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Day 1

Exercise sessions

Exercise 1

Introduction to Notebooks, PyTorch fundamentals.

  • 01-pytorch-test-setup.ipynb
    Open In Colab

Exercise 2

MNIST classification with MLPs.

  • 02-pytorch-mnist-mlp.ipynb
    Open In Colab

Exercise 3

Image classification with CNNs.

  • 03-pytorch-mnist-cnn.ipynb
    Open In Colab

Exercise 4

Text sentiment classification with RNNs.

  • 04-pytorch-imdb-rnn.ipynb
    Open In Colab

Setup

We will use Jupyter Notebooks for all exercises on Day 1. There are several ways to set up a Jupyter environment for running the exercises:

1. LUMI web user interface

The default option.

  1. Go to the LUMI web user interface.
  2. Login with Haka (Finnish university or research institute) or CSC account (anyone with valid CSC account)
  3. Click "Jupyter for courses" (this works only if you have been added to the course project)
  4. Make sure the selections are correct:
    • Reservation: PDL_CPU (during course day 1), No reservation (otherwise)
    • Project: project_462000450
    • Course module: PracticalDeepLearning-Feb2024
      • if you do not see the course module listed, try "Restart Web Server" from the top-right "question-mark-inside-a-circle" menu item
    • Working directory: /users/your-username-here
  5. Click "Launch"
  6. Once the applications has started click "Connect to Jupyter"
  7. If you are not familiar with Jupyter, take a moment to get to know the interface
    • open a new notebook (File -> New -> Notebook, on menubar)
    • select "Python 3" as the kernel for the notebook
    • write some Python code to a Jupyter cell
    • execute the cell with shift-enter

2. CSC Notebooks

CSC Notebooks (https://notebooks.csc.fi) provides easy-to-use environments for working with data and programming. You can access everything via your web browser and CSC cloud environment computes on the background. There should be enough resources for launching a notebooks instance for everyone, but unfortunately no GPUs.

  1. Go to the CSC Notebooks frontpage
  2. Login according to selected login method:
    • Haka or Virtu (users from Finnish universities and research institutes)
      1. Press Login button on the frontpage
      2. Press Haka or Virtu button
      3. Select right organization
      4. Enter login information
    • Special login (if you have been given separate username and password for the course)
      1. Press "Special Login" button on the Notebooks frontpage (below the Login button)
      2. Enter login information (username goes to email slot)
  3. Start the "Practical Deep Learning" application
    • You might find it quicker if you select the "Machine Learning" tab
    • Click the round start button next to the "Practical Deep Learning" card
    • Wait for session to launch
  4. Once the Jupyter Notebook dashboard appears, navigate to intro-to-dl/day1
  5. If you are not familiar with Jupyter, take a moment to get to know the interface
    • open a new notebook (File -> New -> Notebook, on menubar)
    • select "Python 3" as the kernel for the notebook
    • write some Python code to a Jupyter cell
    • execute the cell with shift-enter

⚠️ Note

The notebook sessions have a limited time (4h) after which they, and any data or changes, will be destroyed. If you wish to save any files, you need to download them.

3. Running Jupyter on your laptop

If you have a laptop that has both jupyter and the other necessary python packages installed, it is possible to use it. In particular, if the laptop has an NVIDIA or AMD GPU and it that has been properly set up (CUDA, cuDNN or ROCm).

  • git clone https://github.com/csc-training/intro-to-dl.git
  • try to run the day1/01-pytorch-test-setup.ipynb notebook without errors

4. Google Colaboratory

Google has a free Jupyter Notebooks service you may want to try out. No guarantees, but it does have GPUs available! A Google account is needed to use Colaboratory.

  • Click the corresponding Colab link above in this document
  • If needed, sign in to your Google account using the "Sign in" button in the top-right corner
  • To use a GPU, select: Runtime => Change runtime type => Hardware accelerator: GPU