Tensorflow on Spark爬坑指南

2019年5月7日 243次阅读 来源: biggeng

由于机器学习和深度学习不断被炒热，Tensorflow作为Google家(Jeff Dean大神)推出的开源深度学习框架，也获得了很多关注。Tensorflow的灵活性很强，允许用户使用多台机器的多个设备(如不同的CPU和GPU)。但是由于Tensorflow 分布式的方式需要用户在客户端显示指定集群信息，另外需要手动拉起ps， worker等task. 对资源管理和使用上有诸多不便。因此，Yahoo开源了基于Spark的Tensorflow，使用executor执行worker和ps task. 项目地址为：https://github.com/yahoo/TensorFlowOnSpark

写在前面.. 前方高能，请注意！

虽然yahoo提供了如何在Spark集群中运行Tensorflow的步骤，但是由于这个guideline过于简单，一般情况下，根据这个guideline是跑不起来的. :(

Tensorflow on Spark 介绍

TensorflowOnSpark 支持使用Spark/Hadoop集群分布式的运行Tensorflow，号称支持所有的Tensorflow操作。需要注意的是用户需要对原有的TF程序进行简单的改造，就能够运行在Spark集群之上。

如何跑起来Tensorflow on Spark ？

虽然Yahoo在github上说明了安装部署TFS (https://github.com/yahoo/TensorFlowOnSpark/wiki/GetStarted_YARN), 但是根据实际实践，根据这个文档如果能跑起来，那真的要谢天谢地。因为在实际过程中，会因为环境问题遇到一些unexpected error。以下就是我将自己在实践过程中遇到的一些问题总结列举。

  1. 编译python和pip
    yahoo提供的编译步骤为:
# 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

在实际编译过程中，采用的Centos7.2操作系统，可能出现以下问题：

  • 安装pip报错
bin/python get-pip.py
ERROR:root:code for hash sha224 was not found.
Traceback (most recent call last):

报这个错一般是因为python中缺少_ssl.so 和 _hashlib.so库造成，可以从系统python库中找对应版本的拷贝到相应的python文件夹下(例如：lib/python2.7/lib-dynload)。

  • 缺少zlib
 bin/python get-pip.py
Traceback (most recent call last):
  File "get-pip.py", line 20061, in <module>
    main()
  File "get-pip.py", line 194, in main
    bootstrap(tmpdir=tmpdir)
  File "get-pip.py", line 82, in bootstrap
    import pip
zipimport.ZipImportError: can't decompress data; zlib not available

解决这个问题的方法是使用yum安装zlib*后，重新编译python后，即可解决。

  • ssl 报错
bin/python get-pip.py
pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available.
Collecting pip
  Could not fetch URL https://pypi.python.org/simple/pip/: There was a problem confirming the ssl certificate: Can't connect to HTTPS URL because the SSL module is not available. - skipping
  Could not find a version that satisfies the requirement pip (from versions: )
No matching distribution found for pip

解决方法： 在Python安装目录下打开文件lib/python2.7/ssl.py，注释掉 , HAS_ALPN

from _ssl import HAS_SNI, HAS_ECDH, HAS_NPN#, HAS_ALPN
  • pip install pydoop报错
gcc: error trying to exec 'cc1plus': execvp:

解决办法：需要在机器上安装g++编译器

2.安装编译 TensorFlow w/ RDMA Support

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

注意编译过程需要google的bazel和protoc, 这两个工具需要提前装好。

3.接下来的步骤按照https://github.com/yahoo/TensorFlowOnSpark/wiki/GetStarted_YARN 指导的步骤完成。

4.在HDP2.5部署的spark on Yarn环境上运行Tensorflow。

  • 在yarn-env.sh中设置环境变量，增加 * export HADOOP_HDFS_HOME=/usr/hdp/2.5.0.0-1245/hadoop-hdfs/*
    因为这个环境变量需要在执行tensorflow任务时被用到，如果没有export，会报错。
  • 重启YARN，使上述改动生效。
  • 按照Yahoo github上的步骤，执行训练mnist任务时，按下面命令提交作业:
export PYTHON_ROOT=/data2/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=default

  spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 4 \
--executor-memory 1G \
--py-files /data2/tesorflowonSpark/TensorFlowOnSpark/tfspark.zip,/data2/tesorflowonSpark/TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--archives hdfs:///user/${USER}/Python.zip#Python \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/jdk64/jdk1.8.0_77/jre/lib/amd64/server/" \
/data2/tesorflowonSpark/TensorFlowOnSpark/examples/mnist/spark/mnist_spark.py \
--images mnist/csv/test/images \
--labels mnist/csv/test/labels \
--mode inference \
--model mnist_model \
--output predictions

