Q: is there an equivalent env var for sdk.google.storage.pool_connections/pool_maxsize ? My jobs are running remotely and not within a clearml agent at the moment so they get clearml config through env vars.

Hi. Just a reminder that I'd love to know if / when this issue is looked at

thanks KindChimpanzee37 . Where is that minimal example to be found?

here is the log from the failing component:
File "/root/.clearml/venvs-builds/3.8/lib/python3.8/site-packages/clearml/utilities/locks/portalocker.py", line 140, in lock fcntl.flock(file_.fileno(), flags) BlockingIOError: [Errno 11] Resource temporarily unavailable

These paths are pathlib.Path . Would that be a problem?

I should also mention I use clearml==1.6.3rc0

I'm on clearml==1.6.3rc1

console output shows uploads of 500 files on every new dataset. The lineage is as expected, each additional upload is the same size as the previous ones (~50mb) and Dataset.get on the last dataset's ID retreives all the files from the separate parts to one local folder.
Checking the remote storage location (gs://) shows artifact zip files, each with 500 files

also, whereas the pipeline agent's log has:
Executing task id [7a0ad1fb243a4ff3b9e6c477442ded4a]: repository = git@github.com:shpigi/clearml_evaluation.git branch = main version_num = e045904094cf2f4fa61ce92f7b91682f5de64ab8
The component agent's log has:
Executing task id [90de043e354b4b28a84d5cc0788fe63c]: repository = branch = version_num =

essentially, several running processes were performing:
model_evals_dataset = Dataset.get( dataset_project=dataset_project, dataset_name=f"model_evals", ) model_evals_dataset.add_files(run_eval_path) model_evals_dataset.upload()

hmm.
this isn't supported though:
dataset_args = dataset.connect(dataset_args)

another weird thing:
Before my training task is done:
print(task.models['output'].keys())outputs
odict_keys(['Output Model #0', 'Output Model #1', 'Output Model #2'])
after task.close()
I can do:
task = Task.get_task(task_id) for i in range(100): print(task.models["output"].keys())which prints
odict_keys(['Output Model #0', 'Output Model #1', 'Output Model #2'])in the first iteration
and prints the file names in the latter iterations:
` od...

Something else that I feel is missing from the docs regarding pipelines, as someone who has given kubeflow pipelines a try (in the http://vertex.ai pipelines environment), is some explanation of how functions become pipelines and components.
More specifically, I've learned to watch out for kubeflow pipeline code which is run at definition time (at compilation time, to be more accurate) instead of at pipeline execution time.

This whole experiment with random numbers started as my attempt ...

I can't find version 1.8.1rc1 but I believe I see a relevant change in code of Dataset.upload in 1.8.1rc0

For anyone following, you can "inject" a credentials json file for a google cloud service account so at to get access to your google cloud storage from agents on aws ec2 instances that are managed by the AWS autoscaler by providing the following in the ADDITIONAL CLEARML CONFIGURATION when starting the autoscaler:
` sdk.google.storage.credentials_json: "/root/gs.cred"
sdk.google.storage.project: "<my-gcp-project-id>"
files {
gsc {
contents: """<copy-paste the contents of yo...

Thanks for the fix and the mock HPO example code !
Pipeline behaviour with the fix is looking good.
I see the point about changes to data inside the controller possibly causing dependencies for step 3 (or, at least, making it harder for the interpreter to know).

erm,
this parallelization has led to the pipeline task issuing a bunch of:
model_path/run_2022_07_20T22_11_15.209_0.zip , err: [Errno 28] No space left on deviceand quitting on me.
my train_image_classifier_component is programmed to save model files to a local path which is returned (and, thanks to clearml, the path's contents are zipped uploded to the files service).

I take it that these files are also brought into pipeline tasks's local disk?
Why is that? If that is indeed what...

no retry mesages
CLEARML_FILES_HOST is gs
CLEARML_API_HOST is a self hosted clearml server (in google compute engine).

Note that earlier in the process the code uploads a dataset just fine

I tried playing with those parameters on my laptop to no great effect.

Here is code you can use to reproduce the issue:

` import os
from pathlib import Path
from tqdm import tqdm
from clearml import Dataset, Task

def dataset_upload_test(project_id:str, bucket_name:str
):
def _random_file(fpath, sizekb):
fileSizeInBytes = 1024 * sizekb
with open(fpath, "wb") as fout:
fout.write(os.urandom(fileSizeInBytes))

def random_dataset(dataset_path, num_files, file...

That is a good point, I'll make sure we mention it somewhere in the docs. Any thoughts on where?

maybe in (all of) these places:
https://clear.ml/docs/latest/docs/faq
https://clear.ml/docs/latest/docs/fundamentals/task
https://clear.ml/docs/latest/docs/clearml_sdk/task_sdk

I had several pipeline components getting it and uploading files to is concurrently.
Can Datsets handle that?

Ooh nice.
I wasn't aware task.models["output"] also acts like a dict.
I can get the one I care about in my code with something like task.models["output"]["best_model"]
however can you see the inconsistency between the key and the name there:

Is there any chance the experiment itself has a docker image specified?

It does not as far as I know. The decorators do not have docker fields specified

Is there a way to set the default upload destination for all tasks in my ~/clearml.conf

.. yes by setting files_server: gs://clearml-evaluation/

🙏

👍

That would be a better message however, I must have misunderstood the meaning of auto_create=True
I thought that flag made the get function into a "get-or-create"

I get the same error with those added lines

I have tried this several times now. Sometimes one runs an the other fails and sometimes both fail with this same error

Hi again.
Thanks for the previous replies and links but I haven't been able to find the answer to my question: How do I prevent the content of a uri returned by a task from being saved by clearml at all.

I'm using this simplified snippet (that avoids fastai and large data)
` from clearml.automation.controller import PipelineDecorator
from clearml import TaskTypes

@PipelineDecorator.component(
return_values=["run_datasets_path"], cache=False, task_type=TaskTypes.data_processing
)
def ma...