Source code for mlflow.tracking.fluent

"""
Internal module implementing the fluent API, allowing management of an active
MLflow run. This module is exposed to users at the top-level :py:mod:`mlflow` module.
"""
import atexit
import contextlib
import importlib
import inspect
import logging
import os
from copy import deepcopy
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union

from mlflow.data.dataset import Dataset
from mlflow.entities import (
    DatasetInput,
    Experiment,
    InputTag,
    Metric,
    Param,
    Run,
    RunStatus,
    RunTag,
    ViewType,
)
from mlflow.entities.lifecycle_stage import LifecycleStage
from mlflow.environment_variables import (
    MLFLOW_ENABLE_ASYNC_LOGGING,
    MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING,
    MLFLOW_EXPERIMENT_ID,
    MLFLOW_EXPERIMENT_NAME,
    MLFLOW_RUN_ID,
)
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import (
    INVALID_PARAMETER_VALUE,
    RESOURCE_DOES_NOT_EXIST,
)
from mlflow.store.tracking import SEARCH_MAX_RESULTS_DEFAULT
from mlflow.tracking import _get_store, artifact_utils
from mlflow.tracking.client import MlflowClient
from mlflow.tracking.context import registry as context_registry
from mlflow.tracking.default_experiment import registry as default_experiment_registry
from mlflow.utils import get_results_from_paginated_fn
from mlflow.utils.annotations import experimental
from mlflow.utils.async_logging.run_operations import RunOperations
from mlflow.utils.autologging_utils import (
    AUTOLOGGING_CONF_KEY_IS_GLOBALLY_CONFIGURED,
    AUTOLOGGING_INTEGRATIONS,
    autologging_integration,
    autologging_is_disabled,
    is_testing,
)
from mlflow.utils.databricks_utils import is_in_databricks_runtime
from mlflow.utils.import_hooks import register_post_import_hook
from mlflow.utils.mlflow_tags import (
    MLFLOW_DATASET_CONTEXT,
    MLFLOW_EXPERIMENT_PRIMARY_METRIC_GREATER_IS_BETTER,
    MLFLOW_EXPERIMENT_PRIMARY_METRIC_NAME,
    MLFLOW_PARENT_RUN_ID,
    MLFLOW_RUN_NAME,
    MLFLOW_RUN_NOTE,
)
from mlflow.utils.time import get_current_time_millis
from mlflow.utils.validation import _validate_experiment_id_type, _validate_run_id

if TYPE_CHECKING:
    import matplotlib
    import matplotlib.figure
    import numpy
    import pandas
    import PIL
    import plotly


_active_run_stack = []
run_id_to_system_metrics_monitor = {}
_active_experiment_id = None
_last_active_run_id = None

SEARCH_MAX_RESULTS_PANDAS = 100000
NUM_RUNS_PER_PAGE_PANDAS = 10000

_logger = logging.getLogger(__name__)


[docs]def set_experiment( experiment_name: Optional[str] = None, experiment_id: Optional[str] = None ) -> Experiment: """ Set the given experiment as the active experiment. The experiment must either be specified by name via `experiment_name` or by ID via `experiment_id`. The experiment name and ID cannot both be specified. .. note:: If the experiment being set by name does not exist, a new experiment will be created with the given name. After the experiment has been created, it will be set as the active experiment. On certain platforms, such as Databricks, the experiment name must be an absolute path, e.g. ``"/Users/<username>/my-experiment"``. Args: experiment_name: Case sensitive name of the experiment to be activated. experiment_id: ID of the experiment to be activated. If an experiment with this ID does not exist, an exception is thrown. Returns: An instance of :py:class:`mlflow.entities.Experiment` representing the new active experiment. .. code-block:: python :test: :caption: Example import mlflow # Set an experiment name, which must be unique and case-sensitive. experiment = mlflow.set_experiment("Social NLP Experiments") # Get Experiment Details print(f"Experiment_id: {experiment.experiment_id}") print(f"Artifact Location: {experiment.artifact_location}") print(f"Tags: {experiment.tags}") print(f"Lifecycle_stage: {experiment.lifecycle_stage}") .. code-block:: text :caption: Output Experiment_id: 1 Artifact Location: file:///.../mlruns/1 Tags: {} Lifecycle_stage: active """ if (experiment_name is not None and experiment_id is not None) or ( experiment_name is None and experiment_id is None ): raise MlflowException( message="Must specify exactly one of: `experiment_id` or `experiment_name`.", error_code=INVALID_PARAMETER_VALUE, ) client = MlflowClient() if experiment_id is None: experiment = client.get_experiment_by_name(experiment_name) if not experiment: _logger.info( "Experiment with name '%s' does not exist. Creating a new experiment.", experiment_name, ) # NB: If two simultaneous threads or processes attempt to set the same experiment # simultaneously, a race condition may be encountered here wherein experiment creation # fails experiment_id = client.create_experiment(experiment_name) experiment = client.get_experiment(experiment_id) else: experiment = client.get_experiment(experiment_id) if experiment is None: raise MlflowException( message=f"Experiment with ID '{experiment_id}' does not exist.", error_code=RESOURCE_DOES_NOT_EXIST, ) if experiment.lifecycle_stage != LifecycleStage.ACTIVE: raise MlflowException( message=( "Cannot set a deleted experiment '%s' as the active experiment. " "You can restore the experiment, or permanently delete the " "experiment to create a new one." % experiment.name ), error_code=INVALID_PARAMETER_VALUE, ) global _active_experiment_id _active_experiment_id = experiment.experiment_id return experiment
def _set_experiment_primary_metric( experiment_id: str, primary_metric: str, greater_is_better: bool ): client = MlflowClient() client.set_experiment_tag(experiment_id, MLFLOW_EXPERIMENT_PRIMARY_METRIC_NAME, primary_metric) client.set_experiment_tag( experiment_id, MLFLOW_EXPERIMENT_PRIMARY_METRIC_GREATER_IS_BETTER, str(greater_is_better) )
[docs]class ActiveRun(Run): """Wrapper around :py:class:`mlflow.entities.Run` to enable using Python ``with`` syntax.""" def __init__(self, run): Run.__init__(self, run.info, run.data) def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): status = RunStatus.FINISHED if exc_type is None else RunStatus.FAILED end_run(RunStatus.to_string(status)) return exc_type is None
[docs]def start_run( run_id: Optional[str] = None, experiment_id: Optional[str] = None, run_name: Optional[str] = None, nested: bool = False, tags: Optional[Dict[str, Any]] = None, description: Optional[str] = None, log_system_metrics: Optional[bool] = None, ) -> ActiveRun: """ Start a new MLflow run, setting it as the active run under which metrics and parameters will be logged. The return value can be used as a context manager within a ``with`` block; otherwise, you must call ``end_run()`` to terminate the current run. If you pass a ``run_id`` or the ``MLFLOW_RUN_ID`` environment variable is set, ``start_run`` attempts to resume a run with the specified run ID and other parameters are ignored. ``run_id`` takes precedence over ``MLFLOW_RUN_ID``. If resuming an existing run, the run status is set to ``RunStatus.RUNNING``. MLflow sets a variety of default tags on the run, as defined in :ref:`MLflow system tags <system_tags>`. Args: run_id: If specified, get the run with the specified UUID and log parameters and metrics under that run. The run's end time is unset and its status is set to running, but the run's other attributes (``source_version``, ``source_type``, etc.) are not changed. experiment_id: ID of the experiment under which to create the current run (applicable only when ``run_id`` is not specified). If ``experiment_id`` argument is unspecified, will look for valid experiment in the following order: activated using ``set_experiment``, ``MLFLOW_EXPERIMENT_NAME`` environment variable, ``MLFLOW_EXPERIMENT_ID`` environment variable, or the default experiment as defined by the tracking server. run_name: Name of new run. Used only when ``run_id`` is unspecified. If a new run is created and ``run_name`` is not specified, a unique name will be generated for the run. nested: Controls whether run is nested in parent run. ``True`` creates a nested run. tags: An optional dictionary of string keys and values to set as tags on the run. If a run is being resumed, these tags are set on the resumed run. If a new run is being created, these tags are set on the new run. description: An optional string that populates the description box of the run. If a run is being resumed, the description is set on the resumed run. If a new run is being created, the description is set on the new run. log_system_metrics: bool, defaults to None. If True, system metrics will be logged to MLflow, e.g., cpu/gpu utilization. If None, we will check environment variable `MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING` to determine whether to log system metrics. System metrics logging is an experimental feature in MLflow 2.8 and subject to change. Returns: :py:class:`mlflow.ActiveRun` object that acts as a context