Should I make a new issue or just reply on the one I mentioned above?

Maybe a new issue with the merge, and then the hack+fix? what do you think?

no available 😞

LazyFox65 seems like a docker issue.
Can you manually run the docker ?

Hi @<1661542579272945664:profile|SaltySpider22> I'm not sure I understand the answer to my parallel quesion

Hi BoredSquirrel45

as of today, my required packages aren't being recognized in cloned

Are you saying you are editing the code directly in the cloned Task, then enqueue the Task an the agent does not "auto recognize" the package ?

Yep that is it 🙂

Hi @<1691620877822595072:profile|FlutteringMouse14>

In the latest project I created, Hydra conf is not logged automatically.

Any chance the Task.init call is not on the main script (where the Hydra is) ?

PlainSquid19 Trains will analyze the entire repository if this is a git repo code, and a single script file if there is no repository found.

It will not analyze an entire folder if it is not in a git repository, because it will not be able to recreate this folder anyhow. Make sense ?

@<1639799308809146368:profile|TritePigeon86> +1

Hi UnsightlyShark53 , just a quick FYI, you can also log the entire config file config.json this will be stored as model configuration, and you can see it in the input/output models under the artifacts tab.
See example here you can path either the path to the configuration file, or the dictionary itself after you loaded the json, whatever is more convenient :)

The only weird thing to me is not getting any "connection warnings" if this is indeed a network issue ...

Okay that makes sense, if this is the case I'm assuming you have set the files server to point to your S3 bucket is that correct ?
could it be you are missing the credentials for that (it is trying to upload the preprocessing code there, so the clearml-serving container would be able to pull it later)

Hi EnchantingOstrich20
You how doe s clearml get it there?
In runtime it analyzes the code you are running looking for imports then checks the version you have actively used (i.e. active venv / python) and lists it there.
You can also override those in code, or edit them after you clone the ask and before you enqueue it for remote execution

Scheduled training is what I’m looking forward to

I'll try to remember to update here once we pushed into the GitHub repo, feedback is always appropriated 🙂
If in the next two weeks you hear nothing, please ping here to make sure I did not forget 😉

I'll make sure we have conda ignore git:// packages, and pass them to the second pip stage.

Hi @<1523701132025663488:profile|SlimyElephant79>
Did you check None
There are also ready made templates:
None
None
None

This cam be as simple as a pod or a more complete helm chart.

True, and this could be good for batch processing, but if you want restapi service then clearml-serving is probably a better fit
does that make sense ?

Hi FancyWhale93 , in your clear.conf configure default output uri, you can specify the file server as default, or any object storage:
https://github.com/allegroai/clearml-agent/blob/9054ea37c2ef9152f8eca18ee4173893784c5f95/docs/clearml.conf#L409

This is what I think you should end up with
DiscreteParameterRange('General/dataset_url', values=["option 1 for url", "option 2 for url"])If args['dataset_url'] is a list, you should just do values=args['dataset_url']

Hi @<1742355077231808512:profile|DisturbedLizard6>

the problem maybe in returning None in get_local_model_file()

This tracks, it means that the model file cannot be downloaded for some reason,
when you click on the model here: None
what doe sit say under "MODEL URL:"?
![image](https://clearml-web-assets.s3.amazonaws.com/scoold/images/TT9ATQXJ5-F07SV39P6R3/________________...

IrritableGiraffe81 could it be the pipeline component is not importing pandas inside the function? Notice that a function decorated with pipeline component become a stand-alone, this means that if you need pandas you need to import inside the function. The same goes for all the rest of the packages used.
When you are running with run_loclly or debug_pipeline you are using your local env , as opposed to the actual pipeline where a new env is created inside the repo.
Can you send the Entire p...

OutrageousGrasshopper93 could you send an example of the two links from the artifacts (one local one remote) ?

UnevenDolphin73 are you positive, is this reproducible? What are you getting?

The experiment finished completely this time again

With the RC version or the latest ?

Try adding this environment variable:
export TRAINS_CUDA_VERSION=0

You can check the keras example, run it twice, on the second time it will continue from the previous checkpoint and you will have input and output model.
https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py

Notice that you have to Have the task already started by the Master process

Then the dynamic gpu allocation is exactly what you need, I suggest talking to the sales ppl, I'm sure they can help. https://clear.ml/contact-us/

I see, by default it will look for requirements.txt in the root of the repo (the actual repo).
That said in code you can specify the requirements .txt:
Task.force_requirements_env_freeze(requirements_file='repo/project-a/requirements.txt') task = Task.init(...)Notice, you need to call it prior to the Task.init call

Oh
Task.get_project_object().default_output_destination = None
This has no effect on the backend, meaning this does not actually change the value.
from clearml.backend_api.session.client import APIClient c = APIClient() c.projects.update(project="<project_id_here>", default_output_destination="s3://")btw: how/what it is used for in your workflow ?