charts/bitnami/tensorflow-resnet

TensorFlow Serving ResNet

TensorFlow Serving is an open-source software library for serving machine learning models. This chart will specifically serve the ResNet model with already trained data.

TL;DR

$ helm repo add bitnami https://charts.bitnami.com/bitnami
$ helm install my-release bitnami/tensorflow-resnet

Introduction

This chart bootstraps a TensorFlow Serving ResNet deployment on a Kubernetes cluster using the Helm package manager.

Bitnami charts can be used with Kubeapps for deployment and management of Helm Charts in clusters. This Helm chart has been tested on top of Bitnami Kubernetes Production Runtime (BKPR). Deploy BKPR to get automated TLS certificates, logging and monitoring for your applications.

Prerequisites

• Kubernetes 1.12+
• Helm 3.1.0

Get this chart

Download the latest release of the chart from the releases page.

Alternatively, clone the repo if you wish to use the development snapshot:

$ git clone https://github.com/bitnami/charts.git

Installing the Chart

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

$ helm repo add bitnami https://charts.bitnami.com/bitnami
$ helm install my-release bitnami/tensorflow-resnet

These commands deploy Tensorflow Serving ResNet model on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation.

Tip

: List all releases using helm list

Uninstalling the Chart

To uninstall/delete the my-release deployment:

$ helm delete my-release

You can check your releases with:

$ helm list

The command removes all the Kubernetes components associated with the chart and deletes the release.

Parameters

The following tables lists the configurable parameters of the TensorFlow ResNet chart and their default values.

Parameter	Description	Default
global.imageRegistry	Global Docker image registry	nil
global.imagePullSecrets	Global Docker registry secret names as an array	[] (does not add image pull secrets to deployed pods)
server.image.registry	TensorFlow Serving image registry	docker.io
server.image.repository	TensorFlow Serving Image name	bitnami/tensorflow-serving
server.image.tag	TensorFlow Serving Image tag	{TAG_NAME}
server.image.pullPolicy	TensorFlow Serving image pull policy	IfNotPresent
server.image.pullSecrets	Specify docker-registry secret names as an array	[] (does not add image pull secrets to deployed pods)
client.image.registry	TensorFlow ResNet image registry	docker.io
client.image.repository	TensorFlow ResNet Image name	bitnami/tensorflow-resnet
client.image.tag	TensorFlow ResNet Image tag	{TAG_NAME}
client.image.pullPolicy	TensorFlow ResNet image pull policy	IfNotPresent
client.image.pullSecrets	Specify docker-registry secret names as an array	[] (does not add image pull secrets to deployed pods)
nameOverride	String to partially override common.names.fullname template with a string (will prepend the release name)	nil
fullnameOverride	String to fully override common.names.fullname template with a string	nil
containerPorts.server	Tensorflow server port	8500
containerPorts.restApi	TensorFlow Serving Rest API Port	8501
replicaCount	Desired number of pods	1
podAnnotations	Pod annotations	{}
podAffinityPreset	Pod affinity preset. Ignored if affinity is set. Allowed values: soft or hard	""
podAntiAffinityPreset	Pod anti-affinity preset. Ignored if affinity is set. Allowed values: soft or hard	soft
nodeAffinityPreset.type	Node affinity preset type. Ignored if affinity is set. Allowed values: soft or hard	""
nodeAffinityPreset.key	Node label key to match Ignored if affinity is set.	""
nodeAffinityPreset.values	Node label values to match. Ignored if affinity is set.	[]
affinity	Affinity for pod assignment	{} (evaluated as a template)
nodeSelector	Node labels for pod assignment	{} (evaluated as a template)
hostAliases	Add deployment host aliases	[]
tolerations	Tolerations for pod assignment	[] (evaluated as a template)
resources	Resource requests/limit	{}
livenessProbe.enabled	Would you like a livessProbed to be enabled	true
livenessProbe.initialDelaySeconds	Delay before liveness probe is initiated	30
livenessProbe.periodSeconds	How often to perform the probe	5
livenessProbe.timeoutSeconds	When the probe times out	5
livenessProbe.failureThreshold	Minimum consecutive failures for the probe to be considered failed after having succeeded	6
livenessProbe.successThreshold	Minimum consecutive successes for the probe to be considered successful after having failed	1
readinessProbe.enabled	Would you like a readinessProbe to be enabled	true
readinessProbe.initialDelaySeconds	Delay before liveness probe is initiated	15
readinessProbe.periodSeconds	How often to perform the probe	5
readinessProbe.timeoutSeconds	When the probe times out	5
readinessProbe.failureThreshold	Minimum consecutive failures for the probe to be considered failed after having succeeded	6
readinessProbe.successThreshold	Minimum consecutive successes for the probe to be considered successful after having failed	1
service.type	Kubernetes Service type	LoadBalancer
service.port	TensorFlow Serving server port	8500
service.restApiPort	TensorFlow Serving Rest API port	8501
service.nodePorts.server	Kubernetes server node port	""
service.nodePorts.restApi	Kubernetes Rest API node port	""
service.loadBalancerIP	LoadBalancer service IP address	""
service.annotations	Service annotations	{}
metrics.enabled	Enable Prometheus exporter to expose Tensorflow server metrics	false
metrics.podAnnotations	Additional annotations for Metrics exporter pod	{prometheus.io/scrape: "true", prometheus.io/path: "/monitoring/prometheus/metrics", prometheus.io/port: "8501"}

