chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# This is a version of DockerfileLeanFoundation for ARM
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# Some packages from the AMD image are excluded because they are not available on ARM or take too long to build
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# Use base system for cleaning up wayward processes
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FROM phusion/baseimage:jammy-1.0.1
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MAINTAINER QuantConnect <contact@quantconnect.com>
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# Use baseimage-docker's init system.
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CMD ["/sbin/my_init"]
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# Install OS Packages:
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# Misc tools for running Python.NET and IB inside a headless container.
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RUN add-apt-repository ppa:ubuntu-toolchain-r/test && apt-get update \
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&& apt-get install -y git libgtk2.0.0 bzip2 curl unzip wget python3-pip python3-opengl zlib1g-dev \
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xvfb libxrender1 libxtst6 libxi6 libglib2.0-dev libopenmpi-dev libstdc++6 openmpi-bin \
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r-base pandoc libcurl4-openssl-dev \
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openjdk-11-jdk openjdk-11-jre bbe \
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&& apt-get clean && apt-get autoclean && apt-get autoremove --purge -y \
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&& rm -rf /var/lib/apt/lists/*
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# Set PythonDLL variable for PythonNet
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ENV PYTHONNET_PYDLL="/opt/miniconda3/lib/libpython3.11.so"
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# Install miniconda
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ENV CONDA="Miniconda3-py311_24.9.2-0-Linux-aarch64.sh"
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ENV PATH="/opt/miniconda3/bin:${PATH}"
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RUN wget -q https://cdn.quantconnect.com/miniconda/${CONDA} && \
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bash ${CONDA} -b -p /opt/miniconda3 && rm -rf ${CONDA}
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# Install java runtime for h2o lib
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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 \
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&& alien -i jdk-17.0.12_linux-aarch64_bin.rpm \
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&& update-alternatives --install /usr/bin/java java /usr/lib/jvm/jdk-17.0.12-oracle-aarch64/bin/java 1 \
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&& rm jdk-17.0.12_linux-aarch64_bin.rpm
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# Avoid pip install read timeouts
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ENV PIP_DEFAULT_TIMEOUT=120
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# Install numpy first to avoid it not being resolved when installing libraries that depend on it next
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RUN pip install --no-cache-dir numpy==1.26.4
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# Install newer (than provided by ubuntu) cmake required by scikit build process
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RUN conda install -c conda-forge cmake==3.28.4 && conda clean -y --all
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# The list of packages in this image is shorter than the list in the AMD images
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# This list only includes packages that can be installed within 2 minutes on ARM
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RUN pip install --no-cache-dir \
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cython==3.2.3 \
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pandas==2.3.3 \
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scipy==1.13.1 \
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numpy==1.26.4 \
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wrapt==1.17.3 \
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astropy==7.2.0 \
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beautifulsoup4==4.14.3 \
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dill==0.3.8 \
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jsonschema==4.25.1 \
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lxml==6.0.2 \
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msgpack==1.1.2 \
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numba==0.61.2 \
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xarray==2025.12.0 \
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plotly==5.24.1 \
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jupyterlab==4.5.1 \
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ipywidgets==8.1.8 \
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jupyterlab-widgets==3.0.16 \
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tensorflow==2.19.1 \
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docutils==0.22.4 \
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gensim==4.4.0 \
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keras==3.13.0 \
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lightgbm==4.6.0 \
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nltk==3.9.2 \
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graphviz==0.21 \
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cmdstanpy==1.3.0 \
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copulae==0.7.9 \
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featuretools==1.31.0 \
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PuLP==3.3.0 \
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pymc==5.25.1 \
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rauth==0.7.3 \
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scikit-learn==1.6.1 \
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scikit-optimize==0.10.2 \
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tsfresh==0.20.2 \
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tslearn==0.7.0 \
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tweepy==4.16.0 \
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PyWavelets==1.9.0 \
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umap-learn==0.5.9.post2 \
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fastai==2.8.6 \
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arch==8.0.0 \
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copulas==0.12.3 \
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cufflinks==0.17.3 \
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gym==0.26.2 \
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deap==1.4.3 \