此时，通过Spark界面可以观察到worker0处于阻塞状态。

17/03/21 18:17:18 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 28.4 KB, free 542.6 KB)
17/03/21 18:17:18 INFO TorrentBroadcast: Reading broadcast variable 1 took 17 ms
17/03/21 18:17:18 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 440.6 KB, free 983.3 KB)
2017-03-21 18:17:18,404 INFO (MainThread-14872) Connected to TFSparkNode.mgr on ochadoop03, ppid=14685, state='running'
2017-03-21 18:17:18,411 INFO (MainThread-14872) mgr.state='running'
2017-03-21 18:17:18,411 INFO (MainThread-14872) Feeding partition <generator object load_stream at 0x7f447f120960> into input queue <multiprocessing.queues.JoinableQueue object at 0x7f447f129890>
17/03/21 18:17:20 INFO PythonRunner: Times: total = 2288, boot = -5387, init = 5510, finish = 2165
17/03/21 18:17:20 INFO PythonRunner: Times: total = 101, boot = 3, init = 21, finish = 77
2017-03-21 18:17:20.587060: I tensorflow/core/distributed_runtime/master_session.cc:1011] Start master session b5d9a21a16799e0b with config:

通过分析原因发现，在mnist例子中，logdir设置的是hdfs的路径，可能是由于tf对hdfs的支持有限或者存在bug（惭愧，并没有深究 :)）。将logdir改为本地目录，就可以正常运行。但是由此又带来了另一个问题，因为Spark每次启动时worker0的位置并不确定，有可能每次启动的机器都不同，这就导致在inference的时候没有办法获得训练的模型。

一个解决办法是：在worker 0训练完模型后，将模型同步到hdfs中，在inference的之前，再
将hdfs的checkpoints文件夹拉取到本地执行。以下为我对yahoo提供的mnist example做的类似的修改.

def writeFileToHDFS():
  rootdir = '/tmp/mnist_model'
  client = HdfsClient(hosts='localhost:50070')
  client.mkdirs('/user/root/mnist_model')
  for parent,dirnames,filenames in os.walk(rootdir):
    for dirname in  dirnames:
          print("parent is:{0}".format(parent))
    for filename in filenames:
          client.copy_from_local(os.path.join(parent,filename), os.path.join('/user/root/mnist_model',filename), overwrite=True)

   #logdir = TFNode.hdfs_path(ctx, args.model)
    logdir = "/tmp/" + args.model

      while not sv.should_stop() and step < args.steps:
        # Run a training step asynchronously.
        # See `tf.train.SyncReplicasOptimizer` for additional details on how to
        # perform *synchronous* training.

        # using feed_dict
        batch_xs, batch_ys = feed_dict()
        feed = {x: batch_xs, y_: batch_ys}

        if len(batch_xs) != batch_size:
          print("done feeding")
          break
        else:
          if args.mode == "train":
            _, step = sess.run([train_op, global_step], feed_dict=feed)
            # print accuracy and save model checkpoint to HDFS every 100 steps
            if (step % 100 == 0):
              print("{0} step: {1} accuracy: {2}".format(datetime.now().isoformat(), step, sess.run(accuracy,{x: batch_xs, y_: batch_ys})))
          else: # args.mode == "inference"
              labels, preds, acc = sess.run([label, prediction, accuracy], feed_dict=feed)

              results = ["{0} Label: {1}, Prediction: {2}".format(datetime.now().isoformat(), l, p) for l,p in zip(labels,preds)]
              TFNode.batch_results(ctx.mgr, results)
              print("acc: {0}".format(acc))
      if task_index == 0:
         writeFileToHDFS()

当然这段代码只是为了进行说明，并不是很严谨，在上传hdfs的时候，是需要对文件夹是否存在等要做一系列的判断。。。

5.train & inference

  • 向Spark集群提交训练任务.
spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 3 \
--executor-memory 7G \
--py-files /data2/tesorflowonSpark/TensorFlowOnSpark/tfspark.zip,/data2/tesorflowonSpark/TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--archives hdfs:///user/${USER}/Python.zip#Python \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/jdk64/jdk1.8.0_77/jre/lib/amd64/server/" \
/data2/tesorflowonSpark/TensorFlowOnSpark/examples/mnist/spark/mnist_spark.py \
--images mnist/csv/train/images \
--labels mnist/csv/train/labels \
--mode train \
--model mnist_model

