PyTorch Fundamentals
PyTorch is a Python framework for building and training neural networks. You use tensors, automatic differentiation, and reusable model components to turn data and a learning objective into a trained model.
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Don't Panic
Don't Panic - PyTorch Fundamentals
PyTorch Fundamentals centers on PyTorch. PyTorch is a framework for numerical computing and machine learning. Its central data type is the tensor.
Operate from the project's own resources and APIs. Learn how desired state becomes running state, which status conditions matter, and which dependencies (network, storage, identity, certificates) the control plane assumes.
Upgrades, backups, and credential rotation are part of the product, not optional aftercare. Version skew between clients and servers creates failures that look like application bugs. Pin versions and rehearse rollback.
Convenience features reduce boilerplate and widen blast radius. Enable them when you can observe and reverse the expanded surface. Defaults from quickstarts are starting points, not production policy.
Name owners for upgrades, credentials, and disaster recovery before traffic arrives. Unowned control-plane state becomes an outage with no clear pager. Prefer explicit version pins and tested rollback over floating tags that quietly change behavior between deploys.
Name owners for upgrades, credentials, and disaster recovery before traffic arrives. Unowned control-plane state becomes an outage with no clear pager. Prefer explicit version pins and tested rollback over floating tags that quietly change behavior between deploys.
Name owners for upgrades, credentials, and disaster recovery before traffic arrives. Unowned control-plane state becomes an outage with no clear pager. Prefer explicit version pins and tested rollback over floating tags that quietly change behavior between deploys.
Read the Intro for the mental model. Use the Cheatsheet when you need the resource map. Updates and Upstream track the PyTorch release line that changes these APIs.
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Sources
- https://pytorch.org/
Supports
- PyTorch installation and project ecosystem
- PyTorch ecosystem examples including Captum and PyTorch Geometric
- https://docs.pytorch.org/tutorials/beginner/basics/intro.html
Supports
- Complete workflow spanning data, models, optimization, and saving
- Official Learn the Basics study sequence
- https://docs.pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html
Supports
- Tensor attributes, operations, and accelerator use
- Shape, data type, and device as tensor properties
- https://docs.pytorch.org/tutorials/beginner/basics/data_tutorial.html
Supports
- Dataset and DataLoader responsibilities
- Batching and iteration over data
- https://docs.pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html
Supports
- nn.Module, layers, parameters, and forward computation
- Training and evaluation module modes
- https://docs.pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html
Supports
- Recorded computation and gradient calculation
- Backward calculation and gradient tracking
- https://docs.pytorch.org/tutorials/beginner/basics/optimization_tutorial.html
Supports
- Loss calculation, backward passes, optimizer steps, and gradient clearing
- Standard training-loop structure
- https://docs.pytorch.org/tutorials/beginner/basics/saveloadrun_tutorial.html
Supports
- state_dict saving and loading
- Inference preparation and evaluation mode
- https://docs.pytorch.org/tutorials/intermediate/torch_compile_tutorial.html
Supports
- torch.compile as a later optimization topic
- https://github.com/bharathgs/Awesome-pytorch-list
Supports
- Captum and Lightning ecosystem discovery