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To try it in your project see also the Getting Started User Guide. Dotty uses the Scala Code of Conduct for all communication and discussion. This includes both GitHub, Gitter chat and other more direct lines of communication such as email.
Easy to learn and master, lightning fast and backed by a thriving community of enthusiasts and contributors. For talks, development roadmap, projects using cbt, etc see the wiki. CBT is a build tool meaning it helps orchestrating compilation, code and documentation generation, packaging, deployment and custom tooling for your project. It mainly targets Scala projects but is not exclusive to them.
Rewrite and linting tool for Scala. Fidelity: Scalafix supports formatting-aware rewriting of Scala code. Every detail in the source file is represented with rich data structures and full position information, down to individual space characters.
This is a normal sbt project, you can compile code with sbt compile and run it with sbt run, sbt console will start a Scala 3 REPL. If compiling this example project fails, you probably have a global sbt plugin that does not work with Scala 3, try to disable all plugins in ~/.sbt/1.0/plugins and ~/.sbt/1.0.
Scala & Dotty support for Atom IDE, powered by Metals, Dotty and Ensime language servers. The project is in active development and may have some rough edges. You are welcome to try it out and provide any feedback in the Gitter chat or Github issues.
Scala fuzzer. ~44 bugs found in Dotty so far and many more are still unreported. Powered by a two-layer LSTM (400 units) network trained only for a couple hours. High temperature (≫ 1.0) - lots of random noise, most samples will be syntactically incorrect. Low temperature (≈ 0.0) - little noise, most samples will be syntactically correct.
This is a normal sbt project, you can compile code with sbt compile and run it with sbt run, sbt console will start a Dotty REPL. If compiling this example project fails, you probably have a global sbt plugin that does not work with dotty, try to disable all plugins in ~/.sbt/1.0/plugins and ~/.sbt/1.0.
The run time of the eager NDScala version is ~80% of that of NumPy w/MKL, while if we fuse the ops to a graph it's just ~65%. The PyTorch equivalent is slightly faster, at ~85% of the fused NDScala version run time. This can be accounted for by the copy overhead of passing data between the JVM and native memory.