> ## Documentation Index
> Fetch the complete documentation index at: https://docs.monostate.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# When to Use the API

> Python integration for custom applications

# When to Use the Python API

The API gives you full programmatic control for building custom applications.

## Best For

* **Custom applications** - Build your own tools
* **Complex workflows** - Multi-step pipelines
* **Dynamic configuration** - Adjust on the fly
* **Integration** - Connect with existing code
* **Production systems** - Deploy as services

## What It Looks Like

Write Python code:

```python theme={null}
from aitraining import TextClassification

trainer = TextClassification(
    model="bert-base-uncased",
    learning_rate=2e-5
)

trainer.train(data)
predictions = trainer.predict(texts)
```

## Workflow Example

```python theme={null}
import pandas as pd
from aitraining import AutoTrainer

# Custom preprocessing
data = pd.read_csv("raw_data.csv")
data = clean_and_prepare(data)

# Dynamic configuration
config = {
    "model": get_best_model(data),
    "batch_size": calculate_batch_size(),
    "epochs": 5 if len(data) > 1000 else 10
}

# Train with callbacks
trainer = AutoTrainer(**config)
trainer.train(
    data,
    callbacks=[
        early_stopping,
        checkpoint_best,
        log_to_wandb
    ]
)

# Integrate into application
@app.route("/predict")
def predict():
    result = trainer.predict(request.json)
    return jsonify(result)
```

## Advantages

* **Full control** - Access everything
* **Custom logic** - Your preprocessing
* **Integration** - Works with any Python library
* **Dynamic** - Adjust based on conditions
* **Testable** - Unit test your training

## Limitations

* **More code** - You write the orchestration
* **Complexity** - Handle errors yourself
* **Python only** - Not language agnostic
* **Dependencies** - Manage packages

## When to Switch

Use CLI when you:

* Need simple automation
* Want language agnostic solution
* Prefer configuration over code
* Work with non-Python tools

Use UI when you:

* Need visual feedback
* Teaching others
* Quick experiments
* Data exploration

## Common Use Cases

### Web Service

```python theme={null}
from flask import Flask
from aitraining import load_model

app = Flask(__name__)
model = load_model("./trained_model")

@app.route("/api/classify", methods=["POST"])
def classify():
    text = request.json["text"]
    result = model.predict(text)
    return {"label": result}
```

### Data Pipeline

```python theme={null}
def training_pipeline(df):
    # Custom cleaning
    df = remove_outliers(df)
    df = normalize_features(df)

    # Conditional training
    if df.shape[0] > 10000:
        model = "large-model"
    else:
        model = "small-model"

    # Train
    trainer = AutoTrainer(model=model)
    trainer.train(df)

    return trainer
```

### A/B Testing

```python theme={null}
models = {}

# Train variants
for config in experiments:
    trainer = create_trainer(config)
    trainer.train(data)
    models[config.name] = trainer

# Compare
results = evaluate_all(models, test_data)
best = select_best(results)
```

### Custom Callbacks

```python theme={null}
class CustomCallback:
    def on_epoch_end(self, epoch, logs):
        if logs["loss"] < self.threshold:
            send_notification("Training going well!")

        if should_adjust_lr(logs):
            self.trainer.learning_rate *= 0.5

trainer.train(data, callbacks=[CustomCallback()])
```

## Tips for API Users

1. **Handle exceptions** - Training can fail
2. **Add logging** - Track what happens
3. **Use type hints** - Catch errors early
4. **Write tests** - Ensure reliability
5. **Document code** - Others will use it

## API-Exclusive Features

Things only the API can do:

* Custom callbacks during training
* Dynamic model selection
* Complex data pipelines
* Embedded in applications
* Programmatic hyperparameter tuning

## Essential Patterns

```python theme={null}
# Context manager for resources
with AITraining() as trainer:
    trainer.train(data)
    # Automatically cleanup

# Async training
async def train_async():
    await trainer.train_async(data)

# Streaming predictions
for prediction in trainer.predict_stream(texts):
    process(prediction)

# Model composition
ensemble = Ensemble([
    model1,
    model2,
    model3
])
```

## Integration Examples

```python theme={null}
# With pandas
df = pd.read_csv("data.csv")
trainer.train(df)

# With scikit-learn
from sklearn.model_selection import train_test_split
X_train, X_test = train_test_split(data)

# With weights & biases
import wandb
wandb.init(project="my-training")
trainer.train(data, callbacks=[WandbCallback()])

# With FastAPI
@app.post("/train")
async def train_endpoint(data: TrainingData):
    result = await trainer.train_async(data)
    return {"model_id": result.id}
```

## Next Steps

<CardGroup cols={2}>
  <Card title="API Reference" href="/api/launching-interface">
    Full API documentation
  </Card>

  <Card title="CLI Alternative" href="/foundations/cli-when-to-use">
    When commands are simpler
  </Card>
</CardGroup>