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https://github.com/g-research/fasttrackml

Experiment tracking server focused on speed and scalability
https://github.com/g-research/fasttrackml

ai apache-spark data-science data-visualization experiment-tracking machine-learning metadata metadata-tracking metrics ml mlflow mlflow-tracking-server mlops pytorch tensorboard tensorflow visualization

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Experiment tracking server focused on speed and scalability

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README 

        
[![FastTrackML banner](https://fasttrackml.io/images/github-banner.svg)](https://fasttrackml.io/)



# _FastTrackML_



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.



As the name implies, the emphasis is on speed -- fast logging, fast

retrieval.



### Quickstart



#### Run the tracking server



> [!NOTE]

> For the full guide, see our [quickstart guide](https://github.com/G-Research/fasttrackml/blob/main/docs/quickstart.md).



FastTrackML can be installed and run with `pip`:



```bash

pip install fasttrackml

fml server

```



Alternatively, you can run it within a container with

[Docker](https://docs.docker.com/get-docker/):



```bash

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

```



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



![FastTrackML UI](https://raw.githubusercontent.com/G-Research/fasttrackml/main/docs/images/main_ui.png)



For more info, `--help` is your friend!



#### Track your experiments



Install the MLflow Python package:



```bash

pip install mlflow-skinny

```



Here is an elementary example Python script:



```python

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](https://containers.dev). 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](https://code.visualstudio.com), all you need is to have

[Docker](https://docs.docker.com/get-docker/) and the

[Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)

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](https://apple.stackexchange.com/a/431164) are necessary.



#### CLI



If the CLI is how you roll, then you can install the

[Dev Container CLI](https://github.com/devcontainers/cli) tool and follow the

instruction below.



CLI instructions



> [!WARNING]

> This setup is not recommended or supported. Here be dragons!



You will need to edit the `.devcontainer/docker-compose.yml` file and uncomment

the `services.db.ports` section to expose the ports to the host. You will also

need to add `FML_LISTEN_ADDRESS=:5000` to `.devcontainer/.env`.



You can then issue the following command in your copy of FastTrackML to get up

and running:



```bash

devcontainer up

```



Assuming you cloned the repo into a directory named `fasttrackml` and did not

fiddle with the dev container config, you can enter the dev container with:



```bash

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

```



If any of these is not true, here is how to render a command tailored to your

setup (it requires [`jq`](https://jqlang.github.io/jq/download/) to be

installed):



```bash

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

```



Once in the dev container, use your favorite text editor and `Makefile` targets:



```bash

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.