TFKG is a library for defining, training, saving, and running Tensorflow/Keras models with single GPU acceleration all in Golang.

See ideas-todo.md for what's in store

• macOS Intel: running docker container, no GPU acceleration
• macOS M1 Apple Silicon: running docker container, no GPU acceleration
• Ubuntu 18.04 amd64: running docker container tf-jupyter-golang-gpu with GPU acceleration
• Ubuntu 18.04 amd64: binary execution with CUDA 11.2, cuDNN 8.1, Python 3.8, with GPU acceleration
• Windows 11 amd64: running docker container tf-jupyter-golang-gpu with GPU acceleration

Versions starting with v0 are liable to change radically.

• Tensorflow 2.6 experimental support: go get github.com/codingbeard/tfkg v0.2.6.9

• Docker if using the provided container
• Run make init-docker first to build the container
• If you're using a M1 Apple Silicon Mac on macOS use make init-docker-m1
• For GPU support in the container see: https://www.tensorflow.org/install/docker#gpu_support
• For GPU support on Windows 11 in the container see: https://docs.nvidia.com/cuda/wsl-user-guide/index.html

If not using the container: Make sure to install the correct versions to match the version of this library

• Tensorflow C library: https://www.tensorflow.org/install/lang_c
• Python 3.8 - the binary "python" must be on your path and the correct version
• Tensorflow Python library: https://www.tensorflow.org/install
• CUDA 11.2 and cuDNN 8.1 if using GPU acceleration: https://www.tensorflow.org/install/gpu#hardware_requirements

• Nvidia CUDA support on applicable platforms during Golang training/evaluation due to using the Tensorflow C library
• Define, train, evaluate, save, load, and infer Tensorflow compatible models all in Golang
• Load, shuffle, and preprocess csv datasets efficiently, even very large ones (tested on 330GB csv file)
  • String Tokenizer
  • Float/Int normalization to between 0-1

• tensorflow.keras.Sequential (Single input)
• tensorflow.keras.Model (Multiple input)

• tensorflow.keras.layers.Input
• tensorflow.keras.layers.Dense
• tensorflow.keras.layers.Concatenate
• More coming soon
• Interfaced - you can define custom layers

• Sparse Categorical Cross Entropy:
  • Accuracy
  • False positive rate at true positive rate (Specificity at Sensitivity)
  • True positive rate at false positive rate (Sensitivity at Specificity)

• Python Tensorflow Libraries are still required to use this library, though the docker container has it all
• This is an incomplete port of Tensorflow/Keras: There are many layers, metrics, and optimisers not yet ported
• There is no community support or documentation. You must understand Tensorflow/Keras and Golang well to have a chance of getting this working on a new project
• Loading/Training models saved by vanilla python Tensorflow/Keras is not supported, but it may be possible
• Multiple GPU training is not supported
• Image datasets and preprocessing is not yet supported
• Class weighting only works for datasets with two categories currently: negative/positive. It /will/ cause unintended side effects if you have more than two categories with imbalanced classes

See the full example with comments in ./examples/iris

To test it out run:

docker-compose up -d make examples-iris 

Define a model:

m := model.NewSequentialModel( errorHandler, logger, layer.NewInput(tf.MakeShape(-1, 4), layer.Float32), layer.NewDense(100, layer.Float32, layer.DenseConfig{Activation: "swish"}), layer.NewDense(100, layer.Float32, layer.DenseConfig{Activation: "swish"}), layer.NewDense(3, layer.Float32, layer.DenseConfig{Activation: "softmax"}), ) e = m.CompileAndLoad( 3, // batchSize )

Load a dataset:

dataset, e := data.NewSingleFileDataset( logger, errorHandler, "examples/iris/data/iris.data", cacheDir, 4, //categoryOffset 0.8, //trainPercent 0.1, //valPercent 0.1, //testPercent preprocessor.NewProcessor( errorHandler, "petal_sizes", preprocessor.ProcessorConfig{ CacheDir: cacheDir, LineOffset: 0, DataLength: 4, RequiresFit: true, Divisor: preprocessor.NewDivisor(errorHandler), Reader: preprocessor.ReadCsvFloat32s, Converter: preprocessor.ConvertDivisorToFloat32SliceTensor, }, ), ) e = dataset.SaveProcessors(saveDir) if e != nil { return }

Train a model:

m.Fit( dataset, model.FitConfig{ Epochs: 10, Validation: true, BatchSize: 3, PreFetch: 10, Verbose: 1, Metrics: []metric.Metric{ &metric.SparseCategoricalAccuracy{ Name: "acc", Confidence: 0.5, Average: true, }, }, Callbacks: []callback.Callback{ &callback.Logger{ FileLogger: logger, }, &callback.Checkpoint{ OnEvent: callback.EventEnd, OnMode: callback.ModeVal, MetricName: "val_acc", Compare: callback.CheckpointCompareMax, SaveDir: saveDir, }, }, }, )

