Custom Training Pipelines
Learn custom training pipelines through RunLocalAI's practical lens: training, pipelines, distributed and huggingface, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.
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Why this course matters
Custom Training Pipelines is for builders turning local models into working tools, agents and retrieval systems. It connects training, pipelines, distributed, huggingface and experiment tracking to the questions RunLocalAI wants every reader to answer before they install, upgrade or scale a model: will it run, what will it cost in memory, what setting changes the result, and how do you verify the answer instead of trusting a demo?
What you will be able to do
By the end, you should be able to explain the main tradeoffs in plain language, choose a safe next experiment, and use the chapter exercises as a repeatable operator checklist. The course favors local evidence, hardware fit, context limits, latency and failure modes over generic AI vocabulary.
How to use this course
Start at chapter one if the topic is new. If you already have a working stack, scan for chapters such as Training Pipeline Overview, Data Pipeline Design, Dataset Curation and Data Augmentation and use those lessons as a quality-control pass before changing a workstation, team workflow or production-like local deployment.
- 01Training Pipeline OverviewA training pipeline is five stages with distinct resource profiles—treat each stage independently and connect them through explicit interfaces.15 min
- 02Data Pipeline Design`num_workers`, `prefetch_factor`, and `pin_memory` are the three DataLoader knobs that matter most—tune them through profiling, not guesswork.15 min
- 03Dataset CurationValidate dataset integrity before training begins. Catch corrupted images, missing labels, and class imbalance in the curation phase, not during training.20 min
- 04Data AugmentationAggressive augmentations that destroy real patterns are worse than no augmentation. Always visualize augmented samples to catch destructive transforms.15 min
- 05Dataset StreamingStreaming solves RAM constraints but introduces I/O latency. Memory mapping avoids loading entire shards; WebDataset handles sharding for distributed training.20 min
- 06Multi-GPU TrainingDistributed training multiplies batch size by GPU count—scale the learning rate linearly or face convergence failures.20 min
- 07Data ParallelismDDP's all-reduce synchronizes gradients after every backward pass—slow interconnects or large models increase sync overhead proportionally.20 min
- 08Model ParallelismModel parallelism requires careful orchestration to hide transfer latency—pipeline parallelism with micro-batches is the standard solution.20 min
- 09FSDPFSDP shards parameters, gradients, and optimizers across GPUs—effective memory per GPU equals model size divided by GPU count.20 min
- 10Custom Training LoopA training loop that mixes logging, checkpointing, and validation in the same function is undebuggable. Separate concerns into functions with explicit interfaces.20 min
- 11Loss FunctionsLoss functions are assumptions about what to optimize. Test multiple losses—your initial choice is usually wrong for real-world data with imbalance or outliers.20 min
- 12Optimizers and SchedulersOneCycleLR with warmup is usually the best starting point for new projects—less tuning required than step decay.20 min
- 13Hyperparameter SearchRandom search with 50 trials finds better hyperparameters than grid search with 10—use Bayesian optimization for expensive evaluations.15 min
- 14Experiment Tracking with MLflowLog every experiment with the same structure—params upfront, metrics per epoch, artifacts on completion. Inconsistent logging destroys reproducibility.15 min
- 15Weights and BiasesWandb's sweep feature runs hyperparameter search as-a-service—use it when infrastructure cost exceeds engineering time.20 min
- 16CheckpointingAlways use atomic writes (write to .tmp, then rename) to prevent checkpoint corruption on crashes.20 min
- 17Pipeline OrchestrationPipeline tools enforce execution order, handle failures, and enable reruns from checkpoints—manual scripts can't do this reliably.20 min
- 18Training Pipeline ProjectProduction pipelines are boring—predictable execution, clear failure modes, and full observability beat clever optimizations that hide bugs.25 min