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Add new libraries and updates (#7522)
* Add new libraries and updates * ARM foundation update - Deleting creme library which has been merged into already included river, because it causes copulae build to fail - Deleting Riskfolio-Lib which would try to install old numpy and fail
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@@ -1,11 +1,11 @@ | ||
# | ||
# LEAN Foundation Docker Container 20201214 | ||
# 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. | ||
# | ||
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# Use base system for cleaning up wayward processes | ||
FROM phusion/baseimage:focal-1.0.0 | ||
FROM phusion/baseimage:jammy-1.0.1 | ||
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MAINTAINER QuantConnect <[email protected]> | ||
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@@ -22,13 +22,7 @@ RUN apt-get update && apt-get -y install wget curl unzip \ | |
&& rm -rf /var/lib/apt/lists/* | ||
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# Install dotnet 6 sdk & runtime | ||
RUN wget https://packages.microsoft.com/config/ubuntu/20.04/packages-microsoft-prod.deb -O packages-microsoft-prod.deb && \ | ||
dpkg -i packages-microsoft-prod.deb && \ | ||
apt-get update; \ | ||
apt-get install -y apt-transport-https && \ | ||
apt-get update && \ | ||
apt-get install -y dotnet-sdk-6.0 && \ | ||
rm packages-microsoft-prod.deb && \ | ||
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/* | ||
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# Set PythonDLL variable for PythonNet | ||
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@@ -40,194 +34,208 @@ ENV PATH="/opt/miniconda3/bin:${PATH}" | |
RUN wget -q https://cdn.quantconnect.com/miniconda/${CONDA} && \ | ||
bash ${CONDA} -b -p /opt/miniconda3 && rm -rf ${CONDA} | ||
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# 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 | ||
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# Avoid pip install read timeouts | ||
ENV PIP_DEFAULT_TIMEOUT=120 | ||
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# Install all packages | ||
RUN pip install --no-cache-dir \ | ||
cython==0.29.35 \ | ||
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==0.3.6 \ | ||
jsonschema==4.17.3 \ | ||
lxml==4.9.2 \ | ||
msgpack==1.0.5 \ | ||
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==5.15.0 \ | ||
plotly==5.17.0 \ | ||
jupyterlab==3.4.4 \ | ||
tensorflow==2.12.0 \ | ||
tensorflow==2.13.1 \ | ||
docutils==0.20.1 \ | ||
cvxopt==1.3.1 \ | ||
gensim==4.3.1 \ | ||
keras==2.12.0 \ | ||
lightgbm==3.3.5 \ | ||
mpi4py==3.1.4 \ | ||
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==1.1.0 \ | ||
cmdstanpy==1.2.0 \ | ||
copulae==0.7.8 \ | ||
featuretools==1.26.0 \ | ||
featuretools==1.27.0 \ | ||
PuLP==2.7.0 \ | ||
pymc==5.5.0 \ | ||
pymc==5.6.1 \ | ||
rauth==0.7.3 \ | ||
scikit-learn==1.2.2 \ | ||
scikit-multiflow==0.5.3 \ | ||
scikit-optimize==0.9.0 \ | ||
aesara==2.9.0 \ | ||
aesara==2.9.2 \ | ||
tsfresh==0.20.1 \ | ||
tslearn==0.5.3.2 \ | ||
tslearn==0.6.2 \ | ||
tweepy==4.14.0 \ | ||
PyWavelets==1.4.1 \ | ||
umap-learn==0.5.3 \ | ||
fastai==2.7.12 \ | ||
fastai==2.7.13 \ | ||
arch==5.6.0 \ | ||
copulas==0.9.0 \ | ||
