Applications
Containerized applications for data scientists and speed of use
Last updated
Containerized applications for data scientists and speed of use
Last updated
Applications are docker containers that include all dependencies to run a system. These are provided by hardware vendors or uploaded by the tenant.
The catalog contains versioned apps that Denvr has selected for various uses. They are focused on data scientists who prefer a fast-start environment like PyTorch, RAPIDS, and JAX along with a JupyterLab.
Denvr Dataworks can setup new applications as requested including specific versions.
Denvr Cloud has a single page view to create applications including all of the configuration options that are relevant.
This section provides essential details for setting up a container, including the instance name and where resources are allocated from.
Name | Refers to the unique identifier or label for the application. It helps users manage and track different containers within their infrastructure. |
Resource pool | Defines how compute resources are allocated, either on-demand for dynamic allocation or reserved for dedicated single-tenant resources. |
Difference applications are selected from the Catalog.
Jupyter Authentication Token | Applications that include JupyterLab require a shared password for basic authorization. Please select a difficult password for better security. |
Release | Select a specific version of the container. This also displays the docker hub source repository used. |
Defines the overall configuration of the application container, including the type of hardware (such as GPU or CPU), performance characteristics, and resource allocation.
GPU platform | Refers to the type of GPU hardware available for running AI and computational tasks. Different platforms offer varying levels of performance and specialization for different workloads. |
CPU platform | Refers to the type of central processing unit (CPU) hardware available for provisioning. Different CPU platforms offer varying levels of performance for general-purpose computing, suited for tasks that do not require specialized GPU acceleration. |
Instance size | Defines the specific resource allocation, including the number of GPUs, CPUs, memory, and storage, for a virtual machine. It determines the power and capacity of the compute environment. |
Containers can be configured for SSH access if required. It includes the default user ID and the option to provide SSH keys for secure, passwordless login.
The right panel shows the application summary. Press Launch Application to create the virtual machine.
The application detail screen shows the online readiness, actions to start/stop, and Access to connect with the JupyterLab inferface.
The Applications overview screen displays all running containers and provides fast access to lifecycle actions and access to the JupyterLab interface.
