Yes.
Some mechanism that would allow for followup code execution. Ideally in a way that would not be susceptible to the same things that may cause a task to fail.

There may be cases where failure occurs before my code starts to run (and, perhaps, after it completes)

here is the code in text if you feel like giving it a try:
import tensorboard_logger as tb_logger from clearml import Task task = Task.init(project_name="great project", task_name="test_tb_logging") task_tb_logger = tb_logger.Logger(logdir='./tb/run1', flush_secs=2) for i in range(10): task_tb_logger.log_value("some_metric", 42, i) task.close()

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

yes. several checkpoints + the one that did best on validation data.

That's amazing speed 🚀

Simpler than I had thought, thanks !

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...

I'll try and reproduce this in simpler code

nice !

would setting the max_workers to 1 be a (slower) workaround?

(I see the same thing in some evaluation code that I've written so I thought I'd reproduce it in the standard example)

I'm connecting to the hosted clear.ml
packages in use are:
# Python 3.8.10 (default, Mar 15 2022, 12:22:08) [GCC 9.4.0] clearml == 1.6.2 fastai == 2.7.5
in case it matters, I'm running this code in a jupyter notebook within a docker container (to keep things vell isolated). The /data path is volume mapped to my local filesystem (and, in fact, already contains the dataset files, so the fastai call to untar_data should see the data there and return immediately)
That same make_data fu...

sorry..

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 =

AgitatedDove14
Adding adding repo and repo_branch to the pipeline.component decorator worked (and I can move on to my next issue 🙂 ).
I'm still unclear on why cloning the repo in use happens automatically for the pipeline task and not for component tasks.

I think this should be a valid use of pipelines. for example - at some step I choose to sweep across several values of some parameter and the rest of the steps are duplicated for each value of that parameter.
The additional edges in the graph suggest that these steps somehow contain dependencies that I do not wish them to have.

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

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"

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()

or, barring that, something similar on AWS?

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

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...

trying the AWS Autoscaler for the first time I get his error on instance spin up:
An error occurred (InvalidAMIID.NotFound) when calling the RunInstances operation: The image id '[ami-04c0416d6bd8e4b1f]' does not existI tried both us-west-2 and us-east-1b (thinking it might be zone specific).

I'm not sure if this is a permissions issue or a config issue.

The same occures when I try a different image:
ami-06bafe528da33cdb8
(an aws public image)

Yes. I thought this happened automagically with the current git repo when I send a pipeline for execution from my local python environment. Shouldn't it?
It seems to have happened with the agent running the pipeline task.

I'll try adding repo and repo_branch to the pipeline.component decorator

I'm on the pro tier

I'm looking for a minimal set of permissions because we have other sensitive ec2 instances running in the same account and our IT people are rightfully concerned about providing access to that account externally.

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

in order for the autoscaler to access your git , in the wizard you have to provide the git user/token

git_pass has the token
Perhaps I should have mentined that I start the AWS autoscaler with the https://app.clear.ml/applications/aws-autoscaler/ .

Hmm, how does the decorator of the component looks like ? meaning did you specify a repo/branch/commit there

Neither my pipeline decorator not my component specify any repos:

` # pipeline
@PipelineDecorator.pipeline(
name=...