mirror of
https://github.com/jupyter/docker-stacks.git
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f8cd90ade1
* Add an ability to specify registry when using docker images
* Fix typo
* [TMP] Speedup workflow
* Revert "[TMP] Speedup workflow"
This reverts commit 3af0055ccf.
85 lines
3.2 KiB
Docker
85 lines
3.2 KiB
Docker
# Copyright (c) Jupyter Development Team.
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# Distributed under the terms of the Modified BSD License.
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ARG REGISTRY=docker.io
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ARG OWNER=jupyter
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ARG BASE_CONTAINER=$REGISTRY/$OWNER/scipy-notebook
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FROM $BASE_CONTAINER
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LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"
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# Fix: https://github.com/hadolint/hadolint/wiki/DL4006
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# Fix: https://github.com/koalaman/shellcheck/wiki/SC3014
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SHELL ["/bin/bash", "-o", "pipefail", "-c"]
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USER root
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# Spark dependencies
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# Default values can be overridden at build time
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# (ARGS are in lower case to distinguish them from ENV)
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ARG spark_version="3.5.0"
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ARG hadoop_version="3"
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ARG scala_version
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ARG spark_checksum="8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319"
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ARG openjdk_version="17"
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ENV APACHE_SPARK_VERSION="${spark_version}" \
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HADOOP_VERSION="${hadoop_version}"
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RUN apt-get update --yes && \
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apt-get install --yes --no-install-recommends \
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"openjdk-${openjdk_version}-jre-headless" \
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ca-certificates-java && \
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apt-get clean && rm -rf /var/lib/apt/lists/*
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# Spark installation
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WORKDIR /tmp
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# You need to use https://archive.apache.org/dist/ website if you want to download old Spark versions
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# But it seems to be slower, that's why we use recommended site for download
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RUN if [ -z "${scala_version}" ]; then \
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curl --progress-bar --location --output "spark.tgz" \
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"https://dlcdn.apache.org/spark/spark-${APACHE_SPARK_VERSION}/spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz"; \
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else \
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curl --progress-bar --location --output "spark.tgz" \
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"https://dlcdn.apache.org/spark/spark-${APACHE_SPARK_VERSION}/spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}-scala${scala_version}.tgz"; \
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fi && \
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echo "${spark_checksum} *spark.tgz" | sha512sum -c - && \
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tar xzf "spark.tgz" -C /usr/local --owner root --group root --no-same-owner && \
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rm "spark.tgz"
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# Configure Spark
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ENV SPARK_HOME=/usr/local/spark
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ENV SPARK_OPTS="--driver-java-options=-Xms1024M --driver-java-options=-Xmx4096M --driver-java-options=-Dlog4j.logLevel=info" \
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PATH="${PATH}:${SPARK_HOME}/bin"
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RUN if [ -z "${scala_version}" ]; then \
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ln -s "spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}" "${SPARK_HOME}"; \
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else \
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ln -s "spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}-scala${scala_version}" "${SPARK_HOME}"; \
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fi && \
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# Add a link in the before_notebook hook in order to source automatically PYTHONPATH && \
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ln -s "${SPARK_HOME}/sbin/spark-config.sh" /usr/local/bin/before-notebook.d/spark-config.sh
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# Configure IPython system-wide
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COPY ipython_kernel_config.py "/etc/ipython/"
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RUN fix-permissions "/etc/ipython/"
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USER ${NB_UID}
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# Install pyarrow
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# NOTE: It's important to ensure compatibility between Pandas versions.
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# The pandas version in this Dockerfile should match the version
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# on which the Pandas API for Spark is built.
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# To find the right version:
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# 1. Check out the Spark branch you are on.
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# 2. Find the pandas version in the file spark/dev/infra/Dockerfile.
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RUN mamba install --yes \
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'pandas=2.0.3' \
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'pyarrow' && \
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mamba clean --all -f -y && \
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fix-permissions "${CONDA_DIR}" && \
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fix-permissions "/home/${NB_USER}"
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WORKDIR "${HOME}"
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EXPOSE 4040
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