🚀 Automating AI Model Training with CI/CD, Monitoring & Profiling – AI TrainOps

GitHub Repo Link

🚀 Automating AI Model Training with CI/CD, Monitoring & Profiling – AI TrainOps

A Complete AI Training Workflow with Docker, GitHub Actions, Prometheus, and Profiling Tools

📌 Introduction

Training AI models efficiently is a challenge. Managing the workflow, monitoring resource usage, and profiling performance bottlenecks can be time-consuming.

That’s why I built AI-TrainOps – an automated AI training and optimization pipeline using: ✅ GitHub Actions for CI/CD
✅ Dockerized AI training workflow
✅ Prometheus & Grafana for real-time monitoring
✅ Performance profiling tools (perf, Valgrind, NVIDIA Nsight)

This project simplifies AI model training by automating the entire pipeline, improving efficiency, and enabling seamless deployment.

🚀 What I Built

1️⃣ AI Model Training Pipeline

I implemented an end-to-end AI model training pipeline using PyTorch to train a ResNet model on the CIFAR-10 dataset.

🔹 Tech Stack:

• PyTorch for deep learning
• Docker for containerized training
• GitHub Actions for CI/CD automation

📌 How to Run Locally

git clone https://github.com/Jarvis-Zongao-Bian/AI-TrainOps.git
cd AI-TrainOps
pip install -r requirements.txt
python src/train.py

2️⃣ Automating Training with Docker

To ensure consistency across environments, I containerized the training workflow using Docker.

📌 Build and Run Training in Docker

docker build -t ai-trainops .
docker run --rm -v $(pwd)/logs:/app/logs ai-trainops

✅ Why Docker?

• Portable and runs consistently across machines
• No dependency conflicts
• Easy deployment and scaling

3️⃣ CI/CD with GitHub Actions

Every time I push new code, GitHub Actions automatically: ✅ Builds the Docker image
✅ Runs training & testing
✅ Uploads training logs as artifacts

📌 GitHub Actions Workflow

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

jobs:
  train-test:
    runs-on: ubuntu-latest
    steps:
      - name: 🚀 Checkout Repository
        uses: actions/checkout@v4

      - name: 🛠️ Build Docker Image
        run: docker build -t ai-trainops .

      - name: 🏋️ Run Training
        run: docker run --rm -v $(pwd)/logs:/app/logs ai-trainops

      - name: 📂 Upload Logs
        uses: actions/upload-artifact@v4
        with:
          name: training-logs
          path: logs/*.log

📌 Trigger the CI/CD Pipeline

git add .
git commit -m "Trigger CI/CD pipeline"
git push origin main

👉 View the full GitHub Actions setup

4️⃣ Real-time Monitoring with Prometheus & Grafana

To monitor CPU usage, memory, and training loss, I integrated Prometheus & Grafana.

📌 Start Monitoring Services

docker-compose up -d

✅ Prometheus: http://localhost:9090
✅ Grafana: http://localhost:4000 (Login: admin / admin)

📌 Query Training Metrics in Prometheus

training_loss
cpu_usage_percent
memory_usage_mb

👉 Learn how to set up Grafana Dashboards

5️⃣ AI Training Performance Profiling

To optimize model training, I added profiling tools: ✅ perf (CPU performance monitoring)
✅ Valgrind (Memory leak detection)
✅ NVIDIA Nsight (GPU performance analysis)

📌 Profile CPU Performance

docker run --rm --privileged ai-trainops perf record -F 99 -g -- python3 src/train.py
docker run --rm --privileged ai-trainops perf report > logs/perf_report.txt

📌 Detect Memory Leaks

docker run --rm ai-trainops valgrind --tool=memcheck --leak-check=full python3 src/train.py > logs/valgrind_report.txt

👉 Learn more about NVIDIA Nsight profiling

🎯 What’s Next?

Now that AI-TrainOps is fully automated, my next steps are: 📊 Enhance Grafana dashboards for deeper AI monitoring
📈 Optimize model training with PyTorch Lightning
☁️ Deploy as a cloud-based AI service

🚀 Want to contribute? Fork the repo and submit a pull request!
👉 Check out AI-TrainOps on GitHub

📌 Final Thoughts

Building AI-TrainOps helped me automate, optimize, and monitor AI training workflows.
If you’re working on AI projects, I highly recommend integrating CI/CD, monitoring, and profiling into your pipeline.

💡 Try It Yourself!

1️⃣ Clone the repo:

git clone https://github.com/Jarvis-Zongao-Bian/AI-TrainOps.git
cd AI-TrainOps

2️⃣ Run training:

docker-compose up -d && docker run --rm ai-trainops

3️⃣ Check monitoring at http://localhost:4000

Let me know if you have any questions! 🚀✨

📌 Share Your Thoughts!

🤖 What’s your experience with AI model training automation?
📊 How do you monitor your AI models in production?
Drop a comment below or reach out on GitHub! 🚀