How can I improve Jupyter Notebook performance in VS Code when working with large datasets?

[arjunresha]

Hi everyone,

I've been using the VS Code Jupyter extension to work on some large datasets, but I've noticed significant slowdowns and lag when running cells, especially those that involve heavy computations or large DataFrame manipulations.

• Has anyone experienced similar performance issues?
• Are there recommended settings or best practices to optimize Jupyter Notebook performance in VS Code?
• Would using a different Python environment or hardware setup help?

Any advice or tips would be greatly appreciated!

Thanks in advance!

[Anipaleja]

Hi @arjunresha ! I’ve also worked with large datasets in Jupyter notebooks inside VS Code and experienced performance slowdowns. Here are some tips that might help improve performance:

1. Use a Virtual Environment or Conda Environment
   Make sure your Python environment is clean and has only the necessary packages installed. Sometimes, heavy or conflicting packages slow things down.

2. Enable Jupyter Server in Local Mode
   If you’re running notebooks on a remote server, try running the Jupyter server locally on your machine instead, as network latency can cause lag.

3. Optimize Data Loading and Processing

• Use efficient libraries like pandas with dtype specifications to reduce memory usage.
• Use chunking (read_csv with chunksize) to process data in smaller parts instead of loading entire datasets into memory.

4. Increase VS Code Memory and Performance Settings

• You can adjust the jupyter.jupyterServer.memoryLimit and python.dataScience.textOutputLimit in VS Code settings to allow more memory for notebooks.
• Disable unnecessary extensions while working in Jupyter to reduce CPU/memory overhead.

5. Use Interactive Window or Variable Explorer
   Running code in smaller chunks and monitoring variables through VS Code’s Variable Explorer can help identify bottlenecks or memory leaks.

6. Hardware Considerations
   If possible, use a machine with more RAM and a faster processor. For very large datasets, using a dedicated data processing environment like a cloud VM might be more efficient.

7. Restart and Clear Outputs Frequently
   Long notebook sessions with many outputs can slow VS Code down. Regularly restarting the kernel and clearing outputs can help.

[arjunresha]

Thanks!