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-6.12.1.tar.gz (94.7 kB view details)

Uploaded Source

Built Distribution

jaqpotpy-6.12.1-py3-none-any.whl (184.7 kB view details)

Uploaded Python 3

File details

Details for the file jaqpotpy-6.12.1.tar.gz.

File metadata

  • Download URL: jaqpotpy-6.12.1.tar.gz
  • Upload date:
  • Size: 94.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for jaqpotpy-6.12.1.tar.gz
Algorithm Hash digest
SHA256 bc84a4c4f04e6c7662b12b6315b1889682132d9d9d05ac67f521661674011824
MD5 5fdc53f91d1044334716f51d33061612
BLAKE2b-256 785b440d2dedcb7e492338f5a7a92ddcca6c89173b42b61d5046dcc36214f6b5

See more details on using hashes here.

File details

Details for the file jaqpotpy-6.12.1-py3-none-any.whl.

File metadata

  • Download URL: jaqpotpy-6.12.1-py3-none-any.whl
  • Upload date:
  • Size: 184.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for jaqpotpy-6.12.1-py3-none-any.whl
Algorithm Hash digest
SHA256 abee5613424aec2c3ca2529f8205f6c01ef4b1d11c758e09663fb7c2463efa76
MD5 b33371c34244c6cb9f5644e21d58ddb7
BLAKE2b-256 a7d0641e8bf9315aeea6e8bc3cea47ad18f2b067e54be8d94cab1e8eb61766fa

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