yogadl

Yoga Data Layer: The Flexible Data Layer

A better approach to data loading for Deep Learning. API-transparent caching to disk, GCS, or S3.

Why yogadl?

At Determined AI, we help many of our customers perform high-performance data loading for deep learning models. We believe every data loader should have two layers: the random-access layer and the sequential layer.

The random-access layer is critical for good training infrastructure. Direct random access to any record enables:

• Shuffling (potentially every epoch)
• Pausing/continuing training mid-epoch
• Sharding the dataset efficiently for distributed training

The sequential layer starts as soon as you decide the order in which you will access the records in the dataset. Often the transition is implicit, in which case it starts as soon as you are done modifying the order of access (i.e. via shuffling, sharding, or splitting). This layer is vital to performance optimizations because it enables:

• Prefetching data loading to hide latency costs
• Parallelizing data loading to hide compute costs

Here is a simple code snippet to illustrate what the transition from random-access layer to sequential layer looks like:

# Start of random-access layer.
indices = list(range(100))
indices = indices[skip:]
indices=np.random.shuffle(indices)

# Start of sequential layer.

def record_gen():
    for i in indices:
        yield read_file_at_index(i)

record_ds = tf.data.Dataset.from_generator(record_gen, ...)
final_ds = record_ds.prefetch(...)

Notice that in the above example, the tf.data API is used, but only in the sequential layer. This is because tf.data has no concept of the random access layer. As a result:

• tf.data.Dataset.shuffle() can only approximate a shuffle. Calling .shuffle(N) will read N records into a buffer and choose samples randomly from those N records, while a true shuffle chooses samples randomly from the entire dataset. This shortcoming forces you to choose between memory footprint and the quality of your shuffle. The only true shuffle with tf.data.Dataset.shuffle() is to read the entire dataset into memory.
• tf.data.Dataset.skip(N) is as inefficient as possible. Each of the N skipped records will still be read from disk and processed normally, according to all of the operations preceeding the .skip() call, making .skip() prohibitively expensive for most use cases.
• Pausing and continuing training is only possible by saving the state of a tf.data.Iterator. However, saving a tf.data.Iterator does not work with all datasets. In particular, it does not work with datasets created using from_generator(), which is the easiest way to create a tf.data.Dataset.

We have seen countless instances where tf.data.Dataset shortcomings have made life harder for deep learning practitioners, so we set out to build something better. We set out to build a new data layer which could augment an existing tf.data.Dataset data loader with the properties should come standard with every data loader.

At the same time, we wanted this new data layer to relieve another key pain point: high-performance dataset caching and dataset versioning.

What is yogadl?

We designed yogadl to be two things: a standalone caching layer to imbue existing data loaders with the properties that come from a random-access layer, and a better interface for defining data loaders in general.

A standalone caching tool

Since tf.data.Dataset-based datasets have no random-access layer, yogadl caches them to disk in a random-access-friendly way. The storage mechanism is, in fact, nearly identical to how TensorPack caches datasets to disk, only with some additional abstractions to allow dataset versioning, cloud storage, and all of the wonderful features that a data loader with a random-access layer ought to have.

What does all this do for you? A few things:

• Better training: A yogadl-cached tf.data.Dataset will have better shuffling than a native tf.data.Dataset. Additionally, pausing and continuing training mid-epoch will be simple and robust, and efficient sharding for distributed training comes standard.
• Faster data loading: Slow data loader? Don't waste your time optimizing it. yogadl will save it in a high-performance cache the first time it is used, and all future uses will be fast and efficient.
• API-transparent: Not all operations in the data loader are cacheable. Data augmentation must be done at run time. yogadl allows you to keep your existing data augmentation code.

A better interface

At the core of yogadl is the DataRef interface, which creates an explicit boundary between the random-access layer and the sequential layer.

We are not the first people to think of this: PyTorch separates the DataSet (the random-access layer) from the Sampler (which defines the sequential layer). Keras has a Sequence object which defines the random-access layer, leaving the order of access (the sequential layer) to be decided by the arguments to model.fit(). Both DataSet and Sequence are already 100% compatible with yogadl's DataRef interface (although yogadl does not yet include those adapters).

And yet, the world is still full of data loaders which are lacking. At Determined AI, we are dedicated to advancing the state of the art for training Deep Learning models, and we believe that a better interface for data loading is a critical piece of that goal. Any data loader which implements the DataRef interface is capable of proper shuffling, pausing and continuing training mid-epoch, and efficient multi-machine distributed training.

What is yogadl not?

yogadl is not a data manipulation API. This world has more than enough of those. Instead, yogadl seeks to be API-transparent so that you can continue to use your existing data loading code, but with all the benefits of a high-performance, random-access cache. If you have data augmentation steps which cannot be cached, that code should continue to work without any modifications.

yogadl does not (at this time) work with any data frameworks other than tf.data.Dataset. First-class support for (tf.)Keras Sequence objects, PyTorch DataSet objects, and TensorPack DataFlow objects is on the near-term roadmap.

yogadl offers basic dataset versioning, but it is not (at this time) a full-blown version control for datasets. Offering something like version control for datasets is on the roadmap as well.

Installing yogadl

yogadl can be installed via pip install yogadl.

Further Information

Please refer to the following links for more information:

• YogaDL official documentation
• YogaDL examples
• YogaDL API Reference