TorchForge - Windows Installation & Usage Guide
Complete guide for setting up and running TorchForge on Windows Dell Laptop.
Prerequisites
System Requirements
- Windows 10/11 (64-bit)
- Python 3.8 or higher
- 8GB RAM minimum (16GB recommended)
- 10GB free disk space
- Git for Windows
Optional for GPU Support
- NVIDIA GPU with CUDA 11.8 or higher
- NVIDIA CUDA Toolkit
- cuDNN library
Installation Steps
1. Install Python
Download and install Python from python.org
# Verify installation
python --version
pip --version
2. Install Git
Download and install Git from git-scm.com
# Verify installation
git --version
3. Clone TorchForge Repository
# Open PowerShell or Command Prompt
cd C:\Users\YourUsername\Projects
# Clone repository
git clone https://github.com/anilprasad/torchforge.git
cd torchforge
4. Create Virtual Environment
# Create virtual environment
python -m venv venv
# Activate virtual environment
.\venv\Scripts\activate
# You should see (venv) in your prompt
5. Install TorchForge
# Install in development mode
pip install -e .
# Or install specific extras
pip install -e ".[all]"
# Verify installation
python -c "import torchforge; print(torchforge.__version__)"
Running Examples
Basic Example
# Navigate to examples directory
cd examples
# Run comprehensive examples
python comprehensive_examples.py
Expected output: ```
TorchForge - Comprehensive Examples Author: Anil Prasad
Example 1: Basic Classification ... ✓ Example 1 completed successfully!
### Custom Model Example
Create a file `my_model.py`:
```python
import torch
import torch.nn as nn
from torchforge import ForgeModel, ForgeConfig
# Define your PyTorch model
class MyModel(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(10, 64)
self.fc2 = nn.Linear(64, 2)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
return self.fc2(x)
# Create TorchForge configuration
config = ForgeConfig(
model_name="my_custom_model",
version="1.0.0",
enable_monitoring=True,
enable_governance=True
)
# Wrap with TorchForge
model = ForgeModel(MyModel(), config=config)
# Use the model
x = torch.randn(32, 10)
output = model(x)
print(f"Output shape: {output.shape}")
# Get metrics
metrics = model.get_metrics_summary()
print(f"Metrics: {metrics}")
Run it:
python my_model.py
Running Tests
# Install test dependencies
pip install pytest pytest-cov
# Run all tests
pytest tests/ -v
# Run with coverage
pytest tests/ --cov=torchforge --cov-report=html
# View coverage report
start htmlcov\index.html
Docker Deployment on Windows
1. Install Docker Desktop
Download from docker.com
2. Build Docker Image
# Build image
docker build -t torchforge:1.0.0 .
# Verify image
docker images | findstr torchforge
3. Run Container
# Run container
docker run -p 8000:8000 torchforge:1.0.0
# Run with volume mounts
docker run -p 8000:8000 `
-v ${PWD}\models:/app/models `
-v ${PWD}\logs:/app/logs `
torchforge:1.0.0
4. Run with Docker Compose
# Start services
docker-compose up -d
# Check status
docker-compose ps
# View logs
docker-compose logs -f
# Stop services
docker-compose down
Cloud Deployment
AWS Deployment
from torchforge import ForgeModel, ForgeConfig
from torchforge.cloud import AWSDeployer
# Create model
config = ForgeConfig(model_name="my_model", version="1.0.0")
model = ForgeModel(MyModel(), config=config)
# Deploy to AWS SageMaker
deployer = AWSDeployer(model)
endpoint = deployer.deploy_sagemaker(
instance_type="ml.m5.large",
endpoint_name="torchforge-prod"
)
print(f"Model deployed: {endpoint.url}")
Azure Deployment
from torchforge.cloud import AzureDeployer
deployer = AzureDeployer(model)
service = deployer.deploy_aks(
cluster_name="ml-cluster",
cpu_cores=4,
memory_gb=16
)
GCP Deployment
from torchforge.cloud import GCPDeployer
deployer = GCPDeployer(model)
endpoint = deployer.deploy_vertex(
machine_type="n1-standard-4",
accelerator_type="NVIDIA_TESLA_T4"
)
Common Issues & Solutions
Issue: ModuleNotFoundError
Solution:
# Ensure virtual environment is activated
.\venv\Scripts\activate
# Reinstall TorchForge
pip install -e .
Issue: CUDA Not Available
Solution:
# Install PyTorch with CUDA support
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
Issue: Permission Denied
Solution:
# Run PowerShell as Administrator
# Or add current user to docker-users group
net localgroup docker-users "%USERDOMAIN%\%USERNAME%" /ADD
Issue: Port Already in Use
Solution:
# Find process using port 8000
netstat -ano | findstr :8000
# Kill process (replace PID)
taskkill /PID <PID> /F
Performance Optimization
Enable GPU Support
import torch
# Check CUDA availability
if torch.cuda.is_available():
device = torch.device("cuda")
model = model.to(device)
print(f"Using GPU: {torch.cuda.get_device_name(0)}")
else:
print("CUDA not available, using CPU")
Memory Optimization
# Enable memory optimization
config.optimization.memory_optimization = True
# Enable quantization
config.optimization.quantization = "int8"
Development Workflow
1. Setup Development Environment
# Install dev dependencies
pip install -e ".[dev]"
# Install pre-commit hooks
pre-commit install
2. Run Code Formatters
# Format code with black
black torchforge/
# Sort imports
isort torchforge/
# Check style
flake8 torchforge/
3. Type Checking
# Run mypy
mypy torchforge/
Monitoring in Production
View Metrics
# Get metrics summary
metrics = model.get_metrics_summary()
print(f"Total Inferences: {metrics['inference_count']}")
print(f"Mean Latency: {metrics['latency_mean_ms']:.2f}ms")
print(f"P95 Latency: {metrics['latency_p95_ms']:.2f}ms")
Export Compliance Report
from torchforge.governance import ComplianceChecker
checker = ComplianceChecker()
report = checker.assess_model(model)
# Export reports
report.export_json("compliance_report.json")
report.export_pdf("compliance_report.pdf")
Support & Resources
- GitHub Issues: https://github.com/anilprasad/torchforge/issues
- Documentation: https://torchforge.readthedocs.io
- LinkedIn: Anil Prasad
- Email: anilprasad@example.com
Next Steps
- Try the comprehensive examples
- Build your own model with TorchForge
- Deploy to production
- Check compliance and governance
- Monitor in real-time
- Contribute to the project!
Built with ❤️ by Anil Prasad