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| author | CoprDistGit <infra@openeuler.org> | 2023-05-18 03:42:15 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-18 03:42:15 +0000 |
| commit | f4d9bcbc1fdc082e17d2c45af3415cd208302ba9 (patch) | |
| tree | ed26e1716304eeb3f825ee5eeebad330c1e7d7e4 /python-bavard-ml-utils.spec | |
| parent | a75b3c3ebb1e78eae456f139f49d49f6845f7255 (diff) | |
automatic import of python-bavard-ml-utils
Diffstat (limited to 'python-bavard-ml-utils.spec')
| -rw-r--r-- | python-bavard-ml-utils.spec | 306 |
1 files changed, 306 insertions, 0 deletions
diff --git a/python-bavard-ml-utils.spec b/python-bavard-ml-utils.spec new file mode 100644 index 0000000..1153eb0 --- /dev/null +++ b/python-bavard-ml-utils.spec @@ -0,0 +1,306 @@ +%global _empty_manifest_terminate_build 0 +Name: python-bavard-ml-utils +Version: 0.2.10 +Release: 1 +Summary: Utilities for machine learning, python web services, and cloud infrastructure +License: MIT +URL: https://github.com/bavard-ai/bavard-ml-utils +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b2/95/d76f45db18ecbf1cd50fe9bfac5164fc97b3e2ec87a8c45dd8c1cb7b3eaa/bavard-ml-utils-0.2.10.tar.gz +BuildArch: noarch + +Requires: python3-fastapi +Requires: python3-pydantic +Requires: python3-numpy +Requires: python3-scikit-learn +Requires: python3-networkx +Requires: python3-requests +Requires: python3-loguru +Requires: python3-google-cloud-storage +Requires: python3-google-cloud-pubsub +Requires: python3-google-cloud-error-reporting +Requires: python3-google-cloud-firestore +Requires: python3-boto3 + +%description +# bavard-ml-utils + +[](https://circleci.com/gh/bavard-ai/bavard-ml-utils/tree/main) +[](https://badge.fury.io/py/bavard-ml-utils) +[](https://pepy.tech/project/bavard-ml-utils) +[](https://pypi.org/project/bavard-ml-utils/) +[](https://opensource.org/licenses/MIT) + +A package of common code and utilities for machine learning and MLOps. Includes classes and methods for: + +1. ML model serialization/deserialization +2. Google Cloud Storage IO operations +3. Converting a ML model into a runnable web service +4. Common ML model evaluation utilities +5. Common data structures/models used across the Bavard AI organization +6. ML model artifact persistence and version management +7. And more + +This package maintains common data structures used across our organization. They can all be found in the `bavard_ml_utils.types` sub-package, and are all [Pydantic](https://pydantic-docs.helpmanual.io/) data models. For example the `bavard_ml_utils.types.agent.AgentConfig` class represents a chatbot's configuration and training data, and is used heavily across Bavard. + +API docs for this package can be found [here](https://docs-bavard-ml-utils.web.app/). + +## Getting Started + +To begin using the package, use your favorite package manager to install it from PyPi. For example, using pip: + +``` +pip install bavard-ml-utils +``` + +Some of the features in this repo require more heavy weight dependencies, like Google Cloud Platform related utilities, or utilities specific to machine-learning. If you try to import those features, they will tell you if you do not have the correct package extra installed. For example, many of the features in the `bavard_ml_utils.gcp` sub-package require the `gcp` extra. To install `bavard-ml-utils` with that extra: + +``` +pip install bavard-ml-utils[gcp] +``` + +You can then begin using any package features that require GCP dependencies. + +## Developing Locally + +Before making any new commits or pull requests, please complete these steps. + +1. Install the Poetry package manager for Python if you do not already have it. Installation instructions can be found [here](https://python-poetry.org/docs/#installation). +2. Clone the project: + ``` + git clone https://github.com/bavard-ai/bavard-ml-utils.git + cd bavard-ml-utils + ``` +3. Install the dependencies, including all dev dependencies and extras: + ``` + poetry install --extras "gcp ml" + ``` +4. Install the pre-commit hooks, so they will run before each local commit. This includes linting, auto-formatting, and import sorting: + ``` + pre-commit install + ``` + +## Testing Locally + +With Docker and docker-compose installed, run this script from the project root: + +``` +./scripts/lint-and-test-package.sh +``` + +## Releasing The Package + +Releasing the package is automatically handled by CI, but three steps must be taken to trigger a successful release: + +1. Use Poetry's [`version` command](https://python-poetry.org/docs/cli/#version) to bump the package's version. +2. Commit and tag the repo with the exact same version the package was bumped to, e.g. `1.0.0` (note there is no preceding `v`.) +3. Push the commit and tag to remote. These can be done together using: `git push --atomic origin <branch name> <tag>` + +CI will then build release the package to pypi with that version once the commit and tag are pushed. + + +%package -n python3-bavard-ml-utils +Summary: Utilities for machine learning, python web services, and cloud infrastructure +Provides: python-bavard-ml-utils +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-bavard-ml-utils +# bavard-ml-utils + +[](https://circleci.com/gh/bavard-ai/bavard-ml-utils/tree/main) +[](https://badge.fury.io/py/bavard-ml-utils) +[](https://pepy.tech/project/bavard-ml-utils) +[](https://pypi.org/project/bavard-ml-utils/) +[](https://opensource.org/licenses/MIT) + +A package of common code and utilities for machine learning and MLOps. Includes classes and methods for: + +1. ML model serialization/deserialization +2. Google Cloud Storage IO operations +3. Converting a ML model into a runnable web service +4. Common ML model evaluation utilities +5. Common data structures/models used across the Bavard AI organization +6. ML model artifact persistence and version management +7. And more + +This package maintains common data structures used across our organization. They can all be found in the `bavard_ml_utils.types` sub-package, and are all [Pydantic](https://pydantic-docs.helpmanual.io/) data models. For example