PyTorch Template

The PyTorch template scaffolds a production-ready ML project with PyTorch, torchvision, and common ML dependencies. Two variants are available:

pytorch: basic inference template for deployment
pytorch-train: complete training pipeline with checkpoints, validation, and metrics

Quick Start

# Inference project
cirron init my-pytorch-model --template pytorch

# Training project
cirron init my-training-project --template pytorch-train

The model type (classification, regression, etc.) is selected interactively during cirron init.

Project Structure

my-pytorch-model/
├── src/
│   ├── model.py           # Model architecture (ClassificationModel / RegressionModel)
│   ├── data_loader.py     # Dataset + DataLoader utilities
│   ├── train.py           # Training script (pytorch-train only)
│   └── inference.py       # Inference / prediction
├── data/                  # Data directory
├── checkpoints/           # Saved during training
├── requirements.txt
├── Dockerfile
└── cirron.yaml

For shared files (Dockerfile, .gitignore, README.md, .env.example, tests), see Common files.

Cirron-specific bits

cirron.yaml

The generated config wires the training entrypoint and runtime:

framework: pytorch
model_type: classification
train_entrypoint: src/train.py
inference_entrypoint: src/inference.py

Profiling hooks

The generated train.py and inference.py integrate with the Cirron SDK. Call ci.profile() once at module top so traces show up in cirron traces:

import cirron as ci

ci.profile()

The PyTorch hooks then auto-instrument forward/backward/optimizer steps. For explicit regions the hooks don’t cover, use ci.scope as a context manager:

with ci.scope("validate"):
    val_loss, accuracy = self.validate()

requirements.txt

torch>=2.0.0
torchvision>=0.15.0
numpy>=1.21.0
scikit-learn>=1.3.0
matplotlib>=3.5.0
tqdm>=4.64.0
Pillow>=9.0.0

Model Types

Classification: nn.CrossEntropyLoss, softmax output. Default architecture is a small CNN.
Regression: nn.MSELoss, linear output. Default architecture is a fully-connected MLP.

The interactive cirron init prompt selects which one is generated into src/model.py.

Usage

# Train
python src/train.py

# Build container
cirron build

# Deploy
cirron deploy

GPU Support

The generated code automatically detects CUDA via torch.cuda.is_available() and falls back to CPU. To target a GPU build, set runtime: gpu in cirron.yaml.

Next Steps

TensorFlow Template

Alternative deep learning framework

scikit-learn Template

Traditional ML algorithms

Common files

Dockerfile, tests, and shared scaffolding

Deployment

Deploy your trained model

​PyTorch Template

​Quick Start

​Project Structure

​Cirron-specific bits

​cirron.yaml

​Profiling hooks

​requirements.txt

​Model Types

​Usage

​GPU Support

​Next Steps