and which one if it does

What do you mean by "pull and report multiple trials" ? Spawn multiple processes with different parameters ?Lets say you are doing bayesian sampling of some parameter with your optimizer, that means the next sample will be a function of previous samples. And all of this is contained in the optimizer state (in the optuna optimizer case in the study object). So to have an option to run some optimization in the way described in the example the communication with the optimizer task should hav...

hi TimelyPenguin76 thanks, for some reason it didn't show up in my search or maybe I missed it..
I was wondering specifically about the following case:
lets say I'm cloning the task you created above, now I am editing some of the hyper parameters in the UI and enqueueing it.
would the config be "automatically" synced? I assume not, if not what would be a recommended way to sync it?
I especially wondered if there is a "smart" sync (with parsing) that can take advantage of the type hinting in...

some arbitrary job.. can be executed in parallel to a running job on the same agent.
directly from the UI from the services queue?

CostlyOstrich36 thanks, is there an example for using the post\get in a pythonic way to access the mentioned debug.ping ?

Here are the logs I got:

no, online

I opened some arbitrary project

I get it with the simplest config if I define it as a dataclass, using the example you share as a basis:
config_files/cfg.py
` from hydra.core.config_store import ConfigStore
from dataclasses import dataclass

@dataclass
class MasterConfig:
test: str = 'test'

cs = ConfigStore.instance()
cs.store(name="config", node=MasterConfig) and for the main I had to make some small changes to connect my local server (I'm sharing them just in case): hydra_example.py: # ClearML - Hydra Example

im...

you can see the behavior here:

its only these two files.. nothing else

let me try to explain myself again

am I supposed to change the WeightsFileHandler inplace?

🙏 thanks!

something like in the snippet I shared above

The difference is that I want a single persistent machine, with a single persistent python script that can pull execute and report multiple tasks

something like in the example I shared
<Machine 1> #Init Optimizer <Machine 2> **heavy one time Common Initialization** while True: #sample Optimizer # init task # Execute Something # report results <Machine i> **heavy one time Common Initialization** while True: #sample **same** Optimizer # init task # Execute Something # report results

thoughts?

FrothyDog40 , I am getting the feeling that either I am abusing the framework int the way that I use it, or I am doing a poor jot at explaining myself

we just found it out ourselves , https://github.com/jupyter/nbconvert/issues/754

I am trying to mimic that for debug

thoughts?

I can reproduce the same behavior in the community server.. but for some of the columns I get a slightly different behavior, it does something but I'm not sure exactly what

great! so the agent can be executing some "regular" job while doing some extra "services"

But it does make me think, if instead of changing the optimizer I launch a few workers that "pull" enqueued tasks, and then report values for them in such a way that the optimizer is triggered to collect the results? would it be possible?

changing the queue order cool, but a bit too limited.. I have 30 jobs I want to multi select and push up to first priority.. this is a lot of manual labor..

AgitatedDove14
What I'm looking here is some logic that will allow us not to break backwards compatibility on the one hand, but still will allow you to have something like "first\second" entry. WDYT? any ideas? (I really want to make sure we fix it as soon as possible)Do you mean something like the dictionary structure?
I am not sure about a clean elegant solution yet.. but this patch does some of the job:
str('(' + ', '.join([str(elm) for elm in tuple_value])+')')I am actually curio...

CostlyOstrich36 , I am trying to get the config values as in the example:
task.connect(test_config)I expect that the returned connected dict will override existing keys the local dict with matching keys from the remote task dict
the config I'm talking about is the General section in the Hyper-Parameters under the configuration tab

for keys that are present in both the remote and local configuration the expected behavior is that the remote overrides the local, that what happens in my agent runs

this way I can avoid the heavy computation I describe above for each individual trial