Deploy W&B with Kubernetes Operator - Weights & Biases Documentation

Overview

This page shows platform administrators how to deploy and manage W&B Server on Kubernetes (cloud or on-premises) using the W&B Kubernetes Operator. By the end, you have a running W&B Server installation that the operator manages and upgrades automatically. Use this guide if you self-manage a W&B deployment and need an installation method that works across cloud, on-premises, and air-gapped environments. The W&B Kubernetes Operator is the recommended way to deploy W&B Server on Kubernetes (cloud or on-premises). For an overview of the operator, why W&B uses it, and how configuration hierarchy works, see Self-Managed.

Before you begin

Before deploying W&B with the Kubernetes Operator, ensure your infrastructure meets all requirements:

Review infrastructure requirements: See the Self-Managed infrastructure requirements page for details on:

Software version requirements (Kubernetes, MySQL, Redis, Helm)
Hardware requirements (CPU architecture, sizing recommendations)
Kubernetes cluster configuration
Networking, SSL/TLS, and DNS requirements

Obtain a W&B Server license: See the License section on the Requirements page.
Provision external services: Set up MySQL, Redis, and object storage before deployment.

For additional context, see the reference architecture page.

MySQL database

W&B requires an external MySQL database. For production, W&B strongly recommends using managed database services:

Managed database services provide automated backups, monitoring, high availability, patching, and reduce operational overhead. See the reference architecture for complete MySQL requirements, including sizing recommendations and configuration parameters. For database creation SQL, see the bare-metal guide. For questions about your deployment’s database configuration, contact support or your AISE. For complete MySQL setup instructions including configuration parameters and database creation, see the MySQL section in the requirements page.

Redis

W&B depends on a single-node Redis 7.x deployment used by W&B’s components for job queuing and data caching. For convenience during testing and development of proofs of concept, W&B Self-Managed includes a local Redis deployment that is not appropriate for production deployments. For production deployments, W&B can connect to a Redis instance in the following environments:

AWS Elasticache
Google Cloud Memory Store
Azure Cache for Redis
Redis deployment hosted in your cloud or on-premise infrastructure

See the External Redis configuration section for details on how to configure an external Redis instance in Helm values.

Object storage

W&B requires object storage with pre-signed URL and CORS support. Recommended storage providers:

Amazon S3: Object storage service offering industry-leading scalability, data availability, security, and performance.
Google Cloud Storage: Managed service for storing unstructured data at scale.
Azure Blob Storage: Cloud-based object storage solution for storing massive amounts of unstructured data.
CoreWeave AI Object Storage: High-performance, S3-compatible object storage service optimized for AI workloads.
Enterprise S3-compatible storage: MinIO Enterprise (AIStor), NetApp StorageGRID, or other enterprise-grade solutions

MinIO Open Source is in maintenance mode with no active development or pre-compiled binaries. For production deployments, W&B recommends using managed object storage services or enterprise S3-compatible solutions such as MinIO Enterprise (AIStor).

For detailed bucket provisioning instructions including IAM policies, CORS configuration, and access setup, see the Bring Your Own Bucket (BYOB) guide. See the reference architecture object storage section for complete requirements.

Provision your storage bucket

Before configuring W&B, provision your object storage bucket with proper IAM policies, CORS configuration, and access credentials. See the Bring Your Own Bucket (BYOB) guide for detailed step-by-step provisioning instructions for:

Amazon S3 (including IAM policies and bucket policies)
Google Cloud Storage (including PubSub notifications)
Azure Blob Storage (including managed identities)
CoreWeave AI Object Storage
S3-compatible storage (MinIO Enterprise, NetApp StorageGRID, and other enterprise solutions)

See the Object storage configuration section for details on how to configure object storage in Helm values.

OpenShift Kubernetes clusters

W&B supports deployment on OpenShift Kubernetes clusters in cloud, on-premises, and air-gapped environments.

W&B recommends you install with the official W&B Helm chart.

Run the container as an un-privileged user

OpenShift and similar orchestrators often reject containers that run as root, so W&B containers must be configured to run as a non-root user that still belongs to the root group. By default, containers use a $UID of 999. Specify $UID >= 100000 and a $GID of 0 if your orchestrator requires the container run with a non-root user.

W&B must start as the root group ($GID=0) for file system permissions to function properly.

Configure security contexts for each W&B component. For example, to configure the API component:

api:
  install: true
  image:
    repository: wandb/megabinary
    tag: 0.74.1  # Replace with your actual version
  pod:
    securityContext:
      fsGroup: 10001
      fsGroupChangePolicy: Always
      runAsGroup: 0
      runAsNonRoot: true
      runAsUser: 10001
      seccompProfile:
        type: RuntimeDefault
  container:
    securityContext:
      allowPrivilegeEscalation: false
      capabilities:
        drop:
          - ALL
      privileged: false
      readOnlyRootFilesystem: false

If needed, configure a custom security context for other components like app or console. For details, see Custom security context.

Deploy W&B Server application

The W&B Kubernetes Operator with Helm is the recommended installation method for all W&B self-managed deployments, including cloud, on-premises, and air-gapped environments.

Choose your deployment method:

Helm CLI
Terraform

W&B provides a Helm chart to deploy the W&B Kubernetes Operator to a Kubernetes cluster. This approach lets you deploy W&B Server with Helm CLI or a continuous delivery tool like ArgoCD.For deployment-specific considerations, see Environment-specific considerations and Deploy with Terraform on public cloud. For disconnected environments, see Deploy on Air-Gapped Kubernetes.Follow these steps to install the W&B Kubernetes Operator with Helm CLI:

Add the W&B Helm repository. The W&B Helm chart is available in the W&B Helm repository:
```
helm repo add wandb https://charts.wandb.ai
helm repo update
```

Install the Operator on a Kubernetes cluster:

helm upgrade --install operator wandb/operator -n wandb-cr --create-namespace

Configure the W&B operator custom resource to trigger the W&B Server installation. Create a file named operator.yaml with your W&B deployment configuration. Refer to Configuration Reference for all available options. Here’s a minimal example configuration:

apiVersion: apps.wandb.com/v1
kind: WeightsAndBiases
metadata:
  labels:
    app.kubernetes.io/name: weightsandbiases
    app.kubernetes.io/instance: wandb
  name: wandb
  namespace: default
spec:
  values:
    global:
      host: https://<HOST_URI>
      license: eyJhbGnUzaH...j9ZieKQ2x5GGfw
      bucket:
        <details depend on the provider>
      mysql:
        <redacted>
    ingress:
      annotations:
        <redacted>

Start the Operator with your custom configuration so that it can install, configure, and manage the W&B Server application:
```
kubectl apply -f operator.yaml
```
Wait until the deployment completes. This takes a few minutes.
To verify the installation using the web UI, create the first admin user account, then follow the verification steps outlined in Verify the installation.

After these steps complete, you have a W&B Kubernetes Operator running in the wandb-cr namespace and a W&B Server application that the operator manages from your operator.yaml custom resource.

Deploy W&B using Terraform for infrastructure-as-code deployments. Choose between:

Helm Terraform Module: Deploys the operator to existing Kubernetes infrastructure.
Cloud Terraform Modules: Complete infrastructure plus application deployment for AWS, Google Cloud, and Azure.

For deployment-specific considerations, see Environment-specific considerations and Deploy with Terraform on public cloud. For disconnected environments, see Deploy on Air-Gapped Kubernetes.

