React and python fine tuning

react.js ve python kullanarak fine tuning uygulaması geliştirebilir misin. hem huggingfaceden model indirip eğitebileceğim hem de kendi modelimi sıfırdan geliştirebileceğim bir sistem iistiyorum. geliştirdiğimiz uygulama bilgisayarımdaki pytorch ve cudayı ilk başlarken tanıyıp yüklesin. bir fine tuning uygulamASINda olması gereken her şey olsun

366d ago

AI Model Fine-Tuning Application

I'll create a full-stack application that allows you to fine-tune existing Hugging Face models and develop custom models from scratch. The application will automatically detect and utilize PyTorch and CUDA on your system.

Architecture Overview

Backend Python Implementation

Let's implement the Python backend that will handle the PyTorch and CUDA detection, model training, and fine-tuning:

Fine Tuning backend

detect_system.py

Now let's implement a model fine-tuning script that can be used with Hugging Face models:

Fine Tuning backend

fine_tune.py

Now let's implement a custom model builder that can create models from scratch:

Fine Tuning backend

custom_model.py

Finally, let's create a FastAPI server to tie everything together:

Fine Tuning backend

server.py

366d ago

SyntaxError: incomplete input (577622238.py, line 251)

366d ago

I apologize for the incomplete code. Let me provide the complete version of the server.py file:

Fine Tuning backend

server.py

Now let's create a requirements.txt file for the Python backend:

Fine Tuning backend

requirements.txt

And finally, let's create a simple startup script that will check for PyTorch and CUDA availability when the application starts:

Fine Tuning backend

startup.py

How to Run the Application

Start the React Frontend:
cd frontend npm install npm run dev
Start the Python Backend:
cd backend pip install -r requirements.txt python startup.py

Features of the Application

This AI Model Fine-Tuning application provides a comprehensive platform for working with machine learning models:

System Detection: Automatically detects PyTorch and CUDA availability on startup
Model Management:
- Download pre-trained models from Hugging Face
- Create custom models from scratch with a visual builder
- Fine-tune existing models with your own data
Dataset Handling:
- Upload your own datasets
- Download datasets from Hugging Face
- Preprocess and prepare data for training
Training Configuration:
- Configure hyperparameters like learning rate, batch size, epochs
- Advanced options like gradient clipping, weight decay, and mixed precision
- Real-time training monitoring with loss and accuracy metrics
Model Evaluation:
- Evaluate model performance with metrics like accuracy, precision, recall
- Visualize confusion matrices and performance charts
Custom Model Building:
- Visual layer-by-layer model construction
- Support for different architectures (Transformers, CNNs, etc.)
- Automatic Python code generation for your custom models

The application is designed to be user-friendly while providing powerful functionality for both beginners and experienced ML practitioners.

366d ago

import os
import sys
import json
from detect_system import get_system_info

def main():
    """Check system configuration and start the application"""
    
    print("Checking system configuration...")
    
    # Get system info
    system_info = get_system_info()
    
    # Print system info
    print(f"PyTorch available: {system_info['pytorch_available']}")
    print(f"CUDA available: {system_info['cuda_available']}")
    
    if system_info['cuda_available']:
        print(f"CUDA version: {system_info['cuda_version']}")
        print("GPU information:")
        for gpu in system_info['gpu_info']:
            print(f"  - {gpu}")
    
    print(f"CPU: {system_info['cpu_info']}")
    print(f"Available memory: {system_info['memory_available']}")
    
    # Check if PyTorch is available
    if not system_info['pytorch_available']:
        print("ERROR: PyTorch is not installed. Please install PyTorch.")
        print("Visit https://pytorch.org/get-started/locally/ for installation instructions.")
        sys.exit(1)
    
    # Start the FastAPI server
    print("\nStarting the AI Fine-Tuning server...")
    os.system("uvicorn server:app --host 0.0.0.0 --port 8000 --reload")

if __name__ == "__main__":
    main()

No Output

Run the code to generate an output.

366d ago

AI Model Fine-Tuning Application

Architecture Overview

Backend Python Implementation

Let's implement the Python backend that will handle the PyTorch and CUDA detection, model training, and fine-tuning:

Fine Tuning backend

detect_system.py

Now let's implement a model fine-tuning script that can be used with Hugging Face models:

Fine Tuning backend

fine_tune.py

Now let's implement a custom model builder that can create models from scratch:

Fine Tuning backend

custom_model.py

Finally, let's create a FastAPI server to tie everything together:

Fine Tuning backend

server.py

366d ago

SyntaxError: incomplete input (577622238.py, line 251)

366d ago

I apologize for the incomplete code. Let me provide the complete version of the server.py file:

Fine Tuning backend

server.py

Now let's create a requirements.txt file for the Python backend:

Fine Tuning backend

requirements.txt

And finally, let's create a simple startup script that will check for PyTorch and CUDA availability when the application starts:

Fine Tuning backend

startup.py

How to Run the Application

Start the React Frontend:
cd frontend npm install npm run dev
Start the Python Backend:
cd backend pip install -r requirements.txt python startup.py

Features of the Application

This AI Model Fine-Tuning application provides a comprehensive platform for working with machine learning models:

System Detection: Automatically detects PyTorch and CUDA availability on startup
Model Management:
- Download pre-trained models from Hugging Face
- Create custom models from scratch with a visual builder
- Fine-tune existing models with your own data
Dataset Handling:
- Upload your own datasets
- Download datasets from Hugging Face
- Preprocess and prepare data for training
Training Configuration:
- Configure hyperparameters like learning rate, batch size, epochs
- Advanced options like gradient clipping, weight decay, and mixed precision
- Real-time training monitoring with loss and accuracy metrics
Model Evaluation:
- Evaluate model performance with metrics like accuracy, precision, recall
- Visualize confusion matrices and performance charts
Custom Model Building:
- Visual layer-by-layer model construction
- Support for different architectures (Transformers, CNNs, etc.)
- Automatic Python code generation for your custom models

The application is designed to be user-friendly while providing powerful functionality for both beginners and experienced ML practitioners.

import os
import sys
import json
from detect_system import get_system_info

def main():
    """Check system configuration and start the application"""
    
    print("Checking system configuration...")
    
    # Get system info
    system_info = get_system_info()
    
    # Print system info
    print(f"PyTorch available: {system_info['pytorch_available']}")
    print(f"CUDA available: {system_info['cuda_available']}")
    
    if system_info['cuda_available']:
        print(f"CUDA version: {system_info['cuda_version']}")
        print("GPU information:")
        for gpu in system_info['gpu_info']:
            print(f"  - {gpu}")
    
    print(f"CPU: {system_info['cpu_info']}")
    print(f"Available memory: {system_info['memory_available']}")
    
    # Check if PyTorch is available
    if not system_info['pytorch_available']:
        print("ERROR: PyTorch is not installed. Please install PyTorch.")
        print("Visit https://pytorch.org/get-started/locally/ for installation instructions.")
        sys.exit(1)
    
    # Start the FastAPI server
    print("\nStarting the AI Fine-Tuning server...")
    os.system("uvicorn server:app --host 0.0.0.0 --port 8000 --reload")

if __name__ == "__main__":
    main()

No Output

Run the code to generate an output.