| Name | tfx-bsl JSON |
| Version |
0.28.1
JSON |
| download |
| home_page | https://www.tensorflow.org/tfx |
| Summary | tfx_bsl (TFX Basic Shared Libraries) contains libraries shared by many TFX (TensorFlow eXtended) libraries and components. |
| upload_time | 2021-02-23 17:41:42 |
| maintainer | |
| docs_url | None |
| author | Google LLC |
| requires_python | >=3.6,<4 |
| license | Apache 2.0 |
| keywords |
tfx
bsl
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# TFX Basic Shared Libraries
[](https://github.com/tensorflow/tfx-bsl)
[](https://badge.fury.io/py/tfx-bsl)
TFX Basic Shared Libraries (`tfx_bsl`) contains libraries shared by many
[TensorFlow eXtended (TFX)](https://www.tensorflow.org/tfx) components.
__Only symbols exported by sub-modules under `tfx_bsl/public` are intended for
direct use by TFX users__, including by standalone TFX library (e.g. TFDV, TFMA,
TFT) users, TFX pipeline authors and TFX component authors. Those APIs will
become stable and follow semantic versioning once `tfx_bsl` goes beyond `1.0`.
APIs under other directories should be considered internal to TFX
(and therefore there is no backward or forward compatibility guarantee for
them).
Each minor version of a TFX library or TFX itself, if it needs to
depend on `tfx_bsl`, will depend on a specific minor version of it (e.g.
`tensorflow_data_validation` 0.14.\* will depend on, and only work with,
`tfx_bsl` 0.14.\*)
## Installing from PyPI
`tfx_bsl` is available as a [PyPI package](https://pypi.org/project/tfx-bsl/).
```bash
pip install tfx-bsl
```
### Nightly Packages
TFX-BSL also hosts nightly packages at https://pypi-nightly.tensorflow.org on
Google Cloud. To install the latest nightly package, please use the following
command:
```bash
pip install -i https://pypi-nightly.tensorflow.org/simple tfx-bsl
```
This will install the nightly packages for the major dependencies of TFX-BSL
such as TensorFlow Metadata (TFMD).
However it is a dependency of many TFX components and usually as a user you
don't need to install it directly.
## Build with Docker
If you want to build a TFX component from the master branch, past the latest
release, you may also have to build the latest `tfx_bsl`, as that TFX component
might have depended on new features introduced past the latest `tfx_bsl`
release.
Building from Docker is the recommended way to build `tfx_bsl` under Linux,
and is continuously tested at Google.
### 1. Install Docker
Please first install [`docker`](https://docs.docker.com/install/) and
[`docker-compose`](https://docs.docker.com/compose/install/) by following the
directions.
### 2. Clone the `tfx_bsl` repository
```shell
git clone https://github.com/tensorflow/tfx-bsl
cd tfx-bsl
```
Note that these instructions will install the latest master branch of `tfx-bsl`.
If you want to install a specific branch (such as a release branch), pass
`-b <branchname>` to the `git clone` command.
### 3. Build the pip package
Then, run the following at the project root:
```bash
sudo docker-compose build manylinux2010
sudo docker-compose run -e PYTHON_VERSION=${PYTHON_VERSION} manylinux2010
```
where `PYTHON_VERSION` is one of `{35, 36, 37, 38}`.
A wheel will be produced under `dist/`.
### 4. Install the pip package
```shell
pip install dist/*.whl
```
## Build from source
### 1. Prerequisites
#### Install NumPy
If NumPy is not installed on your system, install it now by following [these
directions](https://www.scipy.org/scipylib/download.html).
#### Install Bazel
If Bazel is not installed on your system, install it now by following [these
directions](https://bazel.build/versions/master/docs/install.html).
### 2. Clone the `tfx_bsl` repository
```shell
git clone https://github.com/tensorflow/tfx-bsl
cd tfx-bsl
```
Note that these instructions will install the latest master branch of `tfx_bsl`
If you want to install a specific branch (such as a release branch),
pass `-b <branchname>` to the `git clone` command.
### 3. Build the pip package
`tfx_bsl` wheel is Python version dependent -- to build the pip package that
works for a specific Python version, use that Python binary to run:
```shell
python setup.py bdist_wheel
```
You can find the generated `.whl` file in the `dist` subdirectory.
