tracking.rst

MLflow Tracking

The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using :ref:`Python <python-api>`, :ref:`REST <rest-api>`, :ref:`R-api`, and :ref:`java_api` APIs.

Table of Contents

• Concepts
• Where Runs Are Recorded
• How Runs and Artifacts are Recorded
  • Scenario 1: MLflow on localhost
  • Scenario 2: MLflow on localhost with SQLite
  • Scenario 3: MLflow on localhost with Tracking Server
  • Scenario 4: MLflow with remote Tracking Server, backend and artifact stores
• Logging Data to Runs
  • Logging Functions
  • Launching Multiple Runs in One Program
  • Performance Tracking with Metrics
  • Visualizing Metrics
• Automatic Logging
  • Scikit-learn
  • TensorFlow and Keras
  • Gluon
  • XGBoost
  • LightGBM
  • Statsmodels
  • Spark
  • Fastai
  • Pytorch
• Organizing Runs in Experiments
  • Managing Experiments and Runs with the Tracking Service API
• Tracking UI
• Querying Runs Programmatically
• MLflow Tracking Servers
  • Storage
  • Networking
  • Logging to a Tracking Server
• System Tags

Concepts

MLflow Tracking is organized around the concept of runs, which are executions of some piece of data science code. Each run records the following information:

Code Version

Git commit hash used for the run, if it was run from an :ref:`MLflow Project <projects>`.

Start & End Time

Start and end time of the run

Source

Name of the file to launch the run, or the project name and entry point for the run if run from an :ref:`MLflow Project <projects>`.

Parameters

Key-value input parameters of your choice. Both keys and values are strings.

Metrics

Key-value metrics, where the value is numeric. Each metric can be updated throughout the course of the run (for example, to track how your model's loss function is converging), and MLflow records and lets you visualize the metric's full history.

Artifacts

Output files in any format. For example, you can record images (for example, PNGs), models (for example, a pickled scikit-learn model), and data files (for example, a Parquet file) as artifacts.

You can record runs using MLflow Python, R, Java, and REST APIs from anywhere you run your code. For example, you can record them in a standalone program, on a remote cloud machine, or in an interactive notebook. If you record runs in an :ref:`MLflow Project <projects>`, MLflow remembers the project URI and source version.

You can optionally organize runs into experiments, which group together runs for a specific task. You can create an experiment using the mlflow experiments CLI, with :py:func:`mlflow.create_experiment`, or using the corresponding REST parameters. The MLflow API and UI let you create and search for experiments.

Once your runs have been recorded, you can query them using the :ref:`tracking_ui` or the MLflow API.

Where Runs Are Recorded

MLflow runs can be recorded to local files, to a SQLAlchemy compatible database, or remotely to a tracking server. By default, the MLflow Python API logs runs locally to files in an mlruns directory wherever you ran your program. You can then run mlflow ui to see the logged runs.

To log runs remotely, set the MLFLOW_TRACKING_URI environment variable to a tracking server's URI or call :py:func:`mlflow.set_tracking_uri`.

There are different kinds of remote tracking URIs:

• Local file path (specified as file:/my/local/dir), where data is just directly stored locally.
• Database encoded as <dialect>+<driver>://<username>:<password>@<host>:<port>/<database>. MLflow supports the dialects mysql, mssql, sqlite, and postgresql. For more details, see SQLAlchemy database uri.
• HTTP server (specified as https://my-server:5000), which is a server hosting an :ref:`MLflow tracking server <tracking_server>`.
• Databricks workspace (specified as databricks or as databricks://<profileName>, a Databricks CLI profile. Refer to Access the MLflow tracking server from outside Databricks [AWS] [Azure], or :ref:`the quickstart <quickstart_logging_to_remote_server>` to easily get started with hosted MLflow on Databricks Community Edition.

How Runs and Artifacts are Recorded

As mentioned above, MLflow runs can be recorded to local files, to a SQLAlchemy compatible database, or remotely to a tracking server. MLflow artifacts can be persisted to local files and a variety of remote file storage solutions. For storing runs and artifacts, MLflow uses two components for storage: backend store and artifact store. While the backend store persists MLflow entities (runs, parameters, metrics, tags, notes, metadata, etc), the artifact store persists artifacts (files, models, images, in-memory objects, or model summary, etc).

The MLflow client can interface with a variety of backend and artifact storage configurations. Here are four common configuration scenarios:

Scenario 1: MLflow on localhost

Many developers run MLflow on their local machine, where both the backend and artifact store share a directory on the local filesystem—./mlruns—as shown in the diagram. The MLflow client directly interfaces with an instance of a FileStore and LocalArtifactRepository.

