diff --git a/docs/platform/tutorials/index.md b/docs/platform/tutorials/index.md index be9e6e39..8982d9fe 100644 --- a/docs/platform/tutorials/index.md +++ b/docs/platform/tutorials/index.md @@ -65,6 +65,19 @@ Each tutorial is divided into parts, so that you can leave a tutorial at a conve +## For the simulation engineer + +
+ +- __MATLAB and Simulink__ + + --- + + Deploy real-time MATLAB transformations and Simulink models to Quix. + + [:octicons-arrow-right-24: MATLAB and Simulink](./matlab/matlab-and-simulink.md) + +
## Need help? 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computing and Model-based Design in academic and industrial applications of science and technology. Ability to deploy MATLAB and Simulink applications with Quix enables engineers, scientists and researchers to develop, test, deploy and orchestrate large clusters of stream processing applications at scale. + +The following sections describe how to deploy MATLAB functions and Simulink models to Quix to perform real-time analytics and simulations with MATLAB and Quix Streams. + +## MATLAB + +This section describes the steps for deploying a MATLAB function that rotates 2D vectors counter-clockwise by a specified angle. + +### Prerequisites + + - A Quix account. You can sign up for a free account from the Quix [website](https://quix.io/product/){target=_blank}. + - MathWorks licenses for MATLAB, MATLAB Compiler and the MATLAB Compiler SDK. + +This tutorial uses MATLAB R2023a. Please refer to the [Working with different MATLAB versions](#working-with-different-matlab-versions) section for information on how to use a different version of MATLAB. + +### Preparing a MATLAB function for deployment + +This section describes the process for packaging MATLAB functions for deployment. The project templates in the Quix Portal have pre-built MATLAB packages. To deploy the default packages without compiling them, go to [deploying a MATLAB function](#deploying-a-matlab-function) section. + + 1. In MATLAB, create a new `*.m` file with the following function and save it as `rot.m`: + ``` + function M = rot(v, theta) + R = [cos(theta) -sin(theta); sin(theta) cos(theta)]; + M = R * v; + end + ``` + 2. In the MATLAB command window, type the following command to compile the MATLAB function for your preferred runtime environment. For more information on compiling MATLAB functions for deployment, please refer to [MATLAB compiler documentation](https://www.mathworks.com/help/compiler/mcc.html#d124e20858){target=_blank}: + + === "Python" + + ``` python + mcc -W python:quixmatlab rot.m -d py + ``` + + === "C\#" + + ``` cs + mcc -W 'dotnet:quixmatlab,api=matlab-data,framework_version=6.0' rot.m -d cs + ``` + +3. Above commands generate deployable packages for Python in `py` directory and `cs` for .NET: + + ![MATLAB](./images/matlab_console.png){width=600} + +### Deploying a MATLAB function + + 1. Sign in to your workspace on the [Quix Portal](https://portal.platform.quix.ai/){target=_blank}. + + 2. Click on the `Code Samples` on the left navigation panel and search for `matlab` in the search bar on the top left to filter code samples: + + ![code samples view on Quix](./images/code_samples.png){width=600} + + 3. Select the MATLAB template for your programming language of choice. Optionally, use the `LANGUAGES` filter on the left to filter templates based on the programming language. + + 4. Click on the `Preview code` button to open a preview of the project, and then click on the `Edit code` button to generate a project from the template: + + ![project preview on Quix](./images/matlab_project_preview.png){width=600} + + 5. Enter `Rotation Transform` as the project name. Enter `matlab-input` for input topic and `matlab-output` for output topic, and click `Save as Project.