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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: Ubuntu <rdadmin@HPCPLTNODE0.n3kgq4m0lhoednrx3hxtad2nha.cdmx.internal.cloudapp.net>

**Description**
Support DirectX test pipeline.

**Major Revision**

- Add an option for pattern to generate mpi_pattern.txt file if
specified the path.
- In mpi pattern, serial_index and parallel_index will add in each
benchmark as environment variables.

**Minor Revision**
- Fix typo

**Description**
Support the following patterns  in `mpi` mode:
* `k-batch`
* `topo-aware`

**Description**
Add stdout logging util module and enable real-time logging flushing in executor

**Major Revision**
- Add stdout logging util module to redirect stdout into file log
- enable stdout logging in executor to write benchmark output into both stdout and file `sb-bench.log`
- enable real-time log flushing in run_command of microbenchmarks through config `log_flushing`

**Minor Revision**
- add log_n_step args to enable regular step time log in model benchmarks 
- udpate related docs

* Extract pair-wise pattern from ib_validation

* add mpi-parallels mode

* update according to comments

* fix and update doc

* update

* merge into 'mpi' mode

* udpate according to comments

* fix testcases

* fix ansible

* regard pattern as field

* udpate

* fix flake8 version

* add flake8 range

* remove map-by from host config

* udpate comments

Support SKU auto detect and using corresponding benchmark config if running on Azure VM.

**Description**
Rename `nvidia_helper` utility as `device_manager` module and support more functions:
```
device_manager.get_device_count()
device_manager.get_device_utilization(idx)
device_manager.get_device_temperature(idx)
device_manager.get_device_power_limit(idx)
device_manager.get_device_memory(idx)
device_manager.get_device_row_remapped_info(idx)
device_manager.get_device_ecc_error(idx)
```