PyTorch / Storage, restoration, and transformed execution

PyTorch β€” Storage, restoration, and transformed execution

Views, reviewed loading, resource evidence, compilation, export domains, and complete synthetic acceptance.

6 lessons Β· No account required

What you will learn

  • Views, reviewed loading, resource evidence, compilation, export domains, and complete synthetic acceptance.

  1. Views, contiguity, and in-place updates

    Separate logical indexing, shared storage, and gradient history before mutating or reshaping tensor state.

    Start learning

  2. State dictionaries and reviewed loading

    Restore a defined function with compatible named state and an explicit loading policy.

    Start learning

  3. Profiling and resource evidence

    Separate correctness, analytical storage counts, profiler observations, and measured user-visible performance.

    Start learning

  4. Compilation and graph-break contracts

    Understand torch.compile as a supported execution transformation with guards, capture limits, and separate performance evidence.

    Start learning

  5. Export and dynamic-shape contracts

    Describe the supported tensor program and input domain rather than treating one captured example as unrestricted portability.

    Start learning

  6. Complete synthetic model acceptance

    Assemble a small PyTorch inference contract and separate local implementation evidence from model-quality claims.

    Start learning
Back to course