Hi ReassuredTiger98

I do not want to share with the clearml-agent workstations.

Long story short, no 😞
The agent is responsible to spin all jobs, regardless of users, basically it has to have a read-only user for all the repositories. I "think" the enterprise version has a vault feature, that allows you to store these kind of secrets on the User itself.
What exactly is the use case?

hen, in the bash console, after some time, I see some messages being logged from clearml

JitteryCoyote63 Hmm that is strange, let me check something

😂

I'm trying to create a task that is not in repository root folder.

JuicyFox94 If the Task is not in a repo folder, you mean in a remote repository right ?
This means the repo should be in the form of " https://github.com/ " or "ssh://"
It failed in deducing this is a remote repository (maybe we can improve the auto detection?!)

Yep, found it, the --name is marked as required and the argparser throws an error ...
I'll make sure this is fixed as well 🙂

I theory this would be doable, but wouldn't it be a bit confusing? Also why not always use containers if the host supports it, there is no real downside, just set the default docker image to something that is a good starting point

Do you want to PR it? should be a quick fix

If I try to connect a dictionary of type 

dict[str, list]

 with 

task.connect

, when retrieving this dictionary with

Wait, this should work out of the box, do you have any specific example?

Hm GiganticTurtle0 let me check quickly it

Hi JitteryCoyote63

I change the project.default_output_destination? I tried setting it to None but it is not updated

How did yo try to change it? and where do you see the effect ?

Because of that, I cannot create a task in this project programmatically locally because it tries to access the bucket and fails. And there is no easy way to change the default output location (not in the web UI, not in the sdk)

JitteryCoyote63 hmm that is a pickle ...
let me check the code ...

RC should be out later today (I hope), this will already be there, I'll ping here when it is out

Hi @<1523701066867150848:profile|JitteryCoyote63>

Setting to redis from version 6.2 to 6.2.11 fixed it but I have new issues now

Was the docker tag incorrect in the docker compose ?

Hi RoughTiger69
A. Yes makes total sense . Basically you can use Task.export Task.import to do achieve this process (notice we assume the dataset artifacts links are available on both, usually this is the case)

B. The easiest way would be to use Process , then one subprocess is exporting from dev , where the credentials and configuration is passed with os environment. The another subprocess imports it to the prod server (again with os environment pointing to the prod server). Make sense?

Hi DilapidatedDucks58

eg, we want max validation accuracy and all other metric values for the corresponding epoch

Is this the equivalent of nested sort ?
Wouldn't you get the requested behavior if you add all metric columns but sort based on the "accuracy" column ?

I reached over 1M API calls in about one week using clearml-serving

Oh that makes sense now 🙂
If I remember correctly, adding an additional model to a signal clearml-serving instance should not actually change the number of API calls, they are mostly affected by the number of clearml-serving / containers and not in the number of models.

Hi @<1572395184505753600:profile|GleamingSeagull15>
Try adjusting:
None
to 30 sec
It will reduce the number of log reports (i.e. API calls)

Hi JitteryCoyote63
Show running experimentsIt doesn't?
Have the legend clickable, to hide/show experiments based on their status:+1:
Have a line connecting points that are SOTA (example in https://paperswithcode.com/sota/image-generation-on-cifar-10 )I like that, how is that selected? (I know FE are thinking of replacing this entire graph library, so maybe good timing in terms of what to look at)

Hmm interesting, will pass it along to FE 🙂 3. That is nice! I wonder if this is built into the graph library

Hi ItchyHippopotamus18
The iteration reporting is automatically detected if you are using tensorboard, matplotlib, or explicitly with trains.Logger
I'm assuming there were no reports, so the monitoring falls back to report every 30seconds where the iterations are seconds from start" (the thing is, this is a time series, so you have to have an X axis...)
Make sense ?

mostly by using

Task.create

instead of

Task.init

.

UnevenDolphin73 , now I'm confused , Task.create is Not meant to be used as a replacement for Task.init, this is so you can manually create an Additional Task (not the current process Task). How are you using it ?

Regarding the second - I'm not doing anything per se. I'm running in offline mode and I'm trying to create a dataset, and this is the error I get...

I think the main thing we need to...

BTW: we are now adding "datasets chunks for a more efficient large dataset storage"

You can always specify diff clearml.conf files with --config-file 🙂

does clearml expect them to be actuall installed to add them as installed packages for a task?

It should add itself to the list (assuming you will end up calling Task.init in your code)

2,3 ) the question is whether the serving is changing from one tenant to another, does it?

Hi SmugOx94
Hmm are you creating the environment manually, or is it done by Task.init ?
(Basically Task.init will store the entire environment of conda, and if the agent is working with conda package manager it will use it to restore it)
https://github.com/allegroai/clearml-agent/blob/77d6ff6630e97ec9a322e6d265cd874d0ab00c87/docs/clearml.conf#L50

Hi PompousParrot44
So do you mean something like:
` task_model_a = Task.get('id_a')
task_model_b = Task.get('id_b')

model_a_file = task_model_a.models['output][-1].get_local_copy()

model_b_file = task_model_b.models['output][-1].get_local_copy() `

Although it's still really weird how it was failing silently

totally agree, I think the main issue was the agent had the correct configuration, but the container / env the agent was spinning was missing it,
I'll double check how come it did not print anything

Hmm, as a quick solution you can use the custom example and load everything manually:
https://github.com/allegroai/clearml-serving/blob/219fa308df2b12732d6fe2c73eea31b72171b342/examples/custom/preprocess.py
But you have a very good point, I'm not sure how one could know what's the xgboost correct class, do you?

Hi GreasyPenguin14
It looks like you are trying to delete a Task that does not exist
Any chance the cleanup service is misconfigured (i.e. accessing the incorrect server) ?

t = Task.get_task('aabbcc') t.update_task(task_data={'task_type': "optimizer"})