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Closed
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
This change is 
size/L lgtm approved cla: yes
opened Aug 30, 2019 by swiftdiaries 101
Closed
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INFO[0000] Deleting cachedir /opt/kubeflow/kf-test/.cache/manifests because Status.ReposCache is out of date filename="kfconfig/types.go:464" INFO[0000] Fetching https://github.com/kubeflow/manifests/archive/v0.7-branch.tar.gz to /opt/kubeflow/kf-test/.cache/manifests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
/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
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
- Define model architecture
- 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
We should have a Kubeflow logo.
priority/p2 priority/p0
opened Jan 31, 2018 by jlewi 65
Closed
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
size/L lgtm approved cla: yes ok-to-test
opened Mar 11, 2020 by wdhorton 60
Open
/kind bug
What steps did you take and what happened: I'm a heavy user of the Kubeflow UI to read through logs to debug pipelines that my company runs. For some reason, starting Aug 10th, a giant div (the footer I believe) has appeared and is blocking most of the screen space which makes debugging and visualizing my pipelines very difficult.
What did you expect to happen: Not to have a giant div blocking the screen.
Anything else you would like to add:
Please see attached screenshot.
Environment:
- Kubeflow version: (version number can be found at the bottom left corner of the Kubeflow dashboard): build version dev_local
- kfctl version: (use
kfctl version): couldn't find this command on kubeflow cluster - Kubernetes platform: (e.g.
minikube): minikube version: v1.26.0 - Kubernetes version: (use
kubectl version): Client Version: v1.24.3 Kustomize Version: v4.5.4 Server Version: v1.21.12-gke.2200 - OS (e.g. from
/etc/os-release):
kind/bug
opened Aug 11, 2022 by cheungringo 0
Open
Required as a part of https://github.com/kubeflow/manifests/pull/2150
Fixes the logout button in the Central Dashboard to work with the recent versions of the oidc-authservice (tested with gcr.io/arrikto/kubeflow/oidc-authservice:e236439 image version)
Changes:
- Pin alpine repository version
- Introduce a new LogoutButton component
size/M
opened Aug 8, 2022 by alembiewski 1
Open
/kind bug
What steps did you take and what happened: [A clear and concise description of what the bug is.]
Created a notebook from the kubectl CLI with this specification using kubectl create -f my-notebook.yaml. This made the Kubeflow Notebooks UI fail to load the list of notebooks and issue a 500 error.
[500] An error occurred in the backend. https://[...]/jupyter/api/namespaces/sample-user/notebooks
apiVersion: kubeflow.org/v1
kind: Notebook
metadata:
annotations:
notebooks.kubeflow.org/server-type: jupyter
creationTimestamp: "2022-08-03T18:15:04Z"
generation: 1
labels:
app: notebook-requests
name: notebook-requests
namespace: sample-user
resourceVersion: "1874092808"
uid: a2ede8a3-98a3-4ae9-8f85-33d2f6a21f50
spec:
template:
spec:
containers:
- args:
- |
/jupyterlab/bin/setup_user_and_run.sh
command:
- /bin/bash
- -c
env:
- name: POD_IP_ADDRESS
valueFrom:
fieldRef:
fieldPath: status.podIP
- name: DC
value: KUBEFLOW
- name: ENV
value: devel
- name: JOB_NAME
value: notebook-requests
- name: USER
value: sample-user
- name: IS_INTERACTIVE
value: "1"
- name: JUPYTERLAB_CHECKPOINTS_MANAGER
value: contents_manager.gcs_contents_manager.CombinedCheckpointsManager
- name: JUPYTERLAB_CONTENTS_MANAGER
value: contents_manager.gcs_contents_manager.CombinedContentsManager
- name: NOTEBOOK_STORAGE__NAME
value: NOTEBOOK_STORAGE_V2
- name: JOB_TRACKING__EXECUTION_ID
value: "113297"
- name: JOB_TRACKING__ENV
value: production
envFrom: null
image: sample_image
imagePullPolicy: Always
livenessProbe: null
name: notebook
ports: null
readinessProbe: null
resources:
limits:
cpu: 6
ephemeral-storage: 50G
memory: 16G
volumeMounts:
- mountPath: /home/jovyan
name: workspace-notebook
- mountPath: /dev/shm
name: dshm
serviceAccountName: default-editor
volumes:
- emptyDir:
medium: Memory
name: dshm
- name: workspace-notebook-requests
persistentVolumeClaim:
claimName: workspace-notebook-requests
If the resources section is modified like so (with requests included, not just limits), the Kubeflow notebook manager UI doesn't hit a 500 error when loading.
resources:
limits:
cpu: 6
ephemeral-storage: 50G
memory: 16G
requests:
cpu: 6
ephemeral-storage: 50G
memory: 16G
What did you expect to happen:
Notebook UI loads list of notebooks in namespace correctly, doesn't hit a 500 error. Gracefully sets requests=limits if unspecified, matching the k8s API behavior.
