Giter Site home page Giter Site logo

atpesc_machinelearning's Introduction

The first two modules in this tutorial will rely on Jupyter Notebooks which are targeted for running on Google's Colaboratory Platform. This 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 latter two modules will be performed with simple Python scripts executed on the ThetaGPU machine.

Before You Arrive

Do the following before you come to the tutorial:

  • You need a Google Account to use Colaboratory
  • Goto Google's Colaboratory Platform
  • You should see this page start_page
  • Click on the New Python Notebook
  • Now you will see a new notebook where you can type in python code. clean_page
  • After you enter code, type <shift>+<enter> to execute the code cell.
  • A full introduction to the notebook environment is out of scope for this tutorial, but many can be found with a simple Google search
  • We will be using notebooks from this repository during the tutorial, so you should be familiar with how to import them into Colaboratory
  • 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.
  • Select the introduction.ipynb file to open and work through it.
  • As each session of the tutorial begins, you will simply select the corresponding notebook from this list and 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 the select Runtime -> Change Runtime Type and you have a dropbox list of hardward settings to choose from where the notebook can run.

atpesc_machinelearning's People

Contributors

felker avatar zhenghh04 avatar bethanyl avatar jtchilders avatar romit-maulik avatar venkat-1 avatar pbalapra avatar coreyjadams avatar memani1 avatar elisej avatar skhairy0 avatar

Watchers

James Cloos avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.