Metadata-Version: 2.1
Name: alectio-sdk
Version: 0.0.1
Summary: Integrate customer side ML application with the Alectio Platform
Home-page: UNKNOWN
Author: Alectio
Author-email: admin@alectio.com
License: UNKNOWN
Description: # 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:
        
        ```python
        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:
        
        ```python
        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](./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:
        ```python
        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](./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](./examples) directory. We cover one example for [topic classification](./examples/topic_classification), one example for [image classification](./examples/image_classification) and one example for [object detection](./examples/object_detection).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
