ComputeEngineGPU-Terraform

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ComputeEngineGPU-Terraform

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Create Compute Engine with GPU and Terraform in Google cloud platform Example

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# 🖥️ ComputeEngineGPU-Terraform
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

Create google cloud GPU server with Terraform

## 🏗️ Architecture
![Group 71](https://github.com/user-attachments/assets/83cd3ae4-7a9a-4ce5-933d-164cc4a45118)

## 📝 Overview
If you are running this script, you will get this cloud resource just after 10 minutes!
* AWS S3
* AWS DynamoDB
* GCP Virtual Private Cloud
* GCP Compute Engine with n1-standard-8 and nvidia-tesla-T4 GPU (this could be changed)
* GCP Cloud Storage
### 🖥️ Available Machine Types and GPU Combinations in GCP

#### 🚀 N1 Series Machines (Previous Generation)
| Machine Type | vCPUs | Memory (GB) | Compatible GPUs | Max GPUs |
|-------------|--------|-------------|-----------------|----------|
| n1-standard-2 | 2 | 7.5 | nvidia-tesla-t4 | 1 |
| n1-standard-4 | 4 | 15 | nvidia-tesla-t4, nvidia-tesla-p4 | 1 |
| n1-standard-8 | 8 | 30 | nvidia-tesla-t4, nvidia-tesla-p4, nvidia-tesla-v100 | 1 |
| n1-standard-16 | 16 | 60 | nvidia-tesla-t4, nvidia-tesla-p4, nvidia-tesla-v100 | 2 |
| n1-standard-32 | 32 | 120 | nvidia-tesla-t4, nvidia-tesla-p4, nvidia-tesla-v100 | 4 |

#### 🎯 G2 Series Machines (Latest Generation)
| Machine Type | vCPUs | Memory (GB) | Compatible GPUs | Max GPUs |
|-------------|--------|-------------|-----------------|----------|
| g2-standard-4 | 4 | 16 | nvidia-l4 | 1 |
| g2-standard-8 | 8 | 32 | nvidia-l4 | 1 |
| g2-standard-12 | 12 | 48 | nvidia-l4 | 2 |
| g2-standard-16 | 16 | 64 | nvidia-l4 | 2 |
| g2-standard-24 | 24 | 96 | nvidia-l4 | 4 |
| g2-standard-32 | 32 | 128 | nvidia-l4 | 4 |
| g2-standard-48 | 48 | 192 | nvidia-l4 | 6 |
| g2-standard-96 | 96 | 384 | nvidia-l4 | 8 |

### 🎮 GPU Specifications
| GPU Type | Memory | Best For | Relative Cost |
|----------|---------|----------|---------------|
| nvidia-tesla-t4 | 16 GB | ML inference, small-scale training | $ |
| nvidia-tesla-p4 | 8 GB | ML inference | $ |
| nvidia-tesla-v100 | 32 GB | Large-scale ML training | $$$ |
| nvidia-l4 | 24 GB | Latest gen for ML/AI workloads | $$ |

#### ⚠️ Note: 
- GPU availability varies by region and zone
- G2 machines are optimized for the latest NVIDIA L4 GPUs
- N1 machines are more flexible with GPU options but are previous generation
- Pricing varies significantly based on configuration and region
- More information -> [here](https://cloud.google.com/compute/docs/gpus?hl=en)

## ⚙️ Prerequisites
```
- Terraform >= 1.10
- AWS CLI configured
- GCP CLI configured
- Github SSH key
- Dockerhub configured
- Bash >= 4.2
```

## 📂 Repository Structure
```
.
├── create_server_with_dynamic_zones.sh
├── credentials.json
├── LICENSE
├── Makefile
├── README.md
├── terraform.prod.tfvars
├── .env
├── .ssh
|   ├── id_ed25519
|   └── id_ed25519.pub
└── src
    ├── main.tf
    ├── provider.tf
    ├── storage.tf
    ├── output.tf
    ├── modules
    │   ├── vpc
    │   │   ├── main.tf
    │   │   └── variables.tf
    │   └── worker
    │       ├── main.tf
    │       └── variables.tf
    ├── s3_init
    │   ├── main.tf
    │   ├── provider.tf
    │   ├── terraform.tfstate
    │   └── terraform.tfstate.backup
    ├── terraform.tfstate
    ├── terraform.tfstate.backup
    └── variables.tf
```

## 📦 Module Documentation

### 🌐 Networking Module

This module establishes the core VPC infrastructure in Google Cloud Platform (GCP).

