# Profiling SLM Lab

## :stopwatch: Profiling Runtime

When developing a feature in SLM Lab, we may want to profile the program to check its performance and runtime, especially since deep RL software is complicated and involves many components.

We recommend Python's built-in `cProfile` and `snakeviz` to profile your program runtime. The example below runs the profiler and visualizes the program runtime broken down hierarchically by components. See an example of the graph: <https://jiffyclub.github.io/snakeviz/#interpreting-results>

```bash
conda activate lab
pip install snakeviz

# say, to profile A2C on Pong
python -m cProfile -o a2c.prof run_lab.py slm_lab/spec/benchmark/a2c_gae_pong.json a2c_gae_pong train

# then Ctrl+C to kill the process after some time to collect runtime data
# use snakeviz to render graphs
snakeviz a2c.prof

# a browser will open, showing the runtime breakdown
```


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://slm-lab.gitbook.io/slm-lab/master/development/profiling-slm-lab.md?ask=<question>
```

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The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.