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ncnn is a high-performance neural network inference framework optimized for the mobile platform
Example
Hi ,
I have tried out both loss.backward() and model_engine.backward(loss) for my code. There are several subtle differences that I have observed , for one retain_graph = True does not work for model_engine.backward(loss) . This is creating a problem since buffers are not being retained every time I run the code for some reason.
Please look into this if you could.
Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
Runtime type system for IO decoding/encoding
Description
If the Triton server build fails due to any reason, I have to delete the /tmp/citritonbuild/<backend> folders to prevent the next rebuild from throwing git repo already exists error.
Triton Information
r21.05
I am building the Triton server myself.
To Reproduce
uninstall one of the dependency needed by a backend.
run build.py with all the backends enabled
TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.
An easy to use PyTorch to TensorRT converter
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DELTA is a deep learning based natural language and speech processing platform.
Deploy a ML inference service on a budget in less than 10 lines of code.
Hi, I am so interesting in your project, and wonder if you need contributor and how could I make my own contribution?
High-efficiency floating-point neural network inference operators for mobile, server, and Web
Pytorch-Named-Entity-Recognition-with-BERT
Bounds check and call [] operator
'max_request_size' seems to refer to bytes, not mb.
Efficient, scalable and enterprise-grade CPU/GPU inference server for
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
TensorFlow models accelerated with NVIDIA TensorRT
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I figured out a way to get the (x,y,z) data points for each frame from one hand previously. but im not sure how to do that for the new holistic model that they released. I am trying to get the all landmark data points for both hands as well as parts of the chest and face. does anyone know how to extract the holistic landmark data/print it to a text file? or at least give me some directions as to h