Environmental Preset
Check the environment preset information used in your project.
Last updated
Check the environment preset information used in your project.
Last updated
Environment preset means providing an IDE environment and library to start data analysis and artificial intelligence algorithm model development. Environment presets are separated by CPU / GPU. Choose the environment preset you want to use for your project and use it!
A CPU-only environment preset with Python 3.8, 2.7, and R kernels. Additionally, we provide an environment for machine learning frameworks such as Python 3.8 version Pytorch, and tensorflow2.
It provides a stable version of R, R3.6, and provides a variety of visualization and data processing packages, including ggplot2, and dplyr.
It provides the latest version of R, R 4.2, and offers a variety of visualization and data processing packages, including ggplot2, and dplyr.
A CPU-only environment preset with a Python 3.8 kernel. Collaborate with two or more users in real time, including real-time collaboration (RTC) capabilities in Jupyterlab. CAUTION: This is an experimental feature that may cause data changes, so be sure to back up your data when using this feature.
A CPU-only container containing MATLAB, a programming and numerical analysis platform used for data analysis, algorithm development, and model generation. You can perform simple tasks using the CPU.
The SSH environment preset enables remote access to projects. Users can access SSH through terminals such as (XShell, PuTTY, SSH Client, etc.) to conduct research from an external IDE to the desired environment
Environment preset with Python 3.8 and 2.7 Kernels. Additionally, we provide an environment for machine learning frameworks such as Python 3.8 version Pytorch, and tensorflow2. Pre-installed CUDA and CuDNN libraries with GPU availability are ideal for professional research.
An environment preset that includes MATLAB, a programming and numerical analysis platform used for data analysis, algorithm development, and model generation. Toolboxes for GPU are used and deep learning is installed for professional work.
