TensorFlow Template

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

tensorflow: basic inference template for deployment
tensorflow-train: full training pipeline with ModelCheckpoint and EarlyStopping callbacks

Quick Start

cirron init my-tensorflow-model --template tensorflow

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

Project Structure

my-tensorflow-model/
├── src/
│   ├── model.py           # create_model() factory
│   ├── data_loader.py     # tf.data.Dataset utilities
│   ├── train.py           # Training script (tensorflow-train only)
│   └── inference.py       # Inference / prediction
├── data/
├── checkpoints/           # best_model.h5
├── models/                # final_model.h5
├── requirements.txt
├── Dockerfile
└── cirron.yaml

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

Cirron-specific bits

cirron.yaml

framework: tensorflow
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 before training begins; the Keras callback auto-registers and tracks model.fit:

import cirron as ci

ci.profile()

def train(self):
    return self.model.fit(self.train_dataset, ...)

requirements.txt

tensorflow>=2.12.0
numpy>=1.21.0
scikit-learn>=1.3.0
matplotlib>=3.5.0
Pillow>=9.0.0

Model Types

Classification: sparse_categorical_crossentropy loss, softmax output, accuracy metric. Default architecture is a small CNN.
Regression: mse loss, linear output, MAE metric. Default architecture is a Keras Sequential MLP.

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

Built-in Callbacks

The generated train.py wires standard Keras callbacks:

ModelCheckpoint: saves best model by validation loss to checkpoints/best_model.h5
EarlyStopping: stops training when val loss stops improving (patience=5)

Add more callbacks (e.g. ReduceLROnPlateau) in get_callbacks().

Usage

python src/train.py     # Train
cirron build            # Container
cirron deploy           # Deploy

GPU Support

TensorFlow automatically detects available GPUs. The tensorflow-train variant supports mixed-precision training:

policy = tf.keras.mixed_precision.Policy('mixed_float16')
tf.keras.mixed_precision.set_global_policy(policy)

Next Steps

PyTorch Template

Alternative deep learning framework

scikit-learn Template

Traditional ML algorithms

Common files

Dockerfile, tests, and shared scaffolding

Deployment

Deploy your trained model

​TensorFlow Template

​Quick Start

​Project Structure

​Cirron-specific bits

​cirron.yaml

​Profiling hooks

​requirements.txt

​Model Types

​Built-in Callbacks

​Usage

​GPU Support

​Next Steps