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Introduction

In this demo, we show an end-to-end workflow of rapids dask-cudf + dask-glm (doc) LogisticRegression on mutliple GPUs. Experiments are done on a DGX-1 with 8xGPUs and 40-core CPUs. With the HIGGS dataset, the GPU solution achieves 14x speedup over CPU using the lbfgs solver. To get a more comprehensive speedup measurement, we run dask-glm on vared sizes of random synthetic data and the GPU solution achieves up to 27x speedup over CPU.

Background

Multi-GPU support of dask-glm is enabled by recent efforts of allowing cupy dask arrays as inputs. dask/dask-glm#87 and dask/dask-glm#89

dask-glm offers 3 estimators:

and 5 solvers:

Currently, all 3 estimators and 5 algorithms work seamlessly with dask cupy arrays and dask-cudf on multiple GPUs.

Install instructions: