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10BC0
/div>
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#
# LEAN Foundation Docker Container
# Cross platform deployment for multiple brokerages
# Intended to be used in conjunction with Dockerfile. This is just the foundation common OS+Dependencies required.
#
# 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 apt-get update && apt-get -y install wget curl unzip \
&& apt-get install -y git bzip2 zlib1g-dev \
xvfb libxrender1 libxtst6 libxi6 libglib2.0-dev libopenmpi-dev libstdc++6 openmpi-bin \
pandoc libcurl4-openssl-dev libgtk2.0.0 build-essential \
# for pyomo solver
liblapack-dev \
&& apt-get clean && apt-get autoclean && apt-get autoremove --purge -y \
&& rm -rf /var/lib/apt/lists/*
# Install dotnet sdk & runtime
RUN add-apt-repository ppa:dotnet/backports && apt-get update && apt-get install -y dotnet-sdk-9.0 && \
apt-get clean && apt-get autoclean && apt-get autoremove --purge -y && rm -rf /var/lib/apt/lists/*
# 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-x86_64.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} && \
conda config --set solver classic && \
conda config --set auto_update_conda false
# Install java runtime for h2o lib
RUN wget https://download.oracle.com/java/17/archive/jdk-17.0.12_linux-x64_bin.deb \
&& dpkg -i jdk-17.0.12_linux-x64_bin.deb \
&& update-alternatives --install /usr/bin/java java /usr/lib/jvm/jdk-17.0.12-oracle-x64/bin/java 1 \
&& rm jdk-17.0.12_linux-x64_bin.deb
# Avoid pip install read timeouts
ENV PIP_DEFAULT_TIMEOUT=120
# Install all packages
RUN pip install --no-cache-dir \
cython==3.1.4 \
pandas==2.3.3 \
scipy==1.13.1 \
numpy==1.26.4 \
wrapt==1.17.3 \
astropy==7.1.0 \
beautifulsoup4==4.14.2 \
dill==0.3.8 \
jsonschema==4.25.1 \
lxml==6.0.2 \
msgpack==1.1.1 \
numba-cuda[cu12]==0.14.1 \
numba==0.61.2 \
xarray==2025.9.1 \
plotly==5.24.1 \
jupyterlab==4.4.9 \
ipywidgets==8.1.7 \
jupyterlab-widgets==3.0.15 \
tensorflow==2.19.1 \
docutils==0.22.2 \
cvxopt==1.3.2 \
gensim==4.3.3 \
keras==3.11.3 \
lightgbm==4.6.0 \
nltk==3.9.2 \
graphviz==0.21 \
cmdstanpy==1.2.5 \
copulae==0.7.9 \
featuretools==1.31.0 \
PuLP==3.3.0 \
pymc==5.25.1 \
rauth==0.7.3 \
scikit-learn==1.6.1 \
scikit-optimize==0.10.2 \
tsfresh==0.20.2 \
tslearn==0.6.4 \
tweepy==4.16.0 \
PyWavelets==1.9.0 \
umap-learn==0.5.9.post2 \
fastai==2.8.4 \
arch==7.2.0 \
copulas==0.12.3 \
creme==0.6.1 \
cufflinks==0.17.3 \
gym==0.26.2 \
deap==1.4.3 \
pykalman==0.10.2 \
cvxpy==1.7.3 \
pyportfolioopt==1.5.6 \
pmdarima==2.0.4 \
pyro-ppl==1.9.1 \
riskparityportfolio==0.6.0 \
sklearn-json==0.1.0 \
statsmodels==0.14.5 \
QuantLib==1.39 \
xgboost==3.0.5 \
dtw-python==1.5.3 \
gluonts==0.16.2 \
jax==0.7.1 \
jaxlib==0.7.1 \
tf2jax==0.3.7 \
keras-rl==0.4.2 \
pennylane==0.42.3 \
PennyLane-Lightning==0.42.0 \
git+https://github.com/PennyLaneAI/pennylane-qiskit.git@12dacf9a867f17b05b26b6d78ef8f9c7987713cb \
autoray==0.7.2 \
qiskit==2.1.2 \
mplfinance==0.12.10b0 \
hmmlearn==0.3.3 \
catboost==1.2.8 \
fastai2==0.0.30 \
scikit-tda==1.1.1 \
ta==0.11.0 \
