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Releases: JDACS4C-IMPROVE/Singularity

Developer release v0.2.1-alpha

21 Dec 04:59
@wilke wilke
v0.2.1-alpha
04f7d37
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IMPROVE: Developer Release v0.2.1 (2023-12-21)

This is a developer release for early testing and feedback. This release provides the ability to import specific benchmark data sets. This will enable the use of the benchmarks for HPO and CSA.

Included in this release are:

  • IMPROVE lib v0.0.1-alpha
  • IMPROVE Documentation v0.6.1
  • Benchmark data for Cross Study Analysis (CSA)
  • IMPROVE models, build and testing code version v0.6.1

IMPROVE Library

Repo: https://github.com/JDACS4C-IMPROVE/Singularity

Release:

Documentation

Repo: https://github.com/JDACS4C-IMPROVE/docs

Release:

New documentation for:

  • Benchmark data sets
  • IMPROVE Library for data handling

CSA Benchmark data

Repo: https://web.cels.anl.gov/projects/IMPROVE_FTP/candle/public/improve/benchmarks/single_drug_drp/benchmark-data-pilot1/csa_data/

Release:

Suggested benchmark data for a drug response cross-study analysis. Detailed information about the data can be found in the documentation

IMPROVE models, build and test code

Repo: https://github.com/JDACS4C-IMPROVE/Singularity

Release:

This version includes templates for the required interface scripts and the usage of candle_lib for wrapping the Python model code.

  • train.sh
  • preprocess.sh
  • template.py

Proof of concept models

For a proof of concept the code has been incorporated into follwoing models for testing and review.

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HPO for supported models on original data

29 Sep 19:12
@wilke wilke
v0.2.0-beta
6fd39d7
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HPO for supported models on original data Pre-release
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IMPROVE: Early Adopter Release v0.2.0 (2023-09-29)

This release allows optimizing hyperparameters for machine learning using data provided or described by the original model code creator. It includes instructions for setting up the infrastructure and performing initial HPO.

To get started please use our guides in the documentation.

Included in this release are:

  • Hyper Parameter Optimization (HPO) workflow version v0.6
  • IMPROVE models, build and testing code version v0.6
  • IMPROVE documentation version v0.6.0
  • CANDLE library v0.8

Documentation:

Repository: https://github.com/JDACS4C-IMPROVE/docs
Documentation: https://jdacs4c-improve.github.io/docs/index.html

Release:

New documentation guides for:

  • Setting up the initial environment for building support model container images and executing HPO workflow
  • Instructions for making container images for supported models
  • Instructions for setting up HPO for a supported model

Build and test framework

Repo: https://github.com/JDACS4C-IMPROVE/Singularity

Release:

This release includes:

  • build instructions for supported model containers.
  • test scripts for models and containers

Workflows

This release of the supervisor supports the Hyper Parameter Optimization Workflow, which was used to optimize multiple IMPROVE models via the DEAP algorithm. We developed a new front-end command-line tool called supervisor that eases the management of configuration files for the system. We also improved the interfaces between Supervisor and IMPROVE containers, and clarified output from the system, including statistics from the models and the overall HPO progress.

Repo: Supervisor

Release:

HPO Examples

Repo: HPO

Release:

This repository contains minimal testing config for HPO and a collection of best HPO results for given HPO and GA parameter space.

CANDLE library

Vendorized version of candle_lib. This version is used by all models and provides a standardized interface for model execution.

Release:

New supported models:

This models have been modified to provide a standardized interface for model training.

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Initial release

29 Sep 19:07
@wilke wilke
v0.1.0-alpha
d788baf
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What's new

  • documentation
  • definition files for supported models
  • build process

Full Changelog: https://github.com/JDACS4C-IMPROVE/Singularity/commits/v0.1.0-alpha

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