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Machine Learning Toolkit for Kubernetes

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adds apply -f to kfctl

Implements case 1 and case 2 in the new kfctl semantics Design doc reference

  • [x] make platform args optional
  • [x] implement new semantics for build and apply

Current workflow for kfctl -

New sematics single command install:

# move into empty directory
kfctl apply -f <path to config file / URL>

New semantics build, edit, apply:

# move into empty directory
kfctl build -f <path to config file / URL>
kfctl apply

Old semantics:

kfctl init kf-app --config <path to config file / URL> 
cd kf-app
kfctl generate <platform>
kfctl apply <platform>

/cc @yanniszark @jlewi @kkasravi

Related to: #3518

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size/L lgtm approved cla: yes 
opened Aug 30, 2019 by swiftdiaries 101
Closed

Kubeflow Install Error

Trying to install Kubeflow v.70 on on-prem cluster (kubernetes v15.7/Ubuntu 18.04) but getting below error:

[email protected]:/opt/kubeflow/kf-test# kfctl apply -V -f ${CONFIG_FILE} INFO[0000]

Notice anonymous usage reporting enabled using spartakus To disable it If you have already deployed it run the following commands: cd $(pwd) kubectl -n ${K8S_NAMESPACE} delete deploy -l app=spartakus

For more info: https://www.kubeflow.org/docs/other-guides/usage-reporting/

filename="coordinator/coordinator.go:120" INFO[0000] Deleting cachedir /opt/kubeflow/kf-test/.cache/manifests because Status.ReposCache is out of date filename="kfconfig/types.go:464" INFO[0000] Fetching https://github.com/kubeflow/manifests/archive/v0.7-branch.tar.gz to /opt/kubeflow/kf-test/.cache/manifests filename="kfconfig/types.go:485" Error: failed to build kfApp from URI /opt/kubeflow/kf-test/kfctl_existing_arrikto.yaml: couldn't generate KfApp: (kubeflow.error): Code 500 with message: could not sync cache. Error: (kubeflow.error): Code 400 with message: couldn't download URI https://github.com/kubeflow/manifests/archive/v0.7-branch.tar.gz Error Error opening a gzip reader for /tmp/getter093015857/archive: EOF Usage: kfctl apply -f ${CONFIG} [flags]

Flags: -f, --file string Static config file to use. Can be either a local path: export CONFIG=./kfctl_gcp_iap.yaml or a URL: export CONFIG=https://raw.githubusercontent.com/kubeflow/manifests/v0.7-branch/kfdef/kfctl_gcp_iap.0.7.0.yaml export CONFIG=https://raw.githubusercontent.com/kubeflow/manifests/v0.7-branch/kfdef/kfctl_existing_arrikto.0.7.0.yaml export CONFIG=https://raw.githubusercontent.com/kubeflow/manifests/v0.7-branch/kfdef/kfctl_aws.0.7.0.yaml export CONFIG=https://raw.githubusercontent.com/kubeflow/manifests/v0.7-branch/kfdef/kfctl_k8s_istio.0.7.0.yaml kfctl apply -V --file=${CONFIG} -h, --help help for apply -V, --verbose verbose output default is false

failed to build kfApp from URI /opt/kubeflow/kf-test/kfctl_existing_arrikto.yaml: couldn't generate KfApp: (kubeflow.error): Code 500 with message: could not sync cache. Error: (kubeflow.error): Code 400 with message: couldn't download URI https://github.com/kubeflow/manifests/archive/v0.7-branch.tar.gz Error Error opening a gzip reader for /tmp/getter093015857/archive: EOF

The Kubeflow installation was working fin when tried it a few days ago. Trying to install it in a new cluster and getting this error. Can you please help?

Thanks, Job Varkey

priority/p0 platform/onprem kind/bug area/enterprise_readiness 
opened Jan 9, 2020 by jobvarkey 84
Closed

Release KF 1.1

/kind process

We need to identify who will be driving the 1.1 release. These folks should then

  • Identify timelines for the release
    • e.g. cutoff dates for branch cuts
    • Target dates for RCs

This would probably be a good opportunity to update some of the processes and policies around releases. https://github.com/kubeflow/kubeflow/blob/master/docs_dev/releasing.md