In this simple scenario, the MLflow client uses the following interfaces to record MLflow entities and artifacts:

• An instance of a LocalArtifactRepository (to store artifacts)
• An instance of a FileStore (to save MLflow entities)

Scenario 2: MLflow on localhost with SQLite

Many users also run MLflow on their local machines with a SQLAlchemy-compatible database: SQLite. In this case, artifacts are stored under the local ./mlruns directory, and MLflow entities are inserted in a SQLite database file mlruns.db.

In this scenario, the MLflow client uses the following interfaces to record MLflow entities and artifacts:

• An instance of a LocalArtifactRepository (to save artifacts)
• An instance of an SQLAlchemyStore (to store MLflow entities to a SQLite file mlruns.db)

Scenario 3: MLflow on localhost with Tracking Server

Similar to scenario 1 but a tracking server is launched, listening for REST request calls at the default port 5000. The arguments supplied to the mlflow server <args> dictate what backend and artifact stores are used. The default is local FileStore. For example, if a user launched a tracking server as mlflow server --backend-store-uri sqlite:///mydb.sqlite, then SQLite would be used for backend storage instead.

As in scenario 1, MLflow uses a local mlruns filesystem directory as a backend store and artifact store. With a tracking server running, the MLflow client interacts with the tracking server via REST requests, as shown in the diagram.

To store all runs' MLflow entities, the MLflow client interacts with the tracking server via a series of REST requests:

• Part 1a and b:

• The MLflow client creates an instance of a RestStore and sends REST API requests to log MLflow entities
• The Tracking Server creates an instance of a FileStore to save MLflow entities and writes directly to the local mlruns directory

For the artifacts, the MLflow client interacts with the tracking server via a REST request:

• Part 2a, b, and c:
  • The MLflow client uses RestStore to send a REST request to fetch the artifact store URI location
  • The Tracking Server responds with an artifact store URI location
  • The MLflow client creates an instance of a LocalArtifactRepository and saves artifacts to the local filesystem location specified by the artifact store URI (a subdirectory of mlruns)

Scenario 4: MLflow with remote Tracking Server, backend and artifact stores

MLflow also supports distributed architectures, where the tracking server, backend store, and artifact store reside on remote hosts. This example scenario depicts an architecture with a remote MLflow Tracking Server, a Postgres database for backend entity storage, and an S3 bucket for artifact storage.

To record all runs' MLflow entities, the MLflow client interacts with the tracking server via a series of REST requests:

• Part 1a and b:

• The MLflow client creates an instance of a RestStore and sends REST API requests to log MLflow entities
• The Tracking Server creates an instance of an SQLAlchemyStore and connects to the remote host to insert MLflow entities in the database

For artifact logging, the MLflow client interacts with the remote Tracking Server and artifact storage host:

• Part 2a, b, and c:

• The MLflow client uses RestStore to send a REST request to fetch the artifact store URI location from the Tracking Server
• The Tracking Server responds with an artifact store URI location (an S3 storage URI in this case)
• The MLflow client creates an instance of an S3ArtifactRepository, connects to the remote AWS host using the boto client libraries, and uploads the artifacts to the S3 bucket URI location

Note

In all scenarios, the MLflow client directly logs artifacts to the remote artifact store. It does not proxy these through the tracking server.

The FileStore, RestStore, and SQLAlchemyStore are concrete implementations of the abstract class AbstractStore, and the LocalArtifactRepository and S3ArtifactRepository are concrete implementations of the abstract class ArtifactRepository.

Logging Data to Runs

You can log data to runs using the MLflow Python, R, Java, or REST API. This section shows the Python API.

In this section:

• Logging Functions
• Launching Multiple Runs in One Program
• Performance Tracking with Metrics
• Visualizing Metrics

Logging Functions

:py:func:`mlflow.set_tracking_uri` connects to a tracking URI. You can also set the MLFLOW_TRACKING_URI environment variable to have MLflow find a URI from there. In both cases, the URI can either be a HTTP/HTTPS URI for a remote server, a database connection string, or a local path to log data to a directory. The URI defaults to mlruns.

:py:func:`mlflow.tracking.get_tracking_uri` returns the current tracking URI.

:py:func:`mlflow.create_experiment` creates a new experiment and returns its ID. Runs can be launched under the experiment by passing the experiment ID to mlflow.start_run.

:py:func:`mlflow.set_experiment` sets an experiment as active. If the experiment does not exist, creates a new experiment. If you do not specify an experiment in :py:func:`mlflow.start_run`, new runs are launched under this experiment.

:py:func:`mlflow.start_run` returns the currently active run (if one exists), or starts a new run and returns a :py:class:`mlflow.ActiveRun` object usable as a context manager for the current run. You do not need to call start_run explicitly: calling one of the logging functions with no active run automatically starts a new one.

:py:func:`mlflow.end_run` ends the currently active run, if any, taking an optional run status.

