When I try to follow the instruction at https://github.com/datarevenue-berlin/OpenMLOps/blob/master/tutorials/set-up-open-source-production-mlops-architecture-aws.md

I got to the step running: terraform apply -var-file=my_vars.tfvars

But I got: Error creating S3 bucket: BucketAlreadyOwnedByYou: Your previous request to create the named bucket succeeded and you already own it. │ │ with aws_s3_bucket.mlflow_artifact_root, │ on main.tf line 11, in resource "aws_s3_bucket" "mlflow_artifact_root": │ 11: resource "aws_s3_bucket" "mlflow_artifact_root"

If I delete the S3 bucket and rerun the command, I got: Error loading state: S3 bucket does not exist.

So maybe it was using that bucket, but then later try to create the bucket again?

Hello, I have a problem following the steps in the tutorial, during the first terraform apply in the creation of the resource : module.mlops-architecture-eks.helm_release.autoscaler I always get the following error:

│ Error: Post "http://localhost/api/v1/namespaces/kube-system/configmaps": dial tcp 127.0.0.1.1:80: connect: connection refused. │ │ with module.mlops-architecture-eks.module.eks.kubernetes_config_map.aws_auth[0], │ on .terraform/modules/mlops-architecture-eks.eks/aws_auth.tf line 63, in resource "kubernetes_config_map" "aws_auth": │ 63: resource "kubernetes_config_map" "aws_auth" {. │ ╵

Hello, and thank you for this great resource. I'm running into an error while going through the architecture set-up tutorial for AWS. I'm able to successfully run everything including "terraform plan -var-file=my_vars.tfvars". I get the following error when I run "terraform apply -var-file=my_vars.tfvars". Let me know if there's any other helpful information I could provide:

I am trying to follow the tutorial Set up your local minikube machine learning architecture for experimenting hands-on with the components of OpenMLOps. Unfortunately, I am not able to follow all the instructions to the end.

Disclaimer: I am new to Terraform and Kubernetes.

To begin with, I tried to start minikube in two ways,

1. with minikube start --kubernetes-version=v1.17.17
2. and without a specified kubernetes version.

In case 1., I could not get minikube to start running. Here is the entire output of the command.

In the latter (without specifying the version), I believe minikube defaults to v1.22.2 as per the output I get from kubectl version. In this case, I am able to get minikube running and start a tunnel as well:

Status:
        machine: minikube
        pid: 10105
        route: 10.96.0.0/12 -> 192.168.49.2
        minikube: Running
        services: [mlflow]
    errors: 
                minikube: no errors
                router: no errors
                loadbalancer emulator: no errors

All ok until I get to the step terraform apply -var-file=my_vars.tfvars, which outputs the following at the end (the only 3 changes not able to complete):

module.ambassador[0].helm_release.ambassador[0]: Creating...
module.dask-jupyterhub.helm_release.dask-jupyterhub: Creating...
module.prefect-server.helm_release.prefect-server: Creating...
╷
│ Error: failed to install CRD crds/filter.yaml: unable to recognize "": no matches for kind "CustomResourceDefinition" in version "apiextensions.k8s.io/v1beta1"
│ 
│   with module.ambassador[0].helm_release.ambassador[0],
│   on modules/ambassador/main.tf line 1, in resource "helm_release" "ambassador":
│    1: resource "helm_release" "ambassador" {
│ 
╵
╷
│ Error: failed to install CRD crds/daskclusters.yaml: unable to recognize "": no matches for kind "CustomResourceDefinition" in version "apiextensions.k8s.io/v1beta1"
│ 
│   with module.dask-jupyterhub.helm_release.dask-jupyterhub,
│   on modules/dask-jupyterhub/main.tf line 1, in resource "helm_release" "dask-jupyterhub":
│    1: resource "helm_release" "dask-jupyterhub" {
│ 
╵
╷
│ Error: unable to build kubernetes objects from release manifest: unable to recognize "": no matches for kind "RoleBinding" in version "rbac.authorization.k8s.io/v1beta1"
│ 
│   with module.prefect-server.helm_release.prefect-server,
│   on modules/prefect-server/main.tf line 1, in resource "helm_release" "prefect-server":
│    1: resource "helm_release" "prefect-server" {
│ 
╵

Why is this happening?

Thanks for sharing your OpenMLOps framework. I hope to find some guidance to configure this framework to make it work on our local Kubernetes cluster. What modifications need to be done for each component?

I have moved the set value from main.tf to the values.yaml file and added a different image to the storageInitializer thing, so that we don't have to change it after deploying using kubectl get configmaps -n seldon

I was following this guide: https://github.com/datarevenue-berlin/OpenMLOps/blob/master/tutorials/set-up-open-source-production-mlops-architecture-aws.md#configuring-the-my_varstfvars-file and have a few questions.

1. Do I need to set additional_aws_users field in my_vars.tfvars to an empty list if I just have only one user (me)?

2. If I am the only user, do I need to fill in oauth2_providers? Or do I just need to make it an empty list? If I need to create a new OAuth application, what should I fill in the Authorization callback URL text box?

3. If I set both 2 attributes above to be empty lists and run terraform init, it asks me to enter an S3 bucket name. What should I fill in this? If it's related to the bucket_name field in the my_vars.tfvars file, I've already set it.

I followed steps and I got this error when I do:

terraform apply -var-file=my_vars.tfvars

Error: error creating EKS Cluster (eks-mlops): InvalidParameterException: unsupported Kubernetes version

I dont know what is the problem here and how to debug

OpenMLOps has no license, according to GitHub[1]:

...without a license, the default copyright laws apply, meaning that you (Data Revenue) retain all rights to your source code and no one may reproduce, distribute, or create derivative works from your work.

So no one external to Data Revenue can use this repo.

This will allow prefect to pull images from private repositories. For this to work with current Prefect version though, we have to set this line image_pull_secrets=["regcred"] into the KubernetesRun flow config init method, because it seems like this Prefect helm chart has quite a few bugs in it.

Fixes the current warnings in docs/**/*rst files

• The "inline strong" and "Bullet list" warnings both affect the rendered output.
• The "Title underline" warning does not affect the rendered output.

docs/components/ory/technical.rst:23: WARNING: Bullet list ends without a blank line; unexpected unindent.
docs/components/ory/usage.rst:14: WARNING: Inline strong start-string without end-string.
docs/components/ory/usage.rst:33: WARNING: Inline strong start-string without end-string.
docs/components/ory/usage.rst:56: WARNING: Title underline too short.

Identities and Providers
~~~~~~~~~~~~~~~~~~~~
docs/components/ory/usage.rst:56: WARNING: Title underline too short.

Identities and Providers
~~~~~~~~~~~~~~~~~~~~

i am following this tutorial and getting this error in this statement "terraform apply -var-file=my_vars.tfvars"

https://github.com/datarevenue-berlin/OpenMLOps/blob/master/tutorials/set-up-minikube-cluster.md

following the versions:

Terraform v1.0.8
kubectl server version v1.21.5 and client version v1.21.5, which includes:
    minikube version: v1.23.2

Minikube start --kubernetes-version=v1.21.5 --vm-driver = hyperv

@sixhobbits @spazm @pedrocwb @timotk

Hi, I was wondering whether there is plan to support Ray for distributed training. I want an ML platform that supports both deep learning and traditional models, and looks like Dask does not have a very support for distributed deep learning. Thanks

Hi all, I am trying to follow the AWS tutorial on setting OpenMLOps and I am getting the bellow error when I run terraform apply.

any help with that? Thanks in advance