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https://github.com/tensorflow/model-analysis
Model analysis tools for TensorFlow
https://github.com/tensorflow/model-analysis
Last synced: 29 days ago
JSON representation
Model analysis tools for TensorFlow
- Host: GitHub
- URL: https://github.com/tensorflow/model-analysis
- Owner: tensorflow
- License: apache-2.0
- Created: 2018-03-23T19:08:49.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-04-01T16:00:41.000Z (7 months ago)
- Last Synced: 2024-04-18T04:11:14.721Z (7 months ago)
- Language: Python
- Size: 21.8 MB
- Stars: 1,241
- Watchers: 73
- Forks: 270
- Open Issues: 33
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# TensorFlow Model Analysis
[](https://github.com/tensorflow/model-analysis)
[](https://badge.fury.io/py/tensorflow-model-analysis)
[](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma)
*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](https://pypi.org/project/tensorflow-model-analysis/):
pip install tensorflow-model-analysis
pip install from https://pypi-nightly.tensorflow.org
pip install -i https://pypi-nightly.tensorflow.org/simple 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/[email protected]#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
Note that protobuf must be installed correctly for the above option since it is
building TFMA from source and it requires protoc and all of its includes
reference-able. Please see [protobuf install instruction](https://github.com/protocolbuffers/protobuf#protocol-compiler-installation)
for see the latest install instructions.
Currently, TFMA requires that TensorFlow is installed but does not have an
explicit dependency on the TensorFlow PyPI package. See the
[TensorFlow install guides](https://www.tensorflow.org/install/) for
instructions.
### Build TFMA from source
To build from source follow the following steps:
Install the protoc as per the link mentioned:
[protoc](https://grpc.io/docs/protoc-installation/#install-pre-compiled-binaries-any-os)
Create a virtual environment by running the commands
```
python3 -m venv
source /bin/activate
pip3 install setuptools wheel
git clone https://github.com/tensorflow/model-analysis.git
cd model-analysis
python3 setup.py bdist_wheel
```
This will build the TFMA wheel in the dist directory. To install the wheel from
dist directory run the commands
```
cd dist
pip3 install tensorflow_model_analysis--py3-none-any.whl
```
### Jupyter Lab
As of writing, because of https://github.com/pypa/pip/issues/9187, `pip install`
might never finish. In that case, you should revert pip to version 19 instead of
20: `pip install "pip<20"`.
Using a JupyterLab extension requires installing dependencies on the command
line. You can do this within the console in the JupyterLab UI or on the command
line. This includes separately installing any pip package dependencies and
JupyterLab labextension plugin dependencies, and the version numbers must be
compatible. JupyterLab labextension packages refer to npm packages
(eg, [tensorflow_model_analysis](https://www.npmjs.com/package/tensorflow_model_analysis).
The examples below use 0.32.0. Check available [versions](#compatible-versions)
below to use the latest.
#### Jupyter Lab 3.0.x
```Shell
pip install tensorflow_model_analysis==0.32.0
jupyter labextension install [email protected]
pip install jupyterlab_widgets==1.0.0
```
#### Jupyter Lab 2.2.x
```Shell
pip install tensorflow_model_analysis==0.32.0
jupyter labextension install [email protected]
jupyter labextension install @jupyter-widgets/jupyterlab-manager@2
```
#### Jupyter Lab 1.2.x
```Shell
pip install tensorflow_model_analysis==0.32.0
jupyter labextension install [email protected]
jupyter labextension install @jupyter-widgets/[email protected]
```
#### Classic Jupyter Notebook
To enable TFMA visualization in the classic Jupyter Notebook (either through
`jupyter notebook` or
[through the JupyterLab UI](https://jupyterlab.readthedocs.io/en/stable/getting_started/starting.html)),
you'll also need to run:
```shell
jupyter nbextension enable --py widgetsnbextension
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.
#### Building TFMA from source
If you want to build TFMA from source and use the UI in JupyterLab, you'll need
to make sure that the source contains valid version numbers. Check that the
Python package version number and npm package version number are exactly the
same, and that both valid version numbers (eg, remove the `-dev` suffix).
#### Troubleshooting
Check pip packages:
```Shell
pip list
```
Check JupyterLab extensions:
```Shell
jupyter labextension list # for JupyterLab
jupyter nbextension list # for classic Jupyter Notebook
```
### Standalone HTML page with `embed_minimal_html`
TFMA notebook extension can be built into a standalone HTML file that also
bundles data into the HTML file. See the Jupyter Widgets docs on
[embed_minimal_html](https://ipywidgets.readthedocs.io/en/latest/embedding.html#python-interface).
