Richard is gaining power.
Named after one of the first programs I ever wrote as a child, Richard started out as a personal effort to learn more about machine learning. The original Richard was meant to be a "virus", but the most malicious thing I could do on my Psion Series 3 personal organiser was print the phrase "Richard is gaining power" in an infinite loop.
The new version of Richard is strictly benevolent.
In its current form, Richard is a CLI application that performs classification using a neural network. Supported layer types include dense, convolutional, and max pooling, but there will likely be others in the future.
GPU acceleration is supported with Vulkan compute shaders.
Install CMake.
Install the Vulkan SDK
# See https://vulkan.lunarg.com/sdk/home
wget -qO- https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo tee /etc/apt/trusted.gpg.d/lunarg.asc
sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-1.3.275-jammy.list https://packages.lunarg.com/vulkan/1.3.275/lunarg-vulkan-1.3.275-jammy.list
sudo apt update
sudo apt install vulkan-sdk
Install development dependencies
sudo apt install \
build-essential \
libboost-program-options-dev
To make a release build
mkdir -p build/release && cd "$_"
cmake -D CMAKE_BUILD_TYPE=Release -G "Unix Makefiles" ../..
make -j8
And for a debug build
mkdir -p build/debug && cd "$_"
cmake -D CMAKE_BUILD_TYPE=Debug -G "Unix Makefiles" ../..
make -j8
Install CMake, Python 3, and the Vulkan SDK.
To build the release configuration, open a powershell and run
cd (mkdir build/release)
cmake -D CMAKE_BUILD_TYPE=Release -G "Visual Studio 17 2022" ../..
cmake --build . --config Release
And for the debug configuration
cd (mkdir build/debug)
cmake -D CMAKE_BUILD_TYPE=Debug -G "Visual Studio 17 2022" ../..
cmake --build . --config Debug
Supply the -D BUILD_TOOLS=1 option if you want to build the tools.
To see usage
./richardcli/richardcli -h
All examples are run from the build directory, e.g. build/release, and assume you have datasets located under data/.
{
"data": {
"classes": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
"shape": [784, 1, 1],
"normalization": {
"min": 0,
"max": 255
}
},
"dataLoader": {
"fetchSize": 512
},
"classifier": {
"network": {
"hyperparams": {
"epochs": 30,
"batchSize": 1024,
"miniBatchSize": 32,
},
"hiddenLayers": [
{
"type": "dense",
"size": 320,
"learnRate": 0.1,
"learnRateDecay": 1.0,
"dropoutRate": 0.0
},
{
"type": "dense",
"size": 64,
"learnRate": 0.1,
"learnRateDecay": 1.0,
"dropoutRate": 0.0
}
],
"outputLayer": {
"size": 10,
"learnRate": 0.1,
"learnRateDecay": 1.0
}
}
}
}
./richardcli/richardcli --train \
--samples ../../data/ocr/train.csv \
--config ../../data/ocr/config.json \
--network ../../data/ocr/network \
--gpu
../richardcli/richardcli --eval \
--samples ../../data/ocr/test.csv \
--network ../../data/ocr/network \
--gpu
{
"data": {
"classes": ["cat", "dog"],
"shape": [100, 100, 3],
"normalization": {
"min": 0,
"max": 255
}
},
"dataLoader": {
"fetchSize": 512
},
"classifier": {
"network": {
"hyperparams": {
"epochs": 10,
"batchSize": 1024,
"miniBatchSize": 32,
},
"hiddenLayers": [
{
"type": "convolutional",
"depth": 32,
"kernelSize": [3, 3],
"learnRate": 0.01,
"learnRateDecay": 1.0,
"dropoutRate": 0.0
},
{
"type": "maxPooling",
"regionSize": [2, 2]
},
{
"type": "convolutional",
"depth": 64,
"kernelSize": [4, 4],
"learnRate": 0.01,
"learnRateDecay": 1.0,
"dropoutRate": 0.0
},
{
"type": "maxPooling",
"regionSize": [2, 2]
},
{
"type": "dense",
"size": 64,
"learnRate": 0.01,
"learnRateDecay": 1.0,
"dropoutRate": 0.0
}
],
"outputLayer": {
"size": 2,
"learnRate": 0.01,
"learnRateDecay": 1.0
}
}
}
}
./richardcli/richardcli --train \
--samples ../../data/catdog/train \
--config ../../data/catdog/config.json \
--network ../../data/catdog/network \
--gpu
./richardcli/richardcli --eval \
--samples ../../data/catdog/test \
--network ../../data/catdog/network \
--gpu
Install google perftools
sudo apt install google-perftools
Make a release build and supply the -D CPU_PROFILE=1 option
cmake -D CMAKE_BUILD_TYPE=Release -D CPU_PROFILE=1 -G "Unix Makefiles" ../..
make -j8
Specify the intermediate file in the CPUPROFILE environment variable and run as usual, e.g.
CPUPROFILE=./prof.out ./richardcli/richardcli --train \
--samples ../../data/ocr/train.csv \
--config ../../data/ocr/config_cnn.json \
--network ../../data/ocr/network
For text output
google-pprof --text ./richardcli/richardcli ./prof.out > ./prof.txt
For graphical output
google-pprof --gv ./richardcli/richardcli ./prof.out
The text file should contain something like this
Total: 2823 samples
1166 41.3% 41.3% 1277 45.2% richard::computeCrossCorrelation
1039 36.8% 78.1% 1145 40.6% richard::computeFullCrossCorrelation
199 7.0% 85.2% 199 7.0% richard::Kernel::at (inline)
...
The first column is the number of samples spent inside the function.
The second column is this same number expressed as a percentage of the total samples taken. So in this case, we spent 41.3% of the time executing computeCrossCorrelation.
The third column is the cumulative time spent inside the function. In this example, 85.2% of the execution time is accounted for by these top 3 functions.
The next two columns tell us for how long the given function was part of the call stack. In other words, it includes time spent executing child calls. So in this example, we spent 40.6% of the time inside computeFullCrossCorrelation (including child calls), but only 36.8% actually within the computeFullCrossCorrelation function itself.