Skip to main content

Metaflow: More Data Science, Less Engineering

Project description

Metaflow_Logo_Horizontal_FullColor_Ribbon_Dark_RGB

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 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 and documentation.

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, support for notebooks, and built-in experiment tracking and versioning.
  2. Horizontal and vertical scalability to the cloud, utilizing both CPUs and GPUs, and fast data access.
  3. Managing dependencies and one-click deployments to highly available production orchestrators.

Getting started

Getting up and running is easy. If you don't know where to start, Metaflow 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:

pip install metaflow

Alternatively, you can also install from conda-forge:

conda install -c conda-forge metaflow

If you are eager to try out Metaflow in practice, you can start with the tutorial. After the tutorial, you can learn more about how Metaflow works here.

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 and to deploy to production-grade workflow orchestrators. To benefit from these features, follow this guide to configure Metaflow and the infrastructure behind it appropriately.

Resources

Slack Community

An active community of thousands of data scientists and ML engineers discussing the ins-and-outs of applied machine learning.

Tutorials

Generative AI and LLM use cases

Get in touch

There are several ways to get in touch with us:

Contributing

We welcome contributions to Metaflow. Please see our contribution guide for more details.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

metaflow-2.9.12.tar.gz (832.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

metaflow-2.9.12-py2.py3-none-any.whl (937.8 kB view details)

Uploaded Python 2Python 3

File details

Details for the file metaflow-2.9.12.tar.gz.

File metadata

  • Download URL: metaflow-2.9.12.tar.gz
  • Upload date:
  • Size: 832.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for metaflow-2.9.12.tar.gz
Algorithm Hash digest
SHA256 8da1a17a987944344543a12793ceb4a38ddc3bc9df5b08cdeb7a30b33fb8dc1f
MD5 2b511d78a0c5c15d9c4ca2d4b8d30ff5
BLAKE2b-256 ec1479c5cdfefca6c89fa703b7fff388a64fcc5253afe619fd2e76b2b0ce4791

See more details on using hashes here.

File details

Details for the file metaflow-2.9.12-py2.py3-none-any.whl.

File metadata

  • Download URL: metaflow-2.9.12-py2.py3-none-any.whl
  • Upload date:
  • Size: 937.8 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for metaflow-2.9.12-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 d1666d370a41c6484e1a71bb9eb20a540474e60118d68d5b558d9a4480bb9206
MD5 30ecfe8904c1909d61c7cc252e21d5c1
BLAKE2b-256 cbbd6388f92e38dab6c19264d1abe29c3ccc0250853cd71ba57bc000dac27d47

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page