我们已尝试使用Dask文档上此页面上的说明和指导：https://yarn.dask.org/en/latest/aws-emr.html在配置AWS EMR管理的Spark集群时部署Dask。我们发现，以上链接中提供的引导操作仅将Dask组件部署到主节点，而不是工作器。理想情况下，YARN将为Dask部署所需的软件包到所有工人。
我在下面提供了引导操作代码。
其他人看到过这个，和/或可以建议如何解决这个问题？
引导程序操作代码如下：
#!/bin/bash
HELP="Usage: bootstrap-dask [OPTIONS]
Example AWS EMR Bootstrap Action to install and configure Dask and Jupyter
By default it does the following things:
- Installs miniconda
- Installs dask, distributed, dask-yarn, pyarrow, and s3fs. This list can be
extended using the --conda-packages flag below.
- Packages this environment for distribution to the workers.
- Installs and starts a jupyter notebook server running on port 8888. This can
be disabled with the --no-jupyter flag below.
Options:
--jupyter / --no-jupyter Whether to also install and start a Jupyter
Notebook Server. Default is True.
--password, -pw Set the password for the Jupyter Notebook
Server. Default is 'dask-user'.
--conda-packages Extra packages to install from conda.
"
set -e
# Parse Inputs. This is specific to this script, and can be ignored
# -----------------------------------------------------------------
JUPYTER_PASSWORD="dask-user"
EXTRA_CONDA_PACKAGES=""
JUPYTER="true"
while [[ $# -gt 0 ]]; do
case $1 in
-h|--help)
echo "$HELP"
exit 0
;;
--no-jupyter)
JUPYTER="false"
shift
;;
--jupyter)
JUPYTER="true"
shift
;;
-pw|--password)
JUPYTER_PASSWORD="$2"
shift
shift
;;
--conda-packages)
shift
PACKAGES=()
while [[ $# -gt 0 ]]; do
case $1 in
-*)
break
;;
*)
PACKAGES+=($1)
shift
;;
esac
done
EXTRA_CONDA_PACKAGES="${PACKAGES[@]}"
;;
*)
echo "error: unrecognized argument: $1"
exit 2
;;
esac
done
# -----------------------------------------------------------------------------
# 1. Check if running on the master node. If not, there's nothing do.
# -----------------------------------------------------------------------------
grep -q '"isMaster": true' /mnt/var/lib/info/instance.json \
|| { echo "Not running on master node, nothing to do" && exit 0; }
# -----------------------------------------------------------------------------
# 2. Install Miniconda
# -----------------------------------------------------------------------------
echo "Installing Miniconda"
curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh
bash /tmp/miniconda.sh -b -p $HOME/miniconda
rm /tmp/miniconda.sh
echo -e '\nexport PATH=$HOME/miniconda/bin:$PATH' >> $HOME/.bashrc
source $HOME/.bashrc
conda update conda -y
# -----------------------------------------------------------------------------
# 3. Install packages to use in packaged environment
#
# We install a few packages by default, and allow users to extend this list
# with a CLI flag:
#
# - dask-yarn >= 0.7.0, for deploying Dask on YARN.
# - pyarrow for working with hdfs, parquet, ORC, etc...
# - s3fs for access to s3
# - conda-pack for packaging the environment for distribution
# - ensure tornado 5, since tornado 6 doesn't work with jupyter-server-proxy
# -----------------------------------------------------------------------------
echo "Installing base packages"
conda install \
-c conda-forge \
-y \
-q \
dask-yarn>=0.7.0 \
pyarrow \
s3fs \
conda-pack \
tornado=5 \
$EXTRA_CONDA_PACKAGES
# -----------------------------------------------------------------------------
# 4. Package the environment to be distributed to worker nodes
# -----------------------------------------------------------------------------
echo "Packaging environment"
conda pack -q -o $HOME/environment.tar.gz
# -----------------------------------------------------------------------------
# 5. List all packages in the worker environment
# -----------------------------------------------------------------------------
echo "Packages installed in the worker environment:"
