VS Code通过SSH连接远程服务器使用Jupyter扩展时出现Session cannot generate requests错误的排查与解决

Hey there, let's unpack what's happening here and how to fix it.

What the Error Actually Means #

That Error: Session cannot generate requests is a sign that communication between your VS Code Jupyter extension and the remote Jupyter kernel has broken down. In your case, this is almost certainly triggered by the massive memory and computation load your code is trying to handle.

Think about the numbers: 251,350 users × 39,729 items equals nearly 10 billion rows—that's way too large to fit into even high-end server memory. When you run that code, the remote Jupyter kernel tries to allocate memory for this giant DataFrame, gets completely overwhelmed, and either crashes or becomes unresponsive. Once the kernel is stuck, it can't send or receive requests from the VS Code extension anymore, hence the error message.

Fixes to Try #

Here are practical steps to resolve this issue:

• Avoid generating the full Cartesian product (if possible)
  Chances are you don't actually need all 10 billion rows for your task. For example, if you're working on a recommendation system, you can filter pairs based on user behavior first, or use matrix factorization techniques that don't require explicit pair generation. Reassess your workflow to skip this unnecessary heavy computation.

• Process data in chunks
  If you absolutely need to work with these pairs, split the job into smaller, manageable chunks. Instead of creating one huge DataFrame, process subsets of users and save results incrementally. Here's an example:

  import pandas as pd
  from itertools import product
  
  # Split user_table into smaller chunks
  chunk_size = 10000
  total_chunks = len(user_table) // chunk_size + 1
  
  for chunk_idx in range(total_chunks):
      # Get a subset of users
      start_idx = chunk_idx * chunk_size
      end_idx = min((chunk_idx + 1) * chunk_size, len(user_table))
      user_chunk = user_table.iloc[start_idx:end_idx]
      
      # Generate pairs for this chunk only
      chunk_df = pd.DataFrame(
          product(item_table['item_id'], user_chunk['user_id']),
          columns=['item_id', 'user_id']
      )
      
      # Save or process the chunk (e.g., write to CSV/database)
      chunk_df.to_csv(f'user_item_pairs_chunk_{chunk_idx}.csv', index=False)
      
      # Clean up memory to avoid buildup
      del chunk_df
  

• Restart the Jupyter kernel
  Your current kernel is likely in an unresponsive state. Go to the Jupyter panel in VS Code, find the "Restart Kernel" button (the circular arrow icon), and click it. This will free up stuck memory and let you start fresh.

• Upgrade your Jupyter tools
  You're using a 2021 version of the VS Code Jupyter extension (ms-toolsai.jupyter-2021.8.1236758218), which is quite outdated. Newer versions have better memory management and bug fixes for remote connections. Update the extension in VS Code, and also make sure the remote server's ipykernel package is up to date (run pip install --upgrade ipykernel on the remote server).

• Monitor server resources
  Use terminal commands like htop or free -h on the remote server to check memory usage while running your code. This will confirm if memory exhaustion is the root cause, and help you decide if you need to upgrade the server's resources.

内容的提问来源于stack exchange，提问作者Eugene Lee