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https://github.com/jameslamb/jameslamb


https://github.com/jameslamb/jameslamb

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README 

        
### oh hey! 👋



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.





  




#### :shipit: open source stuff I'm maintaining



* [LightGBM](https://github.com/microsoft/LightGBM): a lightweight gradient boosting machine

* [lightgbm-dask-testing](https://github.com/jameslamb/lightgbm-dask-testing): containerized setup for testing LightGBM's Dask interface locally and on Amazon ECS

* [pkgnet](https://github.com/uptake/pkgnet): R package for analyzing an R package's dependencies

* [pydistcheck](https://github.com/jameslamb/pydistcheck): linter that finds portability issues in Python package distributions (wheels and sdists)

* [uptasticsearch](https://github.com/uptake/uptasticsearch): an R data frame client for Elasticsearch



#### ✋ some other open source stuff I've contributed on in the past



* [hamilton](https://github.com/stitchfix/hamilton/pulls?q=is%3Apr+author%3Ajameslamb+is%3Aclosed): a "micro-framework" for feature engineering in Python

* [prefect](https://github.com/PrefectHQ/prefect/pulls?q=is%3Apr+author%3Ajameslamb+is%3Aclosed): a workflow management thing in Python that plays nicely with Dask

* [xgboost](https://github.com/dmlc/xgboost/pulls?q=is%3Apr+author%3Ajameslamb+is%3Aclosed): another gradient boosting machine



### 😊 open source contributions I'm proud of



click for details



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.



#### Features



* replacing LightGBM's `setup.py` with `scikit-build-core` for PEP 517/518 compatibility: https://github.com/microsoft/LightGBM/pull/5759

* upstreaming `dask-lightgbm` into LightGBM and guiding community discussion with Dask, XGBoost maintainers

    - https://github.com/dask/community/issues/104

    - https://github.com/microsoft/LightGBM/pull/3515

* adding `Webhook` storage to `prefect`: https://github.com/PrefectHQ/prefect/pull/3000

* adding `autoconf`-based builds of LightGBM's R package: https://github.com/microsoft/LightGBM/pull/3188

* making `snowflake-connector-python` compatible with `pyjwt` 1.x and 2.x: https://github.com/snowflakedb/snowflake-connector-python/pull/604

* allow tight control over ports in LightGBM distributed traiining with Dask: https://github.com/microsoft/LightGBM/pull/3994

* cut compiled size of `{lightgbm}` by ignoring CLI-only objects: https://github.com/microsoft/LightGBM/pull/3566

* allow use of multiple image pull secrets in `prefect` kubernetes agent: https://github.com/PrefectHQ/prefect/pull/3596

* replace single-shot HTTP requests with `httr::RETRY()` in various R packages

    - project I led at Chi R Collab 2020: https://github.com/chircollab/chircollab20/issues/1

    - `{sergeant}` (one example): https://github.com/hrbrmstr/sergeant/pull/42



#### Docs



* tutorial on distributed LightGBM training with Dask: https://github.com/microsoft/LightGBM/pull/4030

* early stopping example in XGBoost Dask docs: https://github.com/dmlc/xgboost/pull/6501

* detailed information on how LightGBM parameters affect training speed: https://github.com/microsoft/LightGBM/pull/3628

* guide on how to find valid memory and CPU combinations for ECS / Fargate clusters in `dask-cloudprovider`: https://github.com/dask/dask-cloudprovider/pull/156



#### Bug Fixes



* detecting debug symbols in `pandas` 2.0 wheels: https://github.com/pandas-dev/pandas/issues/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 (https://github.com/microsoft/LightGBM/pull/5510)

* create a reproducible example for `lightgbm` loading failing with `GLIBCXX` compatibility errors: https://github.com/microsoft/LightGBM/issues/5106#issuecomment-1121925896

* fix `jupyter_server` conda-forge feedstock recipe to prevent broken environments: https://github.com/conda-forge/jupyter_server-feedstock/pull/84

* make multioutput behavior of `dask-ml` regression metrics consistent with `scikit-learn`: https://github.com/dask/dask-ml/pull/820

* fix saving Dask Random Forest models in `cuml`: https://github.com/rapidsai/cuml/pull/3388

* fix checks for availability of `mm_malloc` in `{lightgbm}` autoconf-based builds: https://github.com/microsoft/LightGBM/pull/3510

* fix broken plots in `{lightgbm}`'s docs site: https://github.com/microsoft/LightGBM/pull/3508

* factor out dependency on `gendef.exe` for compiling XGBoost and LightGBM R packages with Visual Studio compilers and R 4.0:

    - `{xgboost}`: https://github.com/dmlc/xgboost/pull/5764

    - `{lightgbm}`: https://github.com/microsoft/LightGBM/pull/3065



#### Infrastructure / CI



* switching LightGBM's Python package jobs to `manylinux_2_28`: https://github.com/microsoft/LightGBM/pull/5580

* automatically publish `prefect-saturn` to PyPI when a new release is created: https://github.com/saturncloud/prefect-saturn/pull/7

* moving LightGBM CI jobs from Travis to GitHub Actions:

    - https://github.com/microsoft/LightGBM/pull/3745

    - https://github.com/microsoft/LightGBM/pull/3726

    - https://github.com/microsoft/LightGBM/pull/3119

* move `{uptasticsearch}` CI to GitHub Actions: https://github.com/uptake/uptasticsearch/pull/217

* add CI job testing `{lightgbm}` within ASAN and UBSAN sanitizers: https://github.com/microsoft/LightGBM/pull/3439

* reduce data loading work in LightGBM tests by caching data loading calls: https://github.com/microsoft/LightGBM/pull/3486

* add Dockerfile to build an image for testing the Apache Arrow R package: https://github.com/apache/arrow/pull/2770



#### 💰 things I do for money



* Sr. Software Engineer at NVIDIA, working on RAPIDS (https://github.com/rapidsai)

* adjunct instructor at Marquette University, where I teach "Intro to R Programming"



#### :computer: conference talks



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.



#### :microphone: talk to me!



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



* :bird: Twitter: https://twitter.com/_jameslamb

* :link: LinkedIn: https://www.linkedin.com/in/jameslamb1/

* 🐘 Mastodon: https://hachyderm.io/@_jameslamb