Platform For AI:Use MaxCompute

Preparation: Configure account information

Before you use PAIIO to read data from or write data to MaxCompute tables, you must configure the AccessKey information used to access MaxCompute resources. PAI allows you to obtain the AccessKey information from a configuration file. To achieve this, you can store the configuration file in a file system and reference the information in your code by using environment variables.

1. Create a configuration file that contains the following content:

   access_id=xxxx
   access_key=xxxx
   end_point=http://xxxx

   Parameter	Description
   access_id	The AccessKey ID of the Alibaba Cloud account.
   access_key	The AccessKey secret of your Alibaba Cloud account.
   end_point	The endpoint of MaxCompute. For example, the endpoint for the China (Shanghai) region is http://service.cn-shanghai.maxcompute.aliyun.com/api. For more information, see Endpoints.

2. Use the following syntax to specify the path of the configuration file in the code:

   os.environ['ODPS_CONFIG_FILE_PATH'] = '<your MaxCompute config file path>'

   Set the <your MaxCompute config file path> parameter to the file path.

TableRecordDataset

Overview

Open source TensorFlow recommends that you use TensorFlow Datasets in TensorFlow 1.2 or later to replace the original threading and queuing interfaces to create data streams. Multiple Dataset interfaces are combined to generate data for computing. This simplifies data input code.

• Syntax (Python)

  class TableRecordDataset(Dataset):
    def __init__(self,
                 filenames,
                 record_defaults,
                 selected_cols=None,
                 excluded_cols=None,
                 slice_id=0,
                 slice_count=1,
                 num_threads=0,
                 capacity=0):

• Parameters

  Parameter	Required	Type	Default value	Description
  filenames	Yes	STRING	None	The names of the tables that you want to read. The tables must use the same schema. Table name format: odps://${your_projectname}/tables/${table_name}/${pt_1}/${pt_2}/....
  record_defaults	Yes	LIST or TUPLE	None	The data type of the column that you want to read. If the column is empty, this parameter indicates the default data type. If the data type is inconsistent with the data type of the column that you read or the data type cannot be automatically converted, the system throws an exception. Valid values: FLOAT32, FLOAT64, INT32, INT64, BOOL, and STRING. For information about the default values of the INT64 data type, use np.array(0, np.int64) syntax for query.
  selected_cols	No	STRING	None	The column that you want to select. Separate multiple columns with commas (,). If you set this parameter to the default value None, all columns are read. You can specify only one of selected_cols and excluded_cols.
  excluded_cols	No	STRING	None	The column that you want to exclude. Separate multiple columns with commas (,). If you set this parameter to the default value None, all columns are read. You can specify only one of selected_cols and excluded_cols.
  slice_id	No	INT	0	The ID of the shard in distributed read mode. The shard ID starts from 0. In distributed read mode, the table is divided into multiple shards based on the value of the slice_count parameter. The system reads data from the shard specified by the slice_id parameter. If slice_id is set to the default value 0 and slice_count is set to 1, the entire table is read. If slice_id is set to the default value 0 and slice_count is set to a value that is greater than 1, the 0th shard is read.
  slice_count	No	INT	1	The number of shards in distributed read mode. In most cases, the value is the number of workers. If you set this parameter to the default value 1, the entire table is read without sharding.
  num_threads	No	INT	0	The number of threads enabled by the built-in reader of each table to prefetch data. The threads are independent of calculating threads. Valid values: 1 to 64. If num_threads is set to 0, the system automatically assigns 25% of the calculating threads to prefetch data. Note I/O has different impacts on the overall computing performance of each model. As a result, the increase in the number of threads used to prefetch data does not necessarily improve the training speed of the overall model.
  capacity	No	INT	0	The number of records that are prefetched. If the value specified by num_threads is greater than 1, each thread prefetches capacity/num_threads data records. The parameter value is rounded up. If capacity is set to 0, the built-in reader configures the total size of data that the threads can prefetch based on the average value of the first N records in the table. The default value of N is 256. As a result, the size of data that each thread prefetches is approximately 64 MB.

  Note

  If the data type of fields in a MaxCompute table is DOUBLE, TensorFlow maps the data type to np.float64.

• Responses

  A Dataset object is returned, which can be used as the input to create pipelines.

