Yes correct . In fact we cannot see any old datasets

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."

Looks like it's picking up the projects but then viewing on the UI they disappear

Hi SuccessfulKoala55 yes I can see the one upload using 1.6.1 but all old datasets have now been remove. I guess you want people to start moving over?

yes exactly

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

the agent it for replicating what you run locally elsewhere i.e. remote GPU machine

I've got it... i just remembered I can call
task_idfrom the cloned tasked and check the status of that 🙂

Hi yes all sorted ! 🙂

thank you guys 😄 😄

nope you'll just need to install clearml

I also noticed that my queue stats haven't been updated since 7/1/2022 @ 12:41am

Okay great thanks SuccessfulKoala55

Hi AgitatedDove14 ,

I noticed that ClearML parses clearml.automation.UniformParameterRange to configuration space to be used with BOHB. When I've used BOHB previously I can use UniformFloatHyperparameter from the configuration space package that allows me to set a parameter in logspace. That is the range is defended by something like numpy.logspace rather than numpy.linspace

cool thanks - I'll try it out

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

I'll let you know what happens

This was the error I was getting from uploads using the old SDK
has been rejected for invalid domain. heap-2443312637.js:2:108655 Referrer Policy: Ignoring the less restricted referrer policy "no-referrer-when-downgrade" for the cross-site request:

great thank you

` python upload_data_to_clearml_copy.py
Generating SHA2 hash for 1 files
100%|████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 733.91it/s]
Hash generation completed
0%| | 0/1 [00:00<?, ?it/s]
Compressing local files, chunk 1 [remaining 1 files]
100%|████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 538.77it/s]
File compression completed: t...

It was the CORS like error we were getting

(deepmirror) ryan@ryan:~$ python -c "import clearml print(clearml.__version__)" 1.1.4

`
import os
import glob
from clearml import Dataset

DATASET_NAME = "Bug"
DATASET_PROJECT = "ProjectFolder"
TARGET_FOLDER = "clearml_bug"
S3_BUCKET = os.getenv('S3_BUCKET')

if not os.path.exists(TARGET_FOLDER):
os.makedirs(TARGET_FOLDER)

with open(f'{TARGET_FOLDER}/data.txt', 'w') as f:
f.writelines('Hello, ClearML')

target_files = glob.glob(TARGET_FOLDER + "/**/*", recursive=True)

# upload dataset

dataset = Dataset.create(dataset_name=DATASET_NAME, dataset_project=DATASET_PR...

<queues.GetQueueMetricsResponse: { "queues": [] }>

I'm sure it used to be in task.artifacts but that's returning an empty dict

prev_task.artifacts {}

Yes on the apps page is the possible to tigger programatically?

remote execution is working now. Internal worker nodes had not spun up the agent correctly 😛

Yes, it's the dependencies. At the moment I'm doing this as a work around.
` autoscaler = AwsAutoScaler(hyper_params, configurations)

startup_bash_script = [
    '...',
]

autoscaler.startup_bash_script = startup_bash_script ` I'd prefer to run it on the Web UI. Also, we seem to have problems when it's executed remotely

yep using the community version

this is the response. We are using the clearml hosted server

In short we clone the repo, build the docker container, and run agent in the container. The reason we do it this, rather than provide a docker image to the clearml-agent is two fold:

We actively develop our custom networks and architectures within a containerised env to make it easy for engineers to have a quick dev cycle for new models. (same repo is cloned and we build the docker container to work inside) We use the same repo to serve models on our backend (in a slightly different contain...