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release: v1.5.3 #404

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19 changes: 19 additions & 0 deletions build_artifacts/v1/v1.5/v1.5.3/CHANGELOG-cpu.md
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# Change log: 1.5.3(cpu)

## Upgrades:

Package | Previous Version | Current Version
---|---|---
python|3.10.13|3.10.14
jupyter-lsp|2.2.3|2.2.5
jupyterlab|4.1.2|4.1.6
amazon-codewhisperer-jupyterlab-ext|2.0.1|2.0.2
jupyter-scheduler|2.5.1|2.5.2
amazon-sagemaker-jupyter-scheduler|3.0.7|3.0.11
scikit-learn|1.4.1.post1|1.4.2
notebook|7.1.1|7.1.3
aws-glue-sessions|1.0.4|1.0.5
langchain|0.1.9|0.1.16
jupyter-server-proxy|4.1.0|4.1.2
matplotlib|3.8.3|3.8.4
python-lsp-server|1.10.0|1.10.1
19 changes: 19 additions & 0 deletions build_artifacts/v1/v1.5/v1.5.3/CHANGELOG-gpu.md
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# Change log: 1.5.3(gpu)

## Upgrades:

Package | Previous Version | Current Version
---|---|---
python|3.10.13|3.10.14
jupyter-lsp|2.2.3|2.2.5
jupyterlab|4.1.2|4.1.6
amazon-codewhisperer-jupyterlab-ext|2.0.1|2.0.2
jupyter-scheduler|2.5.1|2.5.2
amazon-sagemaker-jupyter-scheduler|3.0.7|3.0.11
scikit-learn|1.4.1.post1|1.4.2
notebook|7.1.1|7.1.3
aws-glue-sessions|1.0.4|1.0.5
langchain|0.1.9|0.1.16
jupyter-server-proxy|4.1.0|4.1.2
matplotlib|3.8.3|3.8.4
python-lsp-server|1.10.0|1.10.1
130 changes: 130 additions & 0 deletions build_artifacts/v1/v1.5/v1.5.3/Dockerfile
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ARG TAG_FOR_BASE_MICROMAMBA_IMAGE
FROM mambaorg/micromamba:$TAG_FOR_BASE_MICROMAMBA_IMAGE

ARG CUDA_MAJOR_MINOR_VERSION=''
ARG ENV_IN_FILENAME
ARG ARG_BASED_ENV_IN_FILENAME

ARG AMZN_BASE="/opt/amazon/sagemaker"
ARG DIRECTORY_TREE_STAGE_DIR="${AMZN_BASE}/dir-staging"

ARG NB_USER="sagemaker-user"
ARG NB_UID=1000
ARG NB_GID=100

ENV SAGEMAKER_LOGGING_DIR="/var/log/sagemaker/"
ENV STUDIO_LOGGING_DIR="/var/log/studio/"

USER root
RUN usermod "--login=${NB_USER}" "--home=/home/${NB_USER}" --move-home "-u ${NB_UID}" "${MAMBA_USER}" && \
groupmod "--new-name=${NB_USER}" --non-unique "-g ${NB_GID}" "${MAMBA_USER}" && \
# Update the expected value of MAMBA_USER for the
# _entrypoint.sh consistency check.
echo "${NB_USER}" > "/etc/arg_mamba_user" && \
:
ENV MAMBA_USER=$NB_USER
ENV USER=$NB_USER

RUN apt-get update && apt-get upgrade -y &&\
apt-get install -y --no-install-recommends sudo gettext-base wget curl unzip git rsync build-essential openssh-client nano && \
# We just install tzdata below but leave default time zone as UTC. This helps packages like Pandas to function correctly.
DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends tzdata krb5-user libkrb5-dev libsasl2-dev libsasl2-modules && \
chmod g+w /etc/passwd && \
echo "ALL ALL=(ALL) NOPASSWD: ALL" >> /etc/sudoers && \
touch /etc/krb5.conf.lock && chown ${NB_USER}:${MAMBA_USER} /etc/krb5.conf* && \
# Note that we do NOT run `rm -rf /var/lib/apt/lists/*` here. If we did, anyone building on top of our images will
# not be able to run any `apt-get install` commands and that would hamper customizability of the images.
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" && \
unzip awscliv2.zip && \
sudo ./aws/install && \
rm -rf aws awscliv2.zip && \
:
RUN echo "source /usr/local/bin/_activate_current_env.sh" | tee --append /etc/profile