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pykalman==0.11.0 \
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cvxpy==1.7.5 \
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pyro-ppl==1.9.1 \
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sklearn-json==0.1.0 \
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dtw-python==1.5.3 \
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gluonts==0.16.2 \
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jax==0.7.1 \
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pennylane==0.43.1 \
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PennyLane-Lightning==0.43.0 \
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PennyLane-qiskit==0.43.0 \
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mplfinance==0.12.10b0 \
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hmmlearn==0.3.3 \
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ta==0.11.0 \
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seaborn==0.13.2 \
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optuna==4.6.0 \
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findiff==0.12.2 \
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sktime==0.40.1 \
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hyperopt==0.2.7 \
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bayesian-optimization==3.1.0 \
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matplotlib==3.8.4 \
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sdeint==0.3.0 \
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pandas_market_calendars==5.2.2 \
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ruptures==1.1.10 \
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simpy==4.1.1 \
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scikit-learn-extra==0.3.0 \
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ray==2.53.0 \
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"ray[tune]"==2.53.0 \
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"ray[rllib]"==2.53.0 \
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"ray[data]"==2.53.0 \
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"ray[train]"==2.53.0 \
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fastText==0.9.3 \
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h2o==3.46.0.9 \
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prophet==1.2.1 \
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Riskfolio-Lib==7.0.1 \
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torch==2.8.0 \
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torchvision==0.23.0 \
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ax-platform==1.2.1 \
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alphalens-reloaded==0.4.6 \
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pyfolio-reloaded==0.9.9 \
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altair==6.0.0 \
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modin==0.37.1 \
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persim==0.3.8 \
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ripser==0.6.14 \
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pydmd==2025.8.1 \
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EMD-signal==1.6.4 \
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spacy==3.8.11 \
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pytorch-ignite==0.5.3 \
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tensorly==0.9.0 \
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mlxtend==0.23.4 \
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shap==0.48.0 \
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lime==0.2.0.1 \
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mpmath==1.3.0 \
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polars==1.36.1 \
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stockstats==0.6.5 \
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QuantStats==0.0.77 \
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hurst==0.0.5 \
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numerapi==2.21.0 \
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pymdptoolbox==4.0-b3 \
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panel==1.7.5 \
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hvplot==0.12.2 \
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py-heat==0.0.6 \
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py-heat-magic==0.0.2 \
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bokeh==3.6.3 \
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river==0.21.0 \
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stumpy==1.13.0 \
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pyvinecopulib==0.6.5 \
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ijson==3.4.0.post0 \
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jupyter-resource-usage==1.2.0 \
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injector==0.22.0 \
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openpyxl==3.1.5 \
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xlrd==2.0.2 \
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mljar-supervised==1.1.18 \
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dm-tree==0.1.9 \
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lz4==4.4.4 \
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ortools==9.12.4544 \
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py_vollib==1.0.1 \
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thundergbm==0.3.17 \
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yellowbrick==1.5 \
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livelossplot==0.5.6 \
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gymnasium==1.1.1 \
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interpret==0.7.2 \
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DoubleML==0.10.1 \
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jupyter-bokeh==4.0.5 \
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imbalanced-learn==0.14.1 \
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openai==2.14.0 \
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lazypredict==0.2.16 \
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darts==0.39.0 \
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fastparquet==2025.12.0 \
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tables==3.10.2 \
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dimod==0.12.21 \
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dwave-samplers==1.7.0 \
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python-statemachine==2.5.0 \
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pymannkendall==1.4.3 \
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Pyomo==6.9.5 \
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gpflow==2.10.0 \
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pyarrow==19.0.1 \
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dwave-ocean-sdk==9.2.0 \
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chardet==5.2.0 \
