DeepLearningExamples/MxNet/Classification/RN50v1.5

ResNet-50 v1.5 for MXNet

This repository provides a script and recipe to train the ResNet-50 v1.5 model to achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA.

Table Of Contents

• Model overview
  • Model architecture
  • Default configuration
  • Feature support matrix
    • Features
  • Mixed precision training
    • Enabling mixed precision
    • Enabling TF32
• Setup
  • Requirements
• Quick Start Guide
• Advanced
  • Scripts and sample code
  • Parameters
  • Command-line options
  • Getting the data
    • Dataset guidelines
    • Multi-dataset
  • Training process
  • Inference process
• Performance
  • Benchmarking
    • Training performance benchmark
    • Inference performance benchmark
  • Results
    • Training accuracy results
      • Training accuracy: NVIDIA DGX A100 (8x A100 80GB)
        
      • Training accuracy: NVIDIA DGX-1 (8x V100 16GB)
      • Training stability test
    • Training performance results
      • Training performance: NVIDIA DGX A100 (8x A100 80GB)
      • Training performance: NVIDIA DGX-1 (8x V100 16GB)
      • Training performance: NVIDIA DGX-2 (16x V100 32GB)
    • Inference performance results
      • Inference performance: NVIDIA DGX A100 (1x A100 80GB)
      • Inference performance: NVIDIA DGX-1 (1x V100 16GB)
      • Inference performance: NVIDIA T4
• Release notes
  • Changelog
  • Known issues

Model overview

The ResNet-50 v1.5 model is a modified version of the original ResNet-50 v1 model.

The difference between v1 and v1.5 is in the bottleneck blocks which require downsampling. ResNet v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.

This difference makes ResNet-50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec).

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 3.5x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

The model architecture was present in Deep Residual Learning for Image Recognition paper. The main advantage of the model is the usage of residual layers as a building block that helps with gradient propagation during training.

Image source: Deep Residual Learning for Image Recognition

Default configuration

Optimizer

• SGD with momentum (0.875)
• Learning rate = 0.256 for 256 batch size, for other batch sizes we linearly scale the learning rate
• Learning rate schedule - we use cosine LR schedule
• Linear warmup of the learning rate during the first 5 epochs according to Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.
• Weight decay: 3.0517578125-05 (1/32768)
• We do not apply WD on batch norm trainable parameters (gamma/bias)
• Label Smoothing: 0.1
• We train for:
  • 50 Epochs - configuration that reaches 75.9% top1 accuracy
  • 90 Epochs - 90 epochs is a standard for ResNet-50
  • 250 Epochs - best possible accuracy. For 250 epoch training we also use MixUp regularization.

Data augmentation

For training:

• Normalization
• Random resized crop to 224x224
• Scale from 8% to 100%
• Aspect ratio from 3/4 to 4/3
• Random horizontal flip

For inference:

• Normalization
• Scale to 256x256
• Center crop to 224x224

Feature support matrix

Feature	ResNet-50 MXNet
DALI	yes
Horovod Multi-GPU	yes

Features

The following features are supported by this model.

NVIDIA DALI NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, to accelerate the pre-processing of the input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications.

Horovod Multi-GPU Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the Horovod: Official repository.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:

1. Porting the model to use the FP16 data type where appropriate.
2. Adding loss scaling to preserve small gradient values.

The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.

For information about:

• How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
• Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.

Enabling mixed precision

Using the Gluon API, ensure you perform the following steps to convert a model that supports computation with float16.

1. Cast Gluon Block‘s parameters and expected input type to float16 by calling the cast method of the Block representing the network.

   net = net.cast('float16')
   

2. Ensure the data input to the network is of float16 type. If your DataLoader or Iterator produces output in another datatype, then you have to cast your data. There are different ways you can do this. The easiest way is to use the astype method of NDArrays.

   data = data.astype('float16', copy=False)
   

3. If you are using images and DataLoader, you can also use a Cast transform. It is preferable to use multi_precision mode of optimizer when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios.

   optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)
   

Enabling TF32

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.

For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.

TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.

Setup

The following section lists the requirements that you need to meet in order to start training the ResNet-50 v1.5 model.

