Install for a remote GPU with SSH access#


Local Machine: the machine that has VSCode installed along with the Remote-SSH extension. This is the machine you use to connect to your remote machine.

Remote Machine: the machine that is equipped with a NVIDIA GPU and CUDA installed. This is machine that contains your development environment and runs your Deep Learning workloads.


  • A remote machine equipped with an NVIDIA GPU and Driver installed

  • CUDA versions: 11.1, 11.3, 11.6, 11.7

  • Python: 3.7 - 3.10

  • A local machine with VSCode

Note that most of packages will installed will be on your remote machine.


Create your own virtual environment using your preferred virtual environment manager. Ensure that the appropriate version of PyTorch is installed in your virtual environment.

Installing Deepview.Profile#

On your remote machine, run the following commands to install Deepview.Profile

pip install deepview_profile

For users with PyTorch versions <2.2.0

Due to an issue with PyTorch profiler (which DeepView partially depends on to function), you would need to install an older version of DeepView for profiling to work. The issue relates to PyTorch’s handling of CUPTI contexts, so you will likely get CUPTI_ERROR_NOT_INITIALIZED or CUPTI_ERROR_MULTIPLE_SUBSCRIBERS_NOT_SUPPORTED if you have the incorrect combination of DeepView and PyTorch installed. This error is not present in the latest version of PyTorch.

  • If you have PyTorch version <2.0.1, install DeepView with the following:

    pip install deepview_profile==0.1.4
  • If you have PyTorch versions between 2.1.0 to 2.1.2, please either upgrade (to >=2.2.0) or downgrade (to <2.1.0) to a compatible version.

Installing the VSCode Extension#

Launch VSCode on your local machine and connect to your remote machine with VSCode SSH. Inside your remote VSCode connection, install DeepView.Explore (the VSCode extension) by searching for it in the extension marketplace.

Run your first analysis#

Now you are ready for your first analysis!