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Kunpeng AI Inference-accelerated ECSs

Updated on 2024-11-12 GMT+08:00

Kunpeng AI inference-accelerated ECSs are designed to provide acceleration services for AI services. These ECSs are provided with the Ascend AI Processors and Ascend AI Software Stack.

Kunpeng AI inference-accelerated ECSs use Huawei-developed Ascend 310 processors for AI inference acceleration.

Table 1 Kunpeng AI Inference-accelerated ECSs

Series

Compute

Disk Type

Network

kAi1s

  • vCPU to memory ratio: 1:1 or 1:2
  • Number of vCPUs: 4 to 48
  • Kunpeng 920 processor
  • Base frequency: 2.6 GHz
  • High I/O
  • General Purpose SSD
  • Ultra-high I/O
  • Extreme SSD
  • General Purpose SSD V2
  • Ultra-high packets per second (PPS) throughput
  • An ECS with higher specifications has better network performance.
  • Maximum PPS: 2,000,000
  • Maximum intranet bandwidth: 12 Gbit/s
NOTE:

The driver and CANN used by kAi1s ECSs only support version 21.0.2 (3.0.1) and cannot be upgraded.

Kunpeng Enhanced AI Inference-accelerated kAi1s (Type I)

Overview

Kunpeng AI inference-accelerated kAi1s 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), facilitating the wide application of AI inference. kAi1s ECSs deliver the computing acceleration capabilities of the Ascend 310 processors on the cloud platform. This helps you quickly and simply use the Ascend 310 processors.

kAi1s ECSs are based on Atlas 300I accelerator cards. For details, see Ascend Community.

kAi1s ECSs are used for general technologies, such as computer vision, speech recognition, and natural language processing to support smart retail, smart campus, robot cloud brain, and safe city scenarios.

Specifications

Table 2 kAi1s ECS specifications

Flavor

vCPUs

Memory

(GiB)

Max./Assured Bandwidth (Gbit/s)

Max. PPS

(10,000)

Max. NIC Queues

Max. NICs

Ascend 310 Processors

Virtualization

kai1s.xlarge.1

4

4

3/0.8

20

2

2

1

KVM

kai1s.2xlarge.1

8

8

4/1.5

40

2

3

2

KVM

kai1s.4xlarge.1

16

16

6/3

80

4

4

4

KVM

kai1s.3xlarge.2

12

24

8/4

100

4

4

4

KVM

kai1s.4xlarge.2

16

32

10/6

140

4

5

6

KVM

kai1s.6xlarge.2

24

48

12/8

200

8

6

8

KVM

kai1s.9xlarge.2

36

72

12/8

200

8

6

12

KVM

kai1s.12xlarge.2

48

96

12/8

200

16

6

12

KVM

Features

kAi1s ECSs have the following features:

  • 1:1 or 1:2 ratio of vCPUs to memory
  • CPU: Kunpeng 920 (2.6 GHz)
  • Ascend 310 processors, four of which in an Atlas 300I accelerator card
  • 8 TeraFLOPS of half-precision computing (FP16) on one processor
  • 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
  • Built-in hardware video codec engine, supporting H.264/H.265

Notes

  • kAi1s ECSs support the following OSs:
    • Ubuntu Server 18.04 64bit
    • CentOS 7.6 64-bit
  • kAi1s ECSs support automatic recovery when the hosts accommodating such ECSs become faulty.

Using a kAi1s ECS

Perform the following steps:

  1. Create an ECS. For details, see Purchasing a Custom ECS.
    • In the Specifications field, select kAi1s-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 ECS runs Linux, use an SSH password to log in to the ECS. For details, see Login Using an SSH Password.

  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.

Helpful Links

Ascend Documentation: provides developers with common Ascend development tools to help you learn and use Ascend.

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