class streaming.multimodal.StreamingInsideWebVid(*, streams=None, remote=None, local=None, split=None, download_retry=2, download_timeout=60, validate_hash=None, keep_zip=False, epoch_size=None, predownload=None, cache_limit=None, sampling_method='balanced', sampling_granularity=1, partition_algo='relaxed', num_canonical_nodes=None, batch_size=None, shuffle=False, shuffle_algo='py1e', shuffle_seed=9176, shuffle_block_size=None, batching_method='random')[source]#

Streaming WebVid dataset.

Videos are stored โ€œinsideโ€ the shards, as is typically done.

  • remote (str, optional) โ€“ Remote path or directory to download the dataset from. If None, its data must exist locally. StreamingDataset uses either streams or remote/local. Defaults to None.

  • local (str, optional) โ€“ Local working directory to download shards to. This is where shards are cached while they are being used. Uses a temp directory if not set. StreamingDataset uses either streams or remote/local. Defaults to None.

  • split (str, optional) โ€“ Which dataset split to use, if any. If provided, we stream from/to the split subdirs of remote and local. Defaults to None.

  • download_retry (int) โ€“ Number of download re-attempts before giving up. Defaults to 2.

  • download_timeout (float) โ€“ Number of seconds to wait for a shard to download before raising an exception. Defaults to 60.

  • validate_hash (str, optional) โ€“ Optional hash or checksum algorithm to use to validate shards. Defaults to None.

  • keep_zip (bool) โ€“ Whether to keep or delete the compressed form when decompressing downloaded shards. If False, keep iff remote is local or no remote. Defaults to False.

  • epoch_size (int, optional) โ€“ Number of samples to draw per epoch balanced across all streams. If None, takes its value from the total number of underlying samples. Provide this field if you are weighting streams relatively to target a larger or smaller epoch size. Defaults to None.

  • predownload (int, optional) โ€“ Target number of samples to download per worker in advance of current sample. Workers will attempt to download ahead by this many samples during, but not before, training. Recommendation is to provide a value greater than per device batch size to ensure at-least per device batch size number of samples cached locally. If None, its value gets derived using per device batch size and number of canonical nodes max(batch_size, 256 * batch_size // num_canonical_nodes). Defaults to None.

  • cache_limit (int, optional) โ€“ Maximum size in bytes of this StreamingDatasetโ€™s shard cache. Before downloading a shard, the least recently used resident shard(s) may be evicted (deleted from the local cache) in order to stay under the limit. Set to None to disable shard eviction. Defaults to None.

  • partition_algo (str) โ€“ Which partitioning algorithm to use. Defaults to orig.

  • num_canonical_nodes (int, optional) โ€“

    Canonical number of nodes for shuffling with resumption. The sample space is divided evenly according to the number of canonical nodes. The higher the value, the more independent non-overlapping paths the StreamingDataset replicas take through the shards per model replica (increasing data source diversity). Defaults to None, which is interpreted as 64 times the number of nodes of the initial run.


    For sequential sample ordering, set shuffle to False and num_canonical_nodes to the number of physical nodes of the initial run.

  • batch_size (int, optional) โ€“ Batch size of its DataLoader, which affects how the dataset is partitioned over the workers. Defaults to None.

  • shuffle (bool) โ€“ Whether to iterate over the samples in randomized order. Defaults to False.

  • shuffle_algo (str) โ€“ Which shuffling algorithm to use. Defaults to py1s.

  • shuffle_seed (int) โ€“ Seed for Deterministic data shuffling. Defaults to 9176.

  • shuffle_block_size (int) โ€“ Unit of shuffle. Defaults to 1 << 18.


Get the sample at the index.


idx (int) โ€“ Sample index.


Any โ€“ The sample.