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Experiment tracking server focused on speed and scalability

FastTrackML iconFastTrackML icon


Blazing Fast

FastTrackML is a rewrite of the MLFlow tracking server with a focus on performance and scalability.

Modern Aim UI

Use the modern Aim UI alternative for a seamless experience.

Drop-in Replacement

Use the Classic UI to get the same experience as MLFlow’s tracking server. But even faster.


Run FastTrackML

Using pip
Note: This step requires Python 3 to be installed.

Install FastTrackML:

pip install fasttrackml

Run the server:

fml server
Using a script

Install on Linux and macOS:

curl -fsSL | sh

Install on Windows:

iwr -useb | iex

Run the server:

fml server
Using Docker
Note: This step requires Docker to be running.

Run the server within a container:

docker run --rm -p 5000:5000 -ti gresearch/fasttrackml

Verify that you can see the UI by navigating to http://localhost:5000/.

FastTrackML UI

Track your first experiment

Note: This step requires Python 3 to be installed.

Install the MLflow Python package:

pip install mlflow-skinny

Then, run the following Python script to log a parameter and metric to FastTrackML:

import mlflow
import random

# Set the tracking URI to the FastTrackML server
# Set the experiment name

# Start a run
with mlflow.start_run():
# Log a parameter
mlflow.log_param("param1", random.randint(0, 100))

# Log a metric
mlflow.log_metric("foo", random.random())
# metrics can be updated throughout the run
mlflow.log_metric("foo", random.random() + 1)
mlflow.log_metric("foo", random.random() + 2)

After running this script, you should see the following output from http://localhost:5000/aim/:

FastTrackML UI

From here you can check out the metrics and run information to see more details about the run.

Contact Us

We would love to hear from you! FastTrackML is a brand new project and any contribution would make a difference!

Or, join the #fasttrackml channel on the Slack!