We would like to forward a particular 'key' column which is part of the features to appear alongside the predictions - this is to be able to identify to which set of features a particular prediction belongs to. Here is an example of predictions output using the tensorflow.contrib.estimator.multi_class_head:
I have the same hardware envs, same network, but I could not get the result as you, almost half as you. Any best practices and experience? thanks very much! for bytePS with 1 instance and 8 GPU, I have similar testing result.
It would be really useful if we had a simple Dockerfile containing the build instructions for the library. Then, we could create an image which would be easy to distribute and run for the volunteer participants without setting up the environment themselves.
torchtext (as of 0.4.0) adopts torch.utils.data.DataLoader, and the older iterator interface is deprecated. Ensure AdaptDL's AdaptiveDataLoader supports this new torchtext interface for data loading, and port the example transformer code to the new interface. Then, adaptdl.data.iterator can be deprecated/removed.
HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.
Dynamic training with Apache MXNet reduces cost and time for training deep neural networks by leveraging AWS cloud elasticity and scale. The system reduces training cost and time by dynamically updating the training cluster size during training, with minimal impact on model training accuracy.
Implementation of Protein Classification based on subcellular localization using ProtBert(Rostlab/prot_bert_bfd_localization) model from Hugging Face library, based on BERT model trained on large corpus of protein sequences.
Implementation of Image Classification using Visual Transformers in Amazon SageMaker based on the ideas from research paper - Visual Transformers: Token-based Image Representation and Processing for Computer Vision.
This repository is a tutorial targeting how to train a deep neural network model in a higher efficient way. In this repository, we focus on two main frameworks that are Keras and Tensorflow.
Please can you train ghostnet.
(i don't have the imagenet dataset)