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This example has been tested on EUMETSAT side of the EWC. |
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Tensorflow library has many dependencies that interest all the stack (from application level to hardware) and it is released quite often by the community. For the purpose of this documention for GPUs on Tensorflow. The following assumptions have been considered: |
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You have to have
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It is highly recommended to install Tensorflow inside a virtual environment using Conda or virtualenv, here is a simple example using virtualenv:
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In order to run the following example you need to have the following packages in your environment:
- tensorflow
You can check this documentation for Install package in Python environment and handle python environments for reproducibility.
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CentOS 7
Install the prerequisites and Tensorflow 2.8:
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sudo yum -y install epel-release sudo yum update -y sudo yum -y groupinstall "Development Tools" sudo yum -y install openssl-devel bzip2-devel libffi-devel xz-devel python3 -m pip pipenv install tensorflow==2.8.0 OS=rhel7 && \ sudo yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-${OS}.repo && \ sudo yum clean all && \ sudo yum install -y sudo yum install libcudnn8.x86_64 libcudnn8-devel.x86_64 |
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[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] |
Ubuntu 20.04
Begin by installing the nvidia-cuda-toolkit:
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sudo apt update
sudo apt upgrade
sudo apt install openjdk-11-jdk
sudo apt install nvidia-cuda-toolkit |
After installing the nvidia-cuda-toolkit, you can now install cuDNN 8.4.0 by downloading it from this link. You’ll be asked to login or create an NVIDIA account. After logging in and accepting the terms of cuDNN software license agreement, you will see a list of available cuDNN software.
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Now install Tensorflow with pippipenv (or conda):
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python3 -m pippipenv install tensorflow==2.8.0 |
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