"""
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
import mlflow
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.tracing.provider import _get_trace_exporter
from mlflow.tracking import _get_artifact_repo, _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=(
f"Cannot set a deleted experiment {experiment.name!r} as the active experiment. "
"You can restore the experiment, or permanently delete the "
"experiment to create a new one."
),
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,
parent_run_id: Optional[str] = None,
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 random name will be generated for the run.
nested: Controls whether run is nested in parent run. ``True`` creates a nested run.
parent_run_id: If specified, the current run will be nested under the the run with
the specified UUID. The parent run must be in the ACTIVE state.
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"]])
# Create a nested run under the existing parent run
with mlflow.start_run(
run_name="NEW_CHILD_RUN",
experiment_id=experiment_id,
description="new child",
parent_run_id=parent_run.info.run_id,
) as child_run:
mlflow.log_param("new-child", "yes")
.. 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:
if parent_run_id:
_validate_run_id(parent_run_id)
# Make sure parent_run_id matches the current run id, if there is an active run
if len(_active_run_stack) > 0 and parent_run_id != _active_run_stack[-1].info.run_id:
current_run_id = _active_run_stack[-1].info.run_id
raise MlflowException(
f"Current run with UUID {current_run_id} does not match the specified "
f"parent_run_id {parent_run_id}. To start a new nested run under "
f"the parent run with UUID {current_run_id}, first end the current run "
"with mlflow.end_run()."
)
parent_run_obj = client.get_run(parent_run_id)
# Check if the specified parent_run has been deleted.
if parent_run_obj.info.lifecycle_stage == LifecycleStage.DELETED:
raise MlflowException(
f"Cannot start run under parent run with ID {parent_run_id} "
f"because it is in the deleted state."
)
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()
def _shut_down_async_logging() -> None:
"""Shutdown the async logging and flush all pending data."""
_get_store().shut_down_async_logging()
[docs]def flush_artifact_async_logging() -> None:
"""Flush all pending artifact async logging."""
run_id = _get_or_start_run().info.run_id
_artifact_repo = _get_artifact_repo(run_id)
if _artifact_repo:
_artifact_repo.flush_async_logging()
[docs]def flush_trace_async_logging(terminate=False) -> None:
"""
Flush all pending trace async logging.
Args:
terminate: If True, shut down the logging threads after flushing.
"""
try:
_get_trace_exporter()._async_queue.flush(terminate=terminate)
except Exception as e:
_logger.error(f"Failed to flush trace async logging: {e}")
[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.
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.
timestamp: Time when this metric was calculated. Defaults to the current system time.
run_id: If specified, log the metric to the specified run. If not specified, log the metric
to the currently active run.
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,
timestamp: Optional[int] = 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.
run_id: Run ID. If specified, log metrics to the specified run. If not specified, log
metrics to the currently active run.
timestamp: Time when these metrics were calculated. Defaults to the current system time.
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 = timestamp or 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, run_id: Optional[str] = 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.
run_id: Run ID. If specified, log params to the specified run. If not specified, log
params to the currently active run.
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 = run_id or _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_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.
run_id: If specified, log the artifact to the specified run. If not specified, log the
artifact to the currently active run.
.. 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.
run_id: If specified, log the artifacts to the specified run. If not specified, log the
artifacts to the currently active run.
.. 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").
run_id: If specified, log the artifact to the specified run. If not specified, log the
artifact to the currently active run.
.. 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").
run_id: If specified, log the dictionary to the specified run. If not specified, log the
dictionary to the currently active run.
.. 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_image(
image: Union["numpy.ndarray", "PIL.Image.Image", "mlflow.Image"],
artifact_file: Optional[str] = None,
key: Optional[str] = None,
step: Optional[int] = None,
timestamp: Optional[int] = None,
synchronous: Optional[bool] = False,
) -> None:
"""
Logs an image in MLflow, supporting two use cases:
1. Time-stepped image logging:
Ideal for tracking changes or progressions through iterative processes (e.g.,
during model training phases).
- Usage: :code:`log_image(image, key=key, step=step, timestamp=timestamp)`
2. Artifact file image logging:
Best suited for static image logging where the image is saved directly as a file
artifact.
- Usage: :code:`log_image(image, artifact_file)`
The following image formats 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
- :class:`mlflow.Image`: An MLflow wrapper around PIL image for convenient image logging.
Numpy array support
- data types:
- bool (useful for logging image masks)
- integer [0, 255]
- unsigned integer [0, 255]
- float [0.0, 1.0]
.. warning::
- Out-of-range integer values will raise ValueError.
- Out-of-range float values will auto-scale with min/max and warn.
- 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: The image object to be logged.
artifact_file: Specifies the path, in POSIX format, where the image
will be stored as an artifact relative to the run's root directory (for
example, "dir/image.png"). This parameter is kept for backward compatibility
and should not be used together with `key`, `step`, or `timestamp`.
key: Image name for time-stepped image logging. This string may only contain
alphanumerics, underscores (_), dashes (-), periods (.), spaces ( ), and
slashes (/).
step: Integer training step (iteration) at which the image was saved.
Defaults to 0.
timestamp: Time when this image was saved. Defaults to the current system time.
synchronous: *Experimental* If True, blocks until the image is logged successfully.
.. code-block:: python
:caption: Time-stepped image logging 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, key="dogs", step=3)
.. code-block:: python
:caption: Time-stepped image logging pillow example
import mlflow
from PIL import Image
image = Image.new("RGB", (100, 100))
with mlflow.start_run():
mlflow.log_image(image, key="dogs", step=3)
.. code-block:: python
:caption: Time-stepped image logging with mlflow.Image example
import mlflow
from PIL import Image
# If you have a preexisting saved image
Image.new("RGB", (100, 100)).save("image.png")
image = mlflow.Image("image.png")
with mlflow.start_run() as run:
mlflow.log_image(run.info.run_id, image, key="dogs", step=3)
.. code-block:: python
:caption: Legacy artifact file image logging 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
:caption: Legacy artifact file image logging 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, key, step, timestamp, synchronous)
[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").
run_id: If specified, log the table to the specified run. If not specified, log the
table to the currently active run.
.. 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 a unique string.
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(
f"Unsupported output format: {output_format}. Supported string values are 'pandas' "
"or 'list'"
)
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",
"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)