:py:func:`mlflow.active_run` returns a :py:class:`mlflow.entities.Run` object corresponding to the currently active run, if any. 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.tracking.MlflowClient` as follows:

client = mlflow.tracking.MlflowClient()
data = client.get_run(mlflow.active_run().info.run_id).data

:py:func:`mlflow.log_param` logs a single key-value param in the currently active run. The key and value are both strings. Use :py:func:`mlflow.log_params` to log multiple params at once.

:py:func:`mlflow.log_metric` logs a single key-value metric. The value must always be a number. MLflow remembers the history of values for each metric. Use :py:func:`mlflow.log_metrics` to log multiple metrics at once.

:py:func:`mlflow.set_tag` sets a single key-value tag in the currently active run. The key and value are both strings. Use :py:func:`mlflow.set_tags` to set multiple tags at once.

:py:func:`mlflow.log_artifact` logs a local file or directory as an artifact, optionally taking an artifact_path to place it in within the run's artifact URI. Run artifacts can be organized into directories, so you can place the artifact in a directory this way.

:py:func:`mlflow.log_artifacts` logs all the files in a given directory as artifacts, again taking an optional artifact_path.

:py:func:`mlflow.get_artifact_uri` returns the URI that artifacts from the current run should be logged to.

Launching Multiple Runs in One Program

Sometimes you want to launch multiple MLflow runs in the same program: for example, maybe you are performing a hyperparameter search locally or your experiments are just very fast to run. This is easy to do because the ActiveRun object returned by :py:func:`mlflow.start_run` is a Python context manager. You can "scope" each run to just one block of code as follows:

with mlflow.start_run():
    mlflow.log_param("x", 1)
    mlflow.log_metric("y", 2)
    ...

The run remains open throughout the with statement, and is automatically closed when the statement exits, even if it exits due to an exception.

Performance Tracking with Metrics

You log MLflow metrics with log methods in the Tracking API. The log methods support two alternative methods for distinguishing metric values on the x-axis: timestamp and step.

timestamp is an optional long value that represents the time that the metric was logged. timestamp defaults to the current time. step is an optional integer that represents any measurement of training progress (number of training iterations, number of epochs, and so on). step defaults to 0 and has the following requirements and properties:

• Must be a valid 64-bit integer value.
• Can be negative.
• Can be out of order in successive write calls. For example, (1, 3, 2) is a valid sequence.
• Can have "gaps" in the sequence of values specified in successive write calls. For example, (1, 5, 75, -20) is a valid sequence.

If you specify both a timestamp and a step, metrics are recorded against both axes independently.

Examples

Python

with mlflow.start_run():
    for epoch in range(0, 3):
        mlflow.log_metric(key="quality", value=2*epoch, step=epoch)

Java and Scala

MlflowClient client = new MlflowClient();
RunInfo run = client.createRun();
for (int epoch = 0; epoch < 3; epoch ++) {
    client.logMetric(run.getRunId(), "quality", 2 * epoch, System.currentTimeMillis(), epoch);
}

Visualizing Metrics

Here is an example plot of the :ref:`quick start tutorial <quickstart>` with the step x-axis and two timestamp axes:

X-axis step

X-axis wall time - graphs the absolute time each metric was logged

X-axis relative time - graphs the time relative to the first metric logged, for each run

Automatic Logging

Automatic logging allows you to log metrics, parameters, and models without the need for explicit log statements.

There are two ways to use autologging:

1. Call :py:func:`mlflow.autolog` before your training code. This will enable autologging for each supported library you have installed as soon as you import it.
2. Use library-specific autolog calls for each library you use in your code. See below for examples.

The following libraries support autologging:

• Scikit-learn
• TensorFlow and Keras
• Gluon
• XGBoost
• LightGBM
• Statsmodels
• Spark
• Fastai
• Pytorch

For flavors that automatically save models as an artifact, additional files for dependency management are logged.

Scikit-learn

Call :py:func:`mlflow.sklearn.autolog` before your training code to enable automatic logging of sklearn metrics, params, and models. See example usage here.

Autologging for estimators (e.g. LinearRegression) and meta estimators (e.g. Pipeline) creates a single run and logs:

Metrics	Parameters	Tags	Artifacts
Training score obtained by estimator.score	Parameters obtained by estimator.get_params	Class name Fully qualified class name	Fitted estimator

Autologging for parameter search estimators (e.g. GridSearchCV) creates a single parent run and nested child runs

- Parent run
  - Child run 1
  - Child run 2
  - ...

containing the following data:

Run type	Metrics	Parameters	Tags	Artifacts
Parent	Training score	Parameter search estimator's parameters Best parameter combination	Class name Fully qualified class name	Fitted parameter search estimator Fitted best estimator Search results csv file
Child	CV test score for each parameter combination	Each parameter combination	Class name Fully qualified class name	--

TensorFlow and Keras

Call :py:func:`mlflow.tensorflow.autolog` or :py:func:`mlflow.keras.autolog` before your training code to enable automatic logging of metrics and parameters. See example usages with Keras and TensorFlow.

