Let’s tackle your questions one by one, starting with the most critical one about combining local data access with remote GPU processing.

Can I Use Local Runtime to Access Local Data + Remote GPU? #

Short answer: No—these two modes are mutually exclusive.

Here’s why:

• When you connect to a local Runtime, Colab’s frontend is essentially just a web interface for your local machine’s Jupyter server. All code execution, package installations, and hardware usage (GPU/CPU) happen entirely on your local device. There’s no connection to Colab’s remote cloud GPU instances in this mode.
• When you use Colab’s cloud Runtime, you’re assigned a temporary virtual machine (VM) on Google Cloud Platform (GCP) with access to GPU/TPU resources. In this mode, your code runs on the remote VM, so you can’t directly access local files without transferring them first.

Alternative: Access Local Data on Cloud GPU Runtime (Without Google Drive) #

If you want to use Colab’s remote GPU and access local data (without uploading to Drive), here are two reliable methods:

1. Upload files directly to the cloud VM’s session storage
   Use Colab’s built-in file upload tool for small-to-medium files:

   from google.colab import files
   uploaded = files.upload()
   

   Files uploaded this way are stored in the VM’s temporary storage (they’ll be deleted when the Runtime disconnects).

2. Sync local directories to the cloud VM via SSH
   For larger datasets, you can set up an SSH tunnel to sync files from your local machine to the cloud VM:

   • First, enable SSH access in your Colab notebook (run this code to get SSH credentials):

     from google.colab import output
     output.serve_kernel_port_as_ssh()
     

   • On your local machine, use rsync to copy files to the VM:

     rsync -avz /path/to/local/data/ username@colab-ssh-addr:/content/
     

Why Are GPU Libraries Installing Locally? #

This happens because you’re using a local Runtime—all pip install commands run on your local machine. To install GPU-accelerated libraries (like TensorFlow with CUDA support) on a remote GPU, you need to:

1. Disconnect from your local Runtime (go to Runtime > Disconnect and delete runtime).
2. Reconnect to a cloud Runtime with GPU enabled (Runtime > Change runtime type > Hardware accelerator > GPU > Save).
3. Run your pip install commands now—they’ll execute on the remote GPU VM, and the libraries will use the VM’s CUDA setup automatically.

Colab Runtime Architecture Explained #

Let’s break down how both runtime types work:

Cloud Runtime #

• Backend: Colab provisions a temporary GCP VM (with options for CPU, GPU, or TPU). This VM runs a Jupyter server and has pre-installed ML libraries (TensorFlow, PyTorch, etc.).
• Frontend: The Colab notebook interface in your browser communicates with the VM via WebSockets. Every code cell you run is sent to the VM, executed there, and the output is sent back to your browser.
• Lifecycle: Cloud Runtimes are temporary—they’ll disconnect after 12 hours of inactivity, and all data on the VM is deleted when it’s terminated.

Local Runtime #

• Backend: You run a Jupyter server on your local machine (Colab guides you through installing the required packages like jupyter_http_over_ws).
• Frontend: The Colab browser interface connects directly to your local Jupyter server via a localhost port. All code execution happens locally—Colab is just a UI layer, not a computation layer here.
• Lifecycle: Your local Runtime persists as long as your local Jupyter server is running, and all data/libraries stay on your machine.

内容的提问来源于stack exchange，提问作者user305883