Latest 0.2.0
Homepage https://github.com/tensorflow/tensorflow
License Apache
Platforms ios 9.0

Documentation
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 and C APIs as well as non-guaranteed backwards
compatible API’s for 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).numpy()
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, 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:

CII Best Practices

Continuous build status

Official Builds

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

Community Supported Builds

Build Type Status Artifacts
IBM s390x Build Status TBA
Linux ppc64le CPU Nightly Build Status Nightly
Linux ppc64le CPU Stable Release Build Status Release
Linux ppc64le GPU Nightly Build Status Nightly
Linux ppc64le GPU Stable Release Build Status Release
Linux CPU with Intel® MKL-DNN Nightly Build Status Nightly
Linux CPU with Intel® MKL-DNN
Supports Python 2.7, 3.4, 3.5, and 3.6
Build Status 1.13.1 pypi
Red Hat® Enterprise Linux® 7.6 CPU & GPU
Python 2.7, 3.6
Build Status 1.13.1 pypi

For more information

Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.

License

Apache License 2.0

Latest podspec

{
    "name": "TensorFlowLiteC",
    "version": "0.2.0",
    "authors": "Google Inc.",
    "license": {
        "type": "Apache"
    },
    "homepage": "https://github.com/tensorflow/tensorflow",
    "source": {
        "http": "https://dl.google.com/dl/cpdc/9d0ec5e53f4ff34a/TensorFlowLiteC-0.2.0.tar.gz"
    },
    "summary": "TensorFlow Lite",
    "description": "TensorFlow Lite is TensorFlow's lightweight solution for mobile developers. Itnenables low-latency inference of on-device machine learning models with ansmall binary size and fast performance supporting hardware acceleration.",
    "platforms": {
        "ios": "9.0"
    },
    "module_name": "TensorFlowLiteC",
    "libraries": "c++",
    "vendored_frameworks": "Frameworks/TensorFlowLiteC.framework"
}

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