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This allows users to execute Python scripts directly within SQL for one-stop AI data processing. DataArtsFabric SQL provides a visualized interface and a JDBC driver for easy interaction with existing applications and third-party tools.
Data engineers, data scientists, and AI application developers can collaborate efficiently using familiar tools within a unified workbench, accelerating workflows from development to production.
A larger value (for example, 0.8) leads to a more random output, while a smaller value (for example, 0.2) results in a more centralized and deterministic output. top_p The nucleus sampling strategy, which is used to control the range of tokens the AI model considers based on the cumulative
A larger value (for example, 0.8) leads to a more random output, while a smaller value (for example, 0.2) results in a more centralized and deterministic output. top_p The nucleus sampling strategy, which is used to control the range of tokens the AI model considers based on the cumulative
Default Value: N/A. top_p No Double Definition: Nucleus sampling strategy, which is used to control the range of tokens the AI model considers based on the cumulative probability. If the value is 0, the model considers only the token with the largest logarithmic probability.
Data and AI workloads share a single copy of data, eliminating the need for data replication. Out-of-the-box, elastic, and on-demand resources Mainstream open-source third-party large model inference services are pre-configured.
APU: NPU-based compute unit oriented to AI scenarios. Specifications The differences between DPU resource specifications, such as fabric.ray.dpu.d1x, fabric.ray.dpu.d2x, and fabric.ray.dpu.d4x, lie in the number of CPUs and memory size.
Large Model Inference Process DataArtsFabric provides you with the entire AI development process from data preparation to model deployment in serverless mode. At each stage of the process, you can use DataArtsFabric independently.
LLAMA_3.1_70B Llama3.1 is the first publicly available model and is close to top AI models in terms of common sense, steerability, mathematics, tool usage, and multilingual translation.
Pricing varies by Data Processing Unit (DPU) or AI Compute Unit (ACU) specifications. Both yearly/monthly and pay-per-use billing modes are available. Model compute unit hours Billing is based on the compute unit hours consumed by model instances deployed on inference endpoints.
Default Value: null. npu_resource No Integer Definition: Number of Huawei Cloud AI processing units (PUs). Constraints: N/A. Range: [0, 8]. Default Value: null.
Default Value: null. npu_resource No Integer Definition: Number of Huawei Cloud AI processing units (PUs). Constraints: N/A. Range: [0, 8]. Default Value: null.
Examples of DataFrames with UDFs Scenario In AI data engineering, data preprocessing is a crucial step, typically involving complex cleansing, transformation, and feature engineering operations on data stored in databases.
Pricing varies by Data Processing Unit (DPU) or AI Compute Unit (ACU) specifications. Both yearly/monthly and pay-per-use billing modes are available. Model compute unit hours Billing is based on the compute unit hours consumed by model instances deployed on inference endpoints.
Leveraging the powerful computational capabilities of the DataArts Fabric SQL kernel, it offers easy-to-use and efficient data processing functions for data scientists and AI engineers.
Adaptive Parallelism Scenario In AI data engineering scenarios involving massive data processing, it is necessary to call actors in parallel to enhance data processing efficiency and conduct distributed computing.
