2.3.0
Features
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Python Sagemaker Integration - use Hydrosphere to monitor your machine learning models deployed with Sagemaker. Check hydro-serving-integrations to learn more
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Python SDK introduces Predictors - object made for seamless conversion between your NumPy or Pandas data representation intro proto messages used by Hydrosphere. Sample code:
# Find application deployed at your Hydrosphere cluster
app = Application.find_by_name(cluster, "my-model")
predictor = app.predictor()
# Inference your data without converting it to proto objects
df = pd.read_csv("path/to/data.csv")
for row in df.itertuples(index=False):
predictor.predict(row._as_dict())
Bug Fixes & Other
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Fixed error when docker push failed silently Hydrospheredata/hydro-serving-manager#34
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hydrosdk
model.read_yaml
now does not ignore install_command, training-data and metadata fields -
Fixed UI bug due to which you could not return to the previous page
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Fixed bug when UI sometimes does not handle ModelUpdate event from SSE