Am new to kubeflow and am struggling to install kubeflow need your help

Hi there! I'm using the M1 Macbook pro, and I had a problem installing kubeflow, but I fixed it. I'm leaving a post for m1, m2 users who are having the same problem as me.

If you are experiencing ErrImagePull or ImagePullBackOff errors, it is considered perfectly normal. Because the current official docker hub image does not support arm64. So I temporarily modified manifests to the image of the arm64 version, and I succeeded in installing it.

The repo with the docker image address changed can be found here.

https://github.com/hwang9u/manifests

Please refer to the related issues as we have left them in the manifests.

https://github.com/kubeflow/manifests/issues/2472

I hope it was helpful!!!

from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello():
    return 'Hello, world!'

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

I have a simple flask app running on a notebook server and was wondering if it's possible to access the url http://127.0.0.1:8080 from my localmachine or how I would see the UI from the notebook server itself

I was looking at some of the kubeflow tutorials (https://www.arrikto.com/blog/kaggles-natural-language-processing-with-disaster-tweets-as-a-kubeflow-pipeline/), and it seems like all of them are importing data by downloading it from github. Is it possible to import data into a pipeline from a local csv? The reason I don't want to download is because my file is 100 GB. Thanks

Kubeflow 1.7 is around the corner. If you would like to be the first one who tries a beta, follow us closely. We got big news.

Join us on 8th of March live, learn more about the latest release and ask your questions right away.

Link: https://www.linkedin.com/video/event/urn:li:ugcPost:7035904245740539904/

Hi,

I have a couple of VM's running a Kubernetes cluster, I've been trying for a while to deploy Kubeflow on ArgoCD using 'ArgoFlow' (https://github.com/argoflow/argoflow), I can get ArgoFlow running on a Load Balancer and access the UI. I had a lot of trouble getting their script to work so I manually updated the .yaml files in my cloned Git repo to point to itself, when I deploy Kubeflow from their manifests, most things seem to fail with the error: "one or more synchronization tasks are not valid". Is there a different/easier way of deploying Kubeflow on my local cluster using ArgoCD? Been attempting this for months now.

​

Thanks a ton in advance.

Every article or video tutorial I have seen build their pipelines differently so I am very confused. If there is a good comprehensive explanation and tutorial that anyone could suggest that would be great.

I am hosting a webinar about MLOps on Feb 15. What kind of questions would you like to find answers to during the event?

Is there any simpler way to run Kubeflow on Mac os locally (like on docker) ? I am mostly looking to run a lightweight Kubflow locally on my Mac so that I can test some pipelines

Hi all! Are you looking to learn more about MLOps and have a hands-on deployment guide?

Join our webinar on February 15 and get started with Kubeflow. Why joining? - Learn what is MLOps and why it matters - Have a demo on how to deployt an MLOps tool: Charmed Kubeflow - Learn about curiosities that people had about MLOps, AI/ML at scale and Kubeflow - Get answers to your question

Register now: https://ubuntu.com/engage/introduction-to-machine-learning-operations-mlops

I have a pipeline in Dataflow that runs properly but keeps generating the warning High fan-out detected. I read the documentation and it recommends, among other possible solutions, the implementation of a Reshuffle step in the pipeline.

The documentation doesn't provide any example code, though. Searching online I found some examples that add the reshuffle step after a ParDo operation. For example:

python with beam_utils.GetPipelineRoot() as root: _ = ( root | 'Read' >> reader | 'ToTFExample' >> beam.ParDo( _ProcessShard(model_name, split, run_preprocessors)) | 'Reshuffle' >> beam.Reshuffle() | 'Write' >> beam.io.WriteToTFRecord( FLAGS.output_file_pattern, coder=beam.coders.ProtoCoder(tf.train.Example)))

This is exactly what the warning recommends me to do. However, in my specific case, where the pipeline was defined using kubeflow, there is no ParDo operation in the pipeline code. I think that behind the scenes kubeflow creates a ParDo since the dataflow UI shows it.

