| |=+=+=| | 0 Tesla T4 On | 00000000:00:1E.0 Off | 0 | | N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default | | | | N/A | +-+-+-+ +-+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=| | No running processes found | +-+ Installing on Amazon Linux ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. To install Docker on RHEL 7, first enable this repository: RHEL includes Docker in the Extras repository. The following steps can be used to setup the NVIDIA Container Toolkit on RHEL 7. Share images, automate workflows, and more with a free Docker ID: For more examples and ideas, visit: Setting up NVIDIA Container Toolkit To try something more ambitious, you can run an Ubuntu container with: $ docker run -it ubuntu bash The Docker daemon streamed that output to the Docker client, which sent it to your terminal. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading. The Docker daemon pulled the "hello-world" image from the Docker Hub. The Docker client contacted the Docker daemon. To generate this message, Docker took the following steps: 1. Unable to find image 'hello-world:latest' locally latest: Pulling from library/hello-world 0e03bdcc26d7: Pull complete Digest: sha256:7f0a9f93b4aa3022c3a4c147a449bf11e0941a1fd0bf4a8e6c9408b2600777c5 Status: Downloaded newer image for hello-world:latest Hello from Docker! This message shows that your installation appears to be working correctly. The following steps can be used to setup the NVIDIA Container Toolkit on CentOS 7/8. Check if the import will produce some mistakes.+-+ | NVIDIA-SMI 450.51.06 Driver Version: 450.51.06 CUDA Version: 11.0 | |-+-+-+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr.Sudo apt-get install cuda-command-line-toolsĪnd then install the package using pip sudo pip3 install tensorflow-gpu Open a terminal and install python or python3 and pip.Sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* Sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 Installation steps: tar -xzvf cudnn-9.0-linux-圆4-v7.tgz sudo cp cuda/include/cudnn.h /usr/local/cuda/include Now the correct version of cuDNN is the v.7.1.2 for CUDA 9.0. After the registration, select the version of cudNN, that matches with the version of CUDA, that you have installed on your PC. This step requires a registration to nVidia website. Sudo apt-key add /var/cuda-repo-/7fa2af80.pubĪfter the installation, please also install the patches, if they are available. Sudo dpkg -i cuda-repo-ubuntu-local_9.0.176-1_b Then please install CUDA 9.0 (see the legacy releases box). Actually the latest version is the 9.1, but is not well configured for the use with TensorFlow and Keras. This installation guide is tested on Ubuntu 16.04 LTS (please don’t use no LTS version).
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