diff --git a/develop/doc/_sources/design/cluster_train/data_dispatch.md.txt b/develop/doc/_sources/design/cluster_train/data_dispatch.md.txt index 9f2fc404a09627c6029208a0adf363070e520996..a3eb4e28db0782bbf88520d25023cf031e620a63 100644 --- a/develop/doc/_sources/design/cluster_train/data_dispatch.md.txt +++ b/develop/doc/_sources/design/cluster_train/data_dispatch.md.txt @@ -13,79 +13,108 @@ ### 训练数据的存储 -选择GlusterFS作为训练数据的存储服务(后续的实现考虑HDFS)。 +选择CephFS作为训练数据的存储服务。 在Kubernetes上运行的不同的计算框架,可以通过Volume或PersistentVolume挂载存储空间到每个容器中。 -在GlusterFS存储系统中的公开目录,需要保存一些预置的公开数据集(比如MNIST, BOW, imagenet数据集等),并且可以被提交的job直接使用。 +在CephFS存储系统中的公开目录,需要保存一些预置的公开数据集(比如MNIST, BOW, ImageNet数据集等),并且可以被提交的job直接使用。 -### 上传训练文件 +### 文件预处理 -使用下面命令,可以把本地的训练数据上传到存储集群中,并指定上传数据的`dataset-name`: +在数据集可以被训练之前,文件需要预先被转换成PaddlePaddle集群内部的存储格式(SSTable)。我们提供两个转换方式: -``` -paddle upload train_data.list "dataset-name" -``` +- 提供给用户本地转换的库,用户可以编写程序完成转换。 +- 用户可以上传自己的数据集,在集群运行MapReduce job完成转换。 -其中`.list`文件描述了训练数据的文件和对应的label,对于图像类数据,`.list文件`样例如下,每一行包含了图片文件的路径和其label(用tab分隔开): +转换生成的文件名会是以下格式: -``` -./data/image1.jpg 1 -./data/image2.jpg 5 -./data/image3.jpg 2 -./data/image4.jpg 5 -./data/image5.jpg 1 -./data/image6.jpg 8 -... +```text +name_prefix-aaaaa-of-bbbbb ``` -对于文本类训练数据样例如下(机器翻译),一行中包含源语言,目标语言的文本(label): +"aaaaa"和"bbbbb"都是五位的数字,每一个文件是数据集的一个shard,"aaaaa"代表shard的index,"bbbbb"代表这个shard的最大index。 +比如ImageNet这个数据集可能被分成1000个shard,它们的文件名是: +```text +imagenet-00000-of-00999 +imagenet-00001-of-00999 +... +imagenet-00999-of-00999 ``` -L' inflation , en Europe , a dérapé sur l' alimentation Food : Where European inflation slipped up -L' inflation accélérée , mesurée dans la zone euro , est due principalement à l' augmentation rapide des prix de l' alimentation . The skyward zoom in food prices is the dominant force behind the speed up in eurozone inflation . -... +#### 转换库 + +无论是在本地或是云端转换,我们都提供Python的转换库,接口是: +```python +def convert(output_path, reader, num_shards, name_prefix) ``` -### 使用reader +- `output_path`: directory in which output files will be saved. +- `reader`: a [data reader](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md#data-reader-interface), from which the convert program will read data instances. +- `num_shards`: the number of shards that the dataset will be partitioned into. +- `name_prefix`: the name prefix of generated files. -用户在使用v2 API编写训练任务时,可以使用paddle内置的reader完成对GlusterFS存储中的训练数据的读取,返回文件中的各列,然后在调用`trainer.train()`时传入,完成训练数据的读取: +`reader`每次输出一个data instance,这个instance可以是单个值,或者用tuple表示的多个值: ```python -reader = paddle.dist.reader("dataset-name") -trainer.train(reader, ...) -batch_reader = paddle.batch(paddle.dataset.mnist.train(), 128) -trainer.train(batch_reader, ...) +yield 1 # 单个值 +yield numpy.random.uniform(-1, 1, size=28*28) # 单个值 +yield numpy.random.uniform(-1, 1, size=28*28), 0 # 多个值 ``` -trainer.train内部会获取reader的内容: +每个值的类型可以是整形、浮点型数据、字符串,或者由它们组成的list,以及numpy.ndarray。