the sudo update-ca-certificates ? maybe this will work

Hi @<1581454875005292544:profile|SuccessfulOtter28> ! The logger is likely outdated. Can you please open a Github issue about it?

Can you see your task if you run this minimal example UnevenDolphin73 ?
` from clearml import Task, Dataset

task = Task.init(task_name="name_unique", project_name="project")
d = Dataset.create(dataset_name=task.name, dataset_project=task.get_project_name(), use_current_task=True)
d.upload()
d.finalize() `

UnevenDolphin73 did that fix the logging for you? doesn't seem to work on my machine. This is what I'm running:
` from clearml import Task
import logging

def setup_logging():
level = logging.DEBUG
logging_format = "[%(levelname)s] %(asctime)s - %(message)s"
logging.basicConfig(level=level, format=logging_format)

t = Task.init()
setup_logging()
logging.info("HELLO!")
t.close()
logging.info("HELLO2!") `

You need to specify it. Or you could specify this in your config: https://github.com/allegroai/clearml/blob/54c601eea2f9981bb8e360a8203bc36696a55cfd/clearml/config/default/sdk.conf#L164

Hi @<1643060801088524288:profile|HarebrainedOstrich43> ! At the moment, we don't support default arguments that are typed via a class implemented in the same module as the function.
The way pipelines work is: we copy the code of the function steps (eventually their decorator's as well if declared in the same file), then we copy all the imports in the module. Problem is, we don't copy classes.
You could have your enum in a separate file, import it and it should work

Hi @<1523707653782507520:profile|MelancholyElk85> ! I don't think this is possible at the moment 😕 Feel free to open a GH issue that proposes this feature tho

Hi PricklyRaven28 ! What dict do you connect? Do you have a small script we could use to reproduce?

FlutteringWorm14 we do batch the reported scalars. The flow is like this: the task object will create a Reporter object which will spawn a daemon in another child process that batches multiple report events. The batching is done after a certain time in the child process, or the parent process can force the batching after a certain number of report events are queued.
You could try this hack to achieve what you want:
` from clearml import Task
from clearml.backend_interface.metrics.repor...

Hi @<1643060801088524288:profile|HarebrainedOstrich43> ! The rc is now out and installable via pip install clearml==1.14.1rc0

PanickyMoth78 You might also want to set some lower values for sdk.google.storage.pool_connections/pool_maxsize in your clearml.conf . Newer clearml version set max_workers to 1 by default, and the number of connections should be tweaked using these values. If it doesn't help, please let us know

Btw, to specify a custom package, add the path to that package to your requirements.txt (the path can also be a github link for example).

Hi @<1523701868901961728:profile|ReassuredTiger98> ! Looks like the task actually somehow gets ran by both an agent and locally at the same time, so one of the is aborted. Any idea why this might happen?

There might be something wrong with the agent using ubuntu:22.04 . Anyway, good to know everything works fine now

Hi @<1523705721235968000:profile|GrittyStarfish67> ! Please install the latest RC: pip install clearml==1.12.1rc0 to fix this. We will have an official release soon as well

Hi @<1546303293918023680:profile|MiniatureRobin9> The PipelineController has a property called id , so just doing something like pipeline.id should be enough

Hi LittleShrimp86 ! Looks like something is broken. We are looking into it

Hi HomelyShells16 How about doing things this way? does it work for you?
` class ClearmlLightningCLI(LightningCLI):
def init(self, *args, **kwargs):
Task.add_requirements("requirements.txt")
self.task = Task.init(
project_name="example",
task_name="pytorch_lightning_jsonargparse",
)
super().init(*args, **kwargs)

def instantiate_classes(self, *args, **kwargs):
    super().instantiate_classes(*args, **kwargs)
  ...

Hi @<1590514584836378624:profile|AmiableSeaturtle81> ! What function are you using to upload the data?

Hi!
It is possible to use the same queue for the controller and the steps, but there needs to be at least 2 agents that pull tasks from that queue. Otherwise, if there is only 1 agent, then that agent will be busy running the controller and it won't be able to fetch the steps.

Regarding missing local packages: the step is ran in a temporary directory that is different than the directory the script is originally in. To solve this, you could add all the modules/files you are interested in in a...

Hi @<1693795212020682752:profile|ClumsyChimpanzee88> ! Not sure I understand the question. If the commit ID does not exist remotely, then it can't be pulled. How would you pull the commit to another machine otherwise, is this possible using your current workflow?

Can you please provide a minimal example that may make this happen?

If the task is running remotely and the parameters are populated, then the local run parameters will not be used, instead the parameters that are already on the task will be used. This is because we want to allow users to change these parameters in the UI if they want to - so the paramters that are in the code are ignored in the favor of the ones in the UI

how about this one?

import clearml
import os
print("\n".join(open(os.path.join(clearml.__path__[0], "automation/controller.py")).read().split("\n")[310:320]))

Hi SmugSnake6 ! If you want to delete a project using the APIClient :
from clearml.backend_api.session.client import APIClient from clearml.backend_interface.util import exact_match_regex api_client = APIClient() id = api_client.projects.get_all(name=exact_match_regex("pipeline_project/.pipelines/pipeline_name"), search_hidden=True)[0].id api_client.projects.delete(project=id)Notice that tasks need to be archived

Hi SmallGiraffe94 ! Dataset.squash doesn't set as parents the ids you specify in dataset_ids . Also, notice that the current behaviour of squash is pulling the files from all the datasetes from a temp folder and re-uploading them. How about creating a new dataset with id1, id2, id3 as parents Dataset.create(..., parent_datasets=[id1, id2, id3]) instead? Would this fit your usecase?

Hi @<1554638160548335616:profile|AverageSealion33> ! We pull git repos to copy the directory your task is running in. Because you deleted .git , we can't do that anymore. I think that, to fix this, you could just run the agent in the directory .git previously existed.

Hi DrabOwl94 Looks like this is a bug. Strange no one found it until now. Anyway, you can just add a --params-override at the end of the command line and it should work (and --max-iteration-per-job <YOUR_INT> and --total-max-job <YOUR_INT> as Optuna requires this). We will fix this one in the next patch.
Also, could you please open a Github issue? It should contain your command line and this error.
Thank you

this only affects single files, if you wish to add directories (with wildcards as well) you should be able to

We used to have "<=20" as the default pip version in the agent. Looks like this default value still exists on your machine. But that version of pip doesn't know how to install your version of pytorch...