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Deploy real-time AI to various devices providing next-generation user experiences.

Don’t see the model you want? Bring your own.

Deploy optimized models on real devices in minutes

Qualcomm® AI Hub simplifies deploying AI models for vision, audio, and speech applications to edge devices within minutes. This example shows how you can deploy your own PyTorch model on a real hosted device. See the documentation for more details.

import qai_hub as hub
import torch
from torchvision.models import mobilenet_v2

# Using pre-trained MobileNet
torch_model = mobilenet_v2(pretrained=True)
torch_model.eval()

# Trace model (for on-device deployment)
input_shape = (1, 3, 224, 224)
example_input = torch.rand(input_shape)
traced_torch_model = torch.jit.trace(torch_model, example_input)

# Profile model on a specific device
compile_job, profile_job = hub.submit_compile_and_profile_jobs(
    model=traced_torch_model,
    device=hub.Device("RB3 Gen 2 (Proxy)"),
    input_specs=dict(image=input_shape), 
)
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