Quickstart
This guide will get you all set up and ready to use the Peer AI API. We'll cover how to make your first API request. We'll also look at where to go next to find all the information you need to take full advantage of our API.
Before you can make requests to the Peer AI API, you will need to grab your API key. If you don't have an account yet, you can sign up for free.
Choose your client
Before making your first API request, you need to pick which API client you will use. In addition to good ol' cURL HTTP requests, Peer AI also offers SDK for JavaScript. In the following example, you can see how to install each client.
# cURL is most likely already installed on your machine
curl --version
Making your first API request
After picking your preferred client, you are ready to make your first call to the Peer AI API. Below, you can see how to send a POST request to the pipeline endpoint to run a sentiment analysis model on the text "I love transformers!".
curl -X POST https://api.peer-ai.com/v1/pipeline \
-H "X-API-Group: main" \
-H "X-API-Key: {YOUR_API_KEY}" \
-H "Content-Type: application/json" \
-d "{\"task\": \"sentiment-analysis\", \"inputs\": [\"I love transformers!\"]}"
What's next?
Great, you're now set up to make API requests to Peer AI. Here are some next steps to help you get the most out of our API:
- Learn about how to create and use your own compute groups
- Read the docs on how to use your own models
Tasks
Read the docs on how to run compute on different tasks.
Natural Language Processing
Learn how to run AI models related to natural language processing tasks. For example, text classification, question answering, and summarization.
Vision
Learn how to run AI models related to vision tasks. For example, image classification, object detection, and image segmentation.
Stable Diffusion
Learn how to run AI models related to stable diffusion tasks. For example, image classification, object detection, and image segmentation.
Audio
Learn how to run AI models related to audio tasks. For example, speech recognition, speech synthesis, and speaker recognition.
Tabular
Learn how to run AI models related to tabular data. For example, classification and regression.
Multimodal
Learn how to run AI models related to multimodal tasks. For example, image captioning, visual question answering, and visual reasoning.
Reinforcement Learning
Learn how to run AI models related to reinforcement learning tasks. For example, Atari games, MuJoCo, and ProcGen.