Requirements

This repository contains Dockerfile which extends the MXNet NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:

• NVIDIA Docker
• MXNet 22.10-py3 NGC container Supported GPUs:
• NVIDIA Volta architecture
• NVIDIA Turing architecture
• NVIDIA Ampere architecture

For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:

• Getting Started Using NVIDIA GPU Cloud
• Accessing And Pulling From The NGC Container Registry
• Running MXNet

For those unable to use the MXNet NGC container, to set up the required environment or create your own container, see the versioned NVIDIA Container Support Matrix.

Quick Start Guide

To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the ResNet-50 model on the ImageNet 1k dataset. For the specifics concerning training and inference, see the Advanced section.

1. Clone the repository.

   git clone https://github.com/NVIDIA/DeepLearningExamples
   cd DeepLearningExamples/MxNet/Classification/RN50v1.5
   

2. Build the ResNet-50 MXNet NGC container.

After Docker is set up, you can build the ResNet-50 image with:

docker build . -t nvidia_rn50_mx

1. Start an interactive session in the NGC container to run preprocessing/training/inference.

   nvidia-docker run --rm -it --ipc=host -v <path to dataset>:/data/imagenet/train-val-recordio-passthrough nvidia_rn50_mx
   

2. Download the data.

• Download the images from http://image-net.org/download-images.

• Extract the training and validation data:

  mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
  tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
  find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
  cd ..
  mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf      ILSVRC2012_img_val.tar
  wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
  

1. Preprocess the ImageNet 1k dataset.

   ./scripts/prepare_imagenet.sh <path to raw imagenet> <path where processed dataset will be created>
   

2. Start training.

   ./runner -n <number of gpus> -b <batch size per GPU (default 192)>
   

3. Start validation/evaluation.

   ./runner -n <number of gpus> -b <batch size per GPU (default 192)> --load <path to trained model> --mode val
   

4. Start inference/predictions.

   ./runner --load <path to trained model> --mode pred --data-pred <path to the image>
   

Advanced

The following sections provide greater details of the dataset, running training and inference, and the training results.

Scripts and sample code

In the root directory, the most important files are:

• runner: A wrapper on the train.py script which is the main executable script for training/validation/predicting.
• benchmark.py: A script for benchmarking.
• Dockerfile: Container to build the container.
• fit.py: A file containing most of the training and validation logic.
• data.py: Data loading and preprocessing code.
• dali.py: Data loading and preprocessing code using DALI.
• models.py: The model architecture.
• report.py: A file containing JSON report structure and description of fields.

In the scripts directory, the most important files are:

• prepare_imagenet.sh: A script that converts raw dataset format to RecordIO format.

Parameters

The complete list of available parameters contains:

Model:
  --arch {resnetv1,resnetv15,resnextv1,resnextv15,xception}
                        model architecture (default: resnetv15)
  --num-layers NUM_LAYERS
                        number of layers in the neural network, required by
                        some networks such as resnet (default: 50)
  --num-groups NUM_GROUPS
                        number of groups for grouped convolutions, required by
                        some networks such as resnext (default: 32)
  --num-classes NUM_CLASSES
                        the number of classes (default: 1000)
  --batchnorm-eps BATCHNORM_EPS
                        the amount added to the batchnorm variance to prevent
                        output explosion. (default: 1e-05)
  --batchnorm-mom BATCHNORM_MOM
                        the leaky-integrator factor controling the batchnorm
                        mean and variance. (default: 0.9)
  --fuse-bn-relu FUSE_BN_RELU
                        have batchnorm kernel perform activation relu
                        (default: 0)
  --fuse-bn-add-relu FUSE_BN_ADD_RELU
                        have batchnorm kernel perform add followed by
                        activation relu (default: 0)