Note that autologging for tf.keras is handled by :py:func:`mlflow.tensorflow.autolog`, not :py:func:`mlflow.keras.autolog`. Whether you are using TensorFlow 1.x or 2.x, the respective metrics associated with tf.estimator and EarlyStopping are automatically logged. As an example, try running the MLflow TensorFlow examples.

Autologging captures the following information:

Framework/module	Metrics	Parameters	Tags	Artifacts
keras	Training loss; validation loss; user-specified metrics. Metrics from the EarlyStopping callbacks. For example, stopped_epoch, restored_epoch, restore_best_weight, etc.	fit() or fit_generator() parameters; optimizer name; learning rate; epsilon. Parameters associated with EarlyStopping. For example, min_delta, patience, baseline, restore_best_weights, etc	--	Model summary on training start; MLflow Model (Keras model) on training end
tf.keras	Training loss; validation loss; user-specified metrics	fit() or fit_generator() parameters; optimizer name; learning rate; epsilon	--	Model summary on training start; MLflow Model (Keras model); TensorBoard logs on training end
tf.keras.callbacks.EarlyStopping	Metrics from the EarlyStopping callbacks. For example, stopped_epoch, restored_epoch, restore_best_weight, etc	fit() or fit_generator() parameters from EarlyStopping. For example, min_delta, patience, baseline, restore_best_weights, etc	--	--
tf.estimator	TensorBoard metrics. For example, average_loss, loss etc.	steps, max_steps	--	MLflow Model (TF saved model) on call to tf.estimator.export_saved_model
TensorFlow Core	All tf.summary.scalar calls	--	--	--

If no active run exists when autolog() captures data, MLflow will automatically create a run to log information to. Also, MLflow will then automatically end the run once training ends via calls to tf.estimator.train(), tf.keras.fit(), tf.keras.fit_generator(), keras.fit() or keras.fit_generator(), or once tf.estimator models are exported via tf.estimator.export_saved_model().

If a run already exists when autolog() captures data, MLflow will log to that run but not automatically end that run after training.

Note

• Parameters not explicitly passed by users (parameters that use default values) while using keras.Model.fit_generator() are not currently automatically logged.

Gluon

Call :py:func:`mlflow.gluon.autolog` before your training code to enable automatic logging of metrics and parameters. See example usages with Gluon .

Autologging captures the following information:

Framework	Metrics	Parameters	Tags	Artifacts
Gluon	Training loss; validation loss; user-specified metrics	Number of layers; optimizer name; learning rate; epsilon	--	MLflow Model (Gluon model); on training end

XGBoost

Call :py:func:`mlflow.xgboost.autolog` before your training code to enable automatic logging of metrics and parameters.

Autologging captures the following information:

Framework	Metrics	Parameters	Tags	Artifacts
XGBoost	user-specified metrics	xgboost.train parameters	--	MLflow Model (XGBoost model) with model signature on training end; feature importance; input example

If early stopping is activated, metrics at the best iteration will be logged as an extra step/iteration.

Note

• The scikit-learn API is not supported.

LightGBM

Call :py:func:`mlflow.lightgbm.autolog` before your training code to enable automatic logging of metrics and parameters.

Autologging captures the following information:

Framework	Metrics	Parameters	Tags	Artifacts
LightGBM	user-specified metrics	lightgbm.train parameters	--	MLflow Model (LightGBM model) with model signature on training end; feature importance; input example

If early stopping is activated, metrics at the best iteration will be logged as an extra step/iteration.

Note

• The scikit-learn API is not supported.

Statsmodels

Call :py:func:`mlflow.statsmodels.autolog` before your training code to enable automatic logging of metrics and parameters.

Autologging captures the following information:

Framework	Metrics	Parameters	Tags	Artifacts
Statsmodels	user-specified metrics	statsmodels.base.model.Model.fit parameters	--	MLflow Model (statsmodels.base.wrapper.ResultsWrapper) on training end

Note

• Each model subclass that overrides fit expects and logs its own parameters.

Spark

Initialize a SparkSession with the mlflow-spark JAR attached (e.g. SparkSession.builder.config("spark.jars.packages", "org.mlflow.mlflow-spark")) and then call :py:func:`mlflow.spark.autolog` to enable automatic logging of Spark datasource information at read-time, without the need for explicit log statements. Note that autologging of Spark ML (MLlib) models is not yet supported.

Autologging captures the following information:

Framework	Metrics	Parameters	Tags	Artifacts
Spark	--	--	Single tag containing source path, version, format. The tag contains one line per datasource	--

Note

• Moreover, Spark datasource autologging occurs asynchronously - as such, it's possible (though unlikely) to see race conditions when launching short-lived MLflow runs that result in datasource information not being logged.

Fastai

Call :py:func:`mlflow.fastai.autolog` before your training code to enable automatic logging of metrics and parameters. See an example usage with Fastai.

