Get Pods
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Authorizations
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Response
Unique identifier for the pod, generated as a UUID.
ID of the user associated with this pod, if applicable.
ID of the team owning this pod, if applicable.
ID of the wallet associated with this pod for billing or resource tracking.
Name of the pod.
Type of the pod, based on PodTypeEnum.
HOSTED, EXTERNAL Type of provider associated with the pod, based on ProviderTypeEnum.
runpod, fluidstack, lambdalabs, hyperstack, oblivus, cudocompute, scaleway, tensordock, datacrunch, latitude, crusoecloud, massedcompute, akash, primeintellect, primecompute, dc_impala, dc_kudu, dc_roan Current status of the pod, based on PodStatusEnum.
PROVISIONING, PENDING, ACTIVE, STOPPED, ERROR, TERMINATED Installation status of the pod, based on InstallationStatusEnum.
PENDING, ACTIVE, FINISHED, ERROR, TERMINATED Details about any installation failures that occurred, if applicable.
Percentage of the installation process completed.
Timestamp when the pod was created.
Timestamp when the pod was last updated.
Model of the GPU allocated.
CPU_NODE, A10_24GB, A100_80GB, A100_40GB, A30_24GB, A40_48GB, RTX3070_8GB, RTX3070_8GB, RTX3080_10GB, RTX3080Ti_12GB, RTX3090_24GB, RTX3090Ti_24GB, RTX4070Ti_12GB, RTX4080_16GB, RTX4080Ti_16GB, RTX4090_24GB, H100_80GB, H200_96GB, H200_141GB, GH200_480GB, GH200_624GB, L4_24GB, L40_48GB, L40S_48GB, RTX4000_8GB, RTX5000_16GB, RTX6000_24GB, RTX8000_48GB, RTX4000Ada_20GB, RTX5000Ada_32GB, RTX6000Ada_48GB, A2000_6GB, A4000_16GB, A4500_20GB, A5000_24GB, A6000_48GB, V100_16GB, V100_32GB, P100_16GB, T4_16GB, P4_8GB, P40_24GB Number of GPUs allocated to the node.
Password for accessing the Jupyter environment on the pod, if applicable.
Type of socket used by the GPU.
PCIe, SXM2, SXM3, SXM4, SXM5 Hourly price for running the pod.
Hourly price when the pod is stopped. If empty then priceHr is used.
Hourly price during the provisioning process. If empty then priceHr is used.
Base hourly price for the pod. If the base currency is set.
Currency in which the base price is calculated.
Type of image selected for the pod.
ubuntu_22_cuda_12, cuda_12_1_pytorch_2_2, cuda_11_8_pytorch_2_1, cuda_12_1_pytorch_2_3, cuda_12_1_pytorch_2_4, cuda_12_4_pytorch_2_4, stable_diffusion, axolotl, bittensor, hivemind, petals_llama, vllm_llama_8b, vllm_llama_70b, vllm_llama_405b, custom_template, flux ID of the custom template applied to the pod, if applicable.
Port mapping.
TCP, UDP SSH, JUPYTER_NOTEBOOK, WEB_SERVER SSH connection/connections details.
IP address/addresses of the instance.
Instance attached resources.
Type of resource.
DISK Unique identifier for the resource.
Resource status.
UNATTACHED, ATTACHED, MOUNTED Whether the resource is detachable.
Path to the mount point.
Path to the resource.
Size of the resource.
Whether the instance is spot.
Automatically restart the instance.
Total number of items available in the dataset
Number of items to skip before starting to collect the result set
x > 0Maximum number of items to return
x > 0