  Big Data works perfectly along with Deep learning to extract knowledge from a
huge amount of data. However, this processing could take a lot of training
time. Genomics is a Big Data science with high dimensionality. It relies on
deep learning to solve complicated problems in certain diseases like cancer by
using different DNA information such as the transcription factor. TAL1 is a
transcription factor that is essential for the development of hematopoiesis and
of the vascular system. In this paper, we highlight the potential of deep
learning in the field of genomics and its challenges such as the training time
that takes hours, weeks, and in some cases months. Therefore, we propose to
apply a distributed deep learning implementation based on Convolutional Neural
Networks (CNN) that showed good results in decreasing the training time and
enhancing the accuracy performance with 95% by using multiple GPU and TPU as
accelerators. We proved the efficiency of using a distributed strategy based on
data-parallelism in predicting the transcription-factor TAL1 motif faster.
