# RAPIDS Deployment Documentation > RAPIDS Deployment Documentation documentation 2025, NVIDIA ## Pages - [NVIDIA NIM Microservices](nims/index.html.md): NeMo Retriever - [Local](local/index.html.md): Choose your preferred installation method for running RAPIDS - [HPC](hpc/index.html.md): RAPIDS works extremely well in traditional HPC (High Performance Computing) environments where GPUs ... - [Continuous Integration](index.html.md): GitHub Actions - [Custom RAPIDS Docker Guide](custom-docker/index.html.md): This guide provides instructions for building custom RAPIDS Docker containers. This approach allows ... - [Continuous Integration](index.html.md): GitHub Actions - [Home](_includes/test-rapids-docker-vm/index.html.md): To access Jupyter, navigate to `:8888` in the browser. - [Home](_includes/check-gpu-pod-works/index.html.md): Let’s create a sample Pod that uses some GPU compute to make sure that everything is working as expe... - [NOTE](_includes/install-rapids-with-docker/index.html.md): There are a selection of methods you can use to install RAPIDS which you can see via the [RAPIDS rel... - [Continuous Integration](index.html.md): GitHub Actions - [Does the Dask scheduler need a GPU?](guides/scheduler-gpu-requirements/index.html.md): A common question from users deploying Dask clusters is whether the scheduler has different minimum ... - [Colocate Dask workers on Kubernetes while using nodes with multiple GPUs](guides/colocate-workers/index.html.md): To optimize performance when working with nodes that have multiple GPUs, a best practice is to sched... - [GPU optimization for the Dask scheduler on Kubernetes](guides/scheduler-gpu-optimization/index.html.md): An optimization users can make while deploying Dask clusters is to ensure that the scheduler is plac... - [Continuous Integration](index.html.md): GitHub Actions - [Caching Docker Images For Autoscaling Workloads](guides/caching-docker-images/index.html.md): The [Dask Autoscaler](https://kubernetes.dask.org/en/latest/operator_resources.html#daskautoscaler) ... - [Building RAPIDS containers from a custom base image](guides/rapids-docker-with-cuda/index.html.md): This guide provides instructions to add RAPIDS and CUDA to your existing Docker images. This approac... - [Multi-Instance GPU (MIG)](guides/mig/index.html.md): [Multi-Instance GPU](https://www.nvidia.com/en-us/technologies/multi-instance-gpu/) is a technology ... - [Continuous Integration](index.html.md): GitHub Actions - [dask-cuda](tools/dask-cuda/index.html.md): [Dask-CUDA](https://docs.rapids.ai/api/dask-cuda/nightly/) is a library extending `LocalCluster` fro... - [Kubernetes](platforms/kubernetes/index.html.md): RAPIDS integrates with Kubernetes in many ways depending on your use case. - [Coiled](platforms/coiled/index.html.md): You can deploy RAPIDS on cloud VMs with GPUs using [Coiled](https://www.coiled.io/). - [Kubeflow](platforms/kubeflow/index.html.md): You can use RAPIDS with Kubeflow in a single Pod with [Kubeflow Notebooks](https://www.kubeflow.org/... - [Continuous Integration](index.html.md): GitHub Actions - [Anaconda Cloud Notebooks](platforms/anaconda/index.html.md): You can run RAPIDS workloads on [Anaconda Cloud Notebooks](https://www.anaconda.com/products/noteboo... - [Snowflake](platforms/snowflake/index.html.md): You can access `cuDF` and `cuML` in the [Snowflake Notebooks on Container Runtime for ML](https://do... - [Databricks](platforms/databricks/index.html.md): You can install RAPIDS on Databricks in a few different ways: - [NVIDIA AI