Update docs/access/jupyterlab.md

Co-authored-by: Rocco Meli <[email protected]>

Author: lukasgd

docs/access/jupyterlab.md

@@ -229,7 +229,7 @@ where `/path/to/edf.toml` should be replaced by the TOML file and `venv-<base-im
     Subtle bugs can occur when running multiple Jupyter notebooks concurrently that each assume access to the full node. Also, some notebooks may hold on to resources such as spawned child processes or allocated memory despite having completed. In this case, resources such as a GPU may still be busy, blocking another notebook from using it. Therefore, it is good practice to only keep one such notebook running that occupies the full node and restarting a kernel once a notebook has completed. If in doubt, system monitoring with `htop` and [nvdashboard](https://github.com/rapidsai/jupyterlab-nvdashboard) can be helpful for debugging.
 
 !!! warning "Multi-GPU training from a shared Jupyter process"
-    Running multi-GPU training workloads directly from the shared Jupyter process is generally not recommended due to potential inefficiencies and correctness issues (cf. the [PyTorch docs](https://docs.pytorch.org/docs/stable/notes/cuda.html#use-nn-parallel-distributeddataparallel-instead-of-multiprocessing-or-nn-dataparallel)). However, if you need it to e.g. reproduce existing results, it is possible to do so with utilities like `accelerate`'s `notebook_launcher` or [`transformers`](https://github.com/huggingface/transformers)' `Trainer` class. When using these in containers, you will currently need to unset the environment variables `RANK` and `LOCAL_RANK`, that is have the following in a cell at the top of the notebook:
+    Running multi-GPU training workloads directly from the shared Jupyter process is generally not recommended due to potential inefficiencies and correctness issues (cf. the [PyTorch docs](https://docs.pytorch.org/docs/stable/notes/cuda.html#use-nn-parallel-distributeddataparallel-instead-of-multiprocessing-or-nn-dataparallel)). However, if you need it to e.g. reproduce existing results, it is possible to do so with utilities like `accelerate`'s `notebook_launcher` or [`transformers`](https://github.com/huggingface/transformers)' `Trainer` class. When using these in containers, you will currently need to unset the environment variables `RANK` and `LOCAL_RANK` by adding the following in a cell at the top of the notebook:
 
     ```python
     import os; os.environ.pop("RANK"); os.environ.pop("LOCAL_RANK");