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GPU Optimization Guide

$39

CUDA memory management, mixed precision training, distributed training configs, and GPU utilization monitoring.

📁 28 files
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📁 File Structure 28 files

gpu-optimization-guide/ ├── LICENSE ├── README.md ├── benchmarks/ │ ├── __pycache__/ │ │ └── memory_benchmark.cpython-312.pyc │ └── memory_benchmark.py ├── configs/ │ ├── ddp_config.yaml │ ├── deepspeed_config.json │ └── fsdp_config.yaml ├── free-sample.zip ├── guide/ │ ├── 01-distributed-training.md │ ├── 02-memory-management.md │ ├── 03-mixed-precision.md │ └── 04-profiling.md ├── guides/ │ ├── distributed-training.md │ ├── memory-management.md │ ├── mixed-precision.md │ └── profiling.md ├── index.html ├── requirements.txt └── src/ ├── __init__.py ├── __pycache__/ │ ├── __init__.cpython-312.pyc │ ├── amp_trainer.cpython-312.pyc │ ├── gpu_monitor.cpython-312.pyc │ ├── memory_manager.cpython-312.pyc │ └── profiler.cpython-312.pyc ├── amp_trainer.py ├── gpu_monitor.py ├── memory_manager.py └── profiler.py

📖 Documentation Preview README excerpt

GPU Optimization Guide

A practical toolkit for squeezing maximum performance from NVIDIA GPUs during deep learning training. Covers CUDA memory management, mixed-precision training, gradient accumulation, distributed training (DDP/FSDP/DeepSpeed), profiling, OOM debugging, and GPU monitoring.

Features

  • 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

File Structure


gpu-optimization-guide/
├── README.md
├── LICENSE
├── requirements.txt
├── src/
│   ├── __init__.py              # Package init
│   ├── memory_manager.py        # CUDA memory tracking, OOM diagnostics
│   ├── amp_trainer.py           # Mixed-precision training loop
│   ├── gpu_monitor.py           # nvidia-smi parser + anomaly detection
│   └── profiler.py              # SectionTimer + torch.profiler wrapper
├── configs/
│   ├── ddp_config.yaml          # DistributedDataParallel configuration
│   ├── fsdp_config.yaml         # FullyShardedDataParallel configuration
│   └── deepspeed_config.json    # DeepSpeed ZeRO Stage 2 configuration
├── guides/
│   ├── memory-management.md     # OOM debugging flowchart + optimization ladder
│   ├── mixed-precision.md       # AMP deep dive with troubleshooting
│   ├── distributed-training.md  # DDP vs FSDP vs DeepSpeed decision tree
│   └── profiling.md             # Bottleneck identification workflow
└── benchmarks/
    └── memory_benchmark.py      # Automated memory/speed comparison

Quick Start


pip install -r requirements.txt

# Monitor GPU utilization
python src/gpu_monitor.py

# Run memory benchmark (requires CUDA GPU)
python benchmarks/memory_benchmark.py

Usage

Memory Tracking


from src.memory_manager import memory_tracking, format_bytes

with memory_tracking(label="training_step") as tracker:
    loss = model(batch)

*... continues with setup instructions, usage examples, and more.*

📄 Code Sample .py preview

src/amp_trainer.py """ amp_trainer — Mixed-precision (AMP) training loop with gradient accumulation. Automatic Mixed Precision uses float16 for forward/backward passes and float32 for the optimizer step. This typically: - Cuts GPU memory usage by 30–50 % (tensors are half the size). - Speeds up training by 1.5–3× on Ampere+ GPUs (which have TF32 cores). - Requires NO changes to your model architecture. The GradScaler handles loss scaling automatically to prevent float16 underflow in gradients. This module provides a drop-in training function and a configurable TrainerConfig dataclass. """ from __future__ import annotations import logging import time from dataclasses import dataclass, field from typing import Any, Callable, Dict, List, Optional, Tuple logger = logging.getLogger(__name__) @dataclass class TrainerConfig: """Configuration for the AMP training loop. Attributes: use_amp: Enable automatic mixed precision. gradient_accumulation_steps: Number of micro-batches to accumulate before an optimizer step. Effective batch size = batch_size × accumulation_steps. Set > 1 when the desired batch size doesn't fit in GPU memory. max_grad_norm: Clip gradients to this L2 norm (0 = no clipping). use_gradient_checkpointing: Trade compute for memory: recompute activations during backward pass instead of storing them. Reduces memory by ~60 % at the cost of ~30 % slower training. log_every_n_steps: How often to log training metrics. amp_dtype: "float16" or "bfloat16". bfloat16 doesn't need loss scaling but requires Ampere+ hardware. """ use_amp: bool = True gradient_accumulation_steps: int = 1 max_grad_norm: float = 1.0 use_gradient_checkpointing: bool = False log_every_n_steps: int = 10 # ... 195 more lines ...
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