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• Overview
• Repositories 90
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• Stars 567
• Sponsoring 4

jameslamb (James Lamb) · GitHub

I'm James, an engineer / data scientist from Chicago. My time on GitHub is mostly spent writing Python, R, and shell scripts on projects for data scientists and data engineers. My time off GitHub is spent with family, at hip hop shows, and watching reality TV.

• LightGBM: a lightweight gradient boosting machine
• lightgbm-dask-testing: containerized setup for testing LightGBM's Dask interface locally and on Amazon ECS
• pkgnet: R package for analyzing an R package's dependencies
• pydistcheck: linter that finds portability issues in Python package distributions (wheels, sdists, and conda packages)
• uptasticsearch: an R data frame client for Elasticsearch

• hamilton: a "micro-framework" for feature engineering in Python
• prefect: a workflow management thing in Python that plays nicely with Dask
• xgboost: another gradient boosting machine

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.

• adapting lightgbm and xgboost to scikit-learn 1.6:
  • microsoft/LightGBM#6651
  • microsoft/LightGBM#6733
  • microsoft/LightGBM#6718
  • dmlc/xgboost#11021
• setting up conda packages for legate-boost, legate-dataframe, and legate-raft: rapidsai/legate-boost#115
• 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

• fixing OpenMP conflicts in lightgbm:
  • microsoft/LightGBM#6595 (comment)
  • microsoft/LightGBM#6629
  • microsoft/LightGBM#6489
  • microsoft/LightGBM#6391
• 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

• helping with various migrations for all of the RAPIDS libraries:
  • updating to newer fmt / spdlog: rapidsai/build-planning#56
  • Dropping Python 3.9: rapidsai/build-planning#88
  • CUDA 12.5: rapidsai/build-planning#73
  • Adding Python 3.12: rapidsai/build-planning#40
  • Adding Python 3.11: rapidsai/build-planning#3
• 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

• Sr. Software Engineer at NVIDIA, working on RAPIDS (https://github.com/rapidsai)
• adjunct instructor at Marquette University, where I teach "Intro to R Programming"

I've given talks on Dask, LightGBM, R, Python packaging, and other random stuff. For a full list and links to videos, see https://github.com/jameslamb/talks#gallery.

My DMs are open if you want to talk about open source, data science careers, Bravo shows, or anything else.

• 🔗 LinkedIn: https://www.linkedin.com/in/jameslamb1/
• 🦋 Bluesky: https://bsky.app/profile/jameslamb.bsky.social

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