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.

` # 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

Can you try to go into 'Settings' -> 'Configuration' and verify that you have 'Show Hidden Projects' enabled

Hi yes all sorted ! 🙂

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

queue.avg_waiting_times `

Usually that is shown here... for some reason it's not showing

next one I'm looking for is average queue time

When I get home I'll get the stats to you 👍 thank you :)

thank you guys 😄 😄

echo -e $(aws ssm --region=eu-west-2 get-parameter --name 'my-param' --with-decryption --query "Parameter.Value") | tr -d '"' > .env set -a source .env set +a git clone https://${PAT}@github.com/myrepo/toolbox.git mv .env toolbox/ cd toolbox/ docker-compose up -d --build docker exec -it $(docker-compose ps -q) clearml-agent daemon --detached --gpus 0 --queue default

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

Okay great thanks SuccessfulKoala55

Nope AWS aren't approving the increased vCPU request. I've explained the use case several times and they've not approved

This was the response from AWS:

"Thank you for for sharing the requested details with us. As we discussed, I'd like to share that our internal service team is currently unable to support any G type vCPU increase request for limit increase. 

The issue is we are currently facing capacity scarcity to accommodate P and G instances. Our engineers are working towards fixing this issue. However, until then, we are unable to expand the capacity and process limit increase."

AgitatedDove14 is any working on a GCP or Azura autoscaler at the moment?

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

oh okay

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

The latest commit to the repo is 22.02-py3 ( https://github.com/allegroai/clearml-serving/blob/d15bfcade54c7bdd8f3765408adc480d5ceb4b45/clearml_serving/engines/triton/Dockerfile#L2 ) I will have a look at versions now 🙂

Okay just for clarity...

Originally, my Nvidia drivers were running on an incompatible version for the triton server
This container was built for NVIDIA Driver Release 510.39 or later, but version 470.103.01 was detected and compatibility mode is UNAVAILABLE.
To fix this issue I updated the drivers on my base OS i.e.
sudo apt install nvidia-driver-510 -y sudo reboot
Then it worked. The docker-compose logs from clearml-serving-triton container did not make this clear (i.e. by r...

It might only be a req for the docker/docker-compose-triton-gpu.yml file but I'd need to check

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

I'll add a more detailed response once it's working

I can raise this as an issue on the repo if that is useful?

ahhh okay thank you 🙂

Still debugging.... That fixed the issue with the
nvcr.io/nvidia/tritonserver:22.02-py3 container which now returns
` =============================
== 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 governed by the NVIDIA Deep Learning Co...

okay so this could be a python script that generates the clearml.conf in the working dir in the container?

great thank you it's working. Just wanted to check before adding all env vars 🙂

thanks for the speedy response!