` + + ![create project from template](./images/matlab_set_up_project.png){width=600} + + 6. To use your own MATLAB packages, replace the contents of the `MATLAB` directory in the project with your assets by clicking on the upload icon in the project explorer. + + ![upload MATLAB packages](./images/matlab_pkg_upload.png){width=600} + + 7. Assign a tag to the project by clicking on the `add tag` icon and typing in a release tag such as `1.0`. Click on `Deploy` on the top right to open the deployment dialog: + + ![create a release tag](./images/matlab_deployment_tag.png){width=600} + + 8. In the deployment dialog, choose the release tag and ensure the environment variables are set correctly. In the `Deployment Settings`, choose `Service` for the deployment type, give at least 2 CPUs and sufficient memory (~4GB for the default template), and click on `Deploy`: + + ![deploy the model](./images/matlab_deployment_dialog.png){width=600} + +### Testing the MATLAB function + +This section describes the steps to test the MATLAB function by deploying a service to generate test data. In production environments, data from sensors or the output of another function or simulation takes the place of this service. + + 1. Click on the `Code Samples` on the left navigation, select `Python` under languages, and `Source` under the pipeline stage. Then type `starter` in the search box to filter the starter project for a data source. Follow the steps described in the previous section to create a project named `2D Vector Source` based on this template by clicking on `Preview code` followed by `Edit code`: + + ![create project from starter template](./images/starter_source.png){width=600} + + 2. Set the output topic of the project to the input topic of the deployment containing the MATLAB transformation: + + ![starter project configuration](./images/matlab_starter_project_creation.png){width=600} + + 3. Replace the contents of the `main.py` file of the project with the following script, which generates a random stream of 2D unit vectors: + + ``` + import quixstreams as qx + import time + import datetime + import os + import random, math + + client = qx.QuixStreamingClient() + topic_producer = client.get_topic_producer(topic_id_or_name = os.environ["output"]) + + stream = topic_producer.get_or_create_stream("vectors") + index = 0 + while True: + xa = random.uniform(-1, 1) + ya = random.uniform(-1, 1) + vlen = math.sqrt(xa ** 2 + ya ** 2) + stream.timeseries \ + .buffer \ + .add_timestamp(datetime.datetime.utcnow()) \ + .add_value("x", xa / vlen) \ + .add_value("y", ya / vlen) \ + .publish() + time.sleep(0.5) + ``` + + 4. Create a tag and deploy the `2D Vector Source`. + + ![pipeline](./images/matlab_pipeline_view.png){width=600} + + 5. Click on the `Rotation Transform` node to view the application logs and deployment details: + + ![deployment details](./images/matlab_deployment_details.png){width=600} + + 6. Next, click on `Data explorer` on the left navigation panel, select `Live data` from the top menu, and select the output topic of the MATLAB transformation (for example, `matlab-output`), the stream, and the parameters as shown in the figure below to view the live transformation: + + ![data explorer](./images/matlab_data_explorer.png){width=600} + +## Simulink + +This section describes the steps for deploying a Simulink model of an [internal combustion engine](https://www.mathworks.com/help/simulink/slref/modeling-engine-timing-using-triggered-subsystems.html){target=_blank} developed by MathWorks to Quix. + +### Prerequisites + + - A Quix