fetch_ml/docs/src/quick-start.md

Quick Start

Get Fetch ML running in minutes with Docker Compose and integrated monitoring.

Prerequisites

Container Runtimes:

• Docker Compose: For testing and development only
• Podman: For production experiment execution

Requirements:

• Go 1.25+
• Zig 0.15+
• Docker Compose (testing only)
• 4GB+ RAM
• 2GB+ disk space
• Git

One-Command Setup

# Clone and start
git clone https://github.com/jfraeys/fetch_ml.git
cd fetch_ml
make dev-up

# Wait for services (30 seconds)
sleep 30

# Verify setup
curl http://localhost:8080/health

Note: the development compose runs the API server over HTTP/WS for CLI compatibility. For HTTPS/WSS, terminate TLS at a reverse proxy.

Access Services:

• API Server (via Caddy): http://localhost:8080
• API Server (via Caddy + internal TLS): https://localhost:8443
• Grafana: http://localhost:3000 (admin/admin123)
• Prometheus: http://localhost:9090
• Loki: http://localhost:3100

Development Setup

Build Components

# Build all components
make build

# Development build
make dev

Start Services

# Start development stack with monitoring
make dev-up

# Check status
make dev-status

# Stop services
make dev-down

Verify Setup

# Check API health
curl -f http://localhost:8080/health

# Check monitoring services
curl -f http://localhost:3000/api/health
curl -f http://localhost:9090/api/v1/query?query=up
curl -f http://localhost:3100/ready

# Check Redis
docker exec ml-experiments-redis redis-cli ping

First Experiment

1. Setup CLI

# Build CLI
cd cli && zig build --release=fast

# Initialize CLI config
./cli/zig-out/bin/ml init

2. Queue Job

# Simple test job
echo "test experiment" | ./cli/zig-out/bin/ml queue test-job

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

3. Monitor Progress

# View in Grafana
open http://localhost:3000

# Check logs in Grafana Log Analysis dashboard
# Or view container logs
docker logs ml-experiments-api -f

Key Commands

Development Commands

make help              # Show all commands
make build             # Build all components
make dev-up            # Start dev environment
make dev-down          # Stop dev environment
make dev-status        # Check dev status
make test              # Run tests
make test-unit         # Run unit tests
make test-integration  # Run integration tests

CLI Commands

# Build CLI
cd cli && zig build --release=fast

# Common operations
./cli/zig-out/bin/ml status          # Check system status
./cli/zig-out/bin/ml queue job-name  # Queue job
./cli/zig-out/bin/ml --help         # Show help

Monitoring Commands

# Access monitoring services
open http://localhost:3000  # Grafana
open http://localhost:9090  # Prometheus
open http://localhost:3100  # Loki

# (Optional) Re-generate Grafana provisioning (datasources/providers)
python3 scripts/setup_monitoring.py

Configuration

Environment Setup

# Copy example environment
cp deployments/env.dev.example .env

# Edit as needed
vim .env

Key Variables:

• LOG_LEVEL=info
• GRAFANA_ADMIN_PASSWORD=admin123

CLI Configuration

# Setup CLI config
mkdir -p ~/.ml

# Create config file if needed
touch ~/.ml/config.toml

# Edit configuration
vim ~/.ml/config.toml

Testing

Quick Test

# 5-minute authentication test
make test-auth

# Clean up
make self-cleanup

Full Test Suite

# Run all tests
make test

# Run with coverage
make test-coverage

# Run specific test types
make test-unit
make test-integration
make test-e2e

Load Testing

# Run load tests
make load-test

# Run benchmarks
make benchmark

# Track performance
./scripts/track_performance.sh

Troubleshooting

Common Issues

Port Conflicts:

# Check port usage
lsof -i :8080
lsof -i :8443
lsof -i :3000
lsof -i :9090

# Kill conflicting processes
kill -9 <PID>

Build Issues:

# Fix Go modules
go mod tidy

# Fix Zig build
cd cli && rm -rf zig-out zig-cache && zig build --release=fast

Container Issues:

# Check container status
docker ps --filter "name=ml-"

# View logs
docker logs ml-experiments-api
docker logs ml-experiments-grafana

# Restart services
make dev-down && make dev-up

Monitoring Issues:

# Re-setup monitoring
python3 scripts/setup_monitoring.py

# Restart Grafana
docker restart ml-experiments-grafana

# Check datasources in Grafana
# Settings → Data Sources → Test connection

Debug Mode

# Enable debug logging
export LOG_LEVEL=debug
make dev-up

Next Steps

Explore Features

1. Job Management: Queue and monitor ML experiments
2. WebSocket Communication: Real-time updates
3. Multi-User Authentication: Role-based access control
4. Performance Monitoring: Grafana dashboards and metrics
5. Log Aggregation: Centralized logging with Loki

Advanced Configuration

• Production Setup: See Deployment Guide
• Performance Monitoring: See Performance Monitoring
• Testing Procedures: See Testing Guide
• CLI Reference: See CLI Reference

Production Deployment

For production deployment:

1. Review Deployment Guide
2. Set up production monitoring
3. Configure security and authentication
4. Set up backup procedures

Help and Support

Get Help

make help              # Show all available commands
./cli/zig-out/bin/ml --help  # CLI help

Documentation

• Testing Guide - Comprehensive testing procedures
• Deployment Guide - Production deployment
• Performance Monitoring - Monitoring setup
• Architecture Guide - System architecture
• Troubleshooting - Common issues

Community

• Check logs: docker logs ml-experiments-api
• Review documentation in docs/src/
• Use --debug flag with CLI commands for detailed output

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Ready in minutes!

See Also

• Architecture - System architecture overview
• Scheduler Architecture - Job scheduling and service management
• Jupyter Workflow - Jupyter notebook services
• vLLM Workflow - LLM inference services
• Configuration Reference - Configuration options
• Security Guide - Security best practices