GitLab

* Update gpu-hpcg metrics to encode process and problem shape

* Fix tests

- Add BW150 config
- Update BW1000 config
- Merge summary rules

- Add `numactl` support for local runner modes, including `cpunodebind`, `membind`, and `physcpubind`.
- Add `gpu_affinity` resolution through `sb node topo --get gpu-numa-affinity --gpu-id`.
- Add `sb node topo` support for GPU NUMA topology queries.
- Update BW1000 config to use the new local `numactl` semantics.
- Document the new `numactl` mode fields and limitations.

* Update mem-bw to use BandwidthTest

* Update config and format code

* Support GPU system info collection via hy-smi

* Fix typos in docs

* Fix some lint warnings
* Exclude some paths in cpplint
* Fix some tests and formatting

Adds opt-in deterministic training mode to SuperBench's PyTorch model
benchmarks. When enabled --enable-determinism. PyTorch deterministic
algorithms are enforced, and per-step numerical fingerprints (loss,
activation means) are recorded as metrics. These can be compared across
runs using the existing sb result diagnosis pipeline to verify bit-exact
reproducibility — useful for hardware validation and platform
comparison.
 
Flags added - 

--enable-determinism
--check-frequency: Number of steps after which you want the metrics to
be recorded
--deterministic-seed

Changes - 

Updated pytorch_base.py to handle deterministic settings, logging.
Added a new example script: pytorch_deterministic_example.py
Added a test file: test_pytorch_determinism_all.py to verify everything
works as expected.

Usage - 

Step 1: Run 1 - Run with --enable-determinism and the necessary metrics
will be recorded in the results-summary.jsonl file
Step 2: Generate the baseline file from the Run 1 results using - sb
result generate-baseline
Step 3: Run 2 - Run with --enable-determinism and the necessary metrics
will be recorded in the results-summary.jsonl file on a different
machine (or the same machine)
Step 4: Run diagnosis on the results generated from the 2 runs using the
- sb result diagnosis command

Note - 
1. Make sure all the parameters are constant between the 2 runs 
2. Running the diagnosis command requires the rules.yaml file

---------
Co-authored-by: Aishwarya Tonpe <aishwarya.tonpe25@gmail.com>
Co-authored-by: Ubuntu <rdadmin@HPCPLTNODE0.n3kgq4m0lhoednrx3hxtad2nha.cdmx.internal.cloudapp.net>

- Get ansible_user_dir from facts
- Get hostname from facts
- Update NODE_RANK expression

Configure GitHub Pages