Efficient DataFlow

This tutorial gives an overview of how to build an efficient DataFlow, using ImageNet dataset as an example. Our goal in the end is to have a Python generator which yields preprocessed ImageNet images and labels as fast as possible. Since it is simply a generator interface, you can use the DataFlow in any Python-based frameworks (e.g. PyTorch, Keras) or your own code as well.

What we are going to do: We’ll use ILSVRC12 dataset, which contains 1.28 million images. The original images (JPEG compressed) are 140G in total. The average resolution is about 400x350 [1]. Following the ResNet example, we need images in their original resolution, so we will read the original dataset (instead of a down-sampled version), and then apply complicated preprocessing to it. We aim to reach a speed of, roughly 1k~3k images per second, to keep GPUs busy.

Some things to know before reading:

  1. For smaller datasets (e.g. several GBs of images with lightweight preprocessing), a simple reader plus some multiprocess prefetch should usually work well enough. Therefore you don’t have to understand this tutorial in depth unless you really find your data being the bottleneck. This tutorial could be a bit complicated for people new to system architectures, but you do need these to be able to run fast enough on ImageNet-scale dataset.

  2. Having a fast Python generator alone may or may not improve your overall training speed. You need mechanisms to hide the latency of all preprocessing stages, as mentioned in the previous tutorial.

  3. Reading training set and validation set are different. In training it’s OK to reorder, regroup, or even duplicate some datapoints, as long as the data distribution roughly stays the same. But in validation we often need the exact set of data, to be able to compute a correct and comparable score. This will affect how we build the DataFlow.

  4. The actual performance would depend on not only the disk, but also memory (for caching) and CPU (for data processing). You may need to tune the parameters (#processes, #threads, size of buffer, etc.) or change the pipeline for new tasks and new machines to achieve the best performance.

The benchmark code for this tutorial can be found in tensorpack/benchmarks, including comparison with a similar (but simpler) pipeline built with tf.data.

Random Read

We start from a simple DataFlow:

from tensorpack.dataflow import *
ds0 = dataset.ILSVRC12('/path/to/ILSVRC12', 'train', shuffle=True)
ds1 = BatchData(ds0, 256, use_list=True)

Here ds0 reads original images from the filesystem. It is implemented simply by:

for filename, label in filelist:
  yield [cv2.imread(filename), label]

And ds1 batch the datapoints from ds0, so that we can measure the speed of this DataFlow in terms of “batch per second”. By default, BatchData will stack the datapoints into an numpy.ndarray, but since original images are of different shapes, we use use_list=True so that it just produces lists.

On a good filesystem you probably can already observe good speed here (e.g. 5 it/s, that is 1280 images/s), but on HDD the speed may be just 1 it/s, because we are doing heavy random read on the filesystem (regardless of whether shuffle is True). Image decoding in cv2.imread could also be a bottleneck at this early stage.

We will now add the cheapest pre-processing now to get an ndarray in the end instead of a list (because training will need ndarray eventually):

  ds = dataset.ILSVRC12('/path/to/ILSVRC12', 'train', shuffle=True)
  ds = AugmentImageComponent(ds, [imgaug.Resize(224)])
  ds = BatchData(ds, 256)

You’ll start to observe slow down after adding more pre-processing (such as those in the ResNet example). Now it’s time to add threads or processes:

  ds0 = dataset.ILSVRC12('/path/to/ILSVRC12', 'train', shuffle=True)
  ds1 = AugmentImageComponent(ds0, lots_of_augmentors)
  ds = PrefetchDataZMQ(ds1, nr_proc=25)
  ds = BatchData(ds, 256)

Here we start 25 processes to run ds1, and collect their output through ZMQ IPC protocol, which is faster than multiprocessing.Queue. You can also apply prefetch after batch, of course.

The above DataFlow might be fast, but since it forks the ImageNet reader (ds0), it’s not a good idea to use it for validation (for reasons mentioned at top). Alternatively, you can use multi-threaded preprocessing like this:

  ds0 = dataset.ILSVRC12('/path/to/ILSVRC12', 'train', shuffle=True)
  augmentor = AugmentorList(lots_of_augmentors)
  ds1 = MultiThreadMapData(
          ds0, nr_thread=25,
          map_func=lambda dp: [augmentor.augment(dp[0]), dp[1]], buffer_size=1000)
  # ds1 = PrefetchDataZMQ(ds1, nr_proc=1)
  ds = BatchData(ds1, 256)

MultiThreadMapData launches a thread pool to fetch data and apply the mapping function on a single instance of ds0. This is done by an intermediate buffer of size 1000 to hide the mapping latency. To reduce the effect of GIL to your main training thread, you want to uncomment the line so that everything above it (including all the threads) happen in an independent process.

There is no answer whether it is faster to use threads or processes. Processes avoid the cost of GIL but bring the cost of communication. You can also try a combination of both (several processes each with several threads), but be careful of how forks affect your data distribution.

The above DataFlow still has a potential performance problem: only one thread is doing cv2.imread. If you identify this as a bottleneck, you can also use:

  ds0 = dataset.ILSVRC12Files('/path/to/ILSVRC12', 'train', shuffle=True)
  augmentor = AugmentorList(lots_of_augmentors)
  ds1 = MultiThreadMapData(
          ds0, nr_thread=25,
          map_func=lambda dp:
              [augmentor.augment(cv2.imread(dp[0], cv2.IMREAD_COLOR)), dp[1]],
  ds1 = PrefetchDataZMQ(ds1, nr_proc=1)
  ds = BatchData(ds1, 256)

Let’s summarize what the above dataflow does:

  1. One thread iterates over a shuffled list of (filename, label) pairs, and put them into a queue of size 1000.

  2. 25 worker threads take pairs and make them into (preprocessed image, label) pairs.

