GitLab

* 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

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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

- Added Platform.DTK in the microbenchmark framework.
- Introduced new DTK hipblaslt benchmark class and corresponding tests.
- Updated Dockerfile to include hipblaslt-bench and its permissions.
- Registered DTK benchmarks in the benchmark registry for various performance tests.
- Enhanced GPU detection logic to recognize HYGON GPUs.

This update improves the benchmarking capabilities for DTK, ensuring compatibility and performance testing across platforms.

- Update rocm_commom.cmake for CMake>=3.24
- Prevent isolation build
- Add BabelStream as a submodule
- Update dockerignore

## Summary
- Upgrade Intel Memory Latency Checker from v3.11 to v3.12 in
rocm5.0.x.dockerfile
- Aligns with other dockerfiles that already use v3.12
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

Updated 3rd party submodule gpu-burn to newest version for
implementation & doc support for cuda13.0.
Co-authored-by: guoshzhao <guzhao@microsoft.com>

**Description**

- When building the CUDA 11.1.1 image, pip (Python 3.8) cannot find a
pre-built wheel for the latest wandb release (v0.23.1). As a result, pip
attempts to build wandb from source. However, the build fails because
the image does not have Go installed, which is required for building
wandb from source. Then the error appears.

**Solution**

- For the CUDA 11.1.1 build, install the required build tools (e.g., Go,
Rust, and Cargo) needed for wandb.

---------
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

**Description**
Azure pipeline cpu-unit-test failed for "2025-12-10T03:47:59.0628597Z
ERROR: Could not install packages due to an OSError: [Errno 28] No space
left on device"

**Root Cause**
This happens because the matrix jobs (Python 3.7, 3.10, 3.12) run in
parallel and share the same VM's disk. Python 3.12 downloads
newer/larger packages (especially PyTorch and NVIDIA CUDA libraries
which are ~3GB+), and when multiple jobs run simultaneously, they
exhaust the disk space.

**Fix**
Disable the cache usage when installing SB
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>