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

System information

ONNX version (you are using): develop

What is the problem that this feature solves?

Increase test coverage for ONNX Hub; Simplify existing download code by using ONNX Hub directly.

Describe the alternatives you have considered

Write additional code to test ONNX

Willingness to contribute

No. I cannot contribute a documentation fix at this time.

URL(s) with the issue

https://mlflow.org/docs/latest/tracking.html#artifact-stores

Description of proposal (what needs changing)

I wanted to link to what an “MLflow model URI” could be in a design doc, and searched for it in MLflow docs. It looks like we don’t actually describe supported

/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:

        {

Is there an existing integration?

• I have searched the existing integrations.

Use Case

This will allow to host the software on a serverless and stateless service. This will facilitate scaling because no critical data will be on the disk of the system running the application.

Motivation

Allow users to save resources and money.

Implementation

No response

Anything e

Typo under the description: Returns a containing. Returns a what?

Document Details

⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

• ID: d2dc315d-96d7-e54f-6e90-fec6ed09481c
• Version Independent ID: ab5d0a68-35d6-ef5f-786e-d89e7fee8034
• Content: [DataFrameColumn.Info Method (Microsoft.Data.Analysis)](https://docs.microsoft.com/e

Is your feature request related to a problem? Please describe.
In time series plotting module, lot of plots are customized at the end - template, fig size, etc. Since the same code is repeated in all these plots, maybe this could be modularized and reused.

with fig.batch_update():
    template = _resolve_dict_keys(
        dict_=fig_kwargs, key="template", defaults=fig_default

We currently have read and write capabilities but do not support deleting. We could add a few calls like delete delete_all and some recursive way of deleting.

🚨🚨 Feature Request

• A new implementation (Improvement, Extension)

Is your feature request related to a problem?

Currently, if a user tries to access an index that is larger than the dataset length or tensor length, an internal error is thrown which is not easy to understand.

Description of the possible solution

We can catch the error and throw a more descriptive e

In Ue format string it represent float with comma separator, it crash css style

To fix it you can Round/replace/incluse culture info

samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentiment.Client/Shared/HappinessScale.razor

你好，请问怎么装载 ONNX 模型，目前只看到 Oneflow->ONNX 工具，没有找到 ONNX->Oneflow 工具。

Users might have multiple AWS accounts/credentials, stored in .aws/credentials. In the AWS command line or Terraform one can easily specify which one to use at any given time.

aws s3 ls --profile non-default-profile

Or in TF:

provider "aws" {
  profile  = "non-default-profile"
}

Feast today would always use the default profile. A potential workaround involves defini

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?