the `bavard_ml_utils.types.agent.AgentConfig` class represents a chatbot's configuration and training data, and is used heavily across Bavard. + +API docs for this package can be found [here](https://docs-bavard-ml-utils.web.app/). + +## Getting Started + +To begin using the package, use your favorite package manager to install it from PyPi. For example, using pip: + +``` +pip install bavard-ml-utils +``` + +Some of the features in this repo require more heavy weight dependencies, like Google Cloud Platform related utilities, or utilities specific to machine-learning. If you try to import those features, they will tell you if you do not have the correct package extra installed. For example, many of the features in the `bavard_ml_utils.gcp` sub-package require the `gcp` extra. To install `bavard-ml-utils` with that extra: + +``` +pip install bavard-ml-utils[gcp] +``` + +You can then begin using any package features that require GCP dependencies. + +## Developing Locally + +Before making any new commits or pull requests, please complete these steps. + +1. Install the Poetry package manager for Python if you do not already have it. Installation instructions can be found [here](https://python-poetry.org/docs/#installation). +2. Clone the project: + ``` + git clone https://github.com/bavard-ai/bavard-ml-utils.git + cd bavard-ml-utils + ``` +3. Install the dependencies, including all dev dependencies and extras: + ``` + poetry install --extras "gcp ml" + ``` +4. Install the pre-commit hooks, so they will run before each local commit. This includes linting, auto-formatting, and import sorting: + ``` + pre-commit install + ``` + +## Testing Locally + +With Docker and docker-compose installed, run this script from the project root: + +``` +./scripts/lint-and-test-package.sh +``` + +## Releasing The Package + +Releasing the package is automatically handled by CI, but three steps must be taken to trigger a successful release: + +1. Use Poetry's [`version` command](https://python-poetry.org/docs/cli/#version) to bump the package's version. +2. Commit and tag the repo with the exact same version the package was bumped to, e.g. `1.0.0` (note there is no preceding `v`.) +3. Push the commit and tag to remote. These can be done together using: `git push --atomic origin <branch name> <tag>` + +CI will then build release the package to pypi with that version once the commit and tag are pushed. + + +%package help +Summary: Development documents and examples for bavard-ml-utils +Provides: python3-bavard-ml-utils-doc +%description help +# bavard-ml-utils + +[](https://circleci.com/gh/bavard-ai/bavard-ml-utils/tree/main) +[](https://badge.fury.io/py/bavard-ml-utils) +[](https://pepy.tech/project/bavard-ml-utils) +[](https://pypi.org/project/bavard-ml-utils/) +[](https://opensource.org/licenses/MIT) + +A package of common code and utilities for machine learning and MLOps. Includes classes and methods for: + +1. ML model serialization/deserialization +2. Google Cloud Storage IO operations +3. Converting a ML model into a runnable web service +4. Common ML model evaluation utilities +5. Common data structures/models used across the Bavard AI organization +6. ML model artifact persistence and version management +7. And more + +This package maintains common data structures used across our organization. They can all be found in the `bavard_ml_utils.types` sub-package, and are all [Pydantic](https://pydantic-docs.helpmanual.io/) data models. For example the `bavard_ml_utils.types.agent.AgentConfig` class represents a chatbot's configuration and training data, and is used heavily across Bavard. + +API docs for this package can be found [here](https://docs-bavard-ml-utils.web.app/). + +## Getting Started + +To begin using the package, use your favorite package manager to install it from PyPi. For example, using pip: + +``` +pip install bavard-ml-utils +``` + +Some of the features in this repo require more heavy weight dependencies, like Google Cloud Platform related utilities, or utilities specific to machine-learning. If you try to import those features, they will tell you if you do not have the correct package extra installed. For example, many of the features in the `bavard_ml_utils.gcp` sub-package require the `gcp` extra. To install `bavard-ml-utils` with that extra: + +``` +pip install bavard-ml-utils[gcp] +``` + +You can then begin using any package features that require GCP dependencies. + +## Developing Locally + +Before making any new commits or pull requests, please complete these steps. + +1. Install the Poetry package manager for Python if you do not already have it. Installation instructions can be found [here](https://python-poetry.org/docs/#installation). +2. Clone the project: + ``` + git clone https://github.com/bavard-ai/bavard-ml-utils.git + cd bavard-ml-utils + ``` +3. Install the dependencies, including all dev dependencies and extras: + ``` + poetry install --extras "gcp ml" + ``` +4. Install the pre-commit hooks, so they will run before each local commit. This includes linting, auto-formatting, and import sorting: + ``` + pre-commit install + ``` + +## Testing Locally + +With Docker and docker-compose installed, run this script from the project root: + +``` +./scripts/lint-and-test-package.sh +``` + +## Releasing The Package + +Releasing the package is automatically handled by CI, but three steps must be taken to trigger a successful release: + +1. Use Poetry's [`version` command](https://python-poetry.org/docs/cli/#version) to bump the package's version. +2. Commit and tag the repo with the exact same version the package was bumped to, e.g. `1.0.0` (note there is no preceding `v`.) +3. Push the commit and tag to remote. These can be done together using: `git push --atomic origin <branch name> <tag>` + +CI will then build release the package to pypi with that version once the commit and tag are pushed. + + +%prep +%autosetup -n bavard-ml-utils-0.2.10 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-bavard-ml-utils -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.10-1 +- Package Spec generated |