### Kubeflow Pipelines
[Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/)
includes integrations that embed the TFMA notebook extension ([code](https://github.com/kubeflow/pipelines/blob/1.5.0-rc.2/backend/src/apiserver/visualization/types/tfma.py#L17)).
This integration relies on network access at runtime to load a variant of the
JavaScript build published on unpkg.com (see [config](https://github.com/tensorflow/model-analysis/blob/v0.29.0/tensorflow_model_analysis/notebook/jupyter/js/webpack.config.js#L78)
and [loader code](https://github.com/tensorflow/model-analysis/blob/v0.29.0/tensorflow_model_analysis/notebook/jupyter/js/lib/widget.js#L23)).
### Notable Dependencies
TensorFlow is required.
[Apache Beam](https://beam.apache.org/) is required; it's the way that efficient
distributed computation is supported. By default, Apache Beam runs in local
mode but can also run in distributed mode using
[Google Cloud Dataflow](https://cloud.google.com/dataflow/) and other Apache
Beam
[runners](https://beam.apache.org/documentation/runners/capability-matrix/).
[Apache Arrow](https://arrow.apache.org/) is also required. TFMA uses Arrow to
represent data internally in order to make use of vectorized numpy functions.
## Getting Started
For instructions on using TFMA, see the [get started
guide](https://github.com/tensorflow/model-analysis/blob/master/g3doc/get_started.md).
## 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 |apache-beam[gcp]|pyarrow |tensorflow |tensorflow-metadata |tfx-bsl |
|------------------------------------------------------------------------------------ |----------------|----------|-------------------|--------------------|----------|
|[GitHub master](https://github.com/tensorflow/model-analysis/blob/master/RELEASE.md) | 2.47.0 | 10.0.0 | nightly (2.x) | 1.15.0 | 1.15.1 |
|[0.46.0](https://github.com/tensorflow/model-analysis/blob/v0.46.0/RELEASE.md) | 2.47.0 | 10.0.0 | 2.15 | 1.15.0 | 1.15.1 |
|[0.45.0](https://github.com/tensorflow/model-analysis/blob/v0.45.0/RELEASE.md) | 2.47.0 | 10.0.0 | 2.13 | 1.14.0 | 1.14.0 |
|[0.44.0](https://github.com/tensorflow/model-analysis/blob/v0.44.0/RELEASE.md) | 2.40.0 | 6.0.0 | 2.12 | 1.13.1 | 1.13.0 |
|[0.43.0](https://github.com/tensorflow/model-analysis/blob/v0.43.0/RELEASE.md) | 2.40.0 | 6.0.0 | 2.11 | 1.12.0 | 1.12.0 |
|[0.42.0](https://github.com/tensorflow/model-analysis/blob/v0.42.0/RELEASE.md) | 2.40.0 | 6.0.0 | 1.15.5 / 2.10 | 1.11.0 | 1.11.1 |
|[0.41.0](https://github.com/tensorflow/model-analysis/blob/v0.41.0/RELEASE.md) | 2.40.0 | 6.0.0 | 1.15.5 / 2.9 | 1.10.0 | 1.10.1 |
|[0.40.0](https://github.com/tensorflow/model-analysis/blob/v0.40.0/RELEASE.md) | 2.38.0 | 5.0.0 | 1.15.5 / 2.9 | 1.9.0 | 1.9.0 |
|[0.39.0](https://github.com/tensorflow/model-analysis/blob/v0.39.0/RELEASE.md) | 2.38.0 | 5.0.0 | 1.15.5 / 2.8 | 1.8.0 | 1.8.0 |
|[0.38.0](https://github.com/tensorflow/model-analysis/blob/v0.38.0/RELEASE.md) | 2.36.0 | 5.0.0 | 1.15.5 / 2.8 | 1.7.0 | 1.7.0 |
