Skip to content

Latest commit

 

History

History
345 lines (238 loc) · 11 KB

DEVELOPMENT.md

File metadata and controls

345 lines (238 loc) · 11 KB
Raw

Developer Guide

Before getting started, check out the #bentoml-contributors channel in the BentoML community slack.

If you are interested in contributing to existing issues and feature requets, check out the good-first-issue and help-wanted issues list.

If you are interested in proposing a new feature, make sure to create a new feature request ticket here and share your proposal in the #bentoml-contributors slack channel for feedback.

Start Developing

with the Command Line

  1. Make sure to have Git, pip, Python3.9+, and PDM installed.

    Optionally, make sure to have GNU Make available on your system if you aren't using a UNIX-based system for a better developer experience. If you don't want to use make then please refer to the Makefile for specific commands on a given make target.

  2. Fork the BentoML project on GitHub.

  3. Clone the source code from your fork of BentoML's GitHub repository:

    git clone [email protected]:username/BentoML.git && cd BentoML

  4. Add the BentoML upstream remote to your local BentoML clone:

    git remote add upstream [email protected]:bentoml/BentoML.git

  5. Configure git to pull from the upstream remote:

    git switch main # ensure you're on the main branch
git fetch upstream --tags
git branch --set-upstream-to=upstream/main

  6. Install BentoML in editable and all development dependencies:

    pdm install -G all
pre-commit install

    This installs BentoML with editable mode via pdm and development dependencies in a isolated environment. If you wish not to setup within an isolated environment, pass --no-isolation to pdm

    Note: Make sure to prepend pdm run to all commands within this guide if you are using isolated environment via pdm.

  7. Test the BentoML installation either with bash:

    bentoml --version

    or in a Python session:

    import bentoml
print(bentoml.__version__)

with VS Code

  1. Confirm that you have the following installed:

  2. Fork the BentoML project on GitHub.

  3. Clone the GitHub repository:

    1. Open the command palette with Ctrl+Shift+P and type in 'clone'.
    2. Select 'Git: Clone(Recursive)'.
    3. Clone BentoML.

  4. Add an BentoML upstream remote:

    1. Open the command palette and enter 'add remote'.
    2. Select 'Git: Add Remote'.
    3. Press enter to select 'Add remote' from GitHub.
    4. Enter https://github.com/bentoml/BentoML.git to select the BentoML repository.
    5. Name your remote 'upstream'.

  5. Pull from the BentoML upstream remote to your main branch:

    1. Open the command palette and enter 'checkout'.
    2. Select 'Git: Checkout to...'
    3. Choose 'main' to switch to the main branch.
    4. Open the command palette again and enter 'pull from'.
    5. Click on 'Git: Pull from...'
    6. Select 'upstream'.

  6. Open a new terminal by clicking the Terminal dropdown at the top of the window, followed by the 'New Terminal' option. Next, add a virtual environment with this command:

    python -m venv .venv

  7. Click yes if a popup suggests switching to the virtual environment. Otherwise, go through these steps:

    1. Open any python file in the directory.

    2. Select the interpreter selector on the blue status bar at the bottom of the editor. vscode-status-bar

    3. Switch to the path that includes .venv from the dropdown at the top. vscode-select-venv

  8. Update your PowerShell execution policies. Win+x followed by the 'a' key opens the admin Windows PowerShell. Enter the following command to allow the virtual environment activation script to run:

    Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

Making Changes

using the Command Line

  1. Make sure you're on the main branch.

    git switch main

  2. Use the git pull command to retrieve content from the BentoML Github repository.

    git pull

  3. Create a new branch and switch to it.

    git switch -c my-new-branch-name

  4. Make your changes!

  5. Use the git add command to save the state of files you have changed.

    git add <names of the files you have changed>

  6. Commit your changes.

    git commit

  7. Push all changes to your fork on GitHub.

    git push

using VS Code

  1. Switch to the main branch:

    1. Open the command palette with Ctrl+Shift+P.
    2. Search for 'Git: Checkout to...'
    3. Select 'main'.

  2. Pull from the upstream remote:

    1. Open the command palette.
    2. Enter and select 'Git: Pull...'
    3. Select 'upstream'.

  3. Create and change to a new branch:

    1. Type in 'Git: Create Branch...' in the command palette.
    2. Enter a branch name.

  4. Make your changes!

  5. Stage all your changes:

    1. Enter and select 'Git: Stage All Changes...' in the command palette.

  6. Commit your changes:

    1. Open the command palette and enter 'Git: Commit'.

  7. Push your changes:

    1. Enter and select 'Git: Push...' in the command palette.

Run BentoML with verbose/debug logging

To view internal debug loggings for development, set the BENTOML_DEBUG environment variable to TRUE:

