Jaqpot
Jaqpot platform enables sklearn, xgBoost and other models developed in python to be accessible through a user interface, that allows extensive documentation of the models and sharing through your contacts.
jaqpotpy
jaqpotpy enables model deployment with a simple command.
First register to Jaqpot through https://app.jaqpot.org
jaqpot = Jaqpot() initializes jaqpot upon the standard available API that
is integrated with the application and user interface at https://app.jaqpot.org/ .
Let jaqpot know who you are
Login and have access on the jaqpot services
In order to do so you can use the functions:
jaqpot.login('username', 'password')
Will login and set the api key that is needed.
jaqpot.request_key('username', 'password')
Same as above you request the key and set it on jaqpot
jaqpot.request_key_safe()
Will ask the user for the username and password by hidding the password if jaqpot is used through a jupiter notebook etc
Set Key without login
Some users may have logged in through google or github. At the account page a user can find an api key that can be used in order to have access on the services. These keys have short life and should be updated on each login.
jaqpot.set_api_key("api_key")
Get the key from user interface
Model training and deployment
An example code that demonstrates a model deployemnt.
- Warning! One of the things that may differ from simpler training and validation routes is that you need to train your model with a pandas dataframe as input and not with Numpy arrays!
from jaqpotpy import Jaqpot
import pandas as pd
from sklearn.linear_model import LinearRegression
df = pd.read_csv('/path/to/gdp.csv')
lm = LinearRegression()
y = df['GDP']
X = df[['LFG', 'EQP', 'NEQ', 'GAP']]
model = lm.fit(X=X, y=y)
jaqpot.deploy_sklearn(model, X, y, title="Title", description="Describe")The function will inform you about the model id that is created and is available through the user interface and the API.
- Result
- INFO - Model with id: <model_id> created. Visit the application to proceed
