TensorFlow alternatives and similar libraries
Based on the "Artificial Intelligence" category
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Do you think we are missing an alternative of TensorFlow or a related project?
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.
Keep up to date with release announcements and security updates by subscribing to email@example.com.
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-gpu project on PyPi.
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
>>> 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.
If you want to contribute to TensorFlow, be sure to review the [contribution guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's [code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code.
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
Continuous build status
|Raspberry Pi 0 and 1||Py2 Py3|
|Raspberry Pi 2 and 3||Py2 Py3|
Community Supported Builds
|Linux s390x Nightly||Nightly|
|Linux s390x CPU Stable Release||Release|
|Linux ppc64le CPU Nightly||Nightly|
|Linux ppc64le CPU Stable Release||Release|
|Linux ppc64le GPU Nightly||Nightly|
|Linux ppc64le GPU Stable Release||Release|
|Linux CPU with Intel® MKL-DNN Nightly||Nightly|
|Linux CPU with Intel® MKL-DNN Supports Python 2.7, 3.4, 3.5, and 3.6||1.13.1 pypi|
|Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6||1.13.1 pypi|
For more information
- TensorFlow Website
- TensorFlow Tutorials
- TensorFlow Model Zoo
- TensorFlow Twitter
- TensorFlow Blog
- TensorFlow Course at Stanford
- TensorFlow Roadmap
- TensorFlow White Papers
- TensorFlow YouTube Channel
- TensorFlow Visualization Toolkit
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.
[Apache License 2.0](LICENSE)
*Note that all licence references and agreements mentioned in the TensorFlow README section above are relevant to that project's source code only.