[Docs] Convert rST to MyST (Markdown) (#11145)

Signed-off-by: Rafael Vasquez <rafvasq21@gmail.com>

docs/source/contributing/profiling/profiling_index.md

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+# Profiling vLLM
+
+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/`
+
+The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set.
+
+When using `benchmarks/benchmark_serving.py`, you can enable profiling by passing the `--profile` flag.
+
+```{warning}
+Only enable profiling in a development environment.
+```
+
+Traces can be visualized using <https://ui.perfetto.dev/>.
+
+```{tip}
+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.
+```
+
+```{tip}
+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.
+Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the server. Say something like 30 minutes.
+`export VLLM_RPC_TIMEOUT=1800000`
+```
+
+## Example commands and usage:
+
+### Offline Inference:
+
+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.
+
+### OpenAI Server:
+
+```bash
+VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B
+```
+
+benchmark_serving.py:
+
+```bash
+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
+```