Thank you very much, didnt know about that 🙂

Or does MinIO delay deletion somehow? Deleting a task via the web interface also does not result in deletion of debug samples on MinIO

Or better some cache option. Otherweise the cron job is what I will use 🙂 Thanks again

But this seems like something that is not related to clearml 🙂 Anyways, thanks again for the explanations!

It is not explained there, but do you mean
CLEARML_API_ACCESS_KEY: ${CLEARML_API_ACCESS_KEY:-} CLEARML_API_SECRET_KEY: ${CLEARML_API_SECRET_KEY:-}?

I use fixed users!

The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working.

Thank you very much. I am going to try that.

Also, is max_workers about compression threads or upload threads or both?

Thank you very much!

@<1523701087100473344:profile|SuccessfulKoala55> I just did the following (everything locally, not with clearml-agent)

• Set my credentials and S3 endpoint to A
• Run a task with Task.init() and save a debug sample to S3
• Abort the task
• Change my credentials and S3 endpoint to B
• Restart the taskThe result are lingering files in A that seem not to be associated with the task. I would expect ClearML to instead error the task or to track the lingering files somewhere, so they can ma...

Nvm. I forgot to start my agent with --docker . So here comes my follow up question: It seems like there is no way to define that a Task requires docker support from an agent, right?

I am also wondering how I integrate my (preexisting) main task in the pipeline. I start my main task like this: python my_script.py --myarg "myargs" . How are the arguments captured? I am very confused, how one integrates this correctly...

Perfect, will do! Thank you

2. solves it. I did not know this is possible.

Okay, great! I just want to run the cleanup services, however I am running into ssh issues so I wanted to restart it to try to debug.

Maybe something like this is how it is intended to be used?

` # run_with_clearml.py

def get_main_task():
task = Task.create(project="my_project", name="my_experiment", script="main_script.py")
return task

def run_standalone(task_factory):
Task.enqueue(task_factory())

def run_in_pipeline(task_factory):
pipe = Pipelinecontroller()
pipe.add_step(preprocess, ...)
pipe.add_step(base_task_factory=task_factory, ...)
pipe.add_step(postprocess, ...)
pipe.start()

if...

I mean if I do CLEARML_DOCKER_IMAGE=my_image clearml-task something something it will not work, right?

With remote_execution it is command="[...]" , but on local it is command='train' like it is supposed to be.

Hi @<1523701087100473344:profile|SuccessfulKoala55> Thank you very much.
Is there some way to verify the server uses the correct configuration files? (E.g. see it in the logs/web ui). I Just tried it does not work.
At least I can see the async_delete service complains about a missing secret, so I can start debugging there. I am using the same config as for my agents, but somehow for async_delete it does not work...

Now the pip packages seems to ship with CUDA, so this does not seem to be a problem anymore.

Is ther a way to see the contents of /tmp/conda_envaz1ne897.yml ? Seems to be deleted after the task is finihsed

When is the base_task_factory called? At runtime or definition time?

Thank you. Yes we need to wait for carla to spin up.

I mean, could my hard drive not become full at some point? Can clearml-agent currently detect this?

What I get for args when I print it locally is not the same as what ClearML extracts from args .

Is there a way to specify this on a per task basis? I am running clearml-agent in docker mode btw.

Complete conda log

Locally it works fine.

Perfect, just what I always wanted. Looking forward to the MinIo version. Thank you:)