Skip to main content

Python package for uploading models to Scailable toolchain.

Project description

sclblpy

sclblpy is the core python package provided by Scailable to convert models fit in python to WebAssembly and open them up as a REST endpoint.

sclblpy is only functional in combination with a valid Scailable user account.

Background

The sclblpy package allows users with a valid scailable account (see https://admin.sclbl.net) to upload fitted ML / AI models to the Scailable toolchain server. This will result in:

  1. The model being tested on the client side.
  2. The model being uploaded to Scailable, tested again, and if all test pass it will be converted to WebAssembly.
  3. The model being made available as an easy to access REST endpoint.

Getting started

After installing the package using pip install sclblpy you can easily fit a ML / AI model using your preferred tools and upload it to our toolchains. The following code block provides a simple example:

# Neccesary imports:
import sclblpy as sp

from sklearn import svm
from sklearn import datasets

# Start fitting a simple model:
clf = svm.SVC()
X, y = datasets.load_iris(return_X_y=True)
clf.fit(X, y)

# Create an example feature vector (required):
row = X[130, :]

# Create documentation (optional, but useful):
docs = {}
docs['name'] = "My first fitted model"
docs['documentation'] = "Any documentation you would like to provide."

# Upload the model:
sp.upload(clf, row, docs=docs)

The call to sp.upload() will upload the fitted model, after running a number of local tests, to the Scailable toolchain server and create an associated REST endpoint. Limited user feedback will be printed to show progress, and you will receive an email at the email address associated with your account when the conversion is fully completed (which might take a few minutes). This email also contains further details regarding the usage of your created endpoint.

Note that upon first upload you will be prompted to provide your Scailable username and password; you can choose to store the provided credentials locally to enable easy login on subsequently uploads. (users can signup for an account at https://admin.sclbl.net).

Examples

These examples are merely intended to show the desired syntax for the various packages; we do not intend to fit models that actually have a good predictive performance in these examples.

Currently we support uploading sklearn, statsmodels, and xgboost models (run sp.list_models() to print an overview of all supported models). Here we provide an example for each of these.

sklearn: the elastic net

The elastic net provides a flexible regularized model that is useful for many supervised learning tasks.

import sclblpy as sp
from sklearn import linear_model  # Import linear_model
from sklearn import datasets  # Import sklearn datasets

iris_data = datasets.load_iris(return_X_y=True)
X, y = iris_data

mod = linear_model.ElasticNet()  # Instantiate the model

mod.fit(X, y)  # Fit the model

fv = X[0, :]  # An example feature vector
docs = {'name': "ElasticNet example model", 'documentation' : "Documentation for this model."}

sp.upload(mod, fv, docs)

statsmodels: OLS regression

The statsmodels package provides a number of regression models with flexible error functions. We provide a simple OLS example:

import sclblpy as sp

import statsmodels.api as sm  # Import statsmodels
from sklearn import datasets  # Import sklearn datasets

iris_data = datasets.load_iris(return_X_y=True)
X, y = iris_data

est = sm.OLS(y, X)  # Specify the model
mod = est.fit()  # Fit the model; note that we send the fitted model to scailable

fv = X[0, :]  # An example feature vector
docs = {'name': "OLS example model"}

sp.upload(mod, fv, docs=docs)

xgboost: tree boosting

The xgboost package provides flexibly tree boosting models (see xgboost). This model often performs very well "off the shelf" for many supervised learning tasks.

import sclblpy as sp

from xgboost import XGBClassifier  # Import xgboost classifier
from sklearn import datasets  # Import sklearn datasets

iris_data = datasets.load_iris(return_X_y=True)
X, y = iris_data

mod = XGBClassifier()  # Instantiate the model
mod.fit(X, y)  # Fit the model

fv = X[0, :]  # An example feature vector
docs = {'name': "XGBoost example model"}

sp.upload(mod, fv, docs=docs)

Additional functionality

Next to the main upload() function, the package also exposes the following functions to administer endpoints:

# List all endpoints owned by the current user
sp.endpoints()

# Remove an endpoint
sp.delete_endpoint("cfid-cfid-cfid")

Additionally, the following methods are available:

# List all models currently supported by our toolchains:
sp.list_models()  

# Prevent any user feedback from being printed:
sp.stop_print()  

# Turn user feedback back on:
sp.start_print()  

# Remove locally stored user credentials:
sp.remove_credentials()

Dependencies

sclblpy needs python 3, and has been tested on python > 3.7. Furthermore, dependent on usage, sclblpy will import the following packages:

  • numpy
  • requests
  • uuid
  • sklearn

The statsmodels and xgboost packages are imported when used.

Notes:

  • We try to stick to the naming conventions in http://google.github.io/styleguide/pyguide.html.
  • The methods _set_toolchain_URL(string) and _set_admin_URL(string) can be used to change the default location of the toolchain and user-management function. These are useful when running the Scailable stack locally. Also the method _toggle_debug_mode() can be used for troubleshooting (this will raise exceptions and provide a trace upon errors).

For more information please contact us at go@scailable.net.

Project details


Download files

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

Files for sclblpy, version 0.1.1
Filename, size File type Python version Upload date Hashes
Filename, size sclblpy-0.1.1-py2-none-any.whl (22.1 kB) File type Wheel Python version py2 Upload date Hashes View
Filename, size sclblpy-0.1.1.tar.gz (25.0 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page