Helm Terraform Module

This method supports customized deployments tailored to specific requirements, using Terraform’s infrastructure-as-code approach for consistency and repeatability. The official W&B Helm-based Terraform Module is available on the Terraform Registry.Use the following code as a starting point. It includes all necessary configuration options for a production grade deployment:

module "wandb" {
  source  = "wandb/wandb/helm"

  spec = {
    values = {
      global = {
        host    = "https://<HOST_URI>"
        license = "eyJhbGnUzaH...j9ZieKQ2x5GGfw"

        bucket = {
          <details depend on the provider>
        }

        mysql = {
          <redacted>
        }
      }

      ingress = {
        annotations = {
          "a" = "b"
          "x" = "y"
        }
      }
    }
  }
}

The configuration options are the same as described in Configuration Reference, but the syntax must follow the HashiCorp Configuration Language (HCL). The Terraform module creates the W&B custom resource definition (CRD).To see how W&B themselves use the Helm Terraform module to deploy Dedicated Cloud installations for customers, follow these links:

Cloud Terraform Modules

W&B provides a set of Terraform Modules for AWS, Google Cloud, and Azure. These modules deploy the complete infrastructure, including Kubernetes clusters, load balancers, and MySQL databases, along with the W&B Server application. The W&B Kubernetes Operator is included in these official W&B cloud-specific Terraform Modules with the following versions:

Terraform Registry	Source Code	Version
AWS	https://github.com/wandb/terraform-aws-wandb	v4.0.0+
Azure	https://github.com/wandb/terraform-azurerm-wandb	v2.0.0+
Google Cloud	https://github.com/wandb/terraform-google-wandb	v2.0.0+

These modules install the W&B Kubernetes Operator as part of the deployment, so you can use it to manage W&B Server in your cloud environment without additional setup.For detailed instructions on using these cloud-specific modules, see Deploy with Terraform on public cloud.

Verify the installation

To verify the installation, W&B recommends using the W&B CLI. The verify command executes several tests that verify all components and configurations.

This step assumes that the first admin user account is created with the browser.

Follow these steps to verify the installation:

Install the W&B CLI:

pip install wandb

wandb login --host=https://YOUR_DNS_DOMAIN

For example:

wandb login --host=https://wandb.company-name.com

Verify the installation:

wandb verify

A successful installation and fully working W&B deployment shows the following output:

Default host selected:  https://wandb.company-name.com
Find detailed logs for this test at: /var/folders/pn/b3g3gnc11_sbsykqkm3tx5rh0000gp/T/tmpdtdjbxua/wandb
Checking if logged in...................................................✅
Checking signed URL upload..............................................✅
Checking ability to send large payloads through proxy...................✅
Checking requests to base url...........................................✅
Checking requests made over signed URLs.................................✅
Checking CORs configuration of the bucket...............................✅
Checking wandb package version is up to date............................✅
Checking logged metrics, saving and downloading a file..................✅
Checking artifact save and download workflows...........................✅

Contact W&B Support if you encounter errors.

Enable the MCP server

The W&B MCP Server ships as an optional subchart in operator-wandb. When enabled, the operator deploys an in-cluster MCP server exposed through your existing ingress at <global.host>/mcp, so any MCP-compatible client can connect using a W&B API key. This is the same server W&B runs as the hosted offering at https://mcp.withwandb.com/mcp, but pointed at your deployment’s data. For end-user client configuration and the tool catalog, see Use the W&B MCP server. This section only covers the operator-side enablement.

Prerequisites

Make sure your deployment meets the following requirements before you enable the MCP server:

Chart version: operator-wandb 0.42.3 or later. The mcp-server subchart was introduced in 0.42.1, but the Datadog and privacy fields used in the following example were added later.
Weave Traces enabled: the MCP server depends on Weave Traces for trace tools and for the WF_TRACE_SERVER_URL default. Set weave-trace.install: true. If Weave Traces isn’t enabled, the Helm render fails with mcp-server requires weave-trace.install=true.
Reachable ingress: global.host must already resolve and route to the W&B ingress. The MCP pod reads WANDB_BASE_URL from global.host and is available at <global.host>/mcp.
Node capacity: the MCP pod requests 500m CPU and 1Gi memory by default (limits 2 CPU and 4Gi memory). Confirm your node pool has enough headroom before you enable the subchart.

Enable the subchart

Enable the mcp-server subchart so that the operator deploys an in-cluster MCP server and extends your existing W&B ingress with a /mcp route. Add the following to the spec.values block of your existing WeightsAndBiases custom resource (CR), alongside your existing global, ingress, and other overrides. The Datadog block is optional, but recommended when a Datadog Agent DaemonSet already collects pod logs and traces in your cluster.

spec:
  values:
    weave-trace:
      install: true

    mcp-server:
      install: true
      image:
        repository: us-docker.pkg.dev/wandb-production/public/wandb/mcp-server
        tag: "0.3.3"
      datadog:
        enabled: true
        mode: "agent"
        service: "wandb-mcp-server-<environment>"
        env: "<environment>"
        deploymentType: "self-managed"
        customer: "<customer-name>"
        extraTags:
          - "region:<region>"
          - "tier:<tier>"
      privacy:
        logLevel: "standard"

Configure each block:

weave-trace.install: true: required unless you set mcp-server.env.WF_TRACE_SERVER_URL yourself.
datadog.mode: "agent": use for Kubernetes deployments where the Datadog Agent DaemonSet owns log and trace collection. In agent mode, the MCP pod doesn’t need a Datadog API key.
datadog.service, env, deploymentType, customer, extraTags: set these to match your deployment’s observability naming conventions. Set customer to an empty string if you don’t want a customer tag.
privacy.logLevel: use "standard" for most self-managed Kubernetes installations. This redacts free-text parameter values in logs while preserving deployment identifiers that operators commonly use for debugging. Use "strict" when entity, project, run, or user identifiers should not remain in plaintext logs. Use "off" only when you explicitly want plaintext logging for those values.

Apply the change to trigger reconciliation:

kubectl apply -f operator.yaml

The operator creates a wandb-mcp-server deployment and service in the release namespace, and extends the W&B ingress with a /mcp path.

Verify the MCP server

Wait for the pod to become Running, then check the health endpoint in-cluster and through the ingress:

kubectl get pod -l app.kubernetes.io/component=mcp-server
kubectl port-forward svc/wandb-mcp-server 8080:8080
curl -s http://localhost:8080/mcp/health

curl -s "https://<HOST_URI>/mcp/health"

Both requests should return 200 OK. The in-cluster check confirms the pod is healthy. The ingress check confirms routing. If the in-cluster check returns 200 OK but the ingress check returns 404 Not Found, see Troubleshooting. If you enabled Datadog, MCP server logs should also appear in Datadog with the configured mcp-server.datadog.service and mcp-server.datadog.env values.

Connect a client

After the MCP server is healthy, configure your MCP client to use https://<HOST_URI>/mcp with a W&B API key as the bearer token. For IDE and agent configurations, see Use the W&B MCP server.

