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Tensorflow-GPU with NVIDIA CUDA on Google Cloud | Installation

If you are looking for a guide to help you install Tensorflow-GPU with NVIDIA CUDA on a Google Cloud instance, you are in luck. Our experts have put together this step-by-step guide to help you out:

1. To begin with, we have to set up a Google instance.
2. After that, we have to connect to the SSH server.
3. Then, we have to download a copy of the cuDNN library for CUDA by taking an NVIDIA membership.
4. Next, we have to upload the cuDNN tar folder to our instance with this command:

   gcloud compute scp /Users/meghadesai/Desktop/cudnn-9.0-linux-x64-v7.1.tgz @test-gpu:~/
5. After the file is unpacked, we have to copy it to the cuda directory.
6. Then, we have to install the required python packages: with these commands:

   sudo apt-get install ipython
sudo apt-get install python3-pip
7. Next, we have to run this command:

   sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy python-six python3-six build-essential python-pip python3-pip python-virtualenv swig python-wheel python3-wheel libcurl3-dev libcupti-dev
8. After that, run the following command:

   nano ~/.bashrc
9. At this point, we have to these lines of code, to ensure that the VM instance is aware of the CUDA file from the previous step.

   export LD_LIBRARY_PATH=”$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda
10. Next, we have to reload the file and then add the Bazel repository for Google Cloud APIs.

    echo “deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8” | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub... | sudo apt-key add -
sudo apt-get install bazel
sudo apt-get upgrade bazel
11. Then, we have to clone Tensorflow’s git directory to our instance with this command:

    git clone https://github.com/tensorflow/tensorflow
cd ~/tensorflow
./configure
12. Now, it is time to run these commands in the same sequence:

    /usr/bin/python3.5

    Press enter

    /usr/local/lib/python3.5/dist-packages

    Press ‘y’

    bazel build — config=opt — config=cuda
//tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
13. Finally, we can run this command and exit SSH:

    sudo pip3 install /tmp/tensorflow_pkg/tensorflow [PRESS TAB TO COMPLETE FILENAME]
14. It is now time to reconnect to the SSH server window and run the following code to verify that GPU is set and being used.

    

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Conclusion

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