Programming language: Scala
License: Apache License 2.0
Latest version: v0.5.10

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This library is a Scala API for https://www.tensorflow.org. It attempts to provide most of the functionality provided by the official Python API, while at the same type being strongly-typed and adding some new features. It is a work in progress and a project I started working on for my personal research purposes. Much of the API should be relatively stable by now, but things are still likely to change.

Chat Room

Please refer to the main website for documentation and tutorials. Here are a few useful links:


It would be greatly appreciated if you could cite this project using the following BibTex entry, if you end up using it in your work:

  title        = {{TensorFlow Scala}},
  author       = {Platanios, Emmanouil Antonios},
  howpublished = {\url{https://github.com/eaplatanios/tensorflow_scala}},
  year         = {2018}

Main Features

  • Easy manipulation of tensors and computations involving tensors (similar to NumPy in Python):

    val t1 = Tensor(1.2, 4.5)
    val t2 = Tensor(-0.2, 1.1)
    t1 + t2 == Tensor(1.0, 5.6)
  • Low-level graph construction API, similar to that of the Python API, but strongly typed wherever possible:

    val inputs      = tf.placeholder[Float](Shape(-1, 10))
    val outputs     = tf.placeholder[Float](Shape(-1, 10))
    val predictions = tf.nameScope("Linear") {
      val weights = tf.variable[Float]("weights", Shape(10, 1), tf.ZerosInitializer)
      tf.matmul(inputs, weights)
    val loss        = tf.sum(tf.square(predictions - outputs))
    val optimizer   = tf.train.AdaGrad(1.0f)
    val trainOp     = optimizer.minimize(loss)
  • Numpy-like indexing/slicing for tensors. For example:

    tensor(2 :: 5, ---, 1) // is equivalent to numpy's 'tensor[2:5, ..., 1]'
  • High-level API for creating, training, and using neural networks. For example, the following code shows how simple it is to train a multi-layer perceptron for MNIST using TensorFlow for Scala. Here we omit a lot of very powerful features such as summary and checkpoint savers, for simplicity, but these are also very simple to use.

    // Load and batch data using pre-fetching.
    val dataset = MNISTLoader.load(Paths.get("/tmp"))
    val trainImages = tf.data.datasetFromTensorSlices(dataset.trainImages.toFloat)
    val trainLabels = tf.data.datasetFromTensorSlices(dataset.trainLabels.toLong)
    val trainData =
    // Create the MLP model.
    val input = Input(FLOAT32, Shape(-1, 28, 28))
    val trainInput = Input(INT64, Shape(-1))
    val layer = Flatten[Float]("Input/Flatten") >>
        Linear[Float]("Layer_0", 128) >> ReLU[Float]("Layer_0/Activation", 0.1f) >>
        Linear[Float]("Layer_1", 64) >> ReLU[Float]("Layer_1/Activation", 0.1f) >>
        Linear[Float]("Layer_2", 32) >> ReLU[Float]("Layer_2/Activation", 0.1f) >>
        Linear[Float]("OutputLayer", 10)
    val loss = SparseSoftmaxCrossEntropy[Float, Long, Float]("Loss") >>
    val optimizer = tf.train.GradientDescent(1e-6f)
    val model = Model.simpleSupervised(input, trainInput, layer, loss, optimizer)
    // Create an estimator and train the model.
    val estimator = InMemoryEstimator(model)
    estimator.train(() => trainData, StopCriteria(maxSteps = Some(1000000)))

    And by changing a few lines to the following code, you can get checkpoint capability, summaries, and seamless integration with TensorBoard:

    val loss = SparseSoftmaxCrossEntropy[Float, Long, Float]("Loss") >>
        Mean("Loss/Mean") >>
        ScalarSummary(name = "Loss", tag = "Loss")
    val summariesDir = Paths.get("/tmp/summaries")
    val estimator = InMemoryEstimator(
      modelFunction = model,
      configurationBase = Configuration(Some(summariesDir)),
      trainHooks = Set(
        SummarySaver(summariesDir, StepHookTrigger(100)),
        CheckpointSaver(summariesDir, StepHookTrigger(1000))),
      tensorBoardConfig = TensorBoardConfig(summariesDir))
    estimator.train(() => trainData, StopCriteria(maxSteps = Some(100000)))

    If you now browse to while training, you can see the training progress:

  • Efficient interaction with the native library that avoids unnecessary copying of data. All tensors are created and managed by the native TensorFlow library. When they are passed to the Scala API (e.g., fetched from a TensorFlow session), we use a combination of weak references and a disposing thread running in the background. Please refer to tensorflow/src/main/scala/org/platanios/tensorflow/api/utilities/Disposer.scala, for the implementation.

