Cortex has recently been redesigned and vetted in order to satisfy a few constraints (some conflicting).
- Provide the least amount of cognitive overhead for new developers.
- Create an architecture that will survive a long time.
- Allow simple testable implementation of the majority of the design.
- Enable networks that look more graphlike in structure.
- Enable parameter sharing.
- Coalesce algorithms into as high a level as possible.
- Allow multiple completely independent backends with very different requirements.
- Ensure any backend has complete freedom of expression of desired functionality.
- Provide layer definitions and implement as much of the high level neural network algorithms as possible in pure clojure. This level should be relatively easy to understand and should represent layer metadata and graph traversals in pure clojure datastructures that can be freely serialized in edn, fressian, or nippy format.
- Separation into a few major components with simple data definitions and clear semantics.
- Definition of execution contexts that are have clear semantics but no specification about accomplishing those semantics. The execution contexts are ideally potentially as simple as systems that run commands specified by the cortex layer with using algorithms and utilities defined and tested from the cortex layer. They may also be entire separate neural network frameworks (i.e. mxnet) that take and return (potentially more trained) network.
- Generalized framework for building algorithms meant to run on both a cpu and gpu.
- Implementation of cortex execution context on top of the compute framework enabling a unified cpu/cuda/opencl implementation.
- Specialized layer implementations and protocol definitions for imlementations that cannot be efficiently implemented in a using generalized math operators available across compute framework implementations.
- Cuda implementations of compute framework including neural network algorithms.
- Aggregating of functionality to provide a clear path and example for training various network types using the compute and gpu-compute frameworks.
- Caffe import for cortex using the compute functionality for verification.
- Keras import for cortex using the compute functionality for verification.
Cortex is designed as a specialized graph framework. There are a few steps necessary to use this for neural networks.
- Building an initial minimal description into a network. This calculates layer sizes and creates initial parameter buffers.
- Bind the graph nodes to inputs and outputs. Every node has an id
and data coming form outside is represented by a stream.
Inputs are maps of
id->{:stream stream-name}Outputs for training areid->{:stream stream-name :loss loss-fn}. - Training. Training can be thought of as a function that given a
graph and a sequence if input,answer pairs returns a sequence of
progressively better trained graphs. This builds a traversal which
calculates a forward traversal, backward traversal and io buffer list
for the execution context. A traversal is a sequence of
{:incoming :id: :outgoing}where incoming and outgoing are lists of buffer ids and id is the node for execution. The training system is expected to respect the traversal but it could for instance use a completely separate neural network facility to accomplish the transformation from network->network. - Inference: Inference is a function that given an network and a sequence of data produces a sequence of outputs. In this case the outputs are node-id->output-data maps.
Over time the intention is to aggregate algorithms into cortex out of the execution contexts so that it because easier to test and verify as much of cortex as possible without requiring actual execution of the training or inference algorithms.
- Implement as much of cortex as possible in the base cortex library using simple datastructures. The more logic that can be removed from the compute execution context and tested simple using clojure maps the better.
- Remove anything from cortex that isn't necessary for current execution. Keep experiments in branches.
- The basic design is that given a graph, annotate the graph with better parameters. The graph nodes and such are passed all the way through to the actual execution of the layers so it is easy to, for instance, add a map entry to a layer and then use it during execution or during optimization. It doesn't require chaining it through a set of object constructors and then chaining it back to the graph layer for serialization.
- Push as much functionality up into the cortex layer as possible and out of the various execution context. Implementing graph operations like split or join, for instance, can be largely done in the cortex layer with minimal involvement of the execution contexts aside from implementing some subset of specific operations.
- Cortex is now a graph of id->node map and edge list.
- Parameters are linked to by buffer id from a parameter entry. This allows multiple parameters to point to the same buffer.
- A significant amount of code was removed from the layer implementations of the compute layer and it was split between the compute execution context and the cortex build/traversal/execution system.
- The entire graph, traversal, and supporting information is passed to the execution context. This allows the context complete freedom in its expression of the graph and execution of the traversal.
- Again, this is done by ensuring the interface between cortex proper and any backend is as thin as possible meaning entire blocks of execution (train for this epoch of data) are specified at one function call. Compare this against a design where each layer's interface is specified and the interface then becomes extremely chatty and the backend doesn't have the opportunity to look at the entire traversal as a distinct whole in order to do something like minimize the number of IO buffers created.