I'm good, David; hope you're good too :D
Thanks for the help! It worked :D

http://localhost:9000 http://localhost:9000/%3Cbucket%3E
My minio instance is hosted locally at the 9000 port.

Is there a way to store the return values after each pipeline stage in a format other than pickle?

Also, if I were to move to g pu machines, how do I assign more cores to a worker or so?

A simple StorageManager.download_folder(‘url’)
My minio instance is hosted locally, so I'm providing an url like ‘ http://localhost:9000/bucket-name%E2%80%99

I suppose the issue is only with the elastic search registry

I have hidden the host name as well

That aside, adding mock doesn't really change the error above though

Nop e

Or maybe I'm wrong

We're initialising a task to ensure it appears on the experiments page;
Also not doing so gave us issues of ‘Missing parent pipeline task’ for a set of experiments we had done earlier

version: "3.6"
services:

apiserver:
command:
apiserver
container_name: clearml-apiserver
image: allegroai/clearml:latest
restart: unless-stopped
volumes:
/opt/clearml/logs:/var/log/clearml
/opt/clearml/config:/opt/clearml/config
/opt/clearml/data/fileserver:/mnt/fileserver
depends_on:
redis
mongo
elasticsearch
fileserver
environment:
CLEARML_ELASTIC_SERVICE_HOST: elasticsearch
CLEARML_ELASTIC_SERVICE_PORT: 920...

Hey David , I was able to get things uploaded to the fileserver by a change in the conf

Hey so I was able to get the local .py files imported by adding the folder to my path sys .path

Not local .py files

Exactly what I thought;
That's so odd

How do I access the logs of the init process?

This is the error that I get:

SuccessfulKoala55 Any leads f or the above error i n running the script?

I had initially just pasted the new credentials in place of the existing ones in my conf file;
Running clearml-init now fails at verifying credentials

Thanks a lot David!

What exact ly does the error mean

Also,
How do I just submit a pipeline to the server to be executed by an agent?
Currently I am able to use P ipeline Decorator.run_locally() to run it ;
However I just want to push it to a queue and make the agent do it's trick, any recommendations ?

Simple doubt

I'm asking this because my kwargs is observed as an empty dict if printed

Yep, the pipeline finishes but the status is still at running . Do we need to close a logger that we use for scalers or anything?

Oh