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List of available Amazon Web Services US instances

The list below shows available AWS instances that you can choose and specify as a value for the sbg:AWSInstanceType hint. All instances listed below are available in both US East (N.Virginia) and US West (Oregon) regions that can be selected as project locations on the Platform.

See the AWS page on instance types for details on pricing.

List of instances with ephemeral storage

NameCoresRAM [GB]Storage [GB]
i2.xlarge430.5800
i2.2xlarge8611600
i2.4xlarge161223200
i2.8xlarge322446400
x1.16xlarge649761920
x1.32xlarge12819521920

List of instances with variable attached EBS storage

NameCoresRAM [GB]
c4.large23.75
c4.xlarge47.5
c4.2xlarge815
c4.4xlarge1630
c4.8xlarge3660
c5.large24
c5.xlarge48
c5.2xlarge816
c5.4xlarge1632
c5.9xlarge3672
c5.12xlarge4896
c5.18xlarge72144
c5.24xlarge96192
m4.large28
m4.xlarge416
m4.2xlarge832
m4.4xlarge1664
m4.10xlarge40160
m4.16xlarge64256
m5.large28
m5.xlarge416
m5.2xlarge832
m5.4xlarge1664
m5.8xlarge32128
m5.12xlarge48192
m5.16xlarge64256
m5.24xlarge96384
r4.large215.25
r4.xlarge430.5
r4.2xlarge861
r4.4xlarge16122
r4.8xlarge32244
r4.16xlarge64488
r5.large216
r5.xlarge432
r5.2xlarge864
r5.4xlarge16128
r5.8xlarge32256
r5.12xlarge48384
r5.16xlarge64512
r5.24xlarge96768

GPU Instances

The Platform also supports the following powerful, scalable GPU instances that deliver high performance compute in the cloud. Designed for general-purpose GPU compute applications using CUDA and OpenCL, these instances are ideally suited for machine learning, molecular modeling, genomics, rendering, and other workloads requiring massive parallel floating point processing power.

Name

GPUs

vCPUs

RAM (GiB)

p2.xlarge

1

4

61

p2.8xlarge

8

32

488

p2.16xlarge

16

64

732

p3.2xlarge

1 Tesla v100

8

61

p3.8xlarge

4 Tesla v100

32

244

p3.16xlarge

8 Tesla v100

64

488

Creating Docker images containing tools that are run on GPU instances is similar to the process of creating Docker images with tools that are designed for CPU instances. The only major difference is that GPU tools have additional requirements for interaction with GPUs, which can be either OpenCL or CUDA. NVIDIA drivers come preinstalled and optimized according to the Amazon best practice for the specific instance family and are accessible from the Docker container. It is recommended to use one of Docker images provided by NVIDIA as the base image. For tools that require CUDA, the list of supported images are available at https://hub.docker.com/r/nvidia/cuda/, and for tools that are based on OpenCL at https://hub.docker.com/r/nvidia/opencl. The rest of the procedure for creating and uploading a Docker image is the same as for tools designed to run on CPU instances. In case you have any problems with the setup, please contact our Support Team.

When creating a Docker image containing GPU tools, it should be taken into account that older binaries are usually built for older GPU architectures and might not work on newer GPUs. If that is the case, these binaries can’t be used, and new ones should be built from source code.

Updated about a month ago

List of available Amazon Web Services US instances


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