bitnami/mxnet

Chart version: 1.4.30
Api version: v1
App version: 1.7.0
A flexible and efficient library for deep learning
application
Chart Type
Active
Status
Unknown
License
1849
Downloads
https://charts.bitnami.com/bitnami
Set me up:
helm repo add center https://repo.chartcenter.io
Install Chart:
helm install mxnet center/bitnami/mxnet
Versions (0)

Apache MXNet (Incubating)

Apache MXNet (Incubating) is a deep learning platform that accelerates the transition from research prototyping to production deployment. It is built for full integration into Python that enables you to use it with its libraries and main packages.

TL;DR

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

Introduction

This chart bootstraps an Apache MXNet (Incubating) 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 2.12+ or Helm 3.0-beta3+
  • PV provisioner support in the underlying infrastructure
  • ReadWriteMany volumes for deployment scaling

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/mxnet

These commands deploy Apache MXNet (Incubating) on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured.

Tip: List all releases using helm list

Uninstalling the Chart

To uninstall/delete the my-release deployment:

$ helm delete my-release

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

Parameters

The following table lists the configurable parameters of the MinIO 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)
global.storageClass Global storage class for dynamic provisioning nil
image.registry Apache MXNet (Incubating) image registry docker.io
image.repository Apache MXNet (Incubating) image name bitnami/mxnet
image.tag Apache MXNet (Incubating) image tag {TAG_NAME}
image.pullPolicy Image pull policy IfNotPresent
image.pullSecrets Specify docker-registry secret names as an array [] (does not add image pull secrets to deployed pods)
image.debug Specify if debug logs should be enabled false
git.registry Git image registry docker.io
git.repository Git image name bitnami/git
git.tag Git image tag {TAG_NAME}
git.pullPolicy Git image pull policy IfNotPresent
git.pullSecrets Specify docker-registry secret names as an array [] (does not add image pull secrets to deployed pods)
nameOverride String to partially override mxnet.fullname template with a string (will prepend the release name) nil
fullnameOverride String to fully override mxnet.fullname template with a string nil
volumePermissions.enabled Enable init container that changes volume permissions in the data directory (for cases where the default k8s runAsUser and fsUser values do not work) false
volumePermissions.image.registry Init container volume-permissions image registry docker.io
volumePermissions.image.repository Init container volume-permissions image name bitnami/minideb
volumePermissions.image.tag Init container volume-permissions image tag buster
volumePermissions.image.pullPolicy Init container volume-permissions image pull policy Always
volumePermissions.resources Init container resource requests/limit nil
service.type Kubernetes service type ClusterIP
entrypoint.file Main entrypoint to your application. If not speficied, it will be a sleep infinity command ''
entrypoint.args Args required by your entrypoint nil
entrypoint.workDir Working directory for launching the entrypoint '/app'
podManagementPolicy StatefulSet (worker and server nodes) pod management policy Parallel
mode Run Apache MXNet (Incubating) in standalone or distributed mode (possible values: standalone, distributed) standalone
serverCount Number of server nodes that will execute your code 1
workerCount Number of worker nodes that will execute your code 1
schedulerPort Apache MXNet (Incubating) scheduler port (only for distributed mode) 49875
configMap Config map that contains the files you want to load in Apache MXNet (Incubating) nil
cloneFilesFromGit.enabled Enable in order to download files from git repository false
cloneFilesFromGit.repository Repository that holds the files nil
cloneFilesFromGit.revision Revision from the repository to checkout master
commonExtraEnvVars Extra environment variables to add to server, scheduler and worker nodes nil
workerExtraEnvVars Extra environment variables to add to the worker nodes nil
serverExtraEnvVars Extra environment variables to add to the server nodes nil
schedulerExtraEnvVars Extra environment variables to add to the scheduler node nil
existingSecret Name of a secret with sensitive data to mount in the pods nil
nodeSelector Node labels for pod assignment (this value is evaluated as a template) {}
tolerations Toleration labels for pod assignment (this value is evaluated as a template) []
affinity Map of node/pod affinities (this value is evaluated as a template) {}
resources Pod resources {}
securityContext.enabled Enable security context true
securityContext.fsGroup Group ID for the container 1001
securityContext.runAsUser User ID for the container 1001
livenessProbe.enabled Enable/disable the Liveness probe true
livenessProbe.initialDelaySeconds Delay before liveness probe is initiated 5
livenessProbe.periodSeconds How often to perform the probe 5
livenessProbe.timeoutSeconds When the probe times out 5
livenessProbe.successThreshold Minimum consecutive successes for the probe to be considered successful after having failed. 1
livenessProbe.failureThreshold Minimum consecutive failures for the probe to be considered failed after having succeeded. 5
readinessProbe.enabled Enable/disable the Readiness probe true
readinessProbe.initialDelaySeconds Delay before readiness probe is initiated 5
readinessProbe.periodSeconds How often to perform the probe 5
readinessProbe.timeoutSeconds When the probe times out 1
readinessProbe.successThreshold Minimum consecutive successes for the probe to be considered successful after having failed. 1
readinessProbe.failureThreshold Minimum consecutive failures for the probe to be considered failed after having succeeded. 5
persistence.enabled Use a PVC to persist data false
persistence.mountPath Path to mount the volume at /bitnami/mxnet
persistence.storageClass Storage class of backing PVC nil (uses alpha storage class annotation)
persistence.accessMode Use volume as ReadOnly or ReadWrite ReadWriteOnce
persistence.size Size of data volume 8Gi
persistence.annotations Persistent Volume annotations {}
sidecars Attach additional containers to the pods (scheduler, worker and server nodes) []
initContainers Attach additional init containers to the pods (scheduler, worker and server nodes) []