Load and predict using a saved TFKG model:

inference, e := data.NewInference( logger, errorHandler, saveDir, preprocessor.NewProcessor( errorHandler, "petal_sizes", preprocessor.ProcessorConfig{ Divisor: preprocessor.NewDivisor(errorHandler), Converter: preprocessor.ConvertDivisorToFloat32SliceTensor, }, ), ) if e != nil { return } inputTensors, e := inference.GenerateInputs([][]float32{{6.0, 3.0, 4.8, 1.8}}) if e != nil { return } outputTensor, e := m.Predict(inputTensors...) if e != nil { return } outputValues := outputTensor.Value().([][]float32) logger.InfoF( "main", "Predicted classes: %s: %f, %s: %f, %s: %f", "Iris-setosa", outputValues[0][0], "Iris-versicolor", outputValues[0][1], "Iris-virginica", outputValues[0][2], )

The Tensorflow/Keras python package saves a Graph (see more: https://www.tensorflow.org/guide/intro_to_graphs) which can be executed in other languages using their C library as long as there are C bindings.

The C library does not contain all the functionality of the python library when it comes to defining and saving models, it can only execute Graphs.

The Graph is calculated in python based on your model configuration, and a lot of clever code on the part of the developers in optimising the graph.

While possible, it is not currently feasible for me to generate the Graph in Golang, so I am relying on python to do so.

This means while the model is technically defined and trained in Golang, it just generates a json config string which static python code uses to configure the model and then saves it ready for loading in Golang for training. For the moment this is a needed evil.

If some kind soul wants to replicate Keras and Autograph to generate the Graph in Golang, feel free to make a pull request. I may eventually do it, but it is not likely. There is a branch origin/scratch which allows you to investigate the graph of a saved model.

See: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/lib_package/README.md

See: https://www.tensorflow.org/install/source#docker_linux_builds

Docker did not play nicely with the amd64 precompiled Tensorflow C library so I had to compile it from source with avx disabled on a different linux amd64 machine.

The compiled libraries and licenses can be found at: https://github.com/CodingBeard/tfkg/releases/tag/v0.2.6.5 and need to be placed in ./docker/tf-jupyter-golang-m1/

These are the steps I took to compile the library from sources to make it work:

// On a linux amd64 machine with docker installed: git clone https://github.com/tensorflow/tensorflow cd tensorflow git checkout v2.6.0 docker run -it -w /tensorflow_src -v $PWD:/mnt -v $PWD:/tensorflow_src -e HOST_PERMS="$(id -u):$(id -g)" tensorflow/tensorflow:devel-gpu bash > apt update && apt install apt-transport-https curl gnupg > curl -fsSL https://bazel.build/bazel-release.pub.gpg | gpg --dearmor > bazel.gpg && \ mv bazel.gpg /etc/apt/trusted.gpg.d/ && \ echo "deb [arch=amd64] https://storage.googleapis.com/bazel-apt stable jdk1.8" | tee /etc/apt/sources.list.d/bazel.list > apt update && apt install bazel-3.7.2 nano > nano .bazelrc // add the lines after the existing build:cuda lines: build:cuda --linkopt=-lm build:cuda --linkopt=-ldl build:cuda --host_linkopt=-lm build:cuda --host_linkopt=-ldl > ./configure // take the defaults EXCEPT : // ... "--config=opt" is specified [Default is -Wno-sign-compare]: -mno-avx // The below will compile it for a specific GPU, find your gpu's compute capability and enter it twice separated by a comma (3000 series is 8.6) // ... TensorFlow only supports compute capabilities >= 3.5 [Default is: 3.5,7.0]: 8.6,8.6 > bazel-3.7.2 build --config=cuda --config=opt //tensorflow/tools/lib_package:libtensorflow > mkdir output > cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz ./output/ > cp bazel-bin/tensorflow/tools/lib_package/clicenses.tar ./output/ > rm -r bazel-* > bazel-3.7.2 build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package > ./bazel-bin/tensorflow/tools/pip_package/build_pip_package ./output/tf-2.6.0-gpu-noavx > quit // copy the libs and wheel from ./output into the TFKG project under ./docker/tf-jupyter-golang-m1 ... 

Big shout out to github.com/galeone for their Tensorflow Golang fork for 2.6 and again for their article on how to train a model in golang which helped me figure out how to then save the trained variables: https://pgaleone.eu/tensorflow/go/2020/11/27/deploy-train-tesorflow-models-in-go-human-activity-recognition/