5b900cfe4b
Now python3.6 can be installed from apt and it will be installed with
all submodules.
If we're compiling Python from source, during compilation we get:
```
The necessary bits to build these optional modules were not found:
_bz2 _dbm _gdbm
_lzma _sqlite3 _tkinter
readline
```
which then results in
```
==================== Test output for //bazel_pip/tensorflow/contrib/summary:summary_ops_test:
Running test /tmpfs/src/github/tensorflow/bazel-ci_build-cache/.cache/bazel/_bazel_kbuilder/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/k8-opt/bin/bazel_pip/tensorflow/contrib/summary/summary_ops_test.runfiles/org_tensorflow/bazel_pip/tensorflow/contrib/summary/summary_ops_test on GPU 0
Traceback (most recent call last):
File "/tmpfs/src/github/tensorflow/bazel-ci_build-cache/.cache/bazel/_bazel_kbuilder/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/k8-opt/bin/bazel_pip/tensorflow/contrib/summary/summary_ops_test.runfiles/org_tensorflow/bazel_pip/tensorflow/contrib/summary/summary_ops_test.py", line 23, in <module>
import sqlite3
File "/usr/local/lib/python3.6/sqlite3/__init__.py", line 23, in <module>
from sqlite3.dbapi2 import *
File "/usr/local/lib/python3.6/sqlite3/dbapi2.py", line 27, in <module>
from _sqlite3 import *
ModuleNotFoundError: No module named '_sqlite3'
================================================================================
```
and similar failures which then block releasing patch version.
|
||
|---|---|---|
| tensorflow | ||
| third_party | ||
| tools | ||
| .bazelrc | ||
| .gitignore | ||
| ACKNOWLEDGMENTS | ||
| ADOPTERS.md | ||
| arm_compiler.BUILD | ||
| AUTHORS | ||
| BUILD | ||
| CODE_OF_CONDUCT.md | ||
| CODEOWNERS | ||
| configure | ||
| configure.py | ||
| CONTRIBUTING.md | ||
| ISSUE_TEMPLATE.md | ||
| LICENSE | ||
| models.BUILD | ||
| README.md | ||
| RELEASE.md | ||
| SECURITY.md | ||
| WORKSPACE | ||
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 announce@tensorflow.org.
Installation
See Installing TensorFlow for instructions on how to install our release binaries or 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-nightlyorpip install tf-nightly-gpuin 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 | TBA | |
| Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
| Linux CPU with Intel® MKL-DNN Python 2.7 Linux CPU with Intel® MKL-DNN Python 3.5 Linux CPU with Intel® MKL-DNN Python 3.6 |
1.10.0 py2.7 1.10.0 py3.5 1.10.0 py3.6 |
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
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.