The code is adapted from that of Caffe whose license is attached. More...

Classes
class	Msg
	Msg used to transfer Param info (gradient or value), feature blob, etc between workers, stubs and servers. More...

class	SocketInterface

class	Poller

class	Dealer

class	Router

class	Driver

class	BridgeLayer

class	BridgeDstLayer
	For recv data from layer on other threads which may resident on other nodes due to layer/data partiton. More...

class	BridgeSrcLayer
	For sending data to layer on other threads which may resident on other nodes due to layer/data partition. More...

class	ConcateLayer
	Connect multiple (src) layers with a single (dst) layer. More...

class	SliceLayer
	Connect a single (src) layer with multiple (dst) layers. More...

class	SplitLayer
	Connect a single (src) layer with multiple dst layers. More...

class	DataLayer
	Base layer for reading records from local Shard, HDFS, lmdb, etc. More...

class	ShardDataLayer
	Layer for loading Record from DataShard. More...

class	ParserLayer
	Base layer for parsing the input records into Blobs. More...

class	LabelLayer
	Derived from ParserLayer to parse label from SingaleLabelImageRecord. More...

class	MnistLayer
	Derived from ParserLayer to parse MNIST feature from SingaleLabelImageRecord. More...

class	RGBImageLayer
	Derived from ParserLayer to parse RGB image feature from SingaleLabelImageRecord. More...

class	PrefetchLayer
	Layer for prefetching data records and parsing them. More...

class	Layer
	Base layer class. More...

class	ConnectionLayer
	Base layer for connecting layers when neural net is partitioned. More...

class	InputLayer
	Base layer for getting input data. More...

class	NeuronLayer

class	LossLayer
	Base layer for calculating loss and other metrics, e.g., precison. More...

class	EuclideanLossLayer
	Squared Euclidean loss as 0.5 \|\|predict - ground_truth\|\|^2. More...

class	SoftmaxLossLayer
	Cross-entropy loss applied to the probabilities after Softmax. More...

class	NeuralNet
	The neural network is constructed from user configurations in NetProto. More...

class	ConvolutionLayer
	Convolution layer. More...

class	CConvolutionLayer
	Use im2col from Caffe. More...

class	DropoutLayer

class	LRNLayer
	Local Response Normalization edge. More...

class	PoolingLayer

class	CPoolingLayer
	Use book-keeping for BP following Caffe's pooling implementation. More...

class	ReLULayer

class	InnerProductLayer

class	STanhLayer
	This layer apply scaled Tan function to neuron activations. More...

class	SigmoidLayer
	This layer apply Sigmoid function to neuron activations. More...

class	RBMLayer
	Base layer for RBM models. More...

class	RBMVisLayer
	RBM visible layer. More...

class	RBMHidLayer
	RBM hidden layer. More...

class	Server

class	Trainer
	Every running process has a training object which launches one or more worker (and server) threads. More...

class	Worker
	The Worker class which runs the training algorithm. More...

class	BPWorker

class	CDWorker

class	SyncedMemory
	Manages memory allocation and synchronization between the host (CPU) and device (GPU). More...

class	Blob

class	Cluster
	Cluster is a singleton object, which provides cluster configuations, e.g., the topology of the cluster. More...

struct	RTCallback

struct	JobInfo

class	ZKService

class	ClusterRuntime
	ClusterRuntime is a runtime service that manages dynamic configuration and status of the whole cluster. More...

class	JobManager

class	Metric
	Performance mtrics. More...

class	DataShard
	Data shard stores training/validation/test tuples. More...

class	Node

class	Graph
	Neuralnet is constructed by creating a graph with each node representing one layer at first. More...

class	ParamGenerator
	Base parameter generator which intializes parameter values. More...

class	GaussianGen

class	GaussianSqrtFanInGen

class	UniformGen

class	UniformSqrtFanInGen

class	UniformSqrtFanInOutGen

class	Param
	Base paramter class. More...

class	ParamEntry
	ParamEntry is used for aggregating gradients of Params shared by workers from the same group. More...

class	LRGenerator
	Base learning rate generator. More...

class	FixedStepLRGen

class	StepLRGen

class	LinearLRGen

class	ExpLRGen

class	InvLRGen

class	InvTLRGen

class	Updater
	Updater for Param. More...

class	SGDUpdater

class	AdaGradUpdater

class	NesterovUpdater

Typedefs
typedef void(*	rt_callback )(void *contest)

Functions
int	Addr (int grp, int id_or_proc, int type)
	Wrapper to generate message address. More...

int	AddrGrp (int addr)
	Parse group id from addr. More...

int	AddrID (int addr)
	Parse worker/server id from addr. More...

int	AddrProc (int addr)
	Parse worker/server procs from addr. More...

int	AddrType (int addr)
	Parse msg type from addr. More...

