Testing

Testing and Verification

Checks performed for this documentation package

Check	Result
Python source py_compile	Passed
Jupyter notebook structure	Valid JSON notebook, 16 cells
Tiny model: 1 CPU process, 2 steps	Passed; checkpoint created
Tiny model: 2 CPU processes (Gloo DDP), 2 steps	Passed; checkpoint created
Qwen model load and LoRA fine-tuning	Not executed
Multi-GPU CUDA/NCCL training	Not executed
Real-world training quality, convergence or throughput benchmarks	Not performed

Test commands

python -m py_compile llm_ddp_parallel_training.py
python llm_ddp_parallel_training.py --model tiny --cpu --max-steps 2 --save-dir ddp_example_output\one
python -m torch.distributed.run --standalone --nproc_per_node=2 llm_ddp_parallel_training.py --model tiny --cpu --batch-size 2 --max-steps 2 --save-dir ddp_example_output\two

Check whether each output directory contains tiny_transformer.pt.

Interpreting logs

A lower observed loss on a few toy batches does not prove useful generalization. This example has no validation dataset, task metric, experiment tracking, reproducible evaluation harness or multi-GPU performance report.