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Explore Pricing Docs Blog Changelog Sign in Get started


MACHINE LEARNING DOESN’T NEED TO BE SO HARD.


RUN MODELS IN THE CLOUD AT SCALE.

Explore Pricing Docs Blog Changelog Sign in Get started
Fork this on GitHub


01
RUN

Replicate lets you run machine learning models with a few lines of code, without
needing to understand how machine learning works.

Use our Python library:

import replicate
output = replicate.run(
    "stability-ai/sdxl:2b017d9b67edd2ee1401238df49d75da53c523f36e363881e057f5dc3ed3c5b2",
    input={"prompt": "an astronaut riding a rainbow unicorn"},
)
print(output)



...or query the API directly with your tool of choice:

curl -s -X POST \
    -H "Authorization: Token $REPLICATE_API_TOKEN" \
    -d '{"version": "2b017d9b67edd2ee1401238df49d75da53c523f36e363881e057f5dc3ed3c5b2", \
         "input": { "prompt": "an astronaut riding a rainbow unicorn" } }' \
    https://api.replicate.com/v1/predictions




THOUSANDS OF MODELS, READY TO USE

Machine learning can do some extraordinary things. Replicate's community of
machine learning hackers have shared thousands of models that you can run.

LANGUAGE MODELS

Models that can understand and generate text

VIDEO CREATION AND EDITING

Models that create and edit videos

SUPER RESOLUTION

Upscaling models that create high-quality images from low-quality images

IMAGE RESTORATION

Image and video generation models trained with diffusion processes

IMAGE TO TEXT

Image and video generation models trained with diffusion processes

TEXT TO IMAGE

Image and video generation models trained with diffusion processes

Explore models or, learn more about our API

IMAGINE WHAT YOU CAN BUILD

With Replicate and tools like Next.js and Vercel, you can wake up with an idea
and watch it hit the front page of Hacker News by the time you go to bed.

Here are a few of our favorite projects built on Replicate. They're all
open-source, so you can use them as a starting point for your own projects.
Check out our showcase

ROOMGPT BY HASSAN EL MGHARI

Redesign rooms in seconds using AI

SCRIBBLE DIFFUSION BY ZEKE SIKELIANOS

Sketch to art

ZOO BY CHARLIE HOLTZ

A playground for comparing AI image models


02
PUSH

You're building new products with machine learning. You don't have time to fight
Python dependency hell, get mired in GPU configuration, or cobble together a
Dockerfile.

That's why we built Cog, an open-source tool that lets you package machine
learning models in a standard, production-ready container.

First, define the environment your model runs in with cog.yaml:

build:
  gpu: true
  system_packages:
    - "libgl1-mesa-glx"
    - "libglib2.0-0"
  python_version: "3.10"
  python_packages:
    - "torch==1.13.1"
predict: "predict.py:Predictor"



Next, define how predictions are run on your model with predict.py:

from cog import BasePredictor, Input, Path
import torch

class Predictor(BasePredictor):
    def setup(self):
        """Load the model into memory to make running multiple predictions efficient"""
        self.model = torch.load("./weights.pth")

    # The arguments and types the model takes as input
    def predict(self,
          image: Path = Input(description="Grayscale input image")
    ) -> Path:
        """Run a single prediction on the model"""
        processed_image = preprocess(image)
        output = self.model(processed_image)
        return postprocess(output)



Now, you can run predictions on this model locally:

$ cog predict -i @input.jpg
--> Building Docker image...
--> Running Prediction...
--> Output written to output.jpg



Or, build a Docker image for deployment:

$ cog build -t my-colorization-model
--> Building Docker image...
--> Built my-colorization-model:latest



Finally, push your model to Replicate, and you can run it in the cloud with a
few lines of code:

$ cog push
Pushed model to replicate.com/your-username/my-colorization-model



import replicate
output = replicate.run(
    "your-username/your-model:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
    image=open("input.jpg"),
)



Push a model or, learn more about Cog


03
SCALE

Deploying machine learning models at scale is horrible. If you've tried, you
know. API servers, weird dependencies, enormous model weights, CUDA, GPUs,
batching. If you're building a product fast, you don't want to be dealing with
this stuff.

Replicate makes it easy to deploy machine learning models. You can use
open-source models off the shelf, or you can deploy your own custom, private
models at scale.

 * AUTOMATIC API
   
   Define your model with Cog, and we'll automatically generate a scalable API
   server for it with standard practices and deploy on a big cluster of GPUs.

 * AUTOMATIC SCALE
   
   If you get a ton of traffic, Replicate scales up automatically to handle the
   demand. If you don't get any traffic, we scale down to zero and don't charge
   you a thing.

 * PAY BY THE SECOND
   
   Replicate only bills you for how long your code is running. You don't pay for
   expensive GPUs when you're not using them.

Get started or, learn more about us

Replicate
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