account. You can sign up for a free account from the Quix [website](https://quix.io/product/){target=_blank}. + - MathWorks licenses for MATLAB, MATLAB Compiler, Simulink, Simulink Compiler and MATLAB Compiler SDK. + +This tutorial uses MATLAB R2023a. Please refer to the [Working with different MATLAB versions](#working-with-different-matlab-versions) section for information on how to use a different version of MATLAB. + +### Preparing a Simulink model for deployment + + 1. Download the MATLAB and Simulink assets for the internal combustion engine from the MathWorks [site](https://www.mathworks.com/help/simulink/slref/modeling-engine-timing-using-triggered-subsystems.html){target=_blank} or the `samples` directory in the Quix `Code Samples` for MATLAB. + 2. Convert the input and output of the Simulink model to workspace variables. For other means of interacting with Simulink programmatically, refer to [How to Bring Data from MATLAB Into Simulink](https://www.youtube.com/watch?v=kM2qL__YxBQ){target=_blank} and [Simulate a Simulink® model from Python](https://github.com/mathworks/Call-Simulink-from-Python){target=_blank}. + + ![Simulink](./images/simulink_console.png){width=600} + + 3. Create a MATLAB function in `engine.m` file with the following content to bootstrap the Simulink model and prepare it for deployment: + ``` + function R = engine(throttle_angle, time) + ta = timeseries(throttle_angle, time); + assignin("base", "throttle_angle", ta); + si = Simulink.SimulationInput('sldemo_engine'); + si = simulink.compiler.configureForDeployment(si); + sout = sim(si); + R = sout.engine_speed.Data(end); + end + ``` + 4. If you use workspace variables to pass arguments to Simulink, create them in the workspace before compiling the model. Run the following commands on the MATLAB command window to seed some input data and run the Simulink model using the `engine.m` function: + ``` + throttle_a = [0.2, 0.23, 1.2, 4.2, 5.3 ]; + ts = [1, 2, 3, 4, 5]; + engine(throttle_a, ts); + ``` + 5. On the MATLAB command window, type the following command to compile the MATLAB function to your preferred runtime environment. For information on compiling MATLAB functions for deployment, please refer to [MATLAB compiler documentation](https://www.mathworks.com/help/compiler/mcc.html#d124e20858){target=_blank}: + + === "Python" + + ``` python + mcc -W python:quixmatlab engine.m -d py + ``` + + === "C\#" + + ``` cs + mcc -W 'dotnet:quixmatlab,api=matlab-data,framework_version=6.0' engine.m -d cs + ``` + + 6. Above commands generate deployable packages for Python in `py` directory and `cs` for .NET. + +### Deploying a Simulink model + + 1. Sign in to the workspace on the [Quix Portal](https://portal.platform.quix.ai/){target=_blank}. + + 2. Click on the `Code Samples` on the left navigation panel and search for `simulink` in the search bar on the top left to filter code samples (templates for MATLAB and Simulink are the same): + + ![code samples view on Quix](./images/simulink_code_samples.png){width=600} + + 3. Click on the template for your programming language of choice. You can also use the `LANGUAGES` filters on the left to filter templates based on the programming language. + + 4. Click on the `Preview code` button to open a preview of the project, and then click on the `Edit code` button to generate a new project from the template: + + ![project preview on Quix](./images/matlab_project_preview.png){width=600} + + 5. Enter `Engine Model` for the project name. Enter `simulink-input` for input topic and `simulink-output` for output topic, and click `Save as Project.