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94 × Eureka!this Point class is in repo
I haven't change any port mapping
A sample dummy code
from clearml import Task
from Point import Point
import numpy as np
task = Task.init(project_name="project_demo", task_name="name")
parameters = {
"A": 3,
"B": 0.5
}
task.connect(parameters)
p = Point(2,3)
conf_yaml = task.connect_configuration(
name = "my yaml",
configuration = "config_yaml.yaml"
)
task.upload_artifact("Arti", np.zeros((10,10)))
What is interesting, it works when using virtual environment setup
no, it is everything on my local machine
Can I do this to specify which worker should execute that task?CLEARML_WORKER_NAME=<worker_name> clearml-agent execute --id <task_id>
AgitatedDove14 do you know if it possible not to open ports on machines B_i
where agents reside?
AgitatedDove14 do I need to have the repo that I am running on my account? Even if it is public repo, like repo with your (clearml) examples:
SOURCE CODE
REPOSITORY
https://github.com/allegroai/clearml.git
BRANCH NAME
Latest in branch master
SCRIPT PATH
pytorch_matplotlib.py
WORKING DIRECTORY
examples/frameworks/pytorch
?
I am referring to something like Ray framework has https://docs.ray.io/en/latest/ray-core/tasks.html#specifying-required-resources
version 1.8.1
No, there are no error messages. The behaviour is just very strange (or even incorrect)
Suppose that this is a task that is cloned:
` base_task = replacement_task.create_function_task(
func=some_func, # type: Callable
func_name=f'func_id_run_me_remotely_nr', # type:Optional[str]
task_name=f'a func task', # type:Optional[str]
# everything below will be passed directly to our function as arguments
some_argument=message,
some_argument_2=message,
rand...
Commits, that are not pushed to the repo
clearml-agent daemon --docker --foreground --debug
usage: clearml-agent [-h] [--help] [--version] [--config-file CONFIG_FILE] [--debug]
{execute,build,list,daemon,config,init} ...
clearml-agent: error: unrecognized arguments: --debug
CostlyOstrich36 have you ever seen something like my case maybe?
there is no such option
WARNING: You are using pip version 20.1.1; however, version 21.3.1 is available.
You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip'
command.
Retrying (Retry(total=239, connect=239, read=240, redirect=240, status=240)) after co
nnection broken by 'NewConnectionError('<urllib3.connection.HTTPConnection object at
` 0x7faf9da78400>: Failed to establish a ...
or at least I can't specify such
Because it has no coincidence with some specific actions
Hi @<1523701070390366208:profile|CostlyOstrich36> , sorry for not responding. I would like to return to this subject. The use case - make re-allocation of workers between the queues - depending of the needs include given machine in the testing queue. But when I want to switch if off and switch on on the new queue - it will switch off the experiment by the default. And I would like to wait until the experiments finishes peacefully
There is a git repo 🙂 my question was to clarify if I understand well. Thank you for response :)
No. Hovewer, I see some of running agents, but not all
The problem is that we have a a complex configuration of pipeline. Configuration changes quite frequently and we would not like to run the pipeline every time configuration changes, but we would like to have it scheduled in some defined periods.
Do you have an idea of some workaround / alternative solution for that problem?
But stucks at the same moment when using docker
ClearML Server Version: 1.7.0-232
SuccessfulKoala55 hmm, we are trying to do something like that and we are encountering problems. We are doing big hyperparameter optimization on 200 workers and some tasks are failing (while with less workers they are not failing). Also, UI also has some problems with that. Maybe there are some settings that should be corrected in comparison to classic configuration?
I host the code on my Github