sclblpy 0.0.2

Python package for uploading models to Scailable toolchain.

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.

• Website: https://www.scailable.net
• Docs: https://docs.sclbl.net/sclblpy
• Account: https://admin.sclbl.net
• Source:https://github.com/scailable/sclblpy/

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

# Create an example feature vector (optional, but very useful):
row = X[130, :]

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

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).

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()

Note that many of the exposed functions have an optional argument _verbose which by default is False; setting it to True will provide additional error messaging which might be useful for debugging.

Dependencies

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

• json
• numpy
• requests
• gzip
• pickle
• os
• warnings
• time
• getpass
• platform
• re
• socket
• sys
• uuid
• sklearn

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.

For more information please contact us at [e-mail].