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

$ helm install my-release \
  --set mode=distributed \
  --set serverCount=2 \
  --set workerCount=3 \
    bitnami/mxnet

The above command creates 6 pods for Apache MXNet (Incubating): one scheduler, two servers, and three workers.

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

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

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.

Production configuration

This chart includes a values-production.yaml file where you can find some parameters oriented to production configuration in comparison to the regular values.yaml. You can use this file instead of the default one.

  • Run Apache MXNet (Incubating) in distributed mode: “`diff

  • mode: standalone

  • mode: distributed

    - Number of server nodes that will execute your code:
    ```diff
    - serverCount: 1
    + serverCount: 2
    
  • Number of worker nodes that will execute your code: “`diff

  • workerCount: 1

  • workerCount: 4

    ### Loading your files
    The Apache MXNet (Incubating) chart supports three different ways to load your files. In order of priority, they are:
    1. Existing config map
    2. Files under the `files` directory
    3. Cloning a git repository
    This means that if you specify a config map with your files, it won't look for the `files/` directory nor the git repository.
    In order to use use an existing config map you can set the `configMap=my-config-map` parameter.
    To load your files from the `files/` directory you don't have to set any option. Just copy your files inside and don't specify a `ConfigMap`.
    Finally, if you want to clone a git repository you can use the following parameters:
    ```console
    cloneFilesFromGit.enabled=true
    cloneFilesFromGit.repository=https://github.com/my-user/my-repo
    cloneFilesFromGit.revision=master
    

In case you want to add a file that includes sensitive information, pass a secret object using the existingSecret parameter. All the files in the secret will be mounted in the /secrets folder.

Distributed training example

We will use the gluon example from the Apache MXNet (Incubating) official repository. Launch it with the following values:

mode=distributed
cloneFilesFromGit.enabled=true
cloneFilesFromGit.repository=https://github.com/apache/incubator-mxnet.git
cloneFilesFromGit.revision=master
entrypoint.file=image_classification.py
entrypoint.args="--dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync"
entrypoint.workDir=/app/example/gluon/

Check the logs of the worker node:

