Long story short, the Task requirements are async, so if one puts it after creating the object (at least in theory), it might be too late.

AgitatedDove14 Is there no await/synchronize method to wait for task update?

btw: With the ssh agent forwarding I do not have any issues ( https://github.com/allegroai/clearml-agent/issues/45 )

I think doing all that work is not worth it right now, I am just trying to understand why I clearml seems not to be designed something like this:

` task_name = args.task_name

task = Task()
task = task.load_statedict(await Task.load_or_create(task_name))

task.requirements.add(...)
await task.synchronize()

task.execute_remotely(queue_name, exit=True) `

Here is some context on what I am currently trying to do (pseudocode):

`
def run_experiment(args):
...

def get_task_experiment():
task = Task.init(...)
task.bind_run(run_experiment)

return task

def run_with_pipeline(task):
pipe = PipelineController(...)
pipe.add_step(prepare_something...)
pipe.add_step(task)
pipe.add_step(postprocess_something...)

return pipe 

if name == "main":
task = get_task_experiment()
# Run without Pipeline
if ...

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

Okay, I see. Unfortunetly, I don't get how clearml tasks are intended to be used. Could you help me with that? (see code)

` def start_carla_factory():
task = # How do I create this task?

long_blocking_call_to_start_carla()

return task

pipe = PipelineController(
name="carla-autostart",
project="rlad/carla-servers",
version="0.0.1",
add_pipeline_tags=False,
)
pipe.add_step(name="start-carla", base_task_factory=start_carla_factory)
pipe.start() `

Maybe related question: Will there be some documentation about clearml internals with the new documentation? ClearML seems to store stuff that's relevant to script execution outside of clearml.Task if I am not mistaken. I would like to learn a little bit about what the code structure / internal mechanism is.

Thanks, that makes sense. Can you also explain what task_log_buffer_capacity does?

But would this not have to be a server parameter instead of a clearml.conf parameter then? Maybe someone from clearml can confirm MortifiedDove27 's explaination?

AgitatedDove14

AnxiousSeal95 This bug seems to be affecting me. I just tried forcing clearml-agent to install clearml-agent==1.4.1 in the docker and now it works.
Btw: clearml-agent uses pip install clearml-agent -U to install clearml-agent in the docker. However, instead of using the newest clearml-agent it should use the version that the host machine is using to run clearml-agent in my opinion.

I see. But I just realized: Subsampling means you just show every nth datapoint, right? I still do not get why this leads to some 0.5 values when in my plot there should only be 0 and 1.

Maybe this is something that is only possible with the vault of the enterprise version?

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

Thank you, perfect! I did not try yet, but wil do now.

So if understand correctly, something like this should work?

task = Task.init(...) task.connect_configuration( {"agent.package_manager.system_site_packages": False} ) task.execute_remotely(queue_name, clone=False, exit_process=True)

Too late for what?

Outside of the cleaml.Task?

Ah, nevermind. I thought wrong here.

Wow, thank you very much. And how would I bind my code to task? Should I still use Task.init and it will just use the file it is called in as entrypoint or should I create a task using Task.create and specify the script?

@<1523701435869433856:profile|SmugDolphin23> Good catch. I have a good but unsatisfying message for you guys: I restarted the whole machine (server and agent) and now it works fine ...

Specific step in the pipeline. The main step (the experiment) is currently just a file with a Task.init ` and then the experiment code. I am wondering how to modify this code such that it can be run in the pipeline or as standalone.

Thank you

Thank you, good to know!

(btw: the simulator is called carla, not clara :))

Actually, my current approach looks like this:

carla-server-task : Launch carla server instance on a random port, set the port as param and then block the task/process, so I can kill carla when this task is aborted. This task keeps running the whole time.
start-carla-task : Launch a carla-server-task and wait for the port parameter to be set. Set the launched carla-server-task task-id and the port as param. Set task completed.
main-task : Run experiment when all start-carla-task are...

Or maybe even better: How can I get all the information of the "INFO" page in the WebUI of a task?

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

Can you tell me how I create tasks correctly? The PipelineController.add_step takes the task-id/task-name, but I would rather just define a function that returns the task directly, since the base-task may not be already on the clearml-server.

Another example on what I would expect:
` ### start_carla.py
def get_task():
task = Task.init(project_name="examples", task_name="start-carla", task_type="application")
# experiment is not run here. The experiment is only run when this is executed as standalone or on a clearml-agent.
return task

def run_experiment(task):
...

This task can also be run as standalone or run by a clearml-agent

if name == "main":
task = get_task()
run_experiment(task)

run_pi...

Maybe there is something wrong with my setup. Conda confuses me sometimes.

If you think the explanation takes too much time, no worries! I do not want to waste your time on my confusion 😄