mindspore.dataset.OBSMindDataset

class mindspore.dataset.OBSMindDataset(dataset_files, server, ak, sk, sync_obs_path, columns_list=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, shard_equal_rows=True)

A source dataset that reads and parses MindRecord dataset which stored in cloud storage such as OBS, Minio or AWS S3.

The columns of generated dataset depend on the source MindRecord files.

Note

• This interface accesses the /cache directory for node synchronization and requires the user to ensure access to the /cache directory.

Parameters

• dataset_files (list[str]) – List of files in cloud storage to be read and file path is in the format of s3://bucketName/objectKey.

• server (str) – Endpoint for accessing cloud storage. If it's OBS Service of Huawei Cloud, the endpoint is like <obs.cn-north-4.myhuaweicloud.com> (Region cn-north-4). If it's Minio which starts locally, the endpoint is like <https://127.0.0.1:9000>.

• ak (str) – The access key ID used to access the OBS data.

• sk (str) – The secret access key used to access the OBS data.

• sync_obs_path (str) – Remote dir path used for synchronization, users need to create it on cloud storage in advance. Path is in the format of s3://bucketName/objectKey.

• columns_list (list[str], optional) – List of columns to be read. Default: None , read all columns.

• shuffle (Union[bool, Shuffle], optional) –

  Perform reshuffling of the data every epoch. Default: Shuffle.GLOBAL. Bool type and Shuffle enum are both supported to pass in. If shuffle is False , no shuffling will be performed. If shuffle is True , performs global shuffle. There are three levels of shuffling, desired shuffle enum defined by mindspore.dataset.Shuffle .

  • Shuffle.GLOBAL : Global shuffle of all rows of data in dataset, same as setting shuffle to True.

  • Shuffle.FILES : Shuffle the file sequence but keep the order of data within each file.

  • Shuffle.INFILE : Keep the file sequence the same but shuffle the data within each file.

• num_shards (int, optional) – Number of shards that the dataset will be divided into. Default: None . Used in data parallel training .

• shard_id (int, optional) – The shard ID within num_shards. Default: None . This argument can only be specified when num_shards is also specified.

• shard_equal_rows (bool, optional) – Get equal rows for all shards. Default: True. If shard_equal_rows is false, number of rows of each shard may be not equal, and may lead to a failure in distributed training. When the number of samples of per MindRecord file are not equal, it is suggested to set to True. This argument should only be specified when num_shards is also specified.

Raises

• RuntimeError – If sync_obs_path do not exist.

• ValueError – If columns_list is invalid.

• RuntimeError – If num_shards is specified but shard_id is None.

• RuntimeError – If shard_id is specified but num_shards is None.

• ValueError – If shard_id is not in range of [0, num_shards ).

Note

• It's necessary to create a synchronization directory on cloud storage in advance which be defined by parameter: sync_obs_path .

• If training is offline(no cloud), it's recommended to set the environment variable BATCH_JOB_ID .

• In distributed training, if there are multiple nodes(servers), all 8 devices must be used in each node(server). If there is only one node(server), there is no such restriction.

Examples

>>> import mindspore.dataset as ds
>>> # OBS
>>> bucket = "iris"  # your obs bucket name
>>> # the bucket directory structure is similar to the following:
>>> #  - imagenet21k
>>> #        | - mr_imagenet21k_01
>>> #        | - mr_imagenet21k_02
>>> #  - sync_node
>>> dataset_obs_dir = ["s3://" + bucket + "/imagenet21k/mr_imagenet21k_01",
...                    "s3://" + bucket + "/imagenet21k/mr_imagenet21k_02"]
>>> sync_obs_dir = "s3://" + bucket + "/sync_node"
>>> num_shards = 8
>>> shard_id = 0
>>> dataset = ds.OBSMindDataset(dataset_obs_dir, "obs.cn-north-4.myhuaweicloud.com",
...                             "AK of OBS", "SK of OBS",
...                             sync_obs_dir, shuffle=True, num_shards=num_shards, shard_id=shard_id)

Pre-processing Operation

mindspore.dataset.Dataset.apply	Apply a function in this dataset.
mindspore.dataset.Dataset.concat	Concatenate the dataset objects in the input list.
mindspore.dataset.Dataset.filter	Filter dataset by prediction.
mindspore.dataset.Dataset.flat_map	Map func to each row in dataset and flatten the result.
mindspore.dataset.Dataset.map	Apply each operation in operations to this dataset.
mindspore.dataset.Dataset.project	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
mindspore.dataset.Dataset.rename	Rename the columns in input datasets.
mindspore.dataset.Dataset.repeat	Repeat this dataset count times.
mindspore.dataset.Dataset.reset	Reset the dataset for next epoch.
mindspore.dataset.Dataset.save	Save the dynamic data processed by the dataset pipeline in common dataset format.
mindspore.dataset.Dataset.shuffle	Shuffle the dataset by creating a cache with the size of buffer_size .
mindspore.dataset.Dataset.skip	Skip the first N elements of this dataset.
mindspore.dataset.Dataset.split	Split the dataset into smaller, non-overlapping datasets.
mindspore.dataset.Dataset.take	Take the first specified number of samples from the dataset.
mindspore.dataset.Dataset.zip	Zip the datasets in the sense of input tuple of datasets.

Batch

mindspore.dataset.Dataset.batch	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
mindspore.dataset.Dataset.bucket_batch_by_length	Bucket elements according to their lengths.
mindspore.dataset.Dataset.padded_batch	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

mindspore.dataset.Dataset.create_dict_iterator	Create an iterator over the dataset that yields samples of type dict, while the key is the column name and the value is the data.
mindspore.dataset.Dataset.create_tuple_iterator	Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Attribute

mindspore.dataset.Dataset.get_batch_size	Return the size of batch.
mindspore.dataset.Dataset.get_class_indexing	Get the mapping dictionary from category names to category indexes.
mindspore.dataset.Dataset.get_col_names	Return the names of the columns in dataset.
mindspore.dataset.Dataset.get_dataset_size	Return the number of batches in an epoch.
mindspore.dataset.Dataset.get_repeat_count	Get the replication times in RepeatDataset.
mindspore.dataset.Dataset.input_indexs	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
mindspore.dataset.Dataset.num_classes	Get the number of classes in a dataset.
mindspore.dataset.Dataset.output_shapes	Get the shapes of output data.
mindspore.dataset.Dataset.output_types	Get the types of output data.

Apply Sampler

mindspore.dataset.MappableDataset.add_sampler	Add a child sampler for the current dataset.
mindspore.dataset.MappableDataset.use_sampler	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

mindspore.dataset.Dataset.sync_update	Release a blocking condition and trigger callback with given data.
mindspore.dataset.Dataset.sync_wait	Add a blocking condition to the input Dataset and a synchronize action will be applied.
mindspore.dataset.Dataset.to_json	Serialize a pipeline into JSON string and dump into file if filename is provided.