如果是其它类型,会被Pickle序列化成字符串。 -``` -def paddle.train(batch_reader): - r = batch_reader() # create a iterator for one pass of data - for batch in r: - # train +### 示例程序 + +#### 使用转换库 + +以下`reader_creator`生成的`reader`每次输出一个data instance,每个data instance包涵两个值:numpy.ndarray类型的值和整型的值: +```python +def reader_creator(): + def reader(): + for i in range(1000): + yield numpy.random.uniform(-1, 1, size=28*28), 0 # 多个值 + return reader ``` -这里面batch是含有128个data instance的mini-batch。每一个data instance会是一个tuple,tuple元素的顺序与`.list`文件文件中每一列的顺序是一致的。每一个data instance会是(raw_image_file_binary_data, label)。其中raw_image_file_binary_data是对应图像文件的没有解码的原始二进制数据,用户需要自己解码。label是文本类型(比如:“1“,”2“),这里用户需要的其实是整形,用户需要自己转换成整形。 +把`reader_creator`生成的`reader`传入`convert`函数即可完成转换: +```python +convert("./", reader_creator(), 100, random_images) +``` -### 实现reader +以上命令会在当前目录下生成100个文件: +```text +random_images-00000-of-00099 +random_images-00001-of-00099 +... +random_images-00099-of-00099 +``` -reader的实现需要考虑本地训练程序实现之后,可以不修改程序直接提交集群进行分布式训练。要达到这样的目标,需要实现下面的功能: +#### 进行训练 -paddle会封装一个在集群中使用的reader: `paddle.dist.reader()`。在集群训练时需要使用这个reader指定要使用的数据集开始训练。用户的训练程序需要按照如下方式初始化reader: +PaddlePaddle提供专用的[data reader creator](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md#python-data-reader-design-doc),生成给定SSTable文件对应的data reader。**无论在本地还是在云端,reader的使用方式都是一致的**: ```python -if os.getenv("PADDLE_TRAIN_LOCAL"): - reader = my_local_reader("dataset-name") -else: - reader = paddle.dist.reader("dataset-name") +# ... +reader = paddle.reader.creator.SSTable("/home/random_images-*-of-*") +batch_reader = paddle.batch(paddle.dataset.mnist.train(), 128) +trainer.train(batch_reader, ...) ``` -用户训练程序提交到集群之后,集群会自动设置`PADDLE_TRAIN_LOCAL`环境变量,reader会被配置成集群训练的版本。其中`paddle.dist.reader()`需要从master的队列中获得需要开始执行的训练task,并找到对应的训练数据文件,开始训练任务。如果用户的训练数据源来自于其他服务,比如从集群中的Kafka,zeromq队列读取,也可以根据实际情况实现集群中运行的reader程序。 +以上代码的reader输出的data instance与生成数据集时,reader输出的data instance是一模一样的。 +### 上传训练文件 + +使用下面命令,可以把本地的数据上传到存储集群中。 + +```bash +paddle cp filenames pfs://home/folder/ +``` + +比如,把之前示例中转换完毕的random_images数据集上传到云端的`/home/`可以用以下指令: +```bash +paddle cp random_images-*-of-* pfs://home/ +``` ## TODO -### 支持将数据合并成内部的文件格式(key-value),方便sharding与顺序读取 ### 支持用户自定义的数据预处理job diff --git a/develop/doc/design/cluster_train/data_dispatch.html b/develop/doc/design/cluster_train/data_dispatch.html index 2317caf6751a82f517ab9ce75a8d5c1237018e90..68ebb27a33a87be0b27b1a61dca4b25888331eab 100644 --- a/develop/doc/design/cluster_train/data_dispatch.html +++ b/develop/doc/design/cluster_train/data_dispatch.html @@ -175,13 +175,19 @@
  • 训练数据的存储和分发
  • TODO
  • @@ -227,70 +233,100 @@

