Doing this might actually help with the previous issue as well, because when there are multiple docker containers running they might interfere with each other 🙂

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 🙂

Wow! Awesome to hear :D

Not exactly sure what is going wrong without an exact error or reproducible example.

However, passing around the dataset object is not ideal, because passing info from one step to another in a pipeline requires ClearML to pickle said object and I'm not exactly sure a Dataset obj is picklable.

Next to that, running get_local_copy() in the first step does not guarantee that you can access that data from the other step. Both might be executed in different docker containers or even on different...

What might also help is to look inside the triton docker container while it's running. You can check the example, there should be a pbtxt file in there. Just to doublecheck that it is also in your own folder

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

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 🙂

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 🙂

Can you try setting the env variables to 1 instead of True ? In general, those should indeed be the correct variables to set. For me it works when I start the agent with the following command:

CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1 CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1 clearml-agent daemon --queue "demo-queue"

That might make things harder indeed 🙂 It does explain some things

The above works for me, so if you try and the command line version does not work, there might be a bug. Please post the exact commands you use when you try it 🙂

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 ...

From any computer that has ClearML serving installed. It is simply used to submit configurations, not actually run anything. Please refer to the step by step setup here for more info 🙂

Hi ExuberantParrot61 ! Can you try using a wildcard? E.g. ds.remove_files(dataset_path='folder_to_delete/*')

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!

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

What kind of machine are you running this on?

I've passed it through, we'll update the docs, thanks!!

It seems to be up for me

Here is an example of deploying an sklearn model using ClearML serving.

However, please note that sklearn-like models don't have input and output shapes in the same sense as deep learning models have. Setting the I/O shapes using the CLI is usually meant for GPU-based deep learning models that need to know the sizes for better GPU allocation. In the case of sklearn on CPU, all you have to do is set up your preprocess...

I added a reply to one of the issues 🙂 edit: answered both issues, the third issue is the same as your question here on slack.

Hey! So several things here:

As per the plotly docs, you have to give which axis you want to format, in your example plotly can't know. If you look closely to their example, you'll see it's a nested dict, with the key being 'xaxis' Your numpy data has x values of 1 2 3, but your extra layout has values 1 3 4 which don't match. Plotly took the first element of each subarray to be the x value.
If we fix these 2 things, your example becomes:
` task.logger.report_line_plot('this_is_the_title',
...

Interesting! I'm glad to know it's working now, only I now really want to know what caused it 😄 Let me know if you ever do find out!

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.

Can you run

ls -la /opt/clearml

and paste the results here?

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

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...

Is it the IP of the agent?

Ah I see. So then I would guess it is due to the remote machine (the clearml agent) not being able to properly access your clearml server

Ok I check 3: The command
clearml-serving --id <your_id> model add --engine triton --endpoint "test_model_keras" --preprocess "examples/keras/preprocess.py" --name "train keras model" --project "serving examples" --input-size 1 784 --input-name "dense_input" --input-type float32 --output-size -1 10 --output-name "activation_2" --output-type float32should be
` clearml-serving --id <your_id> model add --engine triton --endpoint "test_model_keras" --preprocess "examples/keras/preprocess.py" ...