Compiling from Source

Note that in order to compile TensorFlow Scala on your machine you will need to first install the TensorFlow Python API. You also need to make sure that you have a python3 alias for your python binary. This is used by CMake to find the TensorFlow header files in your installation.



Funding for the development of this library has been generously provided by the following sponsors:

CMU Presidential Fellowship National Science Foundation Air Force Office of Scientific Research
awarded to Emmanouil Antonios Platanios Grant #: IIS1250956 Grant #: FA95501710218

TensorFlow, the TensorFlow logo, and any related marks are trademarks of Google Inc.


Some TODOs

  • [ ] Figure out what the proper to way to handle Int vs Long shapes is, so that we can use Long shapes without hurting GPU performance.
  • [ ] Make the optimizers typed (with respect to their state, at least).
  • [ ] Make the gradients function retain types (we need a type trait for that).
  • [ ] Dispose dataset iterators automatically.
  • [ ] Fixed all [TYPE] !!! code TODOs.

  • [ ] Session execution context (I'm not sure if that's good to have)

  • [ ] Session reset functionality

  • [ ] Variables slicing

  • [ ] Slice assignment

  • [ ] Support for CriticalSection.

  • [ ] tfdbg / debugging support

  • [ ] tfprof / op statistics collection

  • Switch to using JUnit for all tests.

  • Add convenience implicit conversions for shapes (e.g., from tuples or sequences of integers).

  • Create a "Scope" class and companion object.

  • Variables API:

    • Clean up the implementation of variable scopes and stores and integrate it with "Scope".
    • Make 'PartitionedVariable' extend 'Variable'.
    • After that change, all 'getPartitionedVariable' methods can be integrated with the 'getVariable' methods, which will simplify the variables API.
  • Switch to using "Seq" instead of "Array" wherever possible.

  • Op creation:

    • Reset default graph
    • Register op statistics
  • Fix Travis CI support (somehow load the native library)

    • Website margins are a little large relative to the content in mobile
    • Make the code blocks scroll rather than wrap

To publish a signed snapshot version of the package that is cross-compiled, we use the following commands from within an SBT shell:

set nativeCrossCompilationEnabled in jni := true

You can also test cross-compilation using the following command:

sbt jni/cross:nativeCrossCompile

CUDA Compute Capabilities: 3.5,7.0,7.5,8.0,8.6

Compile the TensorFlow dynamic libraries from source using:

bazel build --config=opt --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0 //tensorflow:libtensorflow.so

On Ubuntu 18.04 you may get some linking errors, in which case you should use:

bazel build --config=opt --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0 --noincompatible_do_not_split_linking_cmdline //tensorflow:libtensorflow.so

On Windows you may get some CUDA-related errors, in which case you should use:

bazel build --config=opt --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0 --define=no_tensorflow_py_deps=true --copt=-DTHRUST_IGNORE_CUB_VERSION_CHECK --copt=-nvcc_options=disable-warnings //tensorflow:tensorflow.lib //tensorflow:tensorflow_framework.lib

For Mac we also need to deal with this currently:

install_name_tool -id @rpath/libtensorflow.2.dylib libtensorflow.2.4.0.dylib
install_name_tool -change @rpath/libtensorflow.so.2 @rpath/libtensorflow.2.dylib libtensorflow_framework.2.4.0.dylib

To publish the documentation website we use the following commands:

sbt docs/previewSite     # To preview the website
sbt docs/ghpagesPushSite # To publish the website

To prepare the precompiled TensorFlow binary packages, use the following commands:

mkdir lib
cp -av /usr/local/lib/libtensorflow* lib/
tar -zcvf libtensorflow-2.2.0-cpu-darwin-x86_64.tar.gz lib
tar -ztvf libtensorflow-2.2.0-cpu-darwin-x86_64.tar.gz


*Note that all licence references and agreements mentioned in the Tensorflow_scala README section above are relevant to that project's source code only.