平安证券-策略周报成长共识已成
百度 过去的2017年，佳能（中国）营业额大幅增长，其影像信息产品业务营业额占集团业务营业额的21%，位列全球首位，并且EOS系列相机的全球市场占有率均排在前列，全画幅相机更是以压倒性的优势排名榜首。This article introduces how to manually set up Python environments for model conversion across different hardware targets, including CPU, QNN, AMD NPU, and Intel NPU.
We recommend installing your environment in a Python virtual environment or a Conda environment.
Note: if you want to do inference on NPU devices with QNN EP, we recommend using a Python virtual environment. The reason is because inference with some models might require the ARM version of Python, while Conda doesn't support creating an ARM version of Python virtual environments yet.
- CPU: Setup CPU environment
- QNN: Setup QNN environment
- AMD NPU: Setup AMD NPU environment
- Intel NPU: Setup Intel NPU environment
Setup CPU environment
Tips: you can directly setup NPU environment such as Setup QNN environment. It will fallback to CPU EP when running inference on a model.
Alternatively, you can choose to setup a CPU environment.
Python version: 3.12.9
pip install requirements-CPU.txt
Setup QNN environment
Python version: 3.12.9
pip install requirements-QNN.txt
Setup AMD NPU environment
Python version: 3.10.9
pip install requirements-AMDNPU.txt
Setup Intel NPU environment
Python version: 3.10.9
pip install requirements-IntelNPU.txt
Install requirements for target project
Navigate to model project folder.
pip install requirements.txt