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README.md

Swift-Jupyter

This is a Jupyter Kernel for Swift, intended to make it possible to use Jupyter with the Swift for TensorFlow project.

Installation Instructions

With TensorFlow toolchain

Requirements

Operating system:

  • Ubuntu 18.04 (64-bit); OR
  • other operating systems may work, but you will have to build Swift from sources.

Dependencies:

  • Python 3 (Ubuntu 18.04 package name: python3)
  • Python 3 Virtualenv (Ubuntu 18.04 package name: python3-venv)

Installation

swift-jupyter requires a Swift toolchain with LLDB Python3 support. Currently, the only prebuilt toolchains with LLDB Python3 support are the Swift for TensorFlow Ubuntu 18.04 Nightly Builds. Alternatively, you can build a toolchain from sources (see the next section for instructions).

Extract the Swift toolchain somewhere.

Create a virtualenv, install the requirements in it, and register the kernel in it:

python3 -m venv venv
. venv/bin/activate
pip install -r requirements.txt
pip install -r requirements_py_graphics.txt
python register.py --sys-prefix --swift-toolchain <path to extracted swift toolchain directory>

Finally, run Jupyter:

. venv/bin/activate
jupyter notebook

You should be able to create Swift notebooks. Installation is done!

(optional) Building toolchain with LLDB Python3 support

Follow the Building Swift for TensorFlow instructions, with some modifications:

  • Also install the Python 3 development headers. (For Ubuntu 18.04, sudo apt-get install libpython3-dev). The LLDB build will automatically find these and build with Python 3 support.
  • Instead of running utils/build-script, run SWIFT_PACKAGE=tensorflow_linux,no_test ./swift/utils/build-toolchain local.swift or SWIFT_PACKAGE=tensorflow_linux ./swift/utils/build-toolchain local.swift,gpu,no_test (depending on whether you want to build tensorflow with GPU support).

This will create a tar file containing the full toolchain. You can now proceed with the installation instructions from the previous section.

Using the Docker Container

This repository also includes a dockerfile which can be used to run a Jupyter Notebook instance which includes this Swift kernel. To build the container, the following command may be used:

# from inside the directory of this repository
docker build -f docker/Dockerfile -t swift-jupyter .

The resulting container comes with the latest Swift for TensorFlow toolchain installed, along with Jupyter and the Swift kernel contained in this repository.

This container can now be run with the following command:

docker run -p 8888:8888 --cap-add SYS_PTRACE -v /my/host/notebooks:/notebooks swift-jupyter

The functions of these parameters are:

  • -p 8888:8888 exposes the port on which Jupyter is running to the host.

  • --cap-add SYS_PTRACE adjusts the privileges with which this container is run, which is required for the Swift REPL.

  • -v <host path>:/notebooks bind mounts a host directory as a volume where notebooks created in the container will be stored. If this command is omitted, any notebooks created using the container will not be persisted when the container is stopped.

Usage Instructions

Rich output

You can call Python libraries using Swift's Python interop to display rich output in your Swift notebooks. (Eventually, we'd like to support Swift libraries that produce rich output too!)

Prerequisites:

  • You must use a Swift toolchain that has Python interop. As of February 2019, only the Swift for TensorFlow toolchains have Python interop.

After taking care of the prerequisites, run %include "EnableIPythonDisplay.swift" in your Swift notebook. Now you should be able to display rich output! For example:

let np = Python.import("numpy")
let plt = Python.import("matplotlib.pyplot")
IPythonDisplay.shell.enable_matplotlib("inline")
let time = np.arange(0, 10, 0.01)
let amplitude = np.exp(-0.1 * time)
let position = amplitude * np.sin(3 * time)

plt.figure(figsize: [15, 10])

plt.plot(time, position)
plt.plot(time, amplitude)
plt.plot(time, -amplitude)

plt.xlabel("time (s)")
plt.ylabel("position (m)")
plt.title("Oscillations")

plt.show()

Screenshot of running the above two snippets of code in Jupyter

let display = Python.import("IPython.display")
let pd = Python.import("pandas")
display.display(pd.DataFrame.from_records([["col 1": 3, "col 2": 5], ["col 1": 8, "col 2": 2]]))

Screenshot of running the above two snippets of code in Jupyter

%install directives

%install directives let you install SwiftPM packages so that your notebook can import them:

// Install the DeckOfPlayingCards package from GitHub.
%install '.package(url: "https://github.com/NSHipster/DeckOfPlayingCards", from: "4.0.0")' DeckOfPlayingCards

// Install the SimplePackage package that's in the kernel's working directory.
%install '.package(path: "$cwd/SimplePackage")' SimplePackage

The first argument to %install is a SwiftPM package dependency specification. The next argument(s) to %install are the products that you want to install from the package.

%install directives currently have some limitations:

  • You can only install packages once before you have to restart the kernel. We recommend having one cell at the beginning of your notebook that installs all the packages that the notebook needs.
  • Packages that (transitively) depend on C source code are not supported.
  • Downloads and build artifacts are not cached.
  • Some parts of packages get installed in a global directory, so two kernels that are running at the same time can clobber each other's installations.

%include directives

%include directives let you include code from files. To use them, put a line %include "<filename>" in your cell. The kernel will preprocess your cell and replace the %include directive with the contents of the file before sending your cell to the Swift interpreter.

<filename> must be relative to the directory containing swift_kernel.py. We'll probably add more search paths later.

Running tests

Locally

Install swift-jupyter locally using the above installation instructions. Now you can activate the virtualenv and run the tests:

. venv/bin/activate
python test/fast_test.py  # Fast tests, should complete in 1-2 min
python test/all_test_local.py  # Much slower, 10+ min
python test/all_test_local.py SimpleNotebookTests.test_simple_successful  # Invoke specific test method

You might also be interested in manually invoking the notebook tester on specific notebooks. See its --help documentation:

python test/notebook_tester.py --help

In Docker

After building the docker image according to the instructions above,

docker run --cap-add SYS_PTRACE swift-jupyter python3 /swift-jupyter/test/all_test_docker.py
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