--- /dev/null
+# Installing TensorFlow on Raspbian
+
+This guide explains how to install TensorFlow on a Raspberry Pi running
+Raspbian. Although these instructions might also work on other Pi variants, we
+have only tested (and we only support) these instructions on machines meeting
+the following requirements:
+
+* Raspberry Pi devices running Raspbian 9.0 or higher
+
+## Determine how to install TensorFlow
+
+You must pick the mechanism by which you install TensorFlow. The supported
+choices are as follows:
+
+* "Native" pip.
+* Cross-compiling from sources.
+
+**We recommend pip installation.**
+
+## Installing with native pip
+
+We have uploaded the TensorFlow binaries to piwheels.org. Therefore, you can
+install TensorFlow through pip.
+
+The [REQUIRED_PACKAGES section of
+setup.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py)
+lists the packages that pip will install or upgrade.
+
+### Prerequisite: Python
+
+In order to install TensorFlow, your system must contain one of the following
+Python versions:
+
+* Python 2.7
+* Python 3.4+
+
+If your system does not already have one of the preceding Python versions,
+[install](https://wiki.python.org/moin/BeginnersGuide/Download) it now. It
+should already be included when Raspbian was installed though, so no extra steps
+should be needed.
+
+### Prerequisite: pip
+
+[Pip](https://en.wikipedia.org/wiki/Pip_\(package_manager\)) installs and
+manages software packages written in Python. If you intend to install with
+native pip, then one of the following flavors of pip must be installed on your
+system:
+
+* `pip3`, for Python 3.n (preferred).
+* `pip`, for Python 2.7.
+
+`pip` or `pip3` was probably installed on your system when you installed Python.
+To determine whether pip or pip3 is actually installed on your system, issue one
+of the following commands:
+
+<pre>$ <b>pip3 -V</b> # for Python 3.n
+$ <b>pip -V</b> # for Python 2.7</pre>
+
+If it gives the error "Command not found", then the package has not been
+installed yet. To install if for the first time, run:
+
+<pre>$ sudo apt-get install python3-pip # for Python 3.n
+sudo apt-get install python-pip # for Python 2.7</pre>
+
+You can find more help on installing and upgrading pip in
+[the Raspberry Pi documentation](https://www.raspberrypi.org/documentation/linux/software/python.md).
+
+### Prerequisite: Atlas
+
+[Atlas](http://math-atlas.sourceforge.net/) is a linear algebra library that
+numpy depends on, and so needs to be installed before TensorFlow. To add it to
+your system, run the following command:
+
+<pre>$ sudo apt install libatlas-base-dev</pre>
+
+### Install TensorFlow
+
+Assuming the prerequisite software is installed on your Pi, install TensorFlow
+by invoking **one** of the following commands:
+
+ <pre> $ <b>pip3 install tensorflow</b> # Python 3.n
+ $ <b>pip install tensorflow</b> # Python 2.7</pre>
+
+This can take some time on certain platforms like the Pi Zero, where some Python
+packages like scipy that TensorFlow depends on need to be compiled before the
+installation can complete. The Python 3 version will typically be faster to
+install because piwheels.org has pre-built versions of the dependencies
+available, so this is our recommended option.
+
+### Next Steps
+
+After installing TensorFlow, [validate your
+installation](#ValidateYourInstallation) to confirm that the installation worked
+properly.
+
+### Uninstalling TensorFlow
+
+To uninstall TensorFlow, issue one of following commands:
+
+<pre>$ <b>pip uninstall tensorflow</b>
+$ <b>pip3 uninstall tensorflow</b> </pre>
+
+## Cross-compiling from sources
+
+Cross-compilation means building on a different machine than than you'll be
+deploying on. Since Raspberry Pi's only have limited RAM and comparatively slow
+processors, and TensorFlow has a large amount of source code to compile, it's
+easier to use a MacOS or Linux desktop or laptop to handle the build process.
+Because it can take over 24 hours to build on a Pi, and requires external swap
+space to cope with the memory shortage, we recommend using cross-compilation if
+you do need to compile TensorFlow from source. To make the dependency management
+process easier, we also recommend using Docker to help simplify building.
