Grid Cortex Client
Python client for GRID Cortex.
Installation
pip install grid-cortex-client
Quick Start
from grid_cortex_client import CortexClient, ModelType
client = CortexClient(api_key="your-api-key")
# Monocular depth estimation
depth_map = client.run(ModelType.ZOEDEPTH, image_input="path/to/image.jpg")
Configuration
Pass your API key and base URL directly, or set them as environment variables:
export GRID_CORTEX_API_KEY="your-api-key"
export GRID_CORTEX_BASE_URL="https://cortex-prod.generalrobotics.dev/cortex"
# Explicit configuration
client = CortexClient(api_key="your-key", base_url="https://...")
# Or rely on environment variables
client = CortexClient()
Input Formats
RGB and stereo model inputs accept multiple input types:
- File path:
"path/to/image.jpg" - URL:
"https://example.com/image.jpg" - PIL Image:
Image.open("image.jpg") - NumPy array:
np.ndarraywith shape(H, W, 3) - GRID Image:
grid_types.Image
For RGB and stereo models, encoded JPEG and PNG GRID Images are forwarded without decoding or re-encoding. Other GRID Image inputs follow the model's existing conversion path; PI05 still resizes its images to 224×224 before sending them. Numerical depth and segmentation inputs retain their existing NumPy/NPY contract.
Async & Concurrent Inference
The async client lets you call multiple models concurrently so total latency equals the slowest model, not the sum of all of them.
Concurrent multi-model example
import asyncio
import numpy as np
from grid_cortex_client import AsyncCortexClient, ModelType
async def run_perception_pipeline(image: np.ndarray):
"""Run depth, detection, and segmentation concurrently on the same frame."""
async with AsyncCortexClient() as client:
depth, detections, mask = await asyncio.gather(
client.run(ModelType.ZOEDEPTH, image_input=image),
client.run(ModelType.OWLV2, image_input=image, prompt="bottle"),
client.run(ModelType.GSAM2, image_input=image, prompt="bottle"),
)
return depth, detections, mask
depth, detections, mask = asyncio.run(
run_perception_pipeline(np.array(Image.open("scene.jpg")))
)
High-throughput streaming with pub/sub
For continuous streams (e.g. camera feeds), the CortexHubClient uses WebSockets to overlap sending and receiving. While frame N's result is being returned, frame N+1 is already being processed server-side:
import asyncio
import numpy as np
from grid_cortex_client import CortexHubClient, ModelType
async def publisher(hub: CortexHubClient, frames: list[np.ndarray]):
"""Send frames as fast as possible."""
for i, frame in enumerate(frames):
await hub.publish(ModelType.ZOEDEPTH, request_id=f"frame_{i}", image_input=frame)
async def subscriber(hub: CortexHubClient, num_frames: int):
"""Receive results as they arrive."""
count = 0
async for result in hub.subscribe():
if result.ok:
print(f"{result.request_id}: shape={result.data.shape}")
count += 1
if count >= num_frames:
break
async def main():
frames = [np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)] * 100
async with CortexHubClient() as hub:
await asyncio.gather(
publisher(hub, frames),
subscriber(hub, len(frames)),
)
asyncio.run(main())
Documentation
For model-specific usage examples, parameter references, and detailed guides, see the full documentation:
docs.generalrobotics.dev/models/cortex
Requirements
- Python >= 3.8
1 maintainer
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