    训练数据的存储

    -

    选择GlusterFS作为训练数据的存储服务(后续的实现考虑HDFS)。

    +

    选择CephFS作为训练数据的存储服务。

    在Kubernetes上运行的不同的计算框架,可以通过Volume或PersistentVolume挂载存储空间到每个容器中。

    -

    在GlusterFS存储系统中的公开目录,需要保存一些预置的公开数据集(比如MNIST, BOW, imagenet数据集等),并且可以被提交的job直接使用。

    +

    在CephFS存储系统中的公开目录,需要保存一些预置的公开数据集(比如MNIST, BOW, ImageNet数据集等),并且可以被提交的job直接使用。

    -

    上传训练文件

    -

    使用下面命令,可以把本地的训练数据上传到存储集群中,并指定上传数据的dataset-name

    -
    paddle upload train_data.list "dataset-name"
    +

    文件预处理

    +

    在数据集可以被训练之前,文件需要预先被转换成PaddlePaddle集群内部的存储格式(SSTable)。我们提供两个转换方式:

    +
      +
    • 提供给用户本地转换的库,用户可以编写程序完成转换。
    • +
    • 用户可以上传自己的数据集,在集群运行MapReduce job完成转换。
    • +
    +

    转换生成的文件名会是以下格式:

    +
    name_prefix-aaaaa-of-bbbbb
     
    -

    其中.list文件描述了训练数据的文件和对应的label,对于图像类数据,.list文件样例如下,每一行包含了图片文件的路径和其label(用tab分隔开):

    -
    ./data/image1.jpg   1
    -./data/image2.jpg   5
    -./data/image3.jpg   2
    -./data/image4.jpg   5
    -./data/image5.jpg   1
    -./data/image6.jpg   8
    -...
    +

    “aaaaa”和”bbbbb”都是五位的数字,每一个文件是数据集的一个shard,”aaaaa”代表shard的index,”bbbbb”代表这个shard的最大index。

    +

    比如ImageNet这个数据集可能被分成1000个shard,它们的文件名是:

    +
    imagenet-00000-of-00999
    +imagenet-00001-of-00999
    +...
    +imagenet-00999-of-00999
     
    -

    对于文本类训练数据样例如下(机器翻译),一行中包含源语言,目标语言的文本(label):

    -
    L' inflation , en Europe , a dérapé sur l' alimentation   Food : Where European inflation slipped up
    -
    -L' inflation accélérée , mesurée dans la zone euro , est due principalement à l' augmentation rapide des prix de l' alimentation .   The skyward zoom in food prices is the dominant force behind the speed up in eurozone inflation .
    -...
    +
    +

    转换库

    +

    无论是在本地或是云端转换,我们都提供Python的转换库,接口是:

    +
    def convert(output_path, reader, num_shards, name_prefix)
    +
    +
    +
      +
    • output_path: directory in which output files will be saved.
    • +
    • reader: a data reader, from which the convert program will read data instances.
    • +
    • num_shards: the number of shards that the dataset will be partitioned into.
    • +
    • name_prefix: the name prefix of generated files.
    • +
    +

    reader每次输出一个data instance,这个instance可以是单个值,或者用tuple表示的多个值:

    +
    yield 1 # 单个值
    +yield numpy.random.uniform(-1, 1, size=28*28) # 单个值
    +yield numpy.random.uniform(-1, 1, size=28*28), 0 # 多个值
    +
    +
    +