seaborn==0.13.2 \
optuna==4.5.0 \
findiff==0.12.1 \
sktime==0.39.0 \
hyperopt==0.2.7 \
bayesian-optimization==3.1.0 \
pingouin==0.5.5 \
quantecon==0.10.1 \
matplotlib==3.8.4 \
sdeint==0.3.0 \
pandas_market_calendars==5.1.1 \
dgl==2.1.0 \
ruptures==1.1.10 \
simpy==4.1.1 \
scikit-learn-extra==0.3.0 \
ray==2.49.2 \
"ray[tune]"==2.49.2 \
"ray[rllib]"==2.49.2 \
"ray[data]"==2.49.2 \
"ray[train]"==2.49.2 \
fastText==0.9.3 \
h2o==3.46.0.7 \
prophet==1.1.7 \
torch==2.8.0 \
torchvision==0.23.0 \
ax-platform==1.1.2 \
alphalens-reloaded==0.4.6 \
pyfolio-reloaded==0.9.9 \
altair==5.5.0 \
modin==0.37.1 \
persim==0.3.8 \
ripser==0.6.12 \
pydmd==2025.8.1 \
spacy==3.8.7 \
pytorch-ignite==0.5.2 \
tensorly==0.9.0 \
mlxtend==0.23.4 \
shap==0.48.0 \
lime==0.2.0.1 \
tensorflow-probability==0.25.0 \
mpmath==1.3.0 \
tensortrade==1.0.3 \
polars==1.34.0 \
stockstats==0.6.5 \
autokeras==2.0.0 \
QuantStats==0.0.77 \
hurst==0.0.5 \
numerapi==2.20.8 \
pymdptoolbox==4.0-b3 \
panel==1.7.5 \
hvplot==0.12.1 \
line-profiler==5.0.0 \
py-heat==0.0.6 \
py-heat-magic==0.0.2 \
bokeh==3.6.3 \
river==0.21.0 \
stumpy==1.13.0 \
pyvinecopulib==0.6.5 \
ijson==3.4.0 \
jupyter-resource-usage==1.2.0 \
injector==0.22.0 \
openpyxl==3.1.5 \
xlrd==2.0.2 \
mljar-supervised==1.1.18 \
dm-tree==0.1.9 \
lz4==4.4.4 \
ortools==9.12.4544 \
py_vollib==1.0.1 \
thundergbm==0.3.17 \
yellowbrick==1.5 \
livelossplot==0.5.6 \
gymnasium==1.1.1 \
interpret==0.7.2 \
DoubleML==0.10.1 \
jupyter-bokeh==4.0.5 \
imbalanced-learn==0.14.0 \
openai==1.109.1 \
lazypredict==0.2.16 \
darts==0.38.0 \
fastparquet==2024.11.0 \
tables==3.10.2 \
dimod==0.12.21 \
dwave-samplers==1.6.0 \
python-statemachine==2.5.0 \
pymannkendall==1.4.3 \
Pyomo==6.9.4 \
gpflow==2.10.0 \
pyarrow==19.0.1 \
dwave-ocean-sdk==9.0.0 \
chardet==5.2.0 \
stable-baselines3==2.7.0 \
sb3-contrib==2.7.0 \
Shimmy==2.0.0 \
pystan==3.10.0 \
FixedEffectModel==0.0.5 \
transformers==4.57.0 \
Rbeast==0.1.23 \
langchain==0.3.27 \
pomegranate==1.1.2 \
MAPIE==1.1.0 \
mlforecast==1.0.2 \
tensorrt==10.13.3.9 \
x-transformers==2.8.2 \
Werkzeug==3.1.3 \
TPOT==0.12.2 \
llama-index==0.12.42 \
mlflow==3.4.0 \
ngboost==0.5.6 \
control==0.10.2 \
pgmpy==1.0.0 \
mgarch==0.3.0 \
jupyter-ai==2.31.6 \
keras-tcn==3.5.6 \
neuralprophet[live]==0.9.0 \
Riskfolio-Lib==7.0.1 \
fuzzy-c-means==1.7.2 \
EMD-signal==1.6.4 \
dask[complete]==2025.7.0 \
nolds==0.6.2 \
feature-engine==1.9.3 \
pytorch-tabnet==4.1.0 \
opencv-contrib-python-headless==4.11.0.86 \
POT==0.9.6.post1 \
datasets==3.6.0 \
scikeras==0.13.0 \
accelerate==1.10.1 \
peft==0.17.1 \
FlagEmbedding==1.3.5 \
contourpy==1.3.3 \
tensorboardX==2.6.4 \
scikit-image==0.22.0 \
scs==3.2.8 \
thinc==8.3.4 \
cesium==0.12.1 \
cvxportfolio==1.5.1 \
tsfel==0.2.0 \
ipympl==0.9.7 \
PyQt6==6.9.1 \
nixtla==0.7.0 \
tigramite==5.2.8.2 \
pytorch-forecasting==1.4.0 \
chronos-forecasting[training]==1.5.3 \
setuptools==73.0.1 \
tinygrad==0.11.0 \
DESlib==0.3.7 \
torchrl==0.10.0 \
tensordict==0.10.0 \
onnxmltools==1.14.0 \
onnxruntime==1.23.0 \
skl2onnx==1.19.1 \
sweetviz==2.3.1 \
filterpy==1.4.5 \
skfolio==0.7.0 \
