airdot 0.2.0b0

A code base for deploying ml api

🚀 Airdot Deployer

Welcome aboard the Airdot Deployer [Beta] 🛠️, your ultimate one-stop solution for seamlessly transitioning your machine learning models from Jupyter notebooks to the live web 🌐. Bid farewell to the tedious process of manually saving and uploading models. Airdot Deployer serves as the perfect assistant, effortlessly managing everything from code and requirements to data objects and beyond, ensuring a smooth deployment experience like never before.

Airdot can deploy your models in a single step.

from airdot.deployer import Deployer
deployer_obj = Deployer().run(<your-ml-predictor>)

Once deployed, your model will be up and running on the web, accessible to users worldwide. No more worrying about complex server setups or manual configuration. Airdot Deployer does all the heavy lifting for you.

curl -XPOST <url> -H 'Content-Type: application/json' -d '{"args": "some-value"}'

Whether you're a data scientist, developer, or tech enthusiast, Airdot Deployer empowers you to showcase your machine learning prowess and share your creations effortlessly. So why wait? Join our beta program now and experience the future of machine learning deployment with Airdot Deployer.

What does Airdot supports ?

This section should list any major frameworks/libraries used to bootstrap your project. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.

• Local Deployment with Docker
• K8s Deployment with seldon core

Want to try Airdot ? follow setup instructions.

📋 Setup Instructions

Before we get started, you'll need to have Docker, Docker Compose, and s2i installed on your machine. If you don't have these installed yet, no worries! Follow the steps below to get them set up:

Docker Install

Please visit the appropriate links to install Docker on your machine:

• For macOS, visit here
• For Windows, visit here
• For Linux, visit here

S2I install

For Mac You can either follow the installation instructions for Linux (and use the darwin-amd64 link) or you can just install source-to-image with Homebrew:

$ brew install source-to-image

For Linux just run following command

curl -s https://api.github.com/repos/openshift/source-to-image/releases/latest| grep browser_download_url | grep linux-amd64 | cut -d '"' -f 4  | wget -qi -

For Windows please follow instruction here

💻 Airdot Installation

Install the Airdot Deployer package using pip:

pip install "git+https://github.com/airdot-io/airdot-Deploy.git@main#egg=airdot"

or

# pypi uri will be added soon

🎯 Let's try out

Local Deployments

Run following in terminal to setup minio and redis on your machine

docker network create minio-network && wget  https://raw.githubusercontent.com/airdot-io/airdot-Deploy/main/docker-compose.yaml && docker-compose -p airdot up

Train your model

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from airdot.deployer import Deployer
from sklearn import datasets
import pandas as pd

iris = datasets.load_iris()
iris = pd.DataFrame(
    data= np.c_[iris['data'], iris['target']],
    columns= iris['feature_names'] + ['target']
)
X = iris.drop(['target','species'], axis=1)
X = X.to_numpy()[:, (2,3)]
y = iris['target']
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.5, random_state=42)
log_reg = LogisticRegression()
log_reg.fit(X_train,y_train)

Test your model

def predict(value)
    return log_reg.predict(value)

Deploy in one step 🤯

deployer_obj = Deployer().run(predict)

Use your deployed Model

curl -XPOST http://127.0.0.1:8000 -H 'Content-Type: application/json' -d '{"value": [[4.7, 1.2]]}'

Want to stop local deployments

deployer.stop('get_value_data') # to stop container

Deployment on k8s using seldon-core deployment

Note - This method will use your current cluster and uses seldon-core to deploy

from airdot import Deployer
import pandas as pd

# this is using default seldon-deployment configuration.
config = {
        'deployment_type':'seldon',
        'bucket_type':'minio',
        'image_uri':'<registry>/get_value_data:latest'
        }
deployer = Deployer(deployment_configuration=config) 


df2 = pd.DataFrame(data=[[10,20],[10,40]], columns=['1', '2'])
def get_value_data(cl_idx='1'):
    return df2[cl_idx].values.tolist()

deployer.run(get_value_data) 

you can also deploy using seldon custom configuration

from airdot import Deployer
import pandas as pd

# this is using default seldon-deployment configuration.
config = {
        'deployment_type':'seldon',
        'bucket_type':'minio',
        'image_uri':'<registry>/get_value_data:latest',
        'seldon_configuration': '' # your custom seldon configuration
        }
deployer = Deployer(deployment_configuration=config) 


df2 = pd.DataFrame(data=[[10,20],[10,40]], columns=['1', '2'])
def get_value_data(cl_idx='1'):
    return df2[cl_idx].values.tolist()

deployer.run(get_value_data)