Workbench](platforms/nvidia-ai-workbench/index.html.md): [NVIDIA AI Workbench](https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/workbench... - [KServe](platforms/kserve/index.html.md): [KServe](https://kserve.github.io/website) is a standard model inference platform built for Kubernet... - [RAPIDS on Google Colab](platforms/colab/index.html.md): RAPIDS cuDF is preinstalled on Google Colab and instantly accelerates Pandas with zero code changes.... - [Continuous Integration](index.html.md): GitHub Actions - [Continuous Integration](index.html.md): GitHub Actions - [Virtual Server for VPC](cloud/ibm/virtual-server/index.html.md): Create a new [Virtual Server (for VPC)](https://www.ibm.com/cloud/virtual-servers) with GPUs, the [N... - [Azure Virtual Machine](cloud/azure/azure-vm/index.html.md): Create a new [Azure Virtual Machine](https://azure.microsoft.com/en-gb/products/virtual-machines/) w... - [Azure Machine Learning](cloud/azure/azureml/index.html.md): RAPIDS can be deployed at scale using [Azure Machine Learning Service](https://learn.microsoft.com/e... - [Continuous Integration](index.html.md): GitHub Actions - [Azure VM Cluster (via Dask)](cloud/azure/azure-vm-multi/index.html.md): The easiest way to setup a multi-node, multi-GPU cluster on Azure is to use [Dask Cloud Provider](ht... - [Azure Kubernetes Service](cloud/azure/aks/index.html.md): RAPIDS can be deployed on Azure via the [Azure Kubernetes Service](https://azure.microsoft.com/en-us... - [Compute Engine Instance](cloud/gcp/compute-engine/index.html.md): Create a new [Compute Engine Instance](https://cloud.google.com/compute/docs/instances) with GPUs, t... - [Vertex AI](cloud/gcp/vertex-ai/index.html.md): RAPIDS can be deployed on [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench). - [Continuous Integration](index.html.md): GitHub Actions - [Google Kubernetes Engine](cloud/gcp/gke/index.html.md): RAPIDS can be deployed on Google Cloud via the [Google Kubernetes Engine](https://cloud.google.com/k... - [Dataproc](cloud/gcp/dataproc/index.html.md): RAPIDS can be deployed on Google Cloud Dataproc using Dask. For more details, see our **[detailed in... - [SageMaker](cloud/aws/sagemaker/index.html.md): RAPIDS can be used in a few ways with [AWS SageMaker](https://aws.amazon.com/sagemaker/). - [Elastic Container Service (ECS)](cloud/aws/ecs/index.html.md): RAPIDS can be deployed on a multi-node ECS cluster using Dask’s dask-cloudprovider management tools.... - [AWS Elastic Kubernetes Service (EKS)](cloud/aws/eks/index.html.md): RAPIDS can be deployed on AWS via the [Elastic Kubernetes Service](https://aws.amazon.com/eks/) (EKS... - [Continuous Integration](index.html.md): GitHub Actions - [EC2 Cluster (via Dask)](cloud/aws/ec2-multi/index.html.md): To launch a multi-node cluster on AWS EC2 we recommend you use [Dask Cloud Provider](https://cloudpr... - [Elastic Compute Cloud (EC2)](cloud/aws/ec2/index.html.md): Create a new [EC2 Instance](https://aws.amazon.com/ec2/) with GPUs, the [NVIDIA Driver](https://www.... - [Continuous Integration](index.html.md): GitHub Actions - [NVIDIA Brev](cloud/nvidia/brev/index.html.md): The [NVIDIA Brev](https://brev.nvidia.com/) platform provides you a one stop menu of available GPU i... - [Home](_includes/menus/gcp/index.html.md): Compute Engine Instance - [Home](_includes/menus/ibm/index.html.md): IBM Virtual Server - [Home](_includes/menus/ci/index.html.md): GitHub Actions - [Home](_includes/menus/azure/index.html.md): Azure Virtual Machine - [Home](_includes/menus/aws/index.html.md): Elastic Compute