CONTAINERS AND DEEP LEARNING FRAMEWORKS

Deep Learning Docker containers and frameworks

Novatech Deep Learning Docker Containers

Docker Containers

A Docker container launches a single application or service for every container. The single application is bundled together with all its data, libraries, and environmental options, providing an efficient, smart application that can execute its environment with exact replicated precision rather than running them on every system you run.

With the success of deep learning being utilised for commercial and research use, the tools used for them have increased in quantity, demanding notable software management challenges. The viable solution is to use a Docker container as a means of simplifying their large management so that issues such as environmental setting dilemmas can be easily fixed by condensing the deep learning software and its entire vast library into a single Docker image.

Novatech Deep Learning Frameworks

Frameworks

There’s a way that you can test ideas and prototype applications without the arduous overhead of having to write bulking amounts of your own code. Deep learning frameworks are taking the process of taking the training and deployment of Deep Learning network’s and turning them into a flexible and adapting system designed to suit your specific tasks.

Using cuDNN to optimise the code, all major frameworks have a community of developers and users dedicated to helping you get the most out of the best. Simply choose what framework you desire and be guided towards modified new features for your application.

Caffe

Interfaces

C++, Python, MATLAB

Multi-GPU ready

Yes

Caffe is very popular for computer vision users. It was developed with expression, modularity and speed in mind.

Visit site

Caffe 2

Interfaces

C++, Python

Multi-GPU ready

Yes

Caffe2 is very popular for computer vision users. It was developed with expression, modularity and speed in mind.

Visit site

Tensorflow

Interfaces

C++, Python

Multi-GPU ready

Yes

Tensorflow is a great libary for users working on numerical computation using data flow graphs.

Visit site

Theano

Interfaces

Python

Multi-GPU ready

Yes

Theano is very efficient at working on mathematical expressions using multi-dimensional arrays.

Visit site

Torch

Interfaces

C, C++, Lua

Multi-GPU ready

Yes

Torch is primaraly a scientific framework which has a wide erray support for ML/DL algorithyms. It also supports Lua.

Visit site

Chainer

Interfaces

Python

Multi-GPU ready

Yes

Chainer is was designed for define-by-run. Lots of frameworks use the define-and-run approach, so Chainer is perfect for modifying networks during runtime.

Visit site

DL4J

Interfaces

Java

Multi-GPU ready

Yes

DL4J is JVM based, industry focused framework.

Visit site

Julia

Interfaces

C++

Multi-GPU ready

No

High-level, high-performance, dynamic, JIT compiled

Keras

Interfaces

Python

Multi-GPU ready

No

Highly modular network written in Python. You can run Keras with either Tensorflow or Theano on top. Keras is very good in the area of fast experimentation run times.

Visit site

MatConvNet

Interfaces

MATLAB

Multi-GPU ready

No

MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient, and can run and learn state-of-the-art CNNs.

Visit site

CNTK

Interfaces

C++, C#

Multi-GPU ready

No

CNTK is a very popular toolkit for combining different models across multiple GPU's and servers. The toolkit offers very efficient CNN and RNN training for text data, speech and image.

Visit site

Mxnet

Interfaces

Python, R, C++, Julia

Multi-GPU ready

Yes

MXNet is a very efficient and flexible framework, you can mix symbolic and imperative programming. It also supports Julia as well.

Visit site

Latest articles