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https://github.com/Netflix/metaflow

:rocket: Build and manage real-life ML, AI, and data science projects with ease!
https://github.com/Netflix/metaflow

ai aws azure data-science datascience gcp high-performance-computing kubernetes machine-learning ml ml-infrastructure ml-platform mlops model-management productivity python r r-package reproducible-research rstats

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:rocket: Build and manage real-life ML, AI, and data science projects with ease!

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README 

        
![Metaflow_Logo_Horizontal_FullColor_Ribbon_Dark_RGB](https://user-images.githubusercontent.com/763451/89453116-96a57e00-d713-11ea-9fa6-82b29d4d6eff.png)



# Metaflow



Metaflow is a human-friendly library that helps scientists and engineers build and manage real-life data science projects. Metaflow was [originally developed at Netflix](https://netflixtechblog.com/open-sourcing-metaflow-a-human-centric-framework-for-data-science-fa72e04a5d9) to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.



For more information, see [Metaflow's website](https://metaflow.org) and [documentation](https://docs.metaflow.org).



## From prototype to production (and back)



Metaflow provides a simple, friendly API that covers foundational needs of ML, AI, and data science projects:




1. [Rapid local prototyping](https://docs.metaflow.org/metaflow/basics), [support for notebooks](https://docs.metaflow.org/metaflow/visualizing-results), and [built-in experiment tracking and versioning](https://docs.metaflow.org/metaflow/client).

2. [Horizontal and vertical scalability to the cloud](https://docs.metaflow.org/scaling/remote-tasks/introduction), utilizing both CPUs and GPUs, and [fast data access](https://docs.metaflow.org/scaling/data).

3. [Managing dependencies](https://docs.metaflow.org/scaling/dependencies) and [one-click deployments to highly available production orchestrators](https://docs.metaflow.org/production/introduction).



## Getting started



Getting up and running is easy. If you don't know where to start, [Metaflow sandbox](https://outerbounds.com/sandbox) will have you running and exploring Metaflow in seconds.



### Installing Metaflow in your Python environment



To install Metaflow in your local environment, you can install from [PyPi](https://pypi.org/project/metaflow/):



```sh

pip install metaflow

```

Alternatively, you can also install from [conda-forge](https://anaconda.org/conda-forge/metaflow):



```sh

conda install -c conda-forge metaflow

```

If you are eager to try out Metaflow in practice, you can start with the [tutorial](https://docs.metaflow.org/getting-started/tutorials). After the tutorial, you can learn more about how Metaflow works [here](https://docs.metaflow.org/metaflow/basics).



### Deploying infrastructure for Metaflow in your cloud




While you can get started with Metaflow easily on your laptop, the main benefits of Metaflow lie in its ability to [scale out to external compute clusters](https://docs.metaflow.org/scaling/remote-tasks/introduction) 

and to [deploy to production-grade workflow orchestrators](https://docs.metaflow.org/production/introduction). To benefit from these features, follow this [guide](https://outerbounds.com/engineering/welcome/) to 

configure Metaflow and the infrastructure behind it appropriately.



## [Resources](https://docs.metaflow.org/introduction/metaflow-resources)



### [Slack Community](http://slack.outerbounds.co/)

An active [community](http://slack.outerbounds.co/) of thousands of data scientists and ML engineers discussing the ins-and-outs of applied machine learning.



### [Tutorials](https://outerbounds.com/docs/tutorials-index/)

- [Introduction to Metaflow](https://outerbounds.com/docs/intro-tutorial-overview/)

- [Natural Language Processing with Metaflow](https://outerbounds.com/docs/nlp-tutorial-overview/)

- [Computer Vision with Metaflow](https://outerbounds.com/docs/cv-tutorial-overview/)

- [Recommender Systems with Metaflow](https://outerbounds.com/docs/recsys-tutorial-overview/)

- And more advanced content [here](https://outerbounds.com/docs/tutorials-index/)



### [Generative AI and LLM use cases](https://outerbounds.com/blog/?category=Foundation%20Models)

- [Infrastructure Stack for Large Language Models](https://outerbounds.com/blog/llm-infrastructure-stack/)

- [Parallelizing Stable Diffusion for Production Use Cases](https://outerbounds.com/blog/parallelizing-stable-diffusion-production-use-cases/)

- [Whisper with Metaflow on Kubernetes](https://outerbounds.com/blog/whisper-kubernetes/)

- [Training a Large Language Model With Metaflow, Featuring Dolly](https://outerbounds.com/blog/train-dolly-metaflow/)



## Get in touch

There are several ways to get in touch with us:

- [Slack Community](http://slack.outerbounds.co/)

- [Github Issues](https://github.com/Netflix/metaflow/issues)



## Contributing

We welcome contributions to Metaflow. Please see our [contribution guide](https://docs.metaflow.org/introduction/contributing-to-metaflow) for more details.