### 4. Install the pip package
```shell
pip install dist/*.whl
```
## Supported platforms
`tfx_bsl` is tested on the following 64-bit operating systems:
* macOS 10.12.6 (Sierra) or later.
* Ubuntu 16.04 or later.
* Windows 7 or later.
## Compatible versions
The following table is the `tfx_bsl` package versions that are compatible with
each other. This is determined by our testing framework, but other *untested*
combinations may also work.
tfx-bsl | apache-beam[gcp] | pyarrow | tensorflow | tensorflow-metadata | tensorflow-serving-api |
------------------------------------------------------------------------------- | -----------------| ---------|-------------------|---------------------|------------------------|
[GitHub master](https://github.com/tensorflow/tfx-bsl/blob/master/RELEASE.md) | 2.28.0 | 2.0.0 | nightly (1.x/2.x) | 0.28.0 | 2.4.0 |
[0.28.0](https://github.com/tensorflow/tfx-bsl/blob/v0.28.0/RELEASE.md) | 2.28.0 | 2.0.0 | 1.15 / 2.4 | 0.28.0 | 2.4.0 |
[0.27.1](https://github.com/tensorflow/tfx-bsl/blob/v0.27.1/RELEASE.md) | 2.27.0 | 2.0.0 | 1.15 / 2.4 | 0.27.0 | 2.4.0 |
[0.27.0](https://github.com/tensorflow/tfx-bsl/blob/v0.27.0/RELEASE.md) | 2.27.0 | 2.0.0 | 1.15 / 2.4 | 0.27.0 | 2.4.0 |
[0.26.1](https://github.com/tensorflow/tfx-bsl/blob/v0.26.1/RELEASE.md) | 2.25.0 | 0.17.0 | 1.15 / 2.3 | 0.27.0 | 2.3.0 |
[0.26.0](https://github.com/tensorflow/tfx-bsl/blob/v0.26.0/RELEASE.md) | 2.25.0 | 0.17.0 | 1.15 / 2.3 | 0.27.0 | 2.3.0 |
Raw data
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"description": "# TFX Basic Shared Libraries\n\n[](https://github.com/tensorflow/tfx-bsl)\n[](https://badge.fury.io/py/tfx-bsl)\n\nTFX Basic Shared Libraries (`tfx_bsl`) contains libraries shared by many\n[TensorFlow eXtended (TFX)](https://www.tensorflow.org/tfx) components.\n\n__Only symbols exported by sub-modules under `tfx_bsl/public` are intended for\ndirect use by TFX users__, including by standalone TFX library (e.g. TFDV, TFMA,\nTFT) users, TFX pipeline authors and TFX component authors. Those APIs will\nbecome stable and follow semantic versioning once `tfx_bsl` goes beyond `1.0`.\n\nAPIs under other directories should be considered internal to TFX\n(and therefore there is no backward or forward compatibility guarantee for\nthem).\n\nEach minor version of a TFX library or TFX itself, if it needs to\ndepend on `tfx_bsl`, will depend on a specific minor version of it (e.g.\n`tensorflow_data_validation` 0.14.\\* will depend on, and only work with,\n`tfx_bsl` 0.14.\\*)\n\n## Installing from PyPI\n\n`tfx_bsl` is available as a [PyPI package](https://pypi.org/project/tfx-bsl/).\n\n```bash\npip install tfx-bsl\n```\n\n### Nightly Packages\n\nTFX-BSL also hosts nightly packages at https://pypi-nightly.tensorflow.org on\nGoogle Cloud. To install the latest nightly package, please use the following\ncommand:\n\n```bash\npip install -i https://pypi-nightly.tensorflow.org/simple tfx-bsl\n```\n\nThis will install the nightly packages for the major dependencies of TFX-BSL\nsuch as TensorFlow Metadata (TFMD).\n\nHowever it is a dependency of many TFX components and usually as a user you\ndon't need to install it directly.\n\n## Build with Docker\n\nIf you want to build a TFX component from the master branch, past the latest\nrelease, you may also have to build the latest `tfx_bsl`, as that TFX component\nmight have depended on new features introduced past the latest `tfx_bsl`\nrelease.\n\nBuilding from Docker is the recommended way to build `tfx_bsl` under Linux,\nand is continuously tested at Google.