imburbank/tensorflow

•Updated almost 8 years ago

Unofficial hosting of Official TensorFlow Docker Images

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imburbank/tensorflow repository overview

`Documentation`

TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard⁠, a data visualization toolkit.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python API and C APIs as well as without API backwards compatibility guarantee like C++, Go, Java, JavaScript and Swift.

Keep up to date with release announcements and security updates by subscribing to [email protected]⁠.

⁠Installation

To install the current release for CPU-only:

pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards:

pip install tensorflow-gpu

See Installing TensorFlow⁠ for detailed instructions, and how to build from source.

People who are a little more adventurous can also try our nightly binaries:

Nightly pip packages

We are pleased to announce that TensorFlow now offers nightly pip packages under the tf-nightly⁠ and tf-nightly-gpu⁠ project on pypi. Simply run pip install tf-nightly or pip install tf-nightly-gpu in a clean environment to install the nightly TensorFlow build. We support CPU and GPU packages on Linux, Mac, and Windows.

⁠Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.enable_eager_execution()
>>> tf.add(1, 2)
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
'Hello, TensorFlow!'

Learn more examples about how to do specific tasks in TensorFlow at the tutorials page of tensorflow.org⁠.

⁠Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines⁠. This project adheres to TensorFlow's code of conduct⁠. By participating, you are expected to uphold this code.

We use GitHub issues⁠ for tracking requests and bugs, so please see TensorFlow Discuss⁠ for general questions and discussion, and please direct specific questions to Stack Overflow⁠.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:

⁠Continuous build status

⁠Official Builds

Build Type	Status	Artifacts
Linux CPU		pypi⁠
Linux GPU		pypi⁠
Linux XLA		TBA
MacOS		pypi⁠
Windows CPU		pypi⁠
Windows GPU		pypi⁠
Android
Raspberry Pi 0 and 1		Py2⁠ Py3⁠
Raspberry Pi 2 and 3		Py2⁠ Py3⁠

⁠Community Supported Builds

Build Type	Status	Artifacts
IBM s390x		TBA
IBM ppc64le CPU		TBA
IBM ppc64le GPU Nightly		Nightly⁠
IBM ppc64le GPU Stable Release		Release⁠
Linux CPU with Intel® MKL-DNN Nightly		Nightly⁠
Linux CPU with Intel® MKL-DNN Python 2.7 Linux CPU with Intel® MKL-DNN Python 3.4 Linux CPU with Intel® MKL-DNN Python 3.5 Linux CPU with Intel® MKL-DNN Python 3.6		1.11.0 py2.7⁠ 1.11.0 py3.4⁠ 1.11.0 py3.5⁠ 1.11.0 py3.6⁠