Specify each parameter using the --set key=value[,key=value] argument to helm install. For example,

$ helm install my-release bitnami/tensorflow-resnet --set imagePullPolicy=Always

Alternatively, a YAML file that specifies the values for the above parameters can be provided while installing the chart. For example,

$ helm install my-release -f values.yaml bitnami/tensorflow-resnet

Tip

: You can use the default values.yaml

Configuration and installation details

Rolling VS Immutable tags

It is strongly recommended to use immutable tags in a production environment. This ensures your deployment does not change automatically if the same tag is updated with a different image.

Bitnami will release a new chart updating its containers if a new version of the main container, significant changes, or critical vulnerabilities exist.

Setting Pod's affinity

This chart allows you to set your custom affinity using the affinity parameter. Find more information about Pod's affinity in the kubernetes documentation.

As an alternative, you can use of the preset configurations for pod affinity, pod anti-affinity, and node affinity available at the bitnami/common chart. To do so, set the podAffinityPreset, podAntiAffinityPreset, or nodeAffinityPreset parameters.

Troubleshooting

Find more information about how to deal with common errors related to Bitnami’s Helm charts in this troubleshooting guide.

Upgrading

To 3.1.0

This version introduces bitnami/common, a library chart as a dependency. More documentation about this new utility could be found here. Please, make sure that you have updated the chart dependencies before executing any upgrade.

To 3.0.0

On November 13, 2020, Helm v2 support was formally finished, this major version is the result of the required changes applied to the Helm Chart to be able to incorporate the different features added in Helm v3 and to be consistent with the Helm project itself regarding the Helm v2 EOL.

What changes were introduced in this major version?

• Previous versions of this Helm Chart use apiVersion: v1 (installable by both Helm 2 and 3), this Helm Chart was updated to apiVersion: v2 (installable by Helm 3 only). Here you can find more information about the apiVersion field.
• The different fields present in the Chart.yaml file has been ordered alphabetically in a homogeneous way for all the Bitnami Helm Charts

Considerations when upgrading to this version

• If you want to upgrade to this version from a previous one installed with Helm v3, you shouldn't face any issues
• If you want to upgrade to this version using Helm v2, this scenario is not supported as this version doesn't support Helm v2 anymore
• If you installed the previous version with Helm v2 and wants to upgrade to this version with Helm v3, please refer to the official Helm documentation about migrating from Helm v2 to v3

Useful links

• https://docs.bitnami.com/tutorials/resolve-helm2-helm3-post-migration-issues/
• https://helm.sh/docs/topics/v2_v3_migration/
• https://helm.sh/blog/migrate-from-helm-v2-to-helm-v3/

To 2.0.0

Backwards compatibility is not guaranteed unless you modify the labels used on the chart's deployments. Use the workaround below to upgrade from versions previous to 2.0.0. The following example assumes that the release name is tensorflow-resnet:

$ kubectl delete deployment  tensorflow-resnet --cascade=false
$ helm upgrade tensorflow-resnet bitnami/tensorflow-resnet
$ kubectl delete rs "$(kubectl get rs -l app=tensorflow-resnet -o jsonpath='{.items[0].metadata.name}')"