am I getting it right that alias = dataset id which can be found in the clearml dashboard?
Not really. It's Alias + dataset_id that will be found in the clearml dashboard 🙂 I'm attaching a screenshot of what that looks like in both the code and the dashboard
Also, the answer to blocking on the pipeline might be in the .wait() function: https://clear.ml/docs/latest/docs/references/sdk/automation_controller_pipelinecontroller#wait-1
TimelyPenguin76 I can't seem to make it work though, on which object should I run the .wait() method?
Hey PanickyMoth78
Here is an easy to reproduce, working example. Mind the multi_instance_support=True parameter in the pipeline itself. This code launches 3 pipelines for me just as it should 🙂
` from clearml.automation.controller import PipelineDecorator
import time
PipelineDecorator.set_default_execution_queue("default")
@PipelineDecorator.component()
def step_one():
time.sleep(2)
@PipelineDecorator.component()
def step_two():
time.sleep(2)
@PipelineDecorator.pipel...
Hi PanickyMoth78 , I have made a minimal example and indeed adding multi_instance_support=True prevents ClearML from killing the process, allowing you to launch pipelines in a loop 🙂
Thanks again for the extra info Jax, we'll take it back to our side and see what we can do 🙂
Hi Jax! Thanks for the feedback, we really appreciate it 😄
MLFlow is able to support a multitude of models from dev to deployment. Is ClearML and ClearML-Serving going to support as much as well?
Do you mean by this that you want to be able to seamlessly deploy models that were tracked using ClearML experiment manager with ClearML serving?
I believe in such scenarios, a custom engine would be required. I would like to know, how difficult is it to create a custom engine wit...
Well I'll be had, you're 100% right, I can recreate the issue. I'm logging it as a bug now and we'll fix it asap! Thanks for sharing!!
Hi LackadaisicalDove24 !
Does this happen with every csv file? If so, I can reproduce it to check if it is a bug 🙂
Just to be sure I understand you correctly: you're saving/dumping an sklearn model in the clearml experiment manager, then want to serve it using clearml serving, but you do not wish to specify the model input and ouput shapes in the CLI?
AgitatedDove14 I was able to recreate the error. Simply by running Lavi's example on clearml==1.6.3rc1 in a fresh env. I don't know what is unique to the flow itself, but it does seem reproducible
Hey ExasperatedCrocodile76 ! Thanks for checking back in and letting me know 😄 Glad I could help!
Hi @<1533257278776414208:profile|SuperiorCockroach75> , the clearml experiment manager will try to detect your package requirements from its original environment. Meaning that if you run the code and it imports e.g. SQLAlchemy, then it will log the exact version of SQLAlchemy you have installed locally.
When you run only get_data,py locally and have the experiment manager track it, can you then look at the task that is made in the clearml webUI and check the installed packages section? ...
Hey! Sorry, didn't fully read your question and missed that you already did it. It should not be done inside the clearm-serving-triton service but instead inside the clearml-serving-inference service. This is where the preprocessing script is ran and it seems to be where the error is coming from.
Well, that's what open source is for 😉 code borrowing is like 90% of the job of software engineers 😄
That's a good idea! I think the YOLO models would be a great fit for a tutorial/example like this. We can add it to our internal list of TODOs, or if you want, you could take a stab at it and we'll try to support you through it 🙂 It might take some engineering though! Serving is never drag and drop 🙂
That said, I think it should be quite easy to do since YOLOv8 supports exporting to tensorrt format, which is native to Triton serving which underlies ClearML serving. So the process shoul...
Hey @<1523701949617147904:profile|PricklyRaven28> I'm checking! Have you updated anything else and on which exact commit of transformers are you now?
Hey! Thanks for all the work you're putting in and the awesome feedback 😄
So, it's weird you get the shm error, this is most likely our fault for not configuring the containers correctly 😞 The containers are brought up using the docker-compose file, so you'll have to add it in there. The service you want is called clearml-serving-triton , you can find it [here](https://github.com/allegroai/clearml-serving/blob/2d3ac1fe63637db1978df2b3f5ea4903ef59788a/docker/docker-...
Hi @<1547028116780617728:profile|TimelyRabbit96> Awesome that you managed to get it working!
You will have to provide more information. What other docker containers are running and how did you start the server?
Hey @<1526371965655322624:profile|NuttyCamel41> Thanks for coming back on this and sorry for the late reply. This looks like a bug indeed, especially because it seems to be working when coming from the clearml servers.
Would you mind just copy pasting this info into a github issue on clearml-serving repo? Then we can track the progress we make at fixing it 🙂
Yes, you will indeed need to add all ensemble endpoints separately 🙂
I'm not quite sure what you mean here? From the docs it seems like you should be able to simply send an HTTP request to the localhost url to get the metrics. Is this not working for you? Otherwise, all the metrics end up in Prometheus, so you can also query that instead or use something like Grafana to visualize it
That would explain why it reports the task id to be 'a' in the error. It tried to index the first element in a list, but took the first character of a string instead.
Wow awesome! Really nice find! Would you mind compiling your findings to a github issue, then we can help you search better :) this info is enough to get us going at least!
When I run the example this way, everything seems to be working.
