Skip to content

Releases: PPPLDeepLearning/plasma-python

SC2017

05 Jan 20:42
@ASvyatkovskiy ASvyatkovskiy
v1.0.0
4572ef2
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
SC2017 Latest
Latest 
Merge pull request #26 from PPPLDeepLearning/jenkins_test

Jenkins test + Python3 and 1D Profile Fixes
Assets 2
Loading

GTC2017

11 May 22:36
@ASvyatkovskiy ASvyatkovskiy
v0.9
09cf67d
Compare
Choose a tag to compare
GTC2017

This release version corresponds to the GTC2017 presentation.

Establish FRNN code workflow similar to that characteristic of typical distributed deep learning projects. First, the raw data is preprocessed and normalized. The pre-processing step involves cutting, resampling, and structuring the data - as well as determining and validating the disruptive properties of the shots considered. Various options for normalization are implemented.

Keep structure of Fusion Recurrent Neural Net (FRNN) deep learning code modular, with 4 main modules:

  1. models: Python classes necessary to construct, train and optimize deep RNN models. Including a distributed data-parallel implementation of mini-batch gradient descent with MPI

  2. preprocessors: signal preprocessing and normalization classes, including the methods necessary to prepare physical data for stateful RNN training.

  3. primitives: contains abstractions specific to the domain implemented as Python classes. For instance: Shot - a measurement of plasma current as a function of time. The Shot object contains attributes corresponding to unique identifier of a shot, disruption time in milliseconds, time profile of the shot converted to time-to- disruption values, validity of a shot (whether plasma current reaches a certain value during the shot), etc

  4. utilities: a set of auxiliary functions for preprocessing, performance evaluation and learning curves analysis

Assets 2
Loading