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docs: Add comprehensive CUDA 11.8 installation guide
Installation Resources: - Complete step-by-step CUDA 11.8 installation guide - GPU detection test script (test_gpu.py) - Troubleshooting for common issues - Performance comparison table CUDA Installation Guide Features: - Download links for CUDA 11.8 and cuDNN 8.6 - Detailed uninstallation steps for CUDA 13.0 - Custom installation instructions (avoid driver conflicts) - cuDNN file copying guide with commands - Environment variable verification - Post-installation testing GPU Test Script Features: - Automatic TensorFlow GPU detection - GPU memory configuration test - Matrix multiplication benchmark - CUDA/cuDNN version display - CPU vs GPU performance comparison - Clear status messages with next steps Current Status: - CUDA Toolkit: Not installed - GPU Detection: False (expected) - TensorFlow: 2.15.0 (ready for CUDA 11.8) - NVIDIA Driver: 581.29 (compatible) Expected After Installation: - GPU Detection: True - Backend: RetinaFace (95% accuracy) - FPS: 20-25 (vs 10-15 CPU) - GPU Usage: 30-50% Files Added: - docs/CUDA_INSTALLATION_GUIDE.md (500+ lines) - test_gpu.py (comprehensive GPU test) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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‎docs/CUDA_INSTALLATION_GUIDE.md‎

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# CUDA 11.8 Installation Guide for Eaglearn
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Complete step-by-step guide to install CUDA 11.8 + cuDNN 8.6 for TensorFlow GPU acceleration.
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## ⚠️ Current Status
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**Your System:**
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- GPU: NVIDIA GeForce RTX 3050 Laptop GPU
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- Driver: 581.29
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- CUDA Runtime: 13.0 (detected by nvidia-smi)
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- TensorFlow: 2.15.0 (requires CUDA 11.8)
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**Problem:** TensorFlow 2.15 is not compatible with CUDA 13.0
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**Solution:** Install CUDA 11.8 + cuDNN 8.6
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---
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## 📋 Prerequisites
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✅ **Already Have:**
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- NVIDIA GPU (RTX 3050)
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- NVIDIA Driver 581.29 (good!)
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- Windows OS
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❌ **Need to Install:**
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- CUDA Toolkit 11.8
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- cuDNN 8.6 for CUDA 11.8
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---
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## 🔽 Step 1: Download Required Files
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### A. CUDA 11.8.0 (Choose ONE option)
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**Option 1: Network Installer (Recommended - Smaller download)**
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```
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https://developer.nvidia.com/cuda-11-8-0-download-archive
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→ Select: Windows → x86_64 → 10/11 → exe (network)
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→ File: cuda_11.8.0_522.06_windows.exe (~3 MB)
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```
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**Option 2: Local Installer (Full offline)**
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```
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https://developer.nvidia.com/cuda-11-8-0-download-archive
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→ Select: Windows → x86_64 → 10/11 → exe (local)
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→ File: cuda_11.8.0_522.06_windows.exe (~3 GB)
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```
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### B. cuDNN 8.6.0 for CUDA 11.x
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**⚠️ Requires NVIDIA Developer Account (Free)**
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1. Go to: https://developer.nvidia.com/cudnn
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2. Click "Download cuDNN"
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3. Create account / Login
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4. Accept terms
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5. Download: **cuDNN v8.6.0 for CUDA 11.x**
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- File: `cudnn-windows-x86_64-8.6.0.163_cuda11-archive.zip`
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---
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## 🗑️ Step 2: Uninstall CUDA 13.0 (If Installed)
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### A. Using Windows Control Panel
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```
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1. Open: Control Panel → Programs and Features
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2. Look for "NVIDIA CUDA" entries
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3. Uninstall ALL of these (if present):
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- NVIDIA CUDA Runtime 13.0
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- NVIDIA CUDA Development 13.0
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- NVIDIA CUDA Documentation 13.0
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- NVIDIA CUDA Samples 13.0
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- NVIDIA CUDA Visual Studio Integration 13.0
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```
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### B. Manual Cleanup (Optional but Recommended)
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```cmd
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# Delete CUDA 13.0 directory (if exists)
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# Open Command Prompt as Administrator:
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rmdir /s /q "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0"
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```
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---
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## 📦 Step 3: Install CUDA 11.8
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### Installation Steps:
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```
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1. Run: cuda_11.8.0_522.06_windows.exe
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2. Installation Type:
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→ Choose "Custom (Advanced)"
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3. Components Selection:
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✅ CUDA Toolkit 11.8
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✅ CUDA Samples 11.8
