Hello_word_on_Sparkling.json

{"paragraphs":[{"text":"%md\n# 通过Analytics Zoo在Apache Spark上使用Keras/Tensorflow\nKeras/Tensorflow是受到广泛使用的机器学习库。在这个Notebook中，我们会看到如何通过Analytics Zoo，在Apache Spark上使用Keras/Tensorflow训练机器学习模型。\n\n## 准备数据\n我们使用MNIST数据。MNIST数据是一个面向初学者的手写数字识别数据集。从[MNIST官方网站](http://yann.lecun.com/exdb/mnist/)上下载数据。\n\n### 上传数据到COS\n[COS](https://cloud.tencent.com/product/cos)是腾讯云上的对象存储系统。我们将刚才下载的四个文件上传到[COS存储桶](https://console.cloud.tencent.com/cos5/bucket/setting?type=filelist&bucketName=ds-1257305158&path=%252F&region=ap-shanghai)中，进行下一步的处理。","user":"anonymous","dateUpdated":"2019-06-18T23:06:06+0800","config":{"editorSetting":{"language":"scala","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/scala","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h1>通过Analytics Zoo在Apache Spark上使用Keras/Tensorflow</h1>\n<p>Keras/Tensorflow是受到广泛使用的机器学习库。在这个Notebook中，我们会看到如何通过Analytics Zoo，在Apache Spark上使用Keras/Tensorflow训练机器学习模型。</p>\n<h2>准备数据</h2>\n<p>我们使用MNIST数据。MNIST数据是一个面向初学者的手写数字识别数据集。从<a href=\"http://yann.lecun.com/exdb/mnist/\">MNIST官方网站</a>上下载数据。</p>\n<h3>上传数据到COS</h3>\n<p><a href=\"https://cloud.tencent.com/product/cos\">COS</a>是腾讯云上的对象存储系统。我们将刚才下载的四个文件上传到<a href=\"https://console.cloud.tencent.com/cos5/bucket/setting?type=filelist&bucketName=ds-1257305158&path=%252F&region=ap-shanghai\">COS存储桶</a>中，进行下一步的处理。</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560870366060_-1836974232","id":"20190529-165353_1299083752","dateCreated":"2019-06-18T23:06:06+0800","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:3866"},{"text":"%pyspark\n\nfrom qcloud_cos import CosConfig\nfrom qcloud_cos import CosS3Client\nimport sys\n\nsecret_id = 'AKIDDIeljXg6rTAiZknLzNaXO3TghfNpadiP'      # 替换为用户的 secretId\nsecret_key = 'cy4uVsQjgT4QWNyUqKbdPvDGvKlwrW8d'      # 替换为用户的 secretKey\nregion = 'ap-shanghai'     # 替换为用户的 Region\ntoken = None                # 使用临时密钥需要传入 Token，默认为空，可不填\nscheme = 'https'            # 指定使用 http/https 协议来访问 COS，默认为 https，可不填\nconfig = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token, Scheme=scheme)\n# 2. 获取客户端对象\nclient = CosS3Client(config)","user":"anonymous","dateUpdated":"2019-06-18T23:20:15+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560870366063_2102991036","id":"20190530-151644_1017445007","dateCreated":"2019-06-18T23:06:06+0800","dateStarted":"2019-06-18T23:20:15+0800","dateFinished":"2019-06-18T23:20:15+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:3867"},{"text":"%md\n2.从COS下载MNIST的数据","user":"anonymous","dateUpdated":"2019-06-18T23:12:37+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>2.从COS下载MNIST的数据</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560870366063_-95990854","id":"20190530-152151_1242351499","dateCreated":"2019-06-18T23:06:06+0800","dateStarted":"2019-06-18T23:12:37+0800","dateFinished":"2019-06-18T23:12:39+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:3868"},{"text":"%pyspark\nfrom bigdl.dataset.mnist import extract_images,extract_labels\n\ndef get_mnist_from_cos():\n    def download_file(from_path, to_path=None):\n        response = client.get_object(\n            Bucket='demo-sh-1258997244',\n            Key=from_path\n        )\n        to_path = '/tmp/{}'.format(from_path.split(\"/\")[-1])\n        response['Body'].get_stream_to_file(to_path)\n        return to_path\n    with open(download_file(\"/mnist/train-images-idx3-ubyte.gz\"), 'rb') as f:\n        train_images = extract_images(f)\n        \n    with open( download_file('/mnist/train-labels-idx1-ubyte.gz'), 'rb') as f:\n        train_labels = extract_labels(f)\n        \n    with open( download_file('/mnist/t10k-images-idx3-ubyte.gz'), 'rb') as f:\n        test_images = extract_images(f)\n    \n    with open( download_file('/mnist/t10k-labels-idx1-ubyte.gz'), 'rb') as f:\n        