Area | release czar | Tracking Issue --- | --- | --- aws | @Jeffwan | ~~#5057~~ | centraldashboard | | ~~#5068~~ docs | | kubeflow/website#1984 fairing | @jinchihe | ~~kubeflow/fairing#503~~ | feast | @woop | | gcp | @jlewi | kubeflow/gcp-blueprints#46 | katib | @andreyvelich | kubeflow/katib#1211 | kfctl | @krishnadurai, @crobby | kubeflow/kfctl#352 | kfserving | @yuzisun @animeshsingh | kubeflow/kfserving#648 | manifests | @krishnadurai | kubeflow/manifests#1252 metadata | | minikf | @vkoukis | | multiuser | @yanniszark @bmorphism | #5067, #5068 notebooks | @kimwnasptd , @jtfogarty | #5060, #5068 | pipelines | @Bobgy | kubeflow/pipelines#3961 | training | @johnugeorge @andreyvelich @Jeffwan | kubeflow/common#97

Target Dates

  • Branch cut June 19
kind/feature priority/p0 kind/process area/engprod 
opened May 18, 2020 by jlewi 73
Closed

Fire off TFJob from Jupyter Notebook

We'd like to make it super easy to go from writing code in a notebook to training that model distributed.

Experience might be something like

  • User writes code in notebook and executes in Jupyter lab
  • User clicks a button which allows user to fill in various settings e.g. number of GPUs
  • User clicks train

Under the hood this would cause

  • A docker image to be built
  • A TFJob/PyTorch/K8s Job to be created and fired off.

I think the biggest challenge is that we probably don't want to execute all code in the notebook. Typically, there's some amount of refactoring that needs to be done to convert a notebook into a python module suitable for execution in a bash job.

As a concrete example

Here's the notebook for our GitHub Issue summarization example

Here's the corresponding python module used when training in a K8s job.

The python module only executes a subset of cells in particular those to

  1. Define model architecture
  2. train the model

Rather than try to auto-convert a notebook like the github issue example, I think we should require users structure their code to facilitate the conversion.

My suggestion would be to allow any functions defined in the notebook to be used as entry points. So for the GitHub issues summarization the user would have a cell like the following

from keras.callbacks import CSVLogger, ModelCheckpoint

def train_model(output)
  script_name_base = 'tutorial_seq2seq'
  csv_logger = CSVLogger('{:}.log'.format(script_name_base))
  model_checkpoint = ModelCheckpoint('{:}.epoch{{epoch:02d}}- 
  val{{val_loss:.5f}}.hdf5'.format(script_name_base),
                                                  save_best_only=True)

  batch_size = 1200
  epochs = 7
  history = seq2seq_Model.fit([encoder_input_data, decoder_input_data], 
  np.expand_dims(decoder_target_data, -1),
           batch_size=batch_size,
           epochs=epochs,
           validation_split=0.12, callbacks=[csv_logger, model_checkpoint])

   seq2seq_Model.save(output)

train('seq2seq_model_tutorial.h5')

If user structures their code this way, we should be able to manually create and invoke a suitable container entry point. Something like the following

  • Use nbconvert to convert from ipynb to python code
  • Post process the python code
    • Strip out any statements not inside a function (except imports)
    • Create a CLI for the functions using a library like PyFire
  • Build a Docker image that is Notebook image + code

A variant of this idea would be to use metaml (by @wbuchwalter ). metaml uses metaparticle to allow people to annotate their python code with information needed to then run it on K8s (e.g. distributed using TFJob). If we went this approach I think the flow would be

  • Run nbconvert to go from ipynb -> py
  • Use metaparticle/metaml tool chain to build the docker image and submit the job.

@willingc @yuvipanda Is there existing tooling in the Jupyter community other than nbconvert to convert notebooks to code suitable for asynchronous batch execution?

/cc @wbuchwalter @gaocegege @yuvipanda @willingc

priority/p1 area/jupyter area/0.4.0 
opened Jul 19, 2018 by jlewi 67
Closed

Kubeflow logo?

We should have a Kubeflow logo.

priority/p2 priority/p0 
opened Jan 31, 2018 by jlewi 65
Closed

Add Tolerations to PodDefault

I want to add tolerations to the pods for my Jupyter server. I am able to inject other things like Labels and Annotations, this would allow me to also use PodDefault to inject tolerations.