manager wrapping the run's state. .. code-block:: python :test: :caption: Example import mlflow # Create nested runs experiment_id = mlflow.create_experiment("experiment1") with mlflow.start_run( run_name="PARENT_RUN", experiment_id=experiment_id, tags={"version": "v1", "priority": "P1"}, description="parent", ) as parent_run: mlflow.log_param("parent", "yes") with mlflow.start_run( run_name="CHILD_RUN", experiment_id=experiment_id, description="child", nested=True, ) as child_run: mlflow.log_param("child", "yes") print("parent run:") print(f"run_id: {parent_run.info.run_id}") print("description: {}".format(parent_run.data.tags.get("mlflow.note.content"))) print("version tag value: {}".format(parent_run.data.tags.get("version"))) print("priority tag value: {}".format(parent_run.data.tags.get("priority"))) print("--") # Search all child runs with a parent id query = f"tags.mlflow.parentRunId = '{parent_run.info.run_id}'" results = mlflow.search_runs(experiment_ids=[experiment_id], filter_string=query) print("child runs:") print(results[["run_id", "params.child", "tags.mlflow.runName"]]) .. code-block:: text :caption: Output parent run: run_id: 8979459433a24a52ab3be87a229a9cdf description: starting a parent for experiment 7 version tag value: v1 priority tag value: P1 -- child runs: run_id params.child tags.mlflow.runName 0 7d175204675e40328e46d9a6a5a7ee6a yes CHILD_RUN """ global _active_run_stack _validate_experiment_id_type(experiment_id) # back compat for int experiment_id experiment_id = str(experiment_id) if isinstance(experiment_id, int) else experiment_id if len(_active_run_stack) > 0 and not nested: raise Exception( ( "Run with UUID {} is already active. To start a new run, first end the " + "current run with mlflow.end_run(). To start a nested " + "run, call start_run with nested=True" ).format(_active_run_stack[0].info.run_id) ) client = MlflowClient() if run_id: existing_run_id = run_id elif run_id := MLFLOW_RUN_ID.get(): existing_run_id = run_id del os.environ[MLFLOW_RUN_ID.name] else: existing_run_id = None if existing_run_id: _validate_run_id(existing_run_id) active_run_obj = client.get_run(existing_run_id) # Check to see if experiment_id from environment matches experiment_id from set_experiment() if ( _active_experiment_id is not None and _active_experiment_id != active_run_obj.info.experiment_id ): raise MlflowException( f"Cannot start run with ID {existing_run_id} because active run ID " "does not match environment run ID. Make sure --experiment-name " "or --experiment-id matches experiment set with " "set_experiment(), or just use command-line arguments" ) # Check if the current run has been deleted. if active_run_obj.info.lifecycle_stage == LifecycleStage.DELETED: raise MlflowException( f"Cannot start run with ID {existing_run_id} because it is in the deleted state." ) # Use previous `end_time` because a value is required for `update_run_info`. end_time = active_run_obj.info.end_time _get_store().update_run_info( existing_run_id, run_status=RunStatus.RUNNING, end_time=end_time, run_name=None ) tags = tags or {} if description: if MLFLOW_RUN_NOTE in tags: raise MlflowException( f"Description is already set via the tag {MLFLOW_RUN_NOTE} in tags." f"Remove the key {MLFLOW_RUN_NOTE} from the tags or omit the description.", error_code=INVALID_PARAMETER_VALUE, ) tags[MLFLOW_RUN_NOTE] = description if tags: client.log_batch( run_id=existing_run_id, tags=[RunTag(key, str(value)) for key, value in tags.items()], ) active_run_obj = client.get_run(existing_run_id) else: parent_run_id = _active_run_stack[-1].info.run_id if len(_active_run_stack) > 0 else None exp_id_for_run = experiment_id if experiment_id is not None else _get_experiment_id() user_specified_tags = deepcopy(tags) or {} if description: if MLFLOW_RUN_NOTE in user_specified_tags: raise MlflowException( f"Description is already set via the tag {MLFLOW_RUN_NOTE} in tags." f"Remove the key {MLFLOW_RUN_NOTE} from the tags or omit the description.", error_code=INVALID_PARAMETER_VALUE, ) user_specified_tags[MLFLOW_RUN_NOTE] = description if parent_run_id is not None: user_specified_tags[MLFLOW_PARENT_RUN_ID] = parent_run_id if run_name: user_specified_tags[MLFLOW_RUN_NAME] = run_name resolved_tags = context_registry.resolve_tags(user_specified_tags) active_run_obj = client.create_run( experiment_id=exp_id_for_run, tags=resolved_tags, run_name=run_name, ) if log_system_metrics is None: # If `log_system_metrics` is not specified, we will check environment variable. log_system_metrics = MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING.get() if log_system_metrics: if importlib.util.find_spec("psutil") is None: raise MlflowException( "Failed to start system metrics monitoring as package `psutil` is not installed. " "Please run `pip install psutil` to resolve the issue, otherwise you can disable " "system metrics logging by passing `log_system_metrics=False` to " "`mlflow.start_run()` or calling `mlflow.disable_system_metrics_logging`." ) try: from mlflow.system_metrics.system_metrics_monitor import SystemMetricsMonitor system_monitor = SystemMetricsMonitor( active_run_obj.info.run_id, resume_logging=existing_run_id is not None, ) global run_id_to_system_metrics_monitor run_id_to_system_metrics_monitor[active_run_obj.info.run_id] = system_monitor system_monitor.start() except Exception as e: _logger.error(f"Failed to start system metrics monitoring: {e}.") _active_run_stack.append(ActiveRun(active_run_obj)) return _active_run_stack[-1]
[docs]def end_run(status: str = RunStatus.to_string(RunStatus.FINISHED)) -> None: """ End an active MLflow run (if there is one). .. code-block:: python :test: :caption: Example import mlflow # Start run and get status mlflow.start_run() run = mlflow.active_run() print(f"run_id: {run.info.run_id}; status: {run.info.status}") # End run and get status mlflow.end_run() run = mlflow.get_run(run.info.run_id) print(f"run_id: {run.info.run_id}; status: {run.info.status}") print("--") # Check for any active runs print(f"Active run: {mlflow.active_run()}") .. code-block:: text :caption: Output run_id: b47ee4563368419880b44ad8535f6371; status: RUNNING run_id: b47ee4563368419880b44ad8535f6371; status: FINISHED -- Active run: None """ global _active_run_stack, _last_active_run_id, run_id_to_system_metrics_monitor if len(_active_run_stack) > 0: # Clear out the global existing run environment variable as well. MLFLOW_RUN_ID.unset() run = _active_run_stack.pop() _last_active_run_id = run.info.run_id MlflowClient().set_terminated(_last_active_run_id, status) if _last_active_run_id in run_id_to_system_metrics_monitor: system_metrics_monitor = run_id_to_system_metrics_monitor.pop(_last_active_run_id) system_metrics_monitor.finish()
def _safe_end_run(): with contextlib.suppress(Exception): end_run() atexit.register(_safe_end_run)
[docs]def active_run() -> Optional[ActiveRun]: """ Get the currently active ``Run``, or None if no such run exists. **Note**: You cannot access currently-active run attributes (parameters, metrics, etc.) through the run returned by ``mlflow.active_run``. In order to access such attributes, use the :py:class:`mlflow.client.MlflowClient` as follows: .. code-block:: python :test: :caption: Example import mlflow mlflow.start_run() run = mlflow.active_run() print(f"Active run_id: {run.info.run_id}") mlflow.end_run() .. code-block:: text :caption: Output Active run_id: 6f252757005748708cd3aad75d1ff462 """ return _active_run_stack[-1] if len(_active_run_stack) > 0 else None
[docs]def last_active_run() -> Optional[Run]: """Gets the most recent active run. Examples: .. code-block:: python :test: :caption: To retrieve the most recent autologged run: import mlflow from sklearn.model_selection import train_test_split from sklearn.datasets import load_diabetes from sklearn.ensemble import RandomForestRegressor mlflow.autolog() db = load_diabetes() X_train, X_test, y_train, y_test = train_test_split(db.data, db.target) # Create and train models. rf = RandomForestRegressor(n_estimators=100, max_depth=6, max_features=3) rf.fit(X_train, y_train) # Use the model to make predictions on the test dataset. predictions = rf.predict(X_test) autolog_run = mlflow.last_active_run() .. code-block:: python :test: :caption: To get the most recently active run that ended: import mlflow mlflow.start_run() mlflow.end_run() run = mlflow.last_active_run() .. code-block:: python :test: :caption: To retrieve the currently active run: import mlflow mlflow.start_run() run = mlflow.last_active_run() mlflow.end_run() Returns: The active run (this is equivalent to ``mlflow.active_run()``) if one exists. Otherwise, the last run started from the current Python process that reached a terminal status (i.e. FINISHED, FAILED, or KILLED). """ _active_run = active_run() if _active_run is not None: return _active_run if _last_active_run_id is None: return None return get_run(_last_active_run_id)
