Machine Learning infra
An opinionated ML development environment
Train pytorch models, hyperparameter tune them with single loc change.
Pytorch is a Python first machine learning library
Ray Provides easy experiment scaling + hyper parameter optimisation
Provides tracking of model metrics and hyperparameters. Also allows for intelligent storing of training artifacts
These components should not need to be customised for model specific use cases
Setups all the required components to train a model
Setups all the required components for inference. Also attempts to download model weights if they are not found locally.
Slim interface into MLFlow, to set the MLFlow server address set the
MLFLOW_TRACKING_URI environment variable either from the CLI or before importing
from os import environ environ['MLFLOW_TRACKING_URI'] = 'http://localhost:5001' import agoge
Loads the dataset and handles the dataset split
User Provided Components
These components need to be inherited by project specific classes
Provides some convenience functions around loading models. This class will hold all model specific code and is used by the train worker and inference workers
solve method with the code required to train your model
Any dataset that is compatiable with the Pytorch map style dataset model
This code is subject to change. I will try not to break anything but can't promise. File an issue if an update breaks your code
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