RyanCCC/Deployment
深度学习应用部署
This project helps machine learning engineers efficiently deploy deep learning models. It takes pre-trained models from frameworks like TensorFlow, Keras, or PyTorch and optimizes them through techniques like quantization and pruning, reducing model size and improving inference speed. The optimized models can then be converted to formats like ONNX or deployed with inference engines like TensorRT for high-performance, real-world application.
No commits in the last 6 months.
Use this if you need to make your deep learning models smaller, faster, and more efficient for production environments, especially on resource-constrained hardware or for real-time applications.
Not ideal if you are looking for an all-in-one solution for model training or if your primary need is general-purpose cloud-based model serving without advanced optimization requirements.
Stars
8
Forks
6
Language
C++
License
—
Category
Last pushed
Jan 05, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/RyanCCC/Deployment"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
microsoft/onnxruntime
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
onnx/onnx
Open standard for machine learning interoperability
PINTO0309/onnx2tf
Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras format (NHWC). The...
NVIDIA/TensorRT
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This...
onnx/onnxmltools
ONNXMLTools enables conversion of models to ONNX