\n\n### 1. Install Docker\n\nPlease first install [`docker`](https://docs.docker.com/install/) and\n[`docker-compose`](https://docs.docker.com/compose/install/) by following the\ndirections.\n\n### 2. Clone the `tfx_bsl` repository\n\n```shell\ngit clone https://github.com/tensorflow/tfx-bsl\ncd tfx-bsl\n```\n\nNote that these instructions will install the latest master branch of `tfx-bsl`.\nIf you want to install a specific branch (such as a release branch), pass\n`-b <branchname>` to the `git clone` command.\n\n### 3. Build the pip package\n\nThen, run the following at the project root:\n\n```bash\nsudo docker-compose build manylinux2010\nsudo docker-compose run -e PYTHON_VERSION=${PYTHON_VERSION} manylinux2010\n```\nwhere `PYTHON_VERSION` is one of `{35, 36, 37, 38}`.\n\nA wheel will be produced under `dist/`.\n\n### 4. Install the pip package\n\n```shell\npip install dist/*.whl\n```\n\n## Build from source\n\n### 1. Prerequisites\n\n#### Install NumPy\n\nIf NumPy is not installed on your system, install it now by following [these\ndirections](https://www.scipy.org/scipylib/download.html).\n\n#### Install Bazel\n\nIf Bazel is not installed on your system, install it now by following [these\ndirections](https://bazel.build/versions/master/docs/install.html).\n\n\n### 2. Clone the `tfx_bsl` repository\n\n```shell\ngit clone https://github.com/tensorflow/tfx-bsl\ncd tfx-bsl\n```\n\nNote that these instructions will install the latest master branch of `tfx_bsl`\nIf you want to install a specific branch (such as a release branch),\npass `-b <branchname>` to the `git clone` command.\n\n### 3. Build the pip package\n\n`tfx_bsl` wheel is Python version dependent -- to build the pip package that\nworks for a specific Python version, use that Python binary to run:\n```shell\npython setup.py bdist_wheel\n```\n\nYou can find the generated `.whl` file in the `dist` subdirectory.\n\n### 4. Install the pip package\n\n```shell\npip install dist/*.whl\n```\n\n## Supported platforms\n\n`tfx_bsl` is tested on the following 64-bit operating systems:\n\n * macOS 10.12.6 (Sierra) or later.\n * Ubuntu 16.04 or later.\n * Windows 7 or later.\n\n\n## Compatible versions\n\nThe following table is the `tfx_bsl` package versions that are compatible with\neach other. This is determined by our testing framework, but other *untested*\ncombinations may also work.\n\ntfx-bsl | apache-beam[gcp] | pyarrow | tensorflow | tensorflow-metadata | tensorflow-serving-api |\n------------------------------------------------------------------------------- | -----------------| ---------|-------------------|---------------------|------------------------|\n[GitHub master](https://github.com/tensorflow/tfx-bsl/blob/master/RELEASE.md) | 2.28.0 | 2.0.0 | nightly (1.x/2.x) | 0.28.0 | 2.4.0 |\n[0.28.0](https://github.com/tensorflow/tfx-bsl/blob/v0.28.0/RELEASE.md) | 2.28.0 | 2.0.0 | 1.15 / 2.4 | 0.28.0 | 2.4.0 |\n[0.27.1](https://github.com/tensorflow/tfx-bsl/blob/v0.27.1/RELEASE.md) | 2.27.0 | 2.0.0 | 1.15 / 2.4 | 0.27.0 | 2.4.0 |\n[0.27.0](https://github.com/tensorflow/tfx-bsl/blob/v0.27.0/RELEASE.md) | 2.27.0 | 2.0.0 | 1.15 / 2.4 | 0.27.0 | 2.4.0 |\n[0.26.1](https://github.com/tensorflow/tfx-bsl/blob/v0.26.1/RELEASE.md) | 2.25.0 | 0.17.0 | 1.15 / 2.3 | 0.27.0 | 2.3.0 |\n[0.26.0](https://github.com/tensorflow/tfx-bsl/blob/v0.26.0/RELEASE.md) | 2.25.0 | 0.17.0 | 1.15 / 2.3 | 0.27.0 | 2.3.0 |\n\n\n",
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