Metaflow: More Data Science, Less Engineering

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for metaflowdev from gravatar.com metaflowdev Avatar for savingoyal from gravatar.com savingoyal

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers
Report project as malware

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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for metaflowdev from gravatar.com metaflowdev Avatar for savingoyal from gravatar.com savingoyal

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

2.19.21

2.19.20

2.19.19

2.19.18

2.19.17

2.19.16

2.19.15

2.19.14

2.19.13

2.19.12

2.19.11

2.19.10

2.19.9

2.19.7

2.19.6

2.19.5

2.19.4

2.19.3

2.19.2

2.19.1

2.19.0

2.18.13

2.18.12

2.18.11

2.18.10

2.18.9

2.18.8

2.18.7

2.18.6

2.18.5

2.18.4

2.18.3

2.18.2

2.18.1

2.18.0

2.17.5

2.17.4

2.17.3

2.17.2

2.17.1

2.17.0

2.16.8

2.16.7

2.16.6

2.16.5

2.16.4

2.16.3

2.16.2

2.16.1

2.16.0

2.15.21

2.15.20

2.15.19

2.15.18

2.15.17

2.15.16

2.15.15

2.15.14

2.15.13

2.15.12

2.15.11

2.15.10

2.15.9

2.15.8

2.15.7

2.15.6

2.15.5

2.15.4

2.15.3

2.15.2

2.15.1

2.15.0

2.14.3

2.14.2

2.14.1

2.14.0

2.13.10

2.13.9

2.13.8

2.13.7

2.13.6

2.13.5

2.13.4

2.13.3

2.13.2

2.13.1

2.13

2.12.39

2.12.38

2.12.37

2.12.36

2.12.35

2.12.34

2.12.33

2.12.32

2.12.31

2.12.30

2.12.29

2.12.28

2.12.27

2.12.26

2.12.25

2.12.24

2.12.23

2.12.22

2.12.21

2.12.20

2.12.19

2.12.18

2.12.17

2.12.16

2.12.15

2.12.14

2.12.13

2.12.12

2.12.11

2.12.10

2.12.9

2.12.8

2.12.7

2.12.6

2.12.5

2.12.4

2.12.3

2.12.2

2.12.1

2.12.0

2.11.16

2.11.15

2.11.14

2.11.13

2.11.12

2.11.11

2.11.10

2.11.9

2.11.8

2.11.7

This version

2.11.6

2.11.5

2.11.4

2.11.3

2.11.2

2.11.1

2.11.0

2.10.11

2.10.10

2.10.9

2.10.8

2.10.7

2.10.6

2.10.5

2.10.4

2.10.3

2.10.2

2.10.0

2.9.15

2.9.14

2.9.13

2.9.12

2.9.11

2.9.10

2.9.9

2.9.8

2.9.7

2.9.6

2.9.5

2.9.4

2.9.3

2.9.2

2.9.1

2.9.0

2.8.6

2.8.5

2.8.4

2.8.3

2.8.2

2.8.1

2.8.0

2.7.23

2.7.22

2.7.21

2.7.20

2.7.19

2.7.18

2.7.17

2.7.16

2.7.15

2.7.14

2.7.13

2.7.12

2.7.11

2.7.10

2.7.9

2.7.8

2.7.7

2.7.6

2.7.5

2.7.4

2.7.3

2.7.2

2.7.1

2.7.0

2.6.3

2.6.2

2.6.1

2.6.0

2.5.4

2.5.3

2.5.2

2.5.1

2.5.0

2.4.9

2.4.8

2.4.7

2.4.5

2.4.4

2.4.3

2.4.2

2.4.1

2.4.0

2.3.6

2.3.5

2.3.4

2.3.3

2.3.2

2.3.1

2.3.0

2.2.13

2.2.12

2.2.11

2.2.10

2.2.9

2.2.8

2.2.7

2.2.6

2.2.5

2.2.4

2.2.3

2.2.2

2.2.1

2.2.0

2.1.1

2.1.0

2.0.5

2.0.4

2.0.3

2.0.2

2.0.1

2.0.0

Download files

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

Source Distribution

metaflow-2.11.6.tar.gz (1.2 MB view details)

Uploaded Source

Built Distribution

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

metaflow-2.11.6-py2.py3-none-any.whl (1.3 MB view details)

Uploaded Python 2Python 3

File details

Details for the file metaflow-2.11.6.tar.gz.

File metadata

  • Download URL: metaflow-2.11.6.tar.gz
  • Upload date:
  • Size: 1.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for metaflow-2.11.6.tar.gz
Algorithm Hash digest
SHA256 8dd57a852a2998406b4ea05d9fa46b13d3e81e5fb7c1a95857acedabf89c7ec5
MD5 ea63d6bba05303f9214ea8ecaa2cdfa3
BLAKE2b-256 80f480815faa1dfe0ed43e2e135bf2968cbe557d90d6bc6430d7a6351fd13923

See more details on using hashes here.

File details

Details for the file metaflow-2.11.6-py2.py3-none-any.whl.

File metadata

  • Download URL: metaflow-2.11.6-py2.py3-none-any.whl
  • Upload date:
  • Size: 1.3 MB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for metaflow-2.11.6-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 4797cf22da398f745b8e3c3fccac876b9148aee95779e58e3083b06d168d53b5
MD5 a4f6c759b27aa1a78e0624e365002919
BLAKE2b-256 384d544286b8274b8a5940b47ea3bc46ad9d66555ba2d777f790d310cb9afe6c

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