入門指南

安裝

我們建議使用 Miniconda 來管理您的 Python 版本和環境。

步驟 1：Python 環境

在您的機器上安裝 miniconda。

Windows：安裝完成後，從開始選單中啟動 Anaconda Prompt。

macOS/Linux：安裝完成後，啟動一個新的 shell 視窗程序。

為 Qualcomm® AI Hub 設置環境：

conda create python=3.10 -n qai_hub
conda activate qai_hub

警告

如果這些步驟中的任何一個因 SSL: CERTIFICATE_VERIFY_FAILED 錯誤而失敗，則表示您已安裝了 SSL 攔截和流量檢查工具。請向您的 IT 部門詢問如何為 Python pip 和 Python Requests 庫設置憑證的說明。他們應該會提供一個新的憑證文件。在創建環境之前，您還需要將此憑證添加到 Miniconda。這可以通過 conda config --set ssl_verify <path to certificate> 完成。

步驟 2：安裝 Python 客戶端

備註

Windows for ARM：由於缺乏對相關程式庫的支持，AI Hub 目前不支持使用原生 ARM Python。當 Python 生態系統趕上 ARM Windows 時，請安裝 x64 版本的 Python（通過 模擬 來實現）。我們推薦從 Python 下載頁面page 安裝 Python 3.10 "64-bit"。

pip3 install qai-hub

步驟 3：登錄

前往 Qualcomm® AI Hub 並使用您的 Qualcomm ID 登錄以查看您創建的作業信息。

登錄後，導航到 Account -> Settings -> API Token。這應該會提供一個 API Token，您可以用來配置您的客戶端。

Step 4, Option A (recommended): Configure Persistent API Token

Configure the client by persisting the the API token on your workstation. You only need to do this once. This is the recommended way of using AI Hub.

Run the following command in your terminal:

qai-hub configure --api_token INSERT_API_TOKEN

You can check that your API token is installed correctly by fetching a list of available devices. To do that, you can type the following in a Python terminal:

import qai_hub as hub
hub.get_devices()

If you go this route, you do not need to follow Option B below.

Step 4, Option B (for ephemeral environments): Configure Session API Token

In ephemeral customer environments, such as Amazon SageMaker, where API tokens cannot be persistently stored on disk, you will need to configure the AI Hub client on every use.

Provide your API token after you import the AI Hub client. For example:

import qai_hub as hub
hub.set_session_token("INSERT_API_TOKEN")

You can check that the API token is working correctly by fetching a list of available devices. Add the following code to your Python script, after initializing the client:

hub.get_devices()

You must provide the API token every time you initialize a session using this token configuration method.

您現在已準備好在 Qualcomm AI Hub 上提交作業。如果遇到任何問題，請 聯繫我們。隨意嘗試以下示例，在所需設備上運行分析文件作業。

快速示例 (PyTorch)

設置好您的 Qualcomm® AI Hub 環境後，下一步是提交配置文件作業。首先，安裝此示例的相關程式庫：

pip3 install "qai-hub[torch]"

警告

如果任何代碼因 API 身份驗證錯誤而失敗，則表示您沒有安裝有效的 API Token。請參閱 安裝 了解如何設置。

提交對您選擇的設備類型上的 MobileNet v2 網絡的分析。您可以使用上面列表中的任何設備來指定不同於以下選項的設備。

移動設備

import numpy as np
import requests
import torch
from PIL import Image
from torchvision.models import mobilenet_v2

import qai_hub as hub

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

# Step 1: Trace model
input_shape = (1, 3, 224, 224)
example_input = torch.rand(input_shape)
traced_torch_model = torch.jit.trace(torch_model, example_input)

# Step 2: Compile model
compile_job = hub.submit_compile_job(
    model=traced_torch_model,
    device=hub.Device("Samsung Galaxy S24 (Family)"),
    input_specs=dict(image=input_shape),
    options="--target_runtime tflite",
)
assert isinstance(compile_job, hub.CompileJob)

# Step 3: Profile on cloud-hosted device
target_model = compile_job.get_target_model()
assert isinstance(target_model, hub.Model)
profile_job = hub.submit_profile_job(
    model=target_model,
    device=hub.Device("Samsung Galaxy S24 (Family)"),
)
assert isinstance(profile_job, hub.ProfileJob)


# Step 4: Run inference on cloud-hosted device
sample_image_url = (
    "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/input_image1.jpg"
)
response = requests.get(sample_image_url, stream=True)
response.raw.decode_content = True
image = Image.open(response.raw).resize((224, 224))
input_array = np.expand_dims(
    np.transpose(np.array(image, dtype=np.float32) / 255.0, (2, 0, 1)), axis=0
)

# Run inference using the on-device model on the input image
inference_job = hub.submit_inference_job(
    model=target_model,
    device=hub.Device("Samsung Galaxy S24 (Family)"),
    inputs=dict(image=[input_array]),
)
assert isinstance(inference_job, hub.InferenceJob)

on_device_output = inference_job.download_output_data()
assert isinstance(on_device_output, dict)

