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18 changes: 10 additions & 8 deletions examples/disaggregated/slurm/benchmark/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -13,9 +13,8 @@ The benchmarking process is orchestrated through a combination of Python scripts
- `start_worker.sh`: Initializes context and generation workers
- `start_server.sh`: Starts the disaggregated serving coordinator
- `wait_server.sh`: Waits for server readiness before benchmarking
- `run_benchmark.sh` / `run_benchmark_nv_sa.sh`: Execute benchmark workloads
- `accuracy_eval.sh`: Runs accuracy evaluation using lm_eval
- `gen_server_config.py`: Generates server configuration from worker settings
- `run_benchmark.sh` / `run_benchmark_nv_sa.sh` / `run_benchmark_aiperf.sh`: Execute benchmark workloads
- `submit.py` invokes `lm_eval` directly when accuracy evaluation is enabled (no separate `accuracy_eval.sh`)

## Configuration (config.yaml)

Expand Down Expand Up @@ -220,12 +219,15 @@ Enable accuracy evaluation using the lm_eval framework:
```yaml
accuracy:
enable_accuracy_test: true
model: "local-completions"
tasks: "gsm8k,hellaswag,mmlu" # Comma-separated task list
model_args_extra: "num_concurrent=512,max_retries=3,tokenized_requests=false,timeout=1200,max_gen_toks=256,max_length=4096"
tasks:
gsm8k:
model: "local-completions"
model_args_extra: "num_concurrent=512,max_retries=3,tokenized_requests=false,timeout=7200,max_gen_toks=16384"
extra_kwargs:
trust_remote_code: true
```

Accuracy results will be saved in `<log_dir>/accuracy_eval/` after benchmark completion.
`submit.py` launches one `lm_eval` command per task. Accuracy results are saved under `<log_dir>/accuracy_eval_<task>/` after benchmark completion.

#### 2. NVIDIA Nsight Systems Profiling

Expand Down Expand Up @@ -272,4 +274,4 @@ environment:
trtllm_wheel_path: "/path/to/tensorrt_llm-*.whl"
trtllm_repo: ""
build_wheel: false
```
```