Profiling

GeneLab forwards profiler flags to torch.profiler through genelab.rl.maybe_profile.

Enabling profiling

genelab train TASK_ID \
  --prof \
  --prof-active 3 \
  --prof-repeat 1 \
  --max_iterations 10

Open traces:

genelab prof open                       # defaults to logs/torch_profile
genelab prof open <log_dir>             # explicit directory
genelab prof open <log_dir> --port 6007 # custom TensorBoard port
genelab prof open <log_dir> --host 0.0.0.0

Flags

Flag	Env-var equivalent	Meaning (default)
--prof	GENELAB_PROFILE	Enable profiling.
--prof-out PATH	GENELAB_PROFILE_OUT	TensorBoard trace directory.
--prof-wait N	GENELAB_PROFILE_WAIT	Initial wait steps (10).
--prof-warmup N	GENELAB_PROFILE_WARMUP	Warmup steps before recording (5).
--prof-active N	GENELAB_PROFILE_ACTIVE	Recorded steps per cycle (10).
--prof-repeat N	GENELAB_PROFILE_REPEAT	Number of cycles (2).
--prof-record-shapes	GENELAB_PROFILE_RECORD_SHAPES	Record tensor shapes.
--prof-with-stack	GENELAB_PROFILE_WITH_STACK	Capture Python stacks. Higher overhead.

CLI flags always take precedence over the env vars.

Practical defaults

Start short. A profiler step is advanced once per env step in GeneLab's RL loop, so traces can grow quickly on large vectorized runs.

Distributed runs

Only the main process writes profiler traces. Keep --prof-active small when profiling distributed training.

prof open prerequisites

• tensorboard must be on PATH (uv pip install tensorboard, or via this checkout's rl extra).
• The directory passed must exist; an empty directory is fine.

See also

• Run RL Experiments
• CLI Reference