#### Resources Created:

**VPC Network (`google_compute_network`):**
- Network name: `rl-vpc-network`
- Configuration:
  - Custom subnet mode enabled (auto-create subnetworks disabled)
  - MTU set to 1460 bytes (GCP standard)

**Subnet (`google_compute_subnetwork`):**
- Subnet name: `my-custom-subnet`
- Configuration:
  - CIDR range: `10.0.1.0/24`
  - Region: Dynamically set via variable
  - Associated with `rl-vpc-network`

**Firewall Rule (`google_compute_firewall`):**
- Rule name: `allow-ingress-from-iap`
- Purpose: Enables SSH access to instances
- Configuration:
  - Protocol: TCP
  - Port: 22 (SSH)
  - Source range: `0.0.0.0/0` (allow from any IP)
  - Direction: INGRESS

#### Usage

To use this module in your Terraform configuration:

```hcl
module "vpc" {
  source = "./modules/vpc"
  
  region = "your-desired-region"
}
```

#### Security Considerations
- The current firewall rule allows SSH access from any IP (`0.0.0.0/0`). Consider restricting this to specific IP ranges for production environments
- Consider implementing additional security measures such as:
  - Cloud NAT for outbound internet access
  - Additional firewall rules for specific services
  - VPC Service Controls

> [!NOTE]
> - The subnet CIDR (`10.0.1.0/24`) provides 254 usable IP addresses
> - Can be extended with additional subnets as needed
> - MTU 1460 is optimized for GCP's network virtualization

### 🖥️ Compute Module

This module provisions a GPU-enabled compute instance in Google Cloud Platform (GCP) configured for deep learning workloads.

#### Instance Configuration (`google_compute_instance`)
- Name: `training-worker-gpu-instance`
- Hardware:
  - Machine type: Configurable via variable
  - GPU: 1x NVIDIA T4 (configurable via `gpu_type` variable)
  - Boot disk: 150GB SSD
  - [Image](https://cloud.google.com/deep-learning-vm/docs/images?hl=en): PyTorch with CUDA 12.4 support (`deeplearning-platform-release/pytorch-latest-cu124`)

#### Network Configuration
- Connected to custom VPC: `rl-vpc-network`
- Subnet: `my-custom-subnet`
- Ephemeral public IP enabled
- Tagged with `ssh-enabled` for firewall rules

#### Security & Access
- SSH access configured via provided SSH keys
- Service account with Google Cloud Storage read/write permissions
- GitHub SSH key deployment for repository access
- Docker Hub authentication configured

#### Instance Scheduling
- Automatic restart disabled
- Non-preemptible instance (for training stability)
- Terminates on host maintenance

#### Provisioning Steps
1. System Updates & Dependencies
   - Updates system packages
   - Installs Git
   - Configures GPU drivers

2. Storage Setup
   - Creates and mounts GCS bucket at `/home/${var.username}/gcs-bucket`
   - Configures appropriate permissions

3. Docker Environment
   - Installs Docker CE and required dependencies
   - Configures Docker service
   - Pulls specified training image (`falconlee236/rl-image:parco-cuda123`)

4. Code Deployment
   - Clones specified Git repository
   - Sets up SSH configurations for GitHub access
   - Copies environment configuration file

#### Usage

```hcl
module "worker" {
  source = "./modules/worker"
  
  machine_type       = "n1-standard-4"
  zone              = "us-central1-a"
  gpu_type          = "nvidia-tesla-t4"
  gpu_count         = 1
  username          = "your-username"
  ssh_file          = "path/to/ssh/public/key"
  ssh_file_private  = "path/to/ssh/private/key"
  env_file          = "path/to/.env"
  git_ssh_url       = "your-repo-ssh-url"
  dockerhub_id      = "your-dockerhub-id"
  dockerhub_pwd     = "your-dockerhub-password"
}
```

> [!NOTE]
> - Instance is optimized for deep learning workloads with CUDA 12.4 support
> - Automatic backups not configured - consider implementing if needed
> - Consider implementing monitoring and logging solutions
> - Review security configurations before deploying to production

## 🚀 How to Use?
For detailed usage instructions, please refer to the [USAGE.md](docs/USAGE.md) file.

## 🔒 State Management

This module sets up the backend infrastructure required for Terraform state management using AWS S3 and DynamoDB.

#### Resources Created:

**S3 Bucket (`aws_s3_bucket`):**
- Bucket name: `sangylee-s3-bucket-tfstate`
- Purpose: Stores Terraform state files
- Configuration:
  - Force destroy enabled for easier cleanup
  - Versioning enabled to maintain state history

**DynamoDB Table (`aws_dynamodb_table`):**
- Table name: `terraform-tfstate-lock`
- Purpose: Provides state locking mechanism to prevent concurrent modifications
- Configuration:
  - Partition key: `LockID` (String)
  - Read capacity: 2 units
  - Write capacity: 2 units

#### Usage

To use this state backend in other Terraform configurations, add the following backend configuration:

```hcl
terraform {
  backend "s3" {
    bucket         = "sangylee-s3-bucket-tfstate"
    key            = "terraform.tfstate"
    region         = "your-region"
    dynamodb_table = "terraform-tfstate-lock"
    encrypt        = true
  }
}
```

> [!NOTE]
> - Ensure proper IAM permissions are configured for access to both S3 and DynamoDB
> - The DynamoDB table uses provisioned capacity mode with minimal read/write units
> - S3 versioning helps maintain state file history and enables recovery if needed

## 🤝 Support
Contact information for infrastructure team or maintainers (@falconlee236)
Or Feel Free to send email to me (`falconlee236@gmail.com`)

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