开始使用Hadoop集群上的TensorFlowOnSpark

Hadoop集群上部署TensorFlowOnSpark

最新推荐文章于 2020-12-25 17:04:26 发布

weixin_33953384

最新推荐文章于 2020-12-25 17:04:26 发布

阅读量264

点赞数

CC 4.0 BY-SA版权

文章标签：大数据人工智能 python

原文链接：https://my.oschina.net/thomas2/blog/863933

本文介绍如何在Hadoop集群上部署并配置TensorFlowOnSpark环境，包括安装Python、TensorFlow及相关依赖，创建Python环境zip包，以及通过Spark进行MNIST数据集的训练与推理。

2019独角兽企业重金招聘Python工程师标准>>>

https://github.com/yahoo/TensorFlowOnSpark/wiki/GetStarted_YARN

开始使用Hadoop集群上的TensorFlowOnSpark

GetStarted_YARN

leewyang编辑本页 20 days ago · 7修订

第6页

本地克隆此wiki

在桌面克隆

开始使用Hadoop集群上的TensorFlowOnSpark

在开始之前，您应该已经熟悉TensorFlow并且可以访问安装了Spark的Hadoop网格。如果你的网格有GPU节点，他们必须在本地安装cuda。

安装Python 2.7

从网格网关，下载/安装Python到本地文件夹。Python的这种安装将分发给Spark执行器，以便任何自定义依赖关系，包括TensorFlow，都可以被执行器使用。

# download and extract Python 2.7
export PYTHON_ROOT=~/Python
curl -O https://www.python.org/ftp/python/2.7.12/Python-2.7.12.tgz
tar -xvf Python-2.7.12.tgz
rm Python-2.7.12.tgz

# compile into local PYTHON_ROOT
pushd Python-2.7.12
./configure --prefix="${PYTHON_ROOT}" --enable-unicode=ucs4
make
make install
popd
rm -rf Python-2.7.12

# install pip
pushd "${PYTHON_ROOT}"
curl -O https://bootstrap.pypa.io/get-pip.py
bin/python get-pip.py
rm get-pip.py

# install tensorflow (and any custom dependencies)
${PYTHON_ROOT}/bin/pip install pydoop
# Note: add any extra dependencies here
popd

安装和编译TensorFlow w / RDMA支持

git clone git@github.com:yahoo/tensorflow.git
# follow build instructions to install into ${PYTHON_ROOT}

为TFRecords安装和编译Hadoop InputFormat / OutputFormat

git clone https://github.com/tensorflow/ecosystem.git
# follow build instructions to generate tensorflow-hadoop-1.0-SNAPSHOT.jar
# copy jar to HDFS for easier reference
hadoop fs -put tensorflow-hadoop-1.0-SNAPSHOT.jar

为Spark创建一个Python w / TensorFlow zip包

pushd "${PYTHON_ROOT}"
zip -r Python.zip *
popd

# copy this Python distribution into HDFS
hadoop fs -put ${PYTHON_ROOT}/Python.zip

安装TensorFlowOnSpark

接下来，克隆这个repo并为Spark构建一个zip包：

git clone git@github.com:yahoo/TensorFlowOnSpark.git
pushd TensorFlowOnSpark/src
zip -r ../tfspark.zip *
popd

运行MNIST示例

下载/压缩MNIST数据集

mkdir ${HOME}/mnist
pushd ${HOME}/mnist >/dev/null
curl -O "http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"
curl -O "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"
curl -O "http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz"
curl -O "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"
popd >/dev/null

将MNIST zip文件转换为HDFS文件

# set environment variables (if not already done)
export PYTHON_ROOT=~/Python
export LD_LIBRARY_PATH=${PATH}
export PYSPARK_PYTHON=${PYTHON_ROOT}/bin/python
export SPARK_YARN_USER_ENV="PYSPARK_PYTHON=Python/bin/python"
export PATH=${PYTHON_ROOT}/bin/:$PATH
export QUEUE=gpu

# for CPU mode:
# export QUEUE=default
# remove --conf spark.executorEnv.LD_LIBRARY_PATH \
# remove --driver-library-path \

# save images and labels as CSV files
${SPARK_HOME}/bin/spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 4 \
--executor-memory 4G \
--archives hdfs:///user/${USER}/Python.zip#Python,mnist/mnist.zip#mnist \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/local/cuda-7.5/lib64" \
--driver-library-path="/usr/local/cuda-7.5/lib64" \
TensorFlowOnSpark/examples/mnist/mnist_data_setup.py \
--output mnist/csv \
--format csv