Troubleshooting

Symptom	Cause and fix
`helm render` fails with `mcp-server requires weave-trace.install=true`	Add `weave-trace.install: true` to `spec.values`. The MCP server depends on Weave Traces for trace tools.
`wandb-mcp-server` pod stuck in `Pending` with `Insufficient cpu` or `Insufficient memory`	Add node capacity, or lower `mcp-server.resources.requests` in your CR. Defaults are `500m` CPU and `1Gi` memory.
`curl https://<HOST_URI>/mcp/health` returns 404	The chart renders the `/mcp` ingress path only when `mcp-server.install: true`. Reapply the CR and wait for the ingress controller to propagate the new path.
MCP logs don’t appear in Datadog	Confirm `mcp-server.datadog.enabled: true`, `mcp-server.datadog.mode: "agent"`, and that the Datadog Agent DaemonSet collects pod stdout. Search Datadog with the configured `service` and `env` values.
MCP logs include more user-supplied text than expected	Set `mcp-server.privacy.logLevel` to `"standard"` or `"strict"`. Use `"strict"` when identifiers such as entity, project, run, or user names should not remain in plaintext logs.
`wandb-mcp-server` pod in `ImagePullBackOff` in an air-gapped or mirrored cluster	Mirror the image to your registry and override `mcp-server.image.repository` in your CR, the same pattern used for other W&B component images in air-gapped installs. See Deploy on Air-Gapped Kubernetes.

Environment-specific considerations

Kubernetes is the same whether it runs on-premises or in the cloud. The main differences are in naming and managed services (for example, MySQL compared to RDS, or S3 compared to on-premises object storage). This section covers considerations that vary by environment.

On-premises and bare metal

When deploying on on-premises or bare-metal Kubernetes, pay attention to the following.

Load balancer configuration

On-premises Kubernetes clusters typically require manual load balancer configuration. Options include:

External load balancer: Configure an existing hardware or software load balancer, such as F5 or HAProxy.
Nginx Ingress Controller: Deploy nginx-ingress-controller with NodePort or host networking.
MetalLB: For bare-metal Kubernetes clusters, MetalLB provides load balancer services.

For detailed load balancer configuration examples, see the Reference Architecture networking section.

Persistent storage

Ensure your Kubernetes cluster has a StorageClass configured for persistent volumes. W&B components might require persistent storage for caching and temporary data. Common on-premises storage options include:

NFS-based storage classes
Ceph/Rook storage
Local persistent volumes
Enterprise storage solutions such as NetApp or Pure Storage

DNS and certificate management

For on-premises deployments, complete the following tasks:

Configure internal DNS records to point to your W&B hostname.
Provision SSL/TLS certificates from your internal Certificate Authority (CA).
If using self-signed certificates, configure the operator to trust your CA certificate.

See the SSL/TLS requirements for certificate configuration details.

OpenShift deployments

W&B fully supports deployment on OpenShift Kubernetes clusters. OpenShift deployments require additional security context configurations due to OpenShift’s stricter security policies. For OpenShift-specific configuration details, see OpenShift Kubernetes clusters. For OpenShift examples in air-gapped environments, see Deploy on Air-Gapped Kubernetes.

Object storage for on-premises and S3-compatible

After provisioning your object storage bucket (see Object storage provisioning), configure it in your W&B Custom Resource. AWS S3 (on-premises) For on-premises AWS S3 (through Outposts or compatible storage):

bucket:
  kmsKey: <kms key arn>  # Optional KMS key for encryption
  name: <bucket name>    # Example: wandb
  path: ""               # Keep as empty string
  provider: s3
  region: <region>       # Example: us-east-1

S3-compatible storage such as MinIO, Ceph, or NetApp For S3-compatible storage systems:

bucket:
  kmsKey: null
  name: <s3 endpoint>    # Example: s3.example.com:9000
  path: <bucket name>    # Example: wandb
  provider: s3
  region: <region>       # Example: us-east-1

To enable TLS for S3-compatible storage, append ?tls=true to the bucket path:

bucket:
  path: "wandb?tls=true"

The certificate must be trusted. Self-signed certificates require additional configuration. See the SSL/TLS requirements for details.

Important considerations for on-premises object storage When running your own object storage, consider:

Storage capacity and performance: Monitor disk capacity carefully. Average W&B usage results in tens to hundreds of gigabytes. Heavy usage can result in petabytes of storage consumption.
Fault tolerance: At minimum, use RAID arrays for physical disks. For S3-compatible storage, use distributed or highly available configurations.
Availability: Configure monitoring to ensure the storage remains available.

MinIO considerations

MinIO Open Source is in maintenance mode with no active development. Pre-compiled binaries are no longer provided, and only critical security fixes are considered case-by-case. For production deployments, W&B recommends using managed object storage services or MinIO Enterprise (AIStor).

Enterprise alternatives for on-premises object storage include:

If you are using an existing MinIO deployment or MinIO Enterprise, you can create a bucket using the MinIO client:

mc config host add local http://$MINIO_HOST:$MINIO_PORT "$MINIO_ACCESS_KEY" "$MINIO_SECRET_KEY" --api s3v4
mc mb --region=us-east-1 local/wandb-files

Public cloud with Terraform

For full infrastructure-plus-application deployment on AWS, Google Cloud, or Azure, see Deploy with Terraform on public cloud.

Deploy with Terraform on public cloud

W&B recommends fully managed deployment options such as W&B Multi-tenant Cloud or W&B Dedicated Cloud deployment types. Fully managed services require little or no configuration.

W&B provides Terraform modules for deploying the platform on public cloud providers. These modules automate the provisioning of infrastructure and installation of W&B Server, so you can stand up a complete environment without manually creating each cloud resource. Before you start, W&B recommends that you choose one of the remote backends available for Terraform to store the State File. The State File is the necessary resource to roll out upgrades or make changes in your deployment without recreating all components. Select your cloud provider:

AWS
Google Cloud
Azure

W&B recommends using the W&B Server AWS Terraform Module to deploy the platform on AWS.The Terraform Module deploys the following mandatory components:

Load Balancer
AWS Identity & Access Management (IAM)
AWS Key Management System (KMS)
Amazon Aurora MySQL
Amazon VPC
Amazon S3
Amazon Route53
Amazon Certificate Manager (ACM)
Amazon Elastic Load Balancing (ALB)
Amazon Secrets Manager

Optional components include:

Elastic Cache for Redis
SQS

Prerequisite permissions

The account that runs Terraform must be able to create all components listed in the preceding section and have permission to create IAM Policies and IAM Roles and assign roles to resources.

General steps

The steps in this section are common for any deployment option.

Prepare the development environment.
- Install Terraform
- W&B recommends creating a Git repository for version control.
Create the terraform.tfvars file. Customize the tvfars file content according to the installation type. The minimum recommended content looks like the following example.
```
namespace                  = "wandb"
license                    = "xxxxxxxxxxyyyyyyyyyyyzzzzzzz"
subdomain                  = "wandb-aws"
domain_name                = "wandb.ml"
zone_id                    = "xxxxxxxxxxxxxxxx"
allowed_inbound_cidr       = ["0.0.0.0/0"]
allowed_inbound_ipv6_cidr  = ["::/0"]
eks_cluster_version        = "1.29"
```
Define variables in your tvfars file before you deploy because the namespace variable is a string that prefixes all resources created by Terraform. The combination of subdomain and domain forms the FQDN for your W&B instance. In the preceding example, the W&B FQDN is wandb-aws.wandb.ml and the DNS zone_id is where Terraform creates the FQDN record. Both allowed_inbound_cidr and allowed_inbound_ipv6_cidr also require setting. In the module, this is a mandatory input. The following example permits access from any source to the W&B installation.
Create the file versions.tf. This file contains the Terraform and Terraform provider versions required to deploy W&B in AWS:
```
provider "aws" {
  region = "eu-central-1"

  default_tags {
    tags = {
      GithubRepo = "terraform-aws-wandb"
      GithubOrg  = "wandb"
      Enviroment = "Example"
      Example    = "PublicDnsExternal"
    }
  }
}
```
Refer to the Terraform Official Documentation to configure the AWS provider. W&B recommends that you also add the remote backend configuration mentioned at the beginning of this documentation.