copulas==0.9.2 \ | ||
creme==0.6.1 \ | ||
cufflinks==0.17.3 \ | ||
gym==0.21 \ | ||
ipywidgets==8.0.6 \ | ||
deap==1.3.3 \ | ||
cvxpy==1.3.2 \ | ||
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==1.8.5 \ | ||
pyro-ppl==1.8.6 \ | ||
riskparityportfolio==0.4 \ | ||
sklearn-json==0.1.0 \ | ||
statsmodels==0.13.5 \ | ||
QuantLib==1.30 \ | ||
xgboost==1.7.6 \ | ||
QuantLib==1.31.1 \ | ||
xgboost==2.0.0 \ | ||
dtw-python==1.3.0 \ | ||
gluonts==0.13.2 \ | ||
gluonts==0.13.7 \ | ||
gplearn==0.4.2 \ | ||
jax==0.4.13 \ | ||
jaxlib==0.4.13 \ | ||
keras-rl==0.4.2 \ | ||
pennylane==0.30.0 \ | ||
PennyLane-Lightning==0.31.0 \ | ||
pennylane-qiskit==0.29.0 \ | ||
qiskit==0.43.2 \ | ||
pennylane==0.32.0 \ | ||
PennyLane-Lightning==0.32.0 \ | ||
pennylane-qiskit==0.32.0 \ | ||
qiskit==0.44.2 \ | ||
neural-tangents==0.6.2 \ | ||
mplfinance==0.12.9b7 \ | ||
mplfinance==0.12.10b0 \ | ||
hmmlearn==0.3.0 \ | ||
catboost==1.2 \ | ||
catboost==1.2.2 \ | ||
fastai2==0.0.30 \ | ||
scikit-tda==1.0.0 \ | ||
ta==0.10.2 \ | ||
seaborn==0.12.2 \ | ||
optuna==3.2.0 \ | ||
findiff==0.9.2 \ | ||
sktime==0.20.0 \ | ||
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==3.7.1 \ | ||
matplotlib==3.7.3 \ | ||
sdeint==0.3.0 \ | ||
pandas_market_calendars==4.1.4 \ | ||
dgl==1.1.1 \ | ||
pandas_market_calendars==4.3.1 \ | ||
dgl==1.1.2 \ | ||
ruptures==1.1.8 \ | ||
simpy==4.0.1 \ | ||
simpy==4.0.2 \ | ||
scikit-learn-extra==0.3.0 \ | ||
ray==2.5.1 \ | ||
"ray[tune]"==2.5.1 \ | ||
"ray[rllib]"==2.5.1 \ | ||
ray==2.7.1 \ | ||
"ray[tune]"==2.7.1 \ | ||
"ray[rllib]"==2.7.1 \ | ||
fastText==0.9.2 \ | ||
h2o==3.40.0.4 \ | ||
prophet==1.1.4 \ | ||
torch==2.0.1 \ | ||
torchvision==0.15.2 \ | ||
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==5.0.1 \ | ||
altair==5.1.2 \ | ||
stellargraph==1.2.1 \ | ||
modin==0.22.2 \ | ||
modin==0.22.3 \ | ||
persim==0.3.1 \ | ||
ripser==0.6.4 \ | ||
pydmd==0.4.1.post2306 \ | ||
EMD-signal==1.5.1 \ | ||
spacy==3.5.3 \ | ||
pydmd==0.4.1.post2308 \ | ||
EMD-signal==1.5.2 \ | ||
spacy==3.7.2 \ | ||
pandas-ta==0.3.14b \ | ||
pytorch-ignite==0.4.12 \ | ||
finrl==0.3.1 \ | ||
tensorly==0.8.1 \ | ||
mlxtend==0.22.0 \ | ||
shap==0.41.0 \ | ||
mlxtend==0.23.0 \ | ||
shap==0.43.0 \ | ||
lime==0.2.0.1 \ | ||
tensorflow-probability==0.20.1 \ | ||
tensorflow-probability==0.21.0 \ | ||
mpmath==1.3.0 \ | ||
tensortrade==1.0.3 \ | ||
polars==0.18.4 \ | ||
polars==0.19.8 \ | ||
stockstats==0.5.4 \ | ||
autokeras==1.1.0 \ | ||
QuantStats==0.0.61 \ | ||
QuantStats==0.0.62 \ | ||
hurst==0.0.5 \ | ||
numerapi==2.14.0 \ | ||
numerapi==2.16.1 \ | ||
pymdptoolbox==4.0-b3 \ | ||
fuzzy-c-means==1.6.3 \ | ||
panel==1.1.1 \ | ||
hvplot==0.8.4 \ | ||
line-profiler==4.0.3 \ | ||
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==1.3.0 \ | ||
tensorflow-decision-forests==1.5.0 \ | ||
river==0.14.0 \ | ||
stumpy==1.11.1 \ | ||
pyvinecopulib==0.6.2 \ | ||
ijson==3.2.2 \ | ||
stumpy==1.12.0 \ | ||
pyvinecopulib==0.6.3 \ | ||
ijson==3.2.3 \ | ||
jupyter-resource-usage==0.7.2 \ | ||
injector==0.20.1 \ | ||
injector==0.21.0 \ | ||
openpyxl==3.1.2 \ | ||
xlrd==2.0.1 \ | ||
mljar-supervised==1.0.0 \ | ||
mljar-supervised==1.0.2 \ | ||
dm-tree==0.1.8 \ | ||
lz4==4.3.2 \ | ||
ortools==9.6.2534 \ | ||
ortools==9.7.2996 \ | ||
py_vollib==1.0.1 \ | ||