[docs]def get_run(run_id: str) -> Run: """ Fetch the run from backend store. The resulting Run contains a collection of run metadata -- RunInfo as well as a collection of run parameters, tags, and metrics -- RunData. It also contains a collection of run inputs (experimental), including information about datasets used by the run -- RunInputs. In the case where multiple metrics with the same key are logged for the run, the RunData contains the most recently logged value at the largest step for each metric. Args: run_id: Unique identifier for the run. Returns: A single Run object, if the run exists. Otherwise, raises an exception. .. code-block:: python :test: :caption: Example import mlflow with mlflow.start_run() as run: mlflow.log_param("p", 0) run_id = run.info.run_id print( f"run_id: {run_id}; lifecycle_stage: {mlflow.get_run(run_id).info.lifecycle_stage}" ) .. code-block:: text :caption: Output run_id: 7472befefc754e388e8e922824a0cca5; lifecycle_stage: active """ return MlflowClient().get_run(run_id)
[docs]def get_parent_run(run_id: str) -> Optional[Run]: """Gets the parent run for the given run id if one exists. Args: run_id: Unique identifier for the child run. Returns: A single :py:class:`mlflow.entities.Run` object, if the parent run exists. Otherwise, returns None. .. code-block:: python :test: :caption: Example import mlflow # Create nested runs with mlflow.start_run(): with mlflow.start_run(nested=True) as child_run: child_run_id = child_run.info.run_id parent_run = mlflow.get_parent_run(child_run_id) print(f"child_run_id: {child_run_id}") print(f"parent_run_id: {parent_run.info.run_id}") .. code-block:: text :caption: Output child_run_id: 7d175204675e40328e46d9a6a5a7ee6a parent_run_id: 8979459433a24a52ab3be87a229a9cdf """ return MlflowClient().get_parent_run(run_id)
[docs]def log_param(key: str, value: Any, synchronous: Optional[bool] = None) -> Any: """ Log a parameter (e.g. model hyperparameter) under the current run. If no run is active, this method will create a new active run. Args: key: Parameter name. This string may only contain alphanumerics, underscores (_), dashes (-), periods (.), spaces ( ), and slashes (/). All backend stores support keys up to length 250, but some may support larger keys. value: Parameter value, but will be string-ified if not. All built-in backend stores support values up to length 6000, but some may support larger values. synchronous: *Experimental* If True, blocks until the parameter is logged successfully. If False, logs the parameter asynchronously and returns a future representing the logging operation. If None, read from environment variable `MLFLOW_ENABLE_ASYNC_LOGGING`, which defaults to False if not set. Returns: When `synchronous=True`, returns parameter value. When `synchronous=False`, returns an :py:class:`mlflow.utils.async_logging.run_operations.RunOperations` instance that represents future for logging operation. .. code-block:: python :test: :caption: Example import mlflow with mlflow.start_run(): value = mlflow.log_param("learning_rate", 0.01) assert value == 0.01 value = mlflow.log_param("learning_rate", 0.02, synchronous=False) """ run_id = _get_or_start_run().info.run_id synchronous = synchronous if synchronous is not None else not MLFLOW_ENABLE_ASYNC_LOGGING.get() return MlflowClient().log_param(run_id, key, value, synchronous=synchronous)
[docs]def flush_async_logging() -> None: """Flush all pending async logging.""" _get_store().flush_async_logging()
[docs]def set_experiment_tag(key: str, value: Any) -> None: """ Set a tag on the current experiment. Value is converted to a string. Args: key: Tag name. This string may only contain alphanumerics, underscores (_), dashes (-), periods (.), spaces ( ), and slashes (/). All backend stores will support keys up to length 250, but some may support larger keys. value: Tag value, but will be string-ified if not. All backend stores will support values up to length 5000, but some may support larger values. .. code-block:: python :test: :caption: Example import mlflow with mlflow.start_run(): mlflow.set_experiment_tag("release.version", "2.2.0") """ experiment_id = _get_experiment_id() MlflowClient().set_experiment_tag(experiment_id, key, value)
[docs]def set_tag(key: str, value: Any, synchronous: Optional[bool] = None) -> Optional[RunOperations]: """ Set a tag under the current run. If no run is active, this method will create a new active run. Args: key: Tag name. This string may only contain alphanumerics, underscores (_), dashes (-), periods (.), spaces ( ), and slashes (/). All backend stores will support keys up to length 250, but some may support larger keys. value: Tag value, but will be string-ified if not. All backend stores will support values up to length 5000, but some may support larger values. synchronous: *Experimental* If True, blocks until the tag is logged successfully. If False, logs the tag asynchronously and returns a future representing the logging operation. If None, read from environment variable `MLFLOW_ENABLE_ASYNC_LOGGING`, which defaults to False if not set. Returns: When `synchronous=True`, returns None. When `synchronous=False`, returns an :py:class:`mlflow.utils.async_logging.run_operations.RunOperations` instance that represents future for logging operation. .. code-block:: python :test: :caption: Example import mlflow # Set a tag. with mlflow.start_run(): mlflow.set_tag("release.version", "2.2.0") # Set a tag in async fashion. with mlflow.start_run(): mlflow.set_tag("release.version", "2.2.1", synchronous=False) """ run_id = _get_or_start_run().info.run_id synchronous = synchronous if synchronous is not None else not MLFLOW_ENABLE_ASYNC_LOGGING.get() return MlflowClient().set_tag(run_id, key, value, synchronous=synchronous)
[docs]def delete_tag(key: str) -> None: """ Delete a tag from a run. This is irreversible. If no run is active, this method will create a new active run. Args: key: Name of the tag .. code-block:: python :test: :caption: Example import mlflow tags = {"engineering": "ML Platform", "engineering_remote": "ML Platform"} with mlflow.start_run() as run: mlflow.set_tags(tags) with mlflow.start_run(run_id=run.info.run_id): mlflow.delete_tag("engineering_remote") """ run_id = _get_or_start_run().info.run_id MlflowClient().delete_tag(run_id, key)
[docs]def log_metric( key: str, value: float, step: Optional[int] = None, synchronous: Optional[bool] = None, timestamp: Optional[int] = None, run_id: Optional[str] = None, ) -> Optional[RunOperations]: """ Log a metric under the current run. If no run is active, this method will create a new active run. Args: key: Metric name. This string may only contain alphanumerics, underscores (_), dashes (-), periods (.), spaces ( ), and slashes (/). All backend stores will support keys up to length 250, but some may support larger keys. value: Metric value. Note that some special values such as +/- Infinity may be replaced by other values depending on the store. For example, the SQLAlchemy store replaces +/- Infinity with max / min float values. All backend stores will support values up to length 5000, but some may support larger values. step: Metric step. Defaults to zero if unspecified. timestamp: Time when this metric was calculated. Defaults to the current system time. synchronous: *Experimental* If True, blocks until the metric is logged successfully. If False, logs the metric asynchronously and returns a future representing the logging operation. If None, read from environment variable `MLFLOW_ENABLE_ASYNC_LOGGING`, which defaults to False if not set. Returns: When `synchronous=True`, returns None. When `synchronous=False`, returns `RunOperations` that represents future for logging operation. .. code-block:: python :test: :caption: Example import mlflow # Log a metric with mlflow.start_run(): mlflow.log_metric("mse", 2500.00) # Log a metric in async fashion. with mlflow.start_run(): mlflow.log_metric("mse", 2500.00, synchronous=False) """ run_id = run_id or _get_or_start_run().info.run_id synchronous = synchronous if synchronous is not None else not MLFLOW_ENABLE_ASYNC_LOGGING.get() return MlflowClient().log_metric( run_id, key, value, timestamp or get_current_time_millis(), step or 0, synchronous=synchronous, )