Create the file variables.tf For every option configured in the terraform.tfvars Terraform requires a correspondent variable declaration.

variable "namespace" {
  type        = string
  description = "Name prefix used for resources"
}

variable "domain_name" {
  type        = string
  description = "Domain name used to access instance."
}

variable "subdomain" {
  type        = string
  default     = null
  description = "Subdomain for accessing the Weights & Biases UI."
}

variable "license" {
  type = string
}

variable "zone_id" {
  type        = string
  description = "Domain for creating the Weights & Biases subdomain on."
}

variable "allowed_inbound_cidr" {
 description = "CIDRs allowed to access wandb-server."
 nullable    = false
 type        = list(string)
}

variable "allowed_inbound_ipv6_cidr" {
 description = "CIDRs allowed to access wandb-server."
 nullable    = false
 type        = list(string)
}

variable "eks_cluster_version" {
 description = "EKS cluster kubernetes version"
 nullable    = false
 type        = string
}

Recommended deployment

This is the most straightforward deployment option configuration that creates all mandatory components and installs in the Kubernetes Cluster the latest version of W&B.

Create the main.tf In the same directory where you created the files in the General Steps, create a file main.tf with the following content:

module "wandb_infra" {
  source  = "wandb/wandb/aws"
  version = "~>7.0"

  namespace   = var.namespace
  domain_name = var.domain_name
  license     = var.license
  subdomain   = var.subdomain
  zone_id     = var.zone_id

  allowed_inbound_cidr           = var.allowed_inbound_cidr
  allowed_inbound_ipv6_cidr      = var.allowed_inbound_ipv6_cidr

  public_access                  = true
  external_dns                   = true
  kubernetes_public_access       = true
  kubernetes_public_access_cidrs = ["0.0.0.0/0"]
  eks_cluster_version            = var.eks_cluster_version
}

 data "aws_eks_cluster" "eks_cluster_id" {
   name = module.wandb_infra.cluster_name
 }

 data "aws_eks_cluster_auth" "eks_cluster_auth" {
   name = module.wandb_infra.cluster_name
 }

 provider "kubernetes" {
   host                   = data.aws_eks_cluster.eks_cluster_id.endpoint
   cluster_ca_certificate = base64decode(data.aws_eks_cluster.eks_cluster_id.certificate_authority.0.data)
   token                  = data.aws_eks_cluster_auth.eks_cluster_auth.token
 }


 provider "helm" {
   kubernetes {
     host                   = data.aws_eks_cluster.eks_cluster_id.endpoint
     cluster_ca_certificate = base64decode(data.aws_eks_cluster.eks_cluster_id.certificate_authority.0.data)
     token                  = data.aws_eks_cluster_auth.eks_cluster_auth.token
   }
 }

 output "url" {
   value = module.wandb_infra.url
 }

 output "bucket" {
   value = module.wandb_infra.bucket_name
 }

Deploy W&B To deploy W&B, execute the following commands:

terraform init
terraform apply -var-file=terraform.tfvars

Enable Redis

To use Redis to cache SQL queries and speed up the application response when loading metrics, add the option create_elasticache_subnet = true to the main.tf file:

module "wandb_infra" {
  source  = "wandb/wandb/aws"
  version = "~>7.0"

  namespace   = var.namespace
  domain_name = var.domain_name
  subdomain   = var.subdomain
  zone_id     = var.zone_id
  create_elasticache_subnet = true
}
[...]

Enable message broker (queue)

To enable an external message broker using SQS, add the option use_internal_queue = false to the main.tf file:

This is optional because W&B includes an embedded broker. This option doesn’t bring a performance improvement.

module "wandb_infra" {
  source  = "wandb/wandb/aws"
  version = "~>7.0"

  namespace   = var.namespace
  domain_name = var.domain_name
  subdomain   = var.subdomain
  zone_id     = var.zone_id
  use_internal_queue = false

[...]
}

Additional resources

W&B recommends using the W&B Server Google Cloud Terraform Module to deploy the platform on Google Cloud.The module documentation lists all available options.Before you start, W&B recommends that you choose one of the remote backends available for Terraform to store the State File. The State File is the necessary resource to roll out upgrades or make changes in your deployment without recreating all components.The Terraform Module deploys the following mandatory components:

VPC
Cloud SQL for MySQL
Cloud Storage Bucket
Google Kubernetes Engine
Memorystore for Redis
KMS Crypto Key
Load Balancer

Optional components include:

Pub/Sub messages system

Prerequisite permissions

The account that runs Terraform must have the role roles/owner in the Google Cloud project used.

General steps

The steps in this section are common for any deployment option.

Prepare the development environment.
- Install Terraform.
- W&B recommends creating a Git repository for your code, but you can keep your files locally.
- Create a project in Google Cloud Console.
- Authenticate with Google Cloud (make sure to install gcloud first) using gcloud auth application-default login.
Create the terraform.tfvars file. Customize the tvfars file content according to the installation type. The minimum recommended content looks like the following example.
```
project_id  = "wandb-project"
region      = "europe-west2"
zone        = "europe-west2-a"
namespace   = "wandb"
license     = "xxxxxxxxxxyyyyyyyyyyyzzzzzzz"
subdomain   = "wandb-gcp"
domain_name = "wandb.ml"
```
You must decide the values for these variables before deployment. The namespace variable is a string that prefixes all resources created by Terraform. The combination of subdomain and domain forms the FQDN that configures W&B. In the preceding example, the W&B FQDN is wandb-gcp.wandb.ml.

Create the file variables.tf. For every option configured in the terraform.tfvars Terraform requires a correspondent variable declaration.

variable "project_id" {
  type        = string
  description = "Project ID"
}

variable "region" {
  type        = string
  description = "Google region"
}

variable "zone" {
  type        = string
  description = "Google zone"
}

variable "namespace" {
  type        = string
  description = "Namespace prefix used for resources"
}

variable "domain_name" {
  type        = string
  description = "Domain name for accessing the Weights & Biases UI."
}

variable "subdomain" {
  type        = string
  description = "Subdomain for access the Weights & Biases UI."
}

variable "license" {
  type        = string
  description = "W&B License"
}

Recommended deployment

This is the most straightforward deployment option configuration that creates all mandatory components and installs in the Kubernetes Cluster the latest version of W&B.

Create the main.tf In the same directory where you created the files in the General Steps, create a file main.tf with the following content:

provider "google" {
 project = var.project_id
 region  = var.region
 zone    = var.zone
}

provider "google-beta" {
 project = var.project_id
 region  = var.region
 zone    = var.zone
}

data "google_client_config" "current" {}

provider "kubernetes" {
  host                   = "https://${module.wandb.cluster_endpoint}"
  cluster_ca_certificate = base64decode(module.wandb.cluster_ca_certificate)
  token                  = data.google_client_config.current.access_token
}

provider "helm" {
  kubernetes {
    host                   = "https://${module.wandb.cluster_endpoint}"
    cluster_ca_certificate = base64decode(module.wandb.cluster_ca_certificate)
    token                  = data.google_client_config.current.access_token
  }
}

# Spin up all required services
module "wandb" {
  source  = "wandb/wandb/google"
  version = "~> 10.0"

  namespace   = var.namespace
  license     = var.license
  domain_name = var.domain_name
  subdomain   = var.subdomain
}

# You'll want to update your DNS with the provisioned IP address
output "url" {
  value = module.wandb.url
}

output "address" {
  value = module.wandb.address
}

output "bucket_name" {
  value = module.wandb.bucket_name
}

Deploy W&B. To deploy W&B, execute the following commands:

terraform init
terraform apply -var-file=terraform.tfvars

Enable Redis

To use Redis to cache SQL queries and speed up the application response when loading metrics, add the option create_redis = true to the main.tf file:

[...]

module "wandb" {
  source  = "wandb/wandb/google"
  version = "~> 10.0"

  namespace    = var.namespace
  license      = var.license
  domain_name  = var.domain_name
  subdomain    = var.subdomain
  create_redis = true
}
[...]

