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.
Currently, AdaptDL will change the batch size whenever:
The job is restarted.
A new epoch is started.
This can cause the batch size to fluctuate frequently.
Instead, we should only change the batch size if the new batch size will cause a noticeable improvement in the predicted speedup (e.g. by 5% or more). Also consider adding a penalty term when finding the preferred batch size to
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.
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)