{"_id":"58dcf01c1f8bff1900b4b5b9","githubsync":"","user":"575e85ac41c8ba0e00259a44","version":{"_id":"5773dcfc255e820e00e1cd50","__v":26,"project":"5773dcfc255e820e00e1cd4d","createdAt":"2016-06-29T14:36:44.812Z","releaseDate":"2016-06-29T14:36:44.812Z","categories":["5773dcfc255e820e00e1cd51","5773df36904b0c0e00ef05ff","577baf92451b1e0e006075ac","577bb183b7ee4a0e007c4e8d","577ce77a1cf3cb0e0048e5ea","577d11865fd4de0e00cc3dab","578e62792c3c790e00937597","578f4fd98335ca0e006d5c84","578f5e5c3d04570e00976ebb","57bc35f7531e000e0075d118","57f801b3760f3a1700219ebb","5804d55d1642890f00803623","581c8d55c0dc651900aa9350","589dcf8ba8c63b3b00c3704f","594cebadd8a2f7001b0b53b2","59a562f46a5d8c00238e309a","5a2aa096e25025003c582b58","5a2e79566c771d003ca0acd4","5a3a5166142db90026f24007","5a3a52b5bcc254001c4bf152","5a3a574a2be213002675c6d2","5a3a66bb2be213002675cb73","5a3a6e4854faf60030b63159","5c8a68278e883901341de571","5cb9971e57bf020024523c7b","5cbf1683e2a36d01d5012ecd"],"is_deprecated":false,"is_hidden":false,"is_beta":false,"is_stable":true,"codename":"","version_clean":"1.0.0","version":"1.0"},"parentDoc":null,"project":"5773dcfc255e820e00e1cd4d","__v":0,"category":{"_id":"577baf92451b1e0e006075ac","__v":0,"project":"5773dcfc255e820e00e1cd4d","version":"5773dcfc255e820e00e1cd50","sync":{"url":"","isSync":false},"reference":false,"createdAt":"2016-07-05T13:01:06.540Z","from_sync":false,"order":6,"slug":"developer-hub","title":"Developer tools"},"metadata":{"title":"","description":"","image":[]},"updates":[],"next":{"pages":[],"description":""},"createdAt":"2017-03-30T11:46:36.158Z","link_external":false,"link_url":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"settings":"","auth":"required","params":[],"url":""},"isReference":false,"order":15,"body":"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](doc:set-computation-instances). See the AWS page on instance types for details on pricing.\n\n## List of instances with ephemeral storage\n\n| Name        | Cores | RAM [GB]    | Storage [GB] | \n|-------------|-------|-------------|--------------| \n| c3.large    | 2     | 3.75        | 32           | \n| c3.xlarge   | 4     | 7.5         | 80           | \n| c3.2xlarge  | 8     | 15          | 160          | \n| c3.4xlarge  | 16    | 30          | 320          | \n| c3.8xlarge  | 32    | 60          | 640          | \n| i2.xlarge   | 4     | 30.5        | 800          | \n| i2.2xlarge  | 8     | 61          | 1600         | \n| i2.4xlarge  | 16    | 122         | 3200         | \n| i2.8xlarge  | 32    | 244         | 6400         | \n| m3.medium   | 1     | 3.75        | 4            | \n| m3.large    | 2     | 7.5         | 32           | \n| m3.xlarge   | 4     | 15          | 80           | \n| m3.2xlarge  | 8     | 30          | 160          | \n| r3.large    | 2     | 15          | 32           | \n| r3.xlarge   | 4     | 30.5        | 80           | \n| r3.2xlarge  | 8     | 61          | 160          | \n| r3.4xlarge  | 16    | 122         | 320          | \n| r3.8xlarge  | 32    | 244         | 640          | \n| x1.16xlarge | 64    | 976         | 1920         | \n| x1.32xlarge | 128   | 1952        | 1920         | \n\n## List of instances with variable attached EBS storage\n\n* The default attached storage size is 1 TB, but it can be changed to anything between 2GB and 4 TB.