Skip to main content

A library for analyzing TensorFlow models

Project description

TensorFlow Model Analysis

Python PyPI Documentation

TensorFlow Model Analysis (TFMA) is a library for evaluating TensorFlow models. It allows users to evaluate their models on large amounts of data in a distributed manner, using the same metrics defined in their trainer. These metrics can be computed over different slices of data and visualized in Jupyter notebooks.

TFMA Slicing Metrics Browser

Caution: TFMA may introduce backwards incompatible changes before version 1.0.

Installation

The recommended way to install TFMA is using the PyPI package:

pip install tensorflow-model-analysis

pip install from the HEAD of the git:

pip install git+https://github.com/tensorflow/model-analysis.git#egg=tensorflow_model_analysis

pip install from a released version directly from git:

pip install git+https://github.com/tensorflow/model-analysis.git@v0.21.3#egg=tensorflow_model_analysis

If you have cloned the repository locally, and want to test your local change, pip install from a local folder:

pip install -e $FOLDER_OF_THE_LOCAL_LOCATION

Currently, TFMA requires that TensorFlow is installed but does not have an explicit dependency on the TensorFlow PyPI package. See the TensorFlow install guides for instructions.

To enable TFMA visualization in Jupyter Notebook:

  jupyter nbextension enable --py widgetsnbextension
  jupyter nbextension install --py --symlink tensorflow_model_analysis
  jupyter nbextension enable --py tensorflow_model_analysis

Note: If Jupyter notebook is already installed in your home directory, add --user to these commands. If Jupyter is installed as root, or using a virtual environment, the parameter --sys-prefix might be required.

Dependencies

Apache Beam is required to run distributed analysis. By default, Apache Beam runs in local mode but can also run in distributed mode using Google Cloud Dataflow. TFMA is designed to be extensible for other Apache Beam runners.

Getting Started

For instructions on using TFMA, see the get started guide.

Compatible Versions

The following table is the TFMA package versions that are compatible with each other. This is determined by our testing framework, but other untested combinations may also work.

tensorflow-model-analysis tensorflow apache-beam[gcp]
GitHub master nightly (1.x/2.x) 2.19.0
0.21.4 1.15 / 2.1 2.19.0
0.21.3 1.15 / 2.1 2.17.0
0.21.2 1.15 / 2.1 2.17.0
0.21.1 1.15 / 2.1 2.17.0
0.21.0 1.15 / 2.1 2.17.0
0.15.4 1.15 / 2.0 2.16.0
0.15.3 1.15 / 2.0 2.16.0
0.15.2 1.15 / 2.0 2.16.0
0.15.1 1.15 / 2.0 2.16.0
0.15.0 1.15 2.16.0
0.14.0 1.14 2.14.0
0.13.1 1.13 2.11.0
0.13.0 1.13 2.11.0
0.12.1 1.12 2.10.0
0.12.0 1.12 2.10.0
0.11.0 1.11 2.8.0
0.9.2 1.9 2.6.0
0.9.1 1.10 2.6.0
0.9.0 1.9 2.5.0
0.6.0 1.6 2.4.0

Questions

Please direct any questions about working with TFMA to Stack Overflow using the tensorflow-model-analysis tag.

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

File details

Details for the file tensorflow_model_analysis-0.21.4-py3-none-any.whl.

File metadata

  • Download URL: tensorflow_model_analysis-0.21.4-py3-none-any.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.15.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/44.0.0 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/2.7.17

File hashes

Hashes for tensorflow_model_analysis-0.21.4-py3-none-any.whl
Algorithm Hash digest
SHA256 ad357aa38865d72f864b882c7e8dbeb227b0e8fb51bef0ef18274b800a8dee93
MD5 7ead24fefbe83f014360776581742b0a
BLAKE2b-256 fbaead61e498ff68c96f0648d9e9f072a1a495935ace4fefb2de8bcbb42e101d

See more details on using hashes here.

File details

Details for the file tensorflow_model_analysis-0.21.4-py2-none-any.whl.

File metadata

  • Download URL: tensorflow_model_analysis-0.21.4-py2-none-any.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: Python 2
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.15.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/44.0.0 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/2.7.17

File hashes

Hashes for tensorflow_model_analysis-0.21.4-py2-none-any.whl
Algorithm Hash digest
SHA256 86530ff45a7de69eede8fd6c5ec9f150c7a6b5a0e03b3aba0768226477075829
MD5 5df8286af16558a7c03c86c5dbe786e7
BLAKE2b-256 0331e983822be1929b5baad2e9a4d20b01403027d68d20ed7c6037a95f3e344b

See more details on using hashes here.

Supported by

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