Autologging captures the following information:

Framework	Metrics	Parameters	Tags	Artifacts
fastai	user-specified metrics	Logs optimizer data as parameters. For example, epochs, lr, opt_func, etc; Logs the parameters of the EarlyStoppingCallback and OneCycleScheduler callbacks	--	Model checkpoints are logged to a ‘models’ directory; MLflow Model (fastai Learner model) on training end; Model summary text is logged

Pytorch

Call :py:func:`mlflow.pytorch.autolog` before your Pytorch Lightning training code to enable automatic logging of metrics, parameters, and models. See example usages here. Note that currently, Pytorch autologging supports only models trained using Pytorch Lightning.

Autologging is triggered on calls to pytorch_lightning.trainer.Trainer.fit and captures the following information:

Framework/module	Metrics	Parameters	Tags	Artifacts
pytorch_lightning.trainer.Trainer	Training loss; validation loss; average_test_accuracy; user-defined-metrics.	fit() parameters; optimizer name; learning rate; epsilon.	--	Model summary on training start, MLflow Model (Pytorch model) on training end;
pytorch_lightning.callbacks.earlystopping	Training loss; validation loss; average_test_accuracy; user-defined-metrics. Metrics from the EarlyStopping callbacks. For example, stopped_epoch, restored_epoch, restore_best_weight, etc.	fit() parameters; optimizer name; learning rate; epsilon Parameters from the EarlyStopping callbacks. For example, min_delta, patience, baseline,``restore_best_weights``, etc	--	Model summary on training start; MLflow Model (Pytorch model) on training end; Best Pytorch model checkpoint, if training stops due to early stopping callback.

If no active run exists when autolog() captures data, MLflow will automatically create a run to log information, ending the run once the call to pytorch_lightning.trainer.Trainer.fit() completes.

If a run already exists when autolog() captures data, MLflow will log to that run but not automatically end that run after training.

Note

• Parameters not explicitly passed by users (parameters that use default values) while using pytorch_lightning.trainer.Trainer.fit() are not currently automatically logged
• In case of a multi-optimizer scenario (such as usage of autoencoder), only the parameters for the first optimizer are logged

Organizing Runs in Experiments

MLflow allows you to group runs under experiments, which can be useful for comparing runs intended to tackle a particular task. You can create experiments using the :ref:`cli` (mlflow experiments) or the :py:func:`mlflow.create_experiment` Python API. You can pass the experiment name for an individual run using the CLI (for example, mlflow run ... --experiment-name [name]) or the MLFLOW_EXPERIMENT_NAME environment variable. Alternatively, you can use the experiment ID instead, via the --experiment-id CLI flag or the MLFLOW_EXPERIMENT_ID environment variable.

# Set the experiment via environment variables
export MLFLOW_EXPERIMENT_NAME=fraud-detection

mlflow experiments create --experiment-name fraud-detection

# Launch a run. The experiment is inferred from the MLFLOW_EXPERIMENT_NAME environment
# variable, or from the --experiment-name parameter passed to the MLflow CLI (the latter
# taking precedence)
with mlflow.start_run():
    mlflow.log_param("a", 1)
    mlflow.log_metric("b", 2)

Managing Experiments and Runs with the Tracking Service API

MLflow provides a more detailed Tracking Service API for managing experiments and runs directly, which is available through client SDK in the :py:mod:`mlflow.tracking` module. This makes it possible to query data about past runs, log additional information about them, create experiments, add tags to a run, and more.

Example

from  mlflow.tracking import MlflowClient
client = MlflowClient()
experiments = client.list_experiments() # returns a list of mlflow.entities.Experiment
run = client.create_run(experiments[0].experiment_id) # returns mlflow.entities.Run
client.log_param(run.info.run_id, "hello", "world")
client.set_terminated(run.info.run_id)

Adding Tags to Runs

The :py:func:`mlflow.tracking.MlflowClient.set_tag` function lets you add custom tags to runs. A tag can only have a single unique value mapped to it at a time. For example:

client.set_tag(run.info.run_id, "tag_key", "tag_value")

Important

Do not use the prefix mlflow for a tag. This prefix is reserved for use by MLflow.

Tracking UI

The Tracking UI lets you visualize, search and compare runs, as well as download run artifacts or metadata for analysis in other tools. If you log runs to a local mlruns directory, run mlflow ui in the directory above it, and it loads the corresponding runs. Alternatively, the :ref:`MLflow tracking server <tracking_server>` serves the same UI and enables remote storage of run artifacts. In that case, you can view the UI using URL http://<ip address of your MLflow tracking server>:5000 in your browser from any machine, including any remote machine that can connect to your tracking server.