test_labels = extract_labels(f)\n    \n    return train_images, train_labels, test_images, test_labels\n    \ntrain_images, train_labels, test_images, test_labels = get_mnist_from_cos()\n","user":"anonymous","dateUpdated":"2019-06-18T23:20:19+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Extracting /tmp/train-images-idx3-ubyte.gz\nExtracting /tmp/train-labels-idx1-ubyte.gz\nExtracting /tmp/t10k-images-idx3-ubyte.gz\nExtracting /tmp/t10k-labels-idx1-ubyte.gz\n"}]},"apps":[],"jobName":"paragraph_1560870366063_100940544","id":"20190610-143358_374957522","dateCreated":"2019-06-18T23:06:06+0800","dateStarted":"2019-06-18T23:20:19+0800","dateFinished":"2019-06-18T23:20:20+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:3869"},{"text":"%md\n4.将数据加载进Spark，并进行归一化","user":"anonymous","dateUpdated":"2019-06-18T23:06:06+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>4.将数据加载进Spark，并进行归一化</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560870366064_1087144257","id":"20190530-194255_299678698","dateCreated":"2019-06-18T23:06:06+0800","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:3870"},{"text":"%pyspark\nfrom bigdl.dataset import mnist\n\ntrain_image_rdd = sc.parallelize(train_images)\ntrain_label_rdd = sc.parallelize(train_labels)\ntest_image_rdd = sc.parallelize(test_images)\ntest_label_rdd = sc.parallelize(test_labels)\n\ntraining_rdd = train_image_rdd.zip(train_label_rdd).map(lambda rec_tuple: ((rec_tuple[0] - mnist.TRAIN_MEAN) / mnist.TRAIN_STD, np.array(rec_tuple[1])))\ntesting_rdd = test_image_rdd.zip(test_label_rdd).map(lambda rec_tuple: ((rec_tuple[0] - mnist.TRAIN_MEAN) / mnist.TRAIN_STD, np.array(rec_tuple[1])))","user":"anonymous","dateUpdated":"2019-06-18T23:20:23+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560870366064_1835093659","id":"20190530-194319_1762329728","dateCreated":"2019-06-18T23:06:06+0800","dateStarted":"2019-06-18T23:20:24+0800","dateFinished":"2019-06-18T23:20:24+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:3871"},{"text":"%md\n## 数据分析\n接下来可以对数据进行一些分析，例如不同类别的样本分布、显示出一些图片\n\n### 数据一览","user":"anonymous","dateUpdated":"2019-06-18T23:06:06+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>数据分析</h2>\n<p>接下来可以对数据进行一些分析，例如不同类别的样本分布、显示出一些图片</p>\n<h3>数据一览</h3>\n</div>"}]},"apps":[],"jobName":"paragraph_1560870366064_-932586876","id":"20190530-194328_2091442862","dateCreated":"2019-06-18T23:06:06+0800","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:3872"},{"text":"%pyspark\nimport numpy as np\nimport matplotlib\nimport io\n\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\ndef show(p):\n    img = io.StringIO()\n    p.savefig(img, format='svg')\n    img.seek(0)\n    print(\"%html <div style='width:600px'>\" + img.getvalue() + \"</div>\")\n\nplt.imshow(np.column_stack(train_images[0:10].reshape(10, 28,28)),cmap='gray')\n# plt.axis('off')\n\nshow(plt)\nprint(\"对应的标签为: \")\nprint(train_labels[0:10])","user":"anonymous","dateUpdated":"2019-06-18T23:20:28+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div style='width:600px'><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"345.6pt\" version=\"1.1\" viewBox=\"0 0 460.8 345.6\" width=\"460.8pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n 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class=\"markdown-body\">\n<h3>分类的种类</h3>\n</div>"}]},"apps":[],"jobName":"paragraph_1560870366065_-1058696580","id":"20190530-194351_136961622","dateCreated":"2019-06-18T23:06:06+0800","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:3874"},{"text":"%pyspark\nclasses = train_label_rdd.distinct().collect()\nclasses.sort()\nprint(classes)","user":"anonymous","dateUpdated":"2019-06-18T23:20:32+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web 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UI","group":"spark","values":["http://10.0.96.12:33819/jobs"],"interpreterSettingId":"spark"}},"apps":[],"jobName":"paragraph_1560870366066_459356038","id":"20190530-203059_1872142748","dateCreated":"2019-06-18T23:06:06+0800","dateStarted":"2019-06-18T23:21:53+0800","dateFinished":"2019-06-18T23:21:53+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:3877"},{"text":"%md\n## 模型训练\n1.