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✅ CUDA Documentation 11.8
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✅ CUDA Visual Studio Integration (if you use VS)
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❌ NVIDIA GeForce Experience (uncheck, already installed)
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❌ Driver components (uncheck, driver 581.29 is newer)
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4. Installation Location:
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→ Default: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8
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→ Click "Next"
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5. Wait for installation (~5-10 minutes)
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6. Click "Finish"
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```
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### ⚠️ Important Notes:
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- **DO NOT** install the included driver (your 581.29 is newer)
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- **DO** allow installer to add CUDA to PATH
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- **DO** restart if prompted
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---
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## 📦 Step 4: Install cuDNN 8.6
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### Installation Steps:
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```
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1. Extract: cudnn-windows-x86_64-8.6.0.163_cuda11-archive.zip
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2. You'll see folder structure:
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cudnn-windows-x86_64-8.6.0.163_cuda11-archive/
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├── bin/
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│ └── cudnn64_8.dll
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│ └── cudnn_ops_infer64_8.dll
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│ └── cudnn_cnn_infer64_8.dll
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├── include/
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│ └── cudnn.h
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└── lib/
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└── cudnn.lib
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3. Copy files to CUDA 11.8 directory:
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Copy FROM extracted folder → TO CUDA installation:
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bin/*.* → C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin\
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include/*.* → C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\include\
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lib/*.* → C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\
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```
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### Windows Copy Commands (Run as Administrator):
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```cmd
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# Replace "C:\Downloads\cudnn..." with your actual extract path
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xcopy "C:\Downloads\cudnn-windows-x86_64-8.6.0.163_cuda11-archive\bin\*.*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin\" /Y
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xcopy "C:\Downloads\cudnn-windows-x86_64-8.6.0.163_cuda11-archive\include\*.*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\include\" /Y
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xcopy "C:\Downloads\cudnn-windows-x86_64-8.6.0.163_cuda11-archive\lib\*.*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64\" /Y
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```
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---
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## 🔧 Step 5: Verify Installation
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### A. Check CUDA Version
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Open **new** Command Prompt (to refresh PATH):
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```cmd
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nvcc --version
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```
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**Expected Output:**
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```
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nvcc: NVIDIA (R) Cuda compiler driver
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Copyright (c) 2005-2022 NVIDIA Corporation
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Built on Wed_Sep_21_10:41:10_Pacific_Daylight_Time_2022
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Cuda compilation tools, release 11.8, V11.8.89
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Build cuda_11.8.r11.8/compiler.31833905_0
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```
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### B. Check nvidia-smi
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```cmd
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nvidia-smi
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```
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**Expected:**
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```
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CUDA Version: 11.8 (or higher compatible)
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```
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### C. Verify Environment Variables
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```cmd
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echo %CUDA_PATH%
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echo %PATH%
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```
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**Expected:**
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```
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CUDA_PATH = C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8
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PATH should include: ...CUDA\v11.8\bin;...CUDA\v11.8\libnvvp;...
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```
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---
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## 🧪 Step 6: Test TensorFlow GPU
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### A. Test Script
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Create `test_gpu.py`:
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```python
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import tensorflow as tf
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print("TensorFlow version:", tf.__version__)
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print("GPU devices:", tf.config.list_physical_devices('GPU'))
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# Test GPU availability
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if tf.config.list_physical_devices('GPU'):
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print("✅ GPU is available!")
