Notebook Feature Overview

Notebook is like your “online programming studio”, where you can write code, conduct experiments, analyze data, without installing any software on your own computer. It’s like having a powerful computer in the cloud, ready to serve you at any time.

Main Features

Programming Environment

Interactive Programming: Can run code line by line, see running results in real-time, support multiple programming languages, code has color marking, easy to read.

Smart Suggestions: When inputting code, there will be suggestions, auto-complete function names, display function descriptions, reduce input errors.

Computing Resources

CPU Environment: 4 CPU cores, fast processing speed, 16GB memory, can process large amounts of data, 100GB storage space, sufficient to store project files.

GPU Support: Support T4 and A100 and other GPUs, 16GB video memory, suitable for deep learning, support CUDA accelerated computing.

Data Processing

Dataset Usage

Data Loading: Can directly use datasets on the platform, support multiple data formats, powerful data preprocessing functions, simple and convenient operation.

Data Storage: Can save processing results, support multiple export formats, data safe and reliable, accessible anytime.

Data Visualization

Basic Charts: Support various types of charts, can customize styles and colors, high chart quality, suitable for reports, simple and intuitive operation.

Interactive Charts: Can zoom in and out to view, mouse hover to display detailed information, support dynamic interaction, better user experience.

Model Development

Model Training

1. Training Configuration

   training_config = {
       "batch_size": 32,
       "epochs": 10,
       "learning_rate": 1e-4,
       "optimizer": "adam",
       "device": "cuda"
   }
   

2. Training Process

   from torch.utils.data import DataLoader
   from tqdm.notebook import tqdm
   
   # Training loop
   for epoch in tqdm(range(epochs)):
       for batch in DataLoader(dataset, batch_size=32):
           # Training step
           loss = train_step(model, batch)
           # Update progress bar
           tqdm.write(f"Loss: {loss:.4f}")
   

Experiment Management

1. Experiment Tracking

   from gitcode.tracking import track_experiment
   
   @track_experiment
   def run_experiment(params):
       # Experiment configuration
       config = {
           "model": "resnet50",
           "params": params
       }
   
       # Run experiment
       results = train_model(config)
   
       return results
   

2. Result Visualization

   # Training curves
   plt.figure(figsize=(12, 6))
   plt.plot(history['loss'], label='train')
   plt.plot(history['val_loss'], label='validation')
   plt.legend()
   plt.title('Training History')
   

Collaboration Features

Version Control

1. Code Version

   # Save checkpoint
   notebook.save_checkpoint("v1.0")
   
   # Restore version
   notebook.restore_checkpoint("v1.0")
   

2. Environment Version

   environment:
     version: "1.0.0"
     python: "3.9"
     packages:
       - torch==2.0.0
       - transformers==4.30.0
   

Sharing Features

1. Export Options

   # Export as Python script
   notebook.export_as_script("script.py")
   
   # Export as HTML
   notebook.export_as_html("report.html")
   

2. Collaboration Settings

   sharing:
     visibility: "public"
     permissions:
       - user: "collaborator@example.com"
         role: "editor"
     comments: true
   

Extension Features

Plugin System

1. Extension Installation

   # Install extension
   !jupyter nbextension install extension-name
   
   # Enable extension
   !jupyter nbextension enable extension-name
   

2. Custom Extensions

   // Custom toolbar button
   define([
       'base/js/namespace'
   ], function(Jupyter) {
       function load_ipython_extension() {
           // Add custom button
           Jupyter.toolbar.add_buttons_group([
               Jupyter.keyboard_manager.actions.register({
                   'help': 'Run all cells',
                   'icon': 'fa-play',
                   'handler': run_all
               }, 'run-all', 'Custom')
           ])
       }
       return {
           load_ipython_extension: load_ipython_extension
       };
   });
   

Resource Monitoring

1. System Monitoring

   def monitor_resources():
       metrics = {
           "cpu_usage": get_cpu_usage(),
           "memory_usage": get_memory_usage(),
           "gpu_usage": get_gpu_usage(),
           "disk_usage": get_disk_usage()
       }
       return metrics
   

2. Performance Analysis

   # Code performance analysis
   %load_ext line_profiler
   %lprun -f function_name function_name(args)
   
   # Memory analysis
   %load_ext memory_profiler
   %memit function_name(args)
   

Usage Suggestions

Development Suggestions

Code Organization: Break code into small pieces for easy understanding, use version control to manage code, add comments to explain code functions, regularly save work results.

Performance Optimization: Choose appropriate data structures, optimize computation-intensive operations, reasonably use caching functions, timely release unnecessary resources.

Collaboration Suggestions

Team Cooperation: Write clear code descriptions, maintain consistent code style, communicate and provide feedback in time, regularly update code versions.

Safe Usage: Protect sensitive information, control access permissions, regularly backup important data, comply with platform usage standards.

Summary

Notebook is a powerful online programming environment. Through Notebook, you can Quick Development (no need to install software, start programming directly), Efficient Experimentation (interactive programming, see results in real-time), Data-Driven (powerful data processing and visualization functions), and Team Collaboration (support multi-person collaborative development).

Remember, Notebook makes programming simple and fun. Start with simple code, learn step by step, and you can master various AI development skills!