This change is Reviewable

size/L lgtm approved cla: yes ok-to-test 
opened Mar 11, 2020 by wdhorton 60
Open

Support dynamic logout URLs in CentralDashboard

/kind bug

We just merged for KF 1.7 RC0 the update on AuthService https://github.com/kubeflow/manifests/pull/2150. But in this commit the logout URL is /authservice/logout, instead of /logout. But since we can't hardcode this value, since not everyone might be using AuthService, we'll need to have a dynamic mechanism for learning this URL.

I'd propose to

  1. have a new ENV Var LOGOUT_URL in the Deployment of the app
  2. The backend will add this for the /api/workgroup/env-info route handler https://github.com/kubeflow/kubeflow/blob/master/components/centraldashboard/app/api_workgroup.ts
  3. The frontend will dynamically use this value from the backend

This way users can configure the app based on the authentication service that they use in a dynamic way

kind/bug 
opened Feb 6, 2023 by kimwnasptd 2
Open

build(deps): bump http-cache-semantics from 4.1.0 to 4.1.1 in /components/centraldashboard-angular/frontend

Bumps http-cache-semantics from 4.1.0 to 4.1.1.

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K8s dependencies size/XS javascript 
opened Feb 4, 2023 by dependabot[bot] 1
Open

build(deps): bump http-cache-semantics from 4.1.0 to 4.1.1 in /components/crud-web-apps/jupyter/frontend

Bumps http-cache-semantics from 4.1.0 to 4.1.1.

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K8s dependencies size/XS javascript 
opened Feb 3, 2023 by dependabot[bot] 1
Open

build(deps): bump http-cache-semantics from 4.1.0 to 4.1.1 in /components/crud-web-apps/volumes/frontend

Bumps http-cache-semantics from 4.1.0 to 4.1.1.

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K8s dependencies size/XS javascript 
opened Feb 3, 2023 by dependabot[bot] 1
Open

How can I have a different StorageClass per team

/kind question

We are running Multi-tenant Kubeflow 1.6.1. in our cluster and we would like to change the StorageClass used by the PVCs created by the pipelines and the notebooks depending on the team. We also would like to avoid other teams to select the StorageClasses that belong to the other teams.

Is there any way to get this behaviour with configuration?

kind/question 
opened Feb 3, 2023 by davidggz 0
Open

gh-actions: Fix KF intergration tests for release branches

This PR addresses https://github.com/kubeflow/kubeflow/pull/6916#issuecomment-1408865920 and is part of https://github.com/kubeflow/kubeflow/issues/6766.

Right now, the KF integration tests do not work when we update the manifests for RCs. The sed command does not work since it tries to replace the latest tag, which doesn't exist in the release branches.

Changes:

  • Update the workflows to use the kustomize edit image <curr_img>=<new_img>:<tag> command (https://github.com/kubernetes-sigs/kustomize/blob/master/examples/image.md) to set the appropriate tag in the manifests before they are applied.

Signed-off-by: Apostolos Gerakaris [email protected]

size/M 
opened Feb 3, 2023 by apo-ger 3

v1.6.0(Sep 7, 2022)

KF 1.6 release 🎉

What's new ✨

  • Expose notebook idleness in Notebook management web app https://github.com/kubeflow/kubeflow/pull/6544 @kimwnasptd
  • Notebook Controller QPS and Burst https://github.com/kubeflow/kubeflow/pull/6453 @mofanke
  • Don't show Notebook groups if they don't have any images https://github.com/kubeflow/kubeflow/pull/6482 @haoxins
  • Upgrade Kubebuilder for Profiles Controller https://github.com/kubeflow/kubeflow/pull/6491 @apo-ger

What's fixed 🐛

  • Reload certificates in PodDefaults webhook https://github.com/kubeflow/kubeflow/pull/6581 @manolis-andr
  • Notebooks controller and K8s 1.22 https://github.com/kubeflow/kubeflow/pull/6374 @samuelvl
  • PodDefaults and K8s 1.22 https://github.com/kubeflow/kubeflow/pull/6459 @aaron-arellano
  • TensorBoard and K8s 1.22 https://github.com/kubeflow/kubeflow/pull/6406 @alembiewski
  • Allow Notebook Controller to patch events https://github.com/kubeflow/kubeflow/pull/6523 @henrysecond1

Upgrade notes ⏫

  • The project is using DockerHub hosted images instead of ECR ones https://github.com/kubeflow/kubeflow/pull/6548. Users will need to update their ovelays accordingly

v1.5.0(Mar 10, 2022)

