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Accessing Remote Jupyter at USC ISI

This guide provides instructions for accessing a Jupyter Notebook on a remote server and configuring a GPU for machine learning tasks using TensorFlow and PyTorch.

Accessing the Remote Jupyter Notebook


  • VPN connection, if required.
  • SSH access to the server.
  • Your server username (e.g.,

Steps to Access

  1. Open Terminal:

    • Windows: Use PowerShell or PuTTY.
    • macOS/Linux: Use the Terminal app.
  2. SSH Connection:

    • Command:
      ssh -L 8080:localhost:8080
    • Replace scho with your server username.
  3. Navigate to Work Directory:

    • Use cd to go to your directory:
      cd path/to/work/directory
  4. Start Jupyter Notebook:

    • Run:
      jupyter lab --no-browser --port=8080
    • Copy the provided URL.
  5. Access Notebook Locally:

    • Paste the URL into your local browser.

Setting Up GPU for TensorFlow


  • TensorFlow and other packages installed.
  • Knowledge of available GPU numbers (0 to 7).

Steps for TensorFlow

  1. Check GPU Availability:

    • In Jupyter, run:
  2. Configure GPU in Notebook:

    • After loading packages, set GPU:

      import os
      import tensorflow as tf

      os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
      os.environ["CUDA_VISIBLE_DEVICES"] = "0" # Choose an available GPU

      physical_devices = tf.config.list_physical_devices('GPU')
      print("Num GPUs Available: ", len(physical_devices))

      if physical_devices:
      tf.config.experimental.set_memory_growth(physical_devices[0], True)

Setting Up GPU for PyTorch


  • PyTorch installed.
  • Knowledge of available GPU numbers.

Steps for PyTorch

  1. Check GPU Availability:

    • Same as for TensorFlow, use !nvidia-smi.
  2. Configure GPU in Notebook:

    • PyTorch automatically uses available GPUs, but you can specify one:

      import torch

      # Check if CUDA is available
      if torch.cuda.is_available():
      device = torch.device("cuda:0") # Replace 0 with your GPU number
      print("Using GPU:", torch.cuda.get_device_name(0))
      device = torch.device("cpu")
      print("Using CPU")
    • Use device to move tensors or models to the selected device:


  • For TensorFlow, CUDA_VISIBLE_DEVICES sets the specific GPU.
  • For PyTorch, torch.device is used to specify the GPU.
  • Always check GPU availability and usage before selecting one.