[docs]def log_metrics( metrics: Dict[str, float], step: Optional[int] = None, synchronous: Optional[bool] = None, run_id: Optional[str] = None, ) -> Optional[RunOperations]: """ Log multiple metrics for the current run. If no run is active, this method will create a new active run. Args: metrics: Dictionary of metric_name: String -> value: Float. Note that some special values such as +/- Infinity may be replaced by other values depending on the store. For example, sql based store may replace +/- Infinity with max / min float values. step: A single integer step at which to log the specified Metrics. If unspecified, each metric is logged at step zero. synchronous: *Experimental* If True, blocks until the metrics are logged successfully. If False, logs the metrics asynchronously and returns a future representing the logging operation. If None, read from environment variable `MLFLOW_ENABLE_ASYNC_LOGGING`, which defaults to False if not set. Returns: When `synchronous=True`, returns None. When `synchronous=False`, returns an :py:class:`mlflow.utils.async_logging.run_operations.RunOperations` instance that represents future for logging operation. .. code-block:: python :test: :caption: Example import mlflow metrics = {"mse": 2500.00, "rmse": 50.00} # Log a batch of metrics with mlflow.start_run(): mlflow.log_metrics(metrics) # Log a batch of metrics in async fashion. with mlflow.start_run(): mlflow.log_metrics(metrics, synchronous=False) """ run_id = run_id or _get_or_start_run().info.run_id timestamp = get_current_time_millis() metrics_arr = [Metric(key, value, timestamp, step or 0) for key, value in metrics.items()] synchronous = synchronous if synchronous is not None else not MLFLOW_ENABLE_ASYNC_LOGGING.get() return MlflowClient().log_batch( run_id=run_id, metrics=metrics_arr, params=[], tags=[], synchronous=synchronous )
[docs]def log_params( params: Dict[str, Any], synchronous: Optional[bool] = None ) -> Optional[RunOperations]: """ Log a batch of params for the current run. If no run is active, this method will create a new active run. Args: params: Dictionary of param_name: String -> value: (String, but will be string-ified if not) synchronous: *Experimental* If True, blocks until the parameters are logged successfully. If False, logs the parameters asynchronously and returns a future representing the logging operation. If None, read from environment variable `MLFLOW_ENABLE_ASYNC_LOGGING`, which defaults to False if not set. Returns: When `synchronous=True`, returns None. When `synchronous=False`, returns an :py:class:`mlflow.utils.async_logging.run_operations.RunOperations` instance that represents future for logging operation. .. code-block:: python :test: :caption: Example import mlflow params = {"learning_rate": 0.01, "n_estimators": 10} # Log a batch of parameters with mlflow.start_run(): mlflow.log_params(params) # Log a batch of parameters in async fashion. with mlflow.start_run(): mlflow.log_params(params, synchronous=False) """ run_id = _get_or_start_run().info.run_id params_arr = [Param(key, str(value)) for key, value in params.items()] synchronous = synchronous if synchronous is not None else not MLFLOW_ENABLE_ASYNC_LOGGING.get() return MlflowClient().log_batch( run_id=run_id, metrics=[], params=params_arr, tags=[], synchronous=synchronous )
[docs]def log_input( dataset: Dataset, context: Optional[str] = None, tags: Optional[Dict[str, str]] = None ) -> None: """ Log a dataset used in the current run. Args: dataset: :py:class:`mlflow.data.dataset.Dataset` object to be logged. context: Context in which the dataset is used. For example: "training", "testing". This will be set as an input tag with key `mlflow.data.context`. tags: Tags to be associated with the dataset. Dictionary of tag_key -> tag_value. .. code-block:: python :test: :caption: Example import numpy as np import mlflow array = np.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) dataset = mlflow.data.from_numpy(array, source="data.csv") # Log an input dataset used for training with mlflow.start_run(): mlflow.log_input(dataset, context="training") """ run_id = _get_or_start_run().info.run_id tags_to_log = [] if tags: tags_to_log.extend([InputTag(key=key, value=value) for key, value in tags.items()]) if context: tags_to_log.append(InputTag(key=MLFLOW_DATASET_CONTEXT, value=context)) dataset_input = DatasetInput(dataset=dataset._to_mlflow_entity(), tags=tags_to_log) MlflowClient().log_inputs(run_id=run_id, datasets=[dataset_input])
[docs]def set_experiment_tags(tags: Dict[str, Any]) -> None: """ Set tags for the current active experiment. Args: tags: Dictionary containing tag names and corresponding values. .. code-block:: python :test: :caption: Example import mlflow tags = { "engineering": "ML Platform", "release.candidate": "RC1", "release.version": "2.2.0", } # Set a batch of tags with mlflow.start_run(): mlflow.set_experiment_tags(tags) """ for key, value in tags.items(): set_experiment_tag(key, value)
[docs]def set_tags(tags: Dict[str, Any], synchronous: Optional[bool] = None) -> Optional[RunOperations]: """ Log a batch of tags for the current run. If no run is active, this method will create a new active run. Args: tags: Dictionary of tag_name: String -> value: (String, but will be string-ified if not) synchronous: *Experimental* If True, blocks until tags are logged successfully. If False, logs tags asynchronously and returns a future representing the logging operation. If None, read from environment variable `MLFLOW_ENABLE_ASYNC_LOGGING`, which defaults to False if not set. Returns: When `synchronous=True`, returns None. When `synchronous=False`, returns an :py:class:`mlflow.utils.async_logging.run_operations.RunOperations` instance that represents future for logging operation. .. code-block:: python :test: :caption: Example import mlflow tags = { "engineering": "ML Platform", "release.candidate": "RC1", "release.version": "2.2.0", } # Set a batch of tags with mlflow.start_run(): mlflow.set_tags(tags) # Set a batch of tags in async fashion. with mlflow.start_run(): mlflow.set_tags(tags, synchronous=False) """ run_id = _get_or_start_run().info.run_id tags_arr = [RunTag(key, str(value)) for key, value in tags.items()] synchronous = synchronous if synchronous is not None else not MLFLOW_ENABLE_ASYNC_LOGGING.get() return MlflowClient().log_batch( run_id=run_id, metrics=[], params=[], tags=tags_arr, synchronous=synchronous )
[docs]def log_artifact( local_path: str, artifact_path: Optional[str] = None, run_id: Optional[str] = None ) -> None: """ Log a local file or directory as an artifact of the currently active run. If no run is active, this method will create a new active run. Args: local_path: Path to the file to write. artifact_path: If provided, the directory in ``artifact_uri`` to write to. .. code-block:: python :test: :caption: Example import tempfile from pathlib import Path import mlflow # Create a features.txt artifact file features = "rooms, zipcode, median_price, school_rating, transport" with tempfile.TemporaryDirectory() as tmp_dir: path = Path(tmp_dir, "features.txt") path.write_text(features) # With artifact_path=None write features.txt under # root artifact_uri/artifacts directory with mlflow.start_run(): mlflow.log_artifact(path) """ run_id = run_id or _get_or_start_run().info.run_id MlflowClient().log_artifact(run_id, local_path, artifact_path)
[docs]def log_artifacts( local_dir: str, artifact_path: Optional[str] = None, run_id: Optional[str] = None ) -> None: """ Log all the contents of a local directory as artifacts of the run. If no run is active, this method will create a new active run. Args: local_dir: Path to the directory of files to write. artifact_path: If provided, the directory in ``artifact_uri`` to write to. .. code-block:: python :test: :caption: Example import json import tempfile from pathlib import Path import mlflow # Create some files to preserve as artifacts features = "rooms, zipcode, median_price, school_rating, transport" data = {"state": "TX", "Available": 25, "Type": "Detached"} with tempfile.TemporaryDirectory() as tmp_dir: tmp_dir = Path(tmp_dir) with (tmp_dir / "data.json").open("w") as f: json.dump(data, f, indent=2) with (tmp_dir / "features.json").open("w") as f: f.write(features) # Write all files in `tmp_dir` to root artifact_uri/states with mlflow.start_run(): mlflow.log_artifacts(tmp_dir, artifact_path="states") """ run_id = run_id or _get_or_start_run().info.run_id MlflowClient().log_artifacts(run_id, local_dir, artifact_path)
[docs]def log_text(text: str, artifact_file: str, run_id: Optional[str] = None) -> None: """ Log text as an artifact. Args: text: String containing text to log. artifact_file: The run-relative artifact file path in posixpath format to which the text is saved (e.g. "dir/file.txt"). .. code-block:: python :test: :caption: Example import mlflow with mlflow.start_run(): # Log text to a file under the run's root artifact directory mlflow.log_text("text1", "file1.txt") # Log text in a subdirectory of the run's root artifact directory mlflow.log_text("text2", "dir/file2.txt") # Log HTML text mlflow.log_text("<h1>header</h1>", "index.html") """ run_id = run_id or _get_or_start_run().info.run_id MlflowClient().log_text(run_id, text, artifact_file)
