Introduction

Welcome to the Truefoundry QuickStart documentation! This guide is your gateway to exploring the capabilities of Truefoundry, a platform designed to simplify the deployment of machine learning models into production environments.

In this guide, we'll immerse ourselves in a real-world problem: the Iris Classification Challenge. By following this documentation, you'll grasp the steps to seamlessly deploy a model via Truefoundry, and then access and utilize the deployed application.

By the conclusion of this QuickStart, you will have a deployed FastAPI service that looks like the following:

Problem Statement: Iris Classification Challenge

The challenge involves the task of distinguishing between different species of iris flowers based on the measurements of their petals and sepals.

We'll work with a dataset that includes attributes of various iris samples. Our ultimate aim is to build a machine learning model that can effectively classify iris species.

Let's get started with the steps for your first deployment:

Step 1: Logging into Your Truefoundry Account

To begin, log in to your Truefoundry account using your credentials. Upon successful login, you will be directed to the Deployments Dashboard for services.

Step 2: Initiating Deployment via UI

1. Navigate to the Test out Service Deployment section on the Deployments Dashboard. If there are existing deployments, you may need to scroll down slightly to find it.
2. Click on the Deploy using UI button to initiate the deployment process.

Step 3: Workspace Selection

A modal will appear with a Where would you like to deploy? option. Click on the Search Workspace bar, and select the desired workspace for your deployment.

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Note:

If you don't have any workspace right now, you can create a new workspace by clicking the Create New Workspace link and following along this Documentation or contact your cluster admin in case of some issues

Once selected click on the Next Step button to continue with the deployment.

Step 4: Configure Deployment

Now, you'll come across a deployment form featuring a range of configuration choices. These choices play a significant role in how your service gets deployed.

To make your first deployment smoother (thanks to the Deploy using UI button), we've already taken care of the basic settings. Nevertheless, it's important to know that you might want to fine-tune these options to suit your needs.

In the next section we are giving a very brief overview of what the options mean:

Explanation of Deployment Options

• Name:
  The Name field allows you to assign a unique identifier to the service within your workspace.
• Build Source (Deploy a Docker Image or Build and Deploy from Source Code):
  The Build Source option lets you specify whether you want to deploy a pre-built Docker image or build and deploy from your source code.
• Build Specification (Build Using Dockerfile or Using Buildpack):
  The Build Specification helps determine the method you want to use for building your service. You can choose to build using a Dockerfile, providing custom configuration for your environment, or you can opt for a Buildpack, which automates the build process based on your application's requirements.
• Ports:
  The Ports field enables you to define the communication channels that your service will utilize. Specify the port numbers that your service will listen on to receive incoming requests.
• Environment Variables:
  Environment Variables allow you to configure runtime settings for your service. You can provide key-value pairs that influence how your service behaves when it's running, such as database connection strings, API keys, or other configuration parameters.
• Resources:
  • CPU:
    The CPU resource allocation determines how much processing power your service can utilize. You can specify the desired amount of CPU capacity that your service can consume to perform its tasks efficiently.
  • Memory:
    Memory allocation dictates the amount of RAM your service can use. Define the memory limit to ensure that your service has enough memory available for its operations without causing performance issues.
  • Storage:
    Storage allocation refers to the amount of disk space your service can access. Specify the storage limit to ensure that your service has sufficient space to store files, logs, and other data it generates during its operation.

Step 5: Submit the form

Now that all the deployment options are filled, you can proceed by clicking the Create button. This will initiate your deployment process. After clicking Create, your Service Dashboard will resemble the following:

While your deployment is in progress, you can hover over the spinner icon to check the status of the deployment. After a brief moment, the deployment should become active, and your Service Dashboard will transform to appear as follows:

With the deployment now active, you can proceed to click on your specific service. Doing so will open up the dashboard dedicated to your service, where you'll have access to various details. Notably, you can retrieve the endpoint for your service:

This endpoint is where your deployed service can be accessed, enabling interactions with your deployed machine learning model.

Copy the following endpoint url, we will need this to make the requests. We will now send the following examples for prediction.

Here is an example Python code snippet to send a request with the above data.

import json
from urllib.parse import urljoin

import requests

# Replace this with the value of your endpoint 
ENDPOINT_URL = "<YOUR_ENDPOINT_URL>" #like https://truefoundry.tfy-ctl-euwe1-production.production.truefoundry.com/fastapi-docs/

response = requests.post(
    urljoin(ENDPOINT_URL, f'predict'),
    json={
            "sepal_length": 5.7,
            "sepal_width": 4.3,
            "petal_width": 3.2,
            "petal_length": 1.5,
    }
)

result = response.json()
print("Predicted Classes:", result["prediction"])

This will give you the following output

Predicted Classes: 0