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GPU Training Toolkit

$39

Multi-GPU training configs, mixed precision training, distributed training, and cloud GPU setup guides.

📁 8 files🏷 v1.0.0
MarkdownYAMLJSONAWSAzureGCP

📁 File Structure 8 files

gpu-training-toolkit/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-distributed-data-parallel.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

GPU Training Toolkit

Multi-GPU training configurations with mixed precision, distributed training patterns, and cloud GPU setup guides. Accelerate model training from single-GPU notebooks to multi-node distributed setups.

What's Included

  • Multi-GPU training configs for PyTorch and TensorFlow
  • Mixed precision training setup (FP16/BF16)
  • Distributed Data Parallel (DDP) templates
  • FSDP (Fully Sharded Data Parallel) configs for large models
  • Cloud GPU setup guides (AWS, GCP, Azure)
  • GPU memory optimization techniques
  • Training profiling and bottleneck identification

Quick Start


# 1. Copy the example config
cp config.example.yaml config.yaml

# 2. Verify GPU availability
python -c "import torch; print(torch.cuda.device_count(), 'GPUs available')"

# 3. Run single-GPU training
python train.py --config config.yaml

# 4. Run multi-GPU training
torchrun --nproc_per_node=4 train.py --config config.yaml

Prerequisites

  • Python 3.9+
  • PyTorch 2.x or TensorFlow 2.x
  • CUDA 11.8+ and cuDNN 8.x
  • NVIDIA GPU(s) with 8GB+ VRAM

Contents


gpu-training-toolkit/
  config.example.yaml
  docs/
    overview.md
    patterns/
      pattern-01-*.md
    checklists/
      pre-deployment.md
  templates/
    config.yaml

Support

For questions or issues, contact: megafolder122122@hotmail.com

License

MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.

📄 Code Sample .yaml preview

config.example.yaml # GPU Training Toolkit - Example Configuration # Copy this file to config.yaml and update values for your environment training: framework: "pytorch" # pytorch or tensorflow device: "cuda" gpus: 1 precision: "fp32" # fp32, fp16, bf16 model: name: "resnet50" pretrained: true data: batch_size: 32 num_workers: 4 pin_memory: true optimizer: type: "adamw" learning_rate: 0.001 weight_decay: 0.01 scheduler: type: "cosine" warmup_steps: 100 epochs: 10 distributed: enabled: false backend: "nccl" strategy: "ddp" # ddp, fsdp, deepspeed mixed_precision: enabled: false dtype: "float16" # float16, bfloat16 logging: level: "INFO"
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