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Copy file name to clipboardexpand all lines: CONTRIBUTING.md
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# How to Contribute
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Owl is a large open source project, to guarantee quality of the software and sustainable development, we enforce the following rules in day-to-day research, development, and project management.
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Besides coding, there are many other ways you can contribute. Bug reporting, typo fix, asking/answering questions, and improvement of existing documents are all well welcome.
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## Owl Team
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To achieve our mission, we are assembling an *Owl Team*. Anyone who can potentially give sufficient contributions recognized by the community can request to be a team member.
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A team member is expected to make several valuable and significant contributions to the project in both quality and quantity.
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Typically a committer focuses on one specific aspect of the project and is a domain expert.
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We hope but cannot guarantee that the team members can cover all aspects of the code base.
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Emphasis are put on the responsibility of each team member.
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Each one should take responsibility of certain aspects of the code base, e.g. a module or maintenance for a specific OS platform.
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Team members, together with their domain aspects and responsibilities, will be listed here and also on the [Owl website](https://ocaml.xyz/).
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For anyone who is willing to contribute, some good starting points could be:
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- Participate in discussion in issues, and help to fix them
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- Fix [documentations](https://ocaml.xyz/docs/), mainly by changing the `.mli` files in the source code, based on which the documentations are generated
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- Fix [tutorials](https://ocaml.xyz/tutorial/); the code could be not runnable, or the content could not display properly
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- Add more examples and tests
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- Fix issues listed in the [TODO list](https://github.com/orgs/owlbarn/projects/2/views/2)
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- Propose to fix/improve anything that interests you during using Owl
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- ...
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**Current team member:**
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-[@jzstark](https://github.com/jzstark): Project leader. Manage overall architecture, roadmap，and tech vision. Community communication. Set research agenda.
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-[@ryanrhymes](https://github.com/ryanrhymes): Potential commercialization, business opportunity & funding seeking.
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## Coding Style
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personal information I submit with it, including my sign-off) is
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maintained indefinitely and may be redistributed consistent with
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this project or the open source license(s) involved.
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```
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## Becoming A Team Member
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Owl Team is open and always welcomes new members to join. If you are enthusiastic about scientific computing and numerical algorithms, you can be one of us. As a team member, you need to be aware of the following things.
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- You will be included in the [owlbarn organisation](https://github.com/owlbarn).
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- You will have access to Team Channel on Slack and join the R&D discussion. Your opinions matter in Owl's future roadmap.
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- You need to be responsible for one aspect (or one or multiple related modules) in Owl and lead its development.
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- You need to review the pull requests related to the area you are specialised in.
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- We value persistence. Being a team member requires you actively get involved in maintaining Owl and contributing to its code base. The team membership becomes invalidated automatically if your contributions cease.
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- Team membership is considered after you have made some significant contributions to Owl's code base or its ecosystem. You can always drop [me]([email protected]) a line to discuss.
Owl is a dedicated system for scientific and engineering computing. The system is developed in OCaml and licensed under MIT. The project is originated by [Liang Wang](https://liang.ocaml.xyz) and currently led by [Jianxin Zhao](https://jianxin.ocaml.xyz). The history of the project is on the [Wikipedia](https://en.wikipedia.org/wiki/Owl_Scientific_Computing).
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Owl is a dedicated system for scientific and engineering computing. The system is developed in OCaml and licensed under MIT. The project is originated by [Liang Wang](https://liang.ocaml.xyz) and currently led by [Jianxin Zhao](https://jianxin.ocaml.xyz). The history of the project can be seen on the [Wikipedia](https://en.wikipedia.org/wiki/Owl_Scientific_Computing) page.
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## Functionalities
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Owl provides a wide range of scientific computing functionalities:
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- mathematical function, from the basic `log`, `sin` etc., to special functions such as the Beta and Gamma functions
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- integration; interpolation and extrapolation
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- statistics and probability; e.g. generation of random number and various distributions
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- various computation on n-dimensional arrays (tensors), including advanced slicing and broadcasting
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- linear algebra
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- ordinary differential equations
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- discrete Fourier Transform algorithms for signal processing
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- algorithmic differentiation, or automatic differentiation
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- various optimization algorithms
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- regression algorithms
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- deep neural network and natural language processing with the optional support of computation graph optimization
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- dataframe processing
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- visualization (with the help of external package `owl-plot`)
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## Installation
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Please follow the [tutorial](https://ocaml.xyz/tutorial/chapters/introduction.html) about installing Owl.
