Release Notes
Version 0.60.0
Updating LAMMPS image to version 29Oct2020 and the corresponding LAMMPS-AWESEMMD
New prototypes for NWChem workflow menus in NWChem NEXT examples
Prototype of new launchpad PySide2 application for starting cloud resources in place.
New quantum chemistry code DALTON image available
- Major revision to Chemistream starting application:
JupyterLab local manager session is deprecated
Startup GUI now manages license file installation and cloud startup functions
Multiple remote compute sessions can now be managed through startup GUI
Improved error handling throughout the startup process
Reworking of compute session workflows for ALFABET, NWChem and Dalton to use ipywidgets for all functions
Improving startup user instructions for all remote workflows
- New streamlined workflows for Dalton and NWChem for
scaling studies
parsing HOMO/LUMO values
series of runs given input SMILES strings
calculating hyperpolarizabilities
Implemented multi-stage builds for Docker images resulting in a further 50% size reduction
Updating panel and panel-chemistry dependencies resulting in faster app/image installs
Updating to JupyterLab=3.3.0
Using jupyterlab_scenes module to enable auto-execution for turnkey workflows
Version 0.20.0
Reworked Chemistream application build system so pyinstaller no longer used, thereby resulting in smaller installers
Streamlined startup page on JupyterLab: Manager tab
Adding menu selection for new RLMolecule (from NREL) image
Adding menu selection for new SPPARKS image with proprietary ALD model pre-compiled
Adding menu selection for new NWChem image with the DDEC6 and Molden packages added
Maintaining separate local ports for remote cloud compute session and local compute session
Fixing temporary directory scratch space for NWChem sims
Improved docker desktop setup for ‘local’ compute session across platforms
Improved checks for installed docker and docker engine start for ‘local’ compute session
Updating LAMMPS and SPPARKS examples for better results parsing
Improved visualization for SPPARKS diffusion 3d example
Reworking of dependencies in Chemistream installer. The installer depends on Makalii and Jupyterlab only. All compute modules and codes are moved to Docker images available from Tech-X repo. All installers are nearly half as large.
Installer setup and configuration now ~2-3x faster due to simplified dependencies.
All Tech-X Docker images have been reworked for fewer dependencies and are smaller
JSME editor is now embedded in Jupyterlab notebooks, available through the Chemistream RDKitBase class
Version 0.12.1
Updating to conda Python 3.8
Updating to JupyterLab > 3
Updating ipywidgets to latest version that simplifies and speeds up installation
Updating pyinstaller and constructor packages to latest version (these can use conda python)
Adding menu selection for new GROMACS image
Using development version of NGLView that simplifies and speeds up installation
Removing STREAMM dependency from Chemistream module as this is only used in remote compute sessions. This has decreased the size of installers on all platforms
Adding ability to start a ‘local’ compute session so users can test workflows without need for configuring cloud accounts. This is valid on MacOS, Linux and Windows
Improving stability of setup of compute session
New example modules for BDE training model
Bug fixes for storage (Waihona) interface methods
Version 0.9.12
Removing py3Dmol dependencies and methods from ChemVis module
Adding NGLView dependencies and migrating from py3Dmol
New methods for NGLView added to ChemVis module
Adding example directories for rDock and QMCPack
Version 0.9.10
Upgrading to JupyterLab v 2.1.0
Examples for remote storage (eg AWS S3 and Azure Blob)
Adding RDKit dependency for a base class RDKitBase that is used by several examples’ derived classes.
Adding example for downloading OPV database info and analyzing with RDKit
Adding extra setup python files to tailor installs for app/cloud and (un)licensed versions
Freezing Miniconda version to version Miniconda3-py37_4.8.3-platform-x86_64.sh”
Version 0.9.8
Examples for machine learning, image recognition for ‘Digits’ and ‘Fashion’
Example for neural net prediction of bond dissociation energies using Alfabet
Example for complex P3HT workflow
Version 0.9.1
Cloud management for Amazon and AWS
Initial development of Jupyterlab framework using ipywidgets
Examples for scaling with NWChem, LAMMPS and SPPARKS
Cloud images all enabled with generation of multi-node HPC cluster
Cloud images all enabled with creation of NFS shared directory