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 both Python and REST APIs.
Table of Contents
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 used to execute the run, if it was executed from an MLflow Project.
- Start & End Time
- Start and end time of the run
- Name of the file executed to launch the run, or the project name and entry point for the run if the run was executed from an MLflow Project.
- Key-value input parameters of your choice. Both keys and values are strings.
- 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 will record and let you visualize the metric’s full history.
- Output files in any format. For example, you can record images (for example, PNGs), models (for example, a pickled scikit-learn model) or even data files (for example, a Parquet file) as artifacts.
Runs can be recorded from anywhere you run your code through MLflow’s Python and REST APIs: 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 MLflow Project, MLflow remembers the project URI and source version.
Finally, runs can optionally be organized into experiments, which group together runs for a
specific task. You can create an experiment via the
mlflow experiments CLI, with
mlflow.create_experiment(), or via 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 Tracking UI or the MLflow API.
MLflow runs can be recorded either locally in files or remotely to a tracking server.
By default, the MLflow Python API logs runs to files in an
mlruns directory wherever you
ran your program. You can then run
mlflow ui to see the logged runs. Set the
MLFLOW_TRACKING_URI environment variable to a server’s URI or call
mlflow.set_tracking_uri() to log runs remotely.
There are a different kinds of remote tracking URIs:
- Local file path (specified as
file:/my/local/dir), where data is just directly stored locally.
- HTTP server (specified as
https://my-server:5000), which is a server hosting your own tracking server.
- Databricks workspace (specified as
databricks, or a specific Databricks CLI profile as
databricks://profileName. For more information on configuring a Databricks CLI, see here. This only works for workspaces for which the Databricks MLflow Tracking Server is enabled; please contact Databricks if interested.
You can log data to runs using either the MLflow Python or REST API. This section shows the Python API.
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, or a local path to log data to a
directory. The URI defaults to
mlflow.get_tracking_uri() returns the current tracking URI.
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() returns the currently active run (if one exists), or starts a new run
and returns a
mlflow.tracking.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 will automatically start a new one.
mlflow.end_run() ends the currently active run, if any, taking an optional run status.
mlflow.active_run() returns a
mlflow.tracking.Run object corresponding to the
currently active run, if any.
mlflow.log_param() logs a key-value parameter in the currently active run. The keys and
values are both strings.
mlflow.log_metric() logs a key-value metric. The value must always be a number. MLflow will
remember the history of values for each metric.
mlflow.log_artifact() logs a local file 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.
mlflow.log_artifacts() logs all the files in a given directory as artifacts, again taking
mlflow.get_artifact_uri() returns the URI that artifacts from the current run should be
Sometimes you want to execute 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
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_parameter("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.
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 via the CLI (
mlflow experiments) or via
create_experiment() Python API. You can pass the experiment ID for a individual run
via the CLI (for example,
mlflow run ... --experiment-id [ID]) or via the
# Prints "created an experiment with ID <id> mlflow experiments create fraud-detection # Set the ID via environment variables export MLFLOW_EXPERIMENT_ID=<id>
# Launch a run. The experiment ID is inferred from the MLFLOW_EXPERIMENT_ID environment # variable, or from the --experiment-id parameter passed to the MLflow CLI (the latter # taking precedence) with mlflow.start_run(): mlflow.log_parameter("a", 1) mlflow.log_metric("b", 2)
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 have been logging runs to a local
mlflow ui in the directory above it, and it will load the corresponding runs.
Alternatively, the MLflow Server serves the same UI, and enables remote storage of run artifacts.
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
All of the functions in the Tracking UI can be accessed programmatically through the
mlflow.tracking module and the REST API. This makes it easy to do several
- 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.
- Load artifacts from past runs as MLflow Models.
- Run automated parameter search algorithms, where you query the metrics from various runs to submit new ones.
The MLflow tracking server launched via
mlflow server also hosts REST APIs for tracking runs,
writing data to the local filesystem. You can specify a tracking server URI
MLFLOW_TRACKING_URI environment variable and MLflow’s tracking APIs automatically
communicate with the tracking server at that URI to create/get run information, log metrics, and so on.
An example configuration for a server is as follows:
mlflow server \ --file-store /mnt/persistent-disk \ --default-artifact-root s3://my-mlflow-bucket/ \ --host 0.0.0.0
The tracking server has two properties related to how data is stored: File Store and Artifact Store.
The File Store (exposed via
--file-store) is where the server stores run and experiment metadata.
It defaults to the local
./mlruns directory (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.
The Artifact Store is a location suitable for large data (such as an S3 bucket or shared NFS file system)
where clients log their artifact output (for example, models). The Artifact Store is a property
of an experiment, but the
--default-artifact-root flag sets the artifact root URI for
newly-created experiments that do not specify one. Once you create an experiment, the
--default-artifact-root is no longer relevant to it.
To allow the clients and server to access the artifact location, you should configure your cloud
provider credentials as normal. For example, for S3, you can set the
AWS_SECRET_ACCESS_KEY environment variables, use an IAM role, or configure a default
~/.aws/credentials. See Set up AWS Credentials and Region for Development for more info.
If you do not specify a
--default-artifact-root or an artifact URI when creating the experiment (for example,
mlflow experiments create --artifact-root s3://<my-bucket>), then the artifact root will be a path inside the File Store. Typically this is not an appropriate location, as the client and server will probably be referring to different physical locations (that is, the same path on different disks).
In addition to local file paths, MLflow supports the following storage systems as artifact stores: Amazon S3, Azure Blob Storage, and Google Cloud Storage.
Specify a URI of the form
s3://<bucket>/<path> to store artifacts in S3. MLflow obtains
credentials to access S3 from your machine’s IAM role, a profile in
the environment variables
AWS_SECRET_ACCESS_KEY depending on which of
these are available. See
Set up AWS Credentials and Region for Development for more information on how to set credentials.
Specify a URI of the form
wasbs://<container>@<storage-account>.blob.core.windows.net/<path> to store
artifacts in Azure Blob Storage. MLflow looks for your Azure Storage access credentials in the
AZURE_STORAGE_ACCESS_KEY environment variables (preferring
a connection string if one is set), so you will need to set one of these variables on both your client
application and your MLflow tracking server. Finally, you will need to
pip install azure-storage
separately (on both your client and the server) to access Azure Blob Storage; MLflow does not declare
a dependency on this package by default.
Specify a URI of the form
gs://<bucket>/<path> to store artifacts in Google Cloud Storage.
You should configure credentials for accessing the GCS container on the client and server as described
in the GCS documentation.
Finally, you will need to
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.
--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.
Additionally, you should ensure that the
--file-store (which defaults to the
points to a persistent (non-ephemeral) disk.
Once you have a server running, set
MLFLOW_TRACKING_URI to the server’s URI, along
with its scheme and port (for example,
http://10.0.0.1:5000). Then you can use
mlflow as normal:
import mlflow with mlflow.start_run(): mlflow.log_metric("a", 1)
mlflow.log_metric calls make API requests to your remote