| # MXNet Architecture |
| |
| Building a high-performance deep learning library |
| requires many systems-level design decisions. |
| In this design note, we share the rationale |
| for the specific choices made when designing _MXNet_. |
| We imagine that these insights may be useful |
| to both deep learning practitioners |
| and builders of other deep learning systems. |
| |
| ## Deep Learning System Design Concepts |
| |
| The following pages address general design concepts for deep learning systems. |
| Mainly, they focus on the following 3 areas: |
| abstraction, optimization, and trade-offs between efficiency and flexibility. |
| Additionally, we provide an overview of the complete MXNet system. |
| |
| * [MXNet System Overview](http://mxnet.io/architecture/overview.html) |
| * [Deep Learning Programming Style: Symbolic vs Imperative](http://mxnet.io/architecture/program_model.html) |
| * [Dependency Engine for Deep Learning](http://mxnet.io/architecture/note_engine.html) |
| * [Optimizing the Memory Consumption in Deep Learning](http://mxnet.io/architecture/note_memory.html) |
| * [Efficient Data Loading Module for Deep Learning](http://mxnet.io/architecture/note_data_loading.html) |
| * [Exception Handling in MXNet](http://mxnet.io/architecture/exception_handling.html) |