Enable message broker (queue)

To enable an external message broker using Pub/Sub, add the option use_internal_queue = false to the main.tf file:

This is optional because W&B includes an embedded broker. This option doesn’t bring a performance improvement.

[...]

module "wandb" {
  source  = "wandb/wandb/google"
  version = "~> 10.0"

  namespace          = var.namespace
  license            = var.license
  domain_name        = var.domain_name
  subdomain          = var.subdomain
  use_internal_queue = false
}

[...]

Additional resources

W&B recommends using the W&B Server Azure Terraform Module to deploy the platform on Azure.The module documentation lists all available options.The Terraform Module deploys the following mandatory components:

Azure Resource Group
Azure Virtual Network (VPC)
Azure MySQL Flexible Server
Azure Storage Account and Blob Storage
Azure Kubernetes Service
Azure Application Gateway

Optional components include:

Azure Cache for Redis
Azure Event Grid

Prerequisite permissions

The simplest way to configure the AzureRM provider is through the Azure CLI. For automation, you can also use an Azure Service Principal.Regardless of the authentication method used, the account that runs Terraform must be able to create all components listed in the preceding section.

General steps

The steps in this section are common for any deployment option.

Prepare the development environment.
- Install Terraform
- W&B recommends creating a Git repository for your code, but you can keep your files locally.
Create the terraform.tfvars file. Customize the tvfars file content according to the installation type. The minimum recommended content looks like the following example.
```
 namespace     = "wandb"
 wandb_license = "xxxxxxxxxxyyyyyyyyyyyzzzzzzz"
 subdomain     = "wandb-azure"
 domain_name   = "wandb.ml"
 location      = "westeurope"
```
You must decide the values for these variables before deployment. The namespace variable is a string that prefixes all resources created by Terraform. The combination of subdomain and domain forms the FQDN that configures W&B. In the preceding example, the W&B FQDN is wandb-azure.wandb.ml.
Create the file versions.tf. This file contains the Terraform and Terraform provider versions required to deploy W&B in Azure:
```
  terraform {
 required_version = "~> 1.3"

 required_providers {
   azurerm = {
     source  = "hashicorp/azurerm"
     version = "~> 3.17"
   }
 }
  }
```
Refer to the Terraform Official Documentation to configure the Azure provider. W&B recommends that you also add the remote backend configuration mentioned at the beginning of this documentation.

Create the file variables.tf For every option configured in the terraform.tfvars Terraform requires a correspondent variable declaration.

    variable "namespace" {
      type        = string
      description = "String used for prefix resources."
    }

    variable "location" {
      type        = string
      description = "Azure Resource Group location"
    }

    variable "domain_name" {
      type        = string
      description = "Domain for accessing the Weights & Biases UI."
    }

    variable "subdomain" {
      type        = string
      default     = null
      description = "Subdomain for accessing the Weights & Biases UI. Default creates record at Route53 Route."
    }

    variable "license" {
      type        = string
      description = "Your wandb/local license"
    }

Recommended deployment

This is the most straightforward deployment option configuration that creates all mandatory components and installs in the Kubernetes Cluster the latest version of W&B.

Create the main.tf In the same directory where you created the files in the General Steps, create a file main.tf with the following content:

  provider "azurerm" {
 features {}
  }

  provider "kubernetes" {
 host                   = module.wandb.cluster_host
 cluster_ca_certificate = base64decode(module.wandb.cluster_ca_certificate)
 client_key             = base64decode(module.wandb.cluster_client_key)
 client_certificate     = base64decode(module.wandb.cluster_client_certificate)
  }

  provider "helm" {
 kubernetes {
   host                   = module.wandb.cluster_host
   cluster_ca_certificate = base64decode(module.wandb.cluster_ca_certificate)
   client_key             = base64decode(module.wandb.cluster_client_key)
   client_certificate     = base64decode(module.wandb.cluster_client_certificate)
 }
  }

  # Spin up all required services
  module "wandb" {
 source  = "wandb/wandb/azurerm"
 version = "~> 1.2"

 namespace   = var.namespace
 location    = var.location
 license     = var.license
 domain_name = var.domain_name
 subdomain   = var.subdomain

 deletion_protection = false

 tags = {
   "Example" : "PublicDns"
 }
  }

  output "address" {
 value = module.wandb.address
  }

  output "url" {
 value = module.wandb.url
  }

Deploy W&B To deploy W&B, execute the following commands:

terraform init
terraform apply -var-file=terraform.tfvars

Enable Redis

To use Redis to cache SQL queries and speed up the application response when loading metrics, add the option create_redis = true to the main.tf file:

# Spin up all required services
module "wandb" {
  source  = "wandb/wandb/azurerm"
  version = "~> 1.2"

  namespace   = var.namespace
  location    = var.location
  license     = var.license
  domain_name = var.domain_name
  subdomain   = var.subdomain

  create_redis = true
  [...]
}

Enable message broker (queue)

To enable an external message broker using Azure Event Grid, add the option use_internal_queue = false to the main.tf file:

This is optional because W&B includes an embedded broker. This option doesn’t bring a performance improvement.

# Spin up all required services
module "wandb" {
  source  = "wandb/wandb/azurerm"
  version = "~> 1.2"

  namespace   = var.namespace
  location    = var.location
  license     = var.license
  domain_name = var.domain_name
  subdomain   = var.subdomain

  use_internal_queue = false
  [...]
}

Additional resources

Other deployment options

You can combine multiple deployment options by adding all configurations to the same file. Each Terraform module provides several options that can be combined with the standard options and the minimal configuration found in the recommended deployment section. Refer to the module documentation for your cloud provider for the full list of available options:

Access the W&B management console

The W&B Kubernetes Operator comes with a management console where you can review deployment status, view component metrics, and adjust operator-level settings. It’s available at ${HOST_URI}/console, for example https://wandb.company-name.com/console. You can log in to the management console in two ways:

Option 1 (Recommended)
Option 2

Open the W&B application in the browser and log in. Log in to the W&B application with ${HOST_URI}/, for example https://wandb.company-name.com/
Access the console. Click the icon in the top right corner and then click System console. Only users with admin privileges can see the System console entry.

W&B recommends you access the console using the following steps only if Option 1 doesn’t work.

Open the console application in your browser. Open the URL described in the preceding section, which redirects you to the login screen:
Retrieve the password from the Kubernetes secret that the installation generates:
```
kubectl get secret wandb-password -o jsonpath='{.data.password}' | base64 -d
```
Copy the password.
Log in to the console. Paste the copied password, then click Login.

Update the W&B Kubernetes Operator

This section describes how to update the W&B Kubernetes Operator itself. Update the operator periodically so that you get bug fixes and new reconciliation features.

Updating the W&B Kubernetes Operator doesn’t update the W&B Server application.
If you use a Helm chart that doesn’t use the W&B Kubernetes Operator, see the migration instructions before following the steps in this section to update the W&B Operator.