# Step 5: Post-processing the on-device output
output_name = list(on_device_output.keys())[0]
out = on_device_output[output_name][0]
on_device_probabilities = np.exp(out) / np.sum(np.exp(out), axis=1)

# Read the class labels for imagenet
sample_classes = "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/imagenet_classes.txt"
response = requests.get(sample_classes, stream=True)
response.raw.decode_content = True
categories = [str(s.strip()) for s in response.raw]

# Print top five predictions for the on-device model
print("Top-5 On-Device predictions:")
top5_classes = np.argsort(on_device_probabilities[0], axis=0)[-5:]
for c in reversed(top5_classes):
    print(f"{c} {categories[c]:20s} {on_device_probabilities[0][c]:>6.1%}")

# Step 6: Download model
target_model = compile_job.get_target_model()
assert isinstance(target_model, hub.Model)
target_model.download("mobilenet_v2.tflite")

IOT

import numpy as np
import requests
import torch
from PIL import Image
from torchvision.models import mobilenet_v2

import qai_hub as hub

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

# Step 1: Trace model
input_shape = (1, 3, 224, 224)
example_input = torch.rand(input_shape)
traced_torch_model = torch.jit.trace(torch_model, example_input)

# Step 2: Compile model
compile_job = hub.submit_compile_job(
    model=traced_torch_model,
    device=hub.Device("RB3 Gen 2 (Proxy)"),
    input_specs=dict(image=input_shape),
    options="--target_runtime tflite",
)
assert isinstance(compile_job, hub.CompileJob)


# Step 3: Profile on cloud-hosted device
target_model = compile_job.get_target_model()
assert isinstance(target_model, hub.Model)
profile_job = hub.submit_profile_job(
    model=target_model,
    device=hub.Device("RB3 Gen 2 (Proxy)"),
)
assert isinstance(profile_job, hub.ProfileJob)


# Step 4: Run inference on cloud-hosted device
sample_image_url = (
    "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/input_image1.jpg"
)
response = requests.get(sample_image_url, stream=True)
response.raw.decode_content = True
image = Image.open(response.raw).resize((224, 224))
input_array = np.expand_dims(
    np.transpose(np.array(image, dtype=np.float32) / 255.0, (2, 0, 1)), axis=0
)

# Run inference using the on-device model on the input image
inference_job = hub.submit_inference_job(
    model=target_model,
    device=hub.Device("RB3 Gen 2 (Proxy)"),
    inputs=dict(image=[input_array]),
)
assert isinstance(inference_job, hub.InferenceJob)

on_device_output = inference_job.download_output_data()
assert isinstance(on_device_output, dict)

# Step 5: Post-processing the on-device output
output_name = list(on_device_output.keys())[0]
out = on_device_output[output_name][0]
on_device_probabilities = np.exp(out) / np.sum(np.exp(out), axis=1)

# Read the class labels for imagenet
sample_classes = "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/imagenet_classes.txt"
response = requests.get(sample_classes, stream=True)
response.raw.decode_content = True
categories = [str(s.strip()) for s in response.raw]

# Print top five predictions for the on-device model
print("Top-5 On-Device predictions:")
top5_classes = np.argsort(on_device_probabilities[0], axis=0)[-5:]
for c in reversed(top5_classes):
    print(f"{c} {categories[c]:20s} {on_device_probabilities[0][c]:>6.1%}")

# Step 6: Download model
target_model = compile_job.get_target_model()
assert isinstance(target_model, hub.Model)
target_model.download("mobilenet_v2.tflite")

計算設備

import numpy as np
import requests
import torch
from PIL import Image
from torchvision.models import mobilenet_v2

import qai_hub as hub

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

# Step 1: Trace model
input_shape = (1, 3, 224, 224)
example_input = torch.rand(input_shape)
traced_torch_model = torch.jit.trace(torch_model, example_input)

# Step 2: Compile model
compile_job = hub.submit_compile_job(
    model=traced_torch_model,
    device=hub.Device("Snapdragon X Elite CRD"),
    input_specs=dict(image=input_shape),
    options="--target_runtime onnx",
)
assert isinstance(compile_job, hub.CompileJob)

# Step 3: Profile on cloud-hosted device
target_model = compile_job.get_target_model()
assert isinstance(target_model, hub.Model)
profile_job = hub.submit_profile_job(
    model=target_model,
    device=hub.Device("Snapdragon X Elite CRD"),
)
assert isinstance(profile_job, hub.ProfileJob)


# Step 4: Run inference on cloud-hosted device
sample_image_url = (
    "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/input_image1.jpg"
)
response = requests.get(sample_image_url, stream=True)
response.raw.decode_content = True
image = Image.open(response.raw).resize((224, 224))
input_array = np.expand_dims(
    np.transpose(np.array(image, dtype=np.float32) / 255.0, (2, 0, 1)), axis=0
)