USER $MAMBA_USER
COPY --chown=$MAMBA_USER:$MAMBA_USER $ENV_IN_FILENAME *.in /tmp/

# Make sure that $ENV_IN_FILENAME has a newline at the end before the `tee` command runs. Otherwise, nasty things
# will happen.
RUN if [[ -z $ARG_BASED_ENV_IN_FILENAME ]] ; \
then echo 'No ARG_BASED_ENV_IN_FILENAME passed' ; \
else envsubst < /tmp/$ARG_BASED_ENV_IN_FILENAME | tee --append /tmp/$ENV_IN_FILENAME ; \
fi

ARG CONDA_OVERRIDE_CUDA=$CUDA_MAJOR_MINOR_VERSION
RUN micromamba install -y --name base --file /tmp/$ENV_IN_FILENAME && \
micromamba clean --all --yes --force-pkgs-dirs && \
rm -rf /tmp/*.in


ARG MAMBA_DOCKERFILE_ACTIVATE=1
RUN sudo ln -s $(which python3) /usr/bin/python

# Update npm version
RUN npm i -g npm

# Install glue kernels, and move to shared directory
# Also patching base kernel so Studio background code doesn't start session silently
RUN install-glue-kernels && \
SITE_PACKAGES=$(pip show aws-glue-sessions | grep Location | awk '{print $2}') && \
jupyter-kernelspec install $SITE_PACKAGES/aws_glue_interactive_sessions_kernel/glue_pyspark --user && \
jupyter-kernelspec install $SITE_PACKAGES/aws_glue_interactive_sessions_kernel/glue_spark --user && \
mv /home/sagemaker-user/.local/share/jupyter/kernels/glue_pyspark /opt/conda/share/jupyter/kernels && \
mv /home/sagemaker-user/.local/share/jupyter/kernels/glue_spark /opt/conda/share/jupyter/kernels && \
sed -i '/if not store_history and (/i\ if "sm_analytics_runtime_check" in code:\n return await self._complete_cell()\n' \
"$SITE_PACKAGES/aws_glue_interactive_sessions_kernel/glue_kernel_base/BaseKernel.py"


# Patch glue kernels to use kernel wrapper
COPY patch_glue_pyspark.json /opt/conda/share/jupyter/kernels/glue_pyspark/kernel.json
COPY patch_glue_spark.json /opt/conda/share/jupyter/kernels/glue_spark/kernel.json

USER root
RUN HOME_DIR="/home/${NB_USER}/licenses" \
&& mkdir -p ${HOME_DIR} \
&& curl -o ${HOME_DIR}/oss_compliance.zip https://aws-dlinfra-utilities.s3.amazonaws.com/oss_compliance.zip \
&& unzip ${HOME_DIR}/oss_compliance.zip -d ${HOME_DIR}/ \
&& cp ${HOME_DIR}/oss_compliance/test/testOSSCompliance /usr/local/bin/testOSSCompliance \
&& chmod +x /usr/local/bin/testOSSCompliance \
&& chmod +x ${HOME_DIR}/oss_compliance/generate_oss_compliance.sh \
&& ${HOME_DIR}/oss_compliance/generate_oss_compliance.sh ${HOME_DIR} python \
&& rm -rf ${HOME_DIR}/oss_compliance*

# Merge in OS directory tree contents.
RUN mkdir -p ${DIRECTORY_TREE_STAGE_DIR}
COPY dirs/ ${DIRECTORY_TREE_STAGE_DIR}/
RUN rsync -a ${DIRECTORY_TREE_STAGE_DIR}/ / && \
rm -rf ${DIRECTORY_TREE_STAGE_DIR}

# Create logging directories for supervisor
RUN mkdir -p $SAGEMAKER_LOGGING_DIR && \
chmod a+rw $SAGEMAKER_LOGGING_DIR && \
mkdir -p ${STUDIO_LOGGING_DIR} && \
chown ${NB_USER}:${MAMBA_USER} ${STUDIO_LOGGING_DIR}