|[0.37.0](https://github.com/tensorflow/model-analysis/blob/v0.37.0/RELEASE.md) | 2.35.0 | 5.0.0 | 1.15.5 / 2.7 | 1.6.0 | 1.6.0 |
|[0.36.0](https://github.com/tensorflow/model-analysis/blob/v0.36.0/RELEASE.md) | 2.34.0 | 5.0.0 | 1.15.5 / 2.7 | 1.5.0 | 1.5.0 |
|[0.35.0](https://github.com/tensorflow/model-analysis/blob/v0.35.0/RELEASE.md) | 2.33.0 | 5.0.0 | 1.15 / 2.6 | 1.4.0 | 1.4.0 |
|[0.34.1](https://github.com/tensorflow/model-analysis/blob/v0.34.1/RELEASE.md) | 2.32.0 | 2.0.0 | 1.15 / 2.6 | 1.2.0 | 1.3.0 |
|[0.34.0](https://github.com/tensorflow/model-analysis/blob/v0.34.0/RELEASE.md) | 2.31.0 | 2.0.0 | 1.15 / 2.6 | 1.2.0 | 1.3.1 |
|[0.33.0](https://github.com/tensorflow/model-analysis/blob/v0.33.0/RELEASE.md) | 2.31.0 | 2.0.0 | 1.15 / 2.5 | 1.2.0 | 1.2.0 |
|[0.32.1](https://github.com/tensorflow/model-analysis/blob/v0.32.1/RELEASE.md) | 2.29.0 | 2.0.0 | 1.15 / 2.5 | 1.1.0 | 1.1.1 |
|[0.32.0](https://github.com/tensorflow/model-analysis/blob/v0.32.0/RELEASE.md) | 2.29.0 | 2.0.0 | 1.15 / 2.5 | 1.1.0 | 1.1.0 |
|[0.31.0](https://github.com/tensorflow/model-analysis/blob/v0.31.0/RELEASE.md) | 2.29.0 | 2.0.0 | 1.15 / 2.5 | 1.0.0 | 1.0.0 |
|[0.30.0](https://github.com/tensorflow/model-analysis/blob/v0.30.0/RELEASE.md) | 2.28.0 | 2.0.0 | 1.15 / 2.4 | 0.30.0 | 0.30.0 |
|[0.29.0](https://github.com/tensorflow/model-analysis/blob/v0.29.0/RELEASE.md) | 2.28.0 | 2.0.0 | 1.15 / 2.4 | 0.29.0 | 0.29.0 |
|[0.28.0](https://github.com/tensorflow/model-analysis/blob/v0.28.0/RELEASE.md) | 2.28.0 | 2.0.0 | 1.15 / 2.4 | 0.28.0 | 0.28.0 |
|[0.27.0](https://github.com/tensorflow/model-analysis/blob/v0.27.0/RELEASE.md) | 2.27.0 | 2.0.0 | 1.15 / 2.4 | 0.27.0 | 0.27.0 |
|[0.26.1](https://github.com/tensorflow/model-analysis/blob/v0.26.1/RELEASE.md) | 2.28.0 | 0.17.0 | 1.15 / 2.3 | 0.26.0 | 0.26.0 |
|[0.26.0](https://github.com/tensorflow/model-analysis/blob/v0.26.0/RELEASE.md) | 2.25.0 | 0.17.0 | 1.15 / 2.3 | 0.26.0 | 0.26.0 |
|[0.25.0](https://github.com/tensorflow/model-analysis/blob/v0.25.0/RELEASE.md) | 2.25.0 | 0.17.0 | 1.15 / 2.3 | 0.25.0 | 0.25.0 |
|[0.24.3](https://github.com/tensorflow/model-analysis/blob/v0.24.3/RELEASE.md) | 2.24.0 | 0.17.0 | 1.15 / 2.3 | 0.24.0 | 0.24.1 |
|[0.24.2](https://github.com/tensorflow/model-analysis/blob/v0.24.2/RELEASE.md) | 2.23.0 | 0.17.0 | 1.15 / 2.3 | 0.24.0 | 0.24.0 |
|[0.24.1](https://github.com/tensorflow/model-analysis/blob/v0.24.1/RELEASE.md) | 2.23.0 | 0.17.0 | 1.15 / 2.3 | 0.24.0 | 0.24.0 |
|[0.24.0](https://github.com/tensorflow/model-analysis/blob/v0.24.0/RELEASE.md) | 2.23.0 | 0.17.0 | 1.15 / 2.3 | 0.24.0 | 0.24.0 |
|[0.23.0](https://github.com/tensorflow/model-analysis/blob/v0.23.0/RELEASE.md) | 2.23.0 | 0.17.0 | 1.15 / 2.3 | 0.23.0 | 0.23.0 |
|[0.22.2](https://github.com/tensorflow/model-analysis/blob/v0.22.2/RELEASE.md) | 2.20.0 | 0.16.0 | 1.15 / 2.2 | 0.22.2 | 0.22.0 |
|[0.22.1](https://github.com/tensorflow/model-analysis/blob/v0.22.1/RELEASE.md) | 2.20.0 | 0.16.0 | 1.15 / 2.2 | 0.22.2 | 0.22.0 |
