wehub-resource-sync
419 lines
18 KiB
Plaintext
419 lines
18 KiB
Plaintext
#
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# LEAN Foundation Docker Container
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# Cross platform deployment for multiple brokerages
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# Intended to be used in conjunction with Dockerfile. This is just the foundation common OS+Dependencies required.
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#
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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 apt-get update && apt-get -y install wget curl unzip \
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&& apt-get install -y git bzip2 zlib1g-dev \
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xvfb libxrender1 libxtst6 libxi6 libglib2.0-dev libopenmpi-dev libstdc++6 openmpi-bin \
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pandoc libcurl4-openssl-dev libgtk2.0.0 build-essential \
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# for pyomo solver
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liblapack-dev \
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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-x86_64.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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conda config --set solver classic && \
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conda config --set auto_update_conda false
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# Install java runtime for h2o lib
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RUN wget https://download.oracle.com/java/17/archive/jdk-17.0.12_linux-x64_bin.deb \
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&& dpkg -i jdk-17.0.12_linux-x64_bin.deb \
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&& update-alternatives --install /usr/bin/java java /usr/lib/jvm/jdk-17.0.12-oracle-x64/bin/java 1 \
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&& rm jdk-17.0.12_linux-x64_bin.deb
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# Avoid pip install read timeouts
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ENV PIP_DEFAULT_TIMEOUT=120
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# Install all packages
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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-cuda[cu12]==0.14.1 \
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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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cvxopt==1.3.2 \
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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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creme==0.6.1 \
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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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pyportfolioopt==1.5.6 \
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pmdarima==2.1.1 \
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pyro-ppl==1.9.1 \
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riskparityportfolio==0.6.0 \
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sklearn-json==0.1.0 \
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statsmodels==0.14.6 \
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QuantLib==1.40 \
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xgboost==3.0.5 \
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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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jaxlib==0.7.1 \
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tf2jax==0.3.7 \
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keras-rl==0.4.2 \
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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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autoray==0.8.0 \
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qiskit==2.1.2 \
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mplfinance==0.12.10b0 \
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hmmlearn==0.3.3 \
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catboost==1.2.8 \
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fastai2==0.0.30 \
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scikit-tda==1.1.1 \
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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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pingouin==0.5.5 \
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quantecon==0.10.1 \
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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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dgl==2.1.0 \
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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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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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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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tensorflow-probability==0.25.0 \
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mpmath==1.3.0 \
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tensortrade==1.0.3 \
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polars==1.36.1 \
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stockstats==0.6.5 \
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autokeras==3.0.0 \
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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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line-profiler==5.0.0 \
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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==0.23.0 \
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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==4.4.5 \
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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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pystan==3.10.0 \
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FixedEffectModel==0.0.5 \
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transformers==4.57.3 \
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Rbeast==0.1.23 \
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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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tensorrt==10.14.1.48.post1 \
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x-transformers==2.11.24 \
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Werkzeug==3.1.4 \
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TPOT==0.12.2 \
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mlflow==3.4.0 \
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ngboost==0.5.6 \
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control==0.10.2 \
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pgmpy==1.0.0 \
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mgarch==0.3.0 \
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jupyter-ai==2.31.7 \
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keras-tcn==3.5.6 \
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neuralprophet[live]==0.9.0 \
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Riskfolio-Lib==7.0.1 \
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fuzzy-c-means==1.7.2 \
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EMD-signal==1.9.0 \
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dask[complete]==2025.7.0 \
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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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accelerate==1.12.0 \
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peft==0.18.0 \
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FlagEmbedding==1.3.5 \
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contourpy==1.3.3 \
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tensorboardX==2.6.4 \
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scikit-image==0.22.0 \
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scs==3.2.9 \
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thinc==8.3.4 \
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cesium==0.12.1 \
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cvxportfolio==1.5.1 \
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tsfel==0.2.0 \
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ipympl==0.9.8 \
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PyQt6==6.9.1 \
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nixtla==0.7.2 \
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tigramite==5.2.9.4 \
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pytorch-forecasting==1.5.0 \
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chronos-forecasting==2.2.2 \
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setuptools==80.9.0 \
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tinygrad==0.11.0 \
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DESlib==0.3.7 \
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torchrl==0.10.1 \
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tensordict==0.10.0 \
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onnx==1.20.0 \
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onnxmltools==1.14.0 \
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onnxruntime==1.23.2 \
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skl2onnx==1.19.1 \
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sweetviz==2.3.1 \
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filterpy==1.4.5 \
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skfolio==0.7.0 \