    每个值的类型可以是整形、浮点型数据、字符串,或者由它们组成的list,以及numpy.ndarray。如果是其它类型,会被Pickle序列化成字符串。

    +
    +
    +
    +

    示例程序

    +
    +

    使用转换库

    +

    以下reader_creator生成的reader每次输出一个data instance,每个data instance包涵两个值:numpy.ndarray类型的值和整型的值:

    +
    def reader_creator():
    +    def reader():
    +        for i in range(1000):
    +            yield numpy.random.uniform(-1, 1, size=28*28), 0 # 多个值
    +    return reader
    +
    +
    +

    reader_creator生成的reader传入convert函数即可完成转换:

    +
    convert("./", reader_creator(), 100, random_images)
    +
    +
    +

    以上命令会在当前目录下生成100个文件:

    +
    random_images-00000-of-00099
    +random_images-00001-of-00099
    +...
    +random_images-00099-of-00099
     
    -
    -

    使用reader

    -

    用户在使用v2 API编写训练任务时,可以使用paddle内置的reader完成对GlusterFS存储中的训练数据的读取,返回文件中的各列,然后在调用trainer.train()时传入,完成训练数据的读取:

    -
    reader = paddle.dist.reader("dataset-name")
    -trainer.train(reader, ...)
    +
    +

    进行训练

    +

    PaddlePaddle提供专用的data reader creator,生成给定SSTable文件对应的data reader。无论在本地还是在云端,reader的使用方式都是一致的

    +
    # ...
    +reader = paddle.reader.creator.SSTable("/home/random_images-*-of-*")
     batch_reader = paddle.batch(paddle.dataset.mnist.train(), 128)
     trainer.train(batch_reader, ...)
     
    -

    trainer.train内部会获取reader的内容:

    -
    def paddle.train(batch_reader):
    -  r = batch_reader() # create a iterator for one pass of data
    -  for batch in r:
    -    # train
    -
    +

    以上代码的reader输出的data instance与生成数据集时,reader输出的data instance是一模一样的。

    -

    这里面batch是含有128个data instance的mini-batch。每一个data instance会是一个tuple,tuple元素的顺序与.list文件文件中每一列的顺序是一致的。每一个data instance会是(raw_image_file_binary_data, label)。其中raw_image_file_binary_data是对应图像文件的没有解码的原始二进制数据,用户需要自己解码。label是文本类型(比如:“1“,”2“),这里用户需要的其实是整形,用户需要自己转换成整形。

    -
    -

    实现reader

    -

    reader的实现需要考虑本地训练程序实现之后,可以不修改程序直接提交集群进行分布式训练。要达到这样的目标,需要实现下面的功能:

    -

    paddle会封装一个在集群中使用的reader: paddle.dist.reader()。在集群训练时需要使用这个reader指定要使用的数据集开始训练。用户的训练程序需要按照如下方式初始化reader:

    -
    if os.getenv("PADDLE_TRAIN_LOCAL"):
    -  reader = my_local_reader("dataset-name")
    -else:
    -  reader = paddle.dist.reader("dataset-name")
    +
    +

    上传训练文件

    +

    使用下面命令,可以把本地的数据上传到存储集群中。

    +
    paddle cp filenames pfs://home/folder/
    +
    +
    +

    比如,把之前示例中转换完毕的random_images数据集上传到云端的/home/可以用以下指令:

    +
    paddle cp random_images-*-of-* pfs://home/
     
    -

    用户训练程序提交到集群之后,集群会自动设置PADDLE_TRAIN_LOCAL环境变量,reader会被配置成集群训练的版本。其中paddle.dist.reader()需要从master的队列中获得需要开始执行的训练task,并找到对应的训练数据文件,开始训练任务。如果用户的训练数据源来自于其他服务,比如从集群中的Kafka,zeromq队列读取,也可以根据实际情况实现集群中运行的reader程序。

