283 lines
6.6 KiB
Markdown
283 lines
6.6 KiB
Markdown
# Cross-Platform GPU Support
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## Overview
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**The SAME codebase works with NVIDIA (CUDA) and AMD (ROCm) GPUs!**
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PyTorch abstracts the hardware differences - your trading code doesn't need to change. Just install the right PyTorch build for your hardware.
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## How It Works
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### Same API, Different Backend
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```python
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# This code works on BOTH NVIDIA and AMD GPUs!
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import torch
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model = model.to(device)
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data = data.to(device)
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```
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**Why it works:**
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- PyTorch uses `torch.cuda` API for both NVIDIA (CUDA) and AMD (ROCm)
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- ROCm implements CUDA compatibility layer (HIP)
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- Your code calls `torch.cuda.*` regardless of hardware
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- PyTorch routes to CUDA or ROCm backend automatically
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## Setup for Different Hardware
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### Automatic Setup (Recommended) ⭐
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```bash
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cd /mnt/shared/DEV/repos/d-popov.com/gogo2
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python -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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# Auto-detects hardware and installs correct PyTorch
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./scripts/setup-pytorch.sh
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```
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The script detects:
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- ✅ NVIDIA GPUs → Installs CUDA PyTorch
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- ✅ AMD GPUs → Installs ROCm PyTorch
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- ✅ No GPU → Installs CPU PyTorch
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### Manual Setup
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**NVIDIA GPU (CUDA 12.1):**
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```bash
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pip install torch --index-url https://download.pytorch.org/whl/cu121
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```
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**AMD GPU (ROCm 6.2):**
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```bash
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pip install torch --index-url https://download.pytorch.org/whl/rocm6.2
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```
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**CPU Only:**
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```bash
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pip install torch --index-url https://download.pytorch.org/whl/cpu
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```
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## Verified Hardware
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### ✅ AMD
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- **AMD Strix Halo** (Radeon 8050S/8060S, RDNA 3.5) - gfx1151
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- **AMD RDNA 3** (RX 7900 XTX, 7800 XT, etc.)
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- **AMD RDNA 2** (RX 6900 XT, 6800 XT, etc.)
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### ✅ NVIDIA
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- **RTX 40 Series** (4090, 4080, 4070, etc.) - CUDA 12.x
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- **RTX 30 Series** (3090, 3080, 3070, etc.) - CUDA 11.x/12.x
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- **RTX 20 Series** (2080 Ti, 2070, etc.) - CUDA 11.x
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### ✅ CPU
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- Any x86_64 CPU (Intel/AMD)
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## Code Compatibility
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### What Works Automatically
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```python
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# ✅ Device management
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device = torch.device('cuda') # Works with both CUDA and ROCm
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tensor.to('cuda') # Works with both
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torch.cuda.is_available() # Returns True on both
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# ✅ Memory management
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torch.cuda.empty_cache() # Works with both
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torch.cuda.synchronize() # Works with both
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torch.cuda.get_device_properties(0) # Works with both
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# ✅ Training operations
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model.cuda() # Works with both
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optimizer.step() # Works with both
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loss.backward() # Works with both
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```
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### No Code Changes Needed
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**All training code works identically:**
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```python
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# ANNOTATE/core/real_training_adapter.py
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# This works on NVIDIA AND AMD without modification!