# save images and labels as TFRecords
${SPARK_HOME}/bin/spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 4 \
--executor-memory 4G \
--archives hdfs:///user/${USER}/Python.zip#Python,mnist/mnist.zip#mnist \
--jars hdfs:///user/${USER}/tensorflow-hadoop-1.0-SNAPSHOT.jar \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/local/cuda-7.5/lib64" \
--driver-library-path="/usr/local/cuda-7.5/lib64" \
TensorFlowOnSpark/examples/mnist/mnist_data_setup.py \
--output mnist/tfr \
--format tfr

运行分布式MNIST训练（使用feed_dict）

# for CPU mode:
# export QUEUE=default
# set --conf spark.executorEnv.LD_LIBRARY_PATH="$JAVA_HOME/jre/lib/amd64/server" \
# remove --driver-library-path \

# for CDH (per @wangyum)
# set "--conf spark.executorEnv.LD_LIBRARY_PATH="/opt/cloudera/parcels/CDH/lib64:$JAVA_HOME/jre/lib/amd64/server"

# hadoop fs -rm -r mnist_model
${SPARK_HOME}/bin/spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 4 \
--executor-memory 27G \
--py-files TensorFlowOnSpark/tfspark.zip,TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--archives hdfs:///user/${USER}/Python.zip#Python \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/local/cuda-7.5/lib64:$JAVA_HOME/jre/lib/amd64/server" \
--driver-library-path="/usr/local/cuda-7.5/lib64" \
TensorFlowOnSpark/examples/mnist/spark/mnist_spark.py \
--images mnist/csv/train/images \
--labels mnist/csv/train/labels \
--mode train \
--model mnist_model
# to use infiniband, add --rdma

运行分布式MNIST推理（使用feed_dict）

${SPARK_HOME}/bin/spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 4 \
--executor-memory 27G \
--py-files TensorFlowOnSpark/tfspark.zip,TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--archives hdfs:///user/${USER}/Python.zip#Python \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/local/cuda-7.5/lib64:$JAVA_HOME/jre/lib/amd64/server" \
--driver-library-path="/usr/local/cuda-7.5/lib64" \
TensorFlowOnSpark/examples/mnist/spark/mnist_spark.py \
--images mnist/csv/test/images \
--labels mnist/csv/test/labels \
--mode inference \
--model mnist_model \
--output predictions

运行分布式MNIST训练（使用QueueRunners）

# for CPU mode:
# export QUEUE=default
# set --conf spark.executorEnv.LD_LIBRARY_PATH="$JAVA_HOME/jre/lib/amd64/server" \
# remove --driver-library-path \

# for CDH (per @wangyum)
# set "--conf spark.executorEnv.LD_LIBRARY_PATH="/opt/cloudera/parcels/CDH/lib64:$JAVA_HOME/jre/lib/amd64/server"

# hadoop fs -rm -r mnist_model
${SPARK_HOME}/bin/spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 4 \
--executor-memory 27G \
--py-files tensorflow/tfspark.zip,tensorflow/examples/mnist/tf/mnist_dist.py \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--archives hdfs:///user/${USER}/Python.zip#Python \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/local/cuda-7.5/lib64:$JAVA_HOME/jre/lib/amd64/server" \
--driver-library-path="/usr/local/cuda-7.5/lib64" \
tensorflow/examples/mnist/tf/mnist_spark.py \
--images mnist/tfr/train \
--format tfr \
--mode train \
--model mnist_model
# to use infiniband, replace the last line with --model mnist_model --rdma

运行分布式MNIST推断（使用QueueRunners）

# hadoop fs -rm -r predictions
${SPARK_HOME}/bin/spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 4 \
--executor-memory 27G \
--py-files TensorFlowOnSpark/tfspark.zip,TensorFlowOnSpark/examples/mnist/tf/mnist_dist.py \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--archives hdfs:///user/${USER}/Python.zip#Python \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/local/cuda-7.5/lib64:$JAVA_HOME/jre/lib/amd64/server" \
--driver-library-path="/usr/local/cuda-7.5/lib64" \
TensorFlowOnSpark/examples/mnist/tf/mnist_spark.py \
--images mnist/tfr/test \
--mode inference \
--model mnist_model \
--output predictions

转载于:https://my.oschina.net/thomas2/blog/863933