\n* Learn more from our [EBS Customization Documentation](doc:set-computation-instances#section-set-attached-storage-size)\n\n| Name        | Cores | RAM [GB] |\n|-------------|-------|----------|\n| c4.large    | 2     | 3.75     |\n| c4.xlarge   | 4     | 7.5      |\n| c4.2xlarge  | 8     | 15       |\n| c4.4xlarge  | 16    | 30       |\n| c4.8xlarge  | 36    | 60       |\n| c5.large    | 2     | 4        |\n| c5.xlarge   | 4     | 8        |\n| c5.2xlarge  | 8     | 16       |\n| c5.4xlarge  | 16    | 32       |\n| c5.9xlarge  | 36    | 72       |\n| c5.18xlarge | 72    | 144      |\n| m4.large    | 2     | 8        |\n| m4.xlarge   | 4     | 16       |\n| m4.2xlarge  | 8     | 32       |\n| m4.4xlarge  | 16    | 64       |\n| m4.10xlarge | 40    | 160      |\n| m4.16xlarge | 64    | 256      |\n| m5.large    | 2     | 8        |\n| m5.xlarge   | 4     | 16       |\n| m5.2xlarge  | 8     | 32       |\n| m5.4xlarge  | 16    | 64       |\n| m5.12xlarge | 48    | 192      |\n| m5.24xlarge | 96    | 384      |\n| r4.large    | 2     | 15.25    |\n| r4.xlarge   | 4     | 30.5     |\n| r4.2xlarge  | 8     | 61       |\n| r4.4xlarge  | 16    | 122      |\n| r4.8xlarge  | 32    | 244      |\n| r4.16xlarge | 64    | 488      |\n| r5.large | 2\t| 16 |\n| r5.xlarge | 4\t| 32 |\n| r5.2xlarge | 8 | 64 |\n| r5.4xlarge | 16 | 128 |\n| r5.12xlarge | 48 | 384 |\n| r5.24xlarge | 96 | 768 |\n\n## GPU Instances \nCavatica 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.\n[block:parameters]\n{\n  \"data\": {\n    \"h-0\": \"Name\",\n    \"h-1\": \"GPUs\",\n    \"h-2\": \"vCPUs\",\n    \"h-3\": \"RAM (GiB)\",\n    \"0-0\": \"p2.xlarge\",\n    \"0-1\": \"1\",\n    \"0-2\": \"4\",\n    \"0-3\": \"61\",\n    \"1-0\": \"p2.8xlarge\",\n    \"2-0\": \"p2.16xlarge\",\n    \"1-1\": \"8\",\n    \"1-2\": \"32\",\n    \"1-3\": \"488\",\n    \"2-1\": \"16\",\n    \"2-2\": \"64\",\n    \"2-3\": \"732\",\n    \"3-0\": \"p3.2xlarge\",\n    \"4-0\": \"p3.8xlarge\",\n    \"5-0\": \"p3.16xlarge\",\n    \"3-1\": \"1 Tesla v100\",\n    \"4-1\": \"4 Tesla v100\",\n    \"5-1\": \"8 Tesla v100\",\n    \"3-2\": \"8\",\n    \"4-2\": \"32\",\n    \"5-2\": \"64\",\n    \"3-3\": \"61\",\n    \"4-3\": \"244\",\n    \"5-3\": \"488\"\n  },\n  \"cols\": 4,\n  \"rows\": 6\n}\n[/block]\n\nCreating 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](https://www.khronos.org/opencl/) or [CUDA](https://developer.nvidia.com/cuda-zone). NVIDIA drivers come preinstalled and optimized according to the [Amazon best practice](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/optimize_gpu.html) 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/](https://hub.docker.com/r/nvidia/cuda/), and for tools that are based on OpenCL at [https://hub.docker.com/r/nvidia/opencl](https://hub.docker.com/r/nvidia/opencl). The rest of the procedure for [creating and uploading](doc:upload-your-docker-image-1) 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.\n\nWhen 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.","excerpt":"","slug":"list-of-available-amazon-web-services-instances","type":"basic","title":"List of available Amazon Web Services US East instances"}

