Workflow Examples#
End-to-end examples of running CUDA-X on your platform. Search, pick where you run, or filter by task, library and scale.
Topic Modeling on HPC with cuML and Dagster
Fit LDA topic models as parallel Slurm jobs and cluster them with cuML UMAP and HDBSCAN, orchestrated by Dagster.
Fraud Detection MLOps Pipeline
Orchestrate a fraud detection model lifecycle with cuDF, Prefect, MLflow, and Triton champion/challenger deployment.
HPO for Random Forest with Ray Tune and cuML
Tune a cuML random forest on the airline dataset with Ray Tune, running one trial per GPU on a 4-GPU Brev instance.
GPU-Accelerated Land Use Land Cover Classification
Classify land cover from Sentinel-2 imagery using Dask and Xarray composites and a cuML random forest.
Getting Started with cuml.accel in Snowflake
Speed up scikit-learn random forest training in Snowflake Notebooks with cuml.accel and zero code changes.
Streaming Sensitive Data Detection on EKS
Deploy a Kafka streaming pipeline on EKS that filters with cuDF and detects sensitive data with Morpheus and Triton.
Accelerating data analysis using cudf.pandas
Analyze 64M NYC ride-share trips from S3 with cudf.pandas on a Coiled GPU notebook without changing pandas code.
Getting Started with cudf.pandas and Snowflake
Analyze NYC parking violations from a Snowflake table with cudf.pandas in Snowpark Container Services.
Multi-GPU XGBoost HPO on Kubernetes
Tune multi-GPU XGBoost on NYC taxi data by running Optuna trials across several Dask clusters on Kubernetes.
Measuring Performance with the One Billion Row Challenge
Generate one billion rows with cuDF and CuPy, then aggregate them with Dask and cuDF about 60x faster than pandas.
Training XGBoost with Dask in Databricks
Train XGBoost on HIGGS data read from Delta Lake with Dask and cuDF on a multi-node Databricks GPU cluster.
Multi-Node XGBoost on Azure VMs
Train XGBoost on NYC taxi data across a multi-node Azure VM cluster with dask-cloudprovider, then predict with FIL.
Time Series Forecasting on GKE
Forecast Walmart M5 sales by engineering features with cuDF and tuning XGBoost with Optuna on a GKE Dask cluster.
Random Forest HPO on Azure ML
Tune a cuML random forest on the airline dataset with an Azure ML sweep job running parallel trials on a GPU cluster.
HPO Benchmarking with CUDA-X libraries and Dask
Benchmark XGBoost and random forest HPO with Optuna on GPU versus CPU EC2 instances, up to 48x faster on GPU.
HPO with dask-ml and cuml
Tune XGBoost and a cuML random forest on airline data with dask-ml grid and random search across local GPUs.
Getting Started with Optuna and CUDA-X libraries for HPO
Tune a cuML logistic regression on the BNP Paribas claims data with Optuna trials spread across local GPUs by Dask.
Autoscaling Multi-Tenant Kubernetes Deep-Dive
Simulate many users running Dask GPU workloads on an autoscaling GKE cluster and measure wait times and GPU utilization.
Multi-node Multi-GPU Example on AWS using dask-cloudprovider
Train a cuML random forest on NYC taxi data on a multi-node GPU Dask cluster launched on EC2 with dask-cloudprovider.
HPO Deep Dive on SageMaker
Tune XGBoost, random forest, or KMeans on airline data across parallel SageMaker jobs, then deploy the best model.
HPO Experiments at Scale on SageMaker
Tune a cuML random forest on the HIGGS dataset with SageMaker Bayesian HPO jobs in a custom container.
Job-Parallel XGBoost HPO on Kubernetes
Run hundreds of parallel XGBoost HPO trials with Optuna on a GPU Dask cluster launched by the Dask Kubernetes operator.
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