AI-accelerated ECSs

AI-accelerated ECS Types

AI inference-accelerated: Enhanced AI Inference-accelerated Ai2 (Type II) and Enhanced AI Inference-accelerated Ai1s (Type I)

Public Images Supported by AI-accelerated ECSs

Enhanced AI Inference-accelerated Ai2 (Type II)

Overview

Ai2 ECSs use Ascend 310P processors for AI acceleration. Featuring low power consumption and high computing power, Ascend 310P processors have significantly improved energy efficiency ratio (EER), promoting the wide application of AI inference. AI inference-accelerated Ai2 ECSs enable you to easily use the powerful processing capability of Ascend 310P processors.

Ai2 ECSs are ideal for computer vision, smart campus, smart city, smart transportation, smart retail, Internet-based real-time communication, and video encoding and decoding scenarios.

Specifications

Features

Ai2 ECSs have the following features:

• 1:4 ratio of vCPUs to memory
• CPU: 3rd Generation Intel® Xeon® Scalable 6348 processors (2.6 GHz of base frequency and 3.5 GHz of turbo frequency), or Intel® Xeon® Scalable 8378A processors (3.0 GHz of base frequency and 3.4 GHz of turbo frequency)
• Each Atlas 300I Pro accelerator card is equipped with one Ascend 310P processor, including eight DaVinci AI cores and eight self-developed CPU cores.
• 140 TeraOPS of integer-precision (INT8) on a single GPU
• 24 GiB of GPU memory with a bandwidth of 204.8 GiB/s on a single GPU
• Built-in hardware video codec engine for HD video decoder (H.264/265) and JPEG image encoding and decoding

Notes

1. Ai2 ECSs support the following public images:
   • Ubuntu Server 18.04 64bit
   • CentOS 7.8 64bit
   • CentOS 7.6 64bit
2. Ai2 ECSs do not support modification of specifications.
3. Ai2 ECSs support automatic recovery when the hosts accommodating such ECSs become faulty.

Enhanced AI Inference-accelerated Ai1s (Type I)

Overview

Ai1s ECSs use Ascend 310 processors for AI acceleration. Ascend 310 processors feature low power consumption, high computing capabilities, and significantly improved energy efficiency ratio (EER). This facilitates the wide application of AI inference. Ai1s ECSs deliver the computing acceleration capabilities of the Ascend 310 processors on the cloud platform.

Ai1s ECSs are based on Atlas 300I accelerator cards. For details, go to Ascend Community.

AI-accelerated ECSs are ideal for computer vision, smart campus, smart city, smart transportation, smart retail, Internet-based real-time communication, and video encoding and decoding scenarios.

Specifications

Features

Ai1s ECSs have the following features:

• vCPU to memory ratio: 1:4
• CPU: 2nd Generation Intel® Xeon® Scalable 6278 processors (2.6 GHz of base frequency and 3.5 GHz of turbo frequency), or Intel® Xeon® Scalable 6151 processors (3.0 GHz of base frequency and 3.4 GHz of turbo frequency)
• Ascend 310 processors, four of which in an Atlas300I accelerator card
• 16 TeraOPS of integer-precision computing (INT8) on one processor
• 8 GiB of GPU memory with a memory bandwidth of 50 GiB/s on one processor
• 5-channel HD video decoder (H.264/H.265) based on built-in hardware video codec engine

Notes

1. Ai1s ECSs support the following public images:
   • Ubuntu Server 18.04 64bit
   • CentOS 7.6 64bit
2. Ai1s ECSs do not support modification of specifications.
3. Ai1s ECSs support automatic recovery when the hosts accommodating such ECSs become faulty.

Using an AI-accelerated ECS

Perform the following steps:

1. Create an ECS. For details, see "Step 1: Configure Basic Settings" in the Elastic Cloud Server User Guide.
   • In the Specifications field, select AI-accelerated specifications.
   • In the Image field, select Public image or Private image.
     • Public image: The CANN 3.1.0 development kit has been included and environment variables have been configured in public images by default. You need to verify the environment availability.
     • Private image: You need to install the driver, firmware, and development kit, and configure environment variables by yourself. For details, see the CANN Software Installation Guide of the corresponding version in Ascend Documentation.
2. Remotely log in to the ECS.

   If your Ai1 ECS runs Linux, use an SSH password to log in to the ECS.

3. Verify the environment availability.

   Use a sample for compilation and running. For details, see "Sample Overview" in the Model Development Learning Map of the corresponding CANN edition in Ascend Documentation.

   The sample shows how to classify images (decode, resize, and infer images) based on the Caffe ResNet-50 network.