conda list
# -----------------------------------------------------------------------------
# 6. Configure Dask
#
# This isn't necessary, but for this particular bootstrap script it will make a
# few things easier:
#
# - Configure the cluster's dashboard link to show the proxied version through
# jupyter-server-proxy. This allows access to the dashboard with only an ssh
# tunnel to the notebook.
#
# - Specify the pre-packaged python environment, so users don't have to
#
# - Set the default deploy-mode to local, so the dashboard proxying works
#
# - Specify the location of the native libhdfs library so pyarrow can find it
# on the workers and the client (if submitting applications).
# ------------------------------------------------------------------------------
echo "Configuring Dask"
mkdir -p $HOME/.config/dask
cat <<EOT >> $HOME/.config/dask/config.yaml
distributed:
dashboard:
link: "/proxy/{port}/status"
yarn:
environment: /home/hadoop/environment.tar.gz
deploy-mode: local
worker:
env:
ARROW_LIBHDFS_DIR: /usr/lib/hadoop/lib/native/
client:
env:
ARROW_LIBHDFS_DIR: /usr/lib/hadoop/lib/native/
EOT
# Also set ARROW_LIBHDFS_DIR in ~/.bashrc so it's set for the local user
echo -e '\nexport ARROW_LIBHDFS_DIR=/usr/lib/hadoop/lib/native' >> $HOME/.bashrc
# -----------------------------------------------------------------------------
# 7. If Jupyter isn't requested, we're done
# -----------------------------------------------------------------------------
if [[ "$JUPYTER" == "false" ]]; then
exit 0
fi
# -----------------------------------------------------------------------------
# 8. Install jupyter notebook server and dependencies
#
# We do this after packaging the worker environments to keep the tar.gz as
# small as possible.
#
# We install the following packages:
#
# - notebook: the Jupyter Notebook Server
# - ipywidgets: used to provide an interactive UI for the YarnCluster objects
# - jupyter-server-proxy: used to proxy the dask dashboard through the notebook server
# -----------------------------------------------------------------------------
if [[ "$JUPYTER" == "true" ]]; then
echo "Installing Jupyter"
conda install \
-c conda-forge \
-y \
-q \
notebook \
ipywidgets \
jupyter-server-proxy
fi
# -----------------------------------------------------------------------------
# 9. List all packages in the client environment
# -----------------------------------------------------------------------------
echo "Packages installed in the client environment:"
conda list
# -----------------------------------------------------------------------------
# 10. Configure Jupyter Notebook
# -----------------------------------------------------------------------------
echo "Configuring Jupyter"
mkdir -p $HOME/.jupyter
HASHED_PASSWORD=`python -c "from notebook.auth import passwd; print(passwd('$JUPYTER_PASSWORD'))"`
cat <<EOF >> $HOME/.jupyter/jupyter_notebook_config.py
c.NotebookApp.password = u'$HASHED_PASSWORD'
c.NotebookApp.open_browser = False
c.NotebookApp.ip = '0.0.0.0'
EOF
# -----------------------------------------------------------------------------
# 11. Define an upstart service for the Jupyter Notebook Server
#
# This sets the notebook server up to properly run as a background service.
# -----------------------------------------------------------------------------
echo "Configuring Jupyter Notebook Upstart Service"
cat <<EOF > /tmp/jupyter-notebook.conf
description "Jupyter Notebook Server"
start on runlevel [2345]
stop on runlevel [016]
respawn
respawn limit unlimited
exec su - hadoop -c "jupyter notebook" >> /var/log/jupyter-notebook.log 2>&1
EOF
sudo mv /tmp/jupyter-notebook.conf /etc/init/
# -----------------------------------------------------------------------------
# 12. Start the Jupyter Notebook Server
# -----------------------------------------------------------------------------
echo "Starting Jupyter Notebook Server"
sudo initctl reload-configuration
sudo initctl start jupyter-notebook
您从dask网站复制的脚本是有条件的boostrap操作。
以下行使它仅在主节点上运行：
grep -q '"isMaster": true' /mnt/var/lib/info/instance.json \
|| { echo "Not running on master node, nothing to do" && exit 0; }
它检查instance.json文件中的条目“ isMaster”。如果为假，则脚本以0退出。
理想情况下，YARN会将Dask所需的软件包部署到所有工人。
这确实取决于您的用例。我尚未将dask部署到多节点群集。例如，Spark允许您在提交任务时指定压缩的依赖关系，因此不一定需要安装它们。
在所有节点上安装软件包使打包作业变得更加容易。但是，人们可能会很快陷入冲突的依赖关系中，因为人们使用完全不同的一组依赖关系来运行完全不相关的工作。一个人也可以为每项工作启动多个助理。
如果取消注释该行，该代码将在所有节点上运行。但是，也许您只希望在节点上安装一些依赖项，而在master上安装更多人员。在这种情况下，您可以提供多个引导操作。
例如，不必在每个节点上都安装并启动jupyter笔记本工作人员。
Dask on yarn guide建议在提交时提供一个存档的环境。因此，scipt的编写方式只能在主节点上运行。