Copy and paste the following code snippets into your terminal.

Update the repo with helm repo update:
```
helm repo update
```

Update the Helm chart with helm upgrade:

helm upgrade operator wandb/operator -n wandb-cr --reuse-values

Update the W&B Server application

You no longer need to update W&B Server application if you use the W&B Kubernetes Operator. The operator automatically updates your W&B Server application when a new version of the software of W&B is released.

Migrate self-managed instances to W&B Operator

The following section describes how to migrate from self-managing your own W&B Server installation to using the W&B Operator to do this for you. Migrating lets the operator handle reconciliation and W&B Server upgrades automatically, so you no longer have to coordinate manifest changes or Helm upgrades for the application. The migration process depends on how you installed W&B Server:

The W&B Operator is the default and recommended installation method for W&B Server. Reach out to Customer Support or your W&B team if you have any questions.

If you used the official W&B Cloud Terraform Modules, navigate to the appropriate documentation and follow the steps there:
- AWS
- Google Cloud
- Azure
If you used the W&B Non-Operator Helm chart, see Migrate to operator-based Helm chart.
If you used the W&B Non-Operator Helm chart with Terraform, see Migrate to operator-based Terraform Helm chart.
If you created the Kubernetes resources with manifests, see Migrate to operator-based Helm chart.

Migrate to operator-based AWS Terraform modules

For a detailed description of the migration process, see the operator-wandb chart documentation.

Migrate to operator-based Google Cloud Terraform modules

Reach out to Customer Support or your W&B team if you have any questions or need assistance.

Migrate to operator-based Azure Terraform modules

Reach out to Customer Support or your W&B team if you have any questions or need assistance.

Migrate to operator-based Helm chart

Follow these steps to migrate to the operator-based Helm chart:

Get the current W&B configuration. If you deployed W&B with a non-operator-based version of the Helm chart, export the values like this:
```
helm get values wandb
```
If you deployed W&B with Kubernetes manifests, export the values like this:
```
kubectl get deployment wandb -o yaml
```
You now have all the configuration values you need for the next step.
Create a file called operator.yaml. Follow the format described in the Configuration Reference. Use the values from step 1.
Scale the current deployment to 0 pods. This step stops the current deployment.
```
kubectl scale --replicas=0 deployment wandb
```
Update the Helm chart repo:
```
helm repo update
```

Install the new Helm chart:

helm upgrade --install operator wandb/operator -n wandb-cr --create-namespace

Configure the new Helm chart and trigger W&B application deployment. Apply the new configuration.
```
kubectl apply -f operator.yaml
```
The deployment takes a few minutes to complete.
Verify the installation. Make sure that everything works by following the steps in Verify the installation.
Remove the old installation. Uninstall the old Helm chart or delete the resources that you created with manifests.

Migrate to operator-based Terraform Helm chart

Follow these steps to migrate to the operator-based Helm chart:

Prepare Terraform config. Replace the Terraform code from the old deployment in your Terraform config with the code described in Deploy W&B with Helm Terraform module. Set the same variables as before. Do not change the .tfvars file if you have one.
Execute Terraform run. Execute terraform init, terraform plan, and terraform apply.
Verify the installation. Make sure that everything works by following the steps in Verify the installation.
Remove the old installation. Uninstall the old Helm chart or delete the resources that you created with manifests.

Configuration reference for W&B Server

This section is a reference for the configuration options that you set in your WeightsAndBiases custom resource. Use it to look up the YAML schema for a specific subsystem (for example, MySQL, Redis, ingress, or OIDC) as you build or update your operator.yaml file. This section describes the configuration options for W&B Server application. The application receives its configuration as custom resource definition named WeightsAndBiases. Some configuration options are exposed with the following configuration. You must set others as environment variables. The documentation has two lists of environment variables: basic and advanced. Only use environment variables if the configuration option that you need is not exposed using the Helm chart.

Basic example

This example defines the minimum set of values required for W&B. For a more realistic production example, see Complete example. This YAML file defines the desired state of your W&B deployment, including the version, environment variables, external resources like databases, and other necessary settings.

apiVersion: apps.wandb.com/v1
kind: WeightsAndBiases
metadata:
  labels:
    app.kubernetes.io/name: weightsandbiases
    app.kubernetes.io/instance: wandb
  name: wandb
  namespace: default
spec:
  values:
    global:
      host: https://<HOST_URI>
      license: eyJhbGnUzaH...j9ZieKQ2x5GGfw
      bucket:
        <details depend on the provider>
      mysql:
        <redacted>
    ingress:
      annotations:
        <redacted>

Find the full set of values in the W&B Helm repository. Change only those values you need to override.

Complete example

This example configuration deploys W&B to Google Cloud Anthos using Google Cloud Storage:

apiVersion: apps.wandb.com/v1
kind: WeightsAndBiases
metadata:
  labels:
    app.kubernetes.io/name: weightsandbiases
    app.kubernetes.io/instance: wandb
  name: wandb
  namespace: default
spec:
  values:
    global:
      host: https://abc-wandb.sandbox-gcp.wandb.ml
      bucket:
        name: abc-wandb-moving-pipefish
        provider: gcs
      mysql:
        database: wandb_local
        host: 10.218.0.2
        name: wandb_local
        password: 8wtX6cJHizAZvYScjDzZcUarK4zZGjpV
        port: 3306
        user: wandb
      redis:
        host: redis.example.com
        port: 6379
        password: password
      api:
        enabled: true
      glue:
        enabled: true
      executor:
        enabled: true
      license: eyJhbGnUzaHgyQjQyQWhEU3...ZieKQ2x5GGfw
    ingress:
      annotations:
        ingress.gcp.kubernetes.io/pre-shared-cert: abc-wandb-cert-creative-puma
        kubernetes.io/ingress.class: gce
        kubernetes.io/ingress.global-static-ip-name: abc-wandb-operator-address

Host

 # Provide the FQDN with protocol
global:
  # example host name, replace with your own
  host: https://wandb.example.com

Object storage (bucket)

AWS

global:
  bucket:
    provider: "s3"
    name: ""
    kmsKey: ""
    region: ""

Google Cloud

global:
  bucket:
    provider: "gcs"
    name: ""

Azure

global:
  bucket:
    provider: "az"
    name: ""
    secretKey: ""

Other providers (Minio, Ceph, and other S3-compatible storage) For other S3 compatible providers, set the bucket configuration as follows:

global:
  bucket:
    # Example values, replace with your own
    provider: s3
    name: storage.example.com
    kmsKey: null
    path: wandb
    region: default
    accessKey: 5WOA500...P5DK7I
    secretKey: HDKYe4Q...JAp1YyjysnX

For S3-compatible storage hosted outside of AWS, kmsKey must be null. To reference accessKey and secretKey from a secret:

global:
  bucket:
    # Example values, replace with your own
    provider: s3
    name: storage.example.com
    kmsKey: null
    path: wandb
    region: default
    secret:
      secretName: bucket-secret
      accessKeyName: ACCESS_KEY
      secretKeyName: SECRET_KEY

MySQL

global:
   mysql:
     # Example values, replace with your own
     host: db.example.com
     port: 3306
     database: wandb_local
     user: wandb
     password: 8wtX6cJH...ZcUarK4zZGjpV 

To reference the password from a secret:

global:
   mysql:
     # Example values, replace with your own
     host: db.example.com
     port: 3306
     database: wandb_local
     user: wandb
     passwordSecret:
       name: database-secret
       passwordKey: MYSQL_WANDB_PASSWORD