  3. Both 1 and 2 happen together in a separate process, and the results are sent back to main process through ZeroMQ.

  4. Main process makes batches, and other tensorpack modules will then take care of how they should go into the graph.

Note that in an actual training setup, I used the above multiprocess version for training set since it’s faster to run heavy preprocessing in processes, and use this multithread version only for validation set.

Sequential Read

Random read may not be a good idea when the data is not on an SSD. We can also dump the dataset into one single LMDB file and read it sequentially.

from tensorpack.dataflow import *
class BinaryILSVRC12(dataset.ILSVRC12Files):
    def __iter__(self):
        for fname, label in super(BinaryILSVRC12, self).__iter__():
            with open(fname, 'rb') as f:
                jpeg = f.read()
            jpeg = np.asarray(bytearray(jpeg), dtype='uint8')
            yield [jpeg, label]
ds0 = BinaryILSVRC12('/path/to/ILSVRC/', 'train')
ds1 = PrefetchDataZMQ(ds0, nr_proc=1)
LMDBSerializer.save(ds1, '/path/to/ILSVRC-train.lmdb')

The above script builds a DataFlow which produces jpeg-encoded ImageNet data. We store the jpeg string as a numpy array because the function cv2.imdecode later expect this format. Please note we can only use 1 prefetch process to speed up. If nr_proc>1, ds1 will take data from several forks of ds0, then neither the content nor the order of ds1 will be the same as ds0. See documentation about caveats of PrefetchDataZMQ.

It will generate a database file of 140G. We load the DataFlow back by reading this LMDB file sequentially:

ds = LMDBSerializer.load('/path/to/ILSVRC-train.lmdb', shuffle=False)
ds = BatchData(ds, 256, use_list=True)

Depending on whether the OS has cached the file for you (and how large the RAM is), the above script can run at a speed of 10~130 it/s, roughly corresponding to 250MB~3.5GB/s bandwidth. You can test your cached and uncached disk read bandwidth with sudo hdparm -Tt /dev/sdX. As a reference, on Samsung SSD 850, the uncached speed is about 16it/s.

ds = LMDBSerializer.load('/path/to/ILSVRC-train.lmdb', shuffle=False)
ds = LocallyShuffleData(ds, 50000)
ds = BatchData(ds, 256, use_list=True)

Instead of shuffling all the training data in every epoch (which would require random read), the added line above maintains a buffer of datapoints and shuffle them once a while. It will not affect the model as long as the buffer is large enough, but it can also consume much memory if too large.

Then we add necessary transformations:

ds = LMDBSerializer.load(db, shuffle=False)
ds = LocallyShuffleData(ds, 50000)
ds = MapDataComponent(ds, lambda x: cv2.imdecode(x, cv2.IMREAD_COLOR), 0)
ds = AugmentImageComponent(ds, lots_of_augmentors)
ds = BatchData(ds, 256)
  1. First we deserialize the datapoints (from raw bytes to [jpeg bytes, label] – what we dumped in RawILSVRC12)

  2. Use OpenCV to decode the first component (jpeg bytes) into ndarray

  3. Apply augmentations to the ndarray

Both imdecode and the augmentors can be quite slow. We can parallelize them like this:

ds = LMDBSerializer.load(db, shuffle=False)
ds = LocallyShuffleData(ds, 50000)
ds = PrefetchData(ds, 5000, 1)
ds = MapDataComponent(ds, lambda x: cv2.imdecode(x, cv2.IMREAD_COLOR), 0)
ds = AugmentImageComponent(ds, lots_of_augmentors)
ds = PrefetchDataZMQ(ds, 25)
ds = BatchData(ds, 256)

Since we are reading the database sequentially, having multiple forked instances of the base LMDB reader will result in biased data distribution. Therefore we use PrefetchData to launch the base DataFlow in only one process, and only parallelize the transformations with another PrefetchDataZMQ (Nesting two PrefetchDataZMQ, however, will result in a different behavior. These differences are explained in the API documentation in more details.). Similar to what we did earlier, you can use MultiThreadMapData to parallelize as well.

Let me summarize what this DataFlow does:

  1. One process reads LMDB file, shuffle them in a buffer and put them into a multiprocessing.Queue (used by PrefetchData).

  2. 25 processes take items from the queue, decode and process them into [image, label] pairs, and send them through ZMQ IPC pipe.

  3. The main process takes data from the pipe, makes batches.

The two DataFlow mentioned in this tutorial (both random read and sequential read) can run at a speed of 1k ~ 2.5k images per second if you have good CPUs, RAM, disks. With fewer augmentations, it can reach 5k images/s. As a reference, tensorpack can train ResNet-18 at 1.2k images/s on 4 old TitanX. 8 P100s can train ResNet-50 at 1.7k images/s according to the official benchmark. So DataFlow will not be a serious bottleneck if configured properly.

Distributed DataFlow

To further scale your DataFlow, you can run it on multiple machines and collect them on the training machine. E.g.:

# Data Machine #1, process 1-20:
df = MyLargeData()
send_dataflow_zmq(df, 'tcp://')
# Data Machine #2, process 1-20:
df = MyLargeData()
send_dataflow_zmq(df, 'tcp://')
# Training Machine, process 1-10:
df = MyLargeData()
send_dataflow_zmq(df, 'ipc://@my-socket')
# Training Machine, training process
df = RemoteDataZMQ('ipc://@my-socket', 'tcp://')

[1]. ImageNet: A Large-Scale Hierarchical Image Database, CVPR09