If you have a large dataset stored in a small number of files, you’re out of luck. You need to split your data set into a larger number of files than the number of workers in your distributed training job.This approach works, but it has two problems: Each worker receives a disjoint set of files to process, which avoids any unnecessary disk I/O. TensorFlow’s recommended approach is to create a dataset of TFRecord file names and apply shard() to this list. Generally it is best if the shard operator is used early in the dataset pipeline. The TensorFlow documentation acknowledges this and observes: It gets even worse if you’re doing on-the-fly data augmentation before the shard() operator in your pipeline - those data augmentation operations will be done redundantly by every worker. If you’re training a model with 64 GPUs, that means you’ll be doing 64x more disk I/O than you probably intended. Unfortunately, there’s a catch: shard() iterates over the entire input dataset, returning every n’th record and ignoring the rest! That means that if you apply shard() to a large dataset during distributed training, each worker in the distributed training job will end up reading the entire dataset. To handle this common task, tf.data provides a method that seems like a perfect fit: shard(n,i) splits a dataset into n shards and returns the i’th shard for further processing in the current worker. When doing data-parallel distributed training, each worker (typically a GPU) is trained on a fraction (or “ shard” ) of the data in each batch. Although it is possible to shuffle your entire dataset ahead of time by loading the data into memory or shuffling a list of filenames, many users might not realize this problem exists in their code! Data Sharding Many practitioners, including us, have made this error and seen their model’s generalization performance suffer as a result.
Dataset from generator tensorflow example full size#
For perfect shuffling, a buffer size greater than or equal to the full size of the dataset is required.įor datasets that don’t fit entirely into memory (the most common case in deep learning), shuffle() doesn’t actually shuffle the full dataset! This means that shuffle() doesn’t have the intended effect in most applications. This dataset fills a buffer with buffer_size elements, then randomly samples elements from this buffer, replacing the selected elements with new elements. In fact, that’s exactly what tf.data’s shuffle() method does! If our data API only supports sequential access, how can we implement random shuffling? A simple but inefficient approach would be to read as much data as we can into memory and shuffle it there. When training a deep learning model, the training set is often shuffled before being fed into the model - this typically improves generalization performance. Random-access data loader interfaces may also require that a user specify the entire length of the dataset ( _len_()).ĭataset > TypeError : 'TensorSliceDataset' object does not support indexing list ( dataset. PyTorch uses this approach to define the map-style dataset interface (implemented above). In Python, random access is often done by indexing into a list (i.e., data), which calls _getitem_() behind the scenes. Random access is the ability to access any element of a dataset efficiently. There are two fundamental patterns that a data loading interface can use, random access and sequential access. In this post, we’ll be taking you behind the scenes of a popular data loading API: TensorFlow Datasets. Machine learning frameworks provide abstractions that attempt to make data loading straightforward, but peeking behind the curtain of these seemingly simple interfaces can reveal surprising problems. Data loading during training is often overlooked, and it can have massive implications for throughput. One of the most common problems involves data loading. There are many pitfalls engineering teams can fall into when building an end-to-end enterprise deep learning platform. Learn more in the YogaDL announcement blog post! Update: We’ve released YogaDL, a library for deep learning data loading that addresses a lot of the concerns described above. We argue that random access should be a key consideration when building deep learning data APIs. This design makes it difficult to efficiently shuffle large data sets, to shard data when doing distributed training, and to implement fault-tolerant training. Although tf.data has a lot of powerful features, it is built around sequential access to the underlying data set. TLDR: TensorFlow’s tf.data API is a popular approach to loading data into deep learning models.
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