# Reduce FasterCNN Training Time with Apache MXNet and Horovod on Amazon SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. As datasets continue to increase in size, additional compute is required to reduce the amount of time it takes to train. One method to scale horizontally and add these additional resources on SageMaker is through the use of Horovod and Apache MXNet. In this post, we will show how users can reduce training time with MXNet and Horovod on SageMaker. Finally, we will demonstrate how you can improve performance even more with advanced sections on Horovod Timeline, Horovod Autotune, Horovod Fusion, and MXNet Optimization.

## Distributed Training

Distributed training of neural networks for computer vision (CV) and natural language processing (NLP) applications has become ubiquitous. With Apache MXNet, you only need to modify a few lines of code to enable distributed training. Distributed training allows you to reduce training time by scaling horizontally. The goal is to split training tasks into independent subtasks and execute these across multiple devices. There are primarily two approaches for training in parallel: * Data parallelism: You distribute the data and share the model across multiple compute resources. * Model parallelism: You distribute the model and share transformed data across multiple compute resources.

In this blog, we focus on data parallelism. Specifically, we discuss how Horovod and MXNet allow you to train efficiently on SageMaker.

## Horovod Overview

Horovod is an open-source distributed deep learning framework. It leverages efficient inter-GPU and inter-node communication methods such as NVIDIA Collective Communications Library (NCCL) and Message Passing Interface (MPI) to distribute and aggregate model parameters between workers. Horovod makes distributed deep learning fast and easy by utilizing a single-GPU training script and scaling it across many GPUs in parallel. It is built on top of the ring-allreduce communication protocol. This approach allows each training process (i.e. process running on a single GPU device) to talk to its peers and exchange gradients by averaging (“reduction”) on a subset of gradients. The diagram below illustrates how ring-allreduce works.

Fig. 1 The ring-allreduce algorithm allows worker nodes to average gradients and disperse them to all nodes without the need for a parameter server (source)

Apache MXNet is integrated with Horovod through the distributed training APIs defined in Horovod and you can convert the non-distributed training by following the higher level code skeleton, which will also be shown below. Although this greatly simplifies the process of using Horovod, other complexities need to be considered. For example, you may need to install additional software and libraries to resolve your incompatibilities for making distributed training work. Horovod requires a certain version of Open MPI, and if you want to leverage high-performance training on NVIDIA GPUs you need to install NCCL libraries. These complexities are amplified when you scale across multiple devices, since you need to make sure all the software and libraries in the new nodes are properly installed and configured. Amazon SageMaker includes all the required libraries to run distributed training with MXNet and Horovod. Prebuilt Sagemaker Docker Images come with popular open-source deep learning frameworks and pre-configured CUDA, cuDNN, MPI, and NCCL libraries. SageMaker manages the difficult process of properly installing and configuring your cluster. Together SageMaker and MXNet simplify training with Horovod by managing the complexities to support distributed training at scale.

## Test Problem and Dataset

In order to benchmark the efficiencies realized by Horovod we trained the notoriously resource-intensive model architecture Faster-RCNN. This model architecture was first introduced in 2016, and is currently considered the baseline model architecture for the popular Computer Vision task of Object Detection (Faster-RCNN). Apache MXNet provides pre-built Faster-RCNN models as part of the GluonCV Model Zoo, simplifying the process of training these models. To train our object detection and instance segmentation models, we used the popular COCO2017 dataset. This dataset provides more than 200,000 images and their corresponding labels. COCO2017 dataset is considered an industry standard for benchmarking CV models. GluonCV is a computer-vision toolkit built on top of MXNet. It provides out-of-the-box support for various CV tasks including data loading and preprocessing for many common algorithm’s available within its model zoo. It also has a tutorial on how to get the COCO2017 dataset. In order to make this process replicable for Amazon SageMaker users, we will show an entire end-to-end process for training Faster-RCNN with Horovod and MXNet. To begin, we first open the Jupyter environment on your Sagemaker Notebook and use the conda_mxnet_p36 kernel. Next, we install the required Python packages:

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!pip install gluoncv==0.8.0b20200723 -q
!pip install pycocotools -q

