Use cuda11.0 with pytorch using Docker

As of October 14, 2020, the pytorch official does not show how to install for cuda11.0. Therefore, I will explain the procedure for building a pytorch environment for cuda11.0 using Docker.


As a prerequisite, it is assumed that Docker and nvidia drivers are installed. Please refer to other articles for these installations.

$ nvidia-smi

Also check that the cuda version is 11.0 with the above command.

Download the Pytorch container

Please access the nvidia site, register your user information, and then open the Pytorch screen.

$ sudo docker pull

Since the pull command has the above command, copy it and execute it in the terminal.

$ sudo docker images

Check if you could install with this command. 20.09-py3 If there is an image that says, it is a success.

Check if Pytorch can use cuda

Execute the command to list the images, check the IMAGE ID of pytorch, copy it, and execute the following command.

$ sudo docker run -it --gpus all [IMAGE ID] bash

Then in bash

$ python
>> import torch
>> print(torch.cuda.is_available())

If true is returned with this, it is successful. You can exit bash with control + d. (Linux)

Create a container and mount the file

Please exit bash once.

$ sudo docker ps -a

You can see the container created when you ran bash earlier with the above command. Use this CONTAINER ID.

$ sudo docker inspect [CONTAINER ID]

Find WorkingDir with control + f. In addition, use the command to check the path of the folder on the PC where you want to work.

$ pwd

Then create a container on the nvidia site earlier as described in the overview.

$ sudo docker run --gpus all -it --rm -v [The path of the folder you want to work with]:[workingDir]

In my case

$ sudo docker run --gpus all -it --rm -v /home/myname/Desktop/hoge:/workspace怀

After that, you should be able to confirm that the container is launched.


Please be careful as there may be typographical errors. I hope it will be a connection until the official posts the installation method.

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