_location: Location to store run artifacts.
tags: A list of :py:class:`mlflow.entities.ExperimentTag` instance... | create_experiment | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def get_experiment(self, experiment_id):
"""
Fetch the experiment from the backend store.
Args:
experiment_id: String id for the experiment
Returns:
A single :py:class:`mlflow.entities.Experiment` object if it exists,
otherwise raises an Exception.
... |
Fetch the experiment from the backend store.
Args:
experiment_id: String id for the experiment
Returns:
A single :py:class:`mlflow.entities.Experiment` object if it exists,
otherwise raises an Exception.
| get_experiment | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def get_run(self, run_id):
"""
Fetch the run from backend store
Args:
run_id: Unique identifier for the run
Returns:
A single Run object if it exists, otherwise raises an Exception
"""
req_body = message_to_json(GetRun(run_uuid=run_id, run_id=run... |
Fetch the run from backend store
Args:
run_id: Unique identifier for the run
Returns:
A single Run object if it exists, otherwise raises an Exception
| get_run | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def update_run_info(self, run_id, run_status, end_time, run_name):
"""Updates the metadata of the specified run."""
req_body = message_to_json(
UpdateRun(
run_uuid=run_id,
run_id=run_id,
status=run_status,
end_time=end_time,
... | Updates the metadata of the specified run. | update_run_info | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def create_run(self, experiment_id, user_id, start_time, tags, run_name):
"""
Create a run under the specified experiment ID, setting the run's status to "RUNNING"
and the start time to the current time.
Args:
experiment_id: ID of the experiment for this run.
use... |
Create a run under the specified experiment ID, setting the run's status to "RUNNING"
and the start time to the current time.
Args:
experiment_id: ID of the experiment for this run.
user_id: ID of the user launching this run.
start_time: timestamp of the ini... | create_run | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def start_trace_v3(self, trace: Trace) -> TraceInfo:
"""
Start a trace using the V3 API format.
NB: This method is named "Start" for internal reason in the backend, but actually
should be called at the end of the trace. We will migrate this to "CreateTrace"
API in the future to ... |
Start a trace using the V3 API format.
NB: This method is named "Start" for internal reason in the backend, but actually
should be called at the end of the trace. We will migrate this to "CreateTrace"
API in the future to avoid confusion.
Args:
trace: The Trace obj... | start_trace_v3 | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def end_trace(
self,
request_id: str,
timestamp_ms: int,
status: TraceStatus,
request_metadata: dict[str, str],
tags: dict[str, str],
) -> TraceInfoV2:
"""
Update the TraceInfo object in the backend store with the completed trace info.
Args:
... |
Update the TraceInfo object in the backend store with the completed trace info.
Args:
request_id: Unique string identifier of the trace.
timestamp_ms: End time of the trace, in milliseconds. The execution time field
in the TraceInfo will be calculated by subtrac... | end_trace | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def _is_databricks_tracking_uri(self):
"""
Check if the tracking URI associated with this store is a Databricks URI.
"""
from mlflow.tracking._tracking_service.utils import get_tracking_uri
from mlflow.utils.uri import is_databricks_uri
try:
tracking_uri = ge... |
Check if the tracking URI associated with this store is a Databricks URI.
| _is_databricks_tracking_uri | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def get_assessment(self, trace_id: str, assessment_id: str) -> Assessment:
"""
Get an assessment entity from the backend store.
"""
is_databricks = self._is_databricks_tracking_uri()
req_body = message_to_json(
GetAssessmentRequest(trace_id=trace_id, assessment_id=ass... |
Get an assessment entity from the backend store.
| get_assessment | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def create_assessment(self, assessment: Assessment) -> Assessment:
"""
Create an assessment entity in the backend store.
Args:
assessment: The assessment to log (without an assessment_id).
Returns:
The created Assessment object.
"""
is_databricks... |
Create an assessment entity in the backend store.
Args:
assessment: The assessment to log (without an assessment_id).
Returns:
The created Assessment object.
| create_assessment | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def update_assessment(
self,
trace_id: str,
assessment_id: str,
name: Optional[str] = None,
expectation: Optional[Expectation] = None,
feedback: Optional[Feedback] = None,
rationale: Optional[str] = None,
metadata: Optional[dict[str, str]] = None,
) ->... |
Update an existing assessment entity in the backend store.
Args:
trace_id: The ID of the trace.
assessment_id: The ID of the assessment to update.
name: The updated name of the assessment.
expectation: The updated expectation value of the assessment.
... | update_assessment | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def delete_assessment(self, trace_id: str, assessment_id: str):
"""
Delete an assessment associated with a trace.
Args:
trace_id: String ID of the trace.
assessment_id: String ID of the assessment to delete.
"""
is_databricks = self._is_databricks_trackin... |
Delete an assessment associated with a trace.
