High-Availability Feature Stores for ML on Kubernetes with Rook-Ceph

Question

Pulumi · Accepted Answer

Creating a high-availability feature store for machine learning on Kubernetes would involve setting up a resilient storage system that can handle the demands of machine learning workloads, such as training and inference, while also providing the necessary features for a feature store, such as low-latency access to data.

Rook is an open-source cloud-native storage orchestrator for Kubernetes, and Ceph is a highly scalable distributed storage solution. Rook enables you to run Ceph storage on your Kubernetes clusters. By leveraging Rook-Ceph, you can create a feature store that is highly available, fault-tolerant, and scalable.

To set this up in Pulumi, we'll need the following resources:

1. A Kubernetes cluster where we will deploy our resources.
2. Rook-Ceph storage system to handle persistent data storage.
3. Kubernetes services and deployments that will serve as our high-availability feature store.

The steps we'll follow in the Pulumi program include:

1. Set up a `StorageClass` which will act as the template for creating our storage volumes in Ceph.
2. Deploy Rook and its components on the Kubernetes cluster.
3. Create CephBlockPool, CephFilesystem, or CephObjectStore depending on your access pattern requirements.
4. Deploy your feature store application (not fully covered in this example, but a placeholder will be provided).

Below is a detailed Pulumi program written in Python that sets up the foundational Rook-Ceph storage systems in a Kubernetes cluster.

```python
import pulumi
import pulumi_kubernetes as k8s

# This example assumes you have a Kubernetes cluster configured and kubectl is pointed to it.
# You can set it up using different Pulumi providers like pulumi_aws, pulumi_azure_native, pulumi_gcp or pulumi_digitalocean.

# Initialize a Kubernetes Provider
k8s_provider = k8s.Provider('k8s-provider', kubeconfig=pulumi.Config('k8s').require('kubeconfig'))

# Deploy Rook Operator
# The Rook Operator is responsible for managing the lifecycle of storage components within Kubernetes.
# The Namespace, ClusterRole, ClusterRoleBinding, and Operator Deployment are the bare minimum for the Rook Operator to run.
rook_operator = k8s.yaml.ConfigFile(
    'rook-operator',
    'https://raw.githubusercontent.com/rook/rook/master/cluster/examples/kubernetes/ceph/operator.yaml',
    provider=k8s_provider
)

# Deploy Rook-Ceph Cluster
# A Rook-Ceph cluster resource creates and configures Ceph components within Kubernetes.
rook_cluster = k8s.yaml.ConfigFile(
    'rook-cluster',
    'https://raw.githubusercontent.com/rook/rook/master/cluster/examples/kubernetes/ceph/cluster.yaml',
    provider=k8s_provider
)

# Deploy the CephBlockPool if block storage is needed.
# The CephBlockPool is a resource that defines the pools where block storage data will reside.
ceph_block_pool = k8s.yaml.ConfigFile(
    'ceph-block-pool',
    'https://raw.githubusercontent.com/rook/rook/master/cluster/examples/kubernetes/ceph/csi/rbd/storageclass.yaml',
    provider=k8s_provider
)

# Deploy a StorageClass using Ceph RBD (Block storage)
# This StorageClass will be used by the Kubernetes PersistentVolumeClaims to dynamically provision the Ceph RBD volumes.
ceph_rbd_storage_class = k8s.yaml.ConfigFile(
    'ceph-rbd-storage-class',
    'https://raw.githubusercontent.com/rook/rook/master/cluster/examples/kubernetes/ceph/csi/rbd/storageclass.yaml',
    provider=k8s_provider
)

# Deploy a StorageClass using CephFS (File storage)
# This StorageClass will provision file-based storage, which can be used by applications requiring a shared filesystem.
ceph_fs_storage_class = k8s.yaml.ConfigFile(
    'ceph-fs-storage-class',
    'https://raw.githubusercontent.com/rook/rook/master/cluster/examples/kubernetes/ceph/csi/cephfs/storageclass.yaml',
    provider=k8s_provider
)

# Here you would deploy your ML feature store application, 
# which would use PersistentVolumeClaims to store data on Ceph through Rook.

# Export the Rook-Ceph storage class name for future use
pulumi.export('ceph_rbd_storage_class', ceph_rbd_storage_class.urn)
pulumi.export('ceph_fs_storage_class', ceph_fs_storage_class.urn)
```

This program first sets up a Kubernetes provider pointing to your cluster. Then it deploys the Rook-Ceph operator, which is responsible for managing the lifecycle of the Rook-Ceph storage cluster in Kubernetes. Once the operator is in place, the next step is to roll out the Rook-Ceph cluster itself.

We define the block and file storage options by deploying `CephBlockPool` and `CephFileSystem` resources, respectively. `StorageClasses` are then defined for each type of storage. These `StorageClasses` will be used to dynamically provision persistent storage for your services.

Finally, when it comes to deploying your ML feature store application, you would need to match the resource requirements with the capabilities provided by Ceph through Rook and reference the appropriate storage class when creating `PersistentVolumeClaims`.

Please remember this is a foundational setup. When you build feature store services for machine learning workloads, it will involve using specific application deployments, configurations for data replication, access patterns, backup solutions, and more, based on your architectural and business requirements.