FastAPI wraps the From root directory of the repository run followings, From root directory of the repository run followings, if gpu enabled, run with if gpu disabled, run with ONNX Runtime inference can lead to faster customer experiences and lower costs. From root directory of the repository run followings, Neural 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 TensorFlow is a free and open-source software library for machine learning and artificial intelligence. From root directory of the repository run followings, From root directory of the repository run followings. We need the TensorFlow Lite is a mobile library for deploying models on mobile, microcontrollers and other edge devices. From root directory of the repository run followings, From root directory of the repository run followings. We need the 
Deployments #
FastAPI#
Satellighte library to serve as RESTful APIInstall Dependency For FastAPI#
pip install fastapi==0.74.1
pip install "uvicorn[standard]"==0.17.5
pip install python-multipart
Run AI Service#
python deployment/fastapi/service.py
Build AI Service As Docker Image#
docker build -t satellighte-fastapi deployment/fastapi/
Run AI Service As Docker Container#
docker run -d --name satellighte-service --rm -p 8080:8080 --gpus all satellighte-fastapi
docker run -d --name satellighte-service --rm -p 8080:8080 satellighte-fastapi
ONNX#
Install Dependency For ONNX#
pip install onnx~=1.11.0
pip install onnxruntime~=1.10.0
Convert Model to ONNX#
python deployment/onnx/export.py
# python deployment/onnx/export.py --model_name mobilenetv2_default_eurosat --version 0
ONNX Runtime#
python 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.jpg
DeepSparse#
ONNX model to use it. Create your onnx model from the above steps. Next,Install Dependency For DeepSparse#
pip install deepsparse~=1.0.2
DeepSparse Runtime#
python 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.jpg
TensorFlow#
Install Dependency For TensorFlow#
pip install onnx-tf~=1.10.0
pip install tensorflow~=2.9.1
pip install tensorflow-probability~=0.17.0
ONNX model to use it. Create your onnx model from the above steps. Next,Convert ONNX Model to TensorFlow#
python deployment/tensorflow/export.py
# python deployment/tensorflow/export.py -m satellighte/models/mobilenetv2_default_eurosat/v0/mobilenetv2_default_eurosat.onnx
TensorFlow Runtime#
python 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#
Install Dependency For TensorFlow Lite#
pip install onnx-tf~=1.10.0
pip install tensorflow~=2.9.1
pip install tensorflow-probability~=0.17.0
TensorFlow model to use it. Create your tensorflow model from the above steps. Next,Convert TensorFlow Model to TensorFlow Lite#
python deployment/tensorflow_lite/export.py
# python deployment/tensorflow_lite/export.py -m satellighte/models/mobilenetv2_default_eurosat/v0/mobilenetv2_default_eurosat_tensorflow
TensorFlow Lite Runtime#
python 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"
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