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 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 it now. It should already be included when Raspbian was installed though, so no extra steps should be needed.
Prerequisite: pip
Pip 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:
$ pip3 -V # for Python 3.n $ pip -V # for Python 2.7
If it gives the error "Command not found", then the package has not been installed yet. To install if for the first time, run:
$ sudo apt-get install python3-pip # for Python 3.n sudo apt-get install python-pip # for Python 2.7
You can find more help on installing and upgrading pip in the Raspberry Pi documentation.
Prerequisite: Atlas
Atlas 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:
$ sudo apt install libatlas-base-dev
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 to confirm that the installation worked properly.
Uninstalling TensorFlow
To uninstall TensorFlow, issue one of following commands:
$ pip uninstall tensorflow $ pip3 uninstall tensorflow
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.
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:
$ git clone https://github.com/tensorflow/tensorflow
The preceding git clone 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:
$ cd tensorflow $ git checkout Branch # where Branch is the desired branch
For example, to work with the r1.0 release instead of the master release, issue the following command:
$ git checkout r1.0
Build from source
To compile TensorFlow and produce a binary pip can install, do the following:
- Start a terminal.
- Navigate to the directory containing the tensorflow source code.
- Run a command to cross-compile the library, for example:
$ 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
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:
$ tensorflow/tools/ci_build/ci_build.sh PI tensorflow/tools/ci_build/pi/build_raspberry_pi.sh PI_ONE
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):
$ pip3 install tensorflow-1.9.0-cp34-none-linux_armv7l.whl
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:
$ git checkout r1.0
Validate your installation
To validate your TensorFlow installation, do the following:
- Ensure that your environment is prepared to run TensorFlow programs.
- Run a short TensorFlow program.
Prepare your environment
If you installed on native pip, Virtualenv, or Anaconda, then do the following:
- Start a terminal.
- 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:
$ python
Enter the following short program inside the python interactive shell:
# 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:
Hello, TensorFlow!
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.
To learn more, see the TensorFlow tutorials.
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.
| Stack Overflow Link | Error Message |
|---|---|
| 42006320 |
ImportError: Traceback (most recent call last): File ".../tensorflow/core/framework/graph_pb2.py", line 6, in from google.protobuf import descriptor as _descriptor ImportError: cannot import name ‘descriptor‘ |
| 33623453 |
IOError: [Errno 2] No such file or directory: ‘/tmp/pip-o6Tpui-build/setup.py‘ |
| 35190574 |
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed |
| 42009190 |
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‘ |
| 33622019 |
ImportError: No module named copyreg |
| 37810228 | During a pip install operation, the system returns:
OSError: [Errno 1] Operation not permitted |
| 33622842 | An import tensorflow statement triggers an error such as the following:
Traceback (most recent call last):
File "", line 1, in
File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py",
line 4, in
from tensorflow.python import *
...
File "/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py",
line 22, in
serialized_pb=_b(‘
,tensorflow/core/framework/tensor_shape.protox12
tensorflow"d
x10TensorShapeProtox12-
x03x64imx18x02
x03(x0bx32
.tensorflow.TensorShapeProto.Dimx1a!
x03x44imx12x0c
x04sizex18x01
x01(x03x12x0c
x04namex18x02 x01( bx06proto3‘)
TypeError: __init__() got an unexpected keyword argument ‘syntax‘
|