42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
# Profiling vLLM
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We support tracing vLLM workers using the `torch.profiler` module. You can enable tracing by setting the `VLLM_TORCH_PROFILER_DIR` environment variable to the directory where you want to save the traces: `VLLM_TORCH_PROFILER_DIR=/mnt/traces/`
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The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set.
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When using `benchmarks/benchmark_serving.py`, you can enable profiling by passing the `--profile` flag.
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```{warning}
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Only enable profiling in a development environment.
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```
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Traces can be visualized using <https://ui.perfetto.dev/>.
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```{tip}
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Only send a few requests through vLLM when profiling, as the traces can get quite large. Also, no need to untar the traces, they can be viewed directly.
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```
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```{tip}
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To stop the profiler - it flushes out all the profile trace files to the directory. This takes time, for example for about 100 requests worth of data for a llama 70b, it takes about 10 minutes to flush out on a H100.
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Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the server. Say something like 30 minutes.
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`export VLLM_RPC_TIMEOUT=1800000`
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```
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## Example commands and usage:
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### Offline Inference:
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Refer to [examples/offline_inference_with_profiler.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_with_profiler.py) for an example.
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### OpenAI Server:
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```bash
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VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B
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```
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benchmark_serving.py:
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```bash
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python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Meta-Llama-3-70B --dataset-name sharegpt --dataset-path sharegpt.json --profile --num-prompts 2
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```
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