A Machine Learning Framework for High Performance written in Rust

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Machine learning polarlight
Overview

polarlight

License Build Audit Rust

polarlight is a machine learning framework for high performance written in Rust.

Key Features

TBA

Quick Start

TBA

How To Contribute

Contributions are always welcome, either reporting issues/bugs or forking the repository and then issuing pull requests when you have completed some additional coding that you feel will be beneficial to the main project. If you are interested in contributing in a more dedicated capacity, then please contact me.

Contact

You can contact me via e-mail (utilForever at gmail.com). I am always happy to answer questions or help with any issues you might have, and please be sure to share any additional work or your creations with me, I love seeing what other people are making.

License

The class is licensed under the MIT License:

Copyright © 2021 polarlight Team

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Comments
  • Implement methods 'unsqueeze()' and 'cat()'

    Implement methods 'unsqueeze()' and 'cat()'

    This revision includes:

    • Implement methods 'unsequeeze()' and 'cat()'
      • unsqueeze: Expand a tensor's dimension
      • cat: Concatenate multiple tensors along a desired axis
    size/L 
    opened by litcoderr 1
  • Implement of trait 'Dataset' and struct 'MNIST'

    Implement of trait 'Dataset' and struct 'MNIST'

    [Done]

    • Defined trait 'Dataset' methods
      • len()
      • get_item()
    • Made built-in MNIST dataset
      • Download file from link
      • Decompress file
      • Implemented len() and get_item() from trait Dataset
      • Implemented trait Debug for CLI visualization image

    [Todo]

    • Make Dataloader for multi-processor batching sequence
    • Specify trait DatasetItem when Tensor is usable
    size/L 
    opened by litcoderr 1
  • Implementation of basic Tensor and Tensor operations

    Implementation of basic Tensor and Tensor operations

    Tensor Implementation -- cargo fmt Pass -- cargo clippy Pass

    Examples of implementations are available at main.rs.

    [Done]

    • Defined basic Tensor for all shapes
    • Implemented element-wise matrix operation
    • Implemented 2d Matrix multiplication
    • Implemented Matrix transpose

    [TODO]

    • 3d, 4d Tensor operations
    • Implement reshape function

    [Description]

    • struct Tensor : { shape, components}
    • fn get : Gets the element in the index of the Tensor.
    • fn print: print Tensor according to the format.
    size/L 
    opened by ONground-Korea 0
  • Implement basic Tensor and its operations

    Implement basic Tensor and its operations

    Examples of implementations are available at main.rs.

    [Done]

    • Defined basic Tensor for all shapes
    • Implemented element-wise matrix operation
    • Implemented 2D Matrix multiplication
    • Implemented Matrix transpose
    • Implemented reshape function

    [TODO]

    • 3D, 4D Tensor operations
    • reshape automatically when one dimension is -1
    • Tensor concat
    • min, max, argmin, argmax

    [Description]

    • Struct Tensor: Consists of shape and components
    • Method get: Gets the element in the index of the Tensor.
    • Method print: Print Tensor according to the format.
    size/L 
    opened by ONground-Korea 0
  • Implement Tensor

    Implement Tensor

    Tensor should be a cpu-based multi-dimensional matrix with changeable shape that supports matrix computations.

    Fields

    • data: Vector of numbers (might vary between i32, f64 and so on -> should be implemented with generics)
    • shape: Vector of i32

    Impl

    • addition
    • subtraction
    • multiplication
    • division
    feature p0 
    opened by litcoderr 0
Owner
Chris Ohk
@corp-momenti Engine Engineer, Microsoft Developer Technologies MVP, @CppKorea Founder and @reinforcement-learning-kr Administrator
Chris Ohk
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