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
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.
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 official examples for details
The logic for applying the model to infer on the unlabeled data should be implemented in this process. The function representing this process looks 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 official examples
git clone https://github.com/hsl89/AlectioSDK.git
cd AlectioSDK
pip install -r requirements.txt
python setup.py install
To help customers using this package, we provide detailed examples that covers a wide range of ML problems