Things to check:
Task.connect called before the dictionary is actually used Just in case, do configs['training_configuration']=Task.connect(configs['training_configuration']) add print(configs['training_configuration']) after the Task.connect call, making sure the parameters were passed correctly

Hi CleanPigeon16
Put the specific git into the "installed packages" section
It should look like:
... git+ ...(No need for the specific commit, you can just take the latest)

There is a version coming out next week, the one after it (probably 2/3 weeks later) will have this feature

ShaggyHare67 are you saying the problem is trains fails discovering the packages in the manual execution ?

Are you hosting your own server? Is it on http://app.clear.ml ?

That said , if you could open a github issue and explain the idea behind it, I think a lot of people will be happy to have such process , i.e. CI process verifying code. And I think we should have a "CI" flag doing exactly what we have in the "hack" wdyt?

Because it lives behind a VPN and github workers don’t have access to it

makes sense
If this is the case, I have to admit that combining offline-mode and remote execution makes sense, no?

oh, if this is the case, why not use the "main" server?

With env caching enabled, it won’t reinstall this private dependency, right?

It will, local packages (".") and git packages are alwyas reinstalled even if using venv caching, exactly for that reason 🙂

Ohh so the setup.py is the one containing these requirements, oops I totally missed that :( let me check what pep has to say about that ... (Basically this is not a clearml issue but a pip one...)

error in my-package setup command:

Okay this seems like an error in the setup.py you have in the "mypackage" folder

Could you post what you see under "installed packages" in the UI ?

No worries, you open the issue on pypa/pip and I will do my best to push forward 🙂
We also have to be realistic I have a PR that is waiting for almost a year now (that said it is a major one and needed to wait until a few more features were merged), basically what I'm saying best case scenario is a month to get a PR merged

JitteryCoyote63 you mean? (notice no brackets)
task.update_requirements(".") Either pass a text or a list of lines:
The safest would be '\n'.join(all_req_lines)

I'm having another problem now because I am using the OptunaOptimizer.

Hmm let me check a sec

TrickyFox41 are you saying that if you add Task.init inthe code it works, but when you are calling "clearml-task" it does not work? (in both cases editing the Args/overrides ?

First that is awesome to hear PanickyFish98 !
Can you send the full exception? You might be on to something...
2. Actually we thought of it, but could not find a use case, can you expand?
3. I'm not sure I follow, do you mean you expect the first execution to happen immediately?

Hi @<1523710243865890816:profile|QuaintPelican38>
What's the clearml version ?

😞 I'll pass to the guys

BTW: what's the clearml-server version ?

Hi @<1551376687504035840:profile|StraightSealion9>

AWS Autoscaler to create a new instance when you enqueue a task to the relevant queue.

Does that mean that you were able to enqueue a Task and have it launch on the remote EC2 machine ?

ReassuredTiger98 maybe we should add an option to send a text next to the abort?
(Actually it is just a matter of passing the argument)
wdyt?

This is an odd error, could it be conda is not installed in the container (or in the Path) ?
Are you trying with the latest RC?

Hi SpotlessLeopard9

I got many tasks that were just hang at the end of the script without ...

I remember this exact issue was fixed with 1.1.5rc0, see here:
https://clearml.slack.com/archives/CTK20V944/p1634910855059900

Can you verify with the latest RC?
pip install clearml==1.1.5rc3

Oh no 😞 I wonder if this is connected to:

Any chance the logger is running (or you have) from a subprocess ?

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

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...

VexedCat68 I think this is the issue described here:
https://github.com/allegroai/clearml/issues/491
Can you test with the latest RC:
pip install clearml==1.1.5rc1

🤞

VexedCat68 actually a few users already suggested we auto log the dataset ID used as an additional configuration section, wdyt?