fournex 0.1.9

Open-source GPU performance profiler and bottleneck analyzer for PyTorch.

Fournex

Open-source GPU performance profiler and bottleneck analyzer for PyTorch.

Fournex wraps your training script, collects GPU telemetry, and tells you exactly what is slowing it down — with ranked, actionable recommendations.

Install

pip install fournex

Quick start

# Profile your workload
frx collect --name my-run -- python train.py

# Analyze and get recommendations
frx analyze runs/run-<id>

# Check your environment
frx doctor

# Validate the pipeline end-to-end
frx smoke-test

Detected bottleneck types

Label	Signal
input_bound	DataLoader wait ≥ 20% of step time
copy_bound	H2D transfer ≥ 15% of step time
sync_bound	Sync wait ≥ 10% of step time
underutilized_gpu	GPU utilization < 35%
memory_pressure	Peak memory ratio ≥ 90%
shape_instability	Shape volatility ≥ 30%
launch_bound	Low utilization + profiler windows, no dominant stall
insufficient_telemetry	No timing or GPU utilization data

Safe config benchmarking

frx tune --safe --max-trials 12 -- python train.py

Fournex sweeps DataLoader and runtime configs, benchmarks each one, and recommends the fastest safe candidate — without changing your code.

Interrupted or repeated tune runs can reuse completed trial artifacts:

frx tune --resume runs/tune-<id> -- python train.py

--resume reuses a trial only when the saved config.yaml, benchmark_window.json, and metrics.json match the current workload command and benchmark settings.

Links

• GitHub
• Documentation
• Website