Onepanel's core concepts can be categorized as follows:
- Data Annotation
Projects are a way for you to organize your code, notebooks and jobs. You can make them public or private, and invite others to collaborate. In projects, your code, job runs and outputs are under version control and your notebooks will always automatically pull the latest code on launch.
See creating projects for more information.
Workspaces are Docker containers that contain preinstalled machine learning and deep learning packages, as well as Jupyter, Zeppelin or H2O.ai notebooks. Once you create a workspace, you can launch one of the above notebooks from the browser, or SSH into the workspace. You can optionally install custom packages into your workspaces.
Workspaces are ideal when you are starting to develop your model or are exploring data. For training multiple models in parallel, we recommend that you use jobs.
See creating workspaces for more information.
Much like workspaces, jobs are also Linux environments that contain preinstalled machine learning and deep learning packages. In addition to the preinstalled packages, jobs automatically pull the latest code for your project and run your commands. Once the job is completed, you will be able to see a snapshot of the code, command, environment and output.
See creating jobs for more information.
We have integrated CVAT - an open source video and image annotation tool. Now you can quickly and easily leverage third-party data annotators and provide direct access to your datasets for annotation all within Onepanel.
See annotation tools for more information.