Workspaces are full Linux computing environments that come pre-installed with all the tools you need to explore data and build and experiment with your models. A single workspace can be upgraded to up to 8 GPUs and downgraded back to CPU with ease. You can also collaborate on workspaces with other users by adding them as members to your project.
NOTE: Once a Workspace is created, it will continue to consume compute resources like CPU, GPU, RAM and storage. If you want to leverage compute resources for run to completion tasks please use jobs instead.
TIP: We recommend using lower cost CPU instances initially with a notebook of your choice to browse files, modify code, import libraries and connect to datasets. You can easily switch to a GPU at any time.
Creating a Workspace is very easy, once you are in a project, click "Workspaces" and then the "Create" button.
Once a workspace is launched, you can easily switch between machine types by clicking the edit button next to the machine type:
Switching between applications in workspaces
You can easily toggle between different applications within a workspace using the application switcher drop-down:
You can select either a CPU or GPU machine type.
Using a CPU will take a much longer time to process data vs. a GPU. Once you are ready to start training your models you may want to select a GPU as this will ultimately save both time and money to achieve results quicker that will help you to iterate through algorithm changes.
Environments are pre-installed with all the popular tools you need to build and train your machine learning or deep learning algorithms.
Some of the popular tools and languages included are:
You can choose from several different sizes of SSD Disk space ranging from 10GB to 10TB.
If you require larger sizes please contact email@example.com
You can terminate workspaces by pressing the 'DELETE' button on the side panel of each individual workspace container.
NOTE: Once you delete a workspace, everything stored in the workspace will be deleted.