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
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re-work pruning to enable different strategies with an outside-model object-oriented software design
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document how to extract a single mask
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document how to initialize a deep directed acyclic network
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document how to train models with data, e.g. even with pytorch ignite
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document the graph transformation via duckpunching
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describe idea of graph themes
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describe architecture of deepstruct with flowcharts / visualizations
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describe idea of mapping between network model and graphs (we use networkx)
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sparse recurrent network models
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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