Description of image

clusters

Validated on 14 Dec 2023 • Last edited on 8 Jul 2024
Note
A new Paperspace API is now available. The Gradient and Core API endpoints will become unavailable on 15 July, 2024. For more information, see the release notes.

list

List your team clusters

Examples

gradient clusters list

curl -g -X GET 'https://api.paperspace.io/clusters/getClusters?filter={"limit": 20, "offset": 0, "where": {"isPrivate": true}}' \
-H 'x-api-key: d44808a2785d6a...'

from gradient import ClustersClient

api_key = 'd44808a2785d6a...'

clusters_client = ClustersClient(api_key)

print(clusters_client.list())

Options

Name Type Attributes Description
--limit integer optional Limit listed clusters per page
--offset integer optional Offset value
--apiKey string optional API key to use this time only
--optionsFile string optional Path to YAML file with predefined options
--createOptionsFile string optional Generate template options file

Response

+-----------+------------------+----------------------------+
| ID        | Name             | Type                       |
+-----------+------------------+----------------------------+
| cl9w..... | demo-cluster     | Kubernetes Processing Site |
+-----------+------------------+----------------------------+

[
  {
    id: "cl9w.....",
    name: "demo-cluster",
    type: "Kubernetes Processing Site",
    region: "private",
    cloud: "paperspace-cloud",
    teamId: "tewr3st2z",
    isDefault: false,
    dtCreated: "2020-04-22T20:39:24.004Z",
    dtModified: "2021-07-16T21:02:47.433Z",
    ...
  },
]

[
  Cluster(
    (id = "cl9w....."),
    (name = "demo-cluster"),
    (type = "Kubernetes Processing Site")
  ),
];

machineTypes list

List available machine types

Examples

gradient clusters machineTypes list

curl -X GET 'https://api.paperspace.io/vmTypes/getVmTypesByClusters' \
-H 'x-api-key: d44808a2785d6a...'

from gradient import MachineTypesClient

api_key = 'd44808a2785d6a...'

machineTypes_client = MachineTypesClient(api_key)

print(machineTypes_client.list())

Options

Name Type Attributes Description
--clusterId string optional Filter machine types by cluster ID
--apiKey string optional API key to use this time only
--optionsFile string optional Path to YAML file with predefined options
--createOptionsFile string optional Generate template options file

Response

+-------------+--------------+-----------+--------------+-----------+--------------+--------------------------------------------+
| Name        | Kind         | CPU Count | RAM [Bytes]  | GPU Count | GPU Model    | Clusters                                   |
+-------------+--------------+-----------+--------------+-----------+--------------+--------------------------------------------+
| P4000       | p4000        | 8         | 32212254720  | 1         | Quadro P4000 | cl9w.....                                  |
| P5000       | p5000        | 8         | 32212254720  | 1         | Quadro P5000 | cl9w.....                                  |
| V100        | v100         | 8         | 32212254720  | 1         | Tesla V100   | cl9w.....                                  |
+-------------+--------------+-----------+--------------+-----------+--------------+--------------------------------------------+

[
  VmType(
    (label = "P4000"),
    (kind = "p4000"),
    (cpu_count = 8),
    (ram_in_bytes = 32212254720),
    (gpu_count = 1),
    (gpu_model = VmTypeGpuModel(
      (label = "Quadro P4000"),
      (model = "passthrough"),
      (memory_in_bytes = 8589934592)
    )),
    (is_preemptible = False),
    (deployment_type = "gpu"),
    (deployment_size = "small"),
    (clusters = ["cl9w....."])
  ),
  VmType(
    (label = "P5000"),
    (kind = "p5000"),
    (cpu_count = 8),
    (ram_in_bytes = 32212254720),
    (gpu_count = 1),
    (gpu_model = VmTypeGpuModel(
      (label = "Quadro P5000"),
      (model = "passthrough"),
      (memory_in_bytes = 17179869184)
    )),
    (is_preemptible = False),
    (deployment_type = "gpu"),
    (deployment_size = "medium"),
    (clusters = ["cl92....."])
  ),
  VmType(
    (label = "V100"),
    (kind = "v100"),
    (cpu_count = 8),
    (ram_in_bytes = 32212254720),
    (gpu_count = 1),
    (gpu_model = VmTypeGpuModel(
      (label = "Tesla V100"),
      (model = "passthrough"),
      (memory_in_bytes = 17179869184)
    )),
    (is_preemptible = False),
    (deployment_type = "gpu"),
    (deployment_size = "large"),
    (clusters = ["cl9w....."])
  ),
];

[
  VmType(
    (label = "P4000"),
    (kind = "p4000"),
    (cpu_count = 8),
    (ram_in_bytes = 32212254720),
    (gpu_count = 1),
    (gpu_model = VmTypeGpuModel(
      (label = "Quadro P4000"),
      (model = "passthrough"),
      (memory_in_bytes = 8589934592)
    )),
    (is_preemptible = False),
    (deployment_type = "gpu"),
    (deployment_size = "small"),
    (clusters = ["cl9w....."])
  ),
  VmType(
    (label = "P5000"),
    (kind = "p5000"),
    (cpu_count = 8),
    (ram_in_bytes = 32212254720),
    (gpu_count = 1),
    (gpu_model = VmTypeGpuModel(
      (label = "Quadro P5000"),
      (model = "passthrough"),
      (memory_in_bytes = 17179869184)
    )),
    (is_preemptible = False),
    (deployment_type = "gpu"),
    (deployment_size = "medium"),
    (clusters = ["cl92....."])
  ),
  VmType(
    (label = "V100"),
    (kind = "v100"),
    (cpu_count = 8),
    (ram_in_bytes = 32212254720),
    (gpu_count = 1),
    (gpu_model = VmTypeGpuModel(
      (label = "Tesla V100"),
      (model = "passthrough"),
      (memory_in_bytes = 17179869184)
    )),
    (is_preemptible = False),
    (deployment_type = "gpu"),
    (deployment_size = "large"),
    (clusters = ["cl9w....."])
  ),
];
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