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.
Also check that the cuda version is 11.0 with the above command.
Please access the nvidia site, register your user information, and then open the Pytorch screen.
$ sudo docker pull nvcr.io/nvidia/pytorch:20.09-py3
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. nvcr.io/nvidia/pytorch 20.09-py3 If there is an image that says, it is a success.
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)
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.
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] nvcr.io/nvidia/pytorch:20.09-py3
In my case
$ sudo docker run --gpus all -it --rm -v /home/myname/Desktop/hoge:/workspace nvcr.io/nvidia/pytorch:20.09-py3
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.