    TODO

    -
    -

    支持将数据合并成内部的文件格式(key-value),方便sharding与顺序读取

    -

    支持用户自定义的数据预处理job

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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/cluster_train/data_dispatch.md.txt b/develop/doc_cn/_sources/design/cluster_train/data_dispatch.md.txt index 9f2fc404a09627c6029208a0adf363070e520996..a3eb4e28db0782bbf88520d25023cf031e620a63 100644 --- a/develop/doc_cn/_sources/design/cluster_train/data_dispatch.md.txt +++ b/develop/doc_cn/_sources/design/cluster_train/data_dispatch.md.txt @@ -13,79 +13,108 @@ ### 训练数据的存储 -选择GlusterFS作为训练数据的存储服务(后续的实现考虑HDFS)。 +选择CephFS作为训练数据的存储服务。 在Kubernetes上运行的不同的计算框架,可以通过Volume或PersistentVolume挂载存储空间到每个容器中。 -在GlusterFS存储系统中的公开目录,需要保存一些预置的公开数据集(比如MNIST, BOW, imagenet数据集等),并且可以被提交的job直接使用。 +在CephFS存储系统中的公开目录,需要保存一些预置的公开数据集(比如MNIST, BOW, ImageNet数据集等),并且可以被提交的job直接使用。 -### 上传训练文件 +### 文件预处理 -使用下面命令,可以把本地的训练数据上传到存储集群中,并指定上传数据的`dataset-name`: +在数据集可以被训练之前,文件需要预先被转换成PaddlePaddle集群内部的存储格式(SSTable)。我们提供两个转换方式: -``` -paddle upload train_data.list "dataset-name" -``` +- 提供给用户本地转换的库,用户可以编写程序完成转换。 +- 用户可以上传自己的数据集,在集群运行MapReduce job完成转换。 -其中`.list`文件描述了训练数据的文件和对应的label,对于图像类数据,`.list文件`样例如下,每一行包含了图片文件的路径和其label(用tab分隔开): +转换生成的文件名会是以下格式: -``` -./data/image1.jpg 1 -./data/image2.jpg 5 -./data/image3.jpg 2 -./data/image4.jpg 5 -./data/image5.jpg 1 -./data/image6.jpg 8 -... +```text +name_prefix-aaaaa-of-bbbbb ``` -对于文本类训练数据样例如下(机器翻译),一行中包含源语言,目标语言的文本(label): +"aaaaa"和"bbbbb"都是五位的数字,每一个文件是数据集的一个shard,"aaaaa"代表shard的index,"bbbbb"代表这个shard的最大index。 +比如ImageNet这个数据集可能被分成1000个shard,它们的文件名是: +```text +imagenet-00000-of-00999 +imagenet-00001-of-00999 +... +imagenet-00999-of-00999 ``` -L' inflation , en Europe , a dérapé sur l' alimentation Food : Where European inflation slipped up -L' inflation accélérée , mesurée dans la zone euro , est due principalement à l' augmentation rapide des prix de l' alimentation . The skyward zoom in food prices is the dominant force behind the speed up in eurozone inflation . -... +#### 转换库 + +无论是在本地或是云端转换,我们都提供Python的转换库,接口是: +```python +def convert(output_path, reader, num_shards, name_prefix) ``` -### 使用reader +- `output_path`: directory in which output files will be saved. +- `reader`: a [data reader](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md#data-reader-interface), from which the convert program will read data instances. +- `num_shards`: the number of shards that the dataset will be partitioned into. +- `name_prefix`: the name prefix of generated files. -用户在使用v2 API编写训练任务时,可以使用paddle内置的reader完成对GlusterFS存储中的训练数据的读取,返回文件中的各列,然后在调用`trainer.train()`时传入,完成训练数据的读取: +`reader`每次输出一个data instance,这个instance可以是单个值,或者用tuple表示的多个值: ```python -reader = paddle.dist.reader("dataset-name") -trainer.train(reader, ...) -batch_reader = paddle.batch(paddle.dataset.mnist.train(), 128) -trainer.train(batch_reader, ...) +yield 1 # 单个值 +yield numpy.random.uniform(-1, 1, size=28*28) # 单个值 +yield numpy.random.uniform(-1, 1, size=28*28), 0 # 多个值 ``` -trainer.train内部会获取reader的内容: +每个值的类型可以是整形、浮点型数据、字符串,或者由它们组成的list,以及numpy.ndarray。