Jad Aboul Hosn

> ```python > import numpy as np > vals= np.abs(shap_values).mean(0) > > feature_importance = pd.DataFrame(list(zip(features.columns, sum(vals))), columns=['col_name','feature_importance_vals']) > feature_importance.sort_values(by=['feature_importance_vals'], ascending=False,inplace=True) > feature_importance.head() > ``` For anyone seeing this more recently,...

> @david-waterworth I will look into this, perhaps some ordering issue with the additional_args vs. log_info workaround. Please continue using the workaround. I will update the thread once I have...

> @FrikadelleHelle no I didn't manage to get it to work, I suspect because it doesn't fit the general sagemaker interface. The other thing that's frustrated me is triton has...

> Hi, I am running into a similar issue. Did you find a fix? @VirginieBfd I haven't heard anything back from the Triton team. Are you also running on SageMaker...

@Tabrizian we've found the root cause. As advised by conda-pack, the OS used to create an environment must match the hosting OS. In addition, these environments must be created within...

> Could you share more around the objective you are trying to acheive. And also the exact failure you are seeing? @nskool, I'm looking for the ability to unload models...

@dyastremsky another interested customer here, any updates about prioritizing this feature?

@anshudaur check [this issue](https://github.com/triton-inference-server/server/issues/7121#issuecomment-2070971047) for more details. I believe this should unblock you as well.

> All changes for SageMaker are upstreamed to Triton's Github Repo - https://github.com/triton-inference-server/server, and so - > > The SM-Triton image is essentially the same image as NGC container, with...

@Tabrizian adding my vote to this ticket. Debugging Python models is challenging, even after importing the triton_python_backend_utils. For example, the new TritonErrors are not defined within the python module, and...