@@ -41,6 +41,8 @@ If you encounter errors while building the container, have a look at `build_dlc-
The image can now be used on our JupyterHub service or via the command line on our HPC systems. We recommend using JupyterHub for the initial phase of a project to set up the workflow and for data exploration. Compute-intensive workloads, such as model training, should be run on the HPC cluster using SLURM. This allows users to execute computational jobs in a very flexible and customisable way, making the best use of the available computing resources.
💎 Building a container can take some time. So we provide an image at the path given below so that you can continue to explore the potential of containers on our cluster: `/scratch/projects/workshops/gpu-workshop/dlc-dlwgpu.sif`
## Using the container on HPC JupyterHub 🎨
Below are two examples of how to use HPC JupyterHub. The first example shows the use of a JupyterNotebook for data exploration. The second example is about testing the software environment to ensure that everything is set up correctly, e.g. CUDA for GPU acceleration.
