How to install Tensorflow in a 32 bits linux system
Following is a copy of the step list that I maintain in this github repository: tensorflow-32-bits-linux
I used the following steps to install tensorflow in a old Asus Eee-Pc 1000H. Granted, it has been upgraded from the original 1 GB of RAM and an 80 GB HDD, to 2 GB of RAM and to 480 GB of SSD storage, that runs Ubuntu Xenial 32 bits without problems.
I also has been able to install it in a Debian 9 (stretch) 32 bits system, and the instructions are the same.
Choose a convenient linux system
I have tested both the Ubuntu 16.04 (Xenial) and Debian 9.11 (Stretch) systems with 2 GB of RAM.
I set up the system to have 4 GB of SWAP space. With only 1 GB of SWAP, some compilations failed.
It's critical that the distribution has the version 8 of the Java SDK: openjdk-8-jdk
Install the Java 8 SDK and build tools
sudo apt-get update
sudo apt-get install openjdk-8-jdk
sudo apt-get install git zip unzip autoconf automake libtool curl zlib1g-dev swig build-essential
Install Python libraries
Next, we install python 3 development libraries and the keras module that will be required by tensorflow.
sudo apt-get install python3-dev python3-pip python3-wheel
sudo python3 -m pip install --upgrade pip
python3 -m pip install --user keras
You can use eithr python 3 or python 2 and compile tensorflow for that version.
Install and compile Bazel from sources
We need the source code bazel 0.19.2 distribution. We can obtain it and install in a new folder.
wget https://github.com/bazelbuild/bazel/releases/download/0.19.2/bazel-0.19.2-dist.zip
mkdir Bazel-0-19.2
cd Bazel-0-19.2
unzip ../bazel-0.19.2-dist.zip
Before compiling, we need to remove line 30 of ./src/tools/singlejar/mapped_file_posix.inc file (#error This code for 64 bit Unix.) that throws an error if we are not in a 64 bit machine. This bazel version works ok in 32 bits.
Also we need to increase the java memory available to Bazel and start compiling it.
export BAZEL_JAVAC_OPTS="-J-Xmx1g"
./compile.sh
When it finishes (It can take several hours), we move the bazel compiled executable to some location in the current user's path
cp output/bazel /home/user/.local/bin
Compile Tensorflow from sources
Create a folder and clone tensorflow's 1.13.2 version to it. Starting from version 1.14, tensorflow uses the Intel MKL DNN optimization library that it only works in 64 bits systems. So 1.13.2 is the last version that runs in 32 bits.
mkdir Tensorflow-1.13.2
cd Tensorflow-1.13.2
git clone -b v1.13.2 --depth=1 https://github.com/tensorflow/tensorflow .
Before compiling, we replace the references to 64 bit libraries to the 32 bit ones.
grep -Rl "lib64"| xargs sed -i 's/lib64/lib/g'
We start the tensorflow configuration. We need to explicity disable the use of several optional libraries that are not available or not supported on 32 bit systems.
export TF_NEED_CUDA=0
export TF_NEED_AWS=0
./configure
We have to take the following considerations:
* When asked to specify the location of python. [Default is /usr/bin/python]: We should respond /usr/bin/python3 to use python 3.
* When asked to input the desired Python library path to use. Default is [/usr/local/lib/python3.5/dist-packages] we just hit Enter
* We should respond N to all the Y/N questions.
* When asked to specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]: Just hit Enter
Now we start compiling tensorflow disabling optional components like aws, kafka, etc.
bazel build --config=noaws --config=nohdfs --config=nokafka --config=noignite --config=nonccl -c opt --verbose_failures //tensorflow/tools/pip_package:build_pip_package
If everything went ok, now we generate the pip package.
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
And we install the pip package
python3 -m pip install --user /tmp/tensorflow_pkg/tensorflow-1.13.2-cp35-cp35m-linux_i686.whl
Test tensorflow
Now we run a small test to check that it works. We create a test.py file with the following contents:
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
And we run the test
python3 test.py
Here is the output
Epoch 1/5
60000/60000 [==============================] - 87s 1ms/sample - loss: 0.2202 - acc: 0.9348
Epoch 2/5
60000/60000 [==============================] - 131s 2ms/sample - loss: 0.0963 - acc: 0.9703
Epoch 3/5
60000/60000 [==============================] - 135s 2ms/sample - loss: 0.0685 - acc: 0.9785
Epoch 4/5
60000/60000 [==============================] - 128s 2ms/sample - loss: 0.0526 - acc: 0.9828
Epoch 5/5
60000/60000 [==============================] - 128s 2ms/sample - loss: 0.0436 - acc: 0.9863
10000/10000 [==============================] - 3s 273us/sample - loss: 0.0666 - acc: 0.9800
Enjoy you new Tensorflow !!
