I wrote about this in an earlier post, How to Install a GPU Driver on CentOS. This one looks at how to run TensorFlow with Docker. 1. Check Which TensorFlow Version the Current CPU Supports On a CPU that does not support AVX instructions, running TensorFlow > 1.15 fails with Illegal instruction (core dumped).

Using CentOS 7.7 and a Tesla P100 GPU as an example. 1. Preparing the Base Environment Install the lspci command 1 yum install -y pciutils Check whether the GPU supports CUDA 1 2 3 lspci | grep -i nvidia 00:09.0 3D controller: NVIDIA Corporation GP100GL [Tesla P100 PCIe 12GB] (rev a1) List of CUDA-capable GPUs: https://developer.

1. Pulling images with docker pull When you pull an image with docker pull {IMAGE_NAME}, there are two cases: The IMAGE_NAME prefix points to a registry Docker recognizes IMAGE_NAME as an image provided by the specified repository. For example, for myregistry.io/space1/image1:latest, Docker requests the image data from the server that myregistry.

1. What Problem S2I Solves It is foreseeable that a large number of applications will be deployed as containers in the future. Container platforms care about images and containers, while application development cares about business code, and ultimately the code needs to be deployed as an image. From code to image — that is Source To Image, or S2I.

1. Docker Storage Volumes 1.2 Volumes in Docker A Docker volume mounts a host directory into a container. Files modified inside the container are persisted to the host. Even if the container is deleted, the files on the host are retained. Docker uses /var/lib/docker/volumes/ to store container volumes. List local volumes: