more-compute 0.1.1

An interactive notebook environment for local and GPU computing

more-compute

An interactive notebook environment similar to Marimo and Google Colab that runs locally.

For references:

https://marimo.io/

https://colab.google/

FOR LOCAL DEVELOPMENT:

pip install -e .

Installation

Recommended: Using uv (fastest, auto-handles PATH)

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install more-compute
uv tool install more-compute

Alternative: Using pip

pip install more-compute
# If "command not found", run: python3 -m kernel_run new

Usage

Create a new notebook

more-compute new

This creates a timestamped notebook like notebook_20241007_153302.ipynb

Or run directly:

python3 kernel_run.py new

Open an existing notebook

# Open a specific notebook
more-compute your_notebook.ipynb

# Or run directly
python3 kernel_run.py your_notebook.ipynb

# If no path provided, opens default notebook
more-compute

Features

• Interactive notebook interface similar to Google Colab
• Support for both .py and .ipynb files
• Real-time cell execution with execution timing
• Magic commands support:
  • !pip install package_name - Install Python packages
  • !ls - List directory contents
  • !pwd - Print working directory
  • !any_shell_command - Run any shell command
• Visual execution feedback:
  • ✅ Green check icon for successful execution
  • ❌ Red X icon for failed execution
  • Execution timing displayed for each cell
• Local development environment - runs on your machine
• Web-based interface accessible via localhost
• Cell management:
  • Add/delete cells
  • Drag and drop to reorder
  • Code and Markdown cell types

Usage Examples

Installing and Using Libraries

# Install packages using magic commands (like Colab)
!pip install pandas numpy matplotlib

# Import and use them
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create some data
df = pd.DataFrame({
    'x': np.range(10),
    'y': np.random.randn(10)
})

print(df.head())

Shell Commands

# List files
!ls -la

# Check current directory
!pwd

# Run any shell command
!echo "Hello from the shell!"

Data Analysis Example

# Load data
data = pd.read_csv('your_data.csv')

# Analyze
data.describe()

# Plot
plt.figure(figsize=(10, 6))
plt.plot(data['x'], data['y'])
plt.title('My Analysis')
plt.show()

Development

To install in development mode:

pip install -e .