执行起来后，查看Spark UI，可以看到当前训练过程中的作业执行情况。

《Tensorflow on Spark爬坑指南》 6.46.43.png

执行完后，检查hdsf，checkpoint目录, 可以看到模型的checkpoints已经上传到hdfs中。

hadoop fs -ls /user/root/mnist_model
Found 8 items
-rwxr-xr-x   3 root hdfs        179 2017-03-21 18:53 /user/root/mnist_model/checkpoint
-rwxr-xr-x   3 root hdfs     117453 2017-03-21 18:53 /user/root/mnist_model/graph.pbtxt
-rwxr-xr-x   3 root hdfs     814164 2017-03-21 18:53 /user/root/mnist_model/model.ckpt-0.data-00000-of-00001
-rwxr-xr-x   3 root hdfs        372 2017-03-21 18:53 /user/root/mnist_model/model.ckpt-0.index
-rwxr-xr-x   3 root hdfs      45557 2017-03-21 18:53 /user/root/mnist_model/model.ckpt-0.meta
-rwxr-xr-x   3 root hdfs     814164 2017-03-21 18:53 /user/root/mnist_model/model.ckpt-338.data-00000-of-00001
-rwxr-xr-x   3 root hdfs        372 2017-03-21 18:53 /user/root/mnist_model/model.ckpt-338.index
-rwxr-xr-x   3 root hdfs      45557 2017-03-21 18:53 /user/root/mnist_model/model.ckpt-338.meta
  • 根据训练的结果，执行模型inference
spark-submit \
--master yarn \
--deploy-mode cluster \
--queue ${QUEUE} \
--num-executors 4 \
--executor-memory 1G \
--py-files /data2/tesorflowonSpark/TensorFlowOnSpark/tfspark.zip,/data2/tesorflowonSpark/TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--archives hdfs:///user/${USER}/Python.zip#Python \
--conf spark.executorEnv.LD_LIBRARY_PATH="/usr/jdk64/jdk1.8.0_77/jre/lib/amd64/server/" \
/data2/tesorflowonSpark/TensorFlowOnSpark/examples/mnist/spark/mnist_spark.py \
--images mnist/csv/test/images \
--labels mnist/csv/test/labels \
--mode inference \
--model mnist_model \
--output predictions

等任务执行完成后，会发现，模型判断的结果已经输出到hdfs相关目录下了。

hadoop fs -ls /user/root/predictions
Found 11 items
-rw-r--r--   3 root hdfs          0 2017-03-21 19:16 /user/root/predictions/_SUCCESS
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00000
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00001
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00002
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00003
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00004
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00005
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00006
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00007
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00008
-rw-r--r--   3 root hdfs      51000 2017-03-21 19:16 /user/root/predictions/part-00009

查看其中的某一个文件，可看到里面保存的是测试集的标签和根据模型预测的结果。

# hadoop fs -cat  /user/root/predictions/part-00000
2017-03-21T19:16:40.795694 Label: 7, Prediction: 7
2017-03-21T19:16:40.795729 Label: 2, Prediction: 2
2017-03-21T19:16:40.795741 Label: 1, Prediction: 1
2017-03-21T19:16:40.795750 Label: 0, Prediction: 0
2017-03-21T19:16:40.795759 Label: 4, Prediction: 4
2017-03-21T19:16:40.795769 Label: 1, Prediction: 1
2017-03-21T19:16:40.795778 Label: 4, Prediction: 4
2017-03-21T19:16:40.795787 Label: 9, Prediction: 9
2017-03-21T19:16:40.795796 Label: 5, Prediction: 6
2017-03-21T19:16:40.795805 Label: 9, Prediction: 9
2017-03-21T19:16:40.795814 Label: 0, Prediction: 0
2017-03-21T19:16:40.795822 Label: 6, Prediction: 6
2017-03-21T19:16:40.795831 Label: 9, Prediction: 9
2017-03-21T19:16:40.795840 Label: 0, Prediction: 0
2017-03-21T19:16:40.795848 Label: 1, Prediction: 1
2017-03-21T19:16:40.795857 Label: 5, Prediction: 5
2017-03-21T19:16:40.795866 Label: 9, Prediction: 9
2017-03-21T19:16:40.795875 Label: 7, Prediction: 7
2017-03-21T19:16:40.795883 Label: 3, Prediction: 3
2017-03-21T19:16:40.795892 Label: 4, Prediction: 4
2017-03-21T19:16:40.795901 Label: 9, Prediction: 9
2017-03-21T19:16:40.795909 Label: 6, Prediction: 6
2017-03-21T19:16:40.795918 Label: 6, Prediction: 6
  • Spark集群和tensorflow job task的对应关系，如下图，spark集群起了4个executor，其中一个作为PS, 另外3个作为worker，而谁做ps谁做worker是由Yarn和spark调度的。

《Tensorflow on Spark爬坑指南》 7.22.23.png

 Cluster spec: {'ps': ['ochadoop02:50060'], 'worker': ['ochadoop04:52150', 'ochadoop03:52733', 'ochadoop04:33289']}
    原文作者：biggeng
    原文地址: https://www.jianshu.com/p/72cb5816a0f7
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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