Problem
The proposal includes PyTorch variants for tabular, sequence, image, and distributed
workloads. CPA should avoid three separate PyTorch templates per data type.
Solution
Create one templates/mlops-pytorch-starter after the MLOps common contract and
sklearn MVP are accepted.
Scope:
- Type:
mlops-pytorch.
- CPU-first PyTorch baseline with a small model and tiny fixture/synthetic data.
- Typed Dataset/DataLoader pattern.
- Training/evaluation loop with deterministic smoke tests.
- MLflow local tracking consistent with the common contract.
- Export path using
state_dict or TorchScript where appropriate.
- Full CPA template docs and tests.
Constraints:
- No mandatory CUDA/GPU dependency.
- No heavyweight pretrained model downloads in tests.
- No GitHub Actions workflows inside the template.
- Data modality examples should be extensions or documented examples, not separate
base templates.
Acceptance criteria:
- Generated project passes local scaffold validation.
- CI registry scripts know the new
mlops-pytorch type.
Problem
The proposal includes PyTorch variants for tabular, sequence, image, and distributed
workloads. CPA should avoid three separate PyTorch templates per data type.
Solution
Create one
templates/mlops-pytorch-starterafter the MLOps common contract andsklearn MVP are accepted.
Scope:
mlops-pytorch.state_dictor TorchScript where appropriate.Constraints:
base templates.
Acceptance criteria:
mlops-pytorchtype.