aixblock-ml 0.0.2

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Project description What is the AIxBlock ML backend? The AIxBlock ML backend is an SDK that lets you wrap your machine learning code and turn it into a web server. You can then connect that server to a AIxBlock instance to perform 2 tasks: Dynamically pre-annotate data based on model inference results Retrain or fine-tune a model based on recently annotated data If you just need to load static pre-annotated data into AIxBlock, running an ML backend might be overkill for you. Instead, you can import preannotated data. How it works Get your model code Wrap it with the AIxBlock SDK Create a running server script Launch the script Connect AIxBlock to ML backend on the UI Quickstart Follow this example tutorial to run an ML backend with a simple text classifier: Clone the repo git clone https://github.com/wowai-3/aixblock-ml-backend

Setup environment It is highly recommended to use venv, virtualenv or conda python environments. You can use the same environment as AIxBlock does. Read more about creating virtual environments via venv. cd aixblock-ml-backend

Install aixblock-ml and its dependencies

pip install -U -e .

Install example dependencies

pip install -r aixblock_ml/examples/requirements.txt

Initialize an ML backend based on an example script: aixblock-ml init my_ml_backend --script aixblock_ml/examples/text_classifier/text_classifier.py

This ML backend is an example provided by AIxBlock. See how to create your own ML backend. Start ML backend server aixblock-ml start my_ml_backend

Start AIxBlock and connect it to the running ML backend on the project settings page. Create your own ML backend Follow this tutorial to wrap existing machine learning model code with the AIxBlock ML SDK to use it as an ML backend with AIxBlock. Before you start, determine the following: The expected inputs and outputs for your model. In other words, the type of labeling that your model supports in AIxBlock, which informs the AIxBlock labeling config. For example, text classification labels of "Dog", "Cat", or "Opossum" could be possible inputs and outputs. The prediction format returned by your ML backend server. This example tutorial outlines how to wrap a simple text classifier based on the scikit-learn framework with the AIxBlock ML SDK. Start by creating a class declaration. You can create a AIxBlock-compatible ML backend server in one command by inheriting it from AIxBlockMLBase. from aixblock_ml.model import AIxBlockMLBase

class MyModel(AIxBlockMLBase):

Then, define loaders & initializers in the init method. def init(self, **kwargs): # don't forget to initialize base class... super(MyModel, self).init(**kwargs) self.model = self.load_my_model()

There are special variables provided by the inherited class: self.parsed_label_config is a Python dict that provides a AIxBlock project config structure. See ref for details. Use might want to use this to align your model input/output with AIxBlock labeling configuration; self.label_config is a raw labeling config string; self.train_output is a Python dict with the results of the previous model training runs (the output of the fit() method described bellow) Use this if you want to load the model for the next updates for active learning and model fine-tuning. After you define the loaders, you can define two methods for your model: an inference call and a training call. Inference call Use an inference call to get pre-annotations from your model on-the-fly. You must update the existing predict method in the example ML backend scripts to make them work for your specific use case. Write your own code to override the predict(tasks, **kwargs) method, which takes JSON-formatted AIxBlock tasks and returns predictions in the format accepted by AIxBlock. Example def predict(self, tasks, **kwargs): predictions = [] # Get annotation tag first, and extract from_name/to_name keys from the labeling config to make predictions from_name, schema = list(self.parsed_label_config.items())[0] to_name = schema['to_name'][0] for task in tasks: # for each task, return classification results in the form of "choices" pre-annotations predictions.append({ 'result': [{ 'from_name': from_name, 'to_name': to_name, 'type': 'choices', 'value': {'choices': ['My Label']} }], # optionally you can include prediction scores that you can use to sort the tasks and do active learning 'score': 0.987 }) return predictions

Training call Use the training call to update your model with new annotations. You don't need to use this call in your code, for example if you just want to pre-annotate tasks without retraining the model. If you do want to retrain the model based on annotations from AIxBlock, use this method. Write your own code to override the fit(annotations, **kwargs) method, which takes JSON-formatted AIxBlock annotations and returns an arbitrary dict where some information about the created model can be stored. Example def fit(self, completions, workdir=None, **kwargs): # ... do some heavy computations, get your model and store checkpoints and resources return {'checkpoints': 'my/model/checkpoints'} # <-- you can retrieve this dict as self.train_output in the subsequent calls

After you wrap your model code with the class, define the loaders, and define the methods, you're ready to run your model as an ML backend with AIxBlock. For other examples of ML backends, refer to the examples in this repository. These examples aren't production-ready, but can help you set up your own code as a AIxBlock ML backend.xem Deploy your ML backend to GCP Before you start: Install gcloud Init billing for account if it's not activated Init gcloud, type the following commands and login in browser: gcloud auth login

Activate your Cloud Build API Find your GCP project ID (Optional) Add GCP_REGION with your default region to your ENV variables To start deployment: Create your own ML backend Start deployment to GCP: aixblock-ml deploy gcp {ml-backend-local-dir}
--from={model-python-script}
--gcp-project-id {gcp-project-id}
--aixblock-host {https://tool.aixblock.org}
--aixblock-api-key {YOUR-AIXBLOCLK-API-KEY}

After AIxBlock deploys the model - you will get model endpoint in console.

python3.9 -m SegmentAnythingBeta.SegmentAnything start (choose from 'init', 'start', 'deploy')