Added additional links to Containerised ROCm material.

Author: mbareford

docs/user-guide/machine-learning.md

@@ -28,6 +28,9 @@ A binary install of PyTorch 1.13.1 suitable for ROCm 5.2.3 has been installed ac
 
 This install can be accessed by loading the `pytorch/1.13.1-gpu` module.
 
+!!! note
+    For GPU, ARCHER2 currently provides access to a legacy version of [ROCm](gpu.md#rocm), `rocm/5.2.3`. This means that users cannot run on GPU a version of PyTorch more recent than 1.13.1. However, it is possible to run PyTorch 2.2.0 via a containerised HPE Cray Programming Environment module, one that features ROCm 5.6.0, see [Containerised ROCm](containers.md/#containerised-rocm) for details.
+
 As DeepCam is an [MLPerf](https://ieeexplore.ieee.org/document/9238612) benchmark, you may wish to base a local python environment on `pytorch/1.13.1-gpu`
 so that you have the opportunity to install additional python packages that support MLPerf logging, as well as extra features pertinent to DeepCam (e.g., dynamic learning rates).
 

docs/user-guide/python.md

@@ -137,6 +137,8 @@ ensuring that the Python packages will be gathered from the local virtual enviro
     The `extend-venv-activate` command becomes available (i.e., its location is placed on the path) only when the ML module is loaded.
     The ML modules are themselves based on `cray-python`. For example, `tensorflow/2.12.0` is based on the `cray-python/3.9.13.1` module.
 
+Further info about running ML frameworks on ARCHER2 can be found on the [Machine Learning page](machine-learning.md).
+
 ## Conda on ARCHER2
 
 Conda-based Python distributions (e.g. Anaconda, Mamba, Miniconda) are an extremely popular way of installing and