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lightweight-charts==2.1 \
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KDEpy==1.1.12 \
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lightning==2.6.0 \
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google-genai==1.56.0 \
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neuralforecast==3.1.2 \
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lingam==1.12.1 \
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econml==0.16.0 \
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networkx==3.6.1 \
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causalml==0.15.5 \
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transitions==0.9.3 \
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sismic==1.6.11 \
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cmaes==0.12.0 \
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cuda-python==12.9.5 \
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click==8.2.1 \
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ydf==0.13.0 \
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wurlitzer==3.1.1 \
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statsforecast==2.0.3 \
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holoviews==1.20.2 \
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faiss-cpu==1.13.1 \
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ImageIO==2.37.2 \
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lifelines==0.30.0 \
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h5py==3.15.1 \
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exchange_calendars==4.11.1 \
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formulaic==1.2.1 \
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arviz==0.23.0 \
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deprecated==1.2.18 \
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pyod==2.0.6
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# llama-index-readers-file has a pandas build contraint, see https://github.com/run-llama/llama_index/pull/20387/files
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RUN pip install --no-cache-dir --no-deps \
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aiosqlite==0.22.0 \
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banks==2.2.0 \
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deprecated==1.2.18 \
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dirtyjson==1.0.8 \
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filetype==1.2.0 \
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griffe==1.15.0 \
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llama-parse==0.6.54 \
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llama-cloud==0.1.35 \
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llama-index==0.14.10 \
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llama-index-cli==0.5.3 \
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llama-index-core==0.14.10 \
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llama-cloud-services==0.6.54 \
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llama-index-workflows==2.11.5 \
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llama-index-llms-openai==0.6.12 \
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llama-index-readers-file==0.5.5 \
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llama-index-instrumentation==0.4.2 \
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llama-index-embeddings-openai==0.5.1 \
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llama-index-readers-llama-parse==0.5.1 \
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llama-index-indices-managed-llama-cloud==0.9.4 \
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pypdf==6.5.0 \
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striprtf==0.0.26 \
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tiktoken==0.12.0
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# they have older dependency versions that can be ignored
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# https://github.com/onnx/tensorflow-onnx/issues/2328#issuecomment-2682046428
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RUN pip install --no-cache-dir --no-dependencies tf2onnx==1.16.1 tensorflow-decision-forests==1.12.0
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RUN conda install -c nvidia -y cuda-compiler=12.8.1 && conda clean -y --all
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# for aesara
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ENV MKL_THREADING_LAYER=GNU
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ENV CUDA_MODULE_LOADING=LAZY
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ENV XLA_FLAGS=--xla_gpu_cuda_data_dir=/opt/miniconda3/
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ENV LD_LIBRARY_PATH=/opt/miniconda3/lib/python3.11/site-packages/nvidia/nvjitlink/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cuda_nvrtc/lib/:$LD_LIBRARY_PATH
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# reduces GPU memory usage
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ENV PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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# required for numba to work correctly
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RUN ln -s /opt/miniconda3/lib/python3.11/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so.12 /opt/miniconda3/lib/python3.11/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so
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# iisignature requires numpy to be already installed. cupy requires cuda installed
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# https://github.com/omadson/fuzzy-c-means/issues/109 requires older tabulate but pandas requires 0.9.0, forcing version
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RUN pip install --no-cache-dir tabulate==0.9.0 iisignature==0.24 cupy-cuda12x==13.6.0 https://github.com/state-spaces/mamba/releases/download/v2.2.5/mamba_ssm-2.2.5+cu12torch2.8cxx11abiTRUE-cp311-cp311-linux_x86_64.whl https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.5.4/causal_conv1d-1.5.4+cu12torch2.8cxx11abiTRUE-cp311-cp311-linux_x86_64.whl
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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 -y ipopt==3.14.19 coincbc==2.10.12 openmpi=5.0.8 \
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&& conda clean -y --all
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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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# Install PyTorch Geometric
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RUN TORCH=$(python -c "import torch; print(torch.__version__)") && \
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CUDA=$(python -c "import torch; print('cu' + torch.version.cuda.replace('.', ''))") && \
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pip install --no-cache-dir -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html \
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torch-scatter==2.1.2 torch-sparse==0.6.18 torch-cluster==1.6.3 torch-spline-conv==1.2.2 torch-geometric==2.6.1
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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 TA-lib and pandas-ta for python
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RUN wget -q https://github.com/ta-lib/ta-lib/releases/download/v0.6.4/ta-lib_0.6.4_amd64.deb && \
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dpkg -i ta-lib_0.6.4_amd64.deb && rm ta-lib_0.6.4_amd64.deb && \
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pip install --no-cache-dir TA-Lib==0.6.7 && \
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wget -q https://cdn.quantconnect.com/ta-lib/pandas_ta-0.3.14b.tar.gz && \
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pip install --no-cache-dir pandas_ta-0.3.14b.tar.gz && rm pandas_ta-0.3.14b.tar.gz
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# chronos-forecasting we manually copy the 'scripts' folder which holds the fine tuning tools
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RUN wget -q https://cdn.quantconnect.com/chronos-forecasting/chronos-forecasting-main-133761a.zip && \
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unzip -q chronos-forecasting-main-133761a.zip && cd chronos-forecasting-main && \
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cp -r scripts /opt/miniconda3/lib/python3.11/site-packages/chronos/ && \
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cd .. && rm -rf chronos-forecasting-main && rm chronos-forecasting-main-133761a.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
|
|
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/"
|
|
|
|
# Install IB Gateway and it's dependencies: Installs to /root/ibgateway
|
|
RUN apt-get update && apt-get -y install libasound2 libnss3 libnspr4 && apt-get clean && apt-get autoclean && apt-get autoremove --purge -y && rm -rf /var/lib/apt/lists/* && \
|
|
mkdir -p /root/ibgateway && \
|
|
wget -q https://cdn.quantconnect.com/interactive/ibgateway-latest-standalone-linux-x64.v10.39.1f.sh && \
|
|
chmod 777 ibgateway-latest-standalone-linux-x64.v10.39.1f.sh && \
|
|
./ibgateway-latest-standalone-linux-x64.v10.39.1f.sh -q -dir /root/ibgateway && \
|
|
rm ibgateway-latest-standalone-linux-x64.v10.39.1f.sh
|
|
|
|
# Install dotnet sdk & runtime
|
|
RUN add-apt-repository ppa:dotnet/backports && apt-get update && apt-get install -y dotnet-sdk-10.0 && \
|
|
apt-get clean && apt-get autoclean && apt-get autoremove --purge -y && rm -rf /var/lib/apt/lists/*
|
|
|
|
# label definitions
|
|
LABEL strict_python_version=3.11.11
|
|
LABEL python_version=3.11
|
|
LABEL target_framework=net10.0 |