FastAPI wraps the Satellighte library to serve as RESTful API
From root directory of the repository run followings,
pip install fastapi==0.74.1
pip install "uvicorn[standard]"==0.17.5
pip install python-multipartpython deployment/fastapi/service.pyFrom root directory of the repository run followings,
docker build -t satellighte-fastapi deployment/fastapi/if gpu enabled, run with
docker run -d --name satellighte-service --rm -p 8080:8080 --gpus all satellighte-fastapiif gpu disabled, run with
docker run -d --name satellighte-service --rm -p 8080:8080 satellighte-fastapiONNX Runtime inference can lead to faster customer experiences and lower costs.
From root directory of the repository run followings,
pip install onnx~=1.11.0
pip install onnxruntime~=1.10.0python deployment/onnx/export.py
# python deployment/onnx/export.py --model_name mobilenetv2_default_eurosat --version 0python deployment/onnx/runtime.py
# python deployment/onnx/runtime.py -m satellighte/models/mobilenetv2_default_eurosat/v0/mobilenetv2_default_eurosat.onnx -s src/eurosat_samples/AnnualCrop.jpgNeural Magic's DeepSparse Engine is able to integrate into popular deep learning libraries allowing you to leverage DeepSparse for loading and deploying sparse models with ONNX.
From root directory of the repository run followings. We need the ONNX model to use it. Create your onnx model from the above steps. Next,
pip install deepsparse~=1.0.2python deployment/deepsparse/runtime.py
# python deployment/deepsparse/runtime.py -m -m satellighte/models/mobilenetv2_default_eurosat/v0/mobilenetv2_default_eurosat.onnx -s src/eurosat_samples/AnnualCrop.jpgTensorFlow is a free and open-source software library for machine learning and artificial intelligence.
From root directory of the repository run followings,
pip install onnx-tf~=1.10.0
pip install tensorflow~=2.9.1
pip install tensorflow-probability~=0.17.0From root directory of the repository run followings. We need the ONNX model to use it. Create your onnx model from the above steps. Next,
python deployment/tensorflow/export.py
# python deployment/tensorflow/export.py -m satellighte/models/mobilenetv2_default_eurosat/v0/mobilenetv2_default_eurosat.onnxpython deployment/tensorflow/runtime.py
# python deployment/tensorflow/runtime.py -m satellighte/models/mobilenetv2_default_eurosat/v0/mobilenetv2_default_eurosat_tensorflow -s src/eurosat_samples/AnnualCrop.jpg -l "AnnualCrop,PermanentCrop,Forest,HerbaceousVegetation,Highway,Industrial,Pasture,Residential,River,SeaLake"TensorFlow Lite is a mobile library for deploying models on mobile, microcontrollers and other edge devices.
From root directory of the repository run followings,
pip install onnx-tf~=1.10.0
pip install tensorflow~=2.9.1
pip install tensorflow-probability~=0.17.0From root directory of the repository run followings. We need the TensorFlow model to use it. Create your tensorflow model from the above steps. Next,
python deployment/tensorflow_lite/export.py
# python deployment/tensorflow_lite/export.py -m satellighte/models/mobilenetv2_default_eurosat/v0/mobilenetv2_default_eurosat_tensorflowpython deployment/tensorflow_lite/runtime.py
# python deployment/tensorflow_lite/runtime.py -m satellighte/models/mobilenetv2_default_eurosat/v0/mobilenetv2_default_eurosat_tensorflow.tflite -s satellighte/src/eurosat_samples/AnnualCrop.jpg -l "AnnualCrop,PermanentCrop,Forest,HerbaceousVegetation,Highway,Industrial,Pasture,Residential,River,SeaLake"