Is this a known issue? E: Fixed

$ docker pull <http://cr.flyte.org/flyteorg/flytescheduler-release:v1.4.3|cr.flyte.org/flyteorg/flytescheduler-release:v1.4.3>
5e5f900059c8: Download complete
0f96f76f982d: Download complete
88dae1745799: Downloading [>                                                  ]       0B/2.343kB
failed to copy: httpReadSeeker: failed open: failed to do request: Get "<https://pkg-containers.githubusercontent.com/ghcr1/blobs/sha256:88dae1745799f4078f859b8ada55bc1f1757d10d9c97cdeb62f88cfc827c3a1f?se=2023-03-24T21%3A05%3A00Z&sig=rMhv3oYchaYycF260vI0pdx6pENdYsKpph9eXak%2BMPw%3D&sp=r&spr=https&sr=b&sv=2019-12-12>": x509: certificate is valid for *.<http://githubassets.com|githubassets.com>, <http://githubassets.com|githubassets.com>, not <http://pkg-containers.githubusercontent.com|pkg-containers.githubusercontent.com>

Hello, I experience a long delay (about 5-10 minutes) before tasks starts to run when I do pyflyte run … locally. It used to run tasks immediately. The difference might have been caused by me installing a new python 3.8 vs previously 3.7. Does anyone have idea on how to resolve this? It greatly slows down my development.

Hi buddies, I tried to deploy flyte with

helm template

and

kubectl apply

on a self-hosted cluster since there are some restrictions that I can not use

helm install

directly. Everything works well but I encountered

Failed to fetch data

error when accessing

/console

. I find a related issue but seems it maybe outdated and for sandbox mostly. Any comments or suggestions are welcome! BTW, the screenshot of error message is like the following:

How do you scale up nodes inside a EKS cluster. If I set my desired + min to 1 and then I spawn a bunch of jobs, shouldn't I expect it to create new nodes if I don't hit the max node? But the default auto scaling group doesn't seem to be doing that. Is there something in the helm chart that I should change for that?

Hey, I started using Flyte today and I really like it! I am experimenting with some workflows that outputs models to a MinIO S3 bucket using the Sandbox deployment. It is not quite clear to me how I query the artifacts in the MinIO S3 bucket e.g., given this output from the workflow - how do I access the MinIO bucket to acquire the model artifact?

model_dir:
  type:
    multi-part
  uri:
    <s3://my-s3-bucket/ja/hfnqehz4ps-n0-0/9828036b2328e522488b3ff0311528e2>

Hey all, I'm having trouble passing an empty list to a map_task. I think it might be a bug, hopefully there is a simple work around I could use for now. In the following example. Workflow

test_fixed_array_map_task

completes successfully in a few seconds, while workflow

test_empty_array_map_task

hangs indefinitely with the status QUEUED.

from flytekit import map_task, task, workflow

@task
def say_hello(name: str) -> str:
    message = f"hello {name}"
    print(message)
    return message

@workflow
def test_empty_array_map_task() -> list[str]:
    names: list[str] = []
    messages = map_task(say_hello)(name=names)
    return messages

@workflow
def test_fixed_array_map_task() -> list[str]:
    names: list[str] = ['jp', 'ketan']
    messages = map_task(say_hello)(name=names)
    return messages

Let me know if there is any additional information I can provide. Thanks

We are running Flyte on GKE cluster, we use podTemplate to configure env vars and secrets. After updading the podTemplate, we were trying to run workflows using

pyflyte --image

but we’re getting this error (occasionally) -

Failed to apply default image tag "{{.image.demo.fqn}}:{{.image.demo.version}}": couldn't parse image reference "{{.image.demo.fqn}}:{{.image.demo.version}}": invalid reference format

Does anyone have any idea why it happens? Tnx

Hi All, I am working on integrating Ray with Flyte. I have been able to register and run the ray task and it completes successfully. But I am not able to find any logs anywhere saying that the task was run through ray. Also, I can't see any pods being created / destroyed. There is a ray cluster created, but it is also not destroyed after the task run. I have installed Ray operator, ray cluster and ray api-server using their helm charts. And I have added the configmap in the

inline

section of the

configuration

in values.yaml.

configuration:
  inline:
    configmap:
      enabled_plugins:
        # -- Task specific configuration [structure](<https://pkg.go.dev/github.com/flyteorg/flytepropeller/pkg/controller/nodes/task/config#GetConfig>)
        tasks:
          # -- Plugins configuration, [structure](<https://pkg.go.dev/github.com/flyteorg/flytepropeller/pkg/controller/nodes/task/config#TaskPluginConfig>)
          task-plugins:
            # -- [Enabled Plugins](<https://pkg.go.dev/github.com/flyteorg/flyteplugins/go/tasks/config#Config>). Enable SageMaker*, Athena if you install the backend
            # plugins
            enabled-plugins:
              - container
              - sidecar
              - k8s-array
              - ray
            default-for-task-types:
              container: container
              sidecar: sidecar
              container_array: k8s-array
              ray: ray

I have all the ray pods running -

NAME                                                 READY   STATUS    RESTARTS   AGE
flyte-flyte-binary-6cfdcfc575-9l42x                  1/1     Running   0          3d2h
flyte-ray-cluster-kuberay-head-9q6jq                 1/1     Running   0          147m
flyte-ray-cluster-kuberay-worker-workergroup-bts8b   1/1     Running   0          147m
kuberay-apiserver-d7bbb9864-htsw4                    1/1     Running   0          97m
kuberay-operator-55c84695b8-vftmn                    1/1     Running   0          11h

And also all the services -

NAME                                 TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)                                         AGE
flyte-flyte-binary-grpc              ClusterIP   x.x.x.x.   <none>        8089/TCP                                        3d3h
flyte-flyte-binary-http              ClusterIP   x.x.x.x.   <none>        8088/TCP                                        3d3h
flyte-flyte-binary-webhook           ClusterIP   x.x.x.x.    <none>        443/TCP                                         3d3h
flyte-ray-cluster-kuberay-head-svc   ClusterIP   x.x.x.x.    <none>        10001/TCP,6379/TCP,8265/TCP,8080/TCP,8000/TCP   166m
kuberay-apiserver-service            NodePort    x.x.x.x.   <none>        8888:31888/TCP,8887:31887/TCP                   116m
kuberay-operator                     ClusterIP   x.x.x.x.    <none>        8080/TCP                                        3d2h

Questions: 1. Have I configured flyte to use ray correctly using the configmap in values.yaml? 2. How do I verify that the ray task that Flyte says was successful was indeed run on a ray cluster?

hello, does anyone know why i am seeing the

cache was disabled for this task

for a simple python task, when I have the cache enabled:

@task(cache=True, cache_version="1.0")
def simple_python_task(name: str):
    print(f"Hello {name}")

Could anyone explain how to setup local env to read from S3 using spark? Running this task remotely, it’s run in the image that has flyte/spark setup. When running locally, it doesn’t use that image, therefore missing spark jars.