if so is there any doc/examples about this?

Good point, passing to docs 🙂
https://github.com/allegroai/clearml/blob/51af6e833ddc5a8ba1efaaf75980f58616b25e85/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py#L123
I mean it is mentioned, but we should highlight it better

@<1671689437261598720:profile|FranticWhale40> could you test the fix? just pull & run

allegroai/clearml-serving-triton:1.3.1
allegroai/clearml-serving-inference:1.3.1

making me realize that this may have been optional

I think it is optional, and this is why it was not entered in the first place.
Could you double check and just remove it from your manual pbtxt ?

Thanks @<1671689437261598720:profile|FranticWhale40> !
I was able to locate the issue, fix should be released later today (or worst case tomorrow)

Hi @<1671689437261598720:profile|FranticWhale40>
Are you positive the Triton container finished syncing ?
Could you provide the docker log (both the serving and the triton)?
What is the clearml-serving version you are using ?
Could you add a print in the "preprocess" function, just to validate you are getting to the correct model version ?

we also provide a custom

aux-config

file. We also had to make sure to update the name inside

config.pbtxt

so that Triton is happy:

Good point, what would be the logic of the auto "config.pbtxt" patching we should employ ?

Ohh I see, so basically the ASG should check if the agent is Idle, rather than the Task is running ?

Okay that kind of makes sense, now my followup question is how are you using the ASG? I mean the clearml autoscaler does not use it, so I just wonder on what the big picture, before we solve this little annoyance 🙂

SparklingHedgehong28 this is actually quite cool! Still not sure why not just use the built in autoscaler https://github.com/allegroai/clearml/tree/master/examples/services/aws-autoscaler , but it is a really cool usage of ASG 🤩

@<1523706266315132928:profile|DefiantHippopotamus88> seems like you are missing the ports 🙂

CLEARML_WEB_HOST="

"
CLEARML_API_HOST="

"
CLEARML_FILES_HOST="

"

If possible, can we have a "only one experiment can be given a single tag"

You mean "moving a tag" automatically (i.e. if someone else had the same tag it is removed from it)?

Try to add here:
None

server_info['url'] = f"http://{server_info['hostname']}:{server_info['port']}/"

Hi @<1691620877822595072:profile|FlutteringMouse14>

Do I have to use Hydra

You can, and then the entire configuration is fully captured by ClearML (automatically) while you can still override values with the manual "key.sub=value" both in the UI and in the CLI

Otherwise you can connect nested dict with task.connect (these will be flattened with / for sub keys).
Or you can connect configuration files ( task.connect_configuration ) and edit them as is in the UI (with override of...

It seems something is wrong with the server itself...

repeat it until they are all dead 🙂

Thanks @<1634001106403069952:profile|DefeatedMole42>
A follow up, (1) how are you spinning the agent ? (2) could it be the docker image "ultralytics/yolov5" does not have Bash as entry point ?
you can force that with

@PipelineDecorator.component(return_values=['int'], cache=False,
                             task_type='training',
                             docker="ultralytics/yolov5",
                             docker_args="--entrypoint /bin/bash",
                             pa...

(i.e. importing the trains package is enough to patch the argparser, only when you call the task.init the arguments will be logged, before they are stored in memory)

Hi @<1547390415320125440:profile|SilkySparrow85>

because it is trying to send a debug-sample to fileserver!

Yes, you should always configure the "files server" to point to your minio S3, basically:
None

files_server: "

"

But do not forget to also configure the credentials here:
[None](https://github.com/allegroai/clearml/blob/40c6db9d95016382c721546d42...

(I'll make sure we reply on the issue as well later)

adding the functionality to clearml-task sounds very attractive!

Hmm, what do you think?
parser.add_argument('--configuration', type=str, default=None, help='Specify local configuration file' ) parser.add_argument('--configuration-name', type=str, default=None, help='configuration section name' ) ... with open(args.configuration, 'rt') as f: create_populate.task.set_configuration_object(args.name, config_text=f.read())Add h...

Hi ContemplativePuppy11
This is really interesting point.
Maybe you can provide a pseudo class abstract of your current pipeline design, this will help in trying to understand what you are trying to achieve and how to make it easier to get there

DilapidatedDucks58 long story short:
if you do:
` from clearml import StorageManager
from clearml.storage.helper import StorageHelper

StorageHelper.get(" ", retries=5) `It should make sure that all the other s3:// links of this bucket will use the same original configuration (i.e. retries)

If this workaround works let's make sure we add it into the conf file, wdyt ?

🙂

Let me know if I can be of help 🙂

it’s not implemented right,

I think we forgot to add it as an argument (the query models supports it, but it is not passed to the call)

JitteryCoyote63 Not sure how/why the X-Pack feature was on (it is not used by the system), but you can disable it with an environment variable in the docker-compose
xpack.security.enabled=falseShould solve the problem ...

From here:
https://medium.com/@ospaarmann/tidbits-solving-the-elasticsearch-x-pack-license-issue-in-docker-d15bb22d82fd

Or you want to generate it from a previously executed run?

https://allegro.ai/clearml/docs/docs/faq/faq.html?highlight=clearml_config_file#clearml-configuration

give me a minute to test

What's the clearml-server version ?