How to use PyTorch with Container Station?
1. About PyTorch
PyTorch is a Python-based scientific computing package for those who want a replacement for NumPy to
use the power of GPUs, and a deep learning research platform that provides maximum flexibility and speed.
2. Installing PyTorch in Container Station
1. Assign GPUs to Container Station.
a. Go to Control Panel > System > Hardware > Graphics Card.
b. Under Resource Use, assign the GPUs to Container Station.
c. Click Apply.
2. Open Container Station.
3. Use the correct image version.
a. Click Images.
b. Click Pull to the desired image is installed.
4. Click Create.
5. Search for the keyword “PyTorch”. Find pytorch/pytorch and click Install.
6. Select a version of PyTorch based on what version of QTS and Nvidia Driver you have installed.
7. Click Next.
8. Click Advanced Settings.
9. Assign GPUs to the container.
a. Go to Device.
b. Click Add.
c. Choose the GPUs to add to the container.
10. Optional: Share a NAS folder with the container.
a. Go to Shared Folder.
b. Over Volume from Host, click Add. A new volume from host is added.
c. Select a Host Path.
d. Specify a Mount Point.
11. Click Create. A Summary of your new container will be displayed.
12. Review the container’s settings.
13. Click OK. The container image is installed.
3. Mounting an NVIDIA GPU via SSH
1. Connect to your NAS via SSH.
2. Mount GPUs to the container.
a. Enter one of the following commands based on the GPU you want to mount.
|GPU to mount||Command|
Example commands based on your QTS and Nvidia Driver versions are listed below:
|QTS and Nvidia Driver versions||Command|
|QTS 4.3.5/4.3.6 and Nvidia Driver v1.3.5||
|QTS 4.4.x and Nvidia Driver v2.0.0||
4. Accessing the Container
1. Open Container Station.
2. Click Overview.
3. Find the container you just installed and open the container’s page.
4. Click .
The bash window opens.