What are some good examples of well-engineered pipelines

• IPSuite

  2 14 8.8 Python

  Machine Learned Interatomic Potential Tools

  

I expaned a bit on them with my own package https://zntrack.readthedocs.io/ - a general framework for building DVC pipelines through python scripts (and more). This finally brings me to the project I'm actually working on https://github.com/zincware/IPSuite which brings all of this together for the specific use case of machine learned interatomic potentials.

• ZnTrack

  2 41 7.7 Python

  Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.

  

I expaned a bit on them with my own package https://zntrack.readthedocs.io/ - a general framework for building DVC pipelines through python scripts (and more). This finally brings me to the project I'm actually working on https://github.com/zincware/IPSuite which brings all of this together for the specific use case of machine learned interatomic potentials.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• messages

  1 5 7.0 CMake

  xcompute sub-module (CAE schema layer) including serialization utilities and bindings (by XplicitComputing)

The foundation layer is the schema, called XC-Messages (libxcmessages), defines the file-and-wire specification. These messages (and their serialization and deserialization functions) are generated for numerous languages. Internally, we mostly use the C++ bindings. This free and open acts as a Rosetta Stone for CAE applications. https://github.com/XplicitComputing/messages

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