# clusters

**Warning: Deprecated**: As of 15 July 2024, Gradient API endpoints are deprecated and no longer available for Paperspace users.

## list

List your team clusters

#### Examples

## CLI

```bash
gradient clusters list
```

## cURL

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

## Python

```python
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

## CLI

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

## cURL

```bash
[
  {
    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",
    ...
  },
]
```

## Python

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

## machineTypes list

List available machine types

#### Examples

## CLI

```bash
gradient clusters machineTypes list
```

## cURL

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

## Python

```python
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

## CLI

```text
+-------------+--------------+-----------+--------------+-----------+--------------+--------------------------------------------+
| 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..... |
+-------------+--------------+-----------+--------------+-----------+--------------+--------------------------------------------+
```

## cURL

```bash
[
  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....."])
  ),
];
```

## Python

```bash
[
  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....."])
  ),
];
```