# Canvas View - [Navigating Your Canvas](https://docs.zerve.ai/guide/canvas-view/navigating-your-canvas.md): This section is for exploring different functionalities of canvas - [Creating a Canvas](https://docs.zerve.ai/guide/canvas-view/navigating-your-canvas/creating-a-canvas.md) - [My Canvases](https://docs.zerve.ai/guide/canvas-view/navigating-your-canvas/my-canvases.md): The canvas view in the my canvases section can be adjusted according to the user's preference. The different views are show below: - [Edit and Sort](https://docs.zerve.ai/guide/canvas-view/navigating-your-canvas/edit-and-sort.md) - [Shared with me](https://docs.zerve.ai/guide/canvas-view/navigating-your-canvas/shared-with-me.md) - [Starred](https://docs.zerve.ai/guide/canvas-view/navigating-your-canvas/starred.md) - [Layers Overview](https://docs.zerve.ai/guide/canvas-view/layers-overview.md): Layers are used for arranging blocks as individual workflows. Each canvas can be set up to contain multiple workflows of same/different types for different parts of a data science problem. - [Development](https://docs.zerve.ai/guide/canvas-view/layers-overview/development.md): The IDE Layer. Development layer is similar to any scripting environment - Jupyter or datascience Coding IDE - but better. - [Deployment](https://docs.zerve.ai/guide/canvas-view/layers-overview/deployment.md): API builder allows developers to define endpoints, map data structures, set up authentication and authorization mechanisms, and generate documentation for their APIs. - [Create Deployment](https://docs.zerve.ai/guide/canvas-view/layers-overview/deployment/create-deployment.md): There are 3 components to setup a deployment in Zerve - API Controller, API Route and the code blocks to execute as part of the deployment. - [Download Deployment](https://docs.zerve.ai/guide/canvas-view/layers-overview/deployment/download-deployment.md) - [API Methods](https://docs.zerve.ai/guide/canvas-view/layers-overview/deployment/api-methods.md) - [Scheduled Jobs](https://docs.zerve.ai/guide/canvas-view/layers-overview/scheduled-jobs.md): Each canvas in Zerve has an inbuilt scheduler as part of it. This can be used to run analytics workflows that need to be run at scheduled intervals - hourly/daily/weekly/custom (cron string) - [Files](https://docs.zerve.ai/guide/canvas-view/files.md): File system is the storage space for all the files required to create, run and execute the canvas. - [Storing Files](https://docs.zerve.ai/guide/canvas-view/files/storing-files.md): Canvas file system is a dynamic environment to store files of any type with a specified size limit. - [Import Files](https://docs.zerve.ai/guide/canvas-view/files/import-files.md): Once the file is uploaded, it can be accessed in the Python or R blocks, and the files can be of any type. - [Download Files](https://docs.zerve.ai/guide/canvas-view/files/download-files.md): The files can also be seamlessly downloaded into file system from Python or R blocks. - [Deleting Files](https://docs.zerve.ai/guide/canvas-view/files/deleting-files.md): The zerve's canvas offers different ways to delete a file from file system - [Files in Folder](https://docs.zerve.ai/guide/canvas-view/files/files-in-folder.md) - [Installing Packages](https://docs.zerve.ai/guide/canvas-view/installing-packages.md): User can install Python, Linux and R packages by using the Environment feature. - [Requirements](https://docs.zerve.ai/guide/canvas-view/installing-packages/requirements.md): By default, zerve canvas run in containers with Python 3.11.1, and a number of pre-installed packages. The user has the option to add new packages, new python versions, etc. from the requirements tab. - [Global Imports](https://docs.zerve.ai/guide/canvas-view/global-imports.md): Imports done under each code block will work only inside it. Users can add a package or function to Global Imports section to make it available across all the blocks in the canvas. - [Blocks and Connections](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections.md) - [Block types](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types.md) - [Python](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/python.md) - [Query](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/query.md) - [R](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/r.md) - [Markdown](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/markdown.md) - [Gen AI](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/gen-ai.md): GenAI blocks can be used to connect to LLMs on Bedrock/OpenAI making it suitable for various applications, including text generation, summarization, translation, text to image, and more. - [R Markdown](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/r-markdown.md): Zerve’s R Markdown block enables users to integrate and execute R code within their analytics environment, facilitating the creation and publication of dynamic, interactive reports. - [UI Blocks (Input and Output)](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/ui-blocks-input-and-output.md): These blocks help the user to create an application in the canvas - [Aggregator](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/aggregator.md) - [Logic Gate](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/logic-gate.md) - [Code and Variable Mode](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/code-and-variable-mode.md) - [Block level Security](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-level-security.md) - [Language Interoperability](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/language-interoperability.md): This functionality of Zerve allows users to share the data between two programming languages - [Compute Settings](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/compute-settings.md) - [Lambda vs Fargate vs GPU](https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/compute-settings/lambda-vs-fargate-vs-gpu.md) - [How Zerve Works](https://docs.zerve.ai/guide/canvas-view/how-zerve-works.md) - [Assets](https://docs.zerve.ai/guide/canvas-view/assets.md): Assets are used for storing functions, queries, constants, secrets, and connections for seamless development. - [Functions & Classes](https://docs.zerve.ai/guide/canvas-view/assets/functions-and-classes.md): "Functions & Classes" are reusable code snippets that encapsulate specific logic or operations or classes. This provides code modularity, readability, and reduces duplication. - [Constants & Secrets](https://docs.zerve.ai/guide/canvas-view/assets/constants-and-secrets.md): "Constants" are used for unchanging data in code to enhance readability and maintainability, "Secrets" refer to sensitive information that should be protected and not exposed in your code. - [Connections](https://docs.zerve.ai/guide/canvas-view/assets/connections.md): Zerve offers support to integrate with popular database solutions which can be queried directly from canvas and can be used across different blocks/languages. - [AI Agent](https://docs.zerve.ai/guide/canvas-view/ai-agent.md) - [Chat](https://docs.zerve.ai/guide/canvas-view/ai-agent/chat.md) - [Search](https://docs.zerve.ai/guide/canvas-view/ai-agent/search.md): Agent can search the internet for you based on your input - [Code](https://docs.zerve.ai/guide/canvas-view/ai-agent/code.md) - [Best Practices](https://docs.zerve.ai/guide/canvas-view/ai-agent/best-practices.md) - [Fleets](https://docs.zerve.ai/guide/canvas-view/fleets.md): Fleets is Zerve's built-in feature for parallel processing. With a single line of code, users can parallelize code to run across multiple blocks. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.zerve.ai/guide/canvas-view.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.