|[0.22.0](https://github.com/tensorflow/model-analysis/blob/v0.22.0/RELEASE.md) | 2.20.0 | 0.16.0 | 1.15 / 2.2 | 0.22.0 | 0.22.0 |
|[0.21.6](https://github.com/tensorflow/model-analysis/blob/v0.21.6/RELEASE.md) | 2.19.0 | 0.15.0 | 1.15 / 2.1 | 0.21.0 | 0.21.3 |
|[0.21.5](https://github.com/tensorflow/model-analysis/blob/v0.21.5/RELEASE.md) | 2.19.0 | 0.15.0 | 1.15 / 2.1 | 0.21.0 | 0.21.3 |
|[0.21.4](https://github.com/tensorflow/model-analysis/blob/v0.21.4/RELEASE.md) | 2.19.0 | 0.15.0 | 1.15 / 2.1 | 0.21.0 | 0.21.3 |
|[0.21.3](https://github.com/tensorflow/model-analysis/blob/v0.21.3/RELEASE.md) | 2.17.0 | 0.15.0 | 1.15 / 2.1 | 0.21.0 | 0.21.0 |
|[0.21.2](https://github.com/tensorflow/model-analysis/blob/v0.21.2/RELEASE.md) | 2.17.0 | 0.15.0 | 1.15 / 2.1 | 0.21.0 | 0.21.0 |
|[0.21.1](https://github.com/tensorflow/model-analysis/blob/v0.21.1/RELEASE.md) | 2.17.0 | 0.15.0 | 1.15 / 2.1 | 0.21.0 | 0.21.0 |
|[0.21.0](https://github.com/tensorflow/model-analysis/blob/v0.21.0/RELEASE.md) | 2.17.0 | 0.15.0 | 1.15 / 2.1 | 0.21.0 | 0.21.0 |
|[0.15.4](https://github.com/tensorflow/model-analysis/blob/v0.15.4/RELEASE.md) | 2.16.0 | 0.15.0 | 1.15 / 2.0 | n/a | 0.15.1 |
|[0.15.3](https://github.com/tensorflow/model-analysis/blob/v0.15.3/RELEASE.md) | 2.16.0 | 0.15.0 | 1.15 / 2.0 | n/a | 0.15.1 |
|[0.15.2](https://github.com/tensorflow/model-analysis/blob/v0.15.2/RELEASE.md) | 2.16.0 | 0.15.0 | 1.15 / 2.0 | n/a | 0.15.1 |
|[0.15.1](https://github.com/tensorflow/model-analysis/blob/v0.15.1/RELEASE.md) | 2.16.0 | 0.15.0 | 1.15 / 2.0 | n/a | 0.15.0 |
|[0.15.0](https://github.com/tensorflow/model-analysis/blob/v0.15.0/RELEASE.md) | 2.16.0 | 0.15.0 | 1.15 | n/a | n/a |
|[0.14.0](https://github.com/tensorflow/model-analysis/blob/v0.14.0/RELEASE.md) | 2.14.0 | n/a | 1.14 | n/a | n/a |
|[0.13.1](https://github.com/tensorflow/model-analysis/blob/v0.13.1/RELEASE.md) | 2.11.0 | n/a | 1.13 | n/a | n/a |
|[0.13.0](https://github.com/tensorflow/model-analysis/blob/v0.13.0/RELEASE.md) | 2.11.0 | n/a | 1.13 | n/a | n/a |
|[0.12.1](https://github.com/tensorflow/model-analysis/blob/v0.12.1/RELEASE.md) | 2.10.0 | n/a | 1.12 | n/a | n/a |
|[0.12.0](https://github.com/tensorflow/model-analysis/blob/v0.12.0/RELEASE.md) | 2.10.0 | n/a | 1.12 | n/a | n/a |
|[0.11.0](https://github.com/tensorflow/model-analysis/blob/v0.11.0/RELEASE.md) | 2.8.0 | n/a | 1.11 | n/a | n/a |
|[0.9.2](https://github.com/tensorflow/model-analysis/blob/v0.9.2/RELEASE.md) | 2.6.0 | n/a | 1.9 | n/a | n/a |
|[0.9.1](https://github.com/tensorflow/model-analysis/blob/v0.9.1/RELEASE.md) | 2.6.0 | n/a | 1.10 | n/a | n/a |
|[0.9.0](https://github.com/tensorflow/model-analysis/blob/v0.9.0/RELEASE.md) | 2.5.0 | n/a | 1.9 | n/a | n/a |
|[0.6.0](https://github.com/tensorflow/model-analysis/blob/v0.6.0/RELEASE.md) | 2.4.0 | n/a | 1.6 | n/a | n/a |
## Questions
Please direct any questions about working with TFMA to
[Stack Overflow](https://stackoverflow.com) using the
[tensorflow-model-analysis](https://stackoverflow.com/questions/tagged/tensorflow-model-analysis)
tag.