Looking into Microsoft CNTK, the cognitive toolkit.
This was originally introduced as CNTK,
listed out as Computational Network Toolkit by Microsoft Research in 2014.
The open source license became available in April of 2015
and it was re-branded to the name of Cognitive Toolkit in January of 2016.
This is a deep learning neural network open-source license
toolkit included with easy to learn
computational steps based on a directed graph.
Python or C++ can be used for programming development in CNTK.
This was original programming model was based upon configuration scripts.
A simplified definition of CNTK network graph nodes are provided here.
Where for input, FeatureNode.
For training labels, LabelNode.
For evaluation- training evaluation, we have CriterionNode
and for result evaluation,
we have EvalNode, and for the output, we have OutputNode.
The CNTK characteristics.
Well, optimized runtime system for
deep learning neural network training and testing is included.
Abstract computation graphs are used in deep learning neural network designing.
And brain script are used in defining custom
networks, improved configuration scripts.
Looking at it and comparing,
CNTK and TensorFlow have many similarities.
Such as, they are both script driven.
These are the references that I used and I recommend them to you.
Next is NVIDIA DGX-1.
Now, this is a deep learning artificial intelligence supercomputer
with fully integrated hardware and software.
And it was made by Nvidia.
These are the system specifications.
Taking a little bit closer look into the GPUs,
the TFLOPS as in terms of performance in terahflops.
The GPU memory, the CPU as well as the maximum power requirement listed down here.
Now, the specifications show that 170 TFLOPS.
That's 10 to the power 12 multiplied to 170 flops.
Deep learning software includes accurate DNN stack and the NVIDIA
NVLink interconnected with
Pascal-powered NVIDIA Tesla P100 GPU accelerators are included.
In addition, it is worthy to note that
the NVIDIA Pascal engine was designed for computer learning, seeing and simulation.
And that's what's included in here.
In addition, the SDK: The Software Development Kit is included.
The SDK helps development of advanced GPU accelerated applications for clouds,
data centers, workstations and embedded platforms.
DIGITS GPU cheap new training system is included.
And drivers and CUDA is included as well.The
SDK also includes GPU accelerated libraries,
debugging and optimization tools,
C and C++ compiler
and a runtime library.
These are the references that I used and I recommend them to you. Thank you.