This update was just to modernize the example itself 🙂

What kind of machine are you running this on?

I'll update you once I have more!

Ping 😄 https://clear.ml/blog/nvidia-tao-clearml-tutorial/

Wow! Awesome to hear :D

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

Hi CourageousKoala93 ! Have you tried https://clear.ml/docs/latest/docs/references/sdk/task#set_comment by any chance? There's a description field under the info tab 🙂

Hi OddShrimp85

Do you have some more information than that? It could be a whole list of things 🙂

Hi Fawad!
You should be able to get a local mutable copy using Dataset.get_mutable_local_copy and then creating a new dataset.
But personally I prefer this workflow:

dataset = Dataset.get(dataset_project=CLEARML_PROJECT, dataset_name=CLEARML_DATASET_NAME, auto_create=True, writable_copy=True) dataset.add_files(path=save_path, dataset_path=save_path) dataset.finalize(auto_upload=True)
The writable_copy argument gets a dataset and creates a child of it (a new dataset with your ...

Can you please post the result of running df -h in this chat? Chances are quite high your actual machine does indeed have no more space left 🙂

Hi Adib!
I saw this question about the datastores before and it was answered then with this:
Redis is used for caching so it's fairly 'lightly' used, you don't need many resources for it. Mongo is for artifacts, system info and some metadata. Elastic is for events and logs, this one might require more resources depending on your usage.Hope it can already help a bit!

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, with docker auto-starting containers is def a thing 🙂 We set the containers to restart automatically (a reboot will do that too) for when the container crashes it will immediately restarts, let's say in a production environment.

So the best thing to do there is to use docker ps to get all running containers and then kill them using docker kill <container_id> . Chatgpt tells me this command should kill all currently running containers:
docker rm -f $(docker ps -aq)And I...

Hi @<1534344450795376640:profile|VividSwallow28> ! I've seen your github issue and will answer you there 🙂 I'll leave a link here for others facing the same issue.

Hi GrittyHawk31 ! ClearML is integrated with a bunch of frameworks from which it tries to automatically gather information. You can find a list here: https://clear.ml/docs/latest/docs/integrations/libraries

For example, if you're already reporting scalars to tensorboard, you won't have to add any clearml code, it will automatically be captured. The same will happen with e.g. LightGBM. Take a look at the example codes in the link to find what is automatically supported for your framework.
...

Please do, if you find any more issues (due to my shitty code or otherwise 😄 ) let me know and I'll fix 'em!

With what error message did it fail? I would expect it to fail, because you finalized this version of your dataset by uploading it 🙂 You'll need a mutable copy of the dataset before you can remove files from it I think, or you could always remove the file on disk and create a new dataset with the uploaded one as a parent. In that way, clearml will keep track of what changed in between versions.

Hmm I think we might have made it more clear in the documentation then? How would you have been helped before you figured it out? (great job BTW, thanks for the updates on it :))

Thank you so much, sorry for the inconvenience and thank you for your patience! I've pushed it internally and we're looking for a patch 🙂

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

If you didn't use git, then clearML saves your .py script completely in the uncommited changes section like you say. You should be able to just copy paste it to get the code. In what format are your uncommited changes logged? Can you paste a screenshot or paste the contents of uncommitted changes ?

I tried answering them as well, let us know what you end up choosing, we're always looking to make clearml better for everyone!

Yeah, I do the same thing all the time. You can limit the amount of tasks that are kept in HPO with the save_top_k_tasks_only parameter and you can create subprojects by simply using a slash in the name 🙂 https://clear.ml/docs/latest/docs/fundamentals/projects#creating-subprojects

I can see 2 kinds of errors:
Error: Failed to initialize NVML and Unable to allocate pinned system memory, pinned memory pool will not be available: CUDA driver version is insufficient for CUDA runtime version
These 2 lines make me think something went wrong with the GPU itself. Chances are you won't be able to run nvidia-smi this looks like a non-clearml issue 🙂 It might be that triton hogs the GPU memory if not properly closed down (doubl ctrl-c). It says the driver ver...

This looks to me like a permission issue on GCP side. Do your GCP credentials have the compute.images.useReadOnly permission set? It looks like the worker needs that permission to be able to pull the images correctly 🙂

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...

Allright, a bit of searching later and I've found 2 things:

• You were right about the task! I've staged a fix here . It basically detects whether a task is already running (e.g. from the pipelinedecorator component) and if so, uses that task instead. We should probably do this for all of our integrations.
• But then I found another bug. Basically the pipeline decorator task wou...

And maybe also what kind of docker install do you have given that you changed off of snap?

I've updated the repo 🙂

I'm using image and machine image interchangeably here. It is quite weird that it is still giving the same error, the error clearly asked for "Required 'compute.images.useReadOnly' permission for 'projects/image-processing/global/images/image-for-clearml'" 🤔

Also, now I see your credentials even have the role of compute admin, which I would expect to be sufficient.
I see 2 ways forward:

• Try running the autoscaler with the default machine image and see if it launches correctly
• Dou...