# Create supervisord runtime directory
RUN mkdir -p /var/run/supervisord && \
chmod a+rw /var/run/supervisord

USER $MAMBA_USER
ENV PATH="/opt/conda/bin:/opt/conda/condabin:$PATH"
WORKDIR "/home/${NB_USER}"

# Install Kerberos.
# Make sure no dependency is added/updated
RUN pip install "krb5>=0.5.1,<0.6" && \
pip show krb5 | grep Require | xargs -i sh -c '[ $(echo {} | cut -d: -f2 | wc -w) -eq 0 ] '

# https://stackoverflow.com/questions/122327
RUN SYSTEM_PYTHON_PATH=$(python3 -c "from __future__ import print_function;import sysconfig; print(sysconfig.get_paths().get('purelib'))") && \
# Remove SparkRKernel as it's not supported \
jupyter-kernelspec remove -f -y sparkrkernel && \
# Patch Sparkmagic lib to support Custom Certificates \
# https://github.com/jupyter-incubator/sparkmagic/pull/435/files \
cp -a ${SYSTEM_PYTHON_PATH}/sagemaker_studio_analytics_extension/patches/configuration.py ${SYSTEM_PYTHON_PATH}/sparkmagic/utils/ && \
cp -a ${SYSTEM_PYTHON_PATH}/sagemaker_studio_analytics_extension/patches/reliablehttpclient.py ${SYSTEM_PYTHON_PATH}/sparkmagic/livyclientlib/reliablehttpclient.py && \
sed -i 's= "python"= "/opt/conda/bin/python"=g' /opt/conda/share/jupyter/kernels/pysparkkernel/kernel.json /opt/conda/share/jupyter/kernels/sparkkernel/kernel.json && \
sed -i 's="Spark"="SparkMagic Spark"=g' /opt/conda/share/jupyter/kernels/sparkkernel/kernel.json && \
sed -i 's="PySpark"="SparkMagic PySpark"=g' /opt/conda/share/jupyter/kernels/pysparkkernel/kernel.json

ENV SHELL=/bin/bash
53 changes: 53 additions & 0 deletions build_artifacts/v1/v1.5/v1.5.3/RELEASE.md
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# Release notes: 1.5.3

