FastTrackML

An experiment tracking server focused on speed and scalability, fully compatible with MLFlow.

Quickstart

Run the tracking server

[!NOTE] For the full guide, see our quickstart guide.

FastTrackML can be installed and run with pip:

pip install fasttrackml
fml server

Alternatively, you can run it within a container with Docker:

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

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

For more info, --help is your friend!

Track your experiments

Install the MLFlow Python package:

pip install mlflow-skinny

Here is an elementary example Python script:

import mlflow
import random

# Set the tracking URI to the FastTrackML server
mlflow.set_tracking_uri("http://localhost:5000")
# Set the experiment name
mlflow.set_experiment("my-first-experiment")

# Start a new 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)

Developer

FastTrackML can be built and tested within a dev container. This is the recommended way as the whole environment comes preconfigured with all the dependencies (Go SDK, Postgres, Minio, etc.) and settings (formatting, linting, extensions, etc.) to get started instantly.

GitHub Codespaces

If you have a GitHub account, you can simply open FastTrackML in a new GitHub Codespace by clicking on the green "Code" button at the top of this page.

You can build, run, and attach the debugger by simply pressing F5. The unit tests can be run from the Test Explorer on the left. There are also many targets within the Makefile that can be used (e.g. build, run, test-go-unit).

Visual Studio Code

If you want to work locally in Visual Studio Code, all you need is to have Docker and the Dev Containers extension installed.

Simply open up your copy of FastTrackML in VS Code and click "Reopen in container" when prompted. Once the project has been opened, you can follow the GitHub Codespaces instructions above.

[!IMPORTANT] Note that on MacOS, port 5000 is already occupied, so some adjustments are necessary.

CLI

If the CLI is how you roll, then you can install the Dev Container CLI tool and follow the instruction below.

devcontainer up

docker compose --project-name fasttrackml_devcontainer exec --user vscode --workdir /workspaces/fasttrackml app zsh

devcontainer up | tail -n1 | jq -r '"docker compose --project-name \(.composeProjectName) exec --user \(.remoteUser) --workdir \(.remoteWorkspaceFolder) app zsh"'

vscode ➜ /workspaces/fasttrackml (main) $ vi main.go
vscode ➜ /workspaces/fasttrackml (main) $ emacs .
vscode ➜ /workspaces/fasttrackml (main) $ make run

License

Copyright 2022-2023 G-Research

Copyright 2019-2022 Aimhub, Inc.

Copyright 2018 Databricks, Inc.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.