Cloud (EC2) - [Home](_includes/menus/nvidia/index.html.md): Deploy and run RAPIDS on NVIDIA Brev - [GitHub Actions](developer/ci/github-actions/index.html.md): GitHub Actions is a popular way to automatically run tests against code hosted on GitHub. - [Continuous Integration](index.html.md): GitHub Actions - [How to Setup InfiniBand on Azure](guides/azure/infiniband/index.html.md): [Azure GPU optimized virtual machines](https://learn.microsoft.com/en-us/azure/virtual-machines/size... - [Dask Operator](tools/kubernetes/dask-operator/index.html.md): Many libraries in RAPIDS can leverage Dask to scale out computation onto multiple GPUs and multiple ... - [Dask Helm Chart](tools/kubernetes/dask-helm-chart/index.html.md): Dask has a [Helm Chart](https://github.com/dask/helm-chart) that creates the following resources: - [Measuring Performance with the One Billion Row Challenge](examples/rapids-1brc-single-node/notebook/index.html.md): *January, 2024* - [HPO Benchmarking with RAPIDS and Dask](examples/xgboost-rf-gpu-cpu-benchmark/notebook/index.html.md): *August, 2023* - [Scaling up Hyperparameter Optimization with Kubernetes and XGBoost GPU Algorithm](examples/xgboost-gpu-hpo-job-parallel-k8s/notebook/index.html.md): *January, 2023* - [Getting Started with cuML’s accelerator mode (cuml.accel) in Snowflake Notebooks](examples/cuml-snowflake-nb/notebook/index.html.md): *July, 2025* - [Getting Started with Optuna and RAPIDS for HPO](examples/rapids-optuna-hpo/notebook/index.html.md): *March, 2023* - [Training XGBoost with Dask RAPIDS in Databricks](examples/xgboost-dask-databricks/notebook/index.html.md): *January, 2024* - [Scaling up Hyperparameter Optimization with Multi-GPU Workload on Kubernetes](examples/xgboost-gpu-hpo-mnmg-parallel-k8s/notebook/index.html.md): *June, 2024* - [Multi-Node Multi-GPU XGBoost Example on Azure using dask-cloudprovider](examples/xgboost-azure-mnmg-daskcloudprovider/notebook/index.html.md): *November, 2023* - [Accelerating data analysis using cudf.pandas](examples/rapids-coiled-cudf/notebook/index.html.md): *April, 2025* - [Perform Time Series Forecasting on Google Kubernetes Engine with NVIDIA GPUs](examples/time-series-forecasting-with-hpo/notebook/index.html.md): *October, 2023* - [Running RAPIDS Hyperparameter Experiments at Scale on Amazon SageMaker](examples/rapids-sagemaker-higgs/notebook/index.html.md): *January, 2023* - [Autoscaling Multi-Tenant Kubernetes Deep-Dive](examples/rapids-autoscaling-multi-tenant-kubernetes/notebook/index.html.md): *February, 2023* - [Multi-node Multi-GPU Example on AWS using dask-cloudprovider](examples/rapids-ec2-mnmg/notebook/index.html.md): *February, 2023* - [HPO with dask-ml and cuml](examples/xgboost-randomforest-gpu-hpo-dask/notebook/index.html.md): *April, 2023* - [GPU-Accelerated Land Use Land Cover Classification](examples/lulc-classification-gpu/notebook/index.html.md): Working with satellite imagery at scale quickly exposes the limitations of CPU-bound workflows. A si... - [Deploying End-to-End Kafka Streaming SI Detection Pipeline with cuDF, Morpheus, and Triton on EKS](examples/rapids-morpheus-pipeline/notebook/index.html.md): *June, 2025* - [Train and Hyperparameter-Tune with RAPIDS on AzureML](examples/rapids-azureml-hpo/notebook/index.html.md): *August, 2023* - [Deep Dive into Running Hyper Parameter Optimization on AWS SageMaker](examples/rapids-sagemaker-hpo/notebook/index.html.md): *February, 2023* - [Getting Started with cudf.pandas and Snowflake](examples/rapids-snowflake-cudf/notebook/index.html.md): *February, 2025*