License

global:
  # Example license, replace with your own
  license: eyJhbGnUzaHgyQjQy...VFnPS_KETXg1hi

To reference the license from a secret:

global:
  licenseSecret:
    name: license-secret
    key: CUSTOMER_WANDB_LICENSE

Ingress

See How to identify the Kubernetes ingress class. Without TLS

global:
# IMPORTANT: Ingress is on the same level in the YAML as 'global' (not a child)
ingress:
  class: ""

With TLS Create a secret that contains the certificate

kubectl create secret tls wandb-ingress-tls --key wandb-ingress-tls.key --cert wandb-ingress-tls.crt

Reference the secret in the ingress configuration

global:
# IMPORTANT: Ingress is on the same level in the YAML as 'global' (not a child)
ingress:
  class: ""
  annotations:
    {}
    # kubernetes.io/ingress.class: nginx
    # kubernetes.io/tls-acme: "true"
  tls: 
    - secretName: wandb-ingress-tls
      hosts:
        - <HOST_URI>

For Nginx, you might have to add the following annotation:

ingress:
  annotations:
    nginx.ingress.kubernetes.io/proxy-body-size: 0

Custom Kubernetes service accounts

Specify custom Kubernetes service accounts to run the W&B pods. The following snippet creates a service account as part of the deployment with the specified name:

app:
  serviceAccount:
    name: custom-service-account
    create: true

parquet:
  serviceAccount:
    name: custom-service-account
    create: true

global:
  ...

The subsystems “app” and “parquet” run under the specified service account. The other subsystems run under the default service account. If the service account already exists on the cluster, set create: false:

app:
  serviceAccount:
    name: custom-service-account
    create: false

parquet:
  serviceAccount:
    name: custom-service-account
    create: false
    
global:
  ...

You can specify service accounts on different subsystems such as app, parquet, console, and others:

app:
  serviceAccount:
    name: custom-service-account
    create: true

console:
  serviceAccount:
    name: custom-service-account
    create: true

global:
  ...

The service accounts can be different between the subsystems:

app:
  serviceAccount:
    name: custom-service-account
    create: false

console:
  serviceAccount:
    name: another-custom-service-account
    create: true

global:
  ...

External Redis

redis:
  install: false

global:
  redis:
    host: ""
    port: 6379
    password: ""
    parameters: {}
    caCert: ""

To reference the password from a secret:

kubectl create secret generic redis-secret --from-literal=redis-password=supersecret

Reference it in the following configuration:

redis:
  install: false

global:
  redis:
    host: redis.example
    port: 9001
    auth:
      enabled: true
      secret: redis-secret
      key: redis-password

LDAP

LDAP configuration support in the current Helm chart is limited. Contact W&B Support or your AISE for assistance configuring LDAP.

Configure LDAP by setting environment variables in global.extraEnv:

global:
  extraEnv:
    LDAP_ADDRESS: ldaps://ldap.company.example.com
    LDAP_BASE_DN: cn=accounts,dc=company,dc=example,dc=com
    LDAP_USER_BASE_DN: cn=users,cn=accounts,dc=company,dc=example,dc=com
    LDAP_GROUP_BASE_DN: cn=groups,cn=accounts,dc=company,dc=example,dc=com
    LDAP_BIND_DN: uid=ldapbind,cn=sysaccounts,cn=etc,dc=company,dc=example,dc=com
    LDAP_BIND_PW: ********************
    LDAP_ATTRIBUTES: email=mail,name=cn
    LDAP_TLS_ENABLE: "true"
    LDAP_LOGIN: "true"
    LDAP_USER_OBJECT_CLASS: user
    LDAP_GROUP_OBJECT_CLASS: group

OIDC SSO

global: 
  auth:
    sessionLengthHours: 720
    oidc:
      clientId: ""
      secret: ""
      # Only include if your IdP requires it.
      authMethod: ""
      issuer: ""

authMethod is optional.

SMTP

global:
  email:
    smtp:
      host: ""
      port: 587
      user: ""
      password: ""

Environment variables

global:
  extraEnv:
    GLOBAL_ENV: "example"

Custom certificate authority

customCACerts is a list and can take many certificates. Certificate authorities specified in customCACerts only apply to the W&B Server application.

global:
  customCACerts:
  - |
    -----BEGIN CERTIFICATE-----
    MIIBnDCCAUKgAwIBAg.....................fucMwCgYIKoZIzj0EAwIwLDEQ
    MA4GA1UEChMHSG9tZU.....................tZUxhYiBSb290IENBMB4XDTI0
    MDQwMTA4MjgzMFoXDT.....................oNWYggsMo8O+0mWLYMAoGCCqG
    SM49BAMCA0gAMEUCIQ.....................hwuJgyQRaqMI149div72V2QIg
    P5GD+5I+02yEp58Cwxd5Bj2CvyQwTjTO4hiVl1Xd0M0=
    -----END CERTIFICATE-----
  - |
    -----BEGIN CERTIFICATE-----
    MIIBxTCCAWugAwIB.......................qaJcwCgYIKoZIzj0EAwIwLDEQ
    MA4GA1UEChMHSG9t.......................tZUxhYiBSb290IENBMB4XDTI0
    MDQwMTA4MjgzMVoX.......................UK+moK4nZYvpNpqfvz/7m5wKU
    SAAwRQIhAIzXZMW4.......................E8UFqsCcILdXjAiA7iTluM0IU
    aIgJYVqKxXt25blH/VyBRzvNhViesfkNUQ==
    -----END CERTIFICATE-----

CA certificates can also be stored in a ConfigMap:

global:
  caCertsConfigMap: custom-ca-certs

The ConfigMap must look like this:

apiVersion: v1
kind: ConfigMap
metadata:
  name: custom-ca-certs
data:
  ca-cert1.crt: |
    -----BEGIN CERTIFICATE-----
    ...
    -----END CERTIFICATE-----
  ca-cert2.crt: |
    -----BEGIN CERTIFICATE-----
    ...
    -----END CERTIFICATE-----

If using a ConfigMap, each key in the ConfigMap must end with .crt (for example, my-cert.crt or ca-cert1.crt). This naming convention is required for update-ca-certificates to parse and add each certificate to the system CA store.

Custom security context

Each W&B component supports custom security context configurations of the following form:

pod:
  securityContext:
    runAsNonRoot: true
    runAsUser: 1001
    runAsGroup: 0
    fsGroup: 1001
    fsGroupChangePolicy: Always
    seccompProfile:
      type: RuntimeDefault
container:
  securityContext:
    capabilities:
      drop:
        - ALL
    readOnlyRootFilesystem: false
    allowPrivilegeEscalation: false 

The only valid value for runAsGroup: is 0. Any other value is an error.

For example, to configure the application pod, add a section app to your configuration:

global:
  ...
app:
  pod:
    securityContext:
      runAsNonRoot: true
      runAsUser: 1001
      runAsGroup: 0
      fsGroup: 1001
      fsGroupChangePolicy: Always
      seccompProfile:
        type: RuntimeDefault
  container:
    securityContext:
      capabilities:
        drop:
          - ALL
      readOnlyRootFilesystem: false
      allowPrivilegeEscalation: false 

The same concept applies to console, weave, weave-trace, and parquet.

Configuration reference for W&B Operator

This section describes configuration options for W&B Kubernetes Operator (wandb-controller-manager). The operator receives its configuration in the form of a YAML file. By default, the W&B Kubernetes Operator doesn’t need a configuration file. Create a configuration file if required. For example, you might need a configuration file to specify custom certificate authorities, deploy in an air gap environment, and so forth. Find the full list of spec customization in the Helm repository.

