Workflow Examples#

Scaling up Hyperparameter Optimization with NVIDIA DGX Cloud and XGBoost GPU Algorithm

library/xgboost library/optuna library/dask library/scikit-learn workflow/hpo cloud/nvidia/pym

Scaling up Hyperparameter Optimization with NVIDIA DGX Cloud and XGBoost GPU Algorithm

Scaling up Hyperparameter Optimization with Kubernetes and XGBoost GPU Algorithm

library/xgboost library/optuna library/dask tools/dask-kubernetes platform/kubernetes platform/kubeflow library/scikit-learn workflow/hpo

Scaling up Hyperparameter Optimization with Kubernetes and XGBoost GPU Algorithm

Scaling up Hyperparameter Optimization with Multi-GPU Workload on Kubernetes

library/xgboost library/optuna library/dask library/dask-kubernetes library/scikit-learn workflow/hpo platforms/kubeflow dataset/nyc-taxi data-storage/gcs data-format/csv platforms/kubernetes

Scaling up Hyperparameter Optimization with Multi-GPU Workload on Kubernetes

Getting Started with Optuna and RAPIDS for HPO

library/optuna library/dask workflow/hpo library/cuml library/numpy dataset/bnp-claims

Getting Started with Optuna and RAPIDS for HPO

Running RAPIDS Hyperparameter Experiments at Scale on Amazon SageMaker

cloud/aws/sagemaker workflow/hpo library/cudf library/cuml library/scikit-learn data-format/csv data-storage/s3

Running RAPIDS Hyperparameter Experiments at Scale on Amazon SageMaker

Deep Dive into Running Hyper Parameter Optimization on AWS SageMaker

cloud/aws/sagemaker workflow/hpo library/xgboost library/cuml library/cupy library/cudf library/dask data-storage/s3 data-format/parquet

Deep Dive into Running Hyper Parameter Optimization on AWS SageMaker

Multi-node Multi-GPU Example on AWS using dask-cloudprovider

cloud/aws/ec2-multi library/cuml library/dask library/numpy library/dask-ml library/cudf workflow/randomforest tools/dask-cloudprovider data-format/csv data-storage/gcs

Multi-node Multi-GPU Example on AWS using dask-cloudprovider

Autoscaling Multi-Tenant Kubernetes Deep-Dive

platform/kubernetes cloud/gcp/gke tools/dask-operator library/cuspatial library/dask library/cudf data-format/parquet data-storage/gcs

Autoscaling Multi-Tenant Kubernetes Deep-Dive

HPO with dask-ml and cuml

dataset/airline library/numpy library/pandas library/xgboost library/dask library/dask-cuda library/dask-ml storage/s3 workflows/hpo library/cuml cloud/aws/ec2 cloud/azure/azure-vm cloud/gcp/compute-engine cloud/ibm/virtual-server library/sklearn

HPO with dask-ml and cuml

Train and Hyperparameter-Tune with RAPIDS on AzureML

workflows/hpo cloud/azure/ml library/cudf library/cuml library/randomforest

Train and Hyperparameter-Tune with RAPIDS on AzureML

Perform Time Series Forecasting on Google Kubernetes Engine with NVIDIA GPUs

platform/kubernetes cloud/gcp/gke tools/dask-operator workflow/hpo workflow/xgboost library/dask library/dask-cuda library/xgboost library/optuna data-storage/gcs

Perform Time Series Forecasting on Google Kubernetes Engine with NVIDIA GPUs

HPO Benchmarking with RAPIDS and Dask

cloud/aws/ec2 data-storage/s3 workflow/randomforest workflow/hpo workflow/xgboost library/dask library/dask-cuda library/xgboost library/optuna library/sklearn library/dask-ml

HPO Benchmarking with RAPIDS and Dask

Training XGBoost with Dask RAPIDS in Databricks

platform/databricks library/dask library/dask-cudf library/xgboost library/dask-deltatable library/dask-databricks library/dask-ml workflow/xgboost dataset/higgs data-format/csv data-storage/databricks-delta-lake

Training XGBoost with Dask RAPIDS in Databricks

Multi-Node Multi-GPU XGBoost Example on Azure using dask-cloudprovider

cloud/azure/azure-vm-multi tools/dask-cloudprovider library/cudf library/cuml library/xgboost library/dask library/fil data-storage/azure-data-lake dataset/nyc-taxi workflow/xgboost

Multi-Node Multi-GPU XGBoost Example on Azure using dask-cloudprovider

Measuring Performance with the One Billion Row Challenge

tools/dask-cuda aws/ec2 aws/sagemaker azure/azure-vm azure/ml gcp/compute-engine gcp/vertex-ai data-format/csv library/cudf library/cupy library/dask library/pandas

Measuring Performance with the One Billion Row Challenge