I'm checking it out today and see if I can put up something

Just tried the code

Apiclient will report

I can comment it on the github issue

sorry typo client.task. should be client.tasks.

works fine awesome!

On sec

So is there any tutorial on this topic

from clearml.backend_api.session.client import APIClient client = APIClient() result = client.queues.get_next_task(queue='queue_ID_here')Seems to work for me (latest RC 1.1.5rc2)

I'm thinking roll out multiple experiments at once

Oh this is one line missing on the above code

How can I do to help extend it?

I tried from clearml.backend_api.session import client no luck

Yeah the ultimate goal I'm trying to achieve is to flexibly running tasks for example before running, could have a claim saying how many resources I can and the agent will run as soon as it find there are enough resources

let me check a sec

Guess my best chance is to check out the agent source code right?

Or can I enable agent in this kind of local mode?

I’ll try it tomorrow and let you know if there is anything wrong

But it seems buggy

Or can I enable agent in this kind of local mode?

You just built a local agent

This is so awesome

So is there any tutorial on this topic

Dude, we just invented it 🙂
Any chance you feel like writing something in a github issue, so other users know how to do this ?

Guess I’ll need to implement job schedule myself

You have a scheduler, it will pull jobs from the queue by order, then run them one after the other (one at a time)

Well it should work, make sure you see the Task "holds" all the information needed (under the execution tab). repo / uncommitted changes / python packages etc.
Then configure your agent (choose pip/conda/poetry as package managers), and spin it up (by default in venv/coda mode, or in docker mode)
Should work 🙂

Sure I'm right here with you

Yeah the ultimate goal I'm trying to achieve is to flexibly running tasks for example before running, could have a claim saying how many resources I can and the agent will run as soon as it find there are enough resources

Checkout Task.execute_remotely() you can push it anywhere in your code, when execution get to it, If you are running without an agent it will stop the process and re-enqueue it to be executed remotely, on the remote machine the call itself becomes a noop,

I can comment it on the github issue

Yes please do 🙂

How can I do to help extend it?

How about a CLI tool, like what we have with "clearml-task" ?

This is so awesome

Thank you ! 😊

Do you think the local agent will be supported someday in the future?

Can get the result now but failed with this