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Introduction

Clouderizer Workspace is a cloud IDE for Data Scientists. It allows us to setup, train and deploy AI/ML projects on any infrastructure (AWS, GCP, Local), without worrying about underlying DevOps.

Optimized for Machine Learning Developers

Built in project templates with ML tools like Tensorflow, Keras, Anaconda, Python, Torch. With few clicks you can select machine type, setup environment, upload your deep learning model, download data sets and kick start training, all automated, in one go.

Run projects locally, on cloud or both

Clouderizer Workspace projects can run locally on any Ubuntu / OS X / Windows machine or on any cloud machine. Projects running on local machine can be switched to run on cloud and vice versa. Code, dataset and output checkpoints are synced with Clouderizer Drive in real time. No matter where your project runs, you always resume from where you last stopped.

Forget DevOps. Focus on Machine Learning.

Clouderizer Workspace by default creates a docker environment with most popular libraries and frameworks, with GPU support, for Machine Learning. You can specify additional apt, brew, conda, pip, torch lua packages or custom shell scripts needed to setup your environment. Once configured, environment is setup automatically on any machine you run.

Secure Terminal, Jupyter and Tensorboard

Access your Clouderizer machine from anywhere using our secure private tunnel. SSH terminal, Jupyter Notebooks and Tensorboard are securely accessible from Clouderizer Web Console.

User Management

Create users and allocate cloud resources to team members. Monitor usage across organization. Share project templates and projects within organization. Ideal for use in schools, universities, training programs and software development teams.