+
+Note that we provide well-tested, pre-built TensorFlow binaries for Raspbian
+systems. So, don't build a TensorFlow binary yourself unless you are very
+comfortable building complex packages from source and dealing with the
+inevitable aftermath should things not go exactly as documented
+
+### Prerequisite: Docker
+
+Install Docker on your machine as described in the [Docker
+documentation](https://docs.docker.com/engine/installation/#/on-macos-and-windows).
+
+### Clone the TensorFlow repository
+
+Start the process of building TensorFlow by cloning a TensorFlow repository.
+
+To clone **the latest** TensorFlow repository, issue the following command:
+
+<pre>$ <b>git clone https://github.com/tensorflow/tensorflow</b> </pre>
+
+The preceding <code>git clone</code> command creates a subdirectory named
+`tensorflow`. After cloning, you may optionally build a **specific branch**
+(such as a release branch) by invoking the following commands:
+
+<pre>
+$ <b>cd tensorflow</b>
+$ <b>git checkout</b> <i>Branch</i> # where <i>Branch</i> is the desired branch
+</pre>
+
+For example, to work with the `r1.0` release instead of the master release,
+issue the following command:
+
+<pre>$ <b>git checkout r1.0</b></pre>
+
+### Build from source
+
+To compile TensorFlow and produce a binary pip can install, do the following:
+
+1. Start a terminal.
+2. Navigate to the directory containing the tensorflow source code.
+3. Run a command to cross-compile the library, for example:
+
+<pre>$ CI_DOCKER_EXTRA_PARAMS="-e CI_BUILD_PYTHON=python3 -e CROSSTOOL_PYTHON_INCLUDE_PATH=/usr/include/python3.4" \
+tensorflow/tools/ci_build/ci_build.sh PI-PYTHON3 tensorflow/tools/ci_build/pi/build_raspberry_pi.sh
+ </pre>
+
+This will build a pip .whl file for Python 3.4, with Arm v7 instructions that
+will only work on the Pi models 2 or 3. These NEON instructions are required for
+the fastest operation on those devices, but you can build a library that will
+run across all Pi devices by passing `PI_ONE` at the end of the command line.
+You can also target Python 2.7 by omitting the initial docker parameters. Here's
+an example of building for Python 2.7 and Raspberry Pi model Zero or One
+devices:
+
+<pre>$ tensorflow/tools/ci_build/ci_build.sh PI tensorflow/tools/ci_build/pi/build_raspberry_pi.sh PI_ONE</pre>
+
+This will take some time to complete, typically twenty or thirty minutes, and
+should produce a .whl file in an output-artifacts sub-folder inside your source
+tree at the end. This wheel file can be installed through pip or pip3 (depending
+on your Python version) by copying it to a Raspberry Pi and running a terminal
+command like this (with the name of your actual file substituted):
+
+<pre>$ pip3 install tensorflow-1.9.0-cp34-none-linux_armv7l.whl</pre>
+
+### Troubleshooting the build
+
+The build script uses Docker internally to create a Linux virtual machine to
+handle the compilation. If you do have problems running the script, first check
+that you're able to run Docker tests like `docker run hello-world` on your
+system.
+
+If you're building from the latest development branch, try syncing to an older
+version that's known to work, for example release 1.9, with a command like this:
+
+<pre>$ <b>git checkout r1.0</b></pre>
+
+<a name="ValidateYourInstallation"></a>
+
+## Validate your installation
+
+To validate your TensorFlow installation, do the following:
+
+1. Ensure that your environment is prepared to run TensorFlow programs.
+2. Run a short TensorFlow program.
+
+### Prepare your environment
+
+If you installed on native pip, Virtualenv, or Anaconda, then do the following:
+
+1. Start a terminal.
+2. If you installed TensorFlow source code, navigate to any directory *except*
+ one containing TensorFlow source code.