INFO:root:Starting new image-classification task:, Namespace(batch_norm=False, batch_size=32, builtin_profiler=0, data_dir='', dataset='cifar10', dtype='float32', epochs=1, gpus='', kvstore='dist_sync', log_interval=50, lr=0.1, lr_factor=0.1, lr_steps='30,60,90', mode=None, model='vgg11', momentum=0.9, num_workers=4, prefix='', profile=False, resume='', save_frequency=10, seed=123, start_epoch=0, use_pretrained=False, use_thumbnail=False, wd=0.0001)
INFO:root:downloaded http://data.mxnet.io/mxnet/data/cifar10.zip into data/cifar10.zip successfully
[10:05:40] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: data/cifar/train.rec, use 1 threads for decoding..
[10:05:45] src/io/iter_image_recordio_2.cc:172: ImageRecordIOParser2: data/cifar/test.rec, use 1 threads for decoding..

If you want to increase the verbosity, set the environment variable PS_VERBOSE=1 or PS_VERBOSE=2 using the commonEnvVars value.

mode=distributed
cloneFilesFromGit.enabled=true
cloneFilesFromGit.repository=https://github.com/apache/incubator-mxnet.git
cloneFilesFromGit.revision=master
entrypoint.file=image_classification.py
entrypoint.args="--dataset cifar10 --model vgg11 --epochs 1 --kvstore dist_sync"
entrypoint.workDir=/app/example/gluon/
commonExtraEnvVars[0].name=PS_VERBOSE
commonExtraEnvVars[0].value=1

You will now see log entries in the scheduler and server nodes.

[14:22:44] src/van.cc:290: Bind to role=scheduler, id=1, ip=10.32.0.11, port=9092, is_recovery=0
[14:22:53] src/van.cc:56: assign rank=9 to node role=worker, ip=10.32.0.17, port=55423, is_recovery=0
[14:22:53] src/van.cc:56: assign rank=11 to node role=worker, ip=10.32.0.16, port=60779, is_recovery=0
[14:22:53] src/van.cc:56: assign rank=13 to node role=worker, ip=10.32.0.15, port=39817, is_recovery=0
[14:22:53] src/van.cc:56: assign rank=15 to node role=worker, ip=10.32.0.14, port=48119, is_recovery=0
[14:22:53] src/van.cc:56: assign rank=8 to node role=server, ip=10.32.0.13, port=56713, is_recovery=0
[14:22:53] src/van.cc:56: assign rank=10 to node role=server, ip=10.32.0.12, port=57099, is_recovery=0
[14:22:53] src/van.cc:83: the scheduler is connected to 4 workers and 2 servers
[14:22:53] src/van.cc:183: Barrier count for 7 : 1
[14:22:53] src/van.cc:183: Barrier count for 7 : 2
[14:22:53] src/van.cc:183: Barrier count for 7 : 3
[14:22:53] src/van.cc:183: Barrier count for 7 : 4
...

Sidecars and Init Containers

If you have a need for additional containers to run within the same pod as Apache MXNet (Incubating) (e.g. an additional metrics or logging exporter), you can do so via the sidecars config parameter. Simply define your container according to the Kubernetes container spec.

sidecars:
- name: your-image-name
  image: your-image
  imagePullPolicy: Always
  ports:
  - name: portname
   containerPort: 1234

Similarly, you can add extra init containers using the initContainers parameter.

initContainers:
- name: your-image-name
  image: your-image
  imagePullPolicy: Always
  ports:
  - name: portname
   containerPort: 1234

Persistence

The Bitnami Apache MXNet (Incubating) image can persist data. If enabled, the persisted path is /bitnami/mxnet by default.

The chart mounts a Persistent Volume at this location. The volume is created using dynamic volume provisioning.

Adjust permissions of persistent volume mountpoint

As the image run as non-root by default, it is necessary to adjust the ownership of the persistent volume so that the container can write data into it.

By default, the chart is configured to use Kubernetes Security Context to automatically change the ownership of the volume. However, this feature does not work in all Kubernetes distributions. As an alternative, this chart supports using an initContainer to change the ownership of the volume before mounting it in the final destination.

You can enable this initContainer by setting volumePermissions.enabled to true.