KF 1.5 release 🎉

What's new ✨

  • notebooks: Extend Notebook Controller to expose idleness for Jupyter #6297 @athamark
  • jwa: Rework the Storage API of the web app #6321 @kimwnasptd
  • Change namespace label for Katib metrics collector injection #6225 @andreyvelich
  • Tell user to select a namespace to access namespaced menu item #6181 @toshi-k
  • frontend: Use responsive tables instead of cards #6316 @kimwnasptd
  • Fix(manifests): Upgrade rbac.authorization.k8s.io from v1beta1 to v1 #6261 @haoxins
  • Synchronize jupyter-web-application role with clusterrole #6241 @juliusvonkohout

What's fixed 🐛

  • Secure access to KFAM #6077 @DavidSpek
  • fix(backend): tensorboard-controller does not work because of missing permissions #6216 @juliusvonkohout
  • fix(notebooks) make culling work with multi-user #5980 @LEDfan
  • notebooks: Graceful handling of events #6338 @kimwnasptd
  • notebooks: Fix endless restarts #6341 @kimwnasptd
  • Centraldashboard/add uncaughtexception handler #6203 @haoxins
  • fix dashboard sending malformed headers to kfam #6186 @thesuperzapper

Upgrade notes ⏫

  1. Update the Jupyter web apps configmap with the new storage structure https://github.com/kubeflow/kubeflow/pull/6321
  2. Notebook Controller is using different ENV Vars for culling https://github.com/kubeflow/kubeflow/blob/master/components/proposals/20220121-jupyter-notebook-idleness.md#upgrade-notes
  3. The Profile Controller is adding different Katib metrics labels https://github.com/kubeflow/kubeflow/pull/6225

v1.5.0-rc.1(Feb 14, 2022)

First RC of the KF 1.5 release 🎉

What's new ✨

  • notebooks: Extend Notebook Controller to expose idleness for Jupyter #6297 @athamark
  • jwa: Rework the Storage API of the web app #6321 @kimwnasptd
  • Change namespace label for Katib metrics collector injection #6225 @andreyvelich
  • Tell user to select a namespace to access namespaced menu item #6181 @toshi-k
  • frontend: Use responsive tables instead of cards #6316 @kimwnasptd
  • Fix(manifests): Upgrade rbac.authorization.k8s.io from v1beta1 to v1 #6261 @haoxins
  • Synchronize jupyter-web-application role with clusterrole #6241 @juliusvonkohout

What's fixed 🐛

  • Secure access to KFAM #6077 @DavidSpek
  • fix(backend): tensorboard-controller does not work because of missing permissions #6216 @juliusvonkohout
  • fix(notebooks) make culling work with multi-user #5980 @LEDfan
  • notebooks: Graceful handling of events #6338 @kimwnasptd
  • notebooks: Fix endless restarts #6341 @kimwnasptd
  • Centraldashboard/add uncaughtexception handler #6203 @haoxins
  • fix dashboard sending malformed headers to kfam #6186 @thesuperzapper

Upgrade notes ⏫

  1. Update the Jupyter web apps configmap with the new storage structure https://github.com/kubeflow/kubeflow/pull/6321
  2. Notebook Controller is using different ENV Vars for culling https://github.com/kubeflow/kubeflow/blob/master/components/proposals/20220121-jupyter-notebook-idleness.md#upgrade-notes
  3. The Profile Controller is adding different Katib metrics labels https://github.com/kubeflow/kubeflow/pull/6225

v1.5.0-rc.0(Feb 14, 2022)

First RC of the KF 1.5 release 🎉

What's new ✨

  • Change namespace label for Katib metrics collector injection #6225 @andreyvelich
  • Tell user to select a namespace to access namespaced menu item #6181 @toshi-k
  • frontend: Use responsive tables instead of cards #6316 @kimwnasptd

What's fixed 🐛

  • Secure access to KFAM #6077 @DavidSpek
  • fix(backend): tensorboard-controller does not work because of missing permissions #6216 @juliusvonkohout
  • fix(notebooks) make culling work with multi-user #5980 @LEDfan
  • Centraldashboard/add uncaughtexception handler #6203 @haoxins
  • fix dashboard sending malformed headers to kfam #6186 @thesuperzapper

v1.4.0(Oct 11, 2021)

Web apps:

  • Migrate to Angular 12 from 8: https://github.com/kubeflow/kubeflow/pull/6004 @DavidSpek

Internationalization progress:

