Understanding Model Adapters: The Magic Behind the Scenes 🎭

Ever wondered how Dataloop seamlessly integrates with your ML models? Let's peek behind the curtain and explore the Model Adapter's inner workings!

The Two Types of Functions 🎭

Think of the Model Adapter as a theater production with two types of actors:

1. 🎬 Wrapper Functions - The stage managers who handle all the behind-the-scenes work

   • Part of the BaseModelAdapter class
   • Handle data movement, file management, and coordination
   • Make your life easier by managing the complex stuff

2. 🎭 User Functions - The star performers you get to direct

   • Functions you implement in your ModelAdapter
   • Contain your model's specific logic
   • Turn your ML model into a Dataloop-ready superstar

Let's meet our cast of characters!

The Stage Managers: Wrapper Functions 🎬

load_from_model: The Setup Crew 📦

What happens:

1. 📥 Downloads your model's artifacts
2. 📂 Creates a cozy home at ~/.dataloop/models/{model.name}
3. 🎯 Calls your load function to bring the model to life

Your files will be organized like this:

DATALOOP_PATH/
└── models/
    └── model.name/
        └── artifacts/

save_to_model: The Preservation Expert 💾

What happens:

1. 💾 Calls your save function to package up the model
2. ☁️ Uploads everything to Dataloop's cloud
3. ✨ Keeps your model safe and sound

predict_items: The Performance Director 🎯

The show must go on! Here's how:

1. 🎬 Creates batches of items
2. 🎨 Prepares each batch using your prepare_item_func
3. 🎯 Makes predictions using your predict function
4. 📝 Saves the results as annotations
5. 🔄 Repeats until all items are processed

train_model: The Training Coach 🏋️‍♂️

Your model's training journey:

1. 📥 Loads the current model state
2. 🎯 Prepares training data
3. 🏋️‍♂️ Trains using your train function
4. 💾 Saves the improved model
5. 🧹 Cleans up temporary files

Your training data will be organized like this:

DATALOOP_PATH/
└── models/
    └── model.name/
        └── artifacts/
└── model_data/
    └── model.id_model.name/
        └── timestamp/
            ├── output/
            └── datasets/
                └── dataset.id/
                    ├── train/
                    │   ├── items/
                    │   └── json/
                    └── validation/
                        ├── items/
                        └── json/

evaluate_model: The Performance Reviewer 📊

How your model gets its report card:

1. 📥 Loads the latest model version
2. 🎯 Makes predictions on test data
3. 📊 Compares with ground truth
4. 📈 Generates performance metrics

embed_items: The Feature Vector Transformer 🎆

The embedding process:

1. 🎬 Creates batches of items
2. 🎨 Prepares each batch using your prepare_item_func
3. 🔢 Generates embeddings using your embed function
4. 💾 Stores the vectors in a feature set
5. 🔄 Repeats until all items are processed

Your Star Performers: User Functions 🌟

Required Functions for Different Use Cases:

For Prediction Only:

• load 📥 - Brings your model to life
• predict 🎯 - Makes predictions on data

For Training:

• Everything above, plus:
• train 🏋️‍♂️ - Teaches your model new tricks
• save 💾 - Preserves your model's knowledge

For Embedding:

• load 📥 - Brings your model to life
• embed 🔢 - Converts items to feature vectors
• prepare_item_func 🎨 - Prepares items for embedding

Function Details

load 📥

def load(self, local_path, **kwargs):
    """Load your model from files"""
    self.model = YourModel.load(local_path)

save 💾

def save(self, local_path, **kwargs):
    """Save your model's state"""
    torch.save(self.model.state_dict(), f'{local_path}/weights.pth')

train 🏋️‍♂️

def train(self, data_path, **kwargs):
    """Train your model on new data"""
    # Your training logic here
    pass

predict 🎯

def predict(self, batch, **kwargs):
    """Make predictions and return Dataloop annotations"""
    predictions = self.model(batch)
    return convert_to_annotations(predictions)

prepare_item_func 🎨

def prepare_item_func(self, item):
    """Prepare items for prediction"""
    return preprocess_for_model(item)

embed 🔢

def embed(self, batch, **kwargs):
    """Convert a batch of items into embedding vectors"""
    embeddings = self.model.encode(item)
    return embeddings

Pro Tips for Model Adapter Success 💡

1. Error Handling 🛡️

   • Add proper logging in each function
   • Handle edge cases gracefully
   • Validate inputs and outputs

2. Performance ⚡

   • Optimize batch processing
   • Use efficient data loading
   • Clean up temporary files

3. Best Practices 📋

   • Document your functions clearly
   • Follow the function signatures
   • Test each component thoroughly

Need more help? Check out our example implementations🚀