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Releases: Hydrospheredata/hydro-serving

3.0.3

12 Nov 16:49
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Releasing global version 3.0.3

3.0.2

08 Nov 14:22
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What's Changed

  • docs: add aws production install guide by @akastav in #400
  • fix: problem with Letsencrypt certificate expire, more details in grpc/grpc#27532

Full Changelog: 3.0.1...3.0.2

3.0.1

22 Oct 18:42
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What's Changed

Full Changelog: 3.0.0...3.0.1

⚠️ Depreciation notations: this version has a problem with Letsencrypt certificate expire, more details in grpc/grpc#27532

3.0.0

20 Aug 11:59
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What's Changed

New Contributors

Full Changelog: 2.4.3...3.0.0

2.4.3

05 Nov 17:24
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Bug Fixes & Others

  • Fix default values for helm chart and add option to deploy internal registry without ingress

2.4.2

30 Oct 14:16
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Bug Fixes & Others

  • Add option for enabling/disabling ingress resources in the helm charts
  • Update documentation

2.4.1

30 Oct 12:26
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Bug Fixes & Others

  • Remove unused manifests from helm

2.4.0

14 Oct 09:42
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Release 2.4.0

Breaking Changes 🚨

  • hydrosdk methods named list_all renamed to list

Major Features and Improvements 🚀

  • Released Deployment Configurations - support for specifying your k8s settings for models inside Applications.
  • Introduced garbage collector for Metrics servables associated with Application without servables
  • Monitoring Batch Size is now configurable
  • Data Drift now makes a feature-wise analysis of interaction between inputs and outputs
  • Data Visualization now plots a training density plot
  • hydrosdk can now upload training data
  • Python runtimes starting from 2.3.2 convert TensorProto into Numpy by default

Bug Fixes and Other Changes 🐞

  • Improved Helm charts readability
  • Improved Data Drift and Visualization UI
  • Fixed bug when Gateway couldn't match shapes for signatures with -1 dimensions.

2.3.1

01 Jul 12:47
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Bug Fixes & Others

  • Fixed image and tag for docker-registry init container in the helm charts.
  • Updated documentation for installing helm charts from source code.

2.3.0

11 Jun 07:27
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Features

  • Python Sagemaker Integration - use Hydrosphere to monitor your machine learning models deployed with Sagemaker. Check hydro-serving-integrations to learn more

  • 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

  • Fixed error when docker push failed silently Hydrospheredata/hydro-serving-manager#34

  • 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

  • Fixed bug when UI sometimes does not handle ModelUpdate event from SSE