PyTorch Workflow Mastery: A Guide to Track and Optimize Model Performance
summary
Hands‑on pipeline that actually trains a ResNet18 on CIFAR‑10 (50,000 train images, batch 128) and logs metrics and artifacts to Weights & Biases, runs short Optuna Bayesian searches for learning rate/optimizer choices via Optuna, and shows saving/loading state_dicts; project reproducibility uses UV for dependency sync. Code is delivered as notebooks then modularized into functions; demos run ~3 epochs so results are illustrative not production‑grade, but the integrations and saving/loading workflows work end‑to‑end.
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chapters (15)
- 0:00PyTorch workflow intro
- 1:26Introduction
- 10:14Setting up project
- 15:18Loading data and config
- 23:49Training model
- 33:50CIFAR-10 dataset
- 41:28Model architecture & transfer
- 54:43Training metrics
- 1:03:03Saving models & inference
- 1:06:16Weights & Biases tracking
- 1:11:11Hyperopt with Optuna
- 1:17:44Optuna trials & analysis
- 1:22:45Modular PyTorch code
- 1:25:38Deployment best practices
- 1:28:47Q&A
