Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The Code Ocean Computational Workbench enhances usability, coding, data tool integration, and DevOps lifecycle processes by bridging technology gaps with a user-friendly, ready-to-use interface. It provides readily accessible tools like RStudio, Jupyter, Shiny, Terminal, and Git, while allowing users to select from a variety of popular programming languages. Users can access diverse data sizes and storage types, configure, and generate Docker environments with ease. Furthermore, it offers one-click access to AWS compute resources, streamlining workflows significantly. Through the app panel of the Code Ocean Computational Workbench, researchers can effortlessly share findings by creating and publishing user-friendly web analysis applications for teams of scientists, all without needing IT support, coding skills, or command-line proficiency. This platform allows for the creation and deployment of interactive analyses that operate seamlessly in standard web browsers. Collaboration and sharing of results are simplified, and resources can be reused and managed with minimal effort. By providing a straightforward application and repository, researchers can efficiently organize, publish, and safeguard project-based Compute Capsules, data assets, and their research outcomes, ultimately promoting a more collaborative and productive research environment. The versatility and ease of use of this workbench make it an invaluable tool for scientists looking to enhance their research capabilities.
Description
JupyterHub allows users to establish a multi-user environment that can spawn, manage, and proxy several instances of the individual Jupyter notebook server. Developed by Project Jupyter, JupyterHub is designed to cater to numerous users simultaneously. This platform can provide notebook servers for a variety of purposes, including educational environments for students, corporate data science teams, collaborative scientific research, or groups utilizing high-performance computing resources. It is important to note that JupyterHub does not officially support Windows operating systems. While it might be possible to run JupyterHub on Windows by utilizing compatible Spawners and Authenticators, the default configurations are not designed for this platform. Furthermore, any bugs reported on Windows will not be addressed, and the testing framework does not operate on Windows systems. Although minor patches to resolve basic Windows compatibility issues may be considered, they are rare. For users on Windows, it is advisable to run JupyterHub within a Docker container or a Linux virtual machine to ensure optimal performance and compatibility. This approach not only enhances functionality but also simplifies the installation process for Windows users.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Jupyter Notebook
Yes
Amazon EC2
Yes
Amazon S3
Yes
Amazon Web Services (AWS)
Yes
Cleanlab
No
Coiled
No
DataOps.live
No
Docker
Yes
Git
Yes
GitHub
Yes
Integrations
Jupyter Notebook
Yes
Amazon EC2
No
Amazon S3
No
Amazon Web Services (AWS)
No
Cleanlab
Yes
Coiled
Yes
DataOps.live
Yes
Docker
No
Git
No
GitHub
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Code Ocean
Country
United States
Website
codeocean.com/product/
Vendor Details
Company Name
JupyterHub
Founded
2014
Website
github.com/jupyterhub/jupyterhub
Product Features
Data Analysis
Data Discovery
No
Data Visualization
No
High Volume Processing
No
Predictive Analytics
No
Regression Analysis
No
Sentiment Analysis
No
Statistical Modeling
No
Text Analytics
No