Deep Learning Studio got an update — I especially liked feature #1

Rajat
Rajat
Sep 6, 2018 · 8 min read

About Deep Learning Studio

Deep Learning Studio is the first robust deep learning platform available in two versions (cloud and desktop) with a visual interface in production since January 2017. The platform provides a comprehensive solution to data ingestion, model development, training, deployment and management. Deep Learning Studio is developed by Deep Cognition which is an AI software company that simplifies the process of developing and deploying Artificial Intelligence. AI Engineers, Data Scientists and researchers across the globe use their AI software platform, Deep Learning Studio, free of charge. By using Deep Learning Studio any one starting from a developer to an engineer or a researcher will acquire the ability to quickly develop and deploy deep learning solutions through robust integration with TensorFlow, MXNet and Keras.

In Deep Learning Studio pre-trained models as well as use built-in assistive features simplify and accelerate the model development process. One can import model code and edit the model with the visual interface. The platform automatically saves each model version as one iterates and tunes hyper-parameters to improve performance. One can compare performance across versions to find their optimal design.

AutoML feature can let one design their first neural network without prior knowledge of deep learning.

If you are not familiar with how to use Deep Learning Studio take a look at this :)

Introduction

Complete Guide

A video walkthrough of Deep Cognition by Favio Vázquez

But as we all know that if we want to have big success, we need to become more. Higher success requires you to be a better version. One need to keep updating with time. One has to come up with new improvements again and again and its a never ending process. That’s the motto of Deep Cognition coming up with new advancement and features.

So don’t be a guy who don’t want to get updated with time. Recently Deep Cognition has come up with Deep Learning Studio — New Release Announcement. It has got a major update & it’s super cool and handy for Jupyterlab, Tensorflow, Caffe, Keras2, MXNet & more.

You can download the latest version from their website or click here.

Here’s what you all need to know about their latest updated features:

Updates:

So let me start with the feature that i personally liked alot

1) Converting models code to any DL framework code

As we all know that the best part of using Deep Learning Studio is that we don’t have to do a single line of code. Deep Leaning Studio uses GRAPHICAL EDITOR which has a simple Drag n Drop feature with which we can build a deep learning model in few mins. But what if we want to use that with any deep learning framework. Python doesn't understand the graphical model so how to convert that model into code???

Now Deep Learning Studio got this amazing feature which makes him different from any other platform. Now we can download model source code into popular framework like Tensorflow, Caffe, Keras2, MXNet, CNTK, Pytorch, or ONNX.

You can now copy the converted code & use it in favorite deep learning framework. Isn't it amazing? Yes I know it is.

To do that open graphical editor in Deep learning Studio.

After building your required model. Click on the view code option.

Select any of the required framework and click on the copy icon to copy the code.

2) Jupyter Lab:

As we know Deep Learning Studio provides the possibility to program inside a Jupyter Notebook or run already existing Notebook in the environments provided (Desktop or Cloud).

As a coder we all love to code in Jupyter Notebook because of its features. Code is written into independent cells, which can each execute independently from the rest of the code. This allows a Python user to quickly test a specific step in a sequential workflow without re-executing code from the beginning of the script. It also has a great visualization for pandas dataframe and plotting graphs.

And with the recent update of Jupyter Notebook as Jupyter Lab which is an interactive development environment for working with notebooks, code and data. Most importantly, JupyterLab has full support for Jupyter notebooks. Additionally, JupyterLab enables you to use text editors, terminals, data file viewers, and other custom components side by side with notebooks in a tabbed work area.

Deep Learning Studio is now come with installed Jupyter lab on both the Desktop and Cloud version

To use Jupyter Lab click on the notebook tab

Benefits of using Jupyter lab :

JupyterLab provides a high level of integration between notebooks, documents, and activities:

  • Drag-and-drop to reorder notebook cells and copy them between notebooks.
  • Run code blocks interactively from text files (.py, .R, .md, .tex, etc.).
  • Link a code console to a notebook kernel to explore code interactively without cluttering up the notebook with temporary scratch work.
  • Edit popular file formats with live preview, such as Markdown, JSON, CSV and more.
  • We can also customize jupyter lab and install extensions.

Features:

Jupyter Lab is consist of :

  1. Terminal
  2. Jupyter Notebook
  3. Python3 console
  4. Text File editor

You can use different commands with Jupyter Notebook and other files available in the Commands Section.

You can also switch between light and dark theme.

We can also run jupyter notebook and python files using terminal in background i.e. if you are using cloud version you don’t have to keep your system running in order to complete process.

You can find all the running notebook and terminals in the running section:

You can also switch to classic jupyter notebook whenever you want by clicking on the Launch Classic Notebook option available in Commands section

Pre-configured Environments

Deep Cognition has introduced pre-configured environment for deep learning programmers. This feature frees AI developers from headache of setting up development environments. This is especially important as many deep learning frameworks and libraries require different version of packages. Conflict in version of these packages often lead to wasted time in debugging.

Currently latest version of Tensorflow, Keras, Caffe 2, Chainer, PyTorch, MxNet, H2o.io and Caffe are available. These enable developers to use various GitHub AI projects very fast. These environments are isolated and supports both CPU and GPU computing.

We can easily use all the installed environments in terminal as well as Jupyter notebook.

For using notebook or python console with any specific environment you will directly find all the install environment in the Launcher section.

Steps for switching between different environments in terminal:

To activate source:

Type “source activate <env-name>”

To deactivate source:

Type “source deactivate”

3) H2O.ai

It is now available as pre-configured environment in Deep Learning Studio. H2O, an open source software, allow users to fit thousands of potential models as part of discovering patterns in data Improved Inter-operativity among different deep learning frameworks. It is more efficient and fast as compare to scikit-learn. The larger the data set, the more dramatic the performance difference. H2O has a whole suite of supervised and unsupervised learning algorithms.

With H2O, you can either use their Steam product for serving ML models, or generate and import POJO/MOJO objects (so that you don’t need to have an H2O cluster running) into your existing Java application and directly use the corresponding prediction/scoring function which supports null values, categoricals, and maps input values by name rather than by the order as sklearn requires.

4) Talend Open Studio

It is now available within Deep Learning Studio. Talend Open Studio delivers a single platform for data integration across public, private, and hybrid cloud, as well as on-premises environments.

Talend Open Studio now comes pre bundled with Deep Learning Studio

To open talend studio click on the “Open Studio” option

Download the talend open studio from the link given

For installing Talend Studio you must have JRE/JDK installed on your system. To install Java env click here.

To learn more about talend studio you can refer to their official tutorial from here.

References:

1) http://deepcognition.ai

2) https://www.mathworks.com/discovery/deep-learning.html

3) https://towardsdatascience.com/deep-learning-made-easy-with-deep-learning-studio-an-introduction-18606a67f198

4) https://towardsdatascience.com/making-deep-learning-user-friendly-possible-8fe3c1220f9

5) https://community.talend.com/t5/custom/page/page-id/Tutorials


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Rajat

Written by

Rajat

Torture the data, and it will confess to anything. My professional portfolio website:- http://rajatgupta.me

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