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import mxnet as mx

# import gluoncv as gcv
import os
import sagemaker
import subprocess
from sagemaker.mxnet.estimator import MXNet

sagemaker_session = sagemaker.Session()  # can use LocalSession() to run container locally
bucket = sagemaker_session.default_bucket()
role = sagemaker.get_execution_role()

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# We will use GluonCV's tool to download our data
)

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# Now to install the dataset. Warning, this may take a while

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bucket_name = #INSERT BUCKET NAME

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# Upload the dataset to your s3 bucket
!aws s3 cp './data/' s3://<INSERT BUCKET NAME>/ --recursive --quiet


Here is the standard way of performing training via paramater servers.

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# Define basic configuration of your Sagemaker Parameter/Horovod cluster.
num_instances = 1  # How many nodes you want to use
gpu_per_instance = 8  # How many gpus are on this instance
bs = 1  # Batch-Size per gpu

# Parameter Server variation
hyperparameters = {
"epochs": 12,
"batch-size": bs,
"horovod": "false",
"lr": 0.01,
"amp": "true",
"val-interval": 6,
"num-workers": 16,
}

for instance_family in ["ml.p3.16xlarge", "ml.p3dn.24xlarge"]:  # Which instance you want to use
estimator = MXNet(
entry_point="train_faster_rcnn.py",
source_dir="./source",
role=role,
train_max_run=72 * 60 * 60,
train_instance_type=instance_family,
train_instance_count=num_instances,
framework_version="1.6.0",
train_volume_size=100,
base_job_name=s.split("_")[1]
+ "rcnn-"
+ str(num_instances)
+ "-"
+ "-".join(instance_family.split(".")[1:]),
py_version="py3",
hyperparameters=hyperparameters,
)

estimator.fit({"data": "s3://" + bucket_name + "/data"}, wait=False)

The Amazon SageMaker MXNet Estimator Class supports Horovod via the “distributions” parameter. We need to add a predefined “mpi” parameter with the “enabled” flag, and define the following additional parameters:
* processes_per_host (int): Number of processes MPI should launch on each host. This parameter is usually equal to number of GPU devices available on any given instance. * custom_mpi_options (str): Any custom mpirun flags passed in this field are added to the mpirun command and executed by Amazon SageMaker for Horovod training.

Here is an example of how to initialize the distributions parameters:

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# Define basic configuration of your Sagemaker Parameter/Horovod cluster.
num_instances = 1  # How many nodes you want to use
gpu_per_instance = 8  # How many gpus are on this instance
bs = 1  # Batch-Size per gpu

distributions = {
"mpi": {
"enabled": True,
"processes_per_host": gpu_per_instance,
}
}

hyperparameters = {
"epochs": 12,
"batch-size": bs,
"horovod": "true",
"lr": 0.01,
"amp": "true",
"val-interval": 6,
"num-workers": 15,
}

for num_instances in [1, 3]:
for instance_family in ["ml.p3.16xlarge", "ml.p3dn.24xlarge"]:  # Which instance you want to use
estimator = MXNet(
entry_point="train_faster_rcnn.py",
source_dir="./source",
role=role,
train_max_run=72 * 60 * 60,
train_instance_type=instance_family,
train_instance_count=num_instances,
framework_version="1.6.0",
train_volume_size=100,
base_job_name=s.split("_")[1]
+ "rcnn-hvd-bs-"
+ str(num_instances)
+ "-"
+ "-".join(instance_family.split(".")[1:]),
py_version="py3",
hyperparameters=hyperparameters,
distributions=distributions,
)

estimator.fit({"data": "s3://" + bucket_name + "/data"}, wait=False)


## Training Script with Horovod Support

In order to use Horovod in your training script, only a few modifications are required. Code samples and instructions are available in the Horovod documentation. In addition, many GluonCV models in the model zoo have scripts which already support Horovod out of the box. Let’s review the key changes that are required for Horovod to correctly work on Amazon SageMaker with Apache MXNet. The following code follows directly from Horovod’s documentation.

import mxnet as mx
import horovod.mxnet as hvd

# Initialize Horovod, this has to be done first as it activates Horovod.
hvd.init()

# GPU setup
context =[mx.gpu(hvd.local_rank())] #local_rank is the specific gpu on that
# instance
num_gpus = hvd.size() #This is how many total GPUs you will be using.

