great ! 😉

Hey
Is this issue solved ?
🙂 thanks !

you might have a proxy error or a firewall blocking somewhere

for instance
export CLEARML_AGENT__AGENT__PACKAGE_MANAGER_ TYPE=conda && clearml-agent daemon --queue my queue

Have you tried try to set your agent in conda mode ( https://clear.ml/docs/latest/docs/clearml_agent#conda-mode ) ?

yes i have the behavior. I think that we have a bug. We will release a fix and will keep you updated 🙂
can you please open a github issue ? Of course if it is a problem, i can do it for you, just let me know 🙂
http://github.com/allegroai/clearml/issues

btw can you screenshot your clearml-agent list and UI please ?

Hi HandsomeGiraffe70
There is a way, this is the API. You can use it this way :
retrieve the task the model belongs to retrieve the model you want (from a lit of input and output models) create the metadata inject them to the model
Here is an example :

` from clearml import Task
from clearml.backend_api import Session
from clearml.backend_api.services import models
from clearml.backend_api.services.v2_13.models import MetadataItem

task = Task.get_task(project_name=project_name, task_name=...

can you share with me an example or part from your code ? I might miss something in wht you intend to achieve

Hi SmugSnake6
I might have found you a solution 🎉 I answered on the GH thread https://github.com/allegroai/clearml-agent/issues/111

hi RobustRat47
the field name is active_duration, and it is expressed in seconds

to access it for the task my_task , do my_task.d ata.active_duration

TenderCoyote78
the status should normally be automatically updated . Do all the steps finish successfully ? And also the pipeline ?

hey WickedElephant66 TenderCoyote78
I'm working on a solution, just hold on, I update you asap

btw here is the content of the imported file:

import torch
from torchvision import datasets, transforms
import os
MY_GLOBAL_VAR = 32

def my_dataloder ():
return torch.utils.data.DataLoader(
datasets.MNIST(os.path.join('./', 'data'), train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor()
` ...

those are the credentials you got from your self hosted server ?
what about the logs before the error ? i think it relevant to have them all. i try to isolate the error, and to understand if it comes from the cred, the servers addresses, a file error or a network error

No, it is supposed to have its status updated automatically. We may have a bug. Can you share some example code with me, so that i could try to figure out what is happening here ?

hi TenderCoyote78

thanks for all those precisions. I will try to reproduce and keep you updated 🙂

AverageRabbit65
Any tool that will permit to edit a text file. I personally use nano . Note that the indentations are not crucial, so any tool, either GUI or CLI will be ok

great to hear that the issue is solved. btw sorry for the time it took me to come back to you

Hello Sergios,
We are working on reproducing your issue. We will update you asap

Hi GentleSwallow91 ,
I can't manage to reproduce the issue, it is working fine for me. I use a local minio docker-based image. The conf file has to be precisely configured, but it seems that you did it ok, because you don't have a denied access here. It is strange that he is waiting for the upload to finish. We have this flag for upload_artefact : wait_on_upload . His default value should be False, but i would try to add it...

Also I don't understand what you mean by " I can see files in ...

ok so here is the example.
the idea is to use the API, and finally reproduce what the WebUI does.

` from clearml.backend_api.session.client import APIClient
from clearml import Task

task = Task.get_task(task_id=xxxx)
#or Task.get_task(project_name=xxx, task_name=xxx)

client = APIClient()
my_data = client.tasks.get_by_id(task.id).to_dict()

with open('./my_data.csv', 'w') as f:
for key in my_data.keys():
f.write("%s, %s\n" % (key, my_data[key]) ) `

hi FiercePenguin76
Can you also send your clearml packages versions ?
I would like to sum your issue up , so that you could check i got it right

you have a task that has a model, that you use to make some inference on a dataset you clone the task, and would like to make inferences on the dataset, but with another modelthe problem is that you have a cloned task with the first model....

How have you registered the second model ? Also can you share your logs ?

ok. Let's first be sure that your conf file is correct.

aws {
s3 {
key: "david"
secret: "supersecret"
use_credentials_chain: false

        credentials: [
            {
                 # This will apply to all buckets in this host (unless key/value is specifically provided for a given bucket)
                 host: "localhost:9000"
                 key: "david"
                 secret: "supersecret"
                 mul...

Hey UnevenDolphin73
When you use the parameter "use_current_task" the dataset and the resulting task will be the same (same id). So to retrieve this dataset for using it into another task, use Task.get(...) to retrieve its id.
Then when you will need it into another task, simply retrieve it from within that task by using Dataset.get(dataset_id=...)

Hey
There is a cache limit that you can change by modifying the conf file.
You simply add this to clearml.conf :

storage {
cache {
default_cache_manager_size: 100
}
}
(100 is the defasult value)

Depending on what you need to achieve, there are more advanced cache management tools.

hi NervousFrog58
Can you share some more details with us please ?
Do you mean that when you have an experiment failing, you would like to have a snippet that reset and relaunch it, the way you do through the UI ?
Your ClearML packages version, and your logs would be very userful too 🙂

Ok. We'll try to reproduce this. If you can send a snippet/example that could help. Anyway we'll keep you updated

hey @<1523704089874010112:profile|FloppyDeer99>
did you manage to get rid of your issue ?
thanks