` + + ![create project from template](./images/simulink_set_up_project.png){width=600} + + 6. To deploy your own functions and models, replace the contents of the `MATLAB` directory in the project with your packages. + + 7. Replace the contents of the main function (`main.py` for Python, `Program.cs` for C#) with the following for your target programming language. They are responsible for calling the `engine` function with the correct arguments: + + === "Python" + + ``` python + import quixstreams as qx + import os + import quixmatlab + import matlab + + qxmlm = quixmatlab.initialize() + + client = qx.QuixStreamingClient() + input_topic = client.get_topic_consumer(os.environ["input"]) + output_topic = client.get_topic_producer(os.environ["output"]) + + def on_data_received_handler(input_stream: qx.StreamConsumer, data: qx.TimeseriesData): + with data: + for ts in data.timestamps: + throttle_angles = matlab.double([ts.parameters["throttle_angle"].numeric_value]) + timestamps = matlab.double([ts.timestamp_milliseconds / 1000]) + rv = qxmlm.engine(throttle_angles, timestamps) + ts.add_value("engine_speed", rv) + print("throttle angle:{}, engine speed:{}".format(throttle_angles[0][0], rv)) + output_stream = output_topic.get_or_create_stream(input_stream.stream_id) + output_stream.timeseries.publish(data) + + def on_stream_received_handler(stream: qx.StreamConsumer): + print("New stream: {}".format(stream.stream_id)) + stream.timeseries.on_data_received = on_data_received_handler + + input_topic.on_stream_received = on_stream_received_handler + + print("Listening to streams. Press CTRL-C to exit.") + qx.App.run() + ``` + + === "C\#" + + ``` cs + using System; + using MathWorks.MATLAB.Runtime; + using MathWorks.MATLAB.Types; + using MathWorks.MATLAB.Exceptions; + using QuixStreams.Streaming; + using QuixStreams.Streaming.Models; + + namespace Service + { + class Program + { + static void Main(string[] args) + { + dynamic matlab = MATLABRuntime.StartMATLAB("quixmatlab.ctf"); + + var quix = new QuixStreamingClient(); + using var producer = quix.GetTopicProducer(Environment.GetEnvironmentVariable("output")); + using var consumer = quix.GetTopicConsumer(Environment.GetEnvironmentVariable("input")); + consumer.OnStreamReceived += (_, stream) => + { + Console.WriteLine($"New stream received: {stream.StreamId}"); + stream.Timeseries.OnDataReceived += (_, args) => + { + foreach (var ts in args.Data.Timestamps) + { + var tin = new double[] { ts.TimestampMilliseconds / 1000.0 }; + var din = new double[] { ts.Parameters["throttle_angle"].NumericValue.Value }; + double rv = matlab.engine(din, tin); + ts.AddValue("engine_speed", rv); + Console.WriteLine($"throttle angle:{din[0]}, engine speed:{rv}"); + } + producer.GetOrCreateStream(stream.StreamId).Timeseries.Publish(args.Data); + }; + }; + Console.WriteLine("Listening for streams"); + App.Run(); + Console.WriteLine("Exiting"); + } + } + } + ``` + + 8. Assign a tag to the project by clicking on the `add tag` icon and typing in a release tag such as `1.0`, and clicking on `Deploy` on the top right to open the deployment dialog: + + ![create a release tag](./images/simulink_deployment_tag.png){width=600} + + 9. In the deployment dialog, choose the release tag or the commit reference and ensure the environment variables are set correctly. In the `Deployment Settings`, choose `Service` for the deployment type, give at least 2 CPU cores and sufficient memory (~4GB for the default template), and click on `Deploy` to deploy the model: + + ![deploy the model](./images/simulink_deployment_dialog.png){width=600} + +### Testing the Simulink model + +This section describes the steps to deploy a service to generate test data for the model. In production environments, data from