The UI contains the following key features:

• Experiment-based run listing and comparison
• Searching for runs by parameter or metric value
• Visualizing run metrics
• Downloading run results

Querying Runs Programmatically

You can access all of the functions in the Tracking UI programmatically. This makes it easy to do several common tasks:

• Query and compare runs using any data analysis tool of your choice, for example, pandas.
• Determine the artifact URI for a run to feed some of its artifacts into a new run when executing a workflow. For an example of querying runs and constructing a multistep workflow, see the MLflow Multistep Workflow Example project.
• Load artifacts from past runs as :ref:`models`. For an example of training, exporting, and loading a model, and predicting using the model, see the MLflow TensorFlow example.
• Run automated parameter search algorithms, where you query the metrics from various runs to submit new ones. For an example of running automated parameter search algorithms, see the MLflow Hyperparameter Tuning Example project.

MLflow Tracking Servers

In this section:

• Storage
  • Backend Stores
  • Artifact Stores
  • File store performance
  • Deletion Behavior
  • SQLAlchemy Options
• Networking
• Logging to a Tracking Server

You run an MLflow tracking server using mlflow server. An example configuration for a server is:

mlflow server \
    --backend-store-uri /mnt/persistent-disk \
    --default-artifact-root s3://my-mlflow-bucket/ \
    --host 0.0.0.0

Storage

An MLflow tracking server has two components for storage: a backend store and an artifact store.

Backend Stores

The backend store is where MLflow Tracking Server stores experiment and run metadata as well as params, metrics, and tags for runs. MLflow supports two types of backend stores: file store and database-backed store.

Note

In order to use model registry functionality, you must run your server using a database-backed store.

Use --backend-store-uri to configure the type of backend store. You specify:

• A file store backend as ./path_to_store or file:/path_to_store
• A database-backed store as SQLAlchemy database URI. The database URI typically takes the format <dialect>+<driver>://<username>:<password>@<host>:<port>/<database>. MLflow supports the database dialects mysql, mssql, sqlite, and postgresql. Drivers are optional. If you do not specify a driver, SQLAlchemy uses a dialect's default driver. For example, --backend-store-uri sqlite:///mlflow.db would use a local SQLite database.

Important

mlflow server will fail against a database-backed store with an out-of-date database schema. To prevent this, upgrade your database schema to the latest supported version using mlflow db upgrade [db_uri]. Schema migrations can result in database downtime, may take longer on larger databases, and are not guaranteed to be transactional. You should always take a backup of your database prior to running mlflow db upgrade - consult your database's documentation for instructions on taking a backup.

By default --backend-store-uri is set to the local ./mlruns directory (the same as when running mlflow run locally), but when running a server, make sure that this points to a persistent (that is, non-ephemeral) file system location.

Artifact Stores

In this section:

• Amazon S3 and S3-compatible storage
• Azure Blob Storage
• Google Cloud Storage
• FTP server
• SFTP Server
• NFS
• HDFS

The artifact store is a location suitable for large data (such as an S3 bucket or shared NFS file system) and is where clients log their artifact output (for example, models). artifact_location is a property recorded on :py:class:`mlflow.entities.Experiment` for default location to store artifacts for all runs in this experiment. Additional, artifact_uri is a property on :py:class:`mlflow.entities.RunInfo` to indicate location where all artifacts for this run are stored.

The MLflow client caches artifact location information on a per-run basis. It is therefore not recommended to alter a run's artifact location before it has terminated.

In addition to local file paths, MLflow supports the following storage systems as artifact stores: Amazon S3, Azure Blob Storage, Google Cloud Storage, SFTP server, and NFS.

Use --default-artifact-root (defaults to local ./mlruns directory) to configure default location to server's artifact store. This will be used as artifact location for newly-created experiments that do not specify one. Once you create an experiment, --default-artifact-root is no longer relevant to that experiment.

To allow the server and clients to access the artifact location, you should configure your cloud provider credentials as normal. For example, for S3, you can set the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables, use an IAM role, or configure a default profile in ~/.aws/credentials. See Set up AWS Credentials and Region for Development for more info.

Important

If you do not specify a --default-artifact-root or an artifact URI when creating the experiment (for example, mlflow experiments create --artifact-location s3://<my-bucket>), the artifact root is a path inside the file store. Typically this is not an appropriate location, as the client and server probably refer to different physical locations (that is, the same path on different disks).

Amazon S3 and S3-compatible storage

To store artifacts in S3 (whether on Amazon S3 or on an S3-compatible alternative, such as MinIO), specify a URI of the form s3://<bucket>/<path>. MLflow obtains credentials to access S3 from your machine's IAM role, a profile in ~/.aws/credentials, or the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY depending on which of these are available. For more information on how to set credentials, see Set up AWS Credentials and Region for Development.