+
+### Run a short TensorFlow program
+
+Invoke python from your shell as follows:
+
+<pre>$ <b>python</b></pre>
+
+Enter the following short program inside the python interactive shell:
+
+```python
+# Python
+import tensorflow as tf
+hello = tf.constant('Hello, TensorFlow!')
+sess = tf.Session()
+print(sess.run(hello))
+```
+
+If the system outputs the following, then you are ready to begin writing
+TensorFlow programs:
+
+<pre>Hello, TensorFlow!</pre>
+
+If you're running with Python 3.5, you may see a warning when you first import
+TensorFlow. This is not an error, and TensorFlow should continue to run with no
+problems, despite the log message.
+
+If the system outputs an error message instead of a greeting, see [Common
+installation problems](#common_installation_problems).
+
+If you are new to machine learning, we recommend the [Machine Learning Crash
+Course](https://developers.google.com/machine-learning/crash-course).
+
+If you are experienced with machine learning but new to TensorFlow, see
+@{$get_started/eager}.
+
+## Common installation problems
+
+We are relying on Stack Overflow to document TensorFlow installation problems
+and their remedies. The following table contains links to Stack Overflow answers
+for some common installation problems. If you encounter an error message or
+other installation problem not listed in the following table, search for it on
+Stack Overflow. If Stack Overflow doesn't show the error message, ask a new
+question about it on Stack Overflow and specify the `tensorflow` tag.
+
+<table>
+<tr> <th>Stack Overflow Link</th> <th>Error Message</th> </tr>
+
+
+<tr>
+ <td><a href="http://stackoverflow.com/q/42006320">42006320</a></td>
+ <td><pre>ImportError: Traceback (most recent call last):
+File ".../tensorflow/core/framework/graph_pb2.py", line 6, in <module>
+from google.protobuf import descriptor as _descriptor
+ImportError: cannot import name 'descriptor'</pre>
+ </td>
+</tr>
+
+<tr>
+ <td><a href="https://stackoverflow.com/q/33623453">33623453</a></td>
+ <td><pre>IOError: [Errno 2] No such file or directory:
+ '/tmp/pip-o6Tpui-build/setup.py'</tt></pre>
+</tr>
+
+<tr>
+ <td><a href="https://stackoverflow.com/questions/35190574">35190574</a> </td>
+ <td><pre>SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify
+ failed</pre></td>
+</tr>
+
+<tr>
+ <td><a href="http://stackoverflow.com/q/42009190">42009190</a></td>
+ <td><pre>
+ Installing collected packages: setuptools, protobuf, wheel, numpy, tensorflow
+ Found existing installation: setuptools 1.1.6
+ Uninstalling setuptools-1.1.6:
+ Exception:
+ ...
+ [Errno 1] Operation not permitted:
+ '/tmp/pip-a1DXRT-uninstall/.../lib/python/_markerlib' </pre></td>
+</tr>
+
+<tr>
+ <td><a href="https://stackoverflow.com/q/33622019">33622019</a></td>
+ <td><pre>ImportError: No module named copyreg</pre></td>
+</tr>
+
+<tr>
+ <td><a href="http://stackoverflow.com/q/37810228">37810228</a></td>
+ <td>During a <tt>pip install</tt> operation, the system returns:
+ <pre>OSError: [Errno 1] Operation not permitted</pre>
+ </td>
+</tr>
+
+<tr>
+ <td><a href="http://stackoverflow.com/q/33622842">33622842</a></td>
+ <td>An <tt>import tensorflow</tt> statement triggers an error such as the
+ following:<pre>Traceback (most recent call last):
+ File "<stdin>", line 1, in <module>
+ File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py",
+ line 4, in <module>
+ from tensorflow.python import *
+ ...
+ File "/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py",
+ line 22, in <module>
+ serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"d\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02
+ \x03(\x0b\x32
+ .tensorflow.TensorShapeProto.Dim\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01
+ \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tb\x06proto3')
+ TypeError: __init__() got an unexpected keyword argument 'syntax'</pre>
+ </td>
+</tr>
+
+
+</table>
projects / platform / upstream / tensorflow.git / commitdiff