code_configuration - 在部署期間,會從開發環境上傳本機檔案,例如評分模型的 Python 來源。 如需關於 YAML 結構描述的詳細資訊,請參閱線上端點 YAML 參考。 注意 若要以 Kubernetes 端點而非受控線上端點做為計算目標,則必須: 使用Azure Machine Learning 工作室建立Kubernetes 叢集做為計算目標,並將其連結至 Azure...
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ExplainaBoard "a tool that inspects your system outputs, identifies what is working and what is not working, and helps inspire you with ideas of where to go next.” explainerdashboard "Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learn...
Later, to build the environment, the deployment uses the image (in this example, it's mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest) for the base image, and the conda_file dependencies are installed on top of the base image. code_configuration - during deployment, the local ...
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If the external script verification step was successful, you can run R or Python commands from SQL Server Management Studio, Visual Studio Code, or any other client that can send T-SQL statements to the server. If you got an error when you ran the command, you might need to make addition...
Introducing Deep Learning with MATLAB Integrating Deep Learning into System-Level Design Deep Learning Tutorials and Examples with MATLAB Select a Web Site Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you sele...
Some software requires you to install NVIDIA CUDA to use your codespace’s GPU. Where this is the case, you can create your own custom configuration, by using adevcontainer.jsonfile, and specify that CUDA should be installed. For more information on creating a custom configuration,...
During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challeng
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical