Can get the result now but failed with this

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 ! 😊

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)

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

You just built a local agent

Guess I’ll need to implement job schedule myself

Sure I'm right here with you

On sec

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

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

Oh this is one line missing on the above code

So is there any tutorial on this topic

But it seems buggy

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

We can take this ode sample and extent it. can't see any harm in that.
It will enable very easy to ran "sweeps" without any "real agent" installed.

I'm thinking roll out multiple experiments at once

You mean as multiple subprocesses, sure if you have the memory for it

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

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

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

I can comment it on the github issue

let me check a sec

works fine awesome!

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

How can I do to help extend it?

This is so awesome

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 🙂

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

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

which client should I import for Client.queues ?