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.model.to(self.device)
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batch = {k: v.to(self.device) for k, v in batch.items()}
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outputs = self.model(**batch)
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loss.backward()
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```
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## Performance Comparison
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### Training Speed (relative to CPU baseline)
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| Hardware | Speed | Notes |
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|----------|-------|-------|
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| **NVIDIA RTX 4090** | 10-15x | Best performance |
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| **NVIDIA RTX 3090** | 8-12x | Excellent |
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| **AMD RX 7900 XTX** | 6-10x | Very good |
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| **AMD Strix Halo (iGPU)** | 2-3x | Good for laptop |
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| **CPU (12+ cores)** | 1.0x | Baseline |
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### Inference Speed (relative to CPU baseline)
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| Hardware | Speed | Notes |
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|----------|-------|-------|
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| **NVIDIA RTX 4090** | 20-30x | Real-time capable |
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| **NVIDIA RTX 3090** | 15-25x | Real-time capable |
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| **AMD RX 7900 XTX** | 12-20x | Real-time capable |
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| **AMD Strix Halo (iGPU)** | 5-10x | Real-time capable |
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| **CPU (12+ cores)** | 1.0x | May lag |
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## Verification
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### Check Your Setup
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```bash
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python -c "
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import torch
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print(f'PyTorch: {torch.__version__}')
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print(f'GPU available: {torch.cuda.is_available()}')
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if torch.cuda.is_available():
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print(f'Device: {torch.cuda.get_device_name(0)}')
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print(f'Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB')
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"
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```
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**Expected output (AMD Strix Halo):**
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```
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PyTorch: 2.5.1+rocm6.2
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GPU available: True
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Device: AMD Radeon Graphics
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Memory: 47.0 GB
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```
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**Expected output (NVIDIA RTX 4090):**
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```
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PyTorch: 2.5.1+cu121
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GPU available: True
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Device: NVIDIA GeForce RTX 4090
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Memory: 24.0 GB
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```
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## Development Workflow
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### Single Dev Machine (Your Current Setup)
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```bash
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# One-time setup
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./scripts/setup-pytorch.sh
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# Daily use
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source venv/bin/activate
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python ANNOTATE/web/app.py
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```
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### Multiple Dev Machines (Team)
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Each developer runs setup once:
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```bash
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# Developer 1 (AMD GPU)
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./scripts/setup-pytorch.sh
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# → Installs ROCm PyTorch
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# Developer 2 (NVIDIA GPU)
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./scripts/setup-pytorch.sh
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# → Installs CUDA PyTorch
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# Developer 3 (No GPU)
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./scripts/setup-pytorch.sh
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# → Installs CPU PyTorch
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```
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**Result:** Same code, different PyTorch builds, everything works!
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### CI/CD Pipeline
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```yaml
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# .github/workflows/test.yml
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- name: Setup PyTorch
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run: |
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pip install -r requirements.txt
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pip install torch --index-url https://download.pytorch.org/whl/cpu
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```
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Use CPU build for CI (fastest for testing, no GPU needed).
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## Troubleshooting
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### GPU Not Detected
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**Check drivers:**
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```bash
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# NVIDIA
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nvidia-smi
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# AMD
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rocm-smi
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```
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**Reinstall PyTorch:**
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```bash
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pip uninstall torch
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./scripts/setup-pytorch.sh
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```
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### Wrong PyTorch Build
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**Symptom:** `torch.cuda.is_available()` returns `False` despite having GPU
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**Solution:**
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```bash
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# Check current build
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python -c "import torch; print(torch.__version__)"
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# If it shows +cpu but you have GPU, reinstall:
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./scripts/setup-pytorch.sh
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```
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### Mixed Builds
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**Symptom:** Team members have different results
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**Solution:** Ensure everyone runs `./scripts/setup-pytorch.sh` - it detects their specific hardware and installs correctly.
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## Best Practices
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### ✅ DO
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- Use `torch.device('cuda')` (works with both CUDA and ROCm)
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- Check `torch.cuda.is_available()` before using GPU
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- Use automatic setup script for new machines
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- Let PyTorch handle device-specific optimizations
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### ❌ DON'T
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- Hardcode CUDA-specific code
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- Assume specific GPU memory sizes
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- Pin PyTorch version in requirements.txt
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- Install torchvision/torchaudio (not needed for trading)
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## Summary
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✅ **Same codebase works everywhere**
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✅ **Auto-setup script handles hardware detection**
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✅ **No code changes needed for different GPUs**
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✅ **PyTorch abstracts CUDA vs ROCm differences**
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✅ **Verified on AMD and NVIDIA hardware**
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---
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**Key Insight:** PyTorch's CUDA API is hardware-agnostic. Whether you have NVIDIA or AMD GPU, the same `torch.cuda.*` calls work. Just install the right PyTorch build for your hardware!
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**Last Updated:** 2025-11-12
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**Tested:** AMD Strix Halo (ROCm 6.2), NVIDIA GPUs (CUDA 12.1)
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