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  • tensorflow

    https://github.com/aymericdamien/TensorFlow-Examples.git

    摘要: 利用Docker和阿里云容器服务轻松在本地和云端搭建Tensorflow的学习环境

    tensorflow_jpeg

    本系列将利用Docker和阿里云容器服务,帮助您上手TensorFlow的机器学习方案

    机器学习作为人工智能重要的技术,已经在计算机视觉、自然语言处理、医学诊断等等领域得到了广泛的应用。TensorFlow 是谷歌推出的开源的分布式机器学习框架,它也是Github社区上最受关注的机器学习项目,目前点赞已经超过3万个星。

    TensorFlow提供了多种安装方式,配置也相对简单,但是对于初学者而言,从零开始搭建一个TensorFlow学习环境依然具有一些挑战。幸运的是TensorFlow提供了基于Docker的部署方式,开发者可以快速上手。

    本文是系列中的第一篇文章,会基于Docker快速创建一个Tensorflow学习环境。

    准备Docker环境

    为了利用Docker和Docker Compose编排搭建实验环境,我们需要

    安装Docker for Mac/Windows 或在Linux上安装Docker和Docker Compose。可以使用阿里云提供Docker EngineDocker Toolbox的镜像网站

    本地环境搭建

    在GitHub上有很多Tensorflow的学习资料, 其中 https://github.com/aymericdamien/TensorFlow-Examples 是一个很好的教程。在文中提供了由浅入深的示例来介绍Tensorflow的功能。

    首先执行如下命令获得教程代码 (包含对Tensorflow 1.0 的支持)

    git clone https://github.com/denverdino/TensorFlow-Examples
    cd TensorFlow-Examples
    

    为了运行这个教程你需要安装Tensorflow的执行环境,并配置"jupyter", "tensorboard"来进行交互操作。

    一个最简单的方法是在当前目录,创建如下的docker-compose.yml模板

    version: '2'
    services:
      jupyter:
        image: registry.cn-hangzhou.aliyuncs.com/denverdino/tensorflow:1.0.0
        container_name: jupyter
        ports:
          - "8888:8888"
        environment:
          - PASSWORD=tensorflow
        volumes:
          - "/tmp/tensorflow_logs"
          - "./notebooks:/root/notebooks"
        command:
          - "/run_jupyter.sh"
          - "/root/notebooks"
      tensorboard:
        image: registry.cn-hangzhou.aliyuncs.com/denverdino/tensorflow:1.0.0
        container_name: tensorboard
        ports:
          - "6006:6006"
        volumes_from:
          - jupyter
        command:
          - "tensorboard"
          - "--logdir"
          - "/tmp/tensorflow_logs"
          - "--host"
          - "0.0.0.0"
    

    执行如下命令一键创建Tensorflow的学习环境

    docker-compose up -d 
    

    我们可以检查启动的Docker容器

    yili@yili-mbp:~/work/TensorFlow-Examples$ docker-compose ps
    
                  Name                            Command               State                Ports               
    ------------------------------------------------------------------------------------------------------------
    tensorflowexamples_jupyter_1       /run_jupyter.sh /root/note ...   Up      6006/tcp, 0.0.0.0:8888->8888/tcp 
    tensorflowexamples_tensorboard_1   tensorboard --logdir /tmp/ ...   Up      0.0.0.0:6006->6006/tcp, 8888/tcp 
    

    可以直接通过 http://127.0.0.1:8888/ 从浏览器中访问Tensorflow的Jupyter交互实验环境

    登录密码为: tensorflow

    14738178907665

    通过 http://127.0.0.1:6006 从浏览器中访问模型可视化工具TensorBoard
    注:可以运行 http://127.0.0.1:8888/notebooks/4_Utils/tensorboard_basic.ipynb 来实验Tensorboard的功能,示例中Tensorboard容器配置的log目录是 “/tmp/tensorflow_logs”。对于用户自己的notebook,可以参照tensorboard_basic在代码中设置log的输出路径。

    14738194651219

    注:

    • 其中registry.cn-hangzhou.aliyuncs.com/denverdino/tensorflow:1.0.0是基于tensorflow/tensorflow:1.0.0镜像构建的,只添加了apt源和pipy源的阿里云镜像。 大家也可以参照https://github.com/denverdino/tensorflow-docker中的Dockerfile自己构建,预先添加自己所需的python库、算法库等资源。
    • 利用volumes机制,jupyter可以直接从当前notebooks目录获取示例。jupyter和tensorboard两个容器也通过可以文件卷来共享事件日志。

