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AI servers will inevitably experience hardware failures. As resource pools grow in large-scale compute scenarios, the likelihood of such failures increases. These issues can disrupt services on affected nodes.
The methods of accessing notebook instances vary depending on the AI engine based on which the instance was created. Remote access: Use PyCharm, VS Code, or SSH in the local IDE. For details, see Connecting to a Notebook Instance Through VS Code Toolkit and .
KooSearch can power knowledge Q&A capabilities for a wide range of intelligent applications, such as intelligent customer service, virtual humans, digital employees, AI assistants, and AI search.
Introduction to Alluxio Application Development Introduction to Alluxio Alluxio is an open source data orchestration technology for analytics and AI for the cloud.
In this way, AI agents can communicate with thousands of external tools and data more efficiently and conveniently. Platform Architecture The agent development platform provides one-stop AI application building capabilities.
In the AI task performance enhanced scheduling pane, select whether to enable DRF. This function helps you enhance the service throughput of the cluster and improve service running performance. Click Confirm. Parent Topic: AI Performance-based Scheduling
Managing Prompts on KooSearch Prompts are carefully crafted inputs designed to guide AI models in generating specific, high-quality outputs. They align the models' responses with user intent.
Gang is mainly used in scenarios that require multi-process collaboration, such as AI and big data scenarios.
It offers a zero-code orchestration tool to help developers quickly create AI applications and uses canvas-based node design to cope with complex service scenarios.
Check the AI frameworks available for training. This step is the same as 5 for debugging a single-node training job. Save the current notebook instance as a new image. This step is the same as 9 for debugging a single-node training job.
Check the AI frameworks that can be used for training. from modelarts.estimatorV2 import Estimator Estimator.get_framework_list(session) session is the initialized data in 1. Skip this step if the AI framework has been specified.
Inference Deployment Process You can import and deploy AI models as inference services. These services can be integrated into your IT platform by calling APIs or generate batch results.
Streaming Response Overview To meet the requirements of web and AI applications for real-time data transmission and large packet transmission, you can configure the streaming response for the function to return response packets to the client in HTTP streaming mode.
Changing a Notebook Instance Image ModelArts allows you to change images on a notebook instance to flexibly adjust its AI engine. Constraints The target notebook instance is stopped. Procedure Log in to the ModelArts management console.
The following device parameters need to be added for the Atlas 500 to load the Atlas 200 AI accelerator module: docker run \ -it \ --device=/dev/davinci_manager \ --device=/dev/hisi_hdc \ --device=/dev/davinci0 \ myapp Parent topic: Deploying the Image
The following device parameters need to be added for the Atlas 500 to load the Atlas 200 AI accelerator module: docker run \ -it \ --device=/dev/davinci_manager \ --device=/dev/hisi_hdc \ --device=/dev/davinci0 \ myapp Parent topic: Deploying the Image
Obtaining the Sample Program Open source code samples are available for the software components and inference services of the Ascend AI processor to improve the code development efficiency. You can refer to the code samples for code development.
Obtaining the Sample Program Open source code samples are available for the software components and inference services of the Ascend AI processor to improve the code development efficiency. You can refer to the code samples for code development.
Application Scenarios Big Data and AI Computing Currently, most big data and AI training applications (such as TensorFlow and Caffe) run in containerized mode. These applications are GPU intensive and require high-performance network and storage.
Alluxio Alluxio is data orchestration technology for analytics and AI for the cloud. In the MRS big data ecosystem, Alluxio lies between computing and storage.