Environment:
- Kubeflow version: Kubeflow 1.4
- Kubernetes platform: GKE
- Kubernetes version: (use
kubectl version): Server Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.12-gke.1700", GitCommit:"6c11aec6ce32cf0d66a2631eed2eb49dd65c89f8", GitTreeState:"clean", BuildDate:"2022-05-13T09:31:25Z", GoVersion:"go1.16.15b7", Compiler:"gc", Platform:"linux/amd64"}
kind/bug
opened Aug 3, 2022 by djwhatle 0
Open
/kind feature
Why you need this feature: Given a pipeline, i can train with big samples (prod) or small samples (dev). For which i would like to use different memory limits.
Describe the solution you'd like:
I would like for function set_memory_limit to accept pipeline params. To be able to do something like
@component(base_image='python:3.9')
def test(dev: str) -> str:
if dev == 'True':
return '16G'
else:
return '32G'
@component(base_image='python:3.9')
def test2() -> str:
print('test')
return 'test'
@pipeline(
name='kfp-test-pipeline',
description='Pipeline test.',
pipeline_root='gs://bucket/test'
)
def pipeline(
dev: str = 'True'):
test_ = test(dev=dev)
test2_ = test2()
test2_.set_memory_limit(test_.output)
# then run pipeline with dev='True' or dev ='False'
kind/feature
opened Aug 2, 2022 by iuiu34 0
Open
/kind question
Question: Hi, We have a cluster that already has an Argo installed on, and we wanted to also install Kubeflow (including Kubeflow pipelines) on the same cluster. Is there an easy and right way to install Kubeflow without the inhouse Argo? The other problem we encountered is that Kubeflow seems to work only with a cluster-wide controller. Is there a way to specify to the Kubeflow pipelines which instanceid to use and what namespace?
I was also wondering what is your recommendation about scaling up Argo's workflow-controller (if it really has to be cluster wide). Argo's recommendation is to install a namespaced controller for that.
Thanks.
kind/question
opened Aug 1, 2022 by ran-haim 0
Open
This fixes a currently invalid, but working, on early kustomize, yaml for the profile-controller manager manifests. In this PR duplicated entries for ports and livenessProbe. Removing the previous items makes no changes to the output so this change is no-op for early kustomize versions, but it actually allows newer kustomize versions to work, they currently break.
tests:
- Used kustomize-3.8.10 to generate template from master with and without the patch and the output is identical
- Used kustomize-4.5.5 to generate template from master and it breaks without the patch but produces identical output to 3.8.10 with the patch in
kustomize-4.5.5 breaking...
❯ ( git co master && cd components/profile-controller/config/manager && ./kustomize-4.5.5 -o master.template.yaml build )
Already on 'master'
Your branch is up to date with 'origin/master'.
Error: map[string]interface {}(nil): yaml: unmarshal errors:
line 37: mapping key "livenessProbe" already defined at line 27
line 49: mapping key "ports" already defined at line 33
Identical outputs after the patch
❯ ( git co master && cd components/profile-controller/config/manager
&& ./kustomize-3.8.10 -o master.template.yaml build
&& git checkout fix-kustomize-profile-manager
&& ./kustomize-3.8.10 -o fixed.template.yaml build
&& ./kustomize-4.5.5 -o modern.template.yaml build
&& diff fixed.template.yaml master.template.yaml
&& diff modern.template.yaml master.template.yaml)
Already on 'master'
Your branch is up to date with 'origin/master'.
Switched to branch 'fix-kustomize-profile-manager'
❯ echo $?
0
size/S
opened Jul 30, 2022 by arkaitzj 1
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 ⏫
- Update the Jupyter web apps configmap with the new storage structure https://github.com/kubeflow/kubeflow/pull/6321
- 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
- 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 ⏫
- Update the Jupyter web apps configmap with the new storage structure https://github.com/kubeflow/kubeflow/pull/6321
- 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
- 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
v1.3.1-rc.0(Jun 25, 2021)
v1.3.1-rc.0 release for components owned by WG-Notebooks.
Contents
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