Merge branch 'mlp-tutorials-update-iii' of github.com:lukasgd/cscs-docs into mlp-tutorials-update-iii

Author: bcumming

docs/build-install/containers.md

@@ -7,7 +7,7 @@ Its command-line interface (CLI) closely mirrors Docker’s, providing a consist
 [](){#ref-build-containers-configure-podman}
 ## Preliminary step: configuring Podman's storage
 
-The first step in order to use Podman on Alps is to create a valid Container Storage configuration file in your home according to the following minimal template:
+The first step in order to use Podman on Alps is to create a valid Container Storage configuration file in your home directory, according to the following minimal template:
 
 ```toml title="$HOME/.config/containers/storage.conf"
 [storage]

docs/software/ml/index.md

@@ -2,7 +2,7 @@
 # Machine learning applications and frameworks
 
 CSCS supports a wide range of machine learning (ML) applications and frameworks on its systems.
-Most ML workloads are containerized to ensure portability, reproducibility, and ease of use across machines.
+Most ML workloads are containerized to ensure portability, reproducibility, and ease of use across systems.
 
 Users can choose between running containers, using provided uenv software stacks, or building custom Python environments tailored to their needs.
 

docs/software/ml/pytorch.md

@@ -126,7 +126,7 @@ user@nidYYYYYY$ source venv-ngc-pt-25.06/bin/activate # (3)!
 The changes made to the virtual environment will outlive the container as they are persisted on the distributed filesystem.
 
 !!! note
-    Keep in mind that
+    Keep in mind that:
 
      * this virtual environment is _specific_ to this particular container and won't actually work unless you are using it from inside this container - it relies on the resources packaged inside the container.
      * every Slurm job making use of this virtual environment will need to activate it first (_inside_ the `srun` command). 

docs/tutorials/ml/index.md

@@ -3,7 +3,7 @@
 
 The LLM tutorials gradually introduce key concepts of the Machine Learning Platform in a series of hands-on examples. A particular focus is on the [Container Engine][ref-container-engine] for managing the runtime environment.
 
-In a [first tutorial][software-ml-llm-inference-tutorial], you will learn how to run inference with a LLM on a single node using a container from the NVIDIA GPU Cloud (NGC). Concepts such as container environment description, layering a thin virtual environment on top of the container image, and job launching and monitoring will be introduced.
+In the [first tutorial][software-ml-llm-inference-tutorial], you will learn how to run inference with a LLM on a single node using a container from the NVIDIA GPU Cloud (NGC). Concepts such as container environment description, layering a thin virtual environment on top of the container image, and job launching/monitoring will be introduced.
 
 Building on the first tutorial, in the [second tutorial][software-ml-llm-fine-tuning-tutorial] you will learn how to train (fine-tune) a LLM on multiple GPUs on a single node. For this purpose, you will use HuggingFace's `accelerate` and see best practices for dataset management.