Integrating MLflow with NVIDIA RAPIDS (GPU)

Training and tuning ML models is a long and computationally expensive operation and is one of the operations that can benefit the most from parallel processing. We will explore in this section the integration of your MLflow training jobs, including hyperparameter optimization, with the NVIDIA RAPIDS framework.

To integrate the NVIDIA RAPIDS library, follow the next steps:

1. Install RAPIDS in the most convenient way for your environment, outlined as follows:

   a. https://rapids.ai/start.html contains detailed information on deployment options.

   b. https://developer.nvidia.com/blog/run-rapids-on-google-colab/ details how to run RAPIDS on Google Colaboratory (Google Colab).

2. Install MLflow in your environment.
3. Import the needed libraries, as follows:

   import argparse
   from functools import partial
   import mlflow
   import mlflow.sklearn
   from cuml.metrics.accuracy import accuracy_score
   from cuml.preprocessing.model_selection import train_test_split...