To add S3 file upload extra arguments, set MLFLOW_S3_UPLOAD_EXTRA_ARGS to a JSON object of key/value pairs. For example, if you want to upload to a KMS Encrypted bucket using the KMS Key 1234:

export MLFLOW_S3_UPLOAD_EXTRA_ARGS='{"ServerSideEncryption": "aws:kms", "SSEKMSKeyId": "1234"}'

For a list of available extra args see Boto3 ExtraArgs Documentation.

To store artifacts in a custom endpoint, set the MLFLOW_S3_ENDPOINT_URL to your endpoint's URL. For example, if you have a MinIO server at 1.2.3.4 on port 9000:

export MLFLOW_S3_ENDPOINT_URL=http://1.2.3.4:9000

If the MinIO server is configured with using SSL self-signed or signed using some internal-only CA certificate, you could set MLFLOW_S3_IGNORE_TLS or AWS_CA_BUNDLE variables (not both at the same time!) to disable certificate signature check, or add a custom CA bundle to perform this check, respectively:

export MLFLOW_S3_IGNORE_TLS=true
#or
export AWS_CA_BUNDLE=/some/ca/bundle.pem

Additionally, if MinIO server is configured with non-default region, you should set AWS_DEFAULT_REGION variable:

export AWS_DEFAULT_REGION=my_region

Complete list of configurable values for an S3 client is available in boto3 documentation.

Azure Blob Storage

To store artifacts in Azure Blob Storage, specify a URI of the form wasbs://<container>@<storage-account>.blob.core.windows.net/<path>. MLflow expects Azure Storage access credentials in the AZURE_STORAGE_CONNECTION_STRING, AZURE_STORAGE_ACCESS_KEY environment variables or having your credentials configured such that the DefaultAzureCredential(). class can pick them up. The order of precedence is: #. AZURE_STORAGE_CONNECTION_STRING #. AZURE_STORAGE_ACCESS_KEY #. DefaultAzureCredential() You must set one of these options on both your client application and your MLflow tracking server. Also, you must run pip install azure-storage-blob separately (on both your client and the server) to access Azure Blob Storage. Finally, if you want to use DefaultAzureCredential, you must pip install azure-identity; MLflow does not declare a dependency on these packages by default.

Google Cloud Storage

To store artifacts in Google Cloud Storage, specify a URI of the form gs://<bucket>/<path>. You should configure credentials for accessing the GCS container on the client and server as described in the GCS documentation. Finally, you must run pip install google-cloud-storage (on both your client and the server) to access Google Cloud Storage; MLflow does not declare a dependency on this package by default.

FTP server

To store artifacts in a FTP server, specify a URI of the form ftp://user@host/path/to/directory . The URI may optionally include a password for logging into the server, e.g. ftp://user:pass@host/path/to/directory

SFTP Server

To store artifacts in an SFTP server, specify a URI of the form sftp://user@host/path/to/directory. You should configure the client to be able to log in to the SFTP server without a password over SSH (e.g. public key, identity file in ssh_config, etc.).

The format sftp://user:pass@host/ is supported for logging in. However, for safety reasons this is not recommended.

When using this store, pysftp must be installed on both the server and the client. Run pip install pysftp to install the required package.

NFS

To store artifacts in an NFS mount, specify a URI as a normal file system path, e.g., /mnt/nfs. This path must be the same on both the server and the client -- you may need to use symlinks or remount the client in order to enforce this property.

HDFS

To store artifacts in HDFS, specify a hdfs: URI. It can contain host and port: hdfs://<host>:<port>/<path> or just the path: hdfs://<path>.

There are also two ways to authenticate to HDFS:

• Use current UNIX account authorization
• Kerberos credentials using following environment variables:

export MLFLOW_KERBEROS_TICKET_CACHE=/tmp/krb5cc_22222222
export MLFLOW_KERBEROS_USER=user_name_to_use

Most of the cluster contest settings are read from hdfs-site.xml accessed by the HDFS native driver using the CLASSPATH environment variable.

The used HDFS driver is libhdfs.

File store performance

MLflow will automatically try to use LibYAML bindings if they are already installed. However if you notice any performance issues when using file store backend, it could mean LibYAML is not installed on your system. On Linux or Mac you can easily install it using your system package manager:

# On Ubuntu/Debian
apt-get install libyaml-cpp-dev libyaml-dev

# On macOS using Homebrew
brew install yaml-cpp libyaml

After installing LibYAML, you need to reinstall PyYAML:

# Reinstall PyYAML
pip --no-cache-dir install --force-reinstall -I pyyaml

Deletion Behavior

In order to allow MLflow Runs to be restored, Run metadata and artifacts are not automatically removed from the backend store or artifact store when a Run is deleted. The :ref:`mlflow gc <cli>` CLI is provided for permanently removing Run metadata and artifacts for deleted runs.

SQLAlchemy Options

You can inject some SQLAlchemy connection pooling options using environment variables.