  • Internationalization to web apps https://github.com/kubeflow/kubeflow/pull/5880 @wg102, @Jose-Matsuda
  • Use Angular's i18n solution https://github.com/kubeflow/kubeflow/pull/6065 @kimwnasptd

Central Dashboard:

  • Namespaced menu items https://github.com/kubeflow/kubeflow/pull/5995 @toshi-k
  • Make it possible to add Namespaced menu items #5871 @toshi-k
  • Scrollable side-bar https://github.com/kubeflow/kubeflow/pull/5964 @toshi-k
  • Add support for Models web app https://github.com/kubeflow/kubeflow/pull/6085 @kimwnasptd

Jupyter web app

  • Jupyter web app fix for autoscaling GPU nodegroups https://github.com/kubeflow/kubeflow/pull/6171 @kimwnasptd
  • Fix limits calculation when limitFactor is none in Jupyter web app #6058 @kimwnasptd
  • Make jupyter-web-app parse workspace volume MountPath #5952 @anencore94
  • JWA: Don't override assets with logos ConfigMap #5942 @kimwnasptd
  • Add fonts as assets to service #5691 @saffaalvi @wg102
  • The number of gpu must be set as string #5891 @juliusvonkohout

Notebooks

  • Remove virtualservice timeout to prevent websocket disconnect #6126 @abe-hpe
  • Correct missing predicates in controller watches#5873 @filintod
  • Improve the README of the example Notebook servers https://github.com/kubeflow/kubeflow/pull/6165 @thesuperzapper

TensorBoards

  • tensorboard-controller: fix binding issue #5925 @DavidSpek

PodDefaults

  • Bump Golang version for PodDefaults, TensorBoard Controller and KFAM https://github.com/kubeflow/kubeflow/pull/6180 @kimwnasptd
  • Use namespace from Admission Review in PodDefaults https://github.com/kubeflow/kubeflow/pull/6052 @henrysecond1
  • Add support for ServiceAccountName and AutomountServiceAccountName to PodDefaults #5939 @hopper-signifyd

Other improvements

  • Add kustomize tests in CI/CD https://github.com/kubeflow/kubeflow/pull/5919 @DavidSpek
  • Format checks for the Volumes web app https://github.com/kubeflow/kubeflow/pull/5820 @kimwnasptd
  • Make i18n work with prefixes https://github.com/kubeflow/kubeflow/pull/6034 @kimwnasptd
  • Allow user to add/delete labels to user namespace using ConfigMap #5761 @zijianjoy

v1.4-rc.0(Sep 14, 2021)

The first RC for the 1.4 release

Contents

    luispedro MILK: Machine Learning Toolkit
    spring-cloud Kubernetes integration with Spring Cloud Discovery Client, Configuration, etc...
    ICT-BDA Easy Machine Learning is a general-purpose dataflow-based system for easing the process of applying machine learning algorithms to real world tasks.
    openvinotoolkit OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
    sogou This toolkit was designed for the fast and efficient development of modern machine comprehension models, including both published models and original prototypes.
    kubernetes Go client for Kubernetes.
    kedacore KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
    fabric8io Java client for Kubernetes & OpenShift
    vmware-archive A standard infrastructure environment for Kubernetes
    kelseyhightower Tutorial on running Nomad on Kubernetes.
    fboukezzoula Provisioning automatically a full Kubernetes Cluster on Hyper-V with Helm and Tiller, Kubernetes Dashboard and several external tools in only one click !
    docker Deploy applications described in Compose onto Kubernetes clusters
    darkbitio MKIT is a Managed Kubernetes Inspection Tool that validates several common security-related configuration settings of managed Kubernetes cluster objects and the workloads/resources running inside the cluster.
    aws Amazon EKS Distro (EKS-D) is a Kubernetes distribution based on and used by Amazon Elastic Kubernetes Service (EKS) to create reliable and secure Kubernetes clusters.
    CiscoDevNet A Kubernetes operator to manage ThousandEyes tests
    srohit0 MIVisionX toolkit is a comprehensive computer vision and machine intelligence libraries, utilities and applications bundled into a single toolkit.
    GPUOpen-ProfessionalCompute-Libraries MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX also delivers a highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions.
    BinRoot Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
    kubeflow Machine Learning Toolkit for Kubernetes
    dotnet ML.NET is an open source and cross-platform machine learning framework for .NET.
    savan77 Machine Learning Tutorials in Python
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