Chapter 5: Framework Integration#

rocAL improves the pipeline efficiency by preprocessing the data and parallelizing the data loading on the CPU and running trainings on the GPU. To separate the data loading from the training, rocAL provides TensorFlow and PyTorch iterators and readers as a plugin. The integration process with PyTorch and TensorFlow is described in the sections below.

5.1 PyTorch Integration#

This section demonstrates how to use rocAL with PyTorch for training. Follow the steps below to get started.

5.1.1 Build PyTorch Docker#

Build a rocAL PyTorch docker by following the steps here.

5.1.2 Create Data-loading Pipeline#

Follow these steps:

Import libraries for rocAL.

from amd.rocal.plugin.pytorch import ROCALClassificationIterator
from amd.rocal.pipeline import Pipeline
import amd.rocal.fn as fn
import amd.rocal.types as types

See a rocAL pipeline for PyTorch below. It reads data from the dataset using a fileReader and uses image_slice to decode the raw images. The other required augmentation operations are also defined in the pipeline.

def trainPipeline(data_path, batch_size, num_classes, one_hot, local_rank, world_size, num_thread, crop, rocal_cpu, fp16):
    pipe = Pipeline(batch_size=batch_size, num_threads=num_thread, device_id=local_rank, seed=local_rank+10, 
                rocal_cpu=rocal_cpu, tensor_dtype = types.FLOAT16 if fp16 else types.FLOAT, tensor_layout=types.NCHW, 
                prefetch_queue_depth = 7)
    with pipe:
        jpegs, labels = fn.readers.file(file_root=data_path, shard_id=local_rank, num_shards=world_size, random_shuffle=True)
        rocal_device = 'cpu' if rocal_cpu else 'gpu'
        # decode = fn.decoders.image(jpegs, output_type=types.RGB,file_root=data_path, shard_id=local_rank, num_shards=world_size, random_shuffle=True)
        decode = fn.decoders.image_slice(jpegs, output_type=types.RGB,
                                                    file_root=data_path, shard_id=local_rank, num_shards=world_size, random_shuffle=True)
        res = fn.resize(decode, resize_x=224, resize_y=224)
        flip_coin = fn.random.coin_flip(probability=0.5)
        cmnp = fn.crop_mirror_normalize(res, device="gpu",
                                        output_dtype=types.FLOAT,
                                        output_layout=types.NCHW,
                                         crop=(crop, crop),
                                         mirror=flip_coin,
                                         image_type=types.RGB,
                                         mean=[0.485,0.456,0.406],
                                         std=[0.229,0.224,0.225])
        if(one_hot):
            _ = fn.one_hot(labels, num_classes)
        pipe.set_outputs(cmnp)
    print('rocal "{0}" variant'.format(rocal_device))
    return pipe

Import libraries for PyTorch.

import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

Call the training pipeline with rocAL classification data loader.

Def get_pytorch_train_loader(self):
        print(“in get_pytorch_train_loader function”)   
        pipe_train = trainPipeline(self.data_path, self.batch_size, self.num_classes, self.one_hot, self.local_rank, 
                                    self.world_size, self.num_thread, self.crop, self.rocal_cpu, self.fp16)
        pipe_train.build()
        train_loader = ROCALClassificationIterator(pipe_train, device=”cpu” if self.rocal_cpu else “cuda”, device_id = self.local_rank)

Run the training.

# Training loop
    for epoch in range(10):  # loop over the dataset multiple times
        print(“\n epoch:: “,epoch)
        running_loss = 0.0

        for i, (inputs,labels) in enumerate(train_loader, 0):

            sys.stdout.write(“\r Mini-batch “ + str(i))
            # print(“Images”,inputs)
            # print(“Labels”,labels)
            inputs, labels = inputs.to(device), labels.to(device)

Run the training as shown here.

To see a sample training script, click here.

5.2 TensorFlow Integration#

This section demonstrates how to use rocAL with TensorFlow for training. Follow the steps below to get started.

5.2.1 Build TensorFlow Docker#

Build a rocAL TensorFlow docker by following the steps here.

5.2.2 Create Data-loading Pipeline#