quantconnect--lean/DockerfileLeanFoundationARM

# This is a version of DockerfileLeanFoundation for ARM
# Some packages from the AMD image are excluded because they are not available on ARM or take too long to build

# Use base system for cleaning up wayward processes
FROM phusion/baseimage:jammy-1.0.1

MAINTAINER QuantConnect <contact@quantconnect.com>

# Use baseimage-docker's init system.
CMD ["/sbin/my_init"]

# Install OS Packages:
# Misc tools for running Python.NET and IB inside a headless container.
RUN add-apt-repository ppa:ubuntu-toolchain-r/test && apt-get update \
    && apt-get install -y git libgtk2.0.0 bzip2 curl unzip wget python3-pip python3-opengl zlib1g-dev \
    xvfb libxrender1 libxtst6 libxi6 libglib2.0-dev libopenmpi-dev libstdc++6 openmpi-bin \
    r-base pandoc libcurl4-openssl-dev \
    openjdk-11-jdk openjdk-11-jre bbe \
    && apt-get clean && apt-get autoclean && apt-get autoremove --purge -y \
    && rm -rf /var/lib/apt/lists/*

# Install dotnet 9 sdk & runtime
# The .deb packages don't support ARM, the install script does
ENV PATH="/root/.dotnet:${PATH}"
RUN wget https://dot.net/v1/dotnet-install.sh && \
    chmod 777 dotnet-install.sh && \
    ./dotnet-install.sh -c 9.0 && \
    rm dotnet-install.sh
ENV DOTNET_ROOT="/root/.dotnet"

# Set PythonDLL variable for PythonNet
ENV PYTHONNET_PYDLL="/opt/miniconda3/lib/libpython3.11.so"

# Install miniconda
ENV CONDA="Miniconda3-py311_24.9.2-0-Linux-aarch64.sh"
ENV PATH="/opt/miniconda3/bin:${PATH}"
RUN wget -q https://cdn.quantconnect.com/miniconda/${CONDA} && \
    bash ${CONDA} -b -p /opt/miniconda3 && rm -rf ${CONDA}

# Install java runtime for h2o lib
RUN apt-get update && apt-get install -y alien dpkg-dev debhelper build-essential && wget https://download.oracle.com/java/17/archive/jdk-17.0.12_linux-aarch64_bin.rpm \
    && alien -i jdk-17.0.12_linux-aarch64_bin.rpm \
    && update-alternatives --install /usr/bin/java java /usr/lib/jvm/jdk-17.0.12-oracle-aarch64/bin/java 1 \
    && rm jdk-17.0.12_linux-aarch64_bin.rpm

# Avoid pip install read timeouts
ENV PIP_DEFAULT_TIMEOUT=120

# Install numpy first to avoid it not being resolved when installing libraries that depend on it next
RUN pip install --no-cache-dir numpy==1.26.4

# Install newer (than provided by ubuntu) cmake required by scikit build process
RUN conda install -c conda-forge cmake==3.28.4 && conda clean -y --all

