Robbie
$4.99 / user / month
- Free 200 Project Hours per month
- $0.05 per hour thereafter
- Unlimited running projects
- Community Support
Democratizing Deep Learning
Affordable Deep Learning For Everyone
Setup. Train. Deploy
What users are saying
TIL about Clouderizer, which (amongst other things) allows you to more easily study @fastdotai lessons with free GPU access thru Google Colabhttps://t.co/KIpztpYzh1
— Jeremy Howard (@jeremyphoward) 8 May 2018
Have been using @ClouderizerT to run my @fastdotai course notebooks on Google Colab for some time now......its awesome!! https://t.co/aNwBv4DBgh #DeepLearning
— shijun (@qqsj789) 4 May 2018
“Fastest way to setup https://t.co/InLseTuKfM course notebooks , for free — using Google Colab and Clouderizer” by @ClouderizerT https://t.co/7AeepvYN1b
— João Victor (@JoaoVictor_AC) 8 May 2018
Fastest way to serve, demo and test your Deep Learning Model with a mobile web app and REST APIs https://t.co/2BB0HXuHWZ by @ClouderizerT #DeepLearning #MachineLearning #tech #API #Python #DataScience #Programming #ObjectDetection #computervision #AI #DL #ArtificialIntelligence pic.twitter.com/q7wepUfzad
— JetRuby (@jetrubyagency) 21 June 2018
“Kaggle Competition on Google Colab — how to easily import datasets and local files and access…” by @ClouderizerT https://t.co/FVswyTNXy3
— Karthick (@phykar) 26 May 2018
Videos
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 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 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.
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Reduce cloud bills
Save upto 90% of cloud bills by running Clouderizer projects on EC2 Spot instances, GCP Preemptible VMs or Paperspace. Clouderizer seamlessly integrates with AWS, auto recommends best bidding price for spot instances across regions and makes working with AWS EC2 spot instances a breeze.
Clouderizer Drive helps reduce EBS volume costs when machines are not in use.
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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.
Community Templates
Search, Select and Clone one of the available Clouderizer templates for Machine Learning and Big Data Analytics and get yourself started in seconds.
Pricing
* For custom plan, Self hosting and On-Premise deployment, please contact sales@clouderizer.com
Frequently asked questions
Does Clouderizer offer hardware platform as well?
Clouderizer offers software platform to address various road blocks & challenges of building deep learning models. Various aspects of DeepLearning (hardware, drivers, pre-requisite packages, training data, model, checkpoints, inference) are brought under one umbrella as Clouderizer projects.
Clouderizer projects can be easily deployed on any hardware (local or cloud). Clouderizer can integrate seamlessly with your AWS account and enable you to spin cheap SPOT instances and kick start Clouderizer projects on them in few clicks.
What are the pre-requisite of using Clouderizer?
Nothing. Clouderizer projects can be run on local hardware as well. In case you wish to use Cloud machines with GPU, you will need an account with some IaaS provider like AWS or GCP.
How do I remotely access these machines?
Clouderizer console gives access to secure in-browser shell and Jupyter Notebook for all running projects.