[PR #1378] [CLOSED] Add MLflow. #1215

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GiteaMirror · 2025-11-06T13:11:45-06:00

GiteaMirror commented

2025-11-06 13:11:45 -06:00

📋 Pull Request Information

Original PR: https://github.com/vinta/awesome-python/pull/1378
Author: @jmrr
Created: 10/6/2019
Status: ❌ Closed

Base: master ← Head: master

📝 Commits (1)

d1754f9 Add MLflow.

📊 Changes

1 file changed (+1 additions, -0 deletions)

View changed files

📝 README.md (+1 -0)

📄 Description

What is this Python project?

MLflow is an open source platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

MLflow is the most comprehensive, platform agnostic project with the aims of encompassing, on a single platform, three main components of the ML lifecycle:

MLflow Tracking: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI.
MLflow Projects: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others.
MLflow Models: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker.

What's the difference between this Python project and similar ones?

MLOps is still a domain in its early stages but some tools already exist based on the Kubernetes containerised ecosystem:

The fact that they're based on Kubernetes appears to be somewhat of a barrier for small scale Data Science teams, whilst with MLflow an individual contributor can easily setup a single tracking server for their own experiments. They also tend to be more Deep Learning oriented. An advantage of Pachyderm is that it provides data reproducibility (apart from the code + model reproducibility provided by MLflow).

Sacred provides experimentation logging, but doesn't provide model packaging and sharing or the possibility of creating reproducible projects with your ML code for other people to use. Also you'd need a frontend (see next entry) to visualise and track your experiments, which is already provided by MLflow tracking server.
Ombniboard would only provide the frontend.

Some other nice tools exist but they're library specific, e.g. to track specific frameworks' simulations: TensorBoard and in the domain of model deployment TFX for TensorFlow.

Anyone who agrees with this pull request could vote for it by adding a 👍 to it, and usually, the maintainer will merge it when votes reach 20.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/vinta/awesome-python/pull/1378 **Author:** [@jmrr](https://github.com/jmrr) **Created:** 10/6/2019 **Status:** ❌ Closed **Base:** `master` ← **Head:** `master` --- ### 📝 Commits (1) - [`d1754f9`](https://github.com/vinta/awesome-python/commit/d1754f953f23c2da2f8b2ef4ca824ee991a172cf) Add MLflow. ### 📊 Changes **1 file changed** (+1 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+1 -0) </details> ### 📄 Description ## What is this Python project? [MLflow](https://github.com/mlflow/mlflow) is an open source platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow is the most comprehensive, platform agnostic project with the aims of encompassing, on a single platform, three main components of the ML lifecycle: * MLflow Tracking: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * MLflow Projects: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * MLflow Models: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. ## What's the difference between this Python project and similar ones? * MLOps is still a domain in its early stages but some tools already exist based on the Kubernetes containerised ecosystem: * [Kubeflow](https://github.com/kubeflow/) * [Pachyderm](https://github.com/pachyderm/pachyderm) * [Polyaxon](https://github.com/polyaxon/polyaxon) The fact that they're based on Kubernetes appears to be somewhat of a barrier for small scale Data Science teams, whilst with MLflow an individual contributor can easily setup a single tracking server for their own experiments. They also tend to be more Deep Learning oriented. An advantage of Pachyderm is that it provides data reproducibility (apart from the code + model reproducibility provided by MLflow). * [Sacred](https://github.com/IDSIA/sacred) provides experimentation logging, but doesn't provide model packaging and sharing or the possibility of creating reproducible projects with your ML code for other people to use. Also you'd need a frontend (see next entry) to visualise and track your experiments, which is already provided by MLflow tracking server. * [Ombniboard](https://github.com/vivekratnavel/omniboard) would only provide the frontend. Some other nice tools exist but they're library specific, e.g. to track specific frameworks' simulations: [TensorBoard](https://www.tensorflow.org/tensorboard) and in the domain of model deployment [TFX](https://www.tensorflow.org/tfx) for TensorFlow. ---- Anyone who agrees with this pull request could vote for it by adding a :+1: to it, and usually, the maintainer will merge it when votes reach **20**. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2025-11-06 13:11:45 -06:00

GiteaMirror closed this issue

2025-11-06 13:11:45 -06:00

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Reference: github-starred/awesome-python#1215