Would you expect this fastai callback to work?
(Uses SummaryWriter):
https://github.com/fastai/fastai/blob/d7f4863f1ee3c0fa9f2d9feeb6a05f0625a53696/fastai/callback/tensorboard.py
It seems to have failed as well (but I'd need to check more carefully)

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...

Unfortunately, waiting a while did not make this go away 🙂

Sure. It is a minor change from the code in the clearml examples for pipelines.
I just repeat the last two pipeline steps from that code in a loop (x3)
https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_decorator.py

I'll try a more carefully checked run a bit later but I know it's getting a bit late in your time zone

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

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

https://github.com/allegroai/clearml/issues/819

That job was using clearml 1.8.3 so I take it that setting max_workers to 1 would not make a difference?
Looking at the docs:
https://clear.ml/docs/latest/docs/references/sdk/dataset/#upload
they say that max_workers = number of cores but looking at the log it does seem like it's doing one chunk every 5 minutes (long time for 500mb upload for a node running in gcp...)

If I run from terminal, I see:
ValueError: Task object can only be updated if created or in_progress [status=stopped fields=['configuration']]

anyhow - looks like the keys are simple enough to use (so I can just ignore the model names)

Thanks 🙂
I wonder if it'll also include the fix that went into in the RC I was using there ( 1.6.3rc0 )

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...

first, thanks for having these discussions. I appreciate this kind of support is an effort 🙏
Yes. i perfectly understand that once a pipeline job (or a task) is sent off in this manner, it executes separately (and, most likely in a different machine) from the process that instantiated it.
I still feel strongly that such a command should not be thought of as a fire and exit operation. I can think of several scenarios where continued execution of the instantiating process is desired:
I ...

or, barring that, something similar on AWS?

👍

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

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

🙏

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

I can find the tasks in the "all experiments" project but there are over 500 tasks there (I guess in includes the archived tasks as well) so that's not much help.

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...

reading this..
https://github.com/allegroai/clearml-agent#kubernetes-integration-optional

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

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...

oops, I deleted two messages here because I had a bug in a test I've done.
I'm retesting now

uploads are a bit slow though (~4 minutes for 50mb)

I tried the first option and it worked 🙂 🙏

I imagine that these phantom dependencies will prevent parallelization. Is there a workaround?