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SkyPilot is an open source framework that simplifies, optimizes, and unifies the execution of AI, LLM, and batch workloads. SkyPilot runs across a variety of compute infrastructure, including CoreWeave Kubernetes Service (CKS). SkyPilot abstracts the complexities of provisioning, scheduling, and managing underlying resources, so you can define your jobs once and then run them. SkyPilot includes features such as the ability to ssh into containers, fuse mount and configure access to object storage, and monitor execution.

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Overview

This guide shows how to install and configure SkyPilot for use with CKS. It covers the initial setup and walks through several common use cases. Examples include creating a development cluster, testing network performance, and deploying an inference service.

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Prerequisites

Before you start, you’ll need the following:

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Install SkyPilot

Install the SkyPilot CLI in an isolated Conda environment, then verify that SkyPilot can detect your CKS cluster.
  1. Create an isolated Conda environment and install the SkyPilot CLI with CoreWeave support to avoid package conflicts: 
    conda create -y -n sky python=3.10
conda activate sky
pip install "skypilot[coreweave]"
    • CKS requires SkyPilot version 0.10.1 or later.
    • SkyPilot requires Python 3.7 to 3.13.
  2. Verify that SkyPilot detects your CKS cluster: 
    sky check
    The output lists your allowed contexts, which are the names of your CoreWeave CKS clusters. In this example, the context is cks-usw04: 
    🎉 Enabled infra 🎉
  CoreWeave [storage]
  Kubernetes [compute]
    Allowed contexts:
    └── cks-usw04
For full installation instructions and directions for installing from source, see SkyPilot’s installation documentation. With SkyPilot installed and able to see your cluster, you can now use it to launch workloads on CKS.

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Use SkyPilot to create a development cluster

This section walks through launching an interactive development Pod (“devpod”) on CKS so you can iterate on code in a GPU-backed container. To create a cluster with a specific container, use the simple-devpod.yaml configuration YAML. Full YAML specifications are described in the SkyPilot YAML specification reference.
This example uses a development container from the CoreWeave ML team.

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Launch a devpod

To launch a development cluster using the simple-devpod.yaml configuration YAML, complete the following steps:
  1. Download the file and complete the following tasks:
    • Run sky show-gpus and note the GPU type in your context.
    • Update accelerators to your GPU type.
    resources:
  # Modify this below to request different resources
  # Change to your GPU type.
  accelerators: H100_NVLINK_80GB:1  # Use 1 H100
  # You can use your own container, but we recommend using a CW one because they are optimized for networking.
  image_id: docker:ghcr.io/coreweave/ml-containers/nightly-torch-extras:8b6c417-base-25110205-cuda12.9.1-ubuntu22.04-torch2.10.0a0-vision0.25.0a0-audio2.10.0a0
  memory: 32+  # Request at least 32GB of RAM
    The accelerators value must match the GPU shown in the output after running sky show-gpus. SkyPilot won’t schedule the job if the accelerators value and the output from sky show-gpus don’t match.
  2. Deploy the configuration to launch the devpod: 
    sky launch -c mysimpledevpod simple-devpod.yaml
    If you modify any configuration settings, or if you run into problems, for example, SkyPilot can’t find the bucket, stop and start the API server, then re-run the launch command:
    sky api stop
sky api start
sky check coreweave kubernetes
sky launch -c mysimpledevpod simple-devpod.yaml
  3. Stream the provisioning logs to monitor progress: 
    sky logs --provision mysimpledevpod
  4. Launch the SkyPilot dashboard to see the status of your clusters: 
    sky dashboard
    From the dashboard, use the commands to ssh into your cluster, connect with VSCode or Cursor, or terminate the Pod. For example, to ssh into the Pod: 
    ssh mysimpledevpod
    To automatically stop the development machine after a period of idle time, for example, after five hours: 
    sky autostop -i 300 mysimpledevpod
    For more information about starting and stopping a development server, see SkyPilot’s interactive development guide.

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Optional: Set up CoreWeave AI Object Storage and launch a devpod

To set up CoreWeave AI Object Storage, complete the following steps in the Cloud Console:
  1. Get AI Object Storage access keys by following the instructions to create access keys. Be sure to save the Key Id and Key Secret so you can access them later.
  2. If you don’t already have one, create an AI Object Storage organization access policy by following the instructions to manage organization access policies. You must have at least one organization access policy set before you can use AI Object Storage.
  3. Create an AI Object Storage bucket by following the instructions to create a bucket.

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Configure your development environment for AI Object Storage

  1. Create a separate CoreWeave profile in a specific location to avoid conflicts with other S3-compatible services: 
    AWS_SHARED_CREDENTIALS_FILE=~/.coreweave/cw.credentials aws configure --profile cw
    When prompted, enter your CoreWeave Object Storage credentials. Replace [ACCESS-KEY-ID] with your access key ID and [SECRET-ACCESS-KEY] with your secret access key. 
    AWS Access Key ID [None]: [ACCESS-KEY-ID]
AWS Secret Access Key [None]: [SECRET-ACCESS-KEY]
Default region name [None]:
Default output format [None]: json
  2. Configure the CoreWeave storage endpoint and set the default addressing style to virtual: 
    AWS_CONFIG_FILE=~/.coreweave/cw.config aws configure set endpoint_url https://cwobject.com --profile cw
AWS_CONFIG_FILE=~/.coreweave/cw.config aws configure set s3.addressing_style virtual --profile cw
    Endpoint selectionUse https://cwobject.com so the bucket’s endpoint is accessible from anywhere and uses secure HTTPS. This endpoint supports uploading local data.Always use http://cwlota.com if you don’t need to upload local data to the bucket. The LOTA endpoint provides faster access within CoreWeave’s network. For more information, see About LOTA.To configure the LOTA endpoint:
    AWS_CONFIG_FILE=~/.coreweave/cw.config aws configure set endpoint_url http://cwlota.com --profile cw
AWS_CONFIG_FILE=~/.coreweave/cw.config aws configure set s3.addressing_style virtual --profile cw
  3. Verify that SkyPilot detects your CKS cluster: 
    sky check
    The output lists your allowed contexts, which are the names of your CoreWeave CKS clusters. In this example, the context is cks-usw04: 
    🎉 Enabled infra 🎉
  CoreWeave [storage]
  Kubernetes [compute]
    Allowed contexts:
    └── cks-usw04
In this example, the CoreWeave AI Object Storage bucket you specify is fuse mounted at /my_data. The code in the my-code directory is copied to ~/sky_workdir on the container.This example also installs the AWS CLI and configures it to find configuration files and credentials in the relevant CoreWeave locations, enabling AWS S3 API access to CoreWeave storage. This is only for convenience. To access files using LOTA cache acceleration, use the LOTA endpoint and S3 interface.