如果是其它类型,会被Pickle序列化成字符串。 -``` -def paddle.train(batch_reader): - r = batch_reader() # create a iterator for one pass of data - for batch in r: - # train +### 示例程序 + +#### 使用转换库 + +以下`reader_creator`生成的`reader`每次输出一个data instance,每个data instance包涵两个值:numpy.ndarray类型的值和整型的值: +```python +def reader_creator(): + def reader(): + for i in range(1000): + yield numpy.random.uniform(-1, 1, size=28*28), 0 # 多个值 + return reader ``` -这里面batch是含有128个data instance的mini-batch。每一个data instance会是一个tuple,tuple元素的顺序与`.list`文件文件中每一列的顺序是一致的。每一个data instance会是(raw_image_file_binary_data, label)。其中raw_image_file_binary_data是对应图像文件的没有解码的原始二进制数据,用户需要自己解码。label是文本类型(比如:“1“,”2“),这里用户需要的其实是整形,用户需要自己转换成整形。 +把`reader_creator`生成的`reader`传入`convert`函数即可完成转换: +```python +convert("./", reader_creator(), 100, random_images) +``` -### 实现reader +以上命令会在当前目录下生成100个文件: +```text +random_images-00000-of-00099 +random_images-00001-of-00099 +... +random_images-00099-of-00099 +``` -reader的实现需要考虑本地训练程序实现之后,可以不修改程序直接提交集群进行分布式训练。要达到这样的目标,需要实现下面的功能: +#### 进行训练 -paddle会封装一个在集群中使用的reader: `paddle.dist.reader()`。在集群训练时需要使用这个reader指定要使用的数据集开始训练。用户的训练程序需要按照如下方式初始化reader: +PaddlePaddle提供专用的[data reader creator](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md#python-data-reader-design-doc),生成给定SSTable文件对应的data reader。**无论在本地还是在云端,reader的使用方式都是一致的**: ```python -if os.getenv("PADDLE_TRAIN_LOCAL"): - reader = my_local_reader("dataset-name") -else: - reader = paddle.dist.reader("dataset-name") +# ... +reader = paddle.reader.creator.SSTable("/home/random_images-*-of-*") +batch_reader = paddle.batch(paddle.dataset.mnist.train(), 128) +trainer.train(batch_reader, ...) ``` -用户训练程序提交到集群之后,集群会自动设置`PADDLE_TRAIN_LOCAL`环境变量,reader会被配置成集群训练的版本。其中`paddle.dist.reader()`需要从master的队列中获得需要开始执行的训练task,并找到对应的训练数据文件,开始训练任务。如果用户的训练数据源来自于其他服务,比如从集群中的Kafka,zeromq队列读取,也可以根据实际情况实现集群中运行的reader程序。 +以上代码的reader输出的data instance与生成数据集时,reader输出的data instance是一模一样的。 +### 上传训练文件 + +使用下面命令,可以把本地的数据上传到存储集群中。 + +```bash +paddle cp filenames pfs://home/folder/ +``` + +比如,把之前示例中转换完毕的random_images数据集上传到云端的`/home/`可以用以下指令: +```bash +paddle cp random_images-*-of-* pfs://home/ +``` ## TODO -### 支持将数据合并成内部的文件格式(key-value),方便sharding与顺序读取 ### 支持用户自定义的数据预处理job diff --git a/develop/doc_cn/design/cluster_train/data_dispatch.html b/develop/doc_cn/design/cluster_train/data_dispatch.html index 12488230322993b2f44a8296ef5a9fbee0440435..ec87285907b955ec8940e6b7bdda793823eb42d0 100644 --- a/develop/doc_cn/design/cluster_train/data_dispatch.html +++ b/develop/doc_cn/design/cluster_train/data_dispatch.html @@ -179,13 +179,19 @@
  • 训练数据的存储和分发
  • TODO
  • @@ -231,70 +237,100 @@