Examples

import os
import tensorflow as tf
import paiio

# Specify the path of the configuration file. Replace the value with the path where the configuration file is stored. 
os.environ['ODPS_CONFIG_FILE_PATH'] = "/mnt/data/odps_config.ini"
# Specify the tables that you want to read. Replace ${your_projectname} with the name of the MaxCompute project and ${table_name} with the name of the table that you want to access. 
table = ["odps://${your_projectname}/tables/${table_name}"]
# Specify the TableRecordDataset interface to read the itemid and price columns of the table. 
dataset = paiio.data.TableRecordDataset(table,
                                       record_defaults=[0, 0.0],
                                       selected_cols="itemid,price",
                                       num_threads=1,
                                       capacity=10)
# Specify epoch 2, batch size 3, and prefetch 100 batch. 
dataset = dataset.repeat(2).batch(3).prefetch(100)

ids, prices = tf.compat.v1.data.make_one_shot_iterator(dataset).get_next()

with tf.compat.v1.Session() as sess:
    sess.run(tf.compat.v1.global_variables_initializer())
    sess.run(tf.compat.v1.local_variables_initializer())
    try:
        while True:
            batch_ids, batch_prices = sess.run([ids, prices])
            print("batch_ids:", batch_ids)
            print("batch_prices:", batch_prices)
    except tf.errors.OutOfRangeError:
        print("End of dataset")

TableReader

Overview

You can use the TableReader interface in a MaxCompute SDK without the need to rely on TensorFlow. This allows you to access MaxCompute tables and obtain real-time I/O results.

• Create a Reader object and open a table

  • Syntax

  reader = paiio.python_io.TableReader(table,
                       selected_cols="",
                      excluded_cols="",
                       slice_id=0,
                      slice_count=1):

  • Parameters

  Parameter	Required	Type	Default value	Description
  table	Yes	STRING	None	The name of the MaxCompute table that you want to open. Table name format: odps://${your_projectname}/tables/${table_name}/${pt_1}/${pt_2}/...
  selected_cols	No	STRING	Empty string ("")	The column that you want to select. Separate multiple columns with commas (,). The value must be of the STRING type. If this parameter is set to the default value, all columns are read. You can specify only one of selected_cols and excluded_cols.
  excluded_cols	No	STRING	Empty string ("")	The column that you want to exclude. Separate multiple columns with commas (,). The value must be of the STRING type. If this parameter is set to the default value, all columns are read. You can specify only one of selected_cols and excluded_cols.
  slice_id	No	INT	0	The ID of the shard in distributed read mode. Valid values: [0, slice_count-1]. In distributed read mode, the table is divided into multiple shards based on the value of slice_count. The system reads data from the shard specified by slice_id. If you set this parameter to the default value 0, all table records are read.
  slice_count	No	INT	1	The number of shards in distributed read mode. In most cases, the value is the number of workers.

  • Responses

    A Reader object is returned.

• Read data records

  • Syntax

  reader.read(num_records=1)

  • Parameters

    num_records specifies the number of data records that are sequentially read. The default value is 1, which specifies that a single record is read. If you set the num_records parameter to a value that is greater than the number of unread records, all records that are read are returned. If no records are returned, PAIIO.python_io.OutOfRangeException is thrown.

  • Responses

    A numpy n-dimensional array (or record array) is returned. Each element in the array is a tuple that consists of a table record.

• Obtain data starting from a specific data record

  • Syntax

  reader.seek(offset=0)

  • Parameters

  offset specifies the ID of the data record starting from which you want to obtain data. The record ID starts from 0. If you specify slice_id and slice_count, data is obtained based on the location of the record specified by offset in the corresponding shard. If offset is set to a value that is greater than the total number of data records in the table, an out-of-range exception is thrown. If the previous seek operation returns a record that does not belong to the table and you proceed with another seek operation, PAIIO.python_io.OutOfRangeException is thrown.

  Important

  If the number of unread data records in the table is less than the batch size you specified for a read operation, the number of unread data records is returned and no exception is thrown. If you proceed with another seek operation, an exception is thrown.

  • Responses

    No value is returned. If an error occurs in an operation, the system throws an exception.

• Obtain the total number of data records in the table

  • Syntax

  reader.get_row_count()

  • Parameters

    None

  • Responses

    The number of data records in the table is returned. If you specify slice_id and slice_count, the number of data records in the shard is returned.

• Obtain the schema of the table

  • Syntax

  reader.get_schema()

  • Parameters

    None

  • Responses

  A one-dimensional array is returned. Each element in the array corresponds to the schema of a column in the table. The following table describes the parameters contained in a schema.

  Parameter	Description
  colname	The name of the column.
  typestr	The name of the MaxCompute data type.
  pytype	The Python data type that corresponds to the value specified by typestr.

