-
Notifications
You must be signed in to change notification settings - Fork 1.9k
Release Process
Jacob Alber edited this page Sep 15, 2022
·
29 revisions
- No warnings emitted when built
- Consistent formatting - Enforced by CI
- No valgrind errors - Enforced by CI
- Update
version.txt
with new version number (Check for any other places in the repo that may reference the version number) - Commit and push the version number update and PR to
master
branch -
Use GitHub UI to draft a new release
- Input the version number in
version.txt
into theTag version
field - Doing it this way ensures there is a single linear history of releases
- Input the version number in
- Install tool:
pip install git+https://github.com/jackgerrits/git-changelog-generator.git
- Run tool:
NEW_VERSION=8.10.0
PREV_VERSION=8.9.0
GIT_REV_RANGE=${PREV_VERSION}..HEAD
VW_REPO_DIR=/path/to/vowpal_wabbit/
VW_PR_URL="https://github.com/VowpalWabbit/vowpal_wabbit/pull/"
git-changelog-generator \
--file utl/release_blog_post_template.md \
--range ${GIT_REV_RANGE} \
--dir ${VW_REPO_DIR} \
--additional_data github_pr_url=${VW_PR_URL} \
--additional_data version=${NEW_VERSION} > blog_post_start.md
See here
Run:
brew bump-formula-pr vowpal-wabbit --version=<version i.e. 9.0.1>
See sample PR https://github.com/Homebrew/homebrew-core/pull/81257
See here
1. Trigger a build on the "Windows Official" pipeline against the release tag
2. Trigger a release on the Windows-Official-NuGet-Public release pipeline (it should automatically pick up the latest generated artifact from the build pipeline in (1))
Currently manual process to upload artifacts generated by the dotnet_nugets.yml GH Action
- Home
- First Steps
- Input
- Command line arguments
- Model saving and loading
- Controlling VW's output
- Audit
- Algorithm details
- Awesome Vowpal Wabbit
- Learning algorithm
- Learning to Search subsystem
- Loss functions
- What is a learner?
- Docker image
- Model merging
- Evaluation of exploration algorithms
- Reductions
- Contextual Bandit algorithms
- Contextual Bandit Exploration with SquareCB
- Contextual Bandit Zeroth Order Optimization
- Conditional Contextual Bandit
- Slates
- CATS, CATS-pdf for Continuous Actions
- Automl
- Epsilon Decay
- Warm starting contextual bandits
- Efficient Second Order Online Learning
- Latent Dirichlet Allocation
- VW Reductions Workflows
- Interaction Grounded Learning
- CB with Large Action Spaces
- CB with Graph Feedback
- FreeGrad
- Marginal
- Active Learning
- Eigen Memory Trees (EMT)
- Element-wise interaction
- Bindings
-
Examples
- Logged Contextual Bandit example
- One Against All (oaa) multi class example
- Weighted All Pairs (wap) multi class example
- Cost Sensitive One Against All (csoaa) multi class example
- Multiclass classification
- Error Correcting Tournament (ect) multi class example
- Malicious URL example
- Daemon example
- Matrix factorization example
- Rcv1 example
- Truncated gradient descent example
- Scripts
- Implement your own joint prediction model
- Predicting probabilities
- murmur2 vs murmur3
- Weight vector
- Matching Label and Prediction Types Between Reductions
- Zhen's Presentation Slides on enhancements to vw
- EZExample Archive
- Design Documents
- Contribute: