> ## Documentation Index > Fetch the complete documentation index at: https://docs.orchestrallabs.ai/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart > Import a pi0.5 checkpoint, deploy near your application, and predict from NumPy arrays. Send NumPy inputs; get action arrays. Your application reads sensors and applies actions. Requires Python 3.12+ on macOS/Linux, a Servo account, and a [connected Hugging Face account](https://accounts.orchestrallabs.ai/account/huggingface). No local GPU or robot registration is needed. Save Python examples using their shown filenames; run them with `python FILENAME.py`. Install Servo and sign in: ```bash your computer theme={null} python -m venv .venv source .venv/bin/activate python -m pip install servo-client servo login servo whoami ``` On a headless computer, follow the printed sign-in link and code. CLI and Python share your saved sign-in; telemetry is configured automatically. Read the checkpoint contract before allocating capacity or creating arrays: Inspect the checkpoint: ```bash your computer theme={null} servo model inspect hf://npow/pi05-yam-red-cap-full-7500 \ --revision 39c6876521b3d8363452bed6731c8705b3944c24 ``` Sample output: ```text Sample output theme={null} Checkpoint: npow/pi05-yam-red-cap-full-7500@39c6876521b3d8363452bed6731c8705b3944c24 Runtime: openpi Input contract: resolved Input middle (image): [224, 224, 3], HWC, uint8 Input left (image): [224, 224, 3], HWC, uint8 Input right (image): [224, 224, 3], HWC, uint8 Input state (state): [7], float32 Action chunk shape: [15, 7] Served from its own files: yes ``` Inspect the checkpoint with Python: ```python inspect_checkpoint.py theme={null} import servo sv = servo.Servo() inspection = sv.models.inspect_checkpoint( "https://huggingface.co/npow/pi05-yam-red-cap-full-7500", revision="39c6876521b3d8363452bed6731c8705b3944c24", ) contract = inspection.input_contract print(contract.resolution) for feature in contract.inputs or []: print(feature.name, feature.shape, feature.dtype, feature.layout) print("Actions:", contract.outputs["actions"].shape) ``` Sample output: ```text Sample output theme={null} resolved middle (224, 224, 3) uint8 HWC left (224, 224, 3) uint8 HWC right (224, 224, 3) uint8 HWC state (7,) float32 None Actions: (15, 7) ``` Use the exact input keys, shapes and dtypes shown above. `HWC` means height, width, channels; `None` means no image layout. Continue only when the contract is `resolved`. The checkpoint documentation defines camera placement, state units and action meanings. Import, then deploy using the returned model ID: ```bash your computer theme={null} servo model import hf://npow/pi05-yam-red-cap-full-7500 \ --revision 39c6876521b3d8363452bed6731c8705b3944c24 ``` Run the returned `MODEL_ID=` assignment in your shell, then deploy: ```bash your computer theme={null} servo model deploy "$MODEL_ID" --city "Zurich, Switzerland" --wait --timeout 900 ``` Save the printed deployment ID; this command waits for readiness. Import the checkpoint and wait for deployment readiness: ```python deploy_checkpoint.py theme={null} import servo sv = servo.Servo() model = sv.models.import_checkpoint( "https://huggingface.co/npow/pi05-yam-red-cap-full-7500", revision="39c6876521b3d8363452bed6731c8705b3944c24", ) deployment = model.deploy(city="Zurich, Switzerland") print("DEPLOYMENT_ID=" + deployment.id) deployment.wait(timeout_s=900) ``` Deployment allocates hosted capacity and requires an organization member or API key. The first import may download weights and compile; both examples wait for readiness. Servo aims to provision for the fastest end-to-end latency. Repeated requests for the same model and site reuse the deployment. A timeout does not cancel it: inspect or stop it below. Create sample arrays matching the inspected contract and make one prediction: ```python first_prediction.py theme={null} import numpy as np from numpy.random import randint import servo sv = servo.Servo() deployment = sv.deployments.get(input("Deployment ID: ")) policy = deployment.policy() inputs = { "middle": randint(0, 256, (224, 224, 3), dtype=np.uint8), "left": randint(0, 256, (224, 224, 3), dtype=np.uint8), "right": randint(0, 256, (224, 224, 3), dtype=np.uint8), "state": np.zeros(7, dtype=np.float32), } with sv.session(policy, instruction="pick up the red cap and place it in the black box") as session: chunk = session.predict(inputs=inputs) print(chunk.actions) ``` Save the code above, or download and run it: ```bash your computer theme={null} curl -fsSL https://api.orchestrallabs.ai/docs/downloads/first_prediction.py -o first_prediction.py python first_prediction.py ``` Enter your saved deployment ID when prompted. Expect `15 × 7` action values. Random images and a zero state check connectivity; they are not useful robot observations. The model rejects a state outside the range its checkpoint declares, so use a real reading, not random state values. Servo handles model preprocessing. Servo does not apply them to hardware; your application owns sensor capture, action interpretation and hardware limits. Inspect the deployment, then stop it: ```bash your computer theme={null} servo deployment show "$DEPLOYMENT_ID" servo deployment logs "$DEPLOYMENT_ID" servo deployment stop "$DEPLOYMENT_ID" servo deployment show "$DEPLOYMENT_ID" ``` Inspect the deployment, then stop it: ```python stop_deployment.py theme={null} import servo sv = servo.Servo() deployment = sv.deployments.get(input("Deployment ID: ")) print(deployment.status) deployment.delete() deployment.wait_retired(timeout_s=300) print(deployment.status) ``` Run the returned `DEPLOYMENT_ID=` assignment in your shell first. CLI `logs` prints authenticated dashboard links. `delete()` and `stop` request teardown; confirm `retired` through `wait_retired()` or repeated `show`. Closing Python or a session leaves the deployment running and capacity allocated. For real sensors and action execution, continue to [your control loop](/guides/your-loop). This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.