List of available Amazon Web Services US East instances

As a CAVATICA user, you have access to the AWS instances listed below. Chose an instance type from the list below and specify it as a value for the sbg:AWSInstanceType hint to set it as your preferred computation instance. See the AWS page on instance types for details on pricing.

List of instances with ephemeral storage

NameCoresRAM [GB]Storage [GB]Network Performance (Gbps)
i2.xlarge430.5800Moderate
i2.2xlarge8611600Moderate
i2.4xlarge161223200High
i2.8xlarge32244640010 Gigabit
i3.large215.251 x 0.475 NVMe SSDUp to 10
i3.xlarge430.51 x 0.95 NVMe SSDUp to 10
i3.2xlarge8611 x 1.9 NVMe SSDUp to 10
i3.4xlarge161222 x 1.9 NVMe SSDUp to 10
i3.8xlarge322444 x 1.9 NVMe SSD10
i3.16xlarge644888 x 1.9 NVMe SSD25
i3en.large2161 x 1.25 NVMe SSDUp to 25
i3en.xlarge4321 x 2.5 NVMe SSDUp to 25
i3en.2xlarge8642 x 2.5 NVMe SSDUp to 25
i3en.3xlarge12961 x 7.5 NVMe SSDUp to 25
i3en.6xlarge241922 x 7.5 NVMe SSD25
i3en.12xlarge483844 x 7.5 NVMe SSD50
i3en.24xlarge967688 x 7.5 NVMe SSD100
x1.16xlarge64976192010
x1.32xlarge1281952192025

List of instances with variable attached EBS storage

NameCoresRAM [GB]Auto-scheduling*EBS Bandwidth (Mbps)Network Performance
c4.large23.75Yes500Moderate
c4.xlarge47.5Yes750High
c4.2xlarge815Yes1000High
c4.4xlarge1630Yes2000High
c4.8xlarge3660Yes400010 Gigabit
c5.large24YesUp to 4,750Up to 10
c5.xlarge48YesUp to 4,750Up to 10
c5.2xlarge816YesUp to 4,750Up to 10
c5.4xlarge1632Yes4,750Up to 10
c5.9xlarge3672Yes9,50010
c5.12xlarge4896Yes9,50012
c5.18xlarge72144No19,00025
c5.24xlarge96192No19,00025
m4.large28Yes450Moderate
m4.xlarge416Yes750High
m4.2xlarge832Yes1000High
m4.4xlarge1664Yes2000High
m4.10xlarge40160Yes400010 Gigabit
m4.16xlarge64256No1000025 Gigabit
m5.large28YesUp to 4,750Up to 10
m5.xlarge416YesUp to 4,750Up to 10
m5.2xlarge832YesUp to 4,750Up to 10
m5.4xlarge1664Yes4,750Up to 10
m5.8xlarge32128Yes6,80010
m5.12xlarge48192Yes9,50012
m5.16xlarge64256No13,60020
m5.24xlarge96384No19,00025
r4.large215.25YesUp to 10
r4.xlarge430.5YesUp to 10
r4.2xlarge861YesUp to 10
r4.4xlarge16122YesUp to 10
r4.8xlarge32244Yes10
r4.16xlarge64488No25
r5.large216YesUp to 4,750Up to 10
r5.xlarge432YesUp to 4,750Up to 10
r5.2xlarge864YesUp to 4,750Up to 10
r5.4xlarge16128Yes4,750Up to 10
r5.8xlarge32256Yes6,80010
r5.12xlarge48384Yes9,50012
r5.16xlarge64512No13,60020
r5.24xlarge96768No19,00025

* Instances labelled with Yes in the auto-scheduling column are the instances that are selected for task execution automatically based on the defined CPU and memory requirements. To be able to use instances that are not available for automatic scheduling, you must set the instance type explicitly using the sbg:AWSInstanceType hint.

GPU Instances

CAVATICA 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)

Network Bandwidth

EBS Bandwidth

p2.xlarge

1

4

61

High

p2.8xlarge

8

32

488

10 Gbps

p2.16xlarge

16

64

732

20 Gbps

p3.2xlarge

1 Tesla v100

8

61

Up to 10 Gbps

1.5 Gbps

p3.8xlarge

4 Tesla v100

32

244

10 Gbps

7 Gbps

p3.16xlarge

8 Tesla v100

64

488

25 Gbps

14 Gbps

g4dn.xlarge

1

4

16

Up to 25 Gbps

Up to 3.5 Gbps

g4dn.2xlarge

1

8

32

Up to 25 Gbps

Up to 3.5 Gbps

g4dn.4xlarge

1

16

64

Up to 25 Gbps

4.75 Gbps

g4dn.8xlarge

1

32

128

50 Gbps

9.5 Gbps

g4dn.16xlarge

1

64

256

50 Gbps

9.5 Gbps

g4dn.12xlarge

4

48

192

50 Gbps

9.5 Gbps

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.

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.


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