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Launch a devpod with storage configured

To launch a development cluster with storage configured, use the mydevpod.yaml configuration YAML and complete the following steps:
  1. Download the file and modify the accelerators and source fields:
    • Run sky show-gpus and note the GPU type in your context.
    • Update accelerators to your GPU type.
    resources:
  # Modify this below to request different resources
  # Change to your GPU type.
  accelerators: H100_NVLINK_80GB:1  # Use 1 H100
  image_id: docker:ghcr.io/coreweave/ml-containers/nightly-torch-extras:8b6c417-base-25110205-cuda12.9.1-ubuntu22.04-torch2.10.0a0-vision0.25.0a0-audio2.10.0a0
  memory: 32+  # Request at least 32GB of RAM

file_mounts:
  /my_data: # Mount storage bucket to /my_data in the container
    # Change to your bucket name.
    source: cw://skypilot # Change this to be your bucket name
    mode: MOUNT  # MOUNT or COPY or MOUNT_CACHED. Defaults to MOUNT. Optional.
# Sync data in my-code/ on local machine to ~/sky_workdir in the container
# Be sure this directory is created on your local machine.
workdir: ./my-code
    Be sure to create a directory on your local machine called my-code.
  2. Deploy the configuration to launch the devpod: 
    sky launch -c mydevpod mydevpod.yaml
    If you run into problems, for example, SkyPilot can’t find the bucket, stop and start the API server, then re-run the launch command:
    sky api stop
sky api start
sky check coreweave kubernetes
sky launch -c mydevpod mydevpod.yaml
  3. Stream the provisioning logs to monitor progress: 
    sky logs --provision mydevpod
  4. Launch the SkyPilot dashboard to see the status of your clusters: 
    sky dashboard
    For more information about starting and stopping a development server, see SkyPilot’s interactive development guide.

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Test network performance

After your devpod is running, you can validate that the high-performance network fabric is working as expected before scaling out to multi-Node workloads. The CoreWeave network fabric is optimized for high-performance workloads, and CoreWeave InfiniBand support for SkyPilot is configured automatically when you specify the network_tier: best configuration option. To test network performance for collective operations, you can use the CoreWeave-specific SkyPilot example as a framework. This example deploys one of the CoreWeave example images that is configured with tested drivers and software to be a base for any HPC application. For more information about CoreWeave NCCL tests, see the nccl-test GitHub repository.

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Deploy an inference service

This section shows how to move from interactive development to serving a model behind an HTTP endpoint using SkyPilot’s launch and serve workflows. You can deploy an inference service on Kubernetes using SkyPilot. The following example uses a CoreWeave configuration YAML for vllm.
  1. To launch the inference service, download the vllm.yaml file and modify the accelerators field to match your GPU type: 
    # Change to your GPU type.
accelerators: H100_NVLINK_80GB:1  # Use 1 H100
  2. Launch the service: 
    sky launch -c vllm-test vllm.yaml
  3. Capture the service endpoint in an environment variable: 
    ENDPOINT=$(sky status --endpoint 8000 vllm-test)
  4. Send a test request to the service: 
    curl http://${ENDPOINT}/v1/completions  -H "Content-Type: application/json" \
  -d '{
    "model": "facebook/opt-125m",
    "prompt": "Once upon a time, there lived a princess who",
    "max_tokens": 20
  }'
  5. To scale the inference service, use the sky serve command: 
    sky serve up -n vllm-serve vllm.yaml
For more serving examples, see SkyPilot’s serving examples.

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Deploy a multinode training job

For training workloads that span multiple Nodes, SkyPilot can launch a distributed job that takes advantage of CoreWeave’s InfiniBand fabric. To launch a multinode training job, you can use the CoreWeave distributed training configuration YAML that’s based on the SkyPilot distributed training with PyTorch example. The CoreWeave example specifies using network_tier: best, which automatically configures optimal InfiniBand support and is configured with tested drivers and software as a base for high-performance training jobs. When using the distributed_training.yaml, be sure to change the accelerators field to match your GPU type: 
# Change to your GPU type.
accelerators: H100_NVLINK_80GB:1  # Use 1 H100

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Set up a production API server

When you’re ready to share SkyPilot resources across a team rather than run it per user, use the SkyPilot API server. All of the preceding examples assume clusters are isolated to individual users. If your team wants to use SkyPilot to share resources, follow the instructions to create a SkyPilot API server.

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Next steps

To learn more about SkyPilot, see the SkyPilot documentation. You can also deploy marimo reactive notebooks with SkyPilot for interactive GPU development or batch processing. See the marimo SkyPilot deployment guide for details.
Last modified on June 10, 2026