Deep Cognition Studio

What is Deep Learning Studio?

Deep Learning Studio (DLS) is a software toolkit and platform that simplifies designing, training, testing and deployment of deep learning AI models. The main features of Deep Learning Studio are the following:

DLS No-Coding Editor and Trainer

This component allows for the design and training of the model without any programming. This also allows expert engineers to save time that otherwise will be spent in coding. Further editor has many innovative features like built in help and design time validation of a neural network. It is geared for making deep learning developers more efficient. DLS provides full flexibility to design arbitrary models including CNN, RNN, and LSTM. Automatic type encoding (for example, one hot encoding) is done by the software automatically based on selected data type.

Dataset Management

DLS allows you to organize and manage all your datasets using an easy to use dashboard.

DLS Neural Network AutoML

AutoML feature of Deep Learning Studio can design a starting neural network for your dataset. This can be a good starting point for more complex neural network designs.

Transparent Multi-GPU Training

DLS can transparently use all available GPUs on the machine it is installed on.

Optimized Data Ingestion

DLS has an optimized engine to read and feed data from your disks so that a GPU does not get under-utilized.

Versioning and Checkpoint

All models and their training results are automatically saved so you don't lose any work. DLS's built in versioning system lets you go back to previous models if required.

Single Click Model Deployment

DLS allows deployment of built models with a single click as REST API and a simple form-based web application.

Model Code Export

Built models can be exported as human readable PyThon code for PyTorch, Keras, MxNet, TensorFlow, CNTK or ONNX.

Development Environment

For coding-based projects, DLS provides pre-configured Conda development environments for almost all deep learning frameworks like TensorFlow and Keras. These environments are accessible through terminal or Jupyter Notebook.

DLS is developed by Deep Cognition and is available for both cloud and local machine. It supports (Linux and Windows) OS.

DLS is free for all developers and can be used for commercial use. You will need a free account at https://deepcognition.ai/ for using this software.

Running Deep Learning Studio

Create persistent directories where your datasets and work will be stored $ mkdir -p $HOME/dls/database $HOME/dls/keras $HOME/dls/data

Sample nvidia-docker run:

$ nvidia-docker run \ -d \ --rm \ --name deep_learning_studio \ -p 8880:80 -p 8881:80 -p 8888:8880 -p 8886:8888 -p 8889:3000 \ -v $HOME/dls/data:/data \ -v $HOME/dls/database:/home/app/database \ -v $HOME/dls/keras:/root/.keras \ -e DLS_EULA_AGREED=y \ nvcr.io/partners/deep-learning-studio:latest

Where: -d: run container in detached mode --name deep_learning_studio: assign name deep_learning_studio to the container -p 8880:80 -p 8881:80 -p 8888:8880 -p 8886:8888 -p 8889:3000 : expose required ports for the application -v $HOME/dls/data:/data: this will be the main directory where your uploaded datasets and project will be stored. -v $HOME/dls/database:/home/app/database: directory where DLS will store settings and configuration. -v $HOME/dls/keras:/root/.keras: settings for Keras will be stored here. -e DLS_EULA_AGREED=y: by selecting this option you accept DLS EULA

Once container is launched you can open http://127.0.0.1:8880/ link from your browser to access DLS.

Suggested Reading

Getting Started Videos: http://deepcognition.ai/resources/videos/

Privacy Policy

Visit https://deepcognition.ai/privacy-policy/ to learn about privacy policy of Deep Learning Studio.

Support

Community forum: https://community.deepcognition.ai/ Email us at support@deepcognition.ai or send us message from https://deepcognition.ai/contact-us/