Workspace
A Katonic workspace is an interactive environment for developing and running code. You can conduct research, analyze data, train models, and more. Use workspaces to work in the development environment of your choice, like Jupyter notebooks, RStudio, VS Code, and many other customizable environments. The environment is pre-configured (meaning all your dependencies are preinstalled). All the files and data in your workspace will be preserved for you, across restarts. Your workspace has automatic version control and scalable compute available so that you can use unlimited compute resources to do your data science research.
Katonic has built-in support for JupyterLab, JupterLab on Spark, R Studio and Visual Studio Code.
Supported Workspaces
There are a variety of supported workspace types that are supported in Katonic:
JupyterLab
JupyterLab is a next-generation web-based user interface for Project Jupyter. JupyterLab enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner.
Learn more about JupyterLab.
Various types of JupyterLab
There are 9 types of JupyterLab that Katonic workspace can support:
katonic-studio - In Katonic Studio you can drag notebooks to create pipleine.
katonic-base - In Katonic Base you can use one notebook with tags to create pipeline.
katonic-base-pytorch - In Katonic Base Pytorch you will be having pre-install pytorch Packages.
katonic-base-tenserflow - In Katonic Base Tesnerflow you will be having pre-install tenserflow Packages.
katonic-base-scikit--learn - In Katonic Base Scikit Learn you will be having pre-install skit-learn Packages.
katonic-R-Julia - In Katonic R-Julia you will be having pre-install R-Julia Packages.
katonic-pyspark-standalone - In Katonic Pyspark Stadalone you will be having pre-install pyspark standalone Packages.
katonic-scala-pyspark-standalone - In Katonic Scala Pyspark Standalone you will be having pre-install scala pyspark standalone Packages.
katonic-base-gpu - In Katonic Base Gpu you will be having pre-install gpu Packages.
Jupyter
Jupyter is a project and community whose goal is to "develop open-source software, open-standards, and services for interactive computing across dozens of programming languages".
Learn more about Jupyter.
Various types of Jupyter
There are 3 types of JupyterLab that Katonic workspace can support:
katonic-base-pytorch - In Katonic Base Pytorch you will be having pre-install pytorch Packages.
katonic-base-tenserflow - In Katonic Base Tesnerflow you will be having pre-install tenserflow Packages.
katonic-base-scikit-learn - In Katonic Base Scikit Learn you will be having pre-install skit-learn Packages.
Visual Studio Code
Visual Studio Code is a lightweight but powerful source code editor which runs on your desktop and is available for Windows, macOS and Linux. It comes with built-in support for JavaScript, TypeScript and Node.js and has a rich ecosystem of extensions for other languages (such as C++, C#, Java, Python, PHP, Go) and runtimes (such as .NET and Unity).
Learn more about Visual Studio Code.
Various types of Visual Studio Code
There are 3 types of Visual Studio Code that Katonic workspace can support:
VSCode - Normal VSCode that will launch from our workspace.
VSCode(Python3) - VSCode that will having Packages of Python installed.
VSCode(WebApps) - Mostly used by data Scientist to create webapps like Dash & Streamlit.
R-Studio
RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.
Learn more about R Studio.
Launch a workspace
Workspace sessions are interactive sessions hosted by a Katonic executor where you can interact with code notebooks like Jupyter, RStudio and Visual Studio Code. The software tools and associated configurations available in your session are called Workspaces.
Launching a workspace with JupyterLab
1.1. Click on Workspace from the left sidebar.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Jupyterlab for example.
1.5. Select Image depending upon what environment you select like for JupyterLab we have Katonic Studio, Katonic Base and many more.
1.6. Select Resources. A resources represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
1.7. Katonic provides Custom JupyterLab Image.
1.8. To use custom image, you need to select JupyterLab and then in Image select Custom Image.
1.9. Add the Cutom Image you want to use, for example we are using katonic/run:1.0 as the Custom Image.
1.10. Select the Resources you want to allocate to your Workspace and Click on "Create".
Launching a workspace with JupyterLab(katonic-studio)
1.1. Click on Workspace from the left sidebar.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select JupyterLab for example.
1.5. Select Image depending upon what environment you select like for Jupyter.(example - katonic-studio)
1.6. Select Resources. A resources represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Launching a workspace with JupyterLab(katonic-base)
1.1. Click on Workspace from the left sidebar.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select JupyterLab for example.
1.5. Select Image depending upon what environment you select like for Jupyter.(example - katonic-base)
1.6. Select Resources. A resources represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Other types of JupyterLab
You can create workspace of various Machine Learning and Deep Learning algorithms by using the following drop down.
2. Start Workspace
2.1. Once you create a workspace you can see it will be in a "processing" state.
2.2. Once the Workspace is in a running state it will show the connect button with which you can connect to the environment server.
2.3. When you connect to a Workspace, a new session is created on a machine and your browser is automatically redirected to the selected environment (JupyterLab) UI in a new tab.
