Upload Your First Model

Upload a custom model to the Clarifai platform

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The Clarifai platform allows you to upload custom models for a wide range of use cases. With just a few simple steps, you can get your models up and running and leverage the platform’s powerful capabilities.

Let’s walk through how to upload a simple custom model that appends the phrase Hello World to any input text.

tip

To learn more about how to upload different types of models, check out this comprehensive guide.

Step 1: Perform Prerequisites

Install Clarifai Package

Install the latest version of the clarifai Python SDK. This also installs the Clarifai Command Line Interface (CLI), which we'll use for uploading the model.

Bash

 pip install --upgrade clarifai 

Set a PAT Key

You need to set the CLARIFAI_PAT (Personal Access Token) as an environment variable. You can generate the PAT key in your personal settings page by navigating to the Security section.

This token is essential for authenticating your connection to the Clarifai platform.

Unix-Like Systems

 export CLARIFAI_PAT=YOUR_PERSONAL_ACCESS_TOKEN_HERE 

Windows

 set CLARIFAI_PAT=YOUR_PERSONAL_ACCESS_TOKEN_HERE 

tip

On Windows, the Clarifai Python SDK expects a HOME environment variable, which isn’t set by default. To ensure compatibility with file paths used by the SDK, set HOME to the value of your USERPROFILE. You can set it in your Command Prompt this way: set HOME=%USERPROFILE%.

Step 2: Create Files

Create a project directory and organize your files as indicated below to fit the requirements of uploading models to the Clarifai platform.

your_model_directory/
├── 1/
│   └── model.py
├── requirements.txt
└── config.yaml

• your_model_directory/ – The root directory containing all files related to your custom model.
  • 1/ – A subdirectory that holds the model file (Note that the folder is named as 1).
    • model.py – Contains the code that defines your model, including running inference.
  • requirements.txt – Lists the Python dependencies required to run your model.
  • config.yaml – Contains metadata and configuration settings, such as compute requirements, needed for uploading the model to Clarifai.

Add the following snippets to each of the respective files.

model.py

Python

from clarifai.runners.models.model_class import ModelClass


class MyFirstModel(ModelClass):
    """A custom model that adds 'Hello World' to the end of a text."""

    @ModelClass.method
    def predict(self, text1: str = "") -> str:
        """
        This is the method that will be called when the model is run.
        It takes in an input and returns an output.
        """
        output_text = text1 + " Hello World"
        return output_text

requirements.txt

Text

clarifai>=11.3.0

config.yaml

important

In the model section of the config.yaml file, specify your model ID, Clarifai user ID, and Clarifai app ID. These will define where your model will be uploaded on the Clarifai platform.

YAML

model:
  id: "my-first-model"
  user_id: "YOUR_USER_ID_HERE"
  app_id: "YOUR_APP_ID_HERE"
  model_type_id: "text-to-text"

build_info:
  python_version: "3.11"

inference_compute_info:
  cpu_limit: "1"
  cpu_memory: "5Gi"
  num_accelerators: 0

Step 3: Upload the Model

Once your custom model is ready, upload it to the Clarifai platform by navigating to the directory containing the model and running the following command:

CLI

 clarifai model upload 

Step 4: Predict With Model

Once your model is successfully uploaded to Clarifai, you can start making predictions with it.

Python

import os
from clarifai.client import Model

# Set your Personal Access Token (PAT)
os.environ["CLARIFAI_PAT"] = "YOUR_PAT_HERE"

# Initialize with model URL
model = Model(url="https://clarifai.com/alfrick/docs-demos/models/my-first-model")

response = model.predict("Yes, I uploaded it!")

print(response)

Node.js SDK

import { Model } from "clarifai-nodejs";

const model = new Model({
  url: "https://clarifai.com/alfrick/docs-demos/models/my-first-model",
  authConfig: {
    pat: process.env.CLARIFAI_PAT,
  },
});

const response = await model.predict({
  // see available methodNames using model.availableMethods()
  methodName: "predict",
  text1: "Yes, I uploaded it!",
});

console.log(JSON.stringify(response));

// get response data from the response object
Model.getOutputDataFromModelResponse(response);

Output Example

Yes, I uploaded it! Hello World

Congratulations!

You've successfully uploaded your first model to the Clarifai platform and run inference with it!

tip

In this example, we used the default deployment setting (Clarifai Shared). To learn how to leverage our Compute Orchestration capabilities for scalable and cost-efficient inference across various use cases, click here.