Apache MXNet (Incubating) packaged by Bitnami¶

Apache MXNet (Incubating) is a flexible and efficient library for deep learning designed to work as a neural network. Bitnami image ships OpenBLAS as math library.

Overview of Apache MXNet (Incubating)

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¶

使用加速地址添加仓库:

helm repo add bitnami "https://helm-charts.itboon.top/bitnami" --force-update
helm update 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.

Looking to use Apache MXNet (Incubating) in production? Try VMware Application Catalog, the enterprise edition of Bitnami Application Catalog.

Prerequisites¶

• Kubernetes 1.19+
• Helm 3.2.0+
• 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 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¶

Global parameters¶

Common parameters¶

Common Mxnet parameters¶

Mxnet Standalone parameters (only for standalone mode)¶

Mxnet Server parameters (only for distributed mode)¶

Mxnet Worker parameters (only for distributed mode)¶

Mxnet Scheduler parameters (only for distributed mode)¶

Init containers parameters¶

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

helm install my-release \
  --set mode=distributed \
  --set server.replicaCount=2 \
  --set worker.replicaCount=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.

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:

cloneFilesFromGit.enabled=true
cloneFilesFromGit.repository=https://github.com/my-user/oci://registry-1.docker.io/bitnamicharts
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.

Setting Pod's affinity¶

This chart allows you to set your custom affinity using the XXX.affinity parameter(s). 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 XXX.podAffinityPreset, XXX.podAntiAffinityPreset, or XXX.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 3.0.0¶

This major release renames several values in this chart and adds missing features, in order to be aligned with the rest of the assets in the Bitnami charts repository.

In addition, the chart structure has been refactored for a clearer distribution and configuration.

The following values have been affected:

• service.* has been renamed as scheduler.service.*. In addition, values service.port and service.nodePort have been renamed as scheduler.service.ports.mxnet and scheduler.service.nodePorts.mxnet respectively.
• hostAliases has been renamed as {standalone/server/worker/scheduler}.hostAliases.
• commonExtraEnvVars has been renamed as extraEnvVars.
• podManagementPolicy has been renamed as worker.podManagementPolicy.
• The following have been moved under standalone,server,worker, and scheduler sections:
• podAffinityPreset.
• podAntiAffinityPreset.
• nodeAffinityPreset.
• affinity.
• nodeSelector.
• tolerations.
• resources.
• livenessProbe.
• readinessProbe.
• securityContext (now renamed as podSecurityContext and containerSecurityContext).
• scheduler.port, previously used for both containerPort and service port configuration, is now configured using scheduler.containerPorts.mxnet and scheduler.service.ports.mxnet values.

To 2.1.0¶

Some parameters disappeared in favor of new ones:

• schedulerExtraEnvVars and schedulerPort -> deprecated in favor of scheduler.extraEnvVars and scheduler.port, respectively.
• serverExtraEnvVars and serverCount -> deprecated in favor of server.extraEnvVars and server.replicaCount, respectively.
• workerExtraEnvVars and workerCount -> deprecated in favor of worker.extraEnvVars and worker.replicaCount, respectively.

This version also 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 2.0.0¶

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/

License¶

Copyright © 2023 VMware, Inc.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.