New Features
- define on graph transformation a level of granularity for structure extraction
- estimate a graph size during the inference-duckpunching-phase of the transformation
- mapping between network models and graphs (partially there)
- scalable graph themes for transformation into function space
General
re-work pruning to enable different strategies with an outside-model object-oriented software design
document how to extract a single mask
document how to initialize a deep directed acyclic network
document how to train models with data, e.g. even with pytorch ignite
document the graph transformation via duckpunching
describe idea of graph themes
describe architecture of deepstruct with flowcharts / visualizations
describe idea of mapping between network model and graphs (we use networkx)
sparse recurrent network models
organize and explain when to use which sparse model in application
Consider a reversed naming scheme for variables, i.e. parameter_lr for a learning rate parameter.
The advantage of it is to have a naming scheme which allows for fast auto-complete etc.
poetry build
twine upload dist/*- Create wheel files in dist/:
poetry build - Install wheel in current environment with pip:
pip install path/to/deepstruct/dist/deepstruct-0.1.0-py3-none-any.whl
Install latest gitlab-runner (version 12.3 or up):
# For Debian/Ubuntu/Mint
curl -L https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh | sudo bash
# For RHEL/CentOS/Fedora
curl -L https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.rpm.sh | sudo bash
apt-get update
apt-get install gitlab-runner
$ gitlab-runner -v
Version: 12.3.0Execute job tests: gitlab-runner exec docker test-python3.9
Install https://github.com/nektos/act.
Run act
- Execute pre-commit manually:
poetry run pre-commit run --all-files - Update pre-commit:
poetry run pre-commit autoupdate - Add pre-commit to your local git:
poetry run pre-commit install