MLflow Environment Variable	SQLAlchemy QueuePool Option
MLFLOW_SQLALCHEMYSTORE_POOL_SIZE	pool_size
MLFLOW_SQLALCHEMYSTORE_MAX_OVERFLOW	max_overflow

Networking

The --host option exposes the service on all interfaces. If running a server in production, we would recommend not exposing the built-in server broadly (as it is unauthenticated and unencrypted), and instead putting it behind a reverse proxy like NGINX or Apache httpd, or connecting over VPN. You can then pass authentication headers to MLflow using these :ref:`environment variables <tracking_auth>`.

Additionally, you should ensure that the --backend-store-uri (which defaults to the ./mlruns directory) points to a persistent (non-ephemeral) disk or database connection.

Logging to a Tracking Server

To log to a tracking server, set the MLFLOW_TRACKING_URI environment variable to the server's URI, along with its scheme and port (for example, http://10.0.0.1:5000) or call :py:func:`mlflow.set_tracking_uri`.

The :py:func:`mlflow.start_run`, :py:func:`mlflow.log_param`, and :py:func:`mlflow.log_metric` calls then make API requests to your remote tracking server.

.. code-section::

  .. code-block:: python

      import mlflow
      remote_server_uri = "..." # set to your server URI
      mlflow.set_tracking_uri(remote_server_uri)
      # Note: on Databricks, the experiment name passed to mlflow_set_experiment must be a
      # valid path in the workspace
      mlflow.set_experiment("/my-experiment")
      with mlflow.start_run():
          mlflow.log_param("a", 1)
          mlflow.log_metric("b", 2)

  .. code-block:: R

      library(mlflow)
      install_mlflow()
      remote_server_uri = "..." # set to your server URI
      mlflow_set_tracking_uri(remote_server_uri)
      # Note: on Databricks, the experiment name passed to mlflow_set_experiment must be a
      # valid path in the workspace
      mlflow_set_experiment("/my-experiment")
      mlflow_log_param("a", "1")

In addition to the MLFLOW_TRACKING_URI environment variable, the following environment variables allow passing HTTP authentication to the tracking server:

• MLFLOW_TRACKING_USERNAME and MLFLOW_TRACKING_PASSWORD - username and password to use with HTTP Basic authentication. To use Basic authentication, you must set both environment variables .
• MLFLOW_TRACKING_TOKEN - token to use with HTTP Bearer authentication. Basic authentication takes precedence if set.
• MLFLOW_TRACKING_INSECURE_TLS - If set to the literal true, MLflow does not verify the TLS connection, meaning it does not validate certificates or hostnames for https:// tracking URIs. This flag is not recommended for production environments. If this is set to true then MLFLOW_TRACKING_SERVER_CERT_PATH must not be set.
• MLFLOW_TRACKING_SERVER_CERT_PATH - Path to a CA bundle to use. Sets the verify param of the requests.request function (see requests main interface). When you use a self-signed server certificate you can use this to verify it on client side. If this is set MLFLOW_TRACKING_INSECURE_TLS must not be set (false).
• MLFLOW_TRACKING_CLIENT_CERT_PATH - Path to ssl client cert file (.pem). Sets the cert param of the requests.request function (see requests main interface). This can be used to use a (self-signed) client certificate.

Note

The client directly pushes artifacts to the artifact store. It does not proxy these through the tracking server.

For this reason, the client needs direct access to the artifact store. For instructions on setting up these credentials, see :ref:`Artifact Stores <artifact-stores>`.

System Tags

You can annotate runs with arbitrary tags. Tag keys that start with mlflow. are reserved for internal use. The following tags are set automatically by MLflow, when appropriate:

Key	Description
mlflow.runName	Human readable name that identifies this run.
mlflow.note.content	A descriptive note about this run. This reserved tag is not set automatically and can be overridden by the user to include additional information about the run. The content is displayed on the run's page under the Notes section.
mlflow.parentRunId	The ID of the parent run, if this is a nested run.
mlflow.user	Identifier of the user who created the run.
mlflow.source.type	Source type. Possible values: "NOTEBOOK", "JOB", "PROJECT", "LOCAL", and "UNKNOWN"
mlflow.source.name	Source identifier (e.g., GitHub URL, local Python filename, name of notebook)
mlflow.source.git.commit	Commit hash of the executed code, if in a git repository.
mlflow.source.git.branch	Name of the branch of the executed code, if in a git repository.
mlflow.source.git.repoURL	URL that the executed code was cloned from.
mlflow.project.env	The runtime context used by the MLflow project. Possible values: "docker" and "conda".
mlflow.project.entryPoint	Name of the project entry point associated with the current run, if any.
mlflow.docker.image.name	Name of the Docker image used to execute this run.
mlflow.docker.image.id	ID of the Docker image used to execute this run.
mlflow.log-model.history	(Experimental) Model metadata collected by log-model calls. Includes the serialized form of the MLModel model files logged to a run, although the exact format and information captured is subject to change.