WTF?!

Hi SmallDeer34
Is the Dataset in clearml-data ? If it is then Dataset.get().get_local_copy() will get you a cached local copy of the entire dataset.
If it is not, then you can use StorageManager.get_local_copy(url_here) to download the dataset.

2. Any Argparser is automatically logged (and later can be overridden from the UI). Specifically HfArgumentParser will be automatically logged https://github.com/huggingface/transformers/blob/e43e11260ff3c0a1b3cb0f4f39782d71a51c0191/examples/pytorc...

AttributeError: 'NoneType' object has no attribute 'base_url'

can you print the model object ?
(I think the error is a bit cryptic, but generally it might be the model is missing an actual URL link?)
print(model.id, model.name, model.url)

So the thing is, regardless of the link you should end with:
helper <clearml.storage.helper.StorageHelper object at 0x....>But the code that failed seemed to return None, which makes me suspect the url itself is somehow broken.
Any chance you have a space before the "s3://" ?
BTW : what's the clearml version you are using ?

Hi AbruptCow41

I just want them to be able to write in them without them appear nor in their clearml.conf nor in their environmental variables.

So where would they put them ? (or is it pre baked into the docker?)

S3 access would return a different error...
Can you do:
` from clearml.storage.helper import StorageHelper
helper = StorageHelper.get("s3://<bucket>/<foo>/local/<env>/<project-name>/v0-0-1/2022-05-12-30-9-rocketclassifier.7b7c02c4dac946518bf6955e83128bc2/models/2022-05-12-30-9-rocketclassifier.pkl.gz")

print("helper", helper) `

That makes no sense to me?!
Are you absolutely sure the nntrain is executed on the same queue? (basically could it be that the nntraining is executed on a different queue in these two cases ?)

Hi NastyFox63
This seems like most of the reports are converted to pngs (which is what the automagic does if it fails to convert the matplotlib into interactive plot).

no more than 114 plots are shown in the plots tab.

Are you saying we have 114 limit on plots ?
Is this true for "full screen" mode (i.e. not in the experiments table but switch to full detailed view)

Yes, actually the first step would be a toggle button for regexp in the search, the second will be even more advanced search.
May I suggest you post it on the UI suggestion issue https://github.com/allegroai/trains/issues/81 ?

RC3 is out with a few fixes, please feel free to test 🙂

GiganticTurtle0 I'm not sure I follow, what do you mean by indexing the arguments? Can you post a short usage example ?

Can you run the entire thing on your own machine (just making sure it doesn't give this odd error) ?

pipeline, can I control the tags that the tasks a pipeline creates? 

add_pipeline_tags

  adds tags from pipeline to the tasks I suppose? But I also need to clear existing tags in those created tasks

add_pipeline_tags will add the unique ID of the pipeline execution, if you want to add specific tags you can use the task_overrides and provide:
pipe.add_step(..., task_overrides={'tags': ['my', 'tags']})

seems working to me

Hi ConfusedPig65
Any keras model will be automatically uploaded if you pass an upload url to the Task init:
task = Task.init('examples', 'keras upload test', output_uri=" ")(You can also pass to output_uri s3://buckket/folder or change the default output_uri in the clearml.conf file)
After this line any keras model will be automatically uploaded (you will see it under the Artifacts Tab)
Accessing models from executed tasks:
` trains_task = Task.get_task('task_uid_here')
last_check...

Hmm let me check, I think we changed the offline mode to use the latest API version (because by definition it cannot know what's the server).
Let me check if you can override it

Ohh you mean ~/clearml.conf ?

SuccessfulKoala55 please post here once the code is available in your pytorch_ignite 🙂

SweetGiraffe8 Task.init will autolog everything (git/python packages/console etc), for your existing process.
Task.create purely creates a new Task in the system, and lets' you manually fill in all the details on that Task
Make sense ?

🤞

Hi JollyChimpanzee19
I found this one:
https://clearml.slack.com/archives/CTK20V944/p1622134271306500

Check the log to see exactly where it downloaded the torch from. Just making sure it used the right repository and did not default to the pip, where it might have gotten a CPU version...

Come to think about it, maybe we should have "parallel_for" as a utility for the pipeline since this is so useful

give me a minute to test

BattyLion34 are you saying you do not have the "APP CREDENTIALS" section in the profile page?

Hi LazyTurkey38

, is it possible to have the agents keep a local version and only download the diff of the job commit to speed things up?

This is what it does, it has a local cached copy and it only pulls the latest changes

Hi SubstantialElk6
You are uploading an artifact, a good use case for numpy artifact would be a feature table.
If you want to upload an image use either report_media or report_image or upload PIL image as artifact.
What do you think?

https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/logger_module/logger_logger.html#clearml.logger.Logger.report_image

🤞

BTW: StickyMonkey98 if you feel like writing a few examples I think it will be easy to push into the docs, so that at least we improve iteratively...