Quickstart

Here is a quick walkthrough to get you started with Inferless to deploy your first machine learning model.

​

Step 1: Clone the Template Repository to your Github

We have created a Template repository that you can use as a base to inject your code you can find a sample here with the GPT Neo model.

Github Repo: https://github.com/infer-less/template-method

## Implement the Load function here for the model 
    def initialize(self):
        self.generator = pipeline("text-generation", model="EleutherAI/gpt-neo-125M",device=0)
    
# Function to perform inference 
    def infer(self, inputs):
        # inputs is a dictionary where the keys are input names and values are actual input data
        # e.g. in the below code the input name is "prompt"
        prompt = inputs["prompt"]
        pipeline_output = self.generator(prompt, do_sample=True, min_length=20)
        generated_txt = pipeline_output[0]["generated_text"]
        # The output generated by the infer function should be a dictionary where keys are output names and values are actual output data
        # e.g. in the below code the output name is "generated_txt"
        return {"generated_text": generated_txt}

# perform any cleanup activity here
    def finalize(self,args):
        self.pipe = None

input_schema.py

INPUT_SCHEMA = {
    "prompt": {
        'datatype': 'STRING',
        'required': True,
        'shape': [1],
        'example': ["There is a fine house in the forest"]
    }
}

Read more about the model code structure here

​

Step 2: Login to the Inferless dashboard and click on Import model button

• Click on "Add a Custom Model" button that you see on the top right

​

Step 3: Follow steps to complete the model import

• Select the GitHub/GitLab Integration option to connect your source code repository with the deployment environment.
• Navigate to the specific GitHub repository that contains your model’s code. Here, you will need to identify and enter the name of the model you wish to import.
• Choose the appropriate type of machine that suits your model’s requirements. Additionally, specify the minimum and maximum number of replicas to define the scalability range for deploying your model.
• Optionally, you have the option to enable automatic build and deployment. This feature triggers a new deployment automatically whenever there is a new code push to your repository.
• If your model requires additional software packages, configure the Custom Runtime settings by including necessary pip or apt packages. Also, set up environment variables such as Inference Timeout, Container Concurrency, and Scale Down Timeout to tailor the runtime environment according to your needs.
• Wait for the validation process to complete, ensuring that all settings are correct and functional. Once validation is successful, click on the “Import” button to finalize the import of your model.

​

Step 4: Wait for the model build to complete usually takes ~5-10 minutes

You can see the progress of the model build on the progress page

​

Step 5: Use the APIs to call the model

Once the model is in ‘Active’ status you can click on the ‘API’ page to try the model with sample inputs.