Training:
  --mode {train_val,train,val,pred}
                        mode (default: train_val)
  --seed SEED           random seed (default: None)
  --gpus GPUS           list of gpus to run, e.g. 0 or 0,2,5 (default: [0])
  --kv-store {device,horovod}
                        key-value store type (default: device)
  --dtype {float32,float16}
                        precision (default: float16)
  --amp                 If enabled, turn on AMP (Automatic Mixed Precision)
                        (default: False)
  --batch-size BATCH_SIZE
                        the batch size (default: 192)
  --num-epochs NUM_EPOCHS
                        number of epochs (default: 90)
  --lr LR               initial learning rate (default: 0.1)
  --lr-schedule {multistep,cosine}
                        learning rate schedule (default: cosine)
  --lr-factor LR_FACTOR
                        the ratio to reduce lr on each step (default: 0.256)
  --lr-steps LR_STEPS   the epochs to reduce the lr, e.g. 30,60 (default: [])
  --warmup-epochs WARMUP_EPOCHS
                        the epochs to ramp-up lr to scaled large-batch value
                        (default: 5)
  --optimizer OPTIMIZER
                        the optimizer type (default: sgd)
  --mom MOM             momentum for sgd (default: 0.875)
  --wd WD               weight decay for sgd (default: 3.0517578125e-05)
  --label-smoothing LABEL_SMOOTHING
                        label smoothing factor (default: 0.1)
  --mixup MIXUP         alpha parameter for mixup (if 0 then mixup is not
                        applied) (default: 0)
  --disp-batches DISP_BATCHES
                        show progress for every n batches (default: 20)
  --model-prefix MODEL_PREFIX
                        model checkpoint prefix (default: model)
  --save-frequency SAVE_FREQUENCY
                        frequency of saving model in epochs (--model-prefix
                        must be specified). If -1 then save only best model.
                        If 0 then do not save anything. (default: -1)
  --begin-epoch BEGIN_EPOCH
                        start the model from an epoch (default: 0)
  --load LOAD           checkpoint to load (default: None)
  --test-io             test reading speed without training (default: False)
  --test-io-mode {train,val}
                        data to test (default: train)
  --log LOG             file where to save the log from the experiment
                        (default: log.log)
  --dllogger-log DLLOGGER_LOG
                        file where to save the dllogger log from the
                        experiment (default: dllogger_log.log)
  --workspace WORKSPACE
                        path to directory where results will be stored
                        (default: ./)
  --no-metrics          do not calculate evaluation metrics (for benchmarking)
                        (default: False)
  --benchmark-iters BENCHMARK_ITERS
                        run only benchmark-iters iterations from each epoch
                        (default: None)

Data:
  --data-train DATA_TRAIN
                        the training data (default: None)
  --data-train-idx DATA_TRAIN_IDX
                        the index of training data (default: )
  --data-val DATA_VAL   the validation data (default: None)
  --data-val-idx DATA_VAL_IDX
                        the index of validation data (default: )
  --data-pred DATA_PRED
                        the image on which run inference (only for pred mode)
                        (default: None)
  --data-backend {dali-gpu,dali-cpu,mxnet,synthetic}
                        set data loading & augmentation backend (default:
                        dali-gpu)
  --image-shape IMAGE_SHAPE
                        the image shape feed into the network (default: [3,
                        224, 224])
  --rgb-mean RGB_MEAN   a tuple of size 3 for the mean rgb (default: [123.68,
                        116.779, 103.939])
  --rgb-std RGB_STD     a tuple of size 3 for the std rgb (default: [58.393,
                        57.12, 57.375])
  --input-layout {NCHW,NHWC}
                        the layout of the input data (default: NCHW)
  --conv-layout {NCHW,NHWC}
                        the layout of the data assumed by the conv operation
                        (default: NCHW)
  --batchnorm-layout {NCHW,NHWC}
                        the layout of the data assumed by the batchnorm
                        operation (default: NCHW)
  --pooling-layout {NCHW,NHWC}
                        the layout of the data assumed by the pooling
                        operation (default: NCHW)
  --num-examples NUM_EXAMPLES
                        the number of training examples (doesn't work with
                        mxnet data backend) (default: 1281167)
  --data-val-resize DATA_VAL_RESIZE
                        base length of shorter edge for validation dataset
                        (default: 256)