# The list of packages in this image is shorter than the list in the AMD images
# This list only includes packages that can be installed within 2 minutes on ARM
RUN pip install --no-cache-dir      \
    cython==3.0.9                   \
    pandas==2.1.4                   \
    scipy==1.11.4                   \
    numpy==1.26.4                   \
    wrapt==1.16.0                   \
    astropy==7.0.0                  \
    beautifulsoup4==4.12.3          \
    dill==0.3.8                     \
    jsonschema==4.23.0              \
    lxml==5.3.0                     \
    msgpack==1.1.0                  \
    numba==0.59.1                   \
    xarray==2024.11.0               \
    plotly==5.24.1                  \
    jupyterlab==4.3.2               \
    ipywidgets==8.1.5               \
    jupyterlab-widgets==3.0.13      \
    tensorflow==2.18.0              \
    docutils==0.21.2                \
    gensim==4.3.3                   \
    keras==3.7.0                    \
    lightgbm==4.5.0                 \
    nltk==3.9.1                     \
    graphviz==0.20.3                \
    cmdstanpy==1.2.4                \
    copulae==0.7.9                  \
    featuretools==1.31.0            \
    PuLP==2.9.0                     \
    pymc==5.19.0                    \
    rauth==0.7.3                    \
    scikit-learn==1.4.2             \
    scikit-optimize==0.10.2         \
    aesara==2.9.4                   \
    tsfresh==0.20.2                 \
    tslearn==0.6.3                  \
    tweepy==4.14.0                  \
    PyWavelets==1.7.0               \
    umap-learn==0.5.7               \
    fastai==2.7.18                  \
    arch==7.2.0                     \
    copulas==0.12.0                 \
    cufflinks==0.17.3               \
    gym==0.26.2                     \
    deap==1.4.1                     \
    pykalman==0.9.7                 \
    cvxpy==1.6.0                    \
    pyro-ppl==1.9.1                 \
    sklearn-json==0.1.0             \
    dtw-python==1.5.3               \
    gluonts==0.16.0                 \
    gplearn==0.4.2                  \
    jax==0.4.35                     \
    pennylane==0.39.0               \
    PennyLane-Lightning==0.39.0     \
    pennylane-qiskit==0.36.0        \
    mplfinance==0.12.10b0           \
    hmmlearn==0.3.3                 \
    ta==0.11.0                      \
    seaborn==0.13.2                 \
    optuna==4.1.0                   \
    findiff==0.10.2                 \
    sktime==0.26.0                  \
    hyperopt==0.2.7                 \
    bayesian-optimization==2.0.0    \
    matplotlib==3.7.5               \
    sdeint==0.3.0                   \
    pandas_market_calendars==4.4.2  \
    ruptures==1.1.9                 \
    simpy==4.1.1                    \
    scikit-learn-extra==0.3.0       \
    ray==2.40.0                     \
    "ray[tune]"==2.40.0             \
    "ray[rllib]"==2.40.0            \
    "ray[data]"==2.40.0             \
    "ray[train]"==2.40.0            \
    fastText==0.9.3                 \
    h2o==3.46.0.6                   \
    prophet==1.1.6                  \
    Riskfolio-Lib==6.1.1            \
    torch==2.5.1                    \
    torchvision==0.20.1             \
    ax-platform==0.4.3              \
    alphalens-reloaded==0.4.5       \
    pyfolio-reloaded==0.9.8         \
    altair==5.5.0                   \
    modin==0.26.1                   \
    persim==0.3.7                   \
    ripser==0.6.10                  \
    pydmd==2024.12.1                \
    EMD-signal==1.6.4               \
    spacy==3.7.5                    \
    pandas-ta==0.3.14b              \
    pytorch-ignite==0.5.1           \
    tensorly==0.9.0                 \
    mlxtend==0.23.3                 \
    shap==0.46.0                    \
    lime==0.2.0.1                   \
    mpmath==1.3.0                   \
    polars==1.16.0                  \
    stockstats==0.6.2               \
    QuantStats==0.0.64              \
    hurst==0.0.5                    \
    numerapi==2.19.1                \
    pymdptoolbox==4.0-b3            \
    panel==1.5.4                    \
    hvplot==0.11.1                  \
    py-heat==0.0.6                  \
    py-heat-magic==0.0.2            \
    bokeh==3.6.2                    \
    river==0.21.0                   \
    stumpy==1.13.0                  \
    pyvinecopulib==0.6.5            \
    ijson==3.3.0                    \
    jupyter-resource-usage==1.1.0   \
    injector==0.22.0                \
    openpyxl==3.1.5                 \
    xlrd==2.0.1                     \
    mljar-supervised==1.1.9         \
    dm-tree==0.1.8                  \
    lz4==4.3.3                      \
    ortools==9.9.3963               \
    py_vollib==1.0.1                \
    thundergbm==0.3.17              \
    yellowbrick==1.5                \
    livelossplot==0.5.5             \
    gymnasium==1.0.0                \
    interpret==0.6.7                \
    DoubleML==0.9.0                 \
    jupyter-bokeh==4.0.5            \
    imbalanced-learn==0.12.4        \
    openai==1.57.0                  \
    lazypredict==0.2.14a1           \
    darts==0.31.0                   \
    fastparquet==2024.11.0          \
    tables==3.10.1                  \
    dimod==0.12.17                  \
    dwave-samplers==1.3.0           \
    python-statemachine==2.5.0      \
    pymannkendall==1.4.3            \
    Pyomo==6.8.2                    \
    gpflow==2.9.2                   \
    pyarrow==15.0.1                 \
    dwave-ocean-sdk==8.0.1          \
    chardet==5.2.0                  \
    stable-baselines3==2.4.0        \
    Shimmy==2.0.0                   \
    FixedEffectModel==0.0.5         \
    transformers==4.46.3            \
    langchain==0.2.17               \
    pomegranate==1.1.1              \
    MAPIE==0.9.1                    \
    mlforecast==0.15.1              \
    x-transformers==1.42.24         \
    Werkzeug==3.1.3                 \
    nolds==0.6.1                    \
    feature-engine==1.6.2           \
    pytorch-tabnet==4.1.0           \
    opencv-contrib-python-headless==4.10.0.84 \
    POT==0.9.5                      \
    alibi-detect==0.12.0            \
    datasets==2.21.0                \
    scikeras==0.13.0                \
    contourpy==1.3.1

