# CLI-Jupyter Integration Workflow
## Workflow Overview

This workflow integrates the FetchML CLI with Jupyter notebooks for seamless data science experiments.

### Step 1: Start Jupyter Server
```bash
# Build the container with ML tools
podman build -f ml-tools-runner.podfile -t ml-tools-runner .

# Start Jupyter server
podman run -d -p 8888:8888 --name ml-jupyter \
  -v "$(pwd)/workspace:/workspace:Z" \
  --entrypoint conda localhost/ml-tools-runner \
  run -n ml_env jupyter notebook --no-browser --ip=0.0.0.0 --port=8888 \
  --NotebookApp.token='' --NotebookApp.password='' --allow-root

# Access at http://localhost:8888
```

### Step 2: Use CLI to Sync Projects
```bash
# From another terminal, sync your project
cd cli && ./zig-out/bin/ml sync ./my_project --queue

# Check status
./zig-out/bin/ml status
```

### Step 3: Run Experiments in Jupyter
```python
# In your Jupyter notebook
import mlflow
import wandb
import pandas as pd

# Start MLflow tracking
mlflow.start_run()
mlflow.log_param("model", "random_forest")
mlflow.log_metric("accuracy", 0.95)
```

### Step 4: Monitor with CLI
```bash
# Monitor jobs from CLI
./zig-out/bin/ml monitor

# View logs
./zig-out/bin/ml experiment log my_experiment
```