Thanks Martin 🙂 just seen this worked as expected

ahhh okay thank you 🙂

Hi SuccessfulKoala55 thanks I didn't know it was possible to use in place of the pw. So in the .conf I can just add the git PAT instead of pw?

git_user: ${GITHUB_USER} git_pass: ${GITHUB_PAT}

Okay thanks for the update 🙂 the account manager got involved and the limit has been approved 🚀

if it's completed or not

Thanks JitteryCoyote63 , I'll double check the permissions of key/secrets and if no luck I'll check with the team

Hey having a few issues with this

I can run clearml.OutputModel(task, framework='pytorch') to get the model from a previous task. but how can I get the pytorch model ( torch.nn.Module ) from the output model object

We use albumentations with scripts that execute remotely and have no issues. Good question from CostlyOstrich36

` # dataset_class.py
from PIL import Image
from torch.utils.data import Dataset as BaseDataset

class Dataset(BaseDataset):

def __init__(
    self,
    images_fps,
    masks_fps,
    augmentation=None,
):
    self.augmentation = augmentation
    self.images_fps = images_fps
    self.masks_fps = masks_fps
    self.ids = len(images_fps)


def __getitem__(self, i):

    # read data
    img = Image.open(self.images_fps[i])
    mask = Image...

we normally do something like that - not sure what why it's freezing for you without more info

dw got it https://clearml.slack.com/archives/CTK20V944/p1634131822017500

Umm no luck

q = client.queues.get_all(name='default')[0] from_date = math.floor(datetime.timestamp(datetime.now() - relativedelta(months=3))) to_date = math.floor(datetime.timestamp(datetime.now())) res = client.queues.get_queue_metrics(from_date=from_date, to_date=to_date, interval=1, queue_ids=[q.id])

Yep figured this out yesterday. I had been tagging G type instances with an alarm as a fail safe if the AWS autoscaler failed. The alarm only stopped the instance and didn't terminate it (which deletes the drive). Thanks anyway CostlyOstrich36 and TimelyPenguin76 🙂

next one I'm looking for is average queue time

Thanks SuccessfulKoala55

the Model object

` client.queues.get_default()

Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/opt/conda/lib/python3.9/site-packages/clearml/backend_api/session/client/client.py", line 378, in new_func
return Response(self.session.send(request_cls(*args, **kwargs)))
File "/opt/conda/lib/python3.9/site-packages/clearml/backend_api/session/client/client.py", line 122, in send
raise APIError(result)
clearml.backend_api.session.client.client.APIError: APIError: code 4...

I was having an issue with availability zone. I was using 'eu-west-2' instead of 'eu-west-2c'

so I don't think it's an access issue

Same with new version
(deepmirror) ryan@ryan:~/GitHub/deepmirror/ml-toolbox$ python -c "import clearml; print(clearml.__version__)" 1.6.1
Generating SHA2 hash for 1 files 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2548.18it/s] Hash generation completed Uploading dataset changes (1 files compressed to 130 B) to BUCKET File compression and upload completed: total size 130 B, 1 chunked stored (average size 130 B)

Hi SuccessfulKoala55 I gave up after 20 mins and also got a notification from firefox "This page is slowing down Firefox. The speed up your browser, stop this page". I'm heading out soon so I could leave it on. Also, had the same behaviour in chrome.

For reference
import subprocess for i in ['1', '2']: command = ['python', 'hyp_op.py', '--testnum', f'{i}'] process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)

When I run in the UI I get the following response
Error: Can not start new instance, An error occurred (InvalidParameterValue) when calling the RunInstances operation: Invalid availability zone: [eu-west-2]When I run programatically it just stalls and I don't get any read out

Just for ref if anyone has this issue. I had to update my cuda drivers to 510 on system os

` docker run --gpus=0 -it nvcr.io/nvidia/tritonserver:22.02-py3

=============================
== Triton Inference Server ==

NVIDIA Release 22.02 (build 32400308)

Copyright (c) 2018-2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION. All rights reserved.

This container image and its contents are gove...

$ curl -X 'POST' ' ' -H 'accept: application/json' -H 'Content-Type: application/json' -d '{ "url": " " }' {"digit":5}

I'm using "allegroai/clearml-serving-triton:latest" container I was just debugging using the base image

From SuccessfulKoala55 suggestion

it would be useful to have some control over this if possible 🙂

Going for something like this:
` >>> queue = QueueMetrics(queue='queueid')

queue.avg_waiting_times `