# example, in the train_mask_rcnn.py script
train_sampler = \
gcv.nn.sampler.SplitSortedBucketSampler(...,
num_parts=hvd.size() if args.horovod else 1,
part_index=hvd.rank() if args.horovod else 0)

#Normally, we would shard the dataset first for Horovod.

# You build and initialize your model as usual.
model = ...

# Fetch and broadcast the parameters.
params = model.collect_params()
if params is not None:

# Create DistributedTrainer, a subclass of gluon.Trainer.
trainer = hvd.DistributedTrainer(params, opt)

# Create loss function and train your model as usual.


## Results

We trained Faster-RCNN and Mask-RCNN with similar parameters, except batch-size and learning rate, on the COCO 2017 dataset to provide training performance and accuracy benchmarks.

Fig. 2 Horovod Training Results.

We used the approach for scaling our batch-size and learning rate from the “Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour “ paper. With the improvement in training time enabled by Horovod and SageMaker, Scientists can focus more on improving their algorithms instead of waiting for jobs to finish training. Using Horovod Scientists can train in parallel across multiple instances with marginal impact to mean Average Precision (mAP).

### Optimizing Horovod Training

Horovod provides several additional utilities which allow you to analyze and optimize training performance. Horovod Autotune Finding the optimal combinations of parameters for a given combination of model and cluster size may require several iterations of trial-and-error. The Autotune feature allows you to automate this trial-and-error activity within a single training job and uses Bayesian optimization to search through the parameter space for the most performant combination of parameters. Horovod will search for the best combination of parameters in the first cycles of a training job, and the once best combination is defined, Horovod will write the best configuration in the Autotune log and use this combination for the remainder of the training job. See more details here.

To enable Autotune and capture the search log, pass the following parameters in your MPI configuration:

{
'mpi':
{
'enabled': True,
'custom_mpi_options': '-x HOROVOD_AUTOTUNE=1 -x HOROVOD_AUTOTUNE_LOG=/opt/ml/output/autotune_log.csv'
}
}


#### Horovod Timeline

Horovod Timeline is a report available after training completion which captures all activities in the Horovod ring. This is useful to understand which operations are taking the longest time and will identify optimization opportunities. Refer to this article for more details. for more details. To generate a Timeline file, add the following parameters in your MPI command:

{
'mpi':
{
'enabled': True,
'custom_mpi_options': '-x HOROVOD_TIMELINE=/opt/ml/output/timeline.json'
}
}


Note, that /opt/ml/output is a directory with specific purpose. After training job completion, Amazon Sagemaker automatically archives all files in this directory and uploads it to S3 location defined by user. That’s where your Timeline report will be available for your further analysis.

Note, that /opt/ml/output is a directory with a specific purpose. After training job completion, Amazon Sagemaker automatically archives all files in this directory and uploads it to an Amazon S3 location defined by the user in the Python SageMaker SDK API.

#### Tensor Fusion

The Tensor Fusion feature allows users to perform batch *allreduce* operations at training time. This typically results in better overall performance, see additional details here. By default, Tensor Fusion is enabled and has a buffer size of 64MB. You can modify buffer size using a custom MPI flag’s as follows (in this case we override the default 64MB buffer value with 32MB):

{
'mpi':
{
'enabled': True,
'custom_mpi_options': '-x HOROVOD_FUSION_THRESHOLD=33554432'
}
}


You can also tweak batch cycles using HOROVOD_CYCLE_TIME parameter. Note that cycle time is defined in miliseconds:

{
'mpi':
{
'enabled': True,
'custom_mpi_options': '-x HOROVOD_CYCLE_TIME=5'
}
}


## Optimizing MXNet Model

Another optimization technique is related to optimizing the MXNet model itself. It is recommended you first run the code with os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '1' Then you can copy the best OS environment variables for future training. In our testing we found the following to be the best results:

os.environ['MXNET_GPU_MEM_POOL_TYPE'] = 'Round'
os.environ['MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF'] = '26'
os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD'] = '999'
os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD'] = '25'

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