[docs]def log_dict(dictionary: Dict[str, Any], artifact_file: str, run_id: Optional[str] = None) -> None: """ Log a JSON/YAML-serializable object (e.g. `dict`) as an artifact. The serialization format (JSON or YAML) is automatically inferred from the extension of `artifact_file`. If the file extension doesn't exist or match any of [".json", ".yml", ".yaml"], JSON format is used. Args: dictionary: Dictionary to log. artifact_file: The run-relative artifact file path in posixpath format to which the dictionary is saved (e.g. "dir/data.json"). .. code-block:: python :test: :caption: Example import mlflow dictionary = {"k": "v"} with mlflow.start_run(): # Log a dictionary as a JSON file under the run's root artifact directory mlflow.log_dict(dictionary, "data.json") # Log a dictionary as a YAML file in a subdirectory of the run's root artifact directory mlflow.log_dict(dictionary, "dir/data.yml") # If the file extension doesn't exist or match any of [".json", ".yaml", ".yml"], # JSON format is used. mlflow.log_dict(dictionary, "data") mlflow.log_dict(dictionary, "data.txt") """ run_id = run_id or _get_or_start_run().info.run_id MlflowClient().log_dict(run_id, dictionary, artifact_file)
[docs]def log_figure( figure: Union["matplotlib.figure.Figure", "plotly.graph_objects.Figure"], artifact_file: str, *, save_kwargs: Optional[Dict[str, Any]] = None, ) -> None: """ Log a figure as an artifact. The following figure objects are supported: - `matplotlib.figure.Figure`_ - `plotly.graph_objects.Figure`_ .. _matplotlib.figure.Figure: https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html .. _plotly.graph_objects.Figure: https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html Args: figure: Figure to log. artifact_file: The run-relative artifact file path in posixpath format to which the figure is saved (e.g. "dir/file.png"). save_kwargs: Additional keyword arguments passed to the method that saves the figure. .. code-block:: python :test: :caption: Matplotlib Example import mlflow import matplotlib.pyplot as plt fig, ax = plt.subplots() ax.plot([0, 1], [2, 3]) with mlflow.start_run(): mlflow.log_figure(fig, "figure.png") .. code-block:: python :test: :caption: Plotly Example import mlflow from plotly import graph_objects as go fig = go.Figure(go.Scatter(x=[0, 1], y=[2, 3])) with mlflow.start_run(): mlflow.log_figure(fig, "figure.html") """ run_id = _get_or_start_run().info.run_id MlflowClient().log_figure(run_id, figure, artifact_file, save_kwargs=save_kwargs)
[docs]def log_image(image: Union["numpy.ndarray", "PIL.Image.Image"], artifact_file: str) -> None: """ Log an image as an artifact. The following image objects are supported: - `numpy.ndarray`_ - `PIL.Image.Image`_ .. _numpy.ndarray: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html .. _PIL.Image.Image: https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image Numpy array support - data type (( ) represents a valid value range): - bool - integer (0 ~ 255) - unsigned integer (0 ~ 255) - float (0.0 ~ 1.0) .. warning:: - Out-of-range integer values will be **clipped** to [0, 255]. - Out-of-range float values will be **clipped** to [0, 1]. - shape (H: height, W: width): - H x W (Grayscale) - H x W x 1 (Grayscale) - H x W x 3 (an RGB channel order is assumed) - H x W x 4 (an RGBA channel order is assumed) Args: image: Image to log. artifact_file: The run-relative artifact file path in posixpath format to which the image is saved (e.g. "dir/image.png"). .. code-block:: python :test: :caption: Numpy Example import mlflow import numpy as np image = np.random.randint(0, 256, size=(100, 100, 3), dtype=np.uint8) with mlflow.start_run(): mlflow.log_image(image, "image.png") .. code-block:: python :test: :caption: Pillow Example import mlflow from PIL import Image image = Image.new("RGB", (100, 100)) with mlflow.start_run(): mlflow.log_image(image, "image.png") """ run_id = _get_or_start_run().info.run_id MlflowClient().log_image(run_id, image, artifact_file)
[docs]@experimental def log_table( data: Union[Dict[str, Any], "pandas.DataFrame"], artifact_file: str, run_id: Optional[str] = None, ) -> None: """ Log a table to MLflow Tracking as a JSON artifact. If the artifact_file already exists in the run, the data would be appended to the existing artifact_file. Args: data: Dictionary or pandas.DataFrame to log. artifact_file: The run-relative artifact file path in posixpath format to which the table is saved (e.g. "dir/file.json"). .. code-block:: python :test: :caption: Dictionary Example import mlflow table_dict = { "inputs": ["What is MLflow?", "What is Databricks?"], "outputs": ["MLflow is ...", "Databricks is ..."], "toxicity": [0.0, 0.0], } with mlflow.start_run(): # Log the dictionary as a table mlflow.log_table(data=table_dict, artifact_file="qabot_eval_results.json") .. code-block:: python :test: :caption: Pandas DF Example import mlflow import pandas as pd table_dict = { "inputs": ["What is MLflow?", "What is Databricks?"], "outputs": ["MLflow is ...", "Databricks is ..."], "toxicity": [0.0, 0.0], } df = pd.DataFrame.from_dict(table_dict) with mlflow.start_run(): # Log the df as a table mlflow.log_table(data=df, artifact_file="qabot_eval_results.json") """ run_id = run_id or _get_or_start_run().info.run_id MlflowClient().log_table(run_id, data, artifact_file)
[docs]@experimental def load_table( artifact_file: str, run_ids: Optional[List[str]] = None, extra_columns: Optional[List[str]] = None, ) -> "pandas.DataFrame": """ Load a table from MLflow Tracking as a pandas.DataFrame. The table is loaded from the specified artifact_file in the specified run_ids. The extra_columns are columns that are not in the table but are augmented with run information and added to the DataFrame. Args: artifact_file: The run-relative artifact file path in posixpath format to which table to load (e.g. "dir/file.json"). run_ids: Optional list of run_ids to load the table from. If no run_ids are specified, the table is loaded from all runs in the current experiment. extra_columns: Optional list of extra columns to add to the returned DataFrame For example, if extra_columns=["run_id"], then the returned DataFrame will have a column named run_id. Returns: pandas.DataFrame containing the loaded table if the artifact exists or else throw a MlflowException. .. code-block:: python :test: :caption: Example with passing run_ids import mlflow table_dict = { "inputs": ["What is MLflow?", "What is Databricks?"], "outputs": ["MLflow is ...", "Databricks is ..."], "toxicity": [0.0, 0.0], } with mlflow.start_run() as run: # Log the dictionary as a table mlflow.log_table(data=table_dict, artifact_file="qabot_eval_results.json") run_id = run.info.run_id loaded_table = mlflow.load_table( artifact_file="qabot_eval_results.json", run_ids=[run_id], # Append a column containing the associated run ID for each row extra_columns=["run_id"], ) .. code-block:: python :test: :caption: Example with passing no run_ids # Loads the table with the specified name for all runs in the given # experiment and joins them together import mlflow table_dict = { "inputs": ["What is MLflow?", "What is Databricks?"], "outputs": ["MLflow is ...", "Databricks is ..."], "toxicity": [0.0, 0.0], } with mlflow.start_run(): # Log the dictionary as a table mlflow.log_table(data=table_dict, artifact_file="qabot_eval_results.json") loaded_table = mlflow.load_table( "qabot_eval_results.json", # Append the run ID and the parent run ID to the table extra_columns=["run_id"], ) """ experiment_id = _get_experiment_id() return MlflowClient().load_table(experiment_id, artifact_file, run_ids, extra_columns)
def _record_logged_model(mlflow_model, run_id=None): run_id = run_id or _get_or_start_run().info.run_id MlflowClient()._record_logged_model(run_id, mlflow_model)
[docs]def get_experiment(experiment_id: str) -> Experiment: """Retrieve an experiment by experiment_id from the backend store Args: experiment_id: The string-ified experiment ID returned from ``create_experiment``. Returns: :py:class:`mlflow.entities.Experiment` .. code-block:: python :test: :caption: Example import mlflow experiment = mlflow.get_experiment("0") print(f"Name: {experiment.name}") print(f"Artifact Location: {experiment.artifact_location}") print(f"Tags: {experiment.tags}") print(f"Lifecycle_stage: {experiment.lifecycle_stage}") print(f"Creation timestamp: {experiment.creation_time}") .. code-block:: text :caption: Output Name: Default Artifact Location: file:///.../mlruns/0 Tags: {} Lifecycle_stage: active Creation timestamp: 1662004217511 """ return MlflowClient().get_experiment(experiment_id)
[docs]def get_experiment_by_name(name: str) -> Optional[Experiment]: """ Retrieve an experiment by experiment name from the backend store Args: name: The case sensitive experiment name. Returns: An instance of :py:class:`mlflow.entities.Experiment` if an experiment with the specified name exists, otherwise None. .. code-block:: python :test: :caption: Example import mlflow # Case sensitive name experiment = mlflow.get_experiment_by_name("Default") print(f"Experiment_id: {experiment.experiment_id}") print(f"Artifact Location: {experiment.artifact_location}") print(f"Tags: {experiment.tags}") print(f"Lifecycle_stage: {experiment.lifecycle_stage}") print(f"Creation timestamp: {experiment.creation_time}") .. code-block:: text :caption: Output Experiment_id: 0 Artifact Location: file:///.../mlruns/0 Tags: {} Lifecycle_stage: active Creation timestamp: 1662004217511 """ return MlflowClient().get_experiment_by_name(name)
