Deep learning is a very computational intensive task. Traditionally GPUs have been used to speed-up computations by several orders of magnitude. TensorFlow is a deep learning framework designed to improve performance further by running on multiple nodes in a distributed system. While TensorFlow has only been available for a little over a year, it has quickly become the most popular open source machine learning project on GitHub. The open source version of TensorFlow was originally only capable of running on a single node while Google’s proprietary version only was capable of leveraging distributed systems. This has now changed. In this paper, we will compare performance of TensorFlow running on different single and cloudnode configurations. As an example, we will train a convolutional neural network to detect number of cells in early mouse embryos. From this research, we have found that using a local node with a local high performance GPU is still the best option for most people who do not have the resources to design bigger system
[1]
Fei-Fei Li,et al.
ImageNet: A large-scale hierarchical image database
,
2009,
2009 IEEE Conference on Computer Vision and Pattern Recognition.
[2]
Luca Maria Gambardella,et al.
Blastomere segmentation and 3D morphology measurements of early embryos from Hoffman Modulation Contrast image stacks
,
2010,
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[3]
Davi Geiger,et al.
Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development
,
2014,
Comput. Biol. Medicine.
[4]
Geoffrey E. Hinton,et al.
ImageNet classification with deep convolutional neural networks
,
2012,
Commun. ACM.
[5]
Yoshua Bengio,et al.
Gradient-based learning applied to document recognition
,
1998,
Proc. IEEE.