通过Tensorflow的Keras API定义一个CNN模型","user":"anonymous","dateUpdated":"2019-06-18T23:06:06+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>模型训练</h2>\n<p>1.通过Tensorflow的Keras API定义一个CNN模型</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560870366066_1386052389","id":"20190530-203123_1350453704","dateCreated":"2019-06-18T23:06:06+0800","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:3878"},{"text":"%pyspark\nimport tensorflow as tf\n\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n    tf.keras.layers.Dense(64, activation='relu'),\n    tf.keras.layers.Dense(64, activation='relu'),\n    tf.keras.layers.Dense(10, activation='softmax'),\n])\n\nmodel.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])","user":"anonymous","dateUpdated":"2019-06-18T23:21:58+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560870366067_1244665371","id":"20190530-203138_2088625855","dateCreated":"2019-06-18T23:06:06+0800","dateStarted":"2019-06-18T23:21:58+0800","dateFinished":"2019-06-18T23:22:01+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:3879"},{"text":"%md\n2.通过Intel Analytics Zoo库将单机模型分布式，从而可以在Spark上进行分布式训练","user":"anonymous","dateUpdated":"2019-06-18T23:06:06+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>2.通过Intel Analytics Zoo库将单机模型分布式，从而可以在Spark上进行分布式训练</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560870366067_35621419","id":"20190530-203148_53726312","dateCreated":"2019-06-18T23:06:06+0800","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:3880"},{"text":"%pyspark\nfrom zoo.tfpark import KerasModel\n\nkeras_model = KerasModel(model)","user":"anonymous","dateUpdated":"2019-06-18T23:22:05+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560870366067_2050785492","id":"20190530-203203_1556135961","dateCreated":"2019-06-18T23:06:06+0800","dateStarted":"2019-06-18T23:22:05+0800","dateFinished":"2019-06-18T23:22:05+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:3881"},{"text":"%md\n3.开始训练模型","user":"anonymous","dateUpdated":"2019-06-18T23:06:06+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>3.开始训练模型</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560870366067_319505272","id":"20190530-203212_1959716136","dateCreated":"2019-06-18T23:06:06+0800","status":"READY","errorMessage":"","progressUpdateIntervalMs":500,"$$hashKey":"object:3882"},{"text":"%pyspark\nfrom zoo.tfpark import TFDataset\nfrom zoo import init_nncontext\nimport numpy as np\n\ninit_nncontext()\nmax_epoch = 5\n\ndataset = TFDataset.from_rdd(\n    training_rdd,\n    features=(tf.float32, [28, 28, 1]),\n    labels=(tf.int32, []),\n    batch_size=320,\n    val_rdd=testing_rdd\n)\n\n\nkeras_model.fit(dataset, epochs=max_epoch, 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`tf.compat.v1.graph_util.convert_variables_to_constants`\nWARNING:tensorflow:From /opt/sparkling/python-extra-lib/zoo/util/tf_graph_util.py:283: extract_sub_graph (from zoo.util.tf_graph_util) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse `tf.compat.v1.graph_util.extract_sub_graph`\ncreating: createTFTrainingHelper\ncreating: createTFValidationMethod\ncreating: createTFValidationMethod\ncreating: createIdentityCriterion\ncreating: createMaxEpoch\ncreating: createDistriOptimizer\ncreating: createEveryEpoch\ncreating: createMaxEpoch\n"}]},"runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web 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