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FastTrackML is an API for logging parameters and metrics when running machine learning code, and it is a UI for visualizing the result. The API is a drop-in replacement for MLflow's tracking server, and it ships with the visualization UI of both MLflow and Aim.
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.
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).
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.
