bitnamicharts/mlflow repository overview

⁠Bitnami Secure Images Helm chart for MLflow

MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle. It allows you to track experiments, package code into reproducible runs, and share and deploy models.

Overview of MLflow⁠

Trademarks: This software listing is packaged by Bitnami. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

⁠ TL;DR

helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/mlflow

⁠ Introduction

This chart bootstraps a MLflow⁠ deployment on a Kubernetes⁠ cluster using the Helm⁠ package manager.

Python is built for full integration into Python that enables you to use it with its libraries and main packages.

⁠ Before you begin

• Kubernetes 1.23+
• Helm 3.8.0+
• PV provisioner support in the underlying infrastructure

⁠ Installing the chart

To install the chart with the release name my-release:

helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/mlflow

The command deploys MLflow on the Kubernetes cluster in the default configuration. The Parameters⁠ section lists the parameters that can be configured during installation.

⁠ Configuration and installation details

This section describes resource settings, metrics, Gateway API, Ingress, TLS, and other options.

⁠ Resource requests and limits

Bitnami charts allow setting resource requests and limits for all containers inside the chart deployment. These are inside the resources value (check parameter table). Setting requests is essential for production workloads and these should be adapted to your specific use case.

To make this process easier, the chart contains the resourcesPreset values, which automatically sets the resources section according to different presets. Check these presets in the bitnami/common chart⁠. However, in production workloads using resourcesPreset is discouraged as it may not fully adapt to your specific needs. Find more information on container resource management in the official Kubernetes documentation⁠.

⁠ Prometheus metrics

This chart can be integrated with Prometheus by setting tracking.metrics.enabled to true. This will expose MLflow native Prometheus endpoint in the service. It will have the necessary annotations to be automatically scraped by Prometheus.

⁠Prometheus requirements

It is necessary to have a working installation of Prometheus or Prometheus Operator for the integration to work. Install the Bitnami Prometheus helm chart⁠ or the Bitnami Kube Prometheus helm chart⁠ to easily have a working Prometheus in your cluster.

⁠Integration with Prometheus Operator

The chart can deploy ServiceMonitor objects for integration with Prometheus Operator installations. To do so, set the value tracking.metrics.serviceMonitor.enabled=true. Ensure that the Prometheus Operator CustomResourceDefinitions are installed in the cluster or it will fail with the following error:

no matches for kind "ServiceMonitor" in version "monitoring.coreos.com/v1"

Install the Bitnami Kube Prometheus helm chart⁠ for having the necessary CRDs and the Prometheus Operator.

⁠ Gateway API

This chart provides support for exposing MLflow using the Gateway API⁠ and its HTTPRoute resource. If you have a Gateway controller installed on your cluster, such as APISIX, Contour, Envoy Gateway, NGINX Gateway Fabric or Kong Ingress Controller you can utilize the Gateway controller to serve your application. To enable Gateway API integration, set tracking.httpRoute.enabled to true. The Gateway to be used can be customized by setting the tracking.httpRoute.parentRefs parameter. By default, it will reference a Gateway named gateway in the same namespace as the release.

You can specify the list of hostnames to be mapped to the deployment using the tracking.httpRoute.hostnames parameter. Additionally, you can customize the rules used to route the traffic to the service by modifying the tracking.httpRoute.matches and tracking.httpRoute.filters parameters or adding new rules using the tracking.httpRoute.extraRules parameter.

This chart also supports creating a BackendTLSPolicy to define the SNI the Gateway should use to connect to the MLflow backend pods and how the certificate served by these pods should be verified. To do so, set the backendTLSPolicy.enabled parameter to true. Please note it's required to secure traffic using TLS as explained in the Securing traffic using TLS⁠ section to be able to use this feature.

⁠ Ingress

This chart provides support for Ingress resources. If you have an ingress controller installed on your cluster, such as NGINX Ingress Controller or Contour you can utilize the ingress controller to serve your application. To enable Ingress integration, set tracking.ingress.enabled to true.

The most common scenario is to have one host name mapped to the deployment. In this case, the tracking.ingress.hostname property can be used to set the host name. The tracking.ingress.tls parameter can be used to add the TLS configuration for this host.

However, it is also possible to have more than one host. To facilitate this, the tracking.ingress.extraHosts parameter (if available) can be set with the host names specified as an array. The tracking.ingress.extraTLS parameter (if available) can also be used to add the TLS configuration for extra hosts.

Adding the TLS parameter (where available) will cause the chart to generate HTTPS URLs, and the application will be available on port 443. The actual TLS secrets do not have to be generated by this chart. However, if TLS is enabled, the Ingress record will not work until the TLS secret exists.

Learn more about Ingress controllers⁠.

⁠ Securing traffic using TLS

MLflow can encrypt communications by setting tracking.tls.enabled=true. The chart allows two configuration options:

• Provide your own secret using the tracking.tls.certificatesSecret value. Also set the correct name of the certificate files using the tracking.tls.certFilename, tracking.tls.certKeyFilename and tracking.tls.certCAFilename values.
• Have the chart auto-generate the certificates using tracking.tls.autoGenerated=true.

⁠ Backup and restore

To back up and restore Helm chart deployments on Kubernetes, you need to back up the persistent volumes from the source deployment and attach them to a new deployment using Velero⁠, a Kubernetes backup/restore tool. Find the instructions for using Velero in this guide⁠.

⁠ FIPS parameters

The FIPS parameters only have effect if you are using images from the Bitnami Secure Images catalog⁠.

For more information on this new support, please refer to the FIPS Compliance section⁠.

⁠ Parameters

The following subsections list global, common, and component-specific parameters.

⁠ Global parameters

⁠ Common parameters

⁠ MLflow common Parameters

⁠ MLflow Tracking parameters

Note: the README for this chart is longer than the DockerHub length limit of 25000, so it has been trimmed. The full README can be found at https://techdocs.broadcom.com/us/en/vmware-tanzu/bitnami-secure-images/bitnami-secure-images/services/bsi-app-doc/apps-charts-mlflow-index.html⁠