Skip to main content

Client library for managing machine learning models on the Jaqpot platform

Project description

Build and test Publish to PyPI 📦

Jaqpotpy

The jaqpotpy library enables you to upload and deploy machine learning models to the Jaqpot platform. Once uploaded, you can manage, document, and share your models via the Jaqpot user interface at https://app.jaqpot.org. You can also make predictions online or programmatically using the Jaqpot API.

Getting Started

Prerequisites

Installation

Install jaqpotpy using pip:

pip install jaqpotpy

Model Training and Deployment

Follow these steps to train and deploy your model on Jaqpot:

1. Train your model using pandas DataFrame as input.
2. Deploy the trained model using the deploy_on_jaqpot function.

Example Code

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from jaqpotpy import Jaqpot
from jaqpotpy.datasets import JaqpotTabularDataset
from jaqpotpy.models import SklearnModel

# Creating a Simulated Dataset for Model Training
np.random.seed(42)
X1 = np.random.rand(100)
X2 = np.random.rand(100)
ACTIVITY = 2 * X1 + 3 * X2 + np.random.randn(100) * 0.1
df = pd.DataFrame({"X1": X1, "X2": X2, "ACTIVITY": ACTIVITY})
y_cols = ["ACTIVITY"]
x_cols = ["X1", "X2"]

# Step 1: Create a Jaqpotpy dataset
dataset = JaqpotTabularDataset(df=df, y_cols=y_cols, x_cols=x_cols, task=ModelTask.REGRESSION)

# Step 2: Build a model
rf = RandomForestRegressor(random_state=42)
myModel = SklearnModel(dataset=dataset, model=rf)
myModel.fit()

# Step 3: Upload the model on Jaqpot
jaqpot = Jaqpot()
jaqpot.login()  # log in to Jaqppt
myModel.deploy_on_jaqpot(
    jaqpot=jaqpot,
    name="Demo: Regression",
    description="This is a description",
    visibility=ModelVisibility.PRIVATE
)

Once your model is successfully deployed on the Jaqpot platform, the function will provide you with the model ID that you can use to manage your model through the user interface and API.

Console Output:

<DATE> - INFO - Model has been successfully uploaded. The url of the model is https://app.jaqpot.org/dashboard/models/<ModelID>

Managing Your Models

You can further manage your models through the Jaqpot user interface at https://app.jaqpot.org. This platform allows you to view detailed documentation, share models with your contacts, and make predictions.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jaqpotpy-7.1.0.tar.gz (98.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

jaqpotpy-7.1.0-py3-none-any.whl (155.3 kB view details)

Uploaded Python 3

File details

Details for the file jaqpotpy-7.1.0.tar.gz.

File metadata

  • Download URL: jaqpotpy-7.1.0.tar.gz
  • Upload date:
  • Size: 98.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for jaqpotpy-7.1.0.tar.gz
Algorithm Hash digest
SHA256 afac52e2c10805b7fc8843f6c106c757051e61643a2a1e1716eb23740e02bbe8
MD5 37882a522bff916f397384cadf9c56a7
BLAKE2b-256 9063e864565e52ffa1c7112b92b946ec592baaa9cef994a05063e1bb69b23f67

See more details on using hashes here.

File details

Details for the file jaqpotpy-7.1.0-py3-none-any.whl.

File metadata

  • Download URL: jaqpotpy-7.1.0-py3-none-any.whl
  • Upload date:
  • Size: 155.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for jaqpotpy-7.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 f976e5b5065735d37cafcdcead570a07bf46265e9a016c5060130b3e619a683d
MD5 dad438680f21f9652fa7318c46fcefd3
BLAKE2b-256 e8f4fa070ccde5b0fd70a95e4b3807c6b03b962b1f203f9de6a307c4620e3fd5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page