ml-training-recipes
ML Training Recipes
Battle-tested patterns for PyTorch training across domains. Drawn from production codebases (Karpathy's autoresearch/nanochat, torchvision, HuggingFace) and modern training practice.
Reference files (read when needed)
references/architecture.md— Transformer/LLM architecture code patterns, weight initreferences/optimizers.md— Muon, AdamW hybrid, per-group LR, compiled optimizer stepsreferences/domain-specific.md— Vision, diffusion, contrastive, distributed, checkpointing, data loadingreferences/scaling-and-selection.md— Scaling laws, compute budget tables, decision trees, DGX Sparkreferences/biomedical.md— Drug discovery, protein models, medical imaging, genomics, clinical NLPreferences/experiment-loop.md— Autonomous experiment loop (autoresearch keep/discard/revert)
Architecture Selection
Pick the right model by data type and data scale:
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268mlflow
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267faiss
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263tensorrt-llm
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
261serving-llms-vllm
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
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