Skip to content
Snippets Groups Projects
Readme.md 2.4 KiB
Newer Older
  • Learn to ignore specific revisions
  • abda's avatar
    abda committed
    # Material for Chapter 10
    
    
    Vedrana Andersen Dahl's avatar
    Vedrana Andersen Dahl committed
    The notebook for the exercise in Chapter 10 is available from here:
    
    abda's avatar
    abda committed
    [Notebook for mini U-net](https://github.com/vedranaa/teaching-notebooks/blob/main/02506_week10_MiniUnet.ipynb)
    
    
    vand's avatar
    vand committed
    Training the network may take long time, especially without GPU. We therefore sketch several ways of running the notebook with GPU support.
    
    ## Run the notebook on Google Colab
    
    You can open the notebook in Google Colab from here, and enable GPU :
    
    abda's avatar
    abda committed
    [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vedranaa/teaching-notebooks/blob/main/02506_week10_MiniUnet.ipynb)
    
    
    ## Run the notebook on DTU Gbar
    
    
    vand's avatar
    vand committed
    You can run the notebook on Gbar on an interactive GPU node. First get to a terminal on the cluster either ssh, ThinLinc client, or browser-based ThinLinc [thinlinc.gbar.dtu.dk](thinlinc.gbar.dtu.dk). Open the terminal (command line).
    
    Log onto an interactive GPU node such as `voltash` by running the following command on the command line:
    
    abda's avatar
    abda committed
    
    `voltash -X`
    
    
    vand's avatar
    vand committed
    To set up a Python environment, you can use the script `env02506.sh` which we provided for you. You should place the file `env02506.sh` in a folder on the Gbar and then run the command:
    
    abda's avatar
    abda committed
    
    `source env02506.sh`
    
    The first time you run this, the script will create a new python environment called `env02506` and activate it.
    
    Then you should install the packages needed which include:
    
    `pip install torch torchvision`
    
    `pip install Pillow`
    
    `pip install notebook`
    
    Now you are good to go. You can navigate to the folder that you wish to store the code, download the notebook from the link above, and open a jupyter notebook by typing the following in the command line:
    
    `jupyter-notebook`
    
    vand's avatar
    vand committed
    
    ## Use QIM platform 
    
    We are working on establishing an easier way of using Gbar functionality which does the steps described above automatically. This is still experimental.
    Go to [https://platform.qim.dk/](https://platform.qim.dk/). Log on. Under Tools start Jupyter launcher. Type in your DTU credentials (it still needs to be done twice). Under basic config QIM environment choose `qim3d` (it has the largest number of packages installed). Under advanced config choose HPC que called `gpuqim`. Tick the checkbox Reset SSH tunnel. Click the button `Start jupyter server`. 
    The platform shold launch your job and a button with `Open Jupyter` should appear, leading you to a jupyter server running in your home directory on the Gbar.