add dockerfile

.dockerignore

@@ -0,0 +1,8 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.env
+.git
+

Dockerfile

@@ -0,0 +1,24 @@
+FROM continuumio/miniconda3:latest
+
+WORKDIR /qlib
+
+COPY . .
+
+RUN apt-get update && \
+    apt-get install -y build-essential
+
+RUN conda create --name qlib_source python=3.8
+RUN echo "conda activate qlib_source" >> ~/.bashrc
+ENV PATH /opt/conda/envs/qlib_source/bin:$PATH
+
+RUN python -m pip install --upgrade pip
+
+RUN python -m pip install numpy==1.23.5
+RUN python -m pip install pandas==1.5.3
+RUN python -m pip install importlib-metadata==5.2.0
+RUN python -m pip install "cloudpickle<3"
+RUN python -m pip install scikit-learn==1.3.2
+
+RUN python -m pip install cython packaging tables matplotlib statsmodels
+
+RUN python -m pip install pyqlib

README.md

@@ -272,6 +272,9 @@ We recommend users to prepare their own data if they have a high-quality dataset
   ```
  -->
 
+## Build docker images
+Running `build_docker_image.sh` is the right choice when you want to create a docker image for `Qlib`. Of course, before running it, please open this file and make some necessary changes according to your docker hub account. For more information, please see: https://qlib.readthedocs.io/en/latest/start/installation.html
+
 ## Auto Quant Research Workflow
 Qlib provides a tool named `qrun` to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). You can start an auto quant research workflow and have a graphical reports analysis according to the following steps: 
 

build_docker_image.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# Build the Docker image
+sudo docker build -t qlib_image -f ./Dockerfile .
+
+# Log in to Docker Hub
+# If you are a new docker hub user, please verify your email address before proceeding with this step.
+sudo docker login
+
+# Tag the Docker image
+sudo docker tag qlib_image <Your docker hub username, not your email>/qlib_image:<version stable or nightly>
+
+# Push the Docker image to Docker Hub
+sudo docker push <Your docker hub username, not your email>/qlib_image:<version stable or nightly>

docs/start/installation.rst

@@ -44,3 +44,23 @@ Use the following code to make sure the installation successful:
    >>> import qlib
    >>> qlib.__version__
    <LATEST VERSION>
+
+
+Running ``build_docker_image.sh`` is the right choice when you want to create a docker image for ``Qlib``.
+Of course, before running it, please open this file and make some necessary changes according to your docker hub account.
+.. code-block:: bash
+
+   #!/bin/bash
+
+   # Build the Docker image
+   sudo docker build -t qlib_image -f ./Dockerfile .
+
+   # Log in to Docker Hub
+   # If you are a new docker hub user, please verify your email address before proceeding with this step.
+   sudo docker login
+
+   # Tag the Docker image
+   sudo docker tag qlib_image <Your docker hub username, not your email>/qlib_image:<version stable or nightly>
+
+   # Push the Docker image to Docker Hub
+   sudo docker push <Your docker hub username, not your email>/qlib_image:<version stable or nightly>

setup.py

@@ -47,6 +47,10 @@ def get_version(rel_path: str) -> str:
     "numpy>=1.12.0, <1.24",
     "pandas>=0.25.1",
     "scipy>=1.7.3",
+    # scs is a dependency package,
+    # and the latest version of scs: scs-3.2.4.post3.tar.gz causes the documentation build to fail,
+    # so we have temporarily limited the version of scs.
+    "scs<=3.2.4",
     "requests>=2.18.0",
     "sacred>=0.7.4",
     "python-socketio",