I started getting

Error: docker sandbox doesn't have sufficient memory available. Please run docker system prune -a --volumes

when starting the sandbox cluster. But there is enough memory according to docker info below. Any ideas?

...
OSType: linux
Architecture: x86_64
CPUs: 16
Total Memory: 62.5GiB
Name: Mercury
ID: fa8608e9-e110-482e-8c5f-908edce3debb
Docker Root Dir: /var/lib/docker
...

Hi.. an special configuration is needed to use ECR? flyte is not able to pull the image but when pull the image using docker works fine. I’m getting this: Back-off pulling image

Has anyone worked with or have any docs on automated workflow registration to a remote flyte cluster, particularly with ArgoCD?

Hello! Is there a way to integrate Flyte with DataHub? Has anyone here did this?

Hey ya’ll! I’m currently looking into using Flyte as a solution for our DS and DE team- I’ve got a quick question about running the Getting Started Demo. For some reason the first task,

generate_normal_df

, is getting stuck on

running

and has been

queued

for a while now. This was done using the local demo Flyte Cluster. Running it locally (

pyflyte run example.py wf --n 500 --mean 42 --sigma 2

) the workflow executes fine. However, running it on the Flyte Cluster (

pyflyte run --remote example.py wf --n 500 --mean 42 --sigma 2

) doesn’t seem to work properly on my end. Any ideas?

for deploying multi-cluster flyte, https://docs.flyte.org/en/latest/deployment/multicluster.html#data-plane-deployment, can I keep the original cluster as is (running both control and the data), and just add a data cluster? and when creating the additional cluster, I should follow https://docs.flyte.org/en/latest/deployment/aws/manual.html (Connect to an EKS Cluster, OIDC Provider for the EKS Cluster, Create an EKS Node Group, the latter half of Create an RDS Database where I just connect new cluster to existing RDS)? I don't need to do this

Install an Amazon Loadbalancer Ingress Controller

or to do the SSL cert stuff again right?

Hi, In the script below I have two tasks, one downloads data from S3 to the "flytekit.current_context().working_directory" and returns the working directory as FlyteDirectory. The second tasks reads files from this directory. How can I ensure that both tasks are always running on the same node in a Kubernetes cluster? Is that automatically handled by passing FlyteDirectory?

@task()

def download_data() -> FlyteDirectory:

working_dir = flytekit.current_context().working_directory

download_to_working_dir()

# now data is in "working_dir"

return FlyteDirectory(path=working_dir)

@task()

def train(data_dir: FlyteDirectory): -> None

train_model(data_dir=data_dir)  # reads files from disk and trains model

Hey Flyte team, happy Friday. When it comes to the maxParallelism config where should I be looking for errors as i start to increase this number?

Has anyone noticed behavior where mypy doesn't catch invalid usages inside `@flytekit.task`/`@flytekit.dynamic`? We have a clear case w/ a missing positional argument that we'd typically expect the type-checker to catch, but it seems like the decorator may be causing some unintended behavior here?

Hi team, Can you help with this cryptic error while registering a workflow

raise _InactiveRpcError(state)
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
	status = StatusCode.RESOURCE_EXHAUSTED
	details = "received initial metadata size exceeds limit"
	debug_error_string = "{"created":"@1667984783.531503108","description":"Error received from peer ipv4:10.162.12.35:443","file":"src/core/lib/surface/call.cc","file_line":953,"grpc_message":"received initial metadata size exceeds limit","grpc_status":8}"