  The following table describes the mappings between values that can be specified by typestr and pytype.

  typestr	pytype
  BIGINT	INT
  DOUBLE	FLOAT
  BOOLEAN	BOOL
  STRING	OBJECT
  DATETIME	INT
  MAP Note This data type is unavailable for TensorFlow that is built into PAI.	OBJECT

• Close the table

  • Syntax

  reader.close()

  • Parameters

    None

  • Responses

    No value is returned. If an error occurs in an operation, the system throws an exception.

Examples

For example, you store a table named test in your MaxCompute project named myproject. The following table lists part of the table content.

The following code provides an example on how to use the TableReader interface to read the data contained in the uid, name, and price columns.

import os
import paiio

# Specify the path of the configuration file. Replace the value with the path where the configuration file is stored. 
os.environ['ODPS_CONFIG_FILE_PATH'] = "/mnt/data/odps_config.ini"
# Open a table and return a Reader object. Replace ${your_projectname} with the name of the MaxCompute project and ${table_name} with the name of the table that you want to access. 
reader = paiio.python_io.TableReader("odps://myproject/tables/test", selected_cols="uid,name,price")

# Obtain the total number of data records in the table. 
total_records_num = reader.get_row_count() # return 3

batch_size = 2
# Read the table and return a record array in the [(uid, name, price)*2] format. 
records = reader.read(batch_size) # Return [(25, "Apple", 5.0), (38, "Pear", 4.5)].
records = reader.read(batch_size) # Return [(17, "Watermelon", 2.2)].
# If you continue to read, an out-of-memory exception is thrown. 

# Close the reader.
reader.close()

TableWriter

You can use the TableReader interface in a MaxCompute SDK without the need to rely on TensorFlow. This allows you to access MaxCompute tables and obtain real-time I/O results.

Overview

• Create a Writer object and open a table

  • Syntax

    writer = paiio.python_io.TableWriter(table, slice_id=0)

    Note

    • This interface writes data to a table without clearing existing data.

    • Newly written data can be read only after the table is closed.

  • Parameters

    Parameter	Required	Type	Default value	Description
    table	Yes	STRING	None	The name of the MaxCompute table that you want to open. Table name format:odps://${your_projectname}/tables/${table_name}/${pt_1}/${pt_2}/....
    slice_id	No	INT	0	The ID of the shard. In distributed mode, the data is written to different shards to prevent write conflicts. In standalone mode, use the default value 0. In distributed mode, the write operation fails if multiple workers, including parameter server (PS) nodes, write data to the same shard specified by slice_id.

  • Responses

    A Writer object is returned.

• Write data records

  • Syntax

    writer.write(values, indices)

  • Parameters

    Parameter	Required	Type	Default value	Description
    values	Yes	STRING	None	The data record that you want to write. You can write one or more records. To write only one record, set values to a tuple, list, or one-dimensional array that consists of scalars. If values is set to a list or one-dimensional array, all columns of the record are of the same data type. To write one or more records, set values to a list or one-dimensional array. Each element in the value corresponds to a record that is a tuple, list, or one-dimensional array.
    indices	Yes	INT	None	The columns of the data record that you want to write. The value can be a tuple, list, or one-dimensional array that consists of integer indexes. Each number in the value specified by indices corresponds to a column of the record. For example, the number i corresponds to the column i. The column number starts from 0.

  • Responses

    No value is returned. If an error occurs during the write operation, the system throws an exception and exits the current process.

• Close the table

  • Syntax

    writer.close()

    Note

    In the WITH statement, you do not need to explicitly call the close() method to close a table.

  • Parameters

    None

  • Responses

    No value is returned. If an error occurs in an operation, the system throws an exception.

  • Sample results

    Use TableWriter in the WITH statement:

    with paiio.python_io.TableWriter(table) as writer:
      # Prepare values for writing.
        writer.write(values, incides)
        # Table would be closed automatically outside this section.

Examples

import paiio
import os

# Specify the path of the configuration file. Replace the value with the path where the configuration file is stored. 
os.environ['ODPS_CONFIG_FILE_PATH'] = "/mnt/data/odps_config.ini"
# Prepare data. 
values = [(25, "Apple", 5.0, False),
          (38, "Pear", 4.5, False),
          (17, "Watermelon", 2.2, False)]

# Open a table and return a Writer object. Replace ${your_projectname} with the name of the MaxCompute project and ${table_name} with the name of the table that you want to access. 
writer = paiio.python_io.TableWriter("odps://project/tables/test")

# Write data to columns 0 to 3 of the table. 
records = writer.write(values, indices=[0, 1, 2, 3])

# Use the Writer object to close the table. 
writer.close()