|author||Miao Wang <firstname.lastname@example.org>||Tue May 14 17:30:35 2019 -0700|
|committer||Miao Wang <email@example.com>||Thu May 16 14:59:52 2019 -0700|
Add Android specific generated test runner. - The test runner is a Android specific trimmed down version of generated_examples_zip_test. - The main difference is the removal of dependencies like absl, re2 and tensorflow core. - The files are forked in to nnapi_tflite_zip_tests/ to avoid problems upon rebasing. - More models will be added in upcoming CLs when the delegate is updated to support these ops Bug: 130762914 Test: mm Test: atest TfliteGeneratedNnapiTest Merged-In: I09b5d5307b4a402c4fa166434744d7fe2f3b90cb Change-Id: I09b5d5307b4a402c4fa166434744d7fe2f3b90cb (cherry picked from commit ea303b6c0d289299527d69763ca007cd592130f6)
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.
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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
pip install tf-nightlyor
pip install tf-nightly-gpuin a clean environment to install the nightly TensorFlow build. We support CPU and GPU packages on Linux, Mac, and Windows.
>>> 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.
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
|Raspberry Pi 0 and 1||Py2 Py3|
|Raspberry Pi 2 and 3||Py2 Py3|
|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 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
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.