alectio-sdk 0.0.1

Integrate customer side ML application with the Alectio Platform

Alectio SDK

AlectioSDK is a package that enables developers to build an ML pipeline as a Flask app to interact with Alectio's platform. It is designed for Alectio's clients, who prefer to keep their model and data on their on server.

The package is currently under active development. More functionalities that aim to enhance robustness will be added soon, but for now the package provides a class alectio_sdk.flask_wrapper.Pipeline that inferfaces with customer-side processes in a consistent manner. Customers need to implement 3 processes as python functions:

• A process to train the model
• A process to test the model
• A process to apply the model to infer on unlabeled data
• A process to assign each data point in the dataset to a unique index (Refer to one of the examples to know how)

Train the Model

The logic for training the model should be implemented in this process. The function should look like:

def train(payload):
    # get indices of the data to be trained
    labeled = payload['labeled']

    # get checkpoint to resume from
    resume_from = payload['resume_from']

    # get checkout to save for this loop
    ckpt_file = payload['ckpt_file']

    # implement your logic to train the model
    # with the selected data indexed by `labeled`
    return

The name of the function can be anything you like. It takes an argument payload, which is a dictionary with 3 keys

key	value
resume_from	a string that specifies which checkpoint to resume from
ckpt_file	a string that specifies the name of checkpoint to be saved for the current loop
labeled	a list of indices of selected samples used to train the model in this loop

Depending on your situation, the samples indicated in labeled might not be labeled (despite the variable name). We call it labeled because in the active learning setting, this list represents the pool of samples iteratively labeled by the human oracle.

Test the Model

The logic for testing the model should be implemented in this process. The function representing this process should look like:

def test(payload):
    # the checkpoint to test
    ckpt_file = payload['ckpt_file']

    # implement your testing logic here


    # put the predictions and labels into
    # two dictionaries

    # lbs <- dictionary of indices of test data and their ground-truth

    # prd <- dictionary of indices of test data and their prediction

    return {'predictions': prd, 'labels': lbs}

The test function takes an argument payload, which is a dictionary with 1 key

key	value
ckpt_file	a string that specifies which checkpoint to test

The test function needs to return a dictionary with two keys

key	value
predictions	a dictionary of an index and a prediction for each test sample
labels	a dictionary of an index and a ground truth label for each test sample

The format of the values depends on the type of ML problem. Please refer to the examples directory for details.

Apply Inference

The logic for applying the model to infer on the unlabeled data should be implemented in this process. The function representing this process should look like:

def infer(payload):
    # get the indices of unlabeled data
    unlabeled = payload['unlabeled']

    # get the checkpoint file to be used for applying inference
    ckpt_file = payload['ckpt_file']

    # implement your inference logic here


    # outputs <- save the output from the model on the unlabeled data as a dictionary
    return {'outputs': outputs}

The infer function takes an argument payload, which is a dictionary with 2 keys:

key	value
ckpt_file	a string that specifies which checkpoint to use to infer on the unlabeled data
unlabeled	a list of of indices of unlabeled data in the training set

The infer function needs to return a dictionary with one key

key	value
outputs	a dictionary of indexes mapped to the models output before an activation function is applied

For example, if it is a classification problem, return the output before applying softmax. For more details about the format of the output, please refer to the examples directory.

Installation

1. Set up a virtual environment

We recommend to set-up a virtual environment.

For example, you can use python's built-in virtual environment via:

python3 -m venv env
source env/bin/activate

2. Install AlectioSDK/requirements

If you are a paying customer, then you will have access to our backend. You will need to take the backend IP address that we give you and you can set it in the alectio_sdk/flask_wrapper/config.json file under the "backend_ip" key, or alternatively use this script to enter replace the currently empty value in the config file.

python set_backend_ip.py <backend-ip>

After setting up the backend ip address, you can proceed to installing the repository. Make sure to set the ip address first, then pip install.

pip install .
pip install -r requirements.txt

3. Run Examples

The remaining installation instructions are detailed in the examples directory. We cover one example for topic classification, one example for image classification and one example for object detection.