tensorflow-addons==0.20.0 \ | ||
tensorflow-addons==0.21.0 \ | ||
thundergbm==0.3.17 \ | ||
yellowbrick==1.5 \ | ||
livelossplot==0.5.5 \ | ||
gymnasium==0.26.3 \ | ||
interpret==0.4.2 \ | ||
DoubleML==0.6.3 \ | ||
gymnasium==0.28.1 \ | ||
interpret==0.4.4 \ | ||
DoubleML==0.7.0 \ | ||
jupyter-bokeh==3.0.7 \ | ||
imbalanced-learn==0.10.1 \ | ||
scikeras==0.11.0 \ | ||
openai==0.27.8 \ | ||
openai[embeddings]==0.27.8 \ | ||
openai[wandb]==0.27.8 \ | ||
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==2023.4.0 \ | ||
fastparquet==2023.8.0 \ | ||
tables==3.8.0 \ | ||
dimod==0.12.3 \ | ||
dwave-samplers==1.0.0 \ | ||
python-statemachine==2.1.0 \ | ||
python-statemachine==2.1.2 \ | ||
pymannkendall==1.4.3 \ | ||
Pyomo==6.6.1 \ | ||
gpflow==2.8.1 \ | ||
pyarrow==12.0.1 \ | ||
Pyomo==6.6.2 \ | ||
gpflow==2.9.0 \ | ||
pyarrow==13.0.0 \ | ||
dwave-ocean-sdk==6.1.1 \ | ||
chardet==5.1.0 \ | ||
stable-baselines3==1.8.0 \ | ||
chardet==5.2.0 \ | ||
stable-baselines3==2.1.0 \ | ||
pystan==3.7.0 \ | ||
FixedEffectModel==0.0.5 \ | ||
tick==0.7.0.1 \ | ||
transformers==4.30.2 \ | ||
Rbeast==0.1.13 \ | ||
langchain==0.0.218 \ | ||
tensorflow-ranking==0.5.1 \ | ||
pomegranate==1.0.0 \ | ||
tigramite==5.2.1.8 | ||
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==0.8.2 \ | ||
tensorrt==8.6.1.post1 | ||
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RUN conda install -c conda-forge -y cudatoolkit=11.8.0 && conda install -c nvidia -y cuda-compiler=12.2.2 | ||
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 | ||
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# Install dwave tool | ||
RUN dwave install --all -y | ||
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@@ -237,20 +245,20 @@ RUN conda install -c conda-forge ipopt==3.14.12 \ | |
&& conda clean -y --all | ||
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# We install need to install separately else fails to find numpy | ||
RUN pip install --no-cache-dir Riskfolio-Lib==4.4.0 iisignature==0.24 | ||
RUN pip install --no-cache-dir Riskfolio-Lib==4.4.2 iisignature==0.24 | ||
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# Install spacy models | ||
RUN python -m spacy download en_core_web_md && python -m spacy download en_core_web_sm | ||
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RUN conda install -y -c conda-forge \ | ||
openmpi=4.1.5 \ | ||
openmpi=4.1.6 \ | ||
&& conda clean -y --all | ||
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# 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==2.1.1 torch-sparse==0.6.17 torch-cluster==1.6.1 torch-spline-conv==1.2.2 torch-geometric==2.3.1 | ||
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 | ||
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# Install nltk data | ||
RUN python -m nltk.downloader -d /usr/share/nltk_data punkt && \ | ||
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@@ -294,6 +302,8 @@ RUN python -m venv /Foundation-Pomegranate --system-site-packages && . /Foundati | |
nbeats-keras==1.8.0 \ | ||
nbeats-pytorch==1.8.0 \ | ||
neuralprophet[live]==0.6.2 \ | ||
autogluon==0.8.2 \ | ||
finrl==0.3.1 \ | ||
&& python -m ipykernel install --name=Foundation-Pomegranate \ | ||
&& deactivate | ||
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