jameslamb / devcontainers Goto Github PK

The pull requests and none-code contributions below were chosen to showcase the types of software work I've done. This list is not exhaustive.

• replacing LightGBM's setup.py with scikit-build-core for PEP 517/518 compatibility: microsoft/LightGBM#5759
• upstreaming dask-lightgbm into LightGBM and guiding community discussion with Dask, XGBoost maintainers
  • dask/community#104
  • microsoft/LightGBM#3515
• adding Webhook storage to prefect: PrefectHQ/prefect#3000
• adding autoconf-based builds of LightGBM's R package: microsoft/LightGBM#3188
• making snowflake-connector-python compatible with pyjwt 1.x and 2.x: snowflakedb/snowflake-connector-python#604
• allow tight control over ports in LightGBM distributed traiining with Dask: microsoft/LightGBM#3994
• cut compiled size of {lightgbm} by ignoring CLI-only objects: microsoft/LightGBM#3566
• allow use of multiple image pull secrets in prefect kubernetes agent: PrefectHQ/prefect#3596
• replace single-shot HTTP requests with httr::RETRY() in various R packages
  • project I led at Chi R Collab 2020: chircollab/chircollab20#1
  • {sergeant} (one example): hrbrmstr/sergeant#42

• tutorial on distributed LightGBM training with Dask: microsoft/LightGBM#4030
• early stopping example in XGBoost Dask docs: dmlc/xgboost#6501
• detailed information on how LightGBM parameters affect training speed: microsoft/LightGBM#3628
• guide on how to find valid memory and CPU combinations for ECS / Fargate clusters in dask-cloudprovider: dask/dask-cloudprovider#156

• detecting debug symbols in pandas 2.0 wheels: pandas-dev/pandas#51900
• prevent conda from "downgrading" Python from CPython to PyPy, while also reducing the risk of a subtle networking error made worse by unpredictability in when Dask garbage collects objects (microsoft/LightGBM#5510)
• create a reproducible example for lightgbm loading failing with GLIBCXX compatibility errors: microsoft/LightGBM#5106 (comment)
• fix jupyter_server conda-forge feedstock recipe to prevent broken environments: conda-forge/jupyter_server-feedstock#84
• make multioutput behavior of dask-ml regression metrics consistent with scikit-learn: dask/dask-ml#820
• fix saving Dask Random Forest models in cuml: rapidsai/cuml#3388
• fix checks for availability of mm_malloc in {lightgbm} autoconf-based builds: microsoft/LightGBM#3510
• fix broken plots in {lightgbm}'s docs site: microsoft/LightGBM#3508
• factor out dependency on gendef.exe for compiling XGBoost and LightGBM R packages with Visual Studio compilers and R 4.0:
  • {xgboost}: dmlc/xgboost#5764
  • {lightgbm}: microsoft/LightGBM#3065

• switching LightGBM's Python package jobs to manylinux_2_28: microsoft/LightGBM#5580
• automatically publish prefect-saturn to PyPI when a new release is created: saturncloud/prefect-saturn#7
• moving LightGBM CI jobs from Travis to GitHub Actions:
  • microsoft/LightGBM#3745
  • microsoft/LightGBM#3726
  • microsoft/LightGBM#3119
• move {uptasticsearch} CI to GitHub Actions: uptake/uptasticsearch#217
• add CI job testing {lightgbm} within ASAN and UBSAN sanitizers: microsoft/LightGBM#3439
• reduce data loading work in LightGBM tests by caching data loading calls: microsoft/LightGBM#3486
• add Dockerfile to build an image for testing the Apache Arrow R package: apache/arrow#2770