eric-haibin-lin commented on a change in pull request #10391: [MXNET-139] 
Tutorial for mixed precision training with float16

 File path: docs/tutorials/python/
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+# Mixed precision training using float16
+The computational resources required for training deep neural networks has 
been increasing of late because of complexity of the architectures and size of 
models. Mixed precision training allows us to reduces the resources required by 
using lower precision arithmetic. In this approach we train using 16 bit 
floating points (half precision) while using 32 bit floating points (single 
precision) for output buffers of float16 computation. This combination of 
single and half precision gives rise to the name Mixed precision. It allows us 
to achieve the same accuracy as training with single precision, while 
decreasing the required memory and training or inference time.
+The float16 data type, is a 16 bit floating point representation according to 
the IEEE 754 standard. It has a dynamic range where the precision can go from 
0.0000000596046 (highest, for values closest to 0) to 32 (lowest, for values in 
the range 32768-65536). Despite the decreased precision when compared to single 
precision (float32), float16 computation can be much faster on supported 
hardware. The motivation for using float16 for deep learning comes from the 
idea that deep neural network architectures have natural resilience to errors 
due to backpropagation. Half precision is typically sufficient for training 
neural networks. This means that on hardware with specialized support for 
float16 computation we can greatly improve the speed of training and inference. 
This speedup results from faster matrix multiplication, saving on memory 
bandwidth and reduced communication costs. It also reduces the size of the 
model, allowing us to train larger models and use larger batch sizes. 
+The Volta range of Graphics Processing Units (GPUs) from Nvidia have Tensor 
Cores which perform efficient float16 computation. A tensor core allows 
accumulation of half precision products into single or half precision outputs. 
For the rest of this tutorial we assume that we are working with Nvidia's 
Tensor Cores on a Volta GPU.
+In this tutorial we will walk through how one can train deep learning neural 
networks with mixed precision on supported hardware. We will first see how to 
use float16 and then some techniques on achieving good performance and accuracy.
+## Prerequisites
+- Volta range of Nvidia GPUs
+- Cuda 9 or higher
+- CUDNN v7 or higher
+## Using the Gluon API
+With Gluon, we need to take care of two things to convert a model to support 
+1. Cast the Gluon Block, so as to cast the parameters of layers and change the 
type of input expected, to float16.
+2. Cast the data to float16 to match the input type expected by the blocks if 
+### Training
+Let us look at an example of training a Resnet50 model with the Caltech101 
dataset with float16. 
+First, let us get some import stuff out of the way.
+import os
+import tarfile
+import multiprocessing
+import time
+import numpy as np
+import mxnet as mx
+from mxnet import nd, autograd, gluon
+from mxnet.gluon.model_zoo import vision as models
+from mxnet.metric import Accuracy
+from import ImageFolderDataset
+Let us start by fetching the Caltech101 dataset and extracting it. 
+url = 
+dataset_name = "101_ObjectCategories"
+data_folder = "data"
+if not os.path.isdir(data_folder):
+    os.makedirs(data_folder)
+tar_path =, path='data')
+if (not os.path.isdir(os.path.join(data_folder, "101_ObjectCategories")) or 
+    not os.path.isdir(os.path.join(data_folder, "101_ObjectCategories_test"))):
+    tar =, "r:gz")
+    tar.extractall(data_folder)
+    tar.close()
+    print('Data extracted')
+training_path = os.path.join(data_folder, dataset_name)
+testing_path = os.path.join(data_folder, "{}_test".format(dataset_name))
+Now we have the images in two folders, one for training and the other for 
test. Let us next create Gluon Dataset from these folders, and then create 
Gluon DataLoader from those datasets. Let us also define a transform function 
so that each image loaded is resized, cropped and transposed. 
+EDGE = 224
+NUM_WORKERS = multiprocessing.cpu_count()
+# Lower batch size if you run out of memory on your GPU
+def transform(image, label):
+    resized = mx.image.resize_short(image, EDGE)
+    cropped, crop_info = mx.image.center_crop(resized, SIZE)
+    transposed = nd.transpose(cropped, (2,0,1))
+    return transposed, label
+dataset_train = ImageFolderDataset(root=training_path, transform=transform)
+dataset_test = ImageFolderDataset(root=testing_path, transform=transform)
+train_data =, BATCH_SIZE, shuffle=True, 
+test_data =, BATCH_SIZE, shuffle=False, 
+Next, we'll define softmax cross entropy as our loss, accuracy as our metric 
and the context on which to run our training jobs. It is set by default to gpu. 
Please note that float16 on CPU might not be supported for all operators, as 
float16 on CPU is slower than float32.
+ctx = mx.gpu(0)
+loss = gluon.loss.SoftmaxCrossEntropyLoss()
+metric = Accuracy()
+Now, let us fetch our model from Gluon ModelZoo and initialize the parameters. 
Let us also hybridize the net for efficiency. Here comes the first change we 
need to make to use float16 for the neural network. We **cast the network** to 
our required data type. Let us keep the data type as an argument so that we can 
compare float32 and float16 easily later.
+# Creating the network
+def get_network(dtype):
+    net = models.get_model(name='resnet50_v2', ctx=ctx, pretrained=False, 
+    net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
+    net.hybridize()
+    net.cast(dtype)
+    return net
+It is preferable to use **multi_precision mode of optimizer** when training in 
float16. This mode of optimizer maintains the weights in float32 even when the 
training is in float16. This helps increase precision of the weights and leads 
to faster convergence for some networks. (Further discussion on this towards 
the end.)
 Review comment:
   Do all optimizers support this mode?

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