sensors or output of another simulation takes the place of this service. + + 1. Click on the `Code Samples` on the left navigation, select `Python` under languages, and `Source` under the pipeline stage. Then type `starter` in the search box to filter the starter project for a data source. Follow the steps described in the previous section to create a project based on this template by clicking on `Preview code` followed by `Edit code`: + + + ![create project from starter template](./images/starter_source.png){width=600} + + 2. Set the output topic of the `Engine Data Source` to the input topic of the deployment containing the Simulink model: + + ![starter project configuration](./images/starter_project_creation.png){width=600} + + 3. Replace the contents of the `main.py` file in the `Engine Data Source` project with the following script, which randomly generates a throttle angle once every second: + + ``` + import quixstreams as qx + import time + import datetime + import os + import random + client = qx.QuixStreamingClient() + topic_producer = client.get_topic_producer(topic_id_or_name = os.environ["output"]) + stream = topic_producer.create_stream() + while True: + throttle_angle = random.uniform(8, 12) + data = qx.TimeseriesData() + data.add_timestamp(datetime.datetime.utcnow()) \ + .add_value("throttle_angle", throttle_angle) + stream.timeseries.publish(data) + time.sleep(1) + ``` + + 4. Follow steps 8 and 9 in the preceding section to deploy the `Engine Data Source.` + + ![pipeline](./images/pipeline_view.png){width=600} + + 5. Click on the `Engine Model` deployment to view the deployment details and application logs: + + ![deployment details](./images/simulink_deployment_details.png){width=600} + + 6. Next, click on `Data explorer` on the left navigation panel, select `Live data` from the top menu, and select the output topic, the stream, and the parameters as shown in the figure below to see the live simulation: + + ![data explorer](./images/simulink_data_explorer.png){width=600} + +## Working with different MATLAB versions + +Quix supports all versions of MATLAB with support for MATLAB Runtime API. To reduce build-time, Quix [Docker Hub](https://hub.docker.com/u/quixanalytics){target=_blank} maintains images with MATLAB R2023a, Python and .NET LTS versions pre-installed. Modify the following dockerfiles to use a different version of MATLAB, Python or .NET: + +=== "Python" + + ``` python + FROM ubuntu:22.04 + ENV PYTHONUNBUFFERED=1 + ENV PYTHONIOENCODING=UTF-8 + + # Python 3.10 + ENV PATH /usr/local/bin:$PATH + ENV LANG C.UTF-8 + RUN set -eux; \ + apt-get update; \ + apt-get install -y --no-install-recommends \ + ca-certificates \ + netbase \ + tzdata \ + ; \ + rm -rf /var/lib/apt/lists/* + ENV GPG_KEY A035C8C19219BA821ECEA86B64E628F8D684696D + ENV PYTHON_VERSION 3.10.11 + RUN set -eux; \ + \ + savedAptMark="$(apt-mark showmanual)"; \ + apt-get update; \ + apt-get install -y --no-install-recommends \ + dpkg-dev \ + gcc \ + gnupg \ + libbluetooth-dev \ + libbz2-dev \ + libc6-dev \ + libdb-dev \ + libexpat1-dev \ + libffi-dev \ + libgdbm-dev \ + liblzma-dev \ + libncursesw5-dev \ + libreadline-dev \ + libsqlite3-dev \ + libssl-dev \ + make \ + tk-dev \ + uuid-dev \ + wget \ + xz-utils \ + zlib1g-dev \ + ; \ + \ + wget -O python.tar.xz "https://www.python.org/ftp/python/${PYTHON_VERSION%%[a-z]*}/Python-$PYTHON_VERSION.tar.xz"; \ + wget -O python.tar.xz.asc "https://www.python.org/ftp/python/${PYTHON_VERSION%%[a-z]*}/Python-$PYTHON_VERSION.tar.xz.asc"; \ + GNUPGHOME="$(mktemp -d)"; export GNUPGHOME; \ + gpg --batch --keyserver hkps://keys.openpgp.org --recv-keys "$GPG_KEY"; \ + gpg --batch --verify python.tar.xz.asc