DALI data backend:
  entire group applies only to dali data backend

  --dali-separ-val      each process will perform independent validation on
                        whole val-set (default: False)
  --dali-threads DALI_THREADS
                        number of threadsper GPU for DALI (default: 3)
  --dali-validation-threads DALI_VALIDATION_THREADS
                        number of threadsper GPU for DALI for validation
                        (default: 10)
  --dali-prefetch-queue DALI_PREFETCH_QUEUE
                        DALI prefetch queue depth (default: 2)
  --dali-nvjpeg-memory-padding DALI_NVJPEG_MEMORY_PADDING
                        Memory padding value for nvJPEG (in MB) (default: 64)
  --dali-fuse-decoder DALI_FUSE_DECODER
                        0 or 1 whether to fuse decoder or not (default: 1)

MXNet data backend:
  entire group applies only to mxnet data backend

  --data-mxnet-threads DATA_MXNET_THREADS
                        number of threads for data decoding for mxnet data
                        backend (default: 40)
  --random-crop RANDOM_CROP
                        if or not randomly crop the image (default: 0)
  --random-mirror RANDOM_MIRROR
                        if or not randomly flip horizontally (default: 1)
  --max-random-h MAX_RANDOM_H
                        max change of hue, whose range is [0, 180] (default:
                        0)
  --max-random-s MAX_RANDOM_S
                        max change of saturation, whose range is [0, 255]
                        (default: 0)
  --max-random-l MAX_RANDOM_L
                        max change of intensity, whose range is [0, 255]
                        (default: 0)
  --min-random-aspect-ratio MIN_RANDOM_ASPECT_RATIO
                        min value of aspect ratio, whose value is either None
                        or a positive value. (default: 0.75)
  --max-random-aspect-ratio MAX_RANDOM_ASPECT_RATIO
                        max value of aspect ratio. If min_random_aspect_ratio
                        is None, the aspect ratio range is
                        [1-max_random_aspect_ratio,
                        1+max_random_aspect_ratio], otherwise it is
                        [min_random_aspect_ratio, max_random_aspect_ratio].
                        (default: 1.33)
  --max-random-rotate-angle MAX_RANDOM_ROTATE_ANGLE
                        max angle to rotate, whose range is [0, 360] (default:
                        0)
  --max-random-shear-ratio MAX_RANDOM_SHEAR_RATIO
                        max ratio to shear, whose range is [0, 1] (default: 0)
  --max-random-scale MAX_RANDOM_SCALE
                        max ratio to scale (default: 1)
  --min-random-scale MIN_RANDOM_SCALE
                        min ratio to scale, should >= img_size/input_shape.
                        otherwise use --pad-size (default: 1)
  --max-random-area MAX_RANDOM_AREA
                        max area to crop in random resized crop, whose range
                        is [0, 1] (default: 1)
  --min-random-area MIN_RANDOM_AREA
                        min area to crop in random resized crop, whose range
                        is [0, 1] (default: 0.05)
  --min-crop-size MIN_CROP_SIZE
                        Crop both width and height into a random size in
                        [min_crop_size, max_crop_size] (default: -1)
  --max-crop-size MAX_CROP_SIZE
                        Crop both width and height into a random size in
                        [min_crop_size, max_crop_size] (default: -1)
  --brightness BRIGHTNESS
                        brightness jittering, whose range is [0, 1] (default:
                        0)
  --contrast CONTRAST   contrast jittering, whose range is [0, 1] (default: 0)
  --saturation SATURATION
                        saturation jittering, whose range is [0, 1] (default:
                        0)
  --pca-noise PCA_NOISE
                        pca noise, whose range is [0, 1] (default: 0)
  --random-resized-crop RANDOM_RESIZED_CROP
                        whether to use random resized crop (default: 1)

Command-line options

To see the full list of available options and their descriptions, use the -h or --help command line option:

./runner --help and python train.py --help

./runner acts as a wrapper on train.py and all additional flags will be passed to train.py.

Getting the data

The MXNet ResNet-50 v1.5 script operates on ImageNet 1k, a widely popular image classification dataset from ILSVRC challenge. You can download the images from http://image-net.org/download-images.

The recommended data format is RecordIO, which concatenates multiple examples into seekable binary files for better read efficiency. MXNet provides a tool called im2rec.py located in the /opt/mxnet/tools/ directory. The tool converts individual images into .rec files.

