# Common Issues

## CUDA Out of Memory

**Symptom**: `RuntimeError: CUDA out of memory`

**Solutions**:
1. Reduce batch size
2. Use smaller model
3. Upgrade to larger GPU

```python
# Example: reduce batch size for memory-heavy variants
batch_size = 16  # Smaller batch to fit VRAM
```

## Timeout

**Symptom**: Function terminates before completion

**Solution**: Increase timeout parameter

```python
@app.function(
    gpu="A100",
    image=image,
    timeout=3600,  # 1 hour (default is 300s)
)
def train():
    ...
```

| Training Type | Recommended Timeout |
|---------------|---------------------|
| Quick test | 300s (default) |
| Large-model training | 3600s (1 hour) |
| Extended training | 86400s (max, 24 hours) |

## Import Errors

**Symptom**: `ModuleNotFoundError`

**Solution**: Ensure packages are in `pip_install()`

```python
# WRONG - package not installed
image = modal.Image.debian_slim(python_version="3.11").pip_install("torch")

@app.function(gpu="A100", image=image)
def train():
    from einops import rearrange  # ModuleNotFoundError!

# CORRECT - all packages installed
image = modal.Image.debian_slim(python_version="3.11").pip_install(
    "torch",
    "einops",  # Now available
    "numpy",
)
```

## Data Download Fails

**Symptom**: Network errors downloading from HuggingFace

**Solutions**:
1. Add retry logic
2. Check if dataset is public
3. Add authentication for private datasets

```python
def get_file_with_retry(fname, max_retries=3):
    for attempt in range(max_retries):
        try:
            hf_hub_download(
                repo_id="your-org/your-dataset",
                filename=fname,
                repo_type="dataset",
                local_dir=data_dir,
            )
            return
        except Exception as e:
            if attempt < max_retries - 1:
                print(f"Retry {attempt + 1}/{max_retries}: {e}")
                time.sleep(5)
            else:
                raise
```

## Token Authentication

**Symptom**: Authentication errors

**Solution**: Set up Modal token correctly

```bash
# Check if token is set
modal token list

# Set token
modal token set --token-id <id> --token-secret <secret>
```

## Container Image Issues

**Symptom**: Old code running despite changes

**Solution**: Force image rebuild

```bash
# Force rebuild
modal run --force-build train_modal.py
```

## Debugging Tips

1. **Print statements**: Appear in Modal logs in real-time
2. **Background mode**: Use `modal run --detach` for long jobs
3. **Logs**: Check Modal dashboard for full logs
4. **Local testing**: Test code locally before running on Modal

```python
@app.function(gpu="A100", image=image, timeout=3600)
def train():
    print("Starting...")  # Appears in logs
    print(f"GPU: {torch.cuda.get_device_name(0)}")

    for step in range(max_steps):
        if step % 100 == 0:
            print(f"Step {step}, Loss: {loss:.4f}")  # Progress updates
```