Create CLI-Jupyter integration workflow

- Add jupyter_launcher.sh script to start Jupyter with ML tools
- Create cli_integration.py helper for CLI operations
- Add sample notebook structure for experiments
- Create workflow documentation for seamless data science integration

podman/cli_integration.py

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+# CLI-Jupyter Integration Helper
+import subprocess
+import os
+def sync_project(project_path):
+    """Sync project using CLI"""
+    cmd = ["../cli/zig-out/bin/ml", "sync", project_path, "--queue"]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    return result.returncode == 0

podman/jupyter_launcher.sh

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+#!/bin/bash
+echo "Starting Jupyter with ML tools..."
+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
+echo "Jupyter available at http://localhost:8888"

podman/jupyter_workflow.md

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+# 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
+```

podman/workspace/notebooks/sample_experiment.ipynb

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+{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# ML Experiment with FetchML\n\nThis notebook demonstrates how to use FetchML CLI with Jupyter notebooks."]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}}, "nbformat": 4, "nbformat_minor": 4}