LynxKite is a complete graph data science platform for very large graphs and other datasets. It seamlessly combines the benefits of a friendly graphical interface and a powerful Python API. It is written in Scala and uses Apache Spark.
LynxKite alternatives and similar packages
Based on the "Machine Learning" category.
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DeepLearning.scala7.7 0.0 LynxKite VS DeepLearning.scalaA simple library for creating complex neural networks
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LynxKite is a complete graph data science platform for very large graphs and other datasets. It seamlessly combines the benefits of a friendly graphical interface and a powerful Python API.
- Hundreds of scalable graph operations, including graph metrics like PageRank, embeddedness, and centrality, machine learning methods including GCNs, graph segmentations like modular clustering, and various transformation tools like aggregations on neighborhoods.
- The two main data types are graphs and relational tables. Switch back and forth between the two as needed to describe complex logical flows. Run SQL on both.
- A friendly web UI for building powerful pipelines of operation boxes. Define your own custom boxes to structure your logic.
- Tight integration with Python lets you implement custom transformations or create whole workflows through a simple API.
- Integrates with the Hadoop ecosystem. Import and export from CSV, JSON, Parquet, ORC, JDBC, Hive, or Neo4j.
- Fully documented.
- Proven in production on large clusters and real datasets.
- Fully configurable graph visualizations and statistical plots. Experimental 3D and ray-traced graph renderings.
LynxKite is under active development. Check out our Roadmap to see what we have planned for future releases.
docker run --rm -p2200:2200 lynxkite/lynxkite
Setup with persistent data:
docker run \ -p 2200:2200 \ -v ~/lynxkite/meta:/metadata -v ~/lynxkite/data:/data \ -e KITE_MASTER_MEMORY_MB=1024 \ --name lynxkite lynxkite/lynxkite
If you find any bugs, have any questions, feature requests or comments, please file an issue or email us at [email protected].
You can install LynxKite's dependencies (Scala, Node.js, Go) with Conda.
Before the first build:
tools/git/setup.sh # Sets up pre-commit hooks. conda env create --name lk --file conda-env.yml conda activate lk cp conf/kiterc_template ~/.kiterc
make for building the whole project.
make target/universal/stage/bin/lynxkite interactive
We have test suites for the different parts of the system:
Backend tests are unit tests for the Scala code. They can also be executed with Sphynx as the backend. If you run
make backend-testit will do both. Or you can start
testOnly *SomethingTestto run just one test. Run
./test_backend.sh -sito start
sbtwith Sphynx as the backend.
Frontend tests use Protractor to simulate a user's actions on the UI.
make frontend-testwill build everything, start a temporary LynxKite instance and run the tests against that. Use
xvfb-runfor headless execution. If you already have a running LynxKite instance and you don't mind erasing all data from it, run
npx gulp testin the
webdirectory. You can start up a dev proxy that watches the frontend source code for changes with
npx gulp serve. Run the test suite against the dev proxy with
npx gulp test:serve.
Python API tests are started with
make remote_api-test. If you already have a running LynxKite that is okay to test on, run
python/remote_api/test.sh. This script can also run a subset of the test suite:
python/remote_api/test.sh -p *something*
*Note that all licence references and agreements mentioned in the LynxKite README section above are relevant to that project's source code only.