Args:
trace_id: String ID of the trace.
assessment_id: String ID of the assessment to delete.
| delete_assessment | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def log_metric(self, run_id: str, metric: Metric):
"""
Log a metric for the specified run
Args:
run_id: String id for the run
metric: Metric instance to log
"""
req_body = message_to_json(
LogMetric(
run_uuid=run_id,
... |
Log a metric for the specified run
Args:
run_id: String id for the run
metric: Metric instance to log
| log_metric | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def log_param(self, run_id, param):
"""
Log a param for the specified run
Args:
run_id: String id for the run
param: Param instance to log
"""
req_body = message_to_json(
LogParam(run_uuid=run_id, run_id=run_id, key=param.key, value=param.valu... |
Log a param for the specified run
Args:
run_id: String id for the run
param: Param instance to log
| log_param | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def set_tag(self, run_id, tag):
"""
Set a tag for the specified run
Args:
run_id: String ID of the run
tag: RunTag instance to log
"""
req_body = message_to_json(
SetTag(run_uuid=run_id, run_id=run_id, key=tag.key, value=tag.value)
)
... |
Set a tag for the specified run
Args:
run_id: String ID of the run
tag: RunTag instance to log
| set_tag | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def get_metric_history(self, run_id, metric_key, max_results=None, page_token=None):
"""
Return all logged values for a given metric.
Args:
run_id: Unique identifier for run.
metric_key: Metric name within the run.
max_results: Maximum number of metric histor... |
Return all logged values for a given metric.
Args:
run_id: Unique identifier for run.
metric_key: Metric name within the run.
max_results: Maximum number of metric history events (steps) to return per paged
query. Only supported in 'databricks' backe... | get_metric_history | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def log_logged_model_params(self, model_id: str, params: list[LoggedModelParameter]) -> None:
"""
Log parameters for a logged model in batches of 100.
Args:
model_id: ID of the model to log parameters for.
params: List of parameters to log.
Returns:
... |
Log parameters for a logged model in batches of 100.
Args:
model_id: ID of the model to log parameters for.
params: List of parameters to log.
Returns:
None
| log_logged_model_params | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def get_logged_model(self, model_id: str) -> LoggedModel:
"""
Fetch the logged model with the specified ID.
Args:
model_id: ID of the model to fetch.
Returns:
The fetched model.
"""
endpoint = get_logged_model_endpoint(model_id)
response_... |
Fetch the logged model with the specified ID.
Args:
model_id: ID of the model to fetch.
Returns:
The fetched model.
| get_logged_model | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def log_inputs(
self,
run_id: str,
datasets: Optional[list[DatasetInput]] = None,
models: Optional[list[LoggedModelInput]] = None,
):
"""
Log inputs, such as datasets, to the specified run.
Args:
run_id: String id for the run
datasets:... |
Log inputs, such as datasets, to the specified run.
Args:
run_id: String id for the run
datasets: List of :py:class:`mlflow.entities.DatasetInput` instances to log
as inputs to the run.
models: List of :py:class:`mlflow.entities.LoggedModelInput` ins... | log_inputs | python | mlflow/mlflow | mlflow/store/tracking/rest_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/rest_store.py | Apache-2.0 |
def _create_default_experiment(self, session):
"""
MLflow UI and client code expects a default experiment with ID 0.
This method uses SQL insert statement to create the default experiment as a hack, since
experiment table uses 'experiment_id' column is a PK and is also set to auto increm... |
MLflow UI and client code expects a default experiment with ID 0.
This method uses SQL insert statement to create the default experiment as a hack, since
experiment table uses 'experiment_id' column is a PK and is also set to auto increment.
MySQL and other implementation do not allow v... | _create_default_experiment | python | mlflow/mlflow | mlflow/store/tracking/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/sqlalchemy_store.py | Apache-2.0 |
def _get_experiment(self, session, experiment_id, view_type, eager=False): # noqa: D417
"""
Args:
eager: If ``True``, eagerly loads the experiments's tags. If ``False``, these tags
are not eagerly loaded and will be loaded if/when their corresponding
object p... |
Args:
eager: If ``True``, eagerly loads the experiments's tags. If ``False``, these tags
are not eagerly loaded and will be loaded if/when their corresponding
object properties are accessed from the resulting ``SqlExperiment`` object.
| _get_experiment | python | mlflow/mlflow | mlflow/store/tracking/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/sqlalchemy_store.py | Apache-2.0 |
def _get_eager_experiment_query_options():
"""
A list of SQLAlchemy query options that can be used to eagerly load the following
experiment attributes when fetching an experiment: ``tags``.
"""
return [
# Use a subquery load rather than a joined load in order to minim... |
A list of SQLAlchemy query options that can be used to eagerly load the following
experiment attributes when fetching an experiment: ``tags``.
| _get_eager_experiment_query_options | python | mlflow/mlflow | mlflow/store/tracking/sqlalchemy_store.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/tracking/sqlalchemy_store.py | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.