    训练数据的存储

    -

    选择GlusterFS作为训练数据的存储服务(后续的实现考虑HDFS)。

    +

    选择CephFS作为训练数据的存储服务。

    在Kubernetes上运行的不同的计算框架,可以通过Volume或PersistentVolume挂载存储空间到每个容器中。

    -

    在GlusterFS存储系统中的公开目录,需要保存一些预置的公开数据集(比如MNIST, BOW, imagenet数据集等),并且可以被提交的job直接使用。

    +

    在CephFS存储系统中的公开目录,需要保存一些预置的公开数据集(比如MNIST, BOW, ImageNet数据集等),并且可以被提交的job直接使用。

    -

    上传训练文件

    -

    使用下面命令,可以把本地的训练数据上传到存储集群中,并指定上传数据的dataset-name

    -
    paddle upload train_data.list "dataset-name"
    +

    文件预处理

    +

    在数据集可以被训练之前,文件需要预先被转换成PaddlePaddle集群内部的存储格式(SSTable)。我们提供两个转换方式:

    +
      +
    • 提供给用户本地转换的库,用户可以编写程序完成转换。
    • +
    • 用户可以上传自己的数据集,在集群运行MapReduce job完成转换。
    • +
    +

    转换生成的文件名会是以下格式:

    +
    name_prefix-aaaaa-of-bbbbb
     
    -

    其中.list文件描述了训练数据的文件和对应的label,对于图像类数据,.list文件样例如下,每一行包含了图片文件的路径和其label(用tab分隔开):

    -
    ./data/image1.jpg   1
    -./data/image2.jpg   5
    -./data/image3.jpg   2
    -./data/image4.jpg   5
    -./data/image5.jpg   1
    -./data/image6.jpg   8
    -...
    +

    “aaaaa”和”bbbbb”都是五位的数字,每一个文件是数据集的一个shard,”aaaaa”代表shard的index,”bbbbb”代表这个shard的最大index。

    +

    比如ImageNet这个数据集可能被分成1000个shard,它们的文件名是:

    +
    imagenet-00000-of-00999
    +imagenet-00001-of-00999
    +...
    +imagenet-00999-of-00999
     
    -

    对于文本类训练数据样例如下(机器翻译),一行中包含源语言,目标语言的文本(label):

    -
    L&apos; inflation , en Europe , a dérapé sur l&apos; alimentation   Food : Where European inflation slipped up
    -
    -L&apos; inflation accélérée , mesurée dans la zone euro , est due principalement à l&apos; augmentation rapide des prix de l&apos; alimentation .   The skyward zoom in food prices is the dominant force behind the speed up in eurozone inflation .
    -...
    +
    +

    转换库

    +

    无论是在本地或是云端转换,我们都提供Python的转换库,接口是:

    +
    def convert(output_path, reader, num_shards, name_prefix)
    +
    +
    +
      +
    • output_path: directory in which output files will be saved.
    • +
    • reader: a data reader, from which the convert program will read data instances.
    • +
    • num_shards: the number of shards that the dataset will be partitioned into.
    • +
    • name_prefix: the name prefix of generated files.
    • +
    +

    reader每次输出一个data instance,这个instance可以是单个值,或者用tuple表示的多个值:

    +
    yield 1 # 单个值
    +yield numpy.random.uniform(-1, 1, size=28*28) # 单个值
    +yield numpy.random.uniform(-1, 1, size=28*28), 0 # 多个值
    +
    +
    +