# Run inference using the on-device model on the input image
inference_job = hub.submit_inference_job(
    model=target_model,
    device=hub.Device("Snapdragon X Elite CRD"),
    inputs=dict(image=[input_array]),
)
assert isinstance(inference_job, hub.InferenceJob)

on_device_output = inference_job.download_output_data()
assert isinstance(on_device_output, dict)

# Step 5: Post-processing the on-device output
output_name = list(on_device_output.keys())[0]
out = on_device_output[output_name][0]
on_device_probabilities = np.exp(out) / np.sum(np.exp(out), axis=1)

# Read the class labels for imagenet
sample_classes = "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/imagenet_classes.txt"
response = requests.get(sample_classes, stream=True)
response.raw.decode_content = True
categories = [str(s.strip()) for s in response.raw]

# Print top five predictions for the on-device model
print("Top-5 On-Device predictions:")
top5_classes = np.argsort(on_device_probabilities[0], axis=0)[-5:]
for c in reversed(top5_classes):
    print(f"{c} {categories[c]:20s} {on_device_probabilities[0][c]:>6.1%}")

# Step 6: Download model
target_model = compile_job.get_target_model()
assert isinstance(target_model, hub.Model)
target_model.download("mobilenet_v2.onnx")

汽車設備

import numpy as np
import requests
import torch
from PIL import Image
from torchvision.models import mobilenet_v2

import qai_hub as hub

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

# Step 1: Trace model
input_shape = (1, 3, 224, 224)
example_input = torch.rand(input_shape)
traced_torch_model = torch.jit.trace(torch_model, example_input)

# Step 2: Compile model
compile_job = hub.submit_compile_job(
    model=traced_torch_model,
    device=hub.Device("SA8650 (Proxy)"),
    input_specs=dict(image=input_shape),
    options="--target_runtime qnn_lib_aarch64_android",
)
assert isinstance(compile_job, hub.CompileJob)

# Step 3: Profile on cloud-hosted device
target_model = compile_job.get_target_model()
assert isinstance(target_model, hub.Model)
profile_job = hub.submit_profile_job(
    model=target_model,
    device=hub.Device("SA8650 (Proxy)"),
)
assert isinstance(profile_job, hub.ProfileJob)

# Step 4: Run inference on cloud-hosted device
sample_image_url = (
    "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/input_image1.jpg"
)
response = requests.get(sample_image_url, stream=True)
response.raw.decode_content = True
image = Image.open(response.raw).resize((224, 224))
input_array = np.expand_dims(
    np.transpose(np.array(image, dtype=np.float32) / 255.0, (2, 0, 1)), axis=0
)

# Run inference using the on-device model on the input image
inference_job = hub.submit_inference_job(
    model=target_model,
    device=hub.Device("SA8650 (Proxy)"),
    inputs=dict(image=[input_array]),
)
assert isinstance(inference_job, hub.InferenceJob)

on_device_output = inference_job.download_output_data()
assert isinstance(on_device_output, dict)

# Step 5: Post-processing the on-device output
output_name = list(on_device_output.keys())[0]
out = on_device_output[output_name][0]
on_device_probabilities = np.exp(out) / np.sum(np.exp(out), axis=1)

# Read the class labels for imagenet
sample_classes = "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/imagenet_classes.txt"
response = requests.get(sample_classes, stream=True)
response.raw.decode_content = True
categories = [str(s.strip()) for s in response.raw]

# Print top five predictions for the on-device model
print("Top-5 On-Device predictions:")
top5_classes = np.argsort(on_device_probabilities[0], axis=0)[-5:]
for c in reversed(top5_classes):
    print(f"{c} {categories[c]:20s} {on_device_probabilities[0][c]:>6.1%}")

# Step 6: Download model
target_model = compile_job.get_target_model()
assert isinstance(target_model, hub.Model)
target_model.download("mobilenet_v2.so")

這將提交一個編譯作業，然後提交一個分析文件作業，印出兩個作業的 URL。最後，代碼在一些示例數據上執行推理作業。在 /jobs/ 查看您所有的作業。

您可以透過程式查詢作業的狀態：

status = profile_job.get_status()
print(status)

您可以使用以下代碼訪問作業結果。主要有三個部分

• 分析文件: JSON 格式的分析文件結果。

• 目標模型: 已優化的模型，準備部署。

• 結果: 包含作業所有工件（包括日誌）的文件夾。

請注意，這些是同步API ，調用後會等待作業完成：

• 下載分析結果（JSON 格式，同步呼叫）

profile = profile_job.download_profile()
print(profile)

• 下載最佳化模型（同步呼叫）

compile_job.model.download("model_filename")

• 將結果下載至目前目錄（同步呼叫）

profile_job.download_results(".")

欲了解更多信息，請繼續參閱 分析模型 或參考 API documentation。