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55 lines
1.7 KiB
Markdown
55 lines
1.7 KiB
Markdown
English | [中文](../../../cn/quick_start/runtime/python.md)
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# Python Inference
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Please check out the FastDeploy is already installed in your environment. You can refer to [FastDeploy Installation](../../build_and_install/) to install the pre-compiled FastDeploy, or customize your installation.
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This document shows an inference sample on the CPU using the PaddleClas classification model MobileNetV2 as an example.
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## 1. Obtaining the Module
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``` python
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import fastdeploy as fd
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model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
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fd.download_and_decompress(model_url, path=".")
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```
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## 2. Backend Configuration
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- For more examples, you can refer to [examples/runtime](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/runtime).
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``` python
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option = fd.RuntimeOption()
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option.set_model_path("mobilenetv2/inference.pdmodel",
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"mobilenetv2/inference.pdiparams")
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# **** CPU Configuration ****
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option.use_cpu()
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option.use_ort_backend()
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option.set_cpu_thread_num(12)
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# Initialise runtime
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runtime = fd.Runtime(option)
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# Get model input name
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input_name = runtime.get_input_info(0).name
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# Constructing random data for inference
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results = runtime.infer({
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input_name: np.random.rand(1, 3, 224, 224).astype("float32")
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})
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print(results[0].shape)
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```
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When loading is complete, you can get the following output information indicating the initialized backend and the hardware devices.
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```
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[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OrtBackend in device Device::CPU.
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```
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## Other Documents
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- [Runtime demos on different backends](../../../../examples/runtime/README.md)
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- [Switching hardware and backend for model inference](../../faq/how_to_change_backend.md)
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