[docs]def search_experiments( view_type: int = ViewType.ACTIVE_ONLY, max_results: Optional[int] = None, filter_string: Optional[str] = None, order_by: Optional[List[str]] = None, ) -> List[Experiment]: """ Search for experiments that match the specified search query. Args: view_type: One of enum values ``ACTIVE_ONLY``, ``DELETED_ONLY``, or ``ALL`` defined in :py:class:`mlflow.entities.ViewType`. max_results: If passed, specifies the maximum number of experiments desired. If not passed, all experiments will be returned. filter_string: Filter query string (e.g., ``"name = 'my_experiment'"``), defaults to searching for all experiments. The following identifiers, comparators, and logical operators are supported. Identifiers - ``name``: Experiment name - ``creation_time``: Experiment creation time - ``last_update_time``: Experiment last update time - ``tags.<tag_key>``: Experiment tag. If ``tag_key`` contains spaces, it must be wrapped with backticks (e.g., ``"tags.`extra key`"``). Comparators for string attributes and tags - ``=``: Equal to - ``!=``: Not equal to - ``LIKE``: Case-sensitive pattern match - ``ILIKE``: Case-insensitive pattern match Comparators for numeric attributes - ``=``: Equal to - ``!=``: Not equal to - ``<``: Less than - ``<=``: Less than or equal to - ``>``: Greater than - ``>=``: Greater than or equal to Logical operators - ``AND``: Combines two sub-queries and returns True if both of them are True. order_by: List of columns to order by. The ``order_by`` column can contain an optional ``DESC`` or ``ASC`` value (e.g., ``"name DESC"``). The default ordering is ``ASC``, so ``"name"`` is equivalent to ``"name ASC"``. If unspecified, defaults to ``["last_update_time DESC"]``, which lists experiments updated most recently first. The following fields are supported: - ``experiment_id``: Experiment ID - ``name``: Experiment name - ``creation_time``: Experiment creation time - ``last_update_time``: Experiment last update time Returns: A list of :py:class:`Experiment <mlflow.entities.Experiment>` objects. .. code-block:: python :test: :caption: Example import mlflow def assert_experiment_names_equal(experiments, expected_names): actual_names = [e.name for e in experiments if e.name != "Default"] assert actual_names == expected_names, (actual_names, expected_names) mlflow.set_tracking_uri("sqlite:///:memory:") # Create experiments for name, tags in [ ("a", None), ("b", None), ("ab", {"k": "v"}), ("bb", {"k": "V"}), ]: mlflow.create_experiment(name, tags=tags) # Search for experiments with name "a" experiments = mlflow.search_experiments(filter_string="name = 'a'") assert_experiment_names_equal(experiments, ["a"]) # Search for experiments with name starting with "a" experiments = mlflow.search_experiments(filter_string="name LIKE 'a%'") assert_experiment_names_equal(experiments, ["ab", "a"]) # Search for experiments with tag key "k" and value ending with "v" or "V" experiments = mlflow.search_experiments(filter_string="tags.k ILIKE '%v'") assert_experiment_names_equal(experiments, ["bb", "ab"]) # Search for experiments with name ending with "b" and tag {"k": "v"} experiments = mlflow.search_experiments(filter_string="name LIKE '%b' AND tags.k = 'v'") assert_experiment_names_equal(experiments, ["ab"]) # Sort experiments by name in ascending order experiments = mlflow.search_experiments(order_by=["name"]) assert_experiment_names_equal(experiments, ["a", "ab", "b", "bb"]) # Sort experiments by ID in descending order experiments = mlflow.search_experiments(order_by=["experiment_id DESC"]) assert_experiment_names_equal(experiments, ["bb", "ab", "b", "a"]) """ def pagination_wrapper_func(number_to_get, next_page_token): return MlflowClient().search_experiments( view_type=view_type, max_results=number_to_get, filter_string=filter_string, order_by=order_by, page_token=next_page_token, ) return get_results_from_paginated_fn( pagination_wrapper_func, SEARCH_MAX_RESULTS_DEFAULT, max_results, )
[docs]def create_experiment( name: str, artifact_location: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, ) -> str: """ Create an experiment. Args: name: The experiment name, which must be unique and is case sensitive. artifact_location: The location to store run artifacts. If not provided, the server picks an appropriate default. tags: An optional dictionary of string keys and values to set as tags on the experiment. Returns: String ID of the created experiment. .. code-block:: python :test: :caption: Example import mlflow from pathlib import Path # Create an experiment name, which must be unique and case sensitive experiment_id = mlflow.create_experiment( "Social NLP Experiments", artifact_location=Path.cwd().joinpath("mlruns").as_uri(), tags={"version": "v1", "priority": "P1"}, ) experiment = mlflow.get_experiment(experiment_id) print(f"Name: {experiment.name}") print(f"Experiment_id: {experiment.experiment_id}") print(f"Artifact Location: {experiment.artifact_location}") print(f"Tags: {experiment.tags}") print(f"Lifecycle_stage: {experiment.lifecycle_stage}") print(f"Creation timestamp: {experiment.creation_time}") .. code-block:: text :caption: Output Name: Social NLP Experiments Experiment_id: 1 Artifact Location: file:///.../mlruns Tags: {'version': 'v1', 'priority': 'P1'} Lifecycle_stage: active Creation timestamp: 1662004217511 """ return MlflowClient().create_experiment(name, artifact_location, tags)
[docs]def delete_experiment(experiment_id: str) -> None: """ Delete an experiment from the backend store. Args: experiment_id: The string-ified experiment ID returned from ``create_experiment``. .. code-block:: python :test: :caption: Example import mlflow experiment_id = mlflow.create_experiment("New Experiment") mlflow.delete_experiment(experiment_id) # Examine the deleted experiment details. experiment = mlflow.get_experiment(experiment_id) print(f"Name: {experiment.name}") print(f"Artifact Location: {experiment.artifact_location}") print(f"Lifecycle_stage: {experiment.lifecycle_stage}") print(f"Last Updated timestamp: {experiment.last_update_time}") .. code-block:: text :caption: Output Name: New Experiment Artifact Location: file:///.../mlruns/2 Lifecycle_stage: deleted Last Updated timestamp: 1662004217511 """ MlflowClient().delete_experiment(experiment_id)
[docs]def delete_run(run_id: str) -> None: """ Deletes a run with the given ID. Args: run_id: Unique identifier for the run to delete. .. code-block:: python :test: :caption: Example import mlflow with mlflow.start_run() as run: mlflow.log_param("p", 0) run_id = run.info.run_id mlflow.delete_run(run_id) lifecycle_stage = mlflow.get_run(run_id).info.lifecycle_stage print(f"run_id: {run_id}; lifecycle_stage: {lifecycle_stage}") .. code-block:: text :caption: Output run_id: 45f4af3e6fd349e58579b27fcb0b8277; lifecycle_stage: deleted """ MlflowClient().delete_run(run_id)
[docs]def get_artifact_uri(artifact_path: Optional[str] = None) -> str: """ Get the absolute URI of the specified artifact in the currently active run. If `path` is not specified, the artifact root URI of the currently active run will be returned; calls to ``log_artifact`` and ``log_artifacts`` write artifact(s) to subdirectories of the artifact root URI. If no run is active, this method will create a new active run. Args: artifact_path: The run-relative artifact path for which to obtain an absolute URI. For example, "path/to/artifact". If unspecified, the artifact root URI for the currently active run will be returned. Returns: An *absolute* URI referring to the specified artifact or the currently active run's artifact root. For example, if an artifact path is provided and the currently active run uses an S3-backed store, this may be a uri of the form ``s3://<bucket_name>/path/to/artifact/root/path/to/artifact``. If an artifact path is not provided and the currently active run uses an S3-backed store, this may be a URI of the form ``s3://<bucket_name>/path/to/artifact/root``. .. code-block:: python :test: :caption: Example import mlflow features = "rooms, zipcode, median_price, school_rating, transport" with open("features.txt", "w") as f: f.write(features) # Log the artifact in a directory "features" under the root artifact_uri/features with mlflow.start_run(): mlflow.log_artifact("features.txt", artifact_path="features") # Fetch the artifact uri root directory artifact_uri = mlflow.get_artifact_uri() print(f"Artifact uri: {artifact_uri}") # Fetch a specific artifact uri artifact_uri = mlflow.get_artifact_uri(artifact_path="features/features.txt") print(f"Artifact uri: {artifact_uri}") .. code-block:: text :caption: Output Artifact uri: file:///.../0/a46a80f1c9644bd8f4e5dd5553fffce/artifacts Artifact uri: file:///.../0/a46a80f1c9644bd8f4e5dd5553fffce/artifacts/features/features.txt """ return artifact_utils.get_artifact_uri( run_id=_get_or_start_run().info.run_id, artifact_path=artifact_path )
