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 JaqpotpyDataset
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 = JaqpotpyDataset(df=df, y_cols=y_cols, x_cols=x_cols, task="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="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-6.15.0.tar.gz (118.4 kB view details)

Uploaded Source

Built Distribution

jaqpotpy-6.15.0-py3-none-any.whl (237.7 kB view details)

Uploaded Python 3

File details

Details for the file jaqpotpy-6.15.0.tar.gz.

File metadata

  • Download URL: jaqpotpy-6.15.0.tar.gz
  • Upload date:
  • Size: 118.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for jaqpotpy-6.15.0.tar.gz
Algorithm Hash digest
SHA256 b865db9623760d1898c33308c7bfc23bf51f9e113e6f6a80aa50b871fb5116bc
MD5 2b97c9e2e77b11e6dbd329a548f1bb74
BLAKE2b-256 42ec170013baefbdcefab460fa024cf59a64cefc5e4dd0c3e1e0285da50183dc

See more details on using hashes here.

File details

Details for the file jaqpotpy-6.15.0-py3-none-any.whl.

File metadata

  • Download URL: jaqpotpy-6.15.0-py3-none-any.whl
  • Upload date:
  • Size: 237.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for jaqpotpy-6.15.0-py3-none-any.whl
Algorithm Hash digest
SHA256 104beb69e2a470057ea2963378cdb9ecf6d325d5a28457af5b992e5a6d0d37a2
MD5 6fd709df4c900ef59fd321b140483e17
BLAKE2b-256 316b30860f5eff6f6c8b4f0795363f1128a9825667361b7c1107b2f5b65f9387

See more details on using hashes here.

Supported by

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