export BENTOML_DEBUG=TRUE

And/or use the --verbose option when running bentoml CLI command, e.g.:

bentoml get IrisClassifier --verbose

Style check, auto-formatting, type-checking

We are using pre-commit to manage our hooks, and buf for formatting and linting of our proto files. Configuration can be found here. Currently, we are running buf with docker, hence we kindly ask our developers to have docker available. Docker installation can be found here.

Run linter/format script:

pre-commit run --all-files

Run type checker:

make type

Editing proto files

The proto files for the BentoML gRPC service are located under bentoml/grpc. The generated python files are not checked in the git repository, and are instead generated via this script. If you edit the proto files, make sure to run ./scripts/generate_grpc_stubs.sh to regenerate the proto stubs.

Deploy with your changes

Test out your changes in an actual BentoML model deployment, you can create a new Bento with your custom BentoML source repo:

  1. Install custom BentoML in editable mode. e.g.:
    • git clone your bentoml fork
    • pip install -e PATH_TO_THE_FORK
  2. Set env var export BENTOML_BUNDLE_LOCAL_BUILD=True
  3. Build a new Bento with bentoml build in your project directory
  4. The new Bento will include a wheel file built from the BentoML source, and bentoml containerize will install it to override the default BentoML installation in base image

Distribute a custom BentoML release for your team

If you want other team members to easily use your custom BentoML distribution, you may publish your branch to your fork of BentoML, and have your users install it this way:

pip install git+https://github.com/{YOUR_GITHUB_USERNAME}/bentoml@{YOUR_REVISION}

And in your BentoML projects' bentofile.yaml, force the Bento to install this distribution, e.g.:

service: 'service:svc'
description: 'file: ./README.md'
include:
  - '*.py'
python:
  packages:
    - pandas
    - git+https://github.com/{YOUR_GITHUB_USERNAME}/bentoml@{YOUR_REVISION}
docker:
  system_packages:
    - git

Testing

Make sure to install all dev dependencies:

pdm install

BentoML tests come with a Pytest plugin. Export PYTEST_PLUGINS:

export PYTEST_PLUGINS=bentoml.testing.pytest.plugin

To run all tests with PDM, do the following:

pdm run nox

Adding new test suite

If you are adding new ML framework support, it is recommended that you also add a separate test suite in our CI. Currently we are using GitHub Actions to manage our CI/CD workflow.

We recommend using nektos/act to run and test Actions locally.

Add a new job for your new framework under framework.yml

Python tools ecosystem

Currently, BentoML is PEP518 compatible. We define package configuration via [pyproject.toml][https://github.com/bentoml/bentoml/blob/main/pyproject.toml].

Benchmark

BentoML has moved its benchmark to bentoml/benchmark.

Creating Pull Requests on GitHub

Push changes to your fork and follow this article on how to create a pull request on github. Name your pull request with one of the following prefixes, e.g. "feat: add support for PyTorch". This is based on the Conventional Commits specification

  • feat: (new feature for the user, not a new feature for build script)
  • fix: (bug fix for the user, not a fix to a build script)
  • docs: (changes to the documentation)
  • style: (formatting, missing semicolons, etc; no production code change)
  • refactor: (refactoring production code, eg. renaming a variable)
  • perf: (code changes that improve performance)
  • test: (adding missing tests, refactoring tests; no production code change)
  • chore: (updating grunt tasks etc; no production code change)
  • build: (changes that affect the build system or external dependencies)
  • ci: (changes to configuration files and scripts)
  • revert: (reverts a previous commit)

Once your pull request is created, an automated test run will be triggered on your branch and the BentoML authors will be notified to review your code changes. Once tests are passed and a reviewer has signed off, we will merge your pull request.

Documentations

Refer to BentoML Documentation Guide for how to build and write docs.