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quantconnect--lean/DockerfileLeanFoundation
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Martin-Molinero 4411d4a8fc
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Add missing Shimmy dependency (#7532) 
- Add missing shimmy dependency
- Update, to normalize arm libraries with amd
2023-10-24 20:21:21 -03:00

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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 \
&& apt-get clean && apt-get autoclean && apt-get autoremove --purge -y \
&& rm -rf /var/lib/apt/lists/*
# Install dotnet 6 sdk & runtime
RUN apt-get update && apt-get install -y dotnet-sdk-6.0 && \
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/libpython3.8.so"
# Install miniconda
ENV CONDA="Miniconda3-py38_23.1.0-1-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}
# Install java runtime for h2o lib
RUN wget https://download.oracle.com/java/17/latest/jdk-17_linux-x64_bin.deb \
&& dpkg -i jdk-17_linux-x64_bin.deb \
&& update-alternatives --install /usr/bin/java java /usr/lib/jvm/jdk-17-oracle-x64/bin/java 1 \
&& rm jdk-17_linux-x64_bin.deb
# Avoid pip install read timeouts
ENV PIP_DEFAULT_TIMEOUT=120
# Install all packages
RUN pip install --no-cache-dir \
cython==0.29.36 \
pandas==1.5.3 \
scipy==1.10.1 \
numpy==1.23.5 \
wrapt==1.14.1 \
astropy==5.2.2 \
beautifulsoup4==4.12.2 \
dill==0.3.7 \
jsonschema==4.19.1 \
lxml==4.9.3 \
msgpack==1.0.7 \
numba==0.56.4 \
xarray==2023.1.0 \
plotly==5.17.0 \
jupyterlab==3.4.4 \
tensorflow==2.13.1 \
docutils==0.20.1 \
cvxopt==1.3.2 \
gensim==4.3.2 \
keras==2.13.1 \
lightgbm==4.1.0 \
mpi4py==3.1.5 \
nltk==3.8.1 \
graphviz==0.20.1 \
cmdstanpy==1.2.0 \
copulae==0.7.8 \
featuretools==1.27.0 \
PuLP==2.7.0 \
pymc==5.6.1 \
rauth==0.7.3 \
scikit-learn==1.3.2 \
scikit-multiflow==0.5.3 \
scikit-optimize==0.9.0 \
aesara==2.9.2 \
tsfresh==0.20.1 \
tslearn==0.6.2 \
tweepy==4.14.0 \
PyWavelets==1.4.1 \
umap-learn==0.5.3 \
fastai==2.7.13 \
arch==5.6.0 \
copulas==0.9.2 \
creme==0.6.1 \
cufflinks==0.17.3 \
gym==0.26.2 \
ipywidgets==8.1.1 \
deap==1.4.1 \
cvxpy==1.4.1 \
pykalman==0.9.5 \
pyportfolioopt==1.5.5 \
pmdarima==2.0.3 \
pyro-ppl==1.8.6 \
riskparityportfolio==0.4 \
sklearn-json==0.1.0 \
statsmodels==0.13.5 \
QuantLib==1.31.1 \
xgboost==2.0.0 \
dtw-python==1.3.0 \
gluonts==0.13.7 \
gplearn==0.4.2 \
jax==0.4.13 \
jaxlib==0.4.13 \
keras-rl==0.4.2 \
pennylane==0.32.0 \
PennyLane-Lightning==0.32.0 \
pennylane-qiskit==0.32.0 \
qiskit==0.44.2 \
neural-tangents==0.6.2 \
mplfinance==0.12.10b0 \
hmmlearn==0.3.0 \
catboost==1.2.2 \
fastai2==0.0.30 \
scikit-tda==1.0.0 \
ta==0.10.2 \
seaborn==0.13.0 \
optuna==3.4.0 \
findiff==0.10.0 \
sktime==0.24.0 \
hyperopt==0.2.7 \
bayesian-optimization==1.4.3 \
pingouin==0.5.3 \
quantecon==0.7.1 \
matplotlib==3.7.3 \
sdeint==0.3.0 \
pandas_market_calendars==4.3.1 \
dgl==1.1.2 \
ruptures==1.1.8 \
simpy==4.0.2 \
scikit-learn-extra==0.3.0 \
ray==2.7.1 \
"ray[tune]"==2.7.1 \
"ray[rllib]"==2.7.1 \
fastText==0.9.2 \
h2o==3.44.0.1 \
prophet==1.1.5 \
torch==2.1.0 \
torchvision==0.16.0 \
ax-platform==0.3.3 \
alphalens-reloaded==0.4.3 \
pyfolio-reloaded==0.9.5 \
altair==5.1.2 \
stellargraph==1.2.1 \
modin==0.22.3 \
persim==0.3.1 \
ripser==0.6.4 \
pydmd==0.4.1.post2308 \
EMD-signal==1.5.2 \
spacy==3.7.2 \
pandas-ta==0.3.14b \
pytorch-ignite==0.4.12 \
tensorly==0.8.1 \
mlxtend==0.23.0 \
shap==0.43.0 \
lime==0.2.0.1 \
tensorflow-probability==0.21.0 \
mpmath==1.3.0 \
tensortrade==1.0.3 \
polars==0.19.11 \
stockstats==0.5.4 \
autokeras==1.1.0 \
QuantStats==0.0.62 \
hurst==0.0.5 \
numerapi==2.16.1 \
pymdptoolbox==4.0-b3 \
fuzzy-c-means==1.6.3 \
panel==1.2.3 \
hvplot==0.9.0 \
