A practical toolkit for squeezing maximum performance from NVIDIA GPUs during deep learning training. Covers CUDA memory management, mixed-precision training, gradient accumulation, distributed traini
Browse the actual product documentation and code examples included in this toolkit.
Key features of GPU Optimization Guide
• Memory Manager — CUDA memory snapshots, fragmentation analysis, leak detection, OOM diagnostics with actionable fix suggestions • AMP Trainer — Drop-in mixed-precision training loop with gradient accumulation, GradScaler, and gradient checkpointing support • GPU Monitor — Parse nvidia-smi output into structured Python objects, detect anomalies (idle GPUs, thermal throttling, memory pressure) • Profiler — stdlib SectionTimer for quick bottleneck detection + PyTorch profiler wrapper for detailed Chrome-trace analysis • Memory Benchmark — Automated comparison of fp32 vs AMP vs checkpointing across batch sizes • Distributed Configs — Production-ready DDP, FSDP, and DeepSpeed ZeRO configurations with extensive inline comments
Memory Manager — CUDA memory snapshots, fragmentation analysis, leak detection, OOM diagnostics with actionable fix suggestions
AMP Trainer — Drop-in mixed-precision training loop with gradient accumulation, GradScaler, and gradient checkpointing support
GPU Monitor — Parse nvidia-smi output into structured Python objects, detect anomalies (idle GPUs, thermal throttling, memory pressure)
Profiler — stdlib SectionTimer for quick bottleneck detection + PyTorch profiler wrapper for detailed Chrome-trace analysis
Memory Benchmark — Automated comparison of fp32 vs AMP vs checkpointing across batch sizes
Distributed Configs — Production-ready DDP, FSDP, and DeepSpeed ZeRO configurations with extensive inline comments
Configure GPU Optimization Guide parameters to see how the product works.
pip install -r requirements.txt # Monitor GPU utilization python src/gpu_monitor.py # Run memory benchmark (requires CUDA GPU) python benchmarks/memory_benchmark.py