Custom CA

A custom certificate authority (customCACerts) is a list and can take many certificates. Those certificate authorities, when added, only apply to the W&B Kubernetes Operator (wandb-controller-manager).

customCACerts:
- |
  -----BEGIN CERTIFICATE-----
  MIIBnDCCAUKgAwIBAg.....................fucMwCgYIKoZIzj0EAwIwLDEQ
  MA4GA1UEChMHSG9tZU.....................tZUxhYiBSb290IENBMB4XDTI0
  MDQwMTA4MjgzMFoXDT.....................oNWYggsMo8O+0mWLYMAoGCCqG
  SM49BAMCA0gAMEUCIQ.....................hwuJgyQRaqMI149div72V2QIg
  P5GD+5I+02yEp58Cwxd5Bj2CvyQwTjTO4hiVl1Xd0M0=
  -----END CERTIFICATE-----
- |
  -----BEGIN CERTIFICATE-----
  MIIBxTCCAWugAwIB.......................qaJcwCgYIKoZIzj0EAwIwLDEQ
  MA4GA1UEChMHSG9t.......................tZUxhYiBSb290IENBMB4XDTI0
  MDQwMTA4MjgzMVoX.......................UK+moK4nZYvpNpqfvz/7m5wKU
  SAAwRQIhAIzXZMW4.......................E8UFqsCcILdXjAiA7iTluM0IU
  aIgJYVqKxXt25blH/VyBRzvNhViesfkNUQ==
  -----END CERTIFICATE-----

CA certificates can also be stored in a ConfigMap:

caCertsConfigMap: custom-ca-certs

The ConfigMap must look like this:

apiVersion: v1
kind: ConfigMap
metadata:
  name: custom-ca-certs
data:
  ca-cert1.crt: |
    -----BEGIN CERTIFICATE-----
    ...
    -----END CERTIFICATE-----
  ca-cert2.crt: |
    -----BEGIN CERTIFICATE-----
    ...
    -----END CERTIFICATE-----

Each key in the ConfigMap must end with .crt (for example, my-cert.crt or ca-cert1.crt). This naming convention is required for update-ca-certificates to parse and add each certificate to the system CA store.

FAQ

Purpose and role of each pod

A W&B Server deployment includes the following pods:

wandb-app: the core of W&B, including the GraphQL API and frontend application. It powers most of the W&B platform’s functionality.
wandb-console: the administration console, accessed through /console.
wandb-otel: the OpenTelemetry agent, which collects metrics and logs from resources at the Kubernetes layer for display in the administration console.
wandb-prometheus: the Prometheus server, which captures metrics from various components for display in the administration console.
wandb-parquet: a backend microservice separate from the wandb-app pod that exports database data to object storage in Parquet format.
wandb-weave: another backend microservice that loads query tables in the UI and supports various core app features.
wandb-weave-trace: a framework for tracking, experimenting with, evaluating, deploying, and improving LLM-based applications. The framework is accessed through the wandb-app pod.

How to get the W&B Operator Console password

See Access the W&B management console.

How to access the W&B Operator Console if Ingress doesn’t work

Execute the following command on a host that can reach the Kubernetes cluster:

kubectl port-forward svc/wandb-console 8082

Access the console in the browser with https://localhost:8082/ console. For how to get the password (Option 2), see Access the W&B management console.

How to view W&B Server logs

The application pod is named wandb-app-xxx.

kubectl get pods
kubectl logs wandb-XXXXX-XXXXX

How to identify the Kubernetes ingress class

You can get the ingress class installed in your cluster by running

kubectl get ingressclass

Documentation Index

​Overview

​Before you begin

​MySQL database

​Redis

​Object storage

​Provision your storage bucket

​OpenShift Kubernetes clusters

​Run the container as an un-privileged user

​Deploy W&B Server application

​Helm Terraform Module

​Cloud Terraform Modules

​Verify the installation

​Enable the MCP server

​Prerequisites

​Enable the subchart

​Verify the MCP server

​Connect a client

​Troubleshooting

​Environment-specific considerations

​On-premises and bare metal

​Load balancer configuration

​Persistent storage

​DNS and certificate management

​OpenShift deployments

​Object storage for on-premises and S3-compatible

​Public cloud with Terraform

​Deploy with Terraform on public cloud

​Prerequisite permissions

​General steps

​Recommended deployment

​Enable Redis

​Enable message broker (queue)

​Additional resources

​Prerequisite permissions

​General steps

​Recommended deployment

​Enable Redis

​Enable message broker (queue)

​Additional resources

​Prerequisite permissions

​General steps

​Recommended deployment

​Enable Redis

​Enable message broker (queue)

​Additional resources

​Other deployment options

​Access the W&B management console

​Update the W&B Kubernetes Operator

​Update the W&B Server application

​Migrate self-managed instances to W&B Operator

​Migrate to operator-based AWS Terraform modules

​Migrate to operator-based Google Cloud Terraform modules

​Migrate to operator-based Azure Terraform modules

​Migrate to operator-based Helm chart

​Migrate to operator-based Terraform Helm chart

​Configuration reference for W&B Server

​Basic example

​Complete example

​Host

​Object storage (bucket)

​MySQL

​License

​Ingress

​Custom Kubernetes service accounts

​External Redis

​LDAP

​OIDC SSO

​SMTP

​Environment variables

​Custom certificate authority

​Custom security context

​Configuration reference for W&B Operator

​Custom CA

​FAQ

​Purpose and role of each pod

​How to get the W&B Operator Console password

​How to access the W&B Operator Console if Ingress doesn’t work

​How to view W&B Server logs

​How to identify the Kubernetes ingress class

Overview

Before you begin

MySQL database

Redis

Object storage

Provision your storage bucket

OpenShift Kubernetes clusters

Run the container as an un-privileged user

Deploy W&B Server application

Helm Terraform Module

Cloud Terraform Modules

Verify the installation

Enable the MCP server

Prerequisites

Enable the subchart

Verify the MCP server

Connect a client

Troubleshooting

Environment-specific considerations

On-premises and bare metal

Load balancer configuration

Persistent storage

DNS and certificate management

OpenShift deployments

Object storage for on-premises and S3-compatible

Public cloud with Terraform

Deploy with Terraform on public cloud

Prerequisite permissions

General steps

Recommended deployment

Enable Redis

Enable message broker (queue)

Additional resources

Prerequisite permissions

General steps

Recommended deployment

Enable Redis

Enable message broker (queue)

Additional resources

Prerequisite permissions

General steps

Recommended deployment

Enable Redis

Enable message broker (queue)

Additional resources

Other deployment options

Access the W&B management console

Update the W&B Kubernetes Operator

Update the W&B Server application

Migrate self-managed instances to W&B Operator

Migrate to operator-based AWS Terraform modules

Migrate to operator-based Google Cloud Terraform modules

Migrate to operator-based Azure Terraform modules

Migrate to operator-based Helm chart

Migrate to operator-based Terraform Helm chart

Configuration reference for W&B Server

Basic example

Complete example

Host

Object storage (bucket)

MySQL

License

Ingress

Custom Kubernetes service accounts

External Redis

LDAP

OIDC SSO

SMTP

Environment variables

Custom certificate authority

Custom security context

Configuration reference for W&B Operator

Custom CA

FAQ

Purpose and role of each pod

How to get the W&B Operator Console password

How to access the W&B Operator Console if Ingress doesn’t work

How to view W&B Server logs

How to identify the Kubernetes ingress class