lightweight-charts==2.1 \
KDEpy==1.1.12 \
lightning==2.5.5 \
google-genai==1.41.0 \
neuralforecast==3.1.2 \
lingam==1.11.0 \
econml==0.16.0 \
networkx==3.5 \
causalml==0.15.5 \
transitions==0.9.3 \
sismic==1.6.10 \
cmaes==0.12.0 \
cuda-python==12.9.2 \
click==8.2.1 \
ydf==0.13.0 \
wurlitzer==3.1.1 \
statsforecast==2.0.2 \
holoviews==1.20.2
# they have older dependency versions that can be ignored
# https://github.com/onnx/tensorflow-onnx/issues/2328#issuecomment-2682046428
RUN pip install --no-cache-dir --no-dependencies tf2onnx==1.16.1 tensorflow-decision-forests==1.12.0
RUN conda install -c nvidia -y cuda-compiler=12.8.1 && conda clean -y --all
# for aesara
ENV MKL_THREADING_LAYER=GNU
ENV CUDA_MODULE_LOADING=LAZY
ENV XLA_FLAGS=--xla_gpu_cuda_data_dir=/opt/miniconda3/
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
# reduces GPU memory usage
ENV PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
# required for numba to work correctly
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
# iisignature requires numpy to be already installed. cupy requires cuda installed
# https://github.com/omadson/fuzzy-c-means/issues/109 requires older tabulate but pandas requires 0.9.0, forcing version
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 mamba-ssm[causal-conv1d]==2.2.5
# Install dwave tool
RUN dwave install --all -y
# Install 'ipopt' solver for 'Pyomo'
RUN conda install -c conda-forge -y ipopt==3.14.19 coincbc==2.10.12 openmpi=5.0.8 \
&& conda clean -y --all
# Install spacy models
RUN python -m spacy download en_core_web_md && python -m spacy download en_core_web_sm
# Install PyTorch Geometric
RUN TORCH=$(python -c "import torch; print(torch.__version__)") && \
CUDA=$(python -c "import torch; print('cu' + torch.version.cuda.replace('.', ''))") && \
pip install --no-cache-dir -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html \
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
# 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 TA-lib and pandas-ta for python
RUN wget -q https://github.com/ta-lib/ta-lib/releases/download/v0.6.4/ta-lib_0.6.4_amd64.deb && \
dpkg -i ta-lib_0.6.4_amd64.deb && rm ta-lib_0.6.4_amd64.deb && \
pip install --no-cache-dir TA-Lib==0.6.7 && \
wget -q https://cdn.quantconnect.com/ta-lib/pandas_ta-0.3.14b.tar.gz && \
pip install --no-cache-dir pandas_ta-0.3.14b.tar.gz && rm pandas_ta-0.3.14b.tar.gz
# chronos-forecasting we manually copy the 'scripts' folder which holds the fine tuning tools
RUN wget -q https://cdn.quantconnect.com/chronos-forecasting/chronos-forecasting-main-133761a.zip && \
unzip -q chronos-forecasting-main-133761a.zip && cd chronos-forecasting-main && \
cp -r scripts /opt/miniconda3/lib/python3.11/site-packages/chronos/ && \
cd .. && rm -rf chronos-forecasting-main && rm chronos-forecasting-main-133761a.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/"
# 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
# label definitions
LABEL strict_python_version=3.11.11
LABEL python_version=3.11
LABEL target_framework=net9.0