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Please follow the [tutorial](https://ocaml.xyz/tutorial/chapters/introduction.html) about installing Owl. You can also start by trying the [docker images](https://hub.docker.com/r/matrixanger/owl).
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## Mission
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The current code base is designed to be concise and self-contained.
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We encourage anyone who would like to build up their own tools based on Owl to create new repositories in the [Owlbarn](https://github.com/owlbarn) organization.
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## Owl Team
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## Owl Code Structure
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To help potential developers to understand the structure of Owl, here we briefly describe its overall design. More detailed description can be found in the [documentation](https://ocaml.xyz/docs/) and the Owl [books](https://ocaml.xyz/).
To achieve this mission, we are assembling an *Owl Team*. Anyone who can potentially give sufficient contributions recognized by the community can request to be a team member.
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A team member is expected to make several valuable and significant contributions to the project in both quality and quantity.
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Typically a committer focuses on one specific aspect of the project and is a domain expert.
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We hope but cannot guarantee that the team members can cover all aspects of the code base.
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Owl provides a basic data structure for modern numerical computing: n-dimensional array (Ndarray). It is based on the mathematics, linear algebra, and statistics functions, which are built on both OCaml and C functions and libraries.
Emphasis are put on the responsibility of each team member.
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Each one should take responsibility of certain aspects of the code base, e.g. a module or maintenance for a specific OS platform.
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Team members, together with their domain aspects and responsibilities, will be listed here and also on the [Owl website](https://ocaml.xyz/).
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Sharing the same set of interface with Ndarray is the `base` system data architecture, which is implemented in pure OCaml. It is also based on modules that are implemented in OCaml. However, though it is sufficient for daily use for normal computing, the base version Ndarray does not implement some advanced functions as in the previous Owl version Ndarray, and its performance is understandably much slower.
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For anyone who is willing to contribute, some good starting points could be:
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- Participate in discussion in issues, and help to fix them
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- Fix [documentations](https://ocaml.xyz/docs/), mainly by changing the `.mli` files in the source code, based on which the documentations are generated
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- Fix [tutorials](https://ocaml.xyz/tutorial/); the code could be not runnable, or the content could not display properly
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- Add more examples and tests
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- Fix issues listed in the [TODO list](https://github.com/orgs/owlbarn/projects/2/views/2)
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- Propose to fix/improve anything that interests you during using Owl
Besides these two types of Ndarray, another type is CGraph-Ndarray, which can be used to support symbolic style computing like TensorFlow v1. It facilitate building computation graph and computation optimization.
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The CGraph-Ndarray can be built up by wrapping up either of the previous two types of Ndarray, which are used for actual execution of computing.
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All three types of Ndarray can be used to support advanced computing modules, including algorithmic differentiation, optimization, and neural networks.
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-[@jzstark](https://github.com/jzstark): Project leader. Manage overall architecture, roadmap，and tech vision. Community communication. Set research agenda.
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-[@ryanrhymes](https://github.com/ryanrhymes): Potential commercialization, business opportunity & funding seeking.
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## Code of Contributing
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## Community
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The Owl community is based on the [OCaml Discourse](https://discuss.ocaml.org/) and [Owl Slack channel](https://owl-dev-team.slack.com/). All participants in the community are encouraged to provide support for new users within the project management infrastructure. Those seeking technical support should also recognize that all support activities within the project is voluntary and is therefore provided as and when time permits.
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## Owl Code Structure
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To help potential developers to understand the structure of Owl, here we briefly describe its overall design. More detailed description can be found in the [documentation](https://ocaml.xyz/docs/) and the Owl [books](https://ocaml.xyz/).
Owl provides a basic data structure for modern numerical computing: n-dimensional array (Ndarray). It is based on the mathematics, linear algebra, and statistics functions, which are built on both OCaml and C functions and libraries.
Sharing the same set of interface with Ndarray is the `base` system data architecture, which is implemented in pure OCaml. It is also based on modules that are implemented in OCaml. However, though it is sufficient for daily use for normal computing, the base version Ndarray does not implement some advanced functions as in the previous Owl version Ndarray, and its performance is understandably much slower.
Besides these two types of Ndarray, another type is CGraph-Ndarray, which can be used to support symbolic style computing like TensorFlow v1. It facilitate building computation graph and computation optimization.
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The CGraph-Ndarray can be built up by wrapping up either of the previous two types of Ndarray, which are used for actual execution of computing.
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All three types of Ndarray can be used to support advanced computing modules, including algorithmic differentiation, optimization, and neural networks.
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