Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements; and to You under the Apache License, Version 2.0.

Welcome to this tutorial for Apache SINGA. Please install conda and SINGA before running the notebooks.

- Introduction and installation
- SINGA classes
- Core classes - Tensor and Device
- Model classes - Layer, Loss, Metric, FeedForwardNet, Optimzier, Initializer

- Simple models
- Deep learning models
- Distributed training (WIP)
- Rafiki (WIP)

$\min_{\theta} \sum_{<x, y>\in D}L(f(x|\theta), y)$

Training procedure

- randomly initialize $\theta$
- get a mini-batch of data $\{<x, y>\}$ and compute $\frac{\partial L}{\partial \theta}$
- $\theta \leftarrow \theta - \alpha \frac{\partial L}{\partial \theta}$
- repeat 2-3 until converge, i.e., $||\frac{\partial L}{\partial \theta}|| < \epsilon$

Components | Feature | Status |
---|---|---|

Core | Tensor implementation | V1.0 |

Device implementation (CppCPU, CudaGPU, OpenclGPU) | V1.1 | |

Model | Basic layers for CNN and RNN | V1.0 |

Basic loss and metric functions | V1.0 | |

Basic optimizer and initializers | V1.0 | |

IO | Message paasing via socket | V1.0 |

Installation | From source on Linux and Mac | V1.0 |

From source on Windows | V1.1 | |

From wheel on Linux and Mac | V1.0 | |

From debian package on Ubuntu | V1.1 | |

Docker images | V1.1 | |

AWS AMI | V1.1 |

Components | Feature | Status |
---|---|---|

Core | Tensor API = Numpy API | V1.2 |

Operation scheduling and memory optimization | V1.3 | |

Model | To support more vison, NLP examples | V1.2 |

General neural net API | V1.2 | |

To support reinforcement learning examples | V1.3 | |

Auto-grad + Functional API | TBD | |

IO | HDFS integration | V1.3 |

Server | Consistency and communication optimization | V1.2 |

Message passing via Infiniband | V1.2 | |

Installation | From wheel on Windows | V1.2 |

From debian on Linux | V1.2 |