carefree-learn 0.1.7.1

Release Notes

carefree-learn 0.1.7 integrated mlflow and cleaned up Experiment API, which completes the machine learning lifecycle.

• v0.1.7.1: Hotfixed a critical bug which will load the worst checkpoint saved.

mlflow

mlflow can help us visualizing, reproducing, and serving our models. In carefree-learn, we can quickly play with mlflow by specifying mlflow_config to an empty dict:

import cflearn
import numpy as np

x = np.random.random([1000, 10])
y = np.random.random([1000, 1])
m = cflearn.make(mlflow_config={}).fit(x, y)

After which, we can execute mlflow ui in the current working directory to inspect the tracking results (e.g. loss curve, metric curve, etc.).

We're planning to add documentation for the mlflow integration and it should be available at v0.1.8.

Experiment

Experiment API was embarrassingly user unfriendly before, but has been cleaned up and is ready to use since v0.1.7. Please refer to the documentation for more details.

Misc

• Integrated DeepSpeed for distributed training on one single model (experimental).
• Enhanced Protocol for downstream usages (e.g. Quantitative Trading, Computer Vision, etc.) (experimental).

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• Fixed other bugs.
• Optimized TrainMonitor (#39)
• Optimized some default settings.