[docs]def search_runs( experiment_ids: Optional[List[str]] = None, filter_string: str = "", run_view_type: int = ViewType.ACTIVE_ONLY, max_results: int = SEARCH_MAX_RESULTS_PANDAS, order_by: Optional[List[str]] = None, output_format: str = "pandas", search_all_experiments: bool = False, experiment_names: Optional[List[str]] = None, ) -> Union[List[Run], "pandas.DataFrame"]: """ Search for Runs that fit the specified criteria. Args: experiment_ids: List of experiment IDs. Search can work with experiment IDs or experiment names, but not both in the same call. Values other than ``None`` or ``[]`` will result in error if ``experiment_names`` is also not ``None`` or ``[]``. ``None`` will default to the active experiment if ``experiment_names`` is ``None`` or ``[]``. filter_string: Filter query string, defaults to searching all runs. run_view_type: one of enum values ``ACTIVE_ONLY``, ``DELETED_ONLY``, or ``ALL`` runs defined in :py:class:`mlflow.entities.ViewType`. max_results: The maximum number of runs to put in the dataframe. Default is 100,000 to avoid causing out-of-memory issues on the user's machine. order_by: List of columns to order by (e.g., "metrics.rmse"). The ``order_by`` column can contain an optional ``DESC`` or ``ASC`` value. The default is ``ASC``. The default ordering is to sort by ``start_time DESC``, then ``run_id``. output_format: The output format to be returned. If ``pandas``, a ``pandas.DataFrame`` is returned and, if ``list``, a list of :py:class:`mlflow.entities.Run` is returned. search_all_experiments: Boolean specifying whether all experiments should be searched. Only honored if ``experiment_ids`` is ``[]`` or ``None``. experiment_names: List of experiment names. Search can work with experiment IDs or experiment names, but not both in the same call. Values other than ``None`` or ``[]`` will result in error if ``experiment_ids`` is also not ``None`` or ``[]``. ``None`` will default to the active experiment if ``experiment_ids`` is ``None`` or ``[]``. Returns: If output_format is ``list``: a list of :py:class:`mlflow.entities.Run`. If output_format is ``pandas``: ``pandas.DataFrame`` of runs, where each metric, parameter, and tag is expanded into its own column named metrics.*, params.*, or tags.* respectively. For runs that don't have a particular metric, parameter, or tag, the value for the corresponding column is (NumPy) ``Nan``, ``None``, or ``None`` respectively. .. code-block:: python :test: :caption: Example import mlflow # Create an experiment and log two runs under it experiment_name = "Social NLP Experiments" experiment_id = mlflow.create_experiment(experiment_name) with mlflow.start_run(experiment_id=experiment_id): mlflow.log_metric("m", 1.55) mlflow.set_tag("s.release", "1.1.0-RC") with mlflow.start_run(experiment_id=experiment_id): mlflow.log_metric("m", 2.50) mlflow.set_tag("s.release", "1.2.0-GA") # Search for all the runs in the experiment with the given experiment ID df = mlflow.search_runs([experiment_id], order_by=["metrics.m DESC"]) print(df[["metrics.m", "tags.s.release", "run_id"]]) print("--") # Search the experiment_id using a filter_string with tag # that has a case insensitive pattern filter_string = "tags.s.release ILIKE '%rc%'" df = mlflow.search_runs([experiment_id], filter_string=filter_string) print(df[["metrics.m", "tags.s.release", "run_id"]]) print("--") # Search for all the runs in the experiment with the given experiment name df = mlflow.search_runs(experiment_names=[experiment_name], order_by=["metrics.m DESC"]) print(df[["metrics.m", "tags.s.release", "run_id"]]) .. code-block:: text :caption: Output metrics.m tags.s.release run_id 0 2.50 1.2.0-GA 147eed886ab44633902cc8e19b2267e2 1 1.55 1.1.0-RC 5cc7feaf532f496f885ad7750809c4d4 -- metrics.m tags.s.release run_id 0 1.55 1.1.0-RC 5cc7feaf532f496f885ad7750809c4d4 -- metrics.m tags.s.release run_id 0 2.50 1.2.0-GA 147eed886ab44633902cc8e19b2267e2 1 1.55 1.1.0-RC 5cc7feaf532f496f885ad7750809c4d4 """ no_ids = experiment_ids is None or len(experiment_ids) == 0 no_names = experiment_names is None or len(experiment_names) == 0 no_ids_or_names = no_ids and no_names if not no_ids and not no_names: raise MlflowException( message="Only experiment_ids or experiment_names can be used, but not both", error_code=INVALID_PARAMETER_VALUE, ) if search_all_experiments and no_ids_or_names: experiment_ids = [ exp.experiment_id for exp in search_experiments(view_type=ViewType.ACTIVE_ONLY) ] elif no_ids_or_names: experiment_ids = [_get_experiment_id()] elif not no_names: experiments = [] for n in experiment_names: if n is not None: experiment_by_name = get_experiment_by_name(n) if experiment_by_name: experiments.append(experiment_by_name) else: _logger.warning("Cannot retrieve experiment by name %s", n) experiment_ids = [e.experiment_id for e in experiments if e is not None] if len(experiment_ids) == 0: runs = [] else: # Using an internal function as the linter doesn't like assigning a lambda, and inlining the # full thing is a mess def pagination_wrapper_func(number_to_get, next_page_token): return MlflowClient().search_runs( experiment_ids, filter_string, run_view_type, number_to_get, order_by, next_page_token, ) runs = get_results_from_paginated_fn( pagination_wrapper_func, NUM_RUNS_PER_PAGE_PANDAS, max_results, ) if output_format == "list": return runs # List[mlflow.entities.run.Run] elif output_format == "pandas": import numpy as np import pandas as pd info = { "run_id": [], "experiment_id": [], "status": [], "artifact_uri": [], "start_time": [], "end_time": [], } params, metrics, tags = ({}, {}, {}) PARAM_NULL, METRIC_NULL, TAG_NULL = (None, np.nan, None) for i, run in enumerate(runs): info["run_id"].append(run.info.run_id) info["experiment_id"].append(run.info.experiment_id) info["status"].append(run.info.status) info["artifact_uri"].append(run.info.artifact_uri) info["start_time"].append(pd.to_datetime(run.info.start_time, unit="ms", utc=True)) info["end_time"].append(pd.to_datetime(run.info.end_time, unit="ms", utc=True)) # Params param_keys = set(params.keys()) for key in param_keys: if key in run.data.params: params[key].append(run.data.params[key]) else: params[key].append(PARAM_NULL) new_params = set(run.data.params.keys()) - param_keys for p in new_params: params[p] = [PARAM_NULL] * i # Fill in null values for all previous runs params[p].append(run.data.params[p]) # Metrics metric_keys = set(metrics.keys()) for key in metric_keys: if key in run.data.metrics: metrics[key].append(run.data.metrics[key]) else: metrics[key].append(METRIC_NULL) new_metrics = set(run.data.metrics.keys()) - metric_keys for m in new_metrics: metrics[m] = [METRIC_NULL] * i metrics[m].append(run.data.metrics[m]) # Tags tag_keys = set(tags.keys()) for key in tag_keys: if key in run.data.tags: tags[key].append(run.data.tags[key]) else: tags[key].append(TAG_NULL) new_tags = set(run.data.tags.keys()) - tag_keys for t in new_tags: tags[t] = [TAG_NULL] * i tags[t].append(run.data.tags[t]) data = {} data.update(info) for key, value in metrics.items(): data["metrics." + key] = value for key, value in params.items(): data["params." + key] = value for key, value in tags.items(): data["tags." + key] = value return pd.DataFrame(data) else: raise ValueError( "Unsupported output format: %s. Supported string values are 'pandas' or 'list'" % output_format )
def _get_or_start_run(): if len(_active_run_stack) > 0: return _active_run_stack[-1] return start_run() def _get_experiment_id_from_env(): experiment_name = MLFLOW_EXPERIMENT_NAME.get() experiment_id = MLFLOW_EXPERIMENT_ID.get() if experiment_name is not None: exp = MlflowClient().get_experiment_by_name(experiment_name) if exp: if experiment_id and experiment_id != exp.experiment_id: raise MlflowException( message=f"The provided {MLFLOW_EXPERIMENT_ID} environment variable " f"value `{experiment_id}` does not match the experiment id " f"`{exp.experiment_id}` for experiment name `{experiment_name}`", error_code=INVALID_PARAMETER_VALUE, ) else: return exp.experiment_id else: return MlflowClient().create_experiment(name=experiment_name) if experiment_id is not None: try: exp = MlflowClient().get_experiment(experiment_id) return exp.experiment_id except MlflowException as exc: raise MlflowException( message=f"The provided {MLFLOW_EXPERIMENT_ID} environment variable " f"value `{experiment_id}` does not exist in the tracking server. Provide a valid " f"experiment_id.", error_code=INVALID_PARAMETER_VALUE, ) from exc def _get_experiment_id(): if _active_experiment_id: return _active_experiment_id else: return _get_experiment_id_from_env() or default_experiment_registry.get_experiment_id()