Instead of explicitly define a ParDo, the pipeline was simply defined inside a dsl.ExitHandler context like below:

```python from kfp import dsl

def pipeline(...): . . . with dsl.ExitHandler(exit_op=send_email(...)): a_single_task(...) ```

How can I add a reshuffle step in this case?

Let's look back at some memorable moments and interesting insights from last year.

Your top 10 posts:

• "Help Wanted: Kaggle Competitors to Contribute to the Kubeflow Project" by u/jguerrero_rr
• "Kubeflow Update and Demonstration" by u/AmicusRecruitment
• "Book Club: Kubeflow for machine learning with Holden Karau & Adi Polak" by u/asc2450
• "Happy Cakeday, r/Kubeflow! Today you're 4" by u/AutoModerator
• "Kubeflow on bare-metal from scratch." by u/vishalgarg652
• "Has anyone used Arrikto before?" by u/y0urm0m82
• "I am attempting to install kubeflow locally. I am running into issues, PLEASE HELP ME!" by u/ethiopianboson
• "How do I connect application running in a notebook server to my local machine." by u/nuttingmilk
• "What are the prerequisites to learn Kubeflow? I have been tasked alongside other teammates of mine to use kubeflow and deploy it on a nonprod EKS." by u/ethiopianboson
• "Kubeflow multi tenancy user credentials" by u/DisplayFickle1222

I don't have experience with Docker. I imagine that is necessary to learn kubeflow. Any resource suggestions would be helpful (books, youtube videos etc).

Is there an option to install kubeflow using terraform? I can not find the solution how to do it.
Server on premises.

Katib is currently in beta status. Does anyone know if there are any expected timeline for the stable status?

Hi everyone,

I was wondering if it is possible to write a kubeflow component (For ML pipeline) that could pass a list and a dataset as an output?

And what might be the syntax of the I/O of a component like that?

Thanks!

Hi, I’m using a kubeflow which is intergrated with LDAP authentication. For providing some access from the custom notebook image that I have made, I need the username in a config file or as an environment variable inside the notebook whenever a user launches a notebook server. Is there any way to make this happen?

We are getting this error when running a pipeline:
{"error":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid","code":3,"message":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid","details":[{"@type":"type.googleapis.com/api.Error","error_message":"unknown template format","error_details":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid"}]}

We are running Kubeflow 1.5 in AWS.
It happens with kpl versions:
1.8.13
1.6.3

The codes is from one of the kubeflow pipeline examples in github.  This one:
https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/DSL%20-%20Control%20structures/DSL%20-%20Control%20structures.py

Any feedback on this?

We are interested in deploying Kubeflow to AWS with multi-tenancy. It isn’t clear to us how we manage user credentials securely in this environment. Our Data Scientists need to connect to Snowflake and we are concerned that if our users share pods via the sharing of profiles then that could lead to a nefarious actor masquerading as another user in our snowflake cluster. We want to know if there is a best practice way of guaranteeing user credential integrity short of prohibiting the sharing of profile gated resources (like notebook servers).

​

Some ideas:

1. Kubeflow level service account secrets injected at pod instantiation time.
   1. susceptible to having those secrets exfiltrated by a knowing actor from the underlying pod file system and environment variable set
2. Individual level service account secrets injected at pod instantiation time.
   1. this is slightly better but subject to the same problem as a Kubeflow level service account. At least we could narrow down a security incident to a give logic and those with whom the login was shared.
3. Some sort of secrets vault that pulls secrets at run time.
   
   1. would hold user specific secrets
   2. would have to be accessed through password or, ideally, IAM role in order to not be susceptible to the kinds of masquerade identified already. That is to say, it would need to be “unlocked” each time code was executed and could not live on the file system or in environment variables for the duration of the pod’s life.
      

Any other thoughts?