Free Notebook Usage

Free Notebook is like a “free trial” that lets you try out Notebook features first and see if they fit your needs. While there are some limitations, it’s fully sufficient for learning and simple projects.

What’s Included in the Free Version?

Basic Resources

Computing Resources:

Software Environment: Python 3.11 with common data science packages pre-installed. You can install additional packages as needed. Multiple programming languages are supported.

What Are the Usage Limitations?

Resource Limitations

Runtime Limitations: Only 1 Notebook can run at a time. Maximum 2 hours per run. Maximum 1000 core-hours per month. Core-hour consumption is calculated based on the specification in use (e.g., using CPU basic · 4v CPU · 8GB for 1 hour consumes 4 core-hours).

Storage Limitations: Workspace up to 50GB, temporary storage up to 2GB, single file cannot exceed 100MB, maximum 10 Notebooks can be created.

Functional Limitations

Usage: No pause while starting. Startup timeout or other errors will terminate the run automatically. No core-hours are consumed if startup fails. To close a running notebook, you must do so from the startup page. (AI Community Workspace -> My Notebook)

API Usage: Maximum 60 requests per minute, maximum 1GB data transfer per day, maximum 5 concurrent requests.

Collaboration Features: Maximum 3 people can view, only read-only sharing is supported, comments can be added, only the latest 5 versions are kept.

Getting Started

Registration Process

Create Account: Visit the registration page, fill in basic information, verify email, and complete setup.

Environment Initialization: Use command-line tools to initialize the workspace and verify the Python environment.

Basic Operations

Create Notebook: Use command-line tools to create a new Notebook. Sample projects can be imported.

Manage Files: Upload and download files are supported. Manage project data.

Resource Optimization

Memory Management

1. Optimization Techniques

   import gc
   import torch
   
   def optimize_memory():
       # Clean up unused objects
       gc.collect()
   
       # Clear PyTorch cache
       torch.cuda.empty_cache()
   
       # Use generators for large data
       def data_generator():
           for chunk in pd.read_csv("large_file.csv", chunksize=1000):
               yield process_chunk(chunk)
   

2. Data Processing

   # Process large files in chunks
   def process_large_file(file_path):
       results = []
       for chunk in pd.read_csv(file_path, chunksize=1000):
           result = process_chunk(chunk)
           results.append(result)
       return pd.concat(results)
   

Computing Optimization

1. Parallel Processing

   from multiprocessing import Pool
   
   def parallel_process(data_list):
       with Pool(processes=2) as pool:
           results = pool.map(process_function, data_list)
       return results
   

2. Code Optimization

   # Use vectorized operations
   import numpy as np
   
   def optimize_calculation(data):
       # Replace loop operations
       result = np.vectorize(process_function)(data)
       return result
   

Usage Tips

Improve Efficiency

Code Organization

• Break code into different functions
• Each function does one thing
• Keep code structure clear
• Easy to understand and maintain

Use Caching

• Use cache for repeated calculations
• Avoid running the same code repeatedly
• Save time and resources
• Improve runtime efficiency

Storage Management

File Management

• Regularly clean temporary files
• Compress old data files
• Delete unnecessary files
• Keep workspace tidy

Data Optimization: Choose appropriate data types, compress large datasets, optimize storage format, and save storage space.

When to Upgrade?

Upgrade Signals

Insufficient Resources: CPU usage frequently exceeds 80%, memory usage frequently exceeds 75%, storage usage exceeds 90%, or you frequently hit various limits.

Functional Needs: Need GPU-accelerated computing, larger storage, longer runtime, or more collaboration features.

Upgrade Steps

Preparation: Evaluate actual needs, choose a suitable paid plan, prepare for data migration, and configure the new environment.

Data Migration: Export the current workspace, migrate to the new environment, verify data integrity, and start using new features.

Common Questions

Resource Issues

How to check resource usage? View resource monitoring in the Notebook interface, use system commands to check CPU and memory, and regularly check storage usage.

How to handle insufficient memory? Close unnecessary Notebooks, clear temporary variables and data, use smaller datasets, or consider upgrading to a paid version. How to optimize runtime? Use efficient algorithms, avoid repeated calculations, use cache appropriately, and optimize data processing workflows.

How to manage storage space? Regularly clean unnecessary files, compress large datasets, use external storage services, and delete temporary files promptly.

Functional Issues

How to install additional packages? Use pip to install Python packages, use conda for scientific computing packages, check package compatibility, and be aware of storage limits.

How to share a Notebook? Set sharing permissions, generate a share link, invite collaborators, and control access.

Limitation Issues

Runtime Limitations: Maximum 2 hours per run, automatically stops on startup timeout or other errors. Plan computing tasks reasonably and save your work in time.

Storage Space Limitations: Workspace up to 50GB, single file cannot exceed 100MB. Clean and compress regularly, or consider upgrading to a paid version.

Summary

Free Notebook is a good way to get started with AI. With reasonable use, you can Experience for Free (use Notebook without paying), Learn the Basics (master basic programming and data processing), Practice on Projects (complete small AI projects), and Evaluate Your Needs (decide whether to upgrade to a paid version).

Remember: the free tier has limits, but it is fully sufficient for learning and simple projects. Plan well, optimize usage, and you can make the most of the free resources!