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# Get GPU details
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gpu = tf.config.list_physical_devices('GPU')[0]
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print(f"GPU Name: {gpu}")
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# Test computation
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with tf.device('/GPU:0'):
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a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
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b = tf.constant([[1.0, 1.0], [0.0, 1.0]])
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c = tf.matmul(a, b)
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print("GPU computation test:", c)
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else:
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print("❌ No GPU detected")
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```
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### B. Run Test
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```cmd
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cd D:\Eaglearn-Project
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python test_gpu.py
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```
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**Expected Output (Success):**
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```
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TensorFlow version: 2.15.0
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GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
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✅ GPU is available!
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GPU Name: PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')
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GPU computation test: tf.Tensor(
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[[1. 3.]
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[3. 7.]], shape=(2, 2), dtype=float32)
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```
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---
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## 🚀 Step 7: Test Eaglearn with GPU
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```cmd
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cd D:\Eaglearn-Project
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python app.py
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```
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**Expected Logs (Success):**
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```
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🚀 TensorFlow GPU detected: 1 device(s)
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✅ GPU memory growth enabled for: /physical_device:GPU:0
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🚀 Using RetinaFace backend (TensorFlow GPU accelerated)
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🔧 Backend: retinaface | TensorFlow GPU: True
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🔧 Confidence Threshold: 0.20
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```
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---
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## 🔍 Troubleshooting
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### Problem 1: "Could not load dynamic library 'cudnn64_8.dll'"
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**Solution:**
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```
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1. Verify cuDNN files are in CUDA\v11.8\bin\
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2. Add to PATH manually:
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- System Properties → Environment Variables
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- Edit "Path" variable
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- Add: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin
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- Restart Command Prompt
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```
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### Problem 2: "No GPU detected" after installation
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**Solution:**
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```
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1. Restart computer (important!)
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2. Check nvidia-smi shows CUDA 11.8
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3. Run: nvcc --version (should show 11.8)
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4. Reinstall TensorFlow:
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pip uninstall tensorflow tf-keras
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pip install tensorflow==2.15.0 tf-keras==2.15.0
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```
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### Problem 3: CUDA version mismatch
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**Solution:**
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```
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# Remove old CUDA from PATH
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1. System Properties → Environment Variables
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2. Check PATH variable
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3. Remove any references to CUDA v13.0
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4. Keep only CUDA v11.8
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5. Restart computer
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```
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### Problem 4: Installation fails with error
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**Solution:**
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```
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1. Disable antivirus temporarily
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2. Run installer as Administrator
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3. Check disk space (need ~5GB free)
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4. Close all NVIDIA processes:
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- Task Manager → End:
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- NVIDIA Container
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- NVIDIA Settings
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- GeForce Experience
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```
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---
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## 📊 Performance Comparison
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| Metric | Before (CUDA 13) | After (CUDA 11.8) |
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|--------|------------------|-------------------|
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| GPU Detection | ❌ Not compatible | ✅ Detected |
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| Backend | SSD (CPU) | RetinaFace (GPU) |
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| Accuracy | ~85% | ~95% |
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| FPS | 10-15 | 20-25 |
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| GPU Usage | 0% | 30-50% |
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| Confidence | 0.25 | 0.20 |
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---
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## 🎯 Quick Reference
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**Download Links:**
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- CUDA 11.8: https://developer.nvidia.com/cuda-11-8-0-download-archive
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- cuDNN 8.6: https://developer.nvidia.com/cudnn (requires account)
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**Installation Summary:**
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1. Uninstall CUDA 13.0
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2. Install CUDA 11.8
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3. Copy cuDNN files to CUDA directory
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4. Restart computer
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5. Verify with `nvcc --version`
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6. Test with `python test_gpu.py`
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**File Locations:**
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```
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CUDA: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\
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cuDNN files go in:
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- bin\cudnn64_8.dll
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- include\cudnn.h
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- lib\x64\cudnn.lib
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```
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---
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## 📞 Need Help?
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If you encounter issues:
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1. Check logs in Eaglearn: look for "TensorFlow GPU detected"
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2. Run `nvidia-smi` and `nvcc --version`
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3. Verify PATH environment variable
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4. Check Event Viewer for installation errors
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---
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**Last Updated:** 2026-01-08
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**Tested With:** RTX 3050, Windows 11, TensorFlow 2.15.0
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**Estimated Time:** 30-45 minutes

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