To prepare a RecordIO file containing ImageNet data, we first need to create .lst files which consist of the labels and image paths. We assume that the original images were downloaded to /data/imagenet/raw/train-jpeg and /data/imagenet/raw/val-jpeg.

python /opt/mxnet/tools/im2rec.py --list --recursive train /data/imagenet/raw/train-jpeg
python /opt/mxnet/tools/im2rec.py --list --recursive val /data/imagenet/raw/val-jpeg

Next, we generate the .rec (RecordIO files with data) and .idx (indexes required by DALI to speed up data loading) files. To obtain the best training accuracy we do not preprocess the images when creating the RecordIO file.

python /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 train /data/imagenet/raw/train-jpeg
python /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 val /data/imagenet/raw/val-jpeg

Dataset guidelines

The process of loading, normalizing, and augmenting the data contained in the dataset can be found in the data.py and dali.py files.

The data is read from RecordIO format, which concatenates multiple examples into seekable binary files for better read efficiency.

Data augmentation techniques are described in the Default configuration section.

Multi-dataset

In most cases, to train a model on a different dataset, no changes in the code are required, but the dataset has to be converted into RecordIO format.

To convert a custom dataset, follow the steps from Getting the data section, and refer to the scripts/prepare_dataset.py script.

Training process

To start training, run: ./runner -n <number of gpus> -b <batch size per GPU> --data-root <path to imagenet> --dtype <float32 or float16>

By default, the training script runs the validation after each epoch:

• The best checkpoint will be stored in the model_best.params file in the working directory.
• The log from training will be saved in the log.log file in the working directory.
• The JSON report with statistics will be saved in the report.json file in the working directory.

If ImageNet is mounted in the /data/imagenet/train-val-recordio-passthrough directory, you don't have to specify the --data-root flag.

Inference process

To start validation, run: ./runner -n <number of gpus> -b <batch size per GPU> --data-root <path to imagenet> --dtype <float32 or float16> --mode val

By default:

• The log from validation will be saved in the log.log file in the working directory.
• The JSON report with statistics will be saved in the report.json file in the working directory.

Performance

The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to NVIDIA Data Center Deep Learning Product Performance.

Benchmarking

To benchmark training and inference, run: python benchmark.py -n <numbers of gpus separated by comma> -b <batch sizes per GPU separated by comma> --data-root <path to imagenet> --dtype <float32 or float16> -o <path to benchmark report>

• To control the benchmark length per epoch, use the -i flag (defaults to 100 iterations).
• To control the number of epochs, use the -e flag.
• To control the number of warmup epochs (epochs which are not taken into account), use the -w flag.
• To limit the length of the dataset, use the --num-examples flag.

By default, the same parameters as in ./runner will be used. Additional flags will be passed to ./runner.

Training performance benchmark

To benchmark only training, use the --mode train flag.

Inference performance benchmark

To benchmark only inference, use the --mode val flag.

Results

The following sections provide details on how we achieved our performance and accuracy in training and inference.

Training accuracy results

Training accuracy: NVIDIA DGX A100 (8x A100 80GB)

90 epochs configuration

Our results were obtained by running 8 times the ./runner -n <number of gpus> -b 512 --dtype float32 script for TF32 and the ./runner -n <number of gpus> -b 512 script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.

GPUs	Accuracy - mixed precision	Accuracy - TF32	Time to train - mixed precision	Time to train - TF32	Time to train - speedup
1	77.185	77.184	8.75	29.39	3.36
8	77.185	77.184	1.14	3.82	3.35

Training accuracy: NVIDIA DGX-1 (8x V100 16GB)

90 epochs configuration

Our results were obtained by running the ./runner -n <number of gpus> -b 96 --dtype float32 training script for FP32 and the ./runner -n <number of gpus> -b 192 training script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.

GPUs	Accuracy - mixed precision	Accuracy - FP32	Time to train - mixed precision	Time to train - FP32	Time to train - speedup
1	77.342	77.160	24.2	84.5	3.49
4	77.196	77.290	6.0	21.4	3.59
8	77.150	77.313	3.0	10.7	3.54

Training stability test

Our results were obtained by running the following commands 8 times with different seeds.