python.tar.xz; \ + gpgconf --kill all; \ + rm -rf "$GNUPGHOME" python.tar.xz.asc; \ + mkdir -p /usr/src/python; \ + tar --extract --directory /usr/src/python --strip-components=1 --file python.tar.xz; \ + rm python.tar.xz; \ + \ + cd /usr/src/python; \ + gnuArch="$(dpkg-architecture --query DEB_BUILD_GNU_TYPE)"; \ + ./configure \ + --build="$gnuArch" \ + --enable-loadable-sqlite-extensions \ + --enable-optimizations \ + --enable-option-checking=fatal \ + --enable-shared \ + --with-lto \ + --with-system-expat \ + --without-ensurepip \ + ; \ + nproc="$(nproc)"; \ + EXTRA_CFLAGS="$(dpkg-buildflags --get CFLAGS)"; \ + LDFLAGS="$(dpkg-buildflags --get LDFLAGS)"; \ + LDFLAGS="${LDFLAGS:--Wl},--strip-all"; \ + make -j "$nproc" \ + "EXTRA_CFLAGS=${EXTRA_CFLAGS:-}" \ + "LDFLAGS=${LDFLAGS:-}" \ + "PROFILE_TASK=${PROFILE_TASK:-}" \ + ; \ + rm python; \ + make -j "$nproc" \ + "EXTRA_CFLAGS=${EXTRA_CFLAGS:-}" \ + "LDFLAGS=${LDFLAGS:--Wl},-rpath='\$\$ORIGIN/../lib'" \ + "PROFILE_TASK=${PROFILE_TASK:-}" \ + python \ + ; \ + make install; \ + \ + cd /; \ + rm -rf /usr/src/python; \ + \ + find /usr/local -depth \ + \( \ + \( -type d -a \( -name test -o -name tests -o -name idle_test \) \) \ + -o \( -type f -a \( -name '*.pyc' -o -name '*.pyo' -o -name 'libpython*.a' \) \) \ + \) -exec rm -rf '{}' + \ + ; \ + \ + ldconfig; \ + \ + apt-mark auto '.*' > /dev/null; \ + apt-mark manual $savedAptMark; \ + find /usr/local -type f -executable -not \( -name '*tkinter*' \) -exec ldd '{}' ';' \ + | awk '/=>/ { print $(NF-1) }' \ + | sort -u \ + | xargs -r dpkg-query --search \ + | cut -d: -f1 \ + | sort -u \ + | xargs -r apt-mark manual \ + ; \ + apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false; \ + rm -rf /var/lib/apt/lists/*; \ + \ + python3 --version + RUN set -eux; \ + for src in idle3 pydoc3 python3 python3-config; do \ + dst="$(echo "$src" | tr -d 3)"; \ + [ -s "/usr/local/bin/$src" ]; \ + [ ! -e "/usr/local/bin/$dst" ]; \ + ln -svT "$src" "/usr/local/bin/$dst"; \ + done + ENV PYTHON_PIP_VERSION 23.0.1 + ENV PYTHON_SETUPTOOLS_VERSION 65.5.1 + ENV PYTHON_GET_PIP_URL https://github.com/pypa/get-pip/raw/0d8570dc44796f4369b652222cf176b3db6ac70e/public/get-pip.py + ENV PYTHON_GET_PIP_SHA256 96461deced5c2a487ddc65207ec5a9cffeca0d34e7af7ea1afc470ff0d746207 + RUN set -eux; \ + \ + savedAptMark="$(apt-mark showmanual)"; \ + apt-get update; \ + apt-get install -y --no-install-recommends wget; \ + \ + wget -O get-pip.py "$PYTHON_GET_PIP_URL"; \ + echo "$PYTHON_GET_PIP_SHA256 *get-pip.py" | sha256sum -c -; \ + \ + apt-mark auto '.*' > /dev/null; \ + [ -z "$savedAptMark" ] || apt-mark manual $savedAptMark > /dev/null; \ + apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false; \ + rm -rf /var/lib/apt/lists/*; \ + \ + export PYTHONDONTWRITEBYTECODE=1; \ + \ + python get-pip.py \ + --disable-pip-version-check \ + --no-cache-dir \ + --no-compile \ + "pip==$PYTHON_PIP_VERSION" \ + "setuptools==$PYTHON_SETUPTOOLS_VERSION" \ + ; \ + rm -f get-pip.py; \ + \ + pip --version + + # MathWorks base dependencies + ENV DEBIAN_FRONTEND="noninteractive" TZ="Etc/UTC" + COPY base-dependencies.txt /tmp/base-dependencies.txt + RUN apt-get update && apt-get install --no-install-recommends -y `cat /tmp/base-dependencies.txt` \ + && apt-get clean && apt-get -y autoremove && rm -rf /var/lib/apt/lists/* + RUN [ -d /usr/share/X11/xkb ] || mkdir -p /usr/share/X11/xkb + + # MATLAB runtime + WORKDIR /tmp + RUN wget https://ssd.mathworks.com/supportfiles/downloads/R2023a/Release/2/deployment_files/installer/complete/glnxa64/MATLAB_Runtime_R2023a_Update_2_glnxa64.zip -O MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ + && unzip MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ + && ./install -agreeToLicense yes -destinationFolder /opt/matlab \ + && rm -rf /tmp/* + ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/opt/matlab/R2023a/runtime/glnxa64:/opt/matlab/R2023a/bin/glnxa64:/opt/matlab/R2023a/sys/os/glnxa64:/opt/matlab/R2023a/sys/opengl/lib/glnxa64" + ``` + +=== "C\# (SDK)" + ``` cs + FROM mcr.microsoft.com/dotnet/sdk:6.0-jammy + + # MathWorks base dependencies + ENV DEBIAN_FRONTEND="noninteractive" TZ="Etc/UTC" + COPY base-dependencies.txt /tmp/base-dependencies.txt + RUN apt-get update && apt-get install --no-install-recommends -y `cat /tmp/base-dependencies.txt` \ + && apt-get clean && apt-get -y autoremove && rm -rf /var/lib/apt/lists/* + RUN [ -d /usr/share/X11/xkb ] || mkdir -p /usr/share/X11/xkb + + # MATLAB runtime + WORKDIR /tmp + RUN wget https://ssd.mathworks.com/supportfiles/downloads/R2023a/Release/2/deployment_files/installer/complete/glnxa64/MATLAB_Runtime_R2023a_Update_2_glnxa64.zip -O MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ + && unzip MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ + && ./install -agreeToLicense yes -destinationFolder /opt/matlab \ + && rm -rf /tmp/* + ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/opt/matlab/R2023a/runtime/glnxa64:/opt/matlab/R2023a/bin/glnxa64:/opt/matlab/R2023a/sys/os/glnxa64:/opt/matlab/R2023a/sys/opengl/lib/glnxa64" + ``` + +=== "C\# (Runtime)" + ``` cs + FROM mcr.microsoft.com/dotnet/runtime:6.0-jammy + + # MathWorks base dependencies + ENV DEBIAN_FRONTEND="noninteractive" TZ="Etc/UTC" + COPY base-dependencies.txt /tmp/base-dependencies.txt + RUN apt-get update && apt-get install --no-install-recommends -y `cat /tmp/base-dependencies.txt` \ + && apt-get clean && apt-get -y autoremove && rm -rf /var/lib/apt/lists/* + RUN [ -d /usr/share/X11/xkb ] || mkdir -p /usr/share/X11/xkb + + # MATLAB runtime + WORKDIR /tmp + RUN wget https://ssd.mathworks.com/supportfiles/downloads/R2023a/Release/2/deployment_files/installer/complete/glnxa64/MATLAB_Runtime_R2023a_Update_2_glnxa64.zip -O MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ + && unzip MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ + && ./install -agreeToLicense yes -destinationFolder /opt/matlab \ + && rm -rf /tmp/* + ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/opt/matlab/R2023a/runtime/glnxa64:/opt/matlab/R2023a/bin/glnxa64:/opt/matlab/R2023a/sys/os/glnxa64:/opt/matlab/R2023a/sys/opengl/lib/glnxa64" + ``` + +=== "Base dependencies" + ``` + ca-certificates libasound2 libc6 libcairo2 libcairo-gobject2 libcap2 libcrypt1 libcrypt-dev libcups2 libdrm2 libdw1 libgbm1 libgdk-pixbuf2.0-0 libgl1 libglib2.0-0 libgomp1 libgstreamer1.0-0 libgstreamer-plugins-base1.0-0 libgtk-3-0 libice6 libnspr4 libnss3 libodbc1 libpam0g libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libsndfile1 libsystemd0 libuuid1 libwayland-client0 libxcomposite1 libxcursor1 libxdamage1 libxfixes3 libxft2 libxinerama1 libxrandr2 libxt6 libxtst6 libxxf86vm1 linux-libc-dev locales locales-all make net-tools odbcinst1debian2 procps sudo unzip wget zlib1g + ``` \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index f36b4f8d..97a97f65 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -82,6 +82,7 @@ nav: - 'Data Stream Processing': 'platform/tutorials/data-stream-processing/data-stream-processing.md' - 'Slack Alerting': 'platform/tutorials/slack-alerting/slack-alerting.md' - 'No code sentiment analysis': 'platform/tutorials/nocode-sentiment/nocode-sentiment-analysis.md' + - 'MATLAB and Simulink': 'platform/tutorials/matlab/matlab-and-simulink.md' - 'Code Samples': 'platform/samples/samples.md' - 'Connectors': - platform/connectors/index.md