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This article will explain in detail how Docker containers build a deep learning environment for running python. Xiaobian thinks it is quite practical, so share it with you as a reference. I hope you can gain something after reading this article.

View virtualization in Task Manager, enabled

If disabled, restart the computer and turn on Bios.

Install Docker Desktop and enable WSL functionality.

Open powershell

docker run --rm -it quay.io/azavea/raster-vision:pytorch-latest images

create container by image id docker create -it --name [name] [image id]docker start [name]

into the container

docker exec -it [container id] /bin/bash

docker cp [local path] containerid:[in-container path]docker cp D:\docker\raster-vision\myPY\test.py 7837a6060 f9d:/opt/src/code/test.pydocker cp containerid:[in-container path] [local path]

View all images docker images View all containers docker ps-a

Expected more than 1 value per channel when training, got input size torch.Size([1, 256, 1, 1])

batch_size=1 results.

Change to even number or set parameter drop_last=True in torch.utils.data.DataLoader class or class inherited from DataLoader created by yourself to discard data less than batch_size.

urllib.error.URLError:

Network issues.

Download it locally and upload it or take a ladder.

About "Docker container how to build a deep learning environment to run python" This article is shared here, I hope the above content can be of some help to everyone, so that you can learn more knowledge, if you think the article is good, please share it for more people to see.

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