Section 8 of 8 · Deep Learning
Reliable PyTorch Execution, State & Profiling
Framework fluency becomes engineering evidence when precision, memory, state, compilation and timing claims can be predicted, measured and reproduced.
9 units 22 topics 0/9
Units
9 in this section
- 8.1 Eager execution, autograd nodes and saved tensors explain3 h
- 8.2 Streams, asynchronous copies and honest timing explain · measure4 h
- 8.3 GPU memory: allocated, reserved, peak and memory models derive · measure5 h
- 8.4 Precision formats, autocast and gradient scaling explain · implement5 h
- 8.5 Overflow, underflow and finding the first invalid value measure4 h
- 8.6 Activation checkpointing and recomputation implement · measure4 h
- 8.7 Complete checkpoints, atomic writes and reproducibility boundaries implement5 h
- 8.8 `torch.compile`: capture, guards, graph breaks and dynamic shapes measure4 h
- 8.9 Profiling training phases and verifying optimizations measure5 h
Before moving on
Checkpoint
Predict memory categories and the slowest phase, produce a synchronized profile, explain one discrepancy, repair a precision, state, graph or performance fault, and show retained learning and resume behavior.
Deliberately out of scope
Leave for later
- Distributed training and sharded state
- Custom kernel development
- Production trainer and fleet operations
Resources for this section
Shared by every unit above
- PyTorch - reliability and performance recipes Use the Profiler, Automatic Mixed Precision, model loading and Benchmark recipes, then the Performance Tuning Guide. Apply each to one compact model from an earlier section and keep a CPU fallback when CUDA is unavailable.
- PyTorch - compiler troubleshooting Read graph breaks, guards, recompilations and logging. Compile one stable fragment, intentionally trigger a graph break or recompilation, inspect it, and compare eager and compiled outputs and gradients.
- PyTorch - checkpointing, numerical accuracy and CUDA semantics Use the official user-guide entries for torch.utils.checkpoint, numerical accuracy, CUDA semantics, reproducibility and serialization. Predict memory categories before measuring and define resume equivalence before running.
- Deep Learning Training Debugger Use the failure-injection, measurement and evidence rules as the deeper standard. The full Tier 2 project is a later route and is not added to this stage milestone.
- TensorTonic - mixed-precision memory and transformer FLOPs Solve only Mixed-Precision Training Memory Accountant and Transformer Training FLOP Estimator. Reconcile both with the compact model's profiler and document every included and excluded term.
- Learn PyTorch - profiler walkthrough Use notebook 10 only if the official profiler route remains unclear. Transfer the relevant annotations into the compact model instead of keeping the tutorial code.
- TrenTorch - precision, checkpointing and resume Solve Loss Scaling, FP16/BF16 Representable Range, Gradient Checkpointing and Resume-from-checkpoint in NumPy, then reproduce each behavior with the PyTorch API in the compact model.
- Python os - `os.replace` Read
os.replacefor the atomic-rename step of checkpoint writes: save to a temp file in the same directory,flushandfsync, then replace the final name. - Hands-On Machine Learning with Scikit-Learn and PyTorch Read Appendix B, Reduced Precision Models and Mixed-Precision Training, for why
float16training underflows and whatfp32primary weights and loss scaling fix.
Apply the section
Mini-project · Profiled, resumable training with one repaired fault (10 to 14 h)
Take your compact model and trainer, predict its memory and slowest phase before measuring, and profile it with synchronized phases. Then repair one precision, state, graph or performance fault, and show the repaired run still learns and resumes.
Steps
- Write predictions first: parameter, gradient, optimizer-state and activation bytes for your model and batch, and which phase (data wait, transfer, forward, backward, optimizer) will be slowest.
- Annotate one training step with
record_functionranges, time data wait outside the step, and use CUDA events orsynchronizeat phase boundaries. Profile a few steps with aschedule; on a CPU, profile there and say so. - Read allocated, reserved and peak memory per phase, reset peaks before each measured region, list the saved tensors of one layer, and explain one gap between prediction and measurement.
- Instrument numerics: count non-finite values per step, capture the first invalid value with forward hooks, and replay a fixed batch under anomaly detection to name the backward node.
- Seed one fault and repair it:
fp16autocastwithout aGradScaler, clipping beforeunscale_, an.item()sync inside the step, a graph break undertorch.compile, or an incomplete checkpoint. - Apply one optimization (
autocast, activation checkpointing, compile or loader settings). Show outputs and gradients matcheagerwithin tolerance, report a measured gain or an honest null result, and rerun your learning controls. - Interrupt the optimized run and resume it from an atomic checkpoint written to a temp file and moved with
os.replace, and compare with an uninterrupted run under a declared tolerance.
Deliverables
- The prediction sheet and the synchronized profile, with a phase table and memory per phase.
- One fault postmortem with hypotheses, the discriminating measurement, the repair and a regression guard.
- The optimization report: equivalence, timing method and results, learning controls and the resume check.
Checks
- external-tests · TensorTonic: Mixed-Precision Training Memory Accountant and Transformer Training FLOP Estimator
- rubric · Self-grade with the rubric below
Rubric
| Criterion | Pass | Strong |
|---|---|---|
| Prediction before measurement | Predictions are committed before the profile is run. | Each gap between predicted and measured memory or time is explained by a named term, such as the cuBLAS workspace, allocator reserve, a sync or recomputation. |
| Honest timing | Phase times use events or synchronize, and data wait is measured outside the step. | You show one timing that changes when a sync moves, and explain why. |
| Fault repair | One seeded fault is found, repaired and guarded by a test. | The postmortem shows the discriminating measurement and one rejected hypothesis. |
| Optimization validity | Optimized outputs and gradients match eager within tolerance, and the learning controls still pass. | The gain is reported with its spread over repeated runs, or a null result is reported with its cause. |
| Resume | Resume from the atomic checkpoint matches within a declared tolerance. | A test that kills the process during a write shows the previous checkpoint survives. |
Feeds into: Capstone artifact 3: the performance or compile postmortem comes from here, and the resume result also serves artifact 2.