line-profiler==4.1.1 \
py-heat==0.0.6 \
py-heat-magic==0.0.2 \
bokeh==3.1.1 \
tensorflow-decision-forests==1.5.0 \
river==0.14.0 \
stumpy==1.12.0 \
pyvinecopulib==0.6.3 \
ijson==3.2.3 \
jupyter-resource-usage==0.7.2 \
injector==0.21.0 \
openpyxl==3.1.2 \
xlrd==2.0.1 \
mljar-supervised==1.0.2 \
dm-tree==0.1.8 \
lz4==4.3.2 \
ortools==9.7.2996 \
py_vollib==1.0.1 \
tensorflow-addons==0.21.0 \
thundergbm==0.3.17 \
yellowbrick==1.5 \
livelossplot==0.5.5 \
gymnasium==0.28.1 \
interpret==0.4.4 \
DoubleML==0.7.0 \
jupyter-bokeh==3.0.7 \
imbalanced-learn==0.11.0 \
scikeras==0.12.0 \
openai==0.28.1 \
openai[embeddings]==0.28.1 \
openai[wandb]==0.28.1 \
lazypredict==0.2.12 \
fracdiff==0.9.0 \
darts==0.24.0 \
fastparquet==2023.8.0 \
tables==3.8.0 \
dimod==0.12.3 \
dwave-samplers==1.0.0 \
python-statemachine==2.1.2 \
pymannkendall==1.4.3 \
Pyomo==6.6.2 \
gpflow==2.9.0 \
pyarrow==13.0.0 \
dwave-ocean-sdk==6.1.1 \
chardet==5.2.0 \
stable-baselines3==2.1.0 \
Shimmy==1.3.0 \
pystan==3.7.0 \
FixedEffectModel==0.0.5 \
tick==0.7.0.1 \
transformers==4.34.0 \
Rbeast==0.1.16 \
langchain==0.0.316 \
tensorflow-ranking==0.5.3 \
pomegranate==1.0.3 \
tigramite==5.2.3.1 \
MAPIE==0.7.0 \
mlforecast==0.9.3 \
functime==0.8.4 \
tensorrt==8.6.1.post1
RUN conda install -c conda-forge -y cudatoolkit=11.8.0 && conda install -c nvidia -y cuda-compiler=12.2.2 && conda clean -y --all
ENV XLA_FLAGS=--xla_gpu_cuda_data_dir=/opt/miniconda3/
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/miniconda3/pkgs/cudatoolkit-11.8.0-h6a678d5_0/lib/:/opt/miniconda3/lib/python3.8/site-packages/nvidia/cudnn/lib/:/opt/miniconda3/lib/python3.8/site-packages/tensorrt_libs/
ENV CUDA_MODULE_LOADING=LAZY
# Install dwave tool
RUN dwave install --all -y
# Install 'ipopt' solver for 'Pyomo'
RUN conda install -c conda-forge ipopt==3.14.12 \
&& conda clean -y --all
# We install need to install separately else fails to find numpy
RUN pip install --no-cache-dir Riskfolio-Lib==4.4.2 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 install -y -c conda-forge \
openmpi=4.1.6 \
&& conda clean -y --all
# 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==2.4.0
# Install nltk data
RUN python -m nltk.downloader -d /usr/share/nltk_data punkt && \
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 ppscore
RUN wget -q https://cdn.quantconnect.com/ppscore/ppscore-master-ce93fa3.zip && \
unzip -q ppscore-master-ce93fa3.zip && cd ppscore-master && \
pip install . && cd .. && rm -rf ppscore-master && rm ppscore-master-ce93fa3.zip
# Install DX Analytics
RUN wget -q https://cdn.quantconnect.com/dx/dx-master-69922c0.zip && \
unzip -q dx-master-69922c0.zip && cd dx-master && \
pip install . && cd .. && rm -rf dx-master && rm dx-master-69922c0.zip
# 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 for python
RUN wget -q https://cdn.quantconnect.com/ta-lib/ta-lib-0.4.0-src.tar.gz && \
tar -zxvf ta-lib-0.4.0-src.tar.gz && cd ta-lib && \
./configure --prefix=/usr && make && make install && \
wget -q https://cdn.quantconnect.com/ta-lib/TA_Lib-0.4.18.zip && \
unzip -q TA_Lib-0.4.18.zip && cd ta-lib-TA_Lib-0.4.18 && \
python setup.py install && cd ../.. && rm -rf ta-lib && rm ta-lib-0.4.0-src.tar.gz
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: Installs to /root/ibgateway
RUN mkdir -p /root/ibgateway && \
wget -q https://cdn.quantconnect.com/interactive/ibgateway-stable-standalone-linux-x64.v10.19.2a.sh && \
chmod 777 ibgateway-stable-standalone-linux-x64.v10.19.2a.sh && \
./ibgateway-stable-standalone-linux-x64.v10.19.2a.sh -q -dir /root/ibgateway && \
rm ibgateway-stable-standalone-linux-x64.v10.19.2a.sh
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