chassis-cloud (Python)

Official Python client for the Chassis public API (/api/v1).

Default base URL: https://chassis.okeymeta.com.ng/api/v1

PyPI: chassis-cloud · import as chassis

Install

pip install chassis-cloud

Requires Python 3.10+ and httpx.

from chassis import Chassis

Auth

Create an API key in the Chassis console under API Keys. Keys look like chs_… and are shown once.

from chassis import Chassis

with Chassis(api_key="chs_...") as client:
    ...

Quickstart

from chassis import Chassis

with Chassis(api_key="chs_...") as client:
    gpus = client.list_gpus()
    for gpu in gpus:
        print(gpu["displayName"], gpu["pricePerHourUsd"], gpu.get("stockStatus"))

    instance = client.spin_up(
        gpuSkuId=gpus[0]["id"],
        name="gpu-host-01",
        imageName="ghcr.io/YOUR_ORG/your-gpu-app:latest",
        ports="8080/http,22/tcp",
    )

Example: list available GPUs

with Chassis(api_key="chs_...") as client:
    gpus = client.list_gpus()
    for gpu in gpus:
        print(
            gpu["id"],
            gpu.get("displayName"),
            gpu.get("memoryGb"),
            f"${gpu.get('pricePerHourUsd')}/hr",
            gpu.get("stockStatus"),
        )

    gpu = next(
        (g for g in gpus if "4090" in str(g.get("displayName", ""))),
        gpus[0],
    )
    # use gpu["id"] as gpuSkuId on spin_up / create_cluster / create_endpoint

Example: host a GPU service

Any CUDA app — APIs, media tools, notebooks, batch workers — not only training.

with Chassis(api_key="chs_...") as client:
    gpus = client.list_gpus()
    instance = client.spin_up(
        gpuSkuId=gpus[0]["id"],
        name="gpu-host-01",
        imageName="ghcr.io/YOUR_ORG/your-gpu-app:latest",
        containerDiskGb=50,
        ports="8080/http,22/tcp",
        env={"MODEL_ID": "your-model"},
    )
    detail = client.get_instance(instance["id"])
    print(detail.get("publicIp"), detail.get("connection"), detail.get("status"))
    # Point your clients at publicIp / published ports
    client.stop(instance["id"])

Example: training job

Spin up a dedicated GPU, run your trainer on the instance, then stop billing.

from chassis import Chassis

with Chassis(api_key="chs_...") as client:
    gpus = client.list_gpus()
    gpu = next(
        (g for g in gpus if "A100" in str(g.get("displayName", ""))),
        gpus[0],
    )

    instance = client.spin_up(
        gpuSkuId=gpu["id"],
        name="finetune-bert",
        gpuCount=1,
        imageName="pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel",
        containerDiskGb=80,
        ports="8888/http,22/tcp",
    )
    print("running", instance["id"], instance["status"])
    # SSH / Jupyter → run train.py on the instance
    client.stop(instance["id"])

Example: multi-node cluster

with Chassis(api_key="chs_...") as client:
    gpus = client.list_gpus()
    cluster = client.create_cluster(
        name="dist-train",
        gpuSkuId=gpus[0]["id"],
        nodeCount=4,
        gpusPerNode=1,
        imageName="pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel",
    )
    print(client.get_cluster(cluster["id"]))
    # Nodes receive CHASSIS_CLUSTER_ID, CHASSIS_NODE_RANK, CHASSIS_NODE_COUNT
    client.stop_cluster(cluster["id"])
    # client.terminate_cluster(cluster["id"])

Example: serverless endpoint

with Chassis(api_key="chs_...") as client:
    gpus = client.list_gpus()
    endpoint = client.create_endpoint(
        name="text-infer",
        gpuSkuId=gpus[0]["id"],
        workersMin=0,
        workersMax=3,
    )

    result = client.run_sync(
        endpoint["id"],
        body={
            "input": {
                "prompt": "Summarize Chassis in one sentence.",
                "max_tokens": 128,
            }
        },
    )
    print(result)

    job = client.run(endpoint["id"], body={"input": {"prompt": "hello"}})
    status = client.get_job(endpoint["id"], job["id"])
    print(status)

Instance options (create_instance / spin_up)

Methods

GPUs & instances

Clusters (multi-node)

Create with name, gpuSkuId, nodeCount (2–8), and optional gpusPerNode, imageName, networkVolumeId, ports. Nodes get CHASSIS_CLUSTER_ID, CHASSIS_NODE_RANK, and CHASSIS_NODE_COUNT.

Templates, volumes, registries

Serverless endpoints

Create options also accept env, interruptible, and publicIp.

Responses use data / error. Failures raise ChassisError with .status, .body, and a clear message.

Docs

Human setup guide: https://chassis.okeymeta.com.ng/docs

License

MIT · OkeyMeta Ltd