Package | gpu| cpu
---|---|---
python|3.10.14|3.10.14
numpy|1.26.4|1.26.4
jinja2|3.1.3|3.1.3
pandas|2.1.4|2.1.4
altair|5.2.0|5.2.0
boto3|1.28.64|1.28.64
ipython|8.21.0|8.21.0
jupyter-lsp|2.2.5|2.2.5
jupyterlab|4.1.6|4.1.6
amazon-codewhisperer-jupyterlab-ext|2.0.2|2.0.2
jupyter-scheduler|2.5.2|2.5.2
amazon-sagemaker-jupyter-scheduler|3.0.11|3.0.11
scipy|1.11.4|1.11.4
scikit-learn|1.4.2|1.4.2
pip|23.3.2|23.3.2
torchvision|0.15.2|0.15.2
autogluon|0.8.2|0.8.2
ipywidgets|8.1.2|8.1.2
notebook|7.1.3|7.1.3
aws-glue-sessions|1.0.5|1.0.5
conda|23.11.0|23.11.0
fastapi|0.103.2|0.103.2
langchain|0.1.16|0.1.16
jupyter-ai|2.9.1|2.9.1
jupyter-dash|0.4.2|0.4.2
jupyter-server-proxy|4.1.2|4.1.2
jupyterlab-git|0.50.0|0.50.0
jupyterlab-lsp|5.0.3|5.0.3
keras|2.12.0|2.12.0
matplotlib|3.8.4|3.8.4
nodejs|18.18.2|18.18.2
py-xgboost-gpu|1.7.6|
thrift_sasl|0.4.3|0.4.3
pyhive|0.7.0|0.7.0
python-gssapi|1.8.3|1.8.3
python-lsp-server|1.10.1|1.10.1
pytorch-gpu|2.0.0|
sagemaker-headless-execution-driver|0.0.12|0.0.12
sagemaker-jupyterlab-emr-extension|0.1.9|0.1.9
sagemaker-jupyterlab-extension|0.2.0|0.2.0
sagemaker-kernel-wrapper|0.0.2|0.0.2
sagemaker-python-sdk|2.198.1|2.198.1
sagemaker-studio-analytics-extension|0.0.21|0.0.21
sasl|0.3.1|0.3.1
supervisor|4.2.5|4.2.5
tensorflow|2.12.1|2.12.1
uvicorn|0.27.1|0.27.1
pytorch| |2.0.0
py-xgboost-cpu| |1.7.6
48 changes: 48 additions & 0 deletions build_artifacts/v1/v1.5/v1.5.3/cpu.env.in
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# This file is auto-generated.
conda-forge::langchain[version='>=0.1.9,<0.2.0']
conda-forge::fastapi[version='>=0.103.2,<0.104.0']
conda-forge::uvicorn[version='>=0.27.1,<0.28.0']
conda-forge::pytorch[version='>=2.0.0,<2.1.0']
conda-forge::tensorflow[version='>=2.12.1,<2.13.0']
conda-forge::python[version='>=3.10.13,<3.11.0']
conda-forge::pip[version='>=23.3.2,<23.4.0']
conda-forge::torchvision[version='>=0.15.2,<0.16.0']
conda-forge::numpy[version='>=1.26.4,<1.27.0']
conda-forge::pandas[version='>=2.1.4,<2.2.0']
conda-forge::scikit-learn[version='>=1.4.1.post1,<1.5.0']
conda-forge::jinja2[version='>=3.1.3,<3.2.0']
conda-forge::matplotlib[version='>=3.8.3,<3.9.0']
conda-forge::sagemaker-headless-execution-driver[version='>=0.0.12,<0.1.0']
conda-forge::ipython[version='>=8.21.0,<8.22.0']
conda-forge::scipy[version='>=1.11.4,<1.12.0']
conda-forge::keras[version='>=2.12.0,<2.13.0']
conda-forge::py-xgboost-cpu[version='>=1.7.6,<1.8.0']
conda-forge::jupyterlab[version='>=4.1.2,<4.2.0']
conda-forge::ipywidgets[version='>=8.1.2,<8.2.0']
conda-forge::conda[version='>=23.11.0,<23.12.0']
conda-forge::boto3[version='>=1.28.64,<1.29.0']
conda-forge::sagemaker-python-sdk[version='>=2.198.1,<2.199.0']
conda-forge::supervisor[version='>=4.2.5,<4.3.0']
conda-forge::autogluon[version='>=0.8.2,<0.9.0']
conda-forge::aws-glue-sessions[version='>=1.0.4,<1.1.0']
conda-forge::sagemaker-kernel-wrapper[version='>=0.0.2,<0.1.0']
conda-forge::jupyter-ai[version='>=2.9.1,<2.10.0']
conda-forge::jupyter-scheduler[version='>=2.5.1,<2.6.0']
conda-forge::nodejs[version='>=18.18.2,<18.19.0']
conda-forge::jupyter-lsp[version='>=2.2.3,<2.3.0']
conda-forge::jupyterlab-lsp[version='>=5.0.3,<5.1.0']
conda-forge::python-lsp-server[version='>=1.10.0,<1.11.0']
conda-forge::notebook[version='>=7.1.1,<7.2.0']
conda-forge::altair[version='>=5.2.0,<5.3.0']
conda-forge::sagemaker-studio-analytics-extension[version='>=0.0.21,<0.1.0']
conda-forge::jupyter-dash[version='>=0.4.2,<0.5.0']
conda-forge::sagemaker-jupyterlab-extension[version='>=0.2.0,<0.3.0']
conda-forge::sagemaker-jupyterlab-emr-extension[version='>=0.1.9,<0.2.0']
conda-forge::amazon-sagemaker-jupyter-scheduler[version='>=3.0.7,<3.1.0']
conda-forge::jupyter-server-proxy[version='>=4.1.0,<4.2.0']
conda-forge::amazon-codewhisperer-jupyterlab-ext[version='>=2.0.1,<2.1.0']
conda-forge::jupyterlab-git[version='>=0.50.0,<0.51.0']
conda-forge::sasl[version='>=0.3.1,<0.4.0']
conda-forge::thrift_sasl[version='>=0.4.3,<0.5.0']
conda-forge::pyhive[version='>=0.7.0,<0.8.0']
conda-forge::python-gssapi[version='>=1.8.3,<1.9.0']
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