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stable-baselines3==2.7.1 \
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sb3-contrib==2.7.1 \
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Shimmy==2.0.0 \
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FixedEffectModel==0.0.5 \
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transformers==4.57.3 \
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langchain==0.3.27 \
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pomegranate==1.1.2 \
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MAPIE==1.2.0 \
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mlforecast==1.0.2 \
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x-transformers==2.11.24 \
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Werkzeug==3.1.4 \
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nolds==0.6.2 \
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feature-engine==1.9.3 \
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pytorch-tabnet==4.1.0 \
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opencv-contrib-python-headless==4.11.0.86 \
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POT==0.9.6.post1 \
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datasets==3.6.0 \
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scikeras==0.13.0 \
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contourpy==1.3.3 \
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click==8.2.1
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# Install dwave tool
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RUN dwave install --all -y
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# Install 'ipopt' solver for 'Pyomo'
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RUN conda install -c conda-forge ipopt==3.14.19 \
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&& conda clean -y --all
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# We install need to install separately else fails to find numpy
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RUN pip install --no-cache-dir iisignature==0.24
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# Install spacy models
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RUN python -m spacy download en_core_web_md && python -m spacy download en_core_web_sm
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RUN conda config --set solver classic && conda install -y -c conda-forge \
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openmpi=5.0.8 \
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&& conda clean -y --all
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# Install nltk data
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RUN python -m nltk.downloader -d /usr/share/nltk_data punkt && \
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python -m nltk.downloader -d /usr/share/nltk_data punkt_tab && \
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python -m nltk.downloader -d /usr/share/nltk_data vader_lexicon && \
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python -m nltk.downloader -d /usr/share/nltk_data stopwords && \
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python -m nltk.downloader -d /usr/share/nltk_data wordnet
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# Install Pyrb
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RUN wget -q https://cdn.quantconnect.com/pyrb/pyrb-master-250054e.zip && \
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unzip -q pyrb-master-250054e.zip && cd pyrb-master && \
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pip install . && cd .. && rm -rf pyrb-master && rm pyrb-master-250054e.zip
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# Install SSM
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RUN wget -q https://cdn.quantconnect.com/ssm/ssm-master-646e188.zip && \
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unzip -q ssm-master-646e188.zip && cd ssm-master && \
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pip install . && cd .. && rm -rf ssm-master && rm ssm-master-646e188.zip
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# Install uni2ts
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RUN wget -q https://cdn.quantconnect.com/uni2ts/uni2ts-main-ffe78db.zip && \
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unzip -q uni2ts-main-ffe78db.zip && cd uni2ts-main && \
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pip install . && cd .. && rm -rf uni2ts-main && rm uni2ts-main-ffe78db.zip
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# Install chronos-forecasting
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RUN wget -q https://cdn.quantconnect.com/chronos-forecasting/chronos-forecasting-main-b0bdbd9.zip && \
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unzip -q chronos-forecasting-main-b0bdbd9.zip && cd chronos-forecasting-main && \
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pip install ".[training]" && cd .. && rm -rf chronos-forecasting-main && rm chronos-forecasting-main-b0bdbd9.zip
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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
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# Install wkhtmltopdf and xvfb to support HTML to PDF conversion of reports
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RUN apt-get update && apt install -y xvfb wkhtmltopdf && \
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apt-get clean && apt-get autoclean && apt-get autoremove --purge -y && rm -rf /var/lib/apt/lists/*
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# Install fonts for matplotlib
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RUN wget -q https://cdn.quantconnect.com/fonts/foundation.zip && unzip -q foundation.zip && rm foundation.zip \
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&& mv "lean fonts/"* /usr/share/fonts/truetype/ && rm -rf "lean fonts/" "__MACOSX/"
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# Install dotnet 9 sdk & runtime
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# The .deb packages don't support ARM, the install script does
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ENV PATH="/root/.dotnet:${PATH}"
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RUN wget https://dot.net/v1/dotnet-install.sh && \
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chmod 777 dotnet-install.sh && \
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./dotnet-install.sh -c 10.0 && \
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rm dotnet-install.sh
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ENV DOTNET_ROOT="/root/.dotnet"
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# label definitions
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LABEL strict_python_version=3.11.11
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LABEL python_version=3.11
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LABEL target_framework=net10.0
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