• For 50 epochs

  • ./runner -n 8 -b 96 --dtype float32 --num-epochs 50 for FP32
  • ./runner -n 8 -b 192 --num-epochs 50 for mixed precision

• For 90 epochs

  • ./runner -n 8 -b 96 --dtype float32 for FP32
  • ./runner -n 8 -b 192 for mixed precision

• For 250 epochs

  • ./runner -n 8 -b 96 --dtype float32 --num-epochs 250 --mixup 0.2 for FP32
  • ./runner -n 8 -b 192 --num-epochs 250 --mixup 0.2 for mixed precision

# of epochs	mixed precision avg top1	FP32 avg top1	mixed precision standard deviation	FP32 standard deviation	mixed precision minimum top1	FP32 minimum top1	mixed precision maximum top1	FP32 maximum top1
50	76.308	76.329	0.00073	0.00094	76.230	76.234	76.440	76.470
90	77.150	77.313	0.00098	0.00085	76.972	77.228	77.266	77.474
250	78.460	78.483	0.00078	0.00065	78.284	78.404	78.560	78.598

Plots for 250 epoch configuration Here are example graphs of FP32 and mixed precision training on 8 GPU 250 epochs configuration:

Training performance results

Training performance: NVIDIA DGX A100 (8x A100 80GB)

The following results were obtained by running the python benchmark.py -n 1,4,8 -b 512 --dtype float32 -o benchmark_report_tf32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train script for TF32 and the python benchmark.py -n 1,4,8 -b 512 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.

Training performance reported as Total IPS (data + compute time taken into account). Weak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.

GPUs	Throughput - mixed precision	Throughput - TF32	Throughput speedup (TF32 - mixed precision)	Weak scaling - mixed precision	Weak scaling - TF32
1	3410.52	1055.78	2.18	1.00	1.00
4	13442.66	4182.30	3.24	3.97	3.96
8	26673.72	8247.44	3.23	7.82	7.81

Training performance: NVIDIA DGX-1 (8x V100 16GB)

The following results were obtained by running the python benchmark.py -n 1,2,4,8 -b 192 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train script for mixed precision and the python benchmark.py -n 1,2,4,8 -b 96 --dtype float32 -o benchmark_report_fp32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.

Training performance reported as Total IPS (data + compute time taken into account). Weak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.

GPUs	Throughput - mixed precision	Throughput - FP32	Throughput speedup (FP32 - mixed precision)	Weak scaling - mixed precision	Weak scaling - FP32
1	1376	384	3.58	1.00	1.00
2	2768	763	3.62	2.01	1.98
4	5357	1513	3.54	3.89	3.94
8	10723	3005	3.56	7.79	7.82

Training performance: NVIDIA DGX-2 (16x V100 32GB)

The following results were obtained by running the python benchmark.py -n 1,2,4,8,16 -b 256 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train script for mixed precision and the python benchmark.py -n 1,2,4,8,16 -b 128 --dtype float32 -o benchmark_report_fp32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-2 with (16x V100 32GB) GPUs.

Training performance reported as Total IPS (data + compute time taken into account). Weak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.

GPUs	Throughput - mixed precision	Throughput - FP32	Throughput speedup (FP32 - mixed precision)	Weak scaling - mixed precision	Weak scaling - FP32
1	1492	417	3.57	1.00	1.00
2	2935	821	3.57	1.96	1.96
4	5726	1623	3.52	3.83	3.92
8	11368	3223	3.52	7.61	7.72
16	21484	6338	3.38	14.39	15.19

Inference performance results

Inference performance: NVIDIA DGX A100 (1x A100 80GB)

The following results were obtained by running the python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val script for mixed precision and the python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_tf32.json -i 500 -e 3 -w 1 --mode val script for TF32 in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.

Inference performance reported as Total IPS (data + compute time taken into account). Reported mixed precision speedups are relative to TF32 numbers for corresponding configuration.