    阿里云容器服务上体验

    阿里云容器服务支持Docker Compose模板部署,通过下面模板我们可以轻松把Tensorflow的学习环境部署到云端

    version: '2'
    services:
      jupyter:
        image: registry.cn-hangzhou.aliyuncs.com/denverdino/tensorflow-examples:1.0.0
        volumes:
          - "/tmp/tensorflow_logs"
        environment:
          - PASSWORD=tensorflow
        labels: 
          aliyun.routing.port_8888: jupyter
        command:
          - "/run_jupyter.sh"
          - "/root/notebooks"
      tensorboard:
        image: registry.cn-hangzhou.aliyuncs.com/denverdino/tensorflow:1.0.0
        labels: 
          aliyun.routing.port_6006: tensorboard
        volumes_from:
          - jupyter
        command:
          - "tensorboard"
          - "--logdir"
          - "/tmp/tensorflow_logs"
          - "--host"
          - "0.0.0.0"
    

    注:

    • 利用aliyun.routing标签,我们可以轻松定义Jupyter和TensorBoard的访问访问端点
    • 如果是老集群,需要点击容器服务agent升级来提供所需特性和稳定性增强。

    几分钟之后,我们就可以在云端有一个学习环境来体验Tensorflow。

    14738212612862

    14738177187646

    14738178032537

    14738178506257

    总结

    我们可以利用Docker和阿里云容器服务轻松在本地和云端搭建Tensorflow的学习环境。Docker作为一个标准化的软件交付手段,可以大大简化应用软件的部署和运维复杂度。阿里云容器服务支持以Docker Compose的方式进行容器编排,并提供了众多扩展,可以方便地支持基于容器的微服务应用的云端部署和管理。

    阿里云容器服务还会和高性能计算(HPC)团队一起配合,之后在阿里云上提供结合GPU加速和Docker集群管理的机器学习解决方案,在云端提升机器学习的效能。

    想了解更多容器服务内容,请访问 https://www.aliyun.com/product/containerservice

    https://yq.aliyun.com/articles/60601

    python convolutional.py
    Extracting data/train-images-idx3-ubyte.gz
    Extracting data/train-labels-idx1-ubyte.gz
    Extracting data/t10k-images-idx3-ubyte.gz
    Extracting data/t10k-labels-idx1-ubyte.gz
    I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 1
    I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 1
    Initialized!
    Epoch 0.00
    Minibatch loss: 12.053, learning rate: 0.010000
    Minibatch error: 90.6%
    W tensorflow/core/kernels/bias_op.cc:42] Resource exhausted: OOM when allocating tensor with shapedim { size: 5000 } dim { size: 28 } dim { size: 28 } dim { size: 32 }
    W tensorflow/core/common_runtime/executor.cc:1027] 0x591ac80 Compute status: Resource exhausted: OOM when allocating tensor with shapedim { size: 5000 } dim { size: 28 } dim { size: 28 } dim { size: 32 }
    [[Node: BiasAdd_2 = BiasAdd[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Conv2D_2, Variable_1)]]
    Traceback (most recent call last):
    File "convolutional.py", line 270, in <module>
    tf.app.run()
    File "/usr/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 11, in run
    sys.exit(main(sys.argv))
    File "convolutional.py", line 258, in main
    validation_prediction.eval(), validation_labels)
    File "/usr/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 405, in eval
    return _eval_using_default_session(self, feed_dict, self.graph, session)
    File "/usr/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2728, in _eval_using_default_session
    return session.run(tensors, feed_dict)
    File "/usr/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 345, in run
    results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
    File "/usr/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 419, in _do_run
    e.code)
    tensorflow.python.framework.errors.ResourceExhaustedError: OOM when allocating tensor with shapedim { size: 5000 } dim { size: 28 } dim { size: 28 } dim { size: 32 }
    [[Node: BiasAdd_2 = BiasAdd[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Conv2D_2, Variable_1)]]
    Caused by op u'BiasAdd_2', defined at:
    File "convolutional.py", line 270, in <module>
    tf.app.run()
    File "/usr/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 11, in run
    sys.exit(main(sys.argv))
    File "convolutional.py", line 229, in main
    validation_prediction = tf.nn.softmax(model(validation_data_node))
    File "convolutional.py", line 169, in model
    relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
    File "/usr/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 101, in bias_add
    return gen_nn_ops._bias_add(value, bias, name=name)
    File "/usr/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 163, in _bias_add
    return _op_def_lib.apply_op("BiasAdd", value=value, bias=bias, name=name)
    File "/usr/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 633, in apply_op
    op_def=op_def)
    File "/usr/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1710, in create_op
    original_op=self._default_original_op, op_def=op_def)
    File "/usr/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 988, in __init__
    self._traceback = _extract_stack()

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  • 原文地址:https://www.cnblogs.com/softidea/p/7719596.html
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