tonuse123 / DistributedTraining_inMXNet
MXNet supports distributed training enabling us to leverage multiple machines for faster training.
☆12Updated 6 years ago
Alternatives and similar repositories for DistributedTraining_inMXNet:
Users that are interested in DistributedTraining_inMXNet are comparing it to the libraries listed below
- Mxnet video io reading performance optimization☆43Updated 7 years ago
- This is the re-implementation of group normalization in MXNet Symbol,Module and Gluon☆23Updated 5 years ago
- An Example of MXNet Models Comilation and Deployment with NNVM in C++☆16Updated 7 years ago
- 一个基于 faiss 的检索服务.☆49Updated 6 years ago
- Example codes appears in lectures☆23Updated 3 years ago
- ☆18Updated 7 years ago
- A Gluon implementation of Mnasnet☆59Updated 6 years ago
- UCloud AI SDK☆33Updated 2 years ago
- PyTorch 1.0 inference in C++ on Windows10 platforms☆89Updated 6 years ago
- A Gluon implement of MobileNetV3☆28Updated 5 years ago
- A simple middleware to improving GPU utilization then speedup online inference.☆19Updated 4 years ago
- VGG16 architecture with BatchNorm☆14Updated 8 years ago
- ☆11Updated 8 years ago
- The benchmark of ncnn that is a high-performance neural network inference framework optimized for the mobile platform☆72Updated 6 years ago
- Tensorflow Serving with support for Caffe☆41Updated 7 years ago
- Reproduction of MobileNetV2 using MXNet☆128Updated 6 years ago
- A Lightweight & Flexible Deep Learning (Neural Network) Framework in Python☆45Updated 10 months ago
- ☆12Updated 6 years ago
- ☆30Updated 2 years ago
- ☆19Updated last year
- MTCNN with pycaffe☆14Updated 8 years ago
- Caffe Computation Graph Optimization.☆29Updated 5 years ago
- A MXNet/Gluon implementation of MobileNetV2☆86Updated 7 years ago
- Move to https://github.com/apache/incubator-tvm-site☆26Updated 4 years ago
- MXNet Gluon Synchronized Batch Normalization Preview☆77Updated 6 years ago
- mtcnn mxnet C++ implementation☆65Updated 5 years ago
- keras实现人证比对论文《Large-scale Bisample Learning on ID vs. Spot Face Recognition》的核心思想; 当类别数非常大时(超过百万)，GPU显存可能无法装载权重参数；可以使用支配原型概念，每个step将相关的类别…☆23Updated 6 years ago
- Ranking made easy☆36Updated 6 years ago
- ☆42Updated 4 years ago
- ☆90Updated 7 years ago