A library for analyzing TensorFlow models
Project description
TensorFlow Model Analysis
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.
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 widgetsnbextensionjupyter 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 |
| 0.21.6 | 1.15 / 2.1 | 2.19.0 |
| 0.21.5 | 1.15 / 2.1 | 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.
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 Distributions
Built Distributions
Hashes for tensorflow_model_analysis-0.21.6-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 1bfdf1d2ee28eb8dc84b5562a4a96a3c3eb84ef8b0d85d90e6a4f2e455f46212 |
|
| MD5 | ab362f1628aa6ab87938a7511a2a0c1c |
|
| BLAKE2b-256 | 47a02e4eef99c8a308fa9e28fd2e9faa730e5e5f5b6c05c93ee8e2bb81075345 |
Hashes for tensorflow_model_analysis-0.21.6-py2-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ba19e6f9ed1d4242ca0a186a44844b3cba85b1352515b09617f054913b43972d |
|
| MD5 | 62ba585ee8799aaf3b459d20764fcad2 |
|
| BLAKE2b-256 | d21dfa43310308d73af66debe2bc0875f12bc66fea5d26f39bf9c28112e7171f |
