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
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
conda activate labpip install snakeviz# say, to profile A2C on Pongpython -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 graphssnakeviz a2c.prof# a browser will open, showing the runtime breakdown