Yelp raw data set9/26/2023 ![]() ![]() The following datasets are currently available. split () tokens = for label, line in train_iter : tokens += tokenize ( label, line ) ![]() # import datasets from torchtext.datasets import IMDB train_iter = IMDB ( split = 'train' ) def tokenize ( label, line ): return line. The rest of the RNG (typically used for transformations) isĭifferent across workers, for maximal entropy and optimal accuracy. The shuffling seed is different across epochs. You might need toĬall _settings.apply_shuffle_seed(dp, rng) The shuffling seed is the same across all workers. Len(datapipe) // num_ddp_workers, but this might not suit all Is to by limit the size of the datapipe within each worker to Note however, that this assumesĮqual number of DataLoader workers for all the ranks.Īll DDP workers work on the same number of batches. Number of shards (DDP workers * DataLoader workers) and shard id (inferred through rankĪnd worker ID of corresponding DataLoader withing rank). Is to create worker_init_fn that calls apply_sharding with appropriate The datasets are already wrapped inside ShardingFilterĪnd you may need to call dp.apply_sharding(num_shards, shard_id) in order to shard theĭata across ranks (DDP workers) and DataLoader workers. If you still wish to use DDP, make sureĪll workers (DDP workers and DataLoader workers) see a different part It will beīetter supported in DataLoaderV2. Stable / supported, and we don’t recommend it at this point. drop_last=True ensures that all batch sizesĭistributed training with DistributedDataParallel is not yet entirely This might affect accuracy greatly especially Without this, the batch sizesĪt the end of an epoch may be very small in some cases (smaller than with This will ensure that data isn’t duplicated across workers. From _compatibility import worker_init_fn DataLoader ( dp, num_workers = 4, worker_init_fn = worker_init_fn, drop_last = True )
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