Current implementation of Go binding can not specify options.

GPUOptions struct is in internal package. And go generate doesn't work for protobuf directory. So we can't specify GPUOptions for NewSession.

Feature request

We currently have 2 monocular depth estimation models in the library, namely DPT and GLPN.

It would be great to have a pipeline for this task, with the following API:

from transformers import pipeline

pipe = pipeline("depth-estimation")
pipe("ca

What happened + What you expected to happen

1. Getting a ton of warnings from pytorch-lightning about deprecation of certain hooks.
2. Expect no warnings.
3. The TuneCallback has old hooks such as keyboard_interrupt which have been deprecated.
   The most list of the deprecated hooks

I've ran into this issue for a couple hours and I ended up editing the dist library adding two new functions called fetchVideo and bufferToVideo that works pretty much like the fetchImage and bufferToImage functions.

I'll leave it here to help somebody else with the same issue and in case someone wants to include it on future releases.

face-api.js

...
exports.fetchVideo = fetc

Adding a Dataset

• Name: Stanford dog dataset
• Description: The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/
• Paper: http://vision.stanford.edu/aditya86/ImageNetDogs/
• Data: *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/Ima

Feature Request

System information

ONNX version (you are using): 1.12

What is the problem that this feature solves?

It would be great if ONNX Hub can download test_data_set. It will be helpful for testing ONNX Model Zoo.

Describe the feature

Currently users can only download models through ONNX Hub. There is no easy API for them to download the test_data_set from.tar.gz

Bug with GPU Model

Currently, while using pruning methods like TaylorFOWeight Pruner, If I use a model on GPU for getting the metrics (as calculated for getting masks), it fails on line while creating masks. The reason why it fails i

/kind bug

What steps did you take and what happened:
Right now the Notebooks controller will be appending new Pod conditions to the Notebook's status https://github.com/kubeflow/kubeflow/blob/master/components/notebook-controller/controllers/notebook_controller.go#L247.

This means that a Notebook could end up with the following conditions being present at the same time:

        {