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Welcome to hosting your scikit-learn model on Algorithmia! This guide is designed as an introduction to hosting a scikit-learn model and publishing an algorithm even if you’ve never used Algorithmia before.


Before you get started hosting your model on Algorithmia there are a few things you’ll want to do first:

Train and pickle your model.

After training your Scikit-learn model, you’ll want to save the pickled model so you can upload it to Algorithmia.

Create a Data Collection

Now you’ll want to create a data collection to host your pickled model.

  • To use the Data API, log into your Algorithmia account and create a data collection via the Data Collections page.

  • Click on “Add Collection” under the “My Collections” section on your data collections page.

  • After you create your collection you can set the read and write access on your data collection. For more information check out: Data Collection Types

Add data collection

Upload your Model into a Collection

Next, upload your pickled model to your newly created data collection.

  • Load model by clicking box “Drop files here to upload”

  • Note the path to your files: data://username/collections_name/pickled_model.pkl

Create a data collection

Create your Algorithm

Creating your algorithm is easy!

  • To add an algorithm, simply click “Add Algorithm” from the user profile icon.
  • Name your algorithm, select the language, choose permissions and make the code either open or closed source.

Note: There is also a checkbox for ‘Standard Execution Environment’ or ‘Advanced GPU’. For machine learning models you will want to check ‘Standard Execution Environment’.

Create your algorithm

Load your Model

Here is where you load your pickled model that is to be called by the apply() function. Our recommendation is to preload your model in a separate function before apply(). The reasoning behind this is because when your model is first loaded it can take some time to load depending on the file size. However, with all subsequent calls only the apply() function gets called which will be much faster since your model is already loaded!

Now to check out a code example using the Random Forest Regressor to build a prediction model in Scikit-learn

import sys
import pickle
import csv
import numpy as np
import Algorithmia

from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor

client = Algorithmia.client()

def load_model():
    # Get file by name
    # Open file and load model
    file_path = 'data://demo/demo/scikit-demo-boston-regression.pkl'
    model_path = client.file(file_path).getFile().name
    # Open file and load model
    with open(model_path, 'rb') as f:
        model = pickle.load(f)
        return model

# Load model outside of the apply function so it only gets loaded once
model = load_model()

def process_input(input):
    # Create numpy array from csv file passed as input in apply()
    if input.startswith('data:'):
        file_url = client.file(input).getFile().name
            np_array = np.genfromtxt(file_url, delimiter=',')
            return np_array
        except Exception as e:
            print("Could not create numpy array from data", e)

def apply(input):
    # Input should be a csv file - model is trained on Sklearn
    # Boston housing dataset using RandomForestRegressor
    np_data = process_input(input)
    prediction = model.predict(np_data)
    return list(prediction)

If you are authoring an algorithm, avoid using the ‘.my’ pseudonym in the source code. When the algorithm is executed, ‘.my’ will be interpreted as the user name of the user who called the algorithm, rather than the author’s user name.

Set your Dependencies

Now is the time to set your dependencies that your model relies on.

  • Click on the “Dependencies” button at the top right of the UI and list your packages under the required ones already listed and click “Save Dependencies” on the bottom right corner.

Set your dependencies

If you’re following along with this tutorial, go ahead and copy and paste the libraries listed into the dependency file, adding to the ones already there:


The dependency file is the equivalent to a requirements.txt file which pulls the dependencies listed from PyPi.

Publish your Algorithm

Last is publishing your algorithm. The best part of hosting your model on Algorithmia is that users can access it via an API that takes only a few lines of code to use! Here is what you can set when publishing your algorithm:

On the upper right hand side of the algorithm page you’ll see a purple button “Publish” which will bring up a modal:

Publish an algorithm

In this modal, you’ll see a Changes tab, a Sample I/O tab, and one called Versioning.

Changes shows you your commit history and release notes.

Sample I/O is where you’ll create your sample input and output for the user to try under Try the API in the Run tab. When you add a sample input, make sure to test it out with all the inputs that you accept since users will be able to test your algorithm with their own inputs.

Under the Versioning tab, you can select whether your algorithm will be for public use or private use as well as set the royalty. The algorithm can either be royalty-free or charge per-call. If you opt to have the algorithm charge a royalty, as the author, you will earn 70% of the royalty cost.

Check out Algorithm Pricing for more information on how much algorithms will cost to run.

Under Semantic Versioning you can choose which kind of release your change should fall under: Major, Minor, or Revision.

If you are satisfied with your algorithm and settings, go ahead and hit publish. Congratulations, you’re an algorithm developer!

For more information and detailed steps: creating and publishing your algorithm

Working Demo

If you would like to check this demo out on the platform you can find it here: Scikit-Learn-demo

That’s it for hosting your scikit-learn model on Algorithmia!