2.4. Once your workspace is up and running, you will see a fresh selected interface like JupyterLab interface.
3. Launching a workspace with Jupyter
1.1. Click on Workspace from the left sidebar.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Jupyter for example.
1.5. Select Image depending upon what environment you select like for Jupyter.(example - katonic-base-pytorch)
1.6. Select Resources. A resources represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Launching a workspace with Jupyter(katonic-base-pytorch)
1.1. Click on Workspace from the left sidebar.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Jupyter for example.
1.5. Select Image depending upon what environment you select like for Jupyter.(example - katonic-base-pytorch)
1.6. Select Resources. A resources represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Launching a workspace with Jupyter(katonic-base-tenserflow)
1.1. Click on Workspace from the left sidebar.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Jupyter for example.
1.5. Select Image depending upon what environment you select like for Jupyter.(example - katonic-base-tenserflow)
1.6. Select Resources. A resources represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Launching a workspace with Jupyter(katonic-base-scikit-learn)
1.1. Click on Workspace from the left sidebar.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Jupyter for example.
1.5. Select Image depending upon what environment you select like for Jupyter.(example - katonic-base-scikit-learn)
1.6. Select Resources. A resources represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
4. Launching a workspace with Visual Studio Code
When using a VSCode-equipped Katonic environment you can launch VSCode from the Workspaces Dashboard just as you would launch an RStudio or Jupyter workspace.
4.1. Click on Workspace from the left sidebar navigation menu.
4.2. Click on "Create Workspace".
4.3. Enter a name for your workspace.
4.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Visual Studio Code.
4.5. Select the Image you want to use for your workspace. For now, we are using image VSCode.
4.6. Select the Additional Port you want to allocate to your workspace. For now, we are using port "8050"
4.7. Select Resources. A resource represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Launching a workspace with Visual Studio Code(VSCode)
1.1 Click on Workspace from the left sidebar navigation menu.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Visual Studio Code.
1.5. Select the Image you want to use for your workspace. For now, we are using image VSCode.
1.6. Select the Additional Port you want to allocate to your workspace. For now, we are using port "8050"
1.7. Select Resources. A resource represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Launching a workspace with Visual Studio Code(VSCode(Python3))
1.1 Click on Workspace from the left sidebar navigation menu.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Visual Studio Code.
1.5. Select the Image you want to use for your workspace. For now, we are using image VSCode(pyhton3) with preinstall packages.
1.6. Select the Additional Port you want to allocate to your workspace. For now, we are using port "8050"
1.7. Select Resources. A resource represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Launching a workspace with Visual Studio Code(VSCode(WebApps))
1.1 Click on Workspace from the left sidebar navigation menu.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select Visual Studio Code.
1.5. Select the Image you want to use for your workspace. For now, we are using image VSCode(WebApps).
1.6. Select the WebApp Environment (Dash or Streamlit).
1.7. Select the Additional Port you want to allocate to your workspace. For now, we are using port "8050"
1.8. Select Resources. A resource represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
5. Launching a workspace with R-Studio
1.1. Click on Workspace from the left sidebar.
1.2. Click on "Create Workspace".
1.3. Enter a name for your workspace.
1.4. Select an environment for your workspace. An environment is the software configuration (such as language and packages) that will be used in your workspace. Select R-Studio for example.
1.5. Select Image depending upon what environment you select like for Jupyter.(example - katonic-rstudio)
1.6. Select Resources. A resources represents the compute hardware used for your run. It can be a virtual instance in a cloud services provider, or a physical machine running in your deployment’s on-premise data center.
Stop a Workspace
You can stop a workspace and resume it at a later time. Frequently stopping and resuming a workspace session is a good way to manage compute costs. To stop a workspace:
- Click the "Stop" Button
Resume a Workspace
To resume a stopped workspace:
1.1. Click Workspace in the left sidebar.
1.2. Go to the workspace in your workspace dashboard. Click Start. Your workspace will resume.
Delete a Workspace
1.1. You can delete a workspace if it’s no longer needed. Workspaces must be stopped before they can be deleted.
1.2. Stop your workspace. You can stop a workspace by clicking Stop in the workspace's section.
1.3. Go to the workspace's section. Click the "red trash bin" icon.
1.4. A pop-up will appear to confirm for deleting the workspace, click on "Delete".
View workspaces
To view workspaces that you’ve launched:
1.1. Click Workspace in the navigation menu.
1.2. Now you can view all the workspaces in the workspace dashboard.
Workspace Logs
To check a Workspace log, click on the Logs icon. A Pop up will appear to show all the logs of the workspace.
You can view “User” logs and about your workspace. User logs include information about actions you take within your workspace. Setup logs contain information about Katonic and Kubernetes. If your workspace fails, the logs are a good place to begin investigating.
Workspace Logs(Processing State)
- To check a Workspace log, click on the Logs icon. A Pop up will appear to show all the logs of the workspace.
- You can view “User” logs and about your workspace. User logs include information about actions you take within your workspace while in it's processing state. Setup logs contain information about Katonic and Kubernetes. If your workspace fails, the logs are a good place to begin investigating.
Workspace Logs(Running State)
- To check a Workspace log, click on the Logs icon. A Pop up will appear to show all the logs of the workspace.
- You can view “User” logs and about your workspace. User logs include information about actions you take within your workspace while in it's running state. Setup logs contain information about Katonic and Kubernetes. If your workspace fails, the logs are a good place to begin investigating.
Filter Workspaces
Workspaces can be filter by clicking on the filter tab with options (VSCode, R-Studio, Jupyter, JupyterLab) select and it will filter your workspace accordingly.
Note: To filter select the filter button and select the filter options you want to filter the workspaces.
Search Workspaces
Workspaces can be searched by clicking the search button and type the workspace name, it will search your workspace accordingly.
