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Lecture and hands-on material for Track 8- Machine Learning of Argonne Training Program on Extreme-Scale Computing

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At the beginning of the day, we will temporarily split into two groups. Attendees can choose between "Introduction to deep learning" (01_deepLearning) and "Building data pipelines" (02_dataPipelines).

The "Introduction to deep learning" session will rely on Jupyter Notebooks which are targeted for running on Google's Colaboratory Platform or ALCF JupyterHub. The Colab platform gives the user a virtual machine in which to run Python codes including machine learning codes. The VM comes with a preinstalled environment that includes most of what is needed for these tutorials.

The other sessions involve Python scripts executed on the Polaris and AI Testbed platforms at ALCF.

Using Google Colab

Google Colab involves running Jupyter notebooks, which you have experience with from earlier in the week.

Do the following before you come to the tutorial:

  • You need a Google Account to use Colaboratory
  • Go to Google's Colaboratory Platform
  • You should see this page start_page
  • Now you can open the File menu at the top left and select Open Notebook which will open a dialogue box.
  • Select the GitHub tab in the dialogue box.
  • From here you can enter the url for the github repo: https://github.com/argonne-lcf/ATPESC_MachineLearning and hit <enter>. open_github
  • This will show you a list of the Notebooks available in the repo. When you select a notebook from this list it will create a copy for you in your Colaboratory account (all *.ipynb files in the Colaboratory account will be stored in your Google Drive).
  • To use a GPU in the notbook select Runtime -> Change Runtime Type and select an accelerator.

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Lecture and hands-on material for Track 8- Machine Learning of Argonne Training Program on Extreme-Scale Computing

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