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Description:

daggerml 0.0.15

Dagger-ML Python Library
Prerequisites
pipx
If pipx is not installed, first do that.
hatch
Then install hatch via: pipx install hatch.
Configuration
# help
python -m daggerml --help

# configure global API endpoint
python -m daggerml configure \
--global \
--profile ${DML_PROFILE} \ # optional
--api-endpoint https://api.${DML_ZONE}-${AWS_REGION}.${DOMAIN}

# configure API key
python -m daggerml login \
--profile ${DML_PROFILE} \ # optional
--username ${USERNAME}

# configure group ID for local project
python -m daggerml configure --group-id ${DML_GROUP_ID}

# bash command completion
pip install argcomplete

# enable completion for dml in this shell
eval "$(register-python-argcomplete dml)"

# enable completion for dml via bashrc
cat <<'EOT' >> ~/.bashrc
eval "$(register-python-argcomplete dml)"
EOT

# or see: https://kislyuk.github.io/argcomplete/#activating-global-completion

Usage
You currently need AWS_DEFAULT_REGION and DML_ZONE environment variables
set. Then you can run python bootstrap-docker.py, for instance.
bootstrap-docker.py sets up the docker-build func, so you can now run docker
stuff in your dags (e.g. as we do in the docs/examples/ directory).
Run Locally
# Start local postgres:
sudo systemctl start postgresql

# Connect to local postgres:
psql -h localhost postgres postgres

# Start local DML API server:
python infra/lib/api/server.py

# Run dag locally
DML_LOCAL_DB=1 python mydag.py

Docs
To build the docs, first make sure bootstrap-docker.py has been run, then
run: hatch run docs:build
To serve the docs: hatch run docs:serve
Tests
To run the tests: hatch run test:cov