[docs]@autologging_integration("mlflow") def autolog( log_input_examples: bool = False, log_model_signatures: bool = True, log_models: bool = True, log_datasets: bool = True, disable: bool = False, exclusive: bool = False, disable_for_unsupported_versions: bool = False, silent: bool = False, extra_tags: Optional[Dict[str, str]] = None, ) -> None: """ Enables (or disables) and configures autologging for all supported integrations. The parameters are passed to any autologging integrations that support them. See the :ref:`tracking docs <automatic-logging>` for a list of supported autologging integrations. Note that framework-specific configurations set at any point will take precedence over any configurations set by this function. For example: .. code-block:: python :test: import mlflow mlflow.autolog(log_models=False, exclusive=True) import sklearn would enable autologging for `sklearn` with `log_models=False` and `exclusive=True`, but .. code-block:: python :test: import mlflow mlflow.autolog(log_models=False, exclusive=True) import sklearn mlflow.sklearn.autolog(log_models=True) would enable autologging for `sklearn` with `log_models=True` and `exclusive=False`, the latter resulting from the default value for `exclusive` in `mlflow.sklearn.autolog`; other framework autolog functions (e.g. `mlflow.tensorflow.autolog`) would use the configurations set by `mlflow.autolog` (in this instance, `log_models=False`, `exclusive=True`), until they are explicitly called by the user. Args: log_input_examples: If ``True``, input examples from training datasets are collected and logged along with model artifacts during training. If ``False``, input examples are not logged. Note: Input examples are MLflow model attributes and are only collected if ``log_models`` is also ``True``. log_model_signatures: If ``True``, :py:class:`ModelSignatures <mlflow.models.ModelSignature>` describing model inputs and outputs are collected and logged along with model artifacts during training. If ``False``, signatures are not logged. Note: Model signatures are MLflow model attributes and are only collected if ``log_models`` is also ``True``. log_models: If ``True``, trained models are logged as MLflow model artifacts. If ``False``, trained models are not logged. Input examples and model signatures, which are attributes of MLflow models, are also omitted when ``log_models`` is ``False``. log_datasets: If ``True``, dataset information is logged to MLflow Tracking. If ``False``, dataset information is not logged. disable: If ``True``, disables all supported autologging integrations. If ``False``, enables all supported autologging integrations. exclusive: If ``True``, autologged content is not logged to user-created fluent runs. If ``False``, autologged content is logged to the active fluent run, which may be user-created. disable_for_unsupported_versions: If ``True``, disable autologging for versions of all integration libraries that have not been tested against this version of the MLflow client or are incompatible. silent: If ``True``, suppress all event logs and warnings from MLflow during autologging setup and training execution. If ``False``, show all events and warnings during autologging setup and training execution. extra_tags: A dictionary of extra tags to set on each managed run created by autologging. .. code-block:: python :test: :caption: Example import numpy as np import mlflow.sklearn from mlflow import MlflowClient from sklearn.linear_model import LinearRegression def print_auto_logged_info(r): tags = {k: v for k, v in r.data.tags.items() if not k.startswith("mlflow.")} artifacts = [f.path for f in MlflowClient().list_artifacts(r.info.run_id, "model")] print(f"run_id: {r.info.run_id}") print(f"artifacts: {artifacts}") print(f"params: {r.data.params}") print(f"metrics: {r.data.metrics}") print(f"tags: {tags}") # prepare training data X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]]) y = np.dot(X, np.array([1, 2])) + 3 # Auto log all the parameters, metrics, and artifacts mlflow.autolog() model = LinearRegression() with mlflow.start_run() as run: model.fit(X, y) # fetch the auto logged parameters and metrics for ended run print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id)) .. code-block:: text :caption: Output run_id: fd10a17d028c47399a55ab8741721ef7 artifacts: ['model/MLmodel', 'model/conda.yaml', 'model/model.pkl'] params: {'copy_X': 'True', 'normalize': 'False', 'fit_intercept': 'True', 'n_jobs': 'None'} metrics: {'training_score': 1.0, 'training_root_mean_squared_error': 4.440892098500626e-16, 'training_r2_score': 1.0, 'training_mean_absolute_error': 2.220446049250313e-16, 'training_mean_squared_error': 1.9721522630525295e-31} tags: {'estimator_class': 'sklearn.linear_model._base.LinearRegression', 'estimator_name': 'LinearRegression'} """ locals_copy = locals().items() # Mapping of library name to specific autolog function name. We use string like # "tensorflow.autolog" to avoid loading all flavor modules, so we only set autologging for # compatible modules. # eg: mxnet.gluon is the actual library, mlflow.gluon.autolog is our autolog function for it LIBRARY_TO_AUTOLOG_MODULE = { "tensorflow": "mlflow.tensorflow", "keras": "mlflow.keras", "mxnet.gluon": "mlflow.gluon", "xgboost": "mlflow.xgboost", "lightgbm": "mlflow.lightgbm", "statsmodels": "mlflow.statsmodels", "sklearn": "mlflow.sklearn", "fastai": "mlflow.fastai", "pyspark": "mlflow.spark", "pyspark.ml": "mlflow.pyspark.ml", # TODO: Broaden this beyond pytorch_lightning as we add autologging support for more # Pytorch frameworks under mlflow.pytorch.autolog "pytorch_lightning": "mlflow.pytorch", "setfit": "mlflow.transformers", "transformers": "mlflow.transformers", # do not enable langchain autologging by default } def get_autologging_params(autolog_fn): try: needed_params = list(inspect.signature(autolog_fn).parameters.keys()) return {k: v for k, v in locals_copy if k in needed_params} except Exception: return {} def setup_autologging(module): try: autologging_params = None autolog_module = importlib.import_module(LIBRARY_TO_AUTOLOG_MODULE[module.__name__]) autolog_fn = autolog_module.autolog # Only call integration's autolog function with `mlflow.autolog` configs # if the integration's autolog function has not already been called by the user. # Logic is as follows: # - if a previous_config exists, that means either `mlflow.autolog` or # `mlflow.integration.autolog` was called. # - if the config contains `AUTOLOGGING_CONF_KEY_IS_GLOBALLY_CONFIGURED`, the # configuration was set by `mlflow.autolog`, and so we can safely call `autolog_fn` # with `autologging_params`. # - if the config doesn't contain this key, the configuration was set by an # `mlflow.integration.autolog` call, so we should not call `autolog_fn` with # new configs. prev_config = AUTOLOGGING_INTEGRATIONS.get(autolog_fn.integration_name) if prev_config and not prev_config.get( AUTOLOGGING_CONF_KEY_IS_GLOBALLY_CONFIGURED, False ): return autologging_params = get_autologging_params(autolog_fn) autolog_fn(**autologging_params) AUTOLOGGING_INTEGRATIONS[autolog_fn.integration_name][ AUTOLOGGING_CONF_KEY_IS_GLOBALLY_CONFIGURED ] = True if not autologging_is_disabled( autolog_fn.integration_name ) and not autologging_params.get("silent", False): _logger.info("Autologging successfully enabled for %s.", module.__name__) except Exception as e: if is_testing(): # Raise unexpected exceptions in test mode in order to detect # errors within dependent autologging integrations raise elif autologging_params is None or not autologging_params.get("silent", False): _logger.warning( "Exception raised while enabling autologging for %s: %s", module.__name__, str(e), ) # for each autolog library (except pyspark), register a post-import hook. # this way, we do not send any errors to the user until we know they are using the library. # the post-import hook also retroactively activates for previously-imported libraries. for module in list(set(LIBRARY_TO_AUTOLOG_MODULE.keys()) - {"pyspark", "pyspark.ml"}): register_post_import_hook(setup_autologging, module, overwrite=True) if is_in_databricks_runtime(): # for pyspark, we activate autologging immediately, without waiting for a module import. # this is because on Databricks a SparkSession already exists and the user can directly # interact with it, and this activity should be logged. import pyspark as pyspark_module import pyspark.ml as pyspark_ml_module setup_autologging(pyspark_module) setup_autologging(pyspark_ml_module) else: register_post_import_hook(setup_autologging, "pyspark", overwrite=True) register_post_import_hook(setup_autologging, "pyspark.ml", overwrite=True)