GitLab

* deploy to aiqm/torchani-test-docs for testing

* Deploy to test PyPI on PR

* update

* fix tag version

* fix docs deploy format

* test

* fix

* deploy docs on release

* update

* deploy pypi

* update readme

* Add unit test for builtin models

* flake8

* add test for scripted ensemble

* use torchani.nn.Sequential

* change OrderedDict to module list

* fix

* fix nn.py

* try more fix

* try

* more

* more

* fix more

* rename

* bring ensemble back

* make ANIModel iterable

* Keep in-progress jobs once a test fails in Actions

By default, GitHub cancels all in-progress jobs if any `matrix` jobs fail in Actions. This PR will change the default to let all tests running in case one of them fails.

* Update unittest.yml

* Update unittest.yml

* Move unittest check to GitHub Actions

Tests are parallelized

* Delete tests.yml

* Update setup.py

* Create test_requirements.txt

* Update unittest.yml

* Update unittest.yml

* Update README.md

* Delete install_dependencies.sh

* Delete install_dependencies_python2.sh

* Update unittest.yml

* split

* split

* fix

* fix

* -v

* more

* more

* fix

* fix

* flake8

* transform

* reduce testBenzeneMD

* version

* reduce number of epoches

* further reduce time

* Update docs.yml

* Add GitHub Actions badges

This PR is to fix #320

* Update README.md

* Update README.md

* Move unittest check to GitHub Actions

Tests are parallelized

* Delete tests.yml

* Update setup.py

* Create test_requirements.txt

* Update unittest.yml

* Update unittest.yml

* Update README.md

* Delete install_dependencies.sh

* Delete install_dependencies_python2.sh

* Update unittest.yml

* Move python2 check to GitHub Actions

* Delete python2.yml

* Rename azure/install_dependencies_python2.sh to ci/install_dependencies_python2.sh

* Create runnable_submodules.yml

* Delete runnable_submodules.yml

* Update runnable_submodules.yml

* Move tools check to GitHub Actions

* Delete tools.yml

* Move docs to github actions

* Delete docs.yml

* Create install_dependencies.sh (#311)

* Rename install_dependencies.sh to ci/install_dependencies.sh

* +x

* Update and rename pythonpackage.yml to flake8.yml

* Delete flake8.yml

* Update flake8.yml

* Update utils.py

if I train molecule like Oxygen which contains only one element, the self.self_energies is a scalar and will make the operation self_energies = self.self_energies[species] crash. Besides, the species of Oxygen is [[3,3]] and is over the boundary of the energy list, so need to minus the minimum.

* Update utils.py

* enable EnergyShifter scripting

* fix

* fix

* make aev,model compatible with jit

* add type annotation to nn

* flake8 fix

* refactor AEVComputer

* fix doc

* an example with padding

* use Optional type instead of padding

* fix

* fix

* make pbc and cell keyword arguments in test_aev

* fix

* make pbc and cell keyword arguments in ase

* fix

* fix

* fix dtype

* fix

* aev_computer dtype to double

* change test files to have aev_computer with keyword argument

* fix JIT types

* add TestAEVJIT

* fix LGTM alerts

* fix TestAEVJIT

* Update aev.py

workaround for dtype in `torch.arange`

* More arange bugs

* Even more arange

* fix LGTM alert

* cached

* typo and comments

* easy to read

* change some names

* fix unit test

* empty line

* fix

* fix

* add docs and add whether include_energies

* docs

* other properties for shuffled dataset

* docs

* dtype for benchmark

* add properties to test

* style

* Fix dependency in setup.py: `torch-nightly` has been renamed to `torch`

`torch-nightly` has been renamed to `torch`

* Update install_dependencies.sh

* Update install_dependencies_python2.sh

* Update deploy-docs.yml

* Update docs.yml

* Update tools.yml

* Update setup.py

* Update start.rst

* Update runnable_submodules.yml

* Update install_dependencies.sh

* Update install_dependencies_python2.sh

* Update start.rst

* Update README.md

* Update README.md

* split

* clean

* docs

* docs

* Update new.py

* Remove unnecessary import

* fix

* Accerate angular AEV computation and reduce memory cost

* reduce number of elementwise product

* outlier removal process fixed

* remove outlier energies if exist

* Support len in ChemicalSymbolsToInts

* fix

* flake8

https://pypi.org/project/torchani/

* Update nnp_training to have identical setup to NC

* fix

* add LR decay scheduler for both optimizers

* fix

* update example files

* fix

* fix

* Update nnp_training to have identical setup to NC

* fix

* add LR decay scheduler for both optimizers

* fix