    每个值的类型可以是整形、浮点型数据、字符串,或者由它们组成的list,以及numpy.ndarray。如果是其它类型,会被Pickle序列化成字符串。

    +
    +
    +
    +

    示例程序

    +
    +

    使用转换库

    +

    以下reader_creator生成的reader每次输出一个data instance,每个data instance包涵两个值:numpy.ndarray类型的值和整型的值:

    +
    def reader_creator():
    +    def reader():
    +        for i in range(1000):
    +            yield numpy.random.uniform(-1, 1, size=28*28), 0 # 多个值
    +    return reader
    +
    +
    +

    reader_creator生成的reader传入convert函数即可完成转换:

    +
    convert("./", reader_creator(), 100, random_images)
    +
    +
    +

    以上命令会在当前目录下生成100个文件:

    +
    random_images-00000-of-00099
    +random_images-00001-of-00099
    +...
    +random_images-00099-of-00099
     
    -
    -

    使用reader

    -

    用户在使用v2 API编写训练任务时,可以使用paddle内置的reader完成对GlusterFS存储中的训练数据的读取,返回文件中的各列,然后在调用trainer.train()时传入,完成训练数据的读取:

    -
    reader = paddle.dist.reader("dataset-name")
    -trainer.train(reader, ...)
    +
    +

    进行训练

    +

    PaddlePaddle提供专用的data reader creator,生成给定SSTable文件对应的data reader。无论在本地还是在云端,reader的使用方式都是一致的

    +
    # ...
    +reader = paddle.reader.creator.SSTable("/home/random_images-*-of-*")
     batch_reader = paddle.batch(paddle.dataset.mnist.train(), 128)
     trainer.train(batch_reader, ...)
     
    -

    trainer.train内部会获取reader的内容:

    -
    def paddle.train(batch_reader):
    -  r = batch_reader() # create a iterator for one pass of data
    -  for batch in r:
    -    # train
    -
    +

    以上代码的reader输出的data instance与生成数据集时,reader输出的data instance是一模一样的。

    -

    这里面batch是含有128个data instance的mini-batch。每一个data instance会是一个tuple,tuple元素的顺序与.list文件文件中每一列的顺序是一致的。每一个data instance会是(raw_image_file_binary_data, label)。其中raw_image_file_binary_data是对应图像文件的没有解码的原始二进制数据,用户需要自己解码。label是文本类型(比如:“1“,”2“),这里用户需要的其实是整形,用户需要自己转换成整形。

    -
    -

    实现reader

    -

    reader的实现需要考虑本地训练程序实现之后,可以不修改程序直接提交集群进行分布式训练。要达到这样的目标,需要实现下面的功能:

    -

    paddle会封装一个在集群中使用的reader: paddle.dist.reader()。在集群训练时需要使用这个reader指定要使用的数据集开始训练。用户的训练程序需要按照如下方式初始化reader:

    -
    if os.getenv("PADDLE_TRAIN_LOCAL"):
    -  reader = my_local_reader("dataset-name")
    -else:
    -  reader = paddle.dist.reader("dataset-name")
    +
    +

    上传训练文件

    +

    使用下面命令,可以把本地的数据上传到存储集群中。

    +
    paddle cp filenames pfs://home/folder/
    +
    +
    +

    比如,把之前示例中转换完毕的random_images数据集上传到云端的/home/可以用以下指令:

    +
    paddle cp random_images-*-of-* pfs://home/
     
    -

    用户训练程序提交到集群之后,集群会自动设置PADDLE_TRAIN_LOCAL环境变量,reader会被配置成集群训练的版本。其中paddle.dist.reader()需要从master的队列中获得需要开始执行的训练task,并找到对应的训练数据文件,开始训练任务。如果用户的训练数据源来自于其他服务,比如从集群中的Kafka,zeromq队列读取,也可以根据实际情况实现集群中运行的reader程序。

    TODO

    -
    -

    支持将数据合并成内部的文件格式(key-value),方便sharding与顺序读取

    -

    支持用户自定义的数据预处理job

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