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](doc:set-computation-instances). See the AWS page on instance types for details on pricing. ## List of instances with ephemeral storage | Name | Cores | RAM [GB] | Storage [GB] | |-------------|-------|-------------|--------------| | c3.large | 2 | 3.75 | 32 | | c3.xlarge | 4 | 7.5 | 80 | | c3.2xlarge | 8 | 15 | 160 | | c3.4xlarge | 16 | 30 | 320 | | c3.8xlarge | 32 | 60 | 640 | | i2.xlarge | 4 | 30.5 | 800 | | i2.2xlarge | 8 | 61 | 1600 | | i2.4xlarge | 16 | 122 | 3200 | | i2.8xlarge | 32 | 244 | 6400 | | m3.medium | 1 | 3.75 | 4 | | m3.large | 2 | 7.5 | 32 | | m3.xlarge | 4 | 15 | 80 | | m3.2xlarge | 8 | 30 | 160 | | r3.large | 2 | 15 | 32 | | r3.xlarge | 4 | 30.5 | 80 | | r3.2xlarge | 8 | 61 | 160 | | r3.4xlarge | 16 | 122 | 320 | | r3.8xlarge | 32 | 244 | 640 | | x1.16xlarge | 64 | 976 | 1920 | | x1.32xlarge | 128 | 1952 | 1920 | ## List of instances with variable attached EBS storage * The default attached storage size is 1 TB, but it can be changed to anything between 2GB and 4 TB. * Learn more from our [EBS Customization Documentation](doc:set-computation-instances#section-set-attached-storage-size) | Name | Cores | RAM [GB] | |-------------|-------|----------| | c4.large | 2 | 3.75 | | c4.xlarge | 4 | 7.5 | | c4.2xlarge | 8 | 15 | | c4.4xlarge | 16 | 30 | | c4.8xlarge | 36 | 60 | | c5.large | 2 | 4 | | c5.xlarge | 4 | 8 | | c5.2xlarge | 8 | 16 | | c5.4xlarge | 16 | 32 | | c5.9xlarge | 36 | 72 | | c5.18xlarge | 72 | 144 | | m4.large | 2 | 8 | | m4.xlarge | 4 | 16 | | m4.2xlarge | 8 | 32 | | m4.4xlarge | 16 | 64 | | m4.10xlarge | 40 | 160 | | m4.16xlarge | 64 | 256 | | m5.large | 2 | 8 | | m5.xlarge | 4 | 16 | | m5.2xlarge | 8 | 32 | | m5.4xlarge | 16 | 64 | | m5.12xlarge | 48 | 192 | | m5.24xlarge | 96 | 384 | | r4.large | 2 | 15.25 | | r4.xlarge | 4 | 30.5 | | r4.2xlarge | 8 | 61 | | r4.4xlarge | 16 | 122 | | r4.8xlarge | 32 | 244 | | r4.16xlarge | 64 | 488 | | r5.large | 2 | 16 | | r5.xlarge | 4 | 32 | | r5.2xlarge | 8 | 64 | | r5.4xlarge | 16 | 128 | | r5.12xlarge | 48 | 384 | | r5.24xlarge | 96 | 768 | ## 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. [block:parameters] { "data": { "h-0": "Name", "h-1": "GPUs", "h-2": "vCPUs", "h-3": "RAM (GiB)", "0-0": "p2.xlarge", "0-1": "1", "0-2": "4", "0-3": "61", "1-0": "p2.8xlarge", "2-0": "p2.16xlarge", "1-1": "8", "1-2": "32", "1-3": "488", "2-1": "16", "2-2": "64", "2-3": "732", "3-0": "p3.2xlarge", "4-0": "p3.8xlarge", "5-0": "p3.16xlarge", "3-1": "1 Tesla v100", "4-1": "4 Tesla v100", "5-1": "8 Tesla v100", "3-2": "8", "4-2": "32", "5-2": "64", "3-3": "61", "4-3": "244", "5-3": "488" }, "cols": 4, "rows": 6 } [/block] 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](https://www.khronos.org/opencl/) or [CUDA](https://developer.nvidia.com/cuda-zone). NVIDIA drivers come preinstalled and optimized according to the [Amazon best practice](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/optimize_gpu.html) 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/](https://hub.docker.com/r/nvidia/cuda/), and for tools that are based on OpenCL at [https://hub.docker.com/r/nvidia/opencl](https://hub.docker.com/r/nvidia/opencl). The rest of the procedure for [creating and uploading](doc:upload-your-docker-image-1) 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.