void	DeleteMsg (Msg **msg)

int	BlobTrgt (int grp, int layer)

int	BlobGrp (int blob_trgt)

int	BlobLayer (int blob_trgt)

void	MallocHost (void **ptr, size_t size)

void	FreeHost (void *ptr)

std::string	GetZKJobWorkspace (int job_id)

std::string	IntVecToString (const std::vector< int > &vec)

std::string	VStringPrintf (std::string fmt, va_list l)

std::string	StringPrintf (std::string fmt,...)

int	ArgPos (int argc, char *arglist, const char arg)
	Locate the position of the arg in arglist. More...

void	CreateFolder (const std::string name)

const std::vector< std::vector < int > >	Slice (int num, const std::vector< int > &sizes)
	Slice a set of large Params into small pieces such that they can be roughtly equally partitioned into a fixed number of boxes. More...

const std::vector< int >	PartitionSlices (int num, const std::vector< int > &slices)
	Partition slices into boxes. More...

int	gcd (int a, int b)

int	LeastCommonMultiple (int a, int b)

std::string	GetHostIP ()

void	SetupLog (const std::string &workspace, const std::string &model)

void	Im2col (const float data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, float data_col)

void	Col2im (const float data_col, const int channels, const int height, const int width, const int patch_h, const int patch_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, float data_im)

void	ForwardMaxPooling (const float bottom, const int num, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, float top, float *mask)

void	BackwardMaxPooling (const float top, const float mask, const int num, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, float *bottom)

void	ForwardAvgPooling (const float bottom, const int num, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, float top)

void	BackwardAvgPooling (const float top, const int num, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, float bottom)

void	ReadProtoFromTextFile (const char filename, Message proto)

void	WriteProtoToTextFile (const Message &proto, const char *filename)

void	ReadProtoFromBinaryFile (const char filename, Message proto)

void	WriteProtoToBinaryFile (const Message &proto, const char *filename)

int	ParamTrgt (int param_id, int slice_id)

int	ParamID (int param_trgt)

int	SliceID (int param_trgt)

Variables
const std::string	kInprocRouterEndpoint = "inproc://router"

const int	kCollectSleepTime =5
	< sleep 5 milliseconds if the Param is not updated to the expected version

const int	kZKBufSize = 100

const std::string	kZKPathSinga = "/singa"

const std::string	kZKPathSys = "/singa/sys"

const std::string	kZKPathJLock = "/singa/sys/job-lock"

const std::string	kZKPathApp = "/singa/app"

const std::string	kZKPathJob = "/singa/app/job-"

const std::string	kZKPathJobGroup = "/group"

const std::string	kZKPathJobProc = "/proc"

const std::string	kZKPathJobPLock = "/proc-lock"

Detailed Description

The code is adapted from that of Caffe whose license is attached.

COPYRIGHT All contributions by the University of California: Copyright (c) 2014, The Regents of the University of California (Regents) All rights reserved. All other contributions: Copyright (c) 2014, the respective contributors All rights reserved. Caffe uses a shared copyright model: each contributor holds copyright over their contributions to Caffe. The project versioning records all such contribution and copyright details. If a contributor wants to further mark their specific copyright on a particular contribution, they should indicate their copyright solely in the commit message of the change when it is committed. LICENSE Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. CONTRIBUTION AGREEMENT By contributing to the BVLC/caffe repository through pull-request, comment, or otherwise, the contributor releases their content to the license and copyright terms herein.

Function Documentation

int singa::Addr	(	int	grp,
		int	id_or_proc,
		int	type
	)

inline

Wrapper to generate message address.

Parameters

grp	worker/server group id
id_or_proc	worker/server id or procs id
type	msg type

int singa::AddrGrp ( int addr )

inline

Parse group id from addr.

Returns: group id

int singa::AddrID ( int addr )

inline

Parse worker/server id from addr.

Returns: id

int singa::AddrProc ( int addr )

inline

Parse worker/server procs from addr.

Returns: procs id

int singa::AddrType ( int addr )

inline

Parse msg type from addr.

Returns: msg type

int singa::ArgPos	(	int	argc,
		char **	arglist,
		const char *	arg
	)

Locate the position of the arg in arglist.

Parameters

argc	total num of arguments
arglist	all arguments
the	searched argument

Returns: the position of arg in the arglist; -1 if not found.

const std::vector<int> singa::PartitionSlices	(	int	num,
		const std::vector< int > &	slices
	)

Partition slices into boxes.

Parameters

num	number of boxes
slices	slice sizes

Returns: box id for each slice

const std::vector<std::vector<int> > singa::Slice	(	int	num,
		const std::vector< int > &	sizes
	)

Slice a set of large Params into small pieces such that they can be roughtly equally partitioned into a fixed number of boxes.

Parameters

num	total number of boxes to store the small pieces
sizes	size of all Params

Returns: all slices for each Param

Classes

Typedefs

Functions

Variables

Detailed Description

Function Documentation