# Install dwave tool
RUN dwave install --all -y

# Install 'ipopt' solver for 'Pyomo'
RUN conda install -c conda-forge ipopt==3.14.16 \
    && conda clean -y --all

# We install need to install separately else fails to find numpy
RUN pip install --no-cache-dir iisignature==0.24

# Install spacy models
RUN python -m spacy download en_core_web_md && python -m spacy download en_core_web_sm

RUN conda config --set solver classic && conda install -y -c conda-forge \
    openmpi=5.0.6                   \
    && conda clean -y --all

# Install nltk data
RUN python -m nltk.downloader -d /usr/share/nltk_data punkt && \
    python -m nltk.downloader -d /usr/share/nltk_data punkt_tab && \
    python -m nltk.downloader -d /usr/share/nltk_data vader_lexicon && \
    python -m nltk.downloader -d /usr/share/nltk_data stopwords && \
    python -m nltk.downloader -d /usr/share/nltk_data wordnet

# Install Pyrb
RUN wget -q https://cdn.quantconnect.com/pyrb/pyrb-master-250054e.zip && \
    unzip -q pyrb-master-250054e.zip && cd pyrb-master && \
    pip install . && cd .. && rm -rf pyrb-master && rm pyrb-master-250054e.zip

# Install SSM
RUN wget -q https://cdn.quantconnect.com/ssm/ssm-master-646e188.zip && \
    unzip -q ssm-master-646e188.zip && cd ssm-master && \
    pip install . && cd .. && rm -rf ssm-master && rm ssm-master-646e188.zip

# Install uni2ts
RUN wget -q https://cdn.quantconnect.com/uni2ts/uni2ts-main-ffe78db.zip && \
    unzip -q uni2ts-main-ffe78db.zip && cd uni2ts-main && \
    pip install . && cd .. && rm -rf uni2ts-main && rm uni2ts-main-ffe78db.zip

# Install chronos-forecasting
RUN wget -q https://cdn.quantconnect.com/chronos-forecasting/chronos-forecasting-main-b0bdbd9.zip && \
    unzip -q chronos-forecasting-main-b0bdbd9.zip && cd chronos-forecasting-main && \
    pip install ".[training]" && cd .. && rm -rf chronos-forecasting-main && rm chronos-forecasting-main-b0bdbd9.zip

RUN echo "{\"argv\":[\"python\",\"-m\",\"ipykernel_launcher\",\"-f\",\"{connection_file}\"],\"display_name\":\"Foundation-Py-Default\",\"language\":\"python\",\"metadata\":{\"debugger\":true}}" > /opt/miniconda3/share/jupyter/kernels/python3/kernel.json

# Install wkhtmltopdf and xvfb to support HTML to PDF conversion of reports
RUN apt-get update && apt install -y xvfb wkhtmltopdf && \
    apt-get clean && apt-get autoclean && apt-get autoremove --purge -y && rm -rf /var/lib/apt/lists/*

# Install fonts for matplotlib
RUN wget -q https://cdn.quantconnect.com/fonts/foundation.zip && unzip -q foundation.zip && rm foundation.zip \
    && mv "lean fonts/"* /usr/share/fonts/truetype/ && rm -rf "lean fonts/" "__MACOSX/"

# label definitions
LABEL strict_python_version=3.11.11
LABEL python_version=3.11
LABEL target_framework=net9.0