Batch size	Throughput (img/sec) - mixed precision	Throughput - speedup	Avg latency (ms) - mixed precision	Avg latency - speedup	50% latency (ms) - mixed precision	50% latency - speedup	90% latency (ms) - mixed precision	90% latency - speedup	95% latency (ms) - mixed precision	95% latency - speedup	99% latency (ms) - mixed precision	99% latency - speedup
1	1431.99	1.9	0.7	1.9	0.68	1.95	0.71	1.9	0.84	1.65	0.88	1.7
	2	2530.66	2.19	0.79	2.19	0.74	2.31	0.86	2.05	0.93	2.0	2.0
	4	3680.74	2.11	1.09	2.11	0.92	2.49	1.21	1.98	1.64	1.51	6.03
	8	2593.88	1.11	3.08	1.11	2.89	1.17	4.09	0.89	4.72	0.8	9.85
	16	4340.08	1.52	3.69	1.52	3.31	1.68	4.73	1.24	6.3	0.95	12.31
	32	6808.22	2.1	4.7	2.1	4.0	2.46	6.44	1.58	9.01	1.15	15.88
	64	7659.96	2.21	8.36	2.21	7.44	2.48	10.76	1.75	13.91	1.37	21.96
	128	8017.67	2.23	15.96	2.23	15.0	2.37	18.95	1.9	21.65	1.67	30.36
	192	8240.8	2.26	23.3	2.26	22.49	2.33	25.65	2.07	27.54	1.94	37.19
	256	7909.62	2.15	32.37	2.15	31.66	2.2	34.27	2.05	37.02	1.9	42.83
	512	7213.43	2.07	70.98	2.07	70.48	2.08	73.21	2.04	74.38	2.03	79.15

Inference performance: NVIDIA DGX-1 (1x V100 16GB)

The following results were obtained by running the python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val script for mixed precision and the python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_fp32.json -i 500 -e 3 -w 1 --mode val script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.

Inference performance reported as Total IPS (data + compute time taken into account). Reported mixed precision speedups are relative to FP32 numbers for corresponding configuration.

| Batch size | Throughput (img/sec) - mixed precision | Throughput - speedup | Avg latency (ms) - mixed precision | Avg latency - speedup | 50% latency (ms) - mixed precision | 50% latency - speedup | 90% latency (ms) - mixed precision | 90% latency - speedup | 95% latency (ms) - mixed precision | 95% latency - speedup | 99% latency (ms) - mixed precision | 99% latency - speedup | |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | 1 | 286 | 1.27 | 3.48 | 1.27 | 3.45 | 1.27 | 3.61 | 1.26| 3.68 | 1.26| 3.86 | 1.24| | 2 | 519 | 1.34 | 3.84 | 1.34 | 3.77 | 1.35 | 4.05 | 1.31| 4.16 | 1.29| 4.59 | 1.27| | 4 | 910 | 1.60 | 4.39 | 1.60 | 4.35 | 1.61 | 4.59 | 1.56| 4.66 | 1.56| 5.19 | 1.47| | 8 | 1642| 2.20 | 4.87 | 2.20 | 4.68 | 2.29 | 5.35 | 2.05| 6.01 | 1.84| 11.06| 1.04| | 16 | 2359| 2.55 | 6.78 | 2.55 | 6.49 | 2.66 | 7.07 | 2.48| 8.33 | 2.12| 13.89| 1.30| | 32 | 2902| 2.86 | 11.02| 2.86 | 10.43| 3.02 | 12.25| 2.60| 13.88| 2.31| 21.41| 1.55| | 64 | 3234| 2.74 | 19.78| 2.74 | 18.89| 2.86 | 22.50| 2.44| 25.38| 2.17| 30.78| 1.81| | 128 | 3362| 2.69 | 38.06| 2.69 | 37.20| 2.75 | 42.32| 2.44| 45.12| 2.30| 50.59| 2.07| | 192 | 3178| 2.52 | 60.40| 2.52 | 59.62| 2.55 | 65.56| 2.35| 68.16| 2.25| 73.72| 2.10| | 256 | 3057| 2.38 | 83.73| 2.38 | 82.77| 2.40 | 92.26| 2.24| 92.26| 2.17|100.84| 2.23|

Inference performance: NVIDIA T4

The following results were obtained by running the python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val script for mixed precision and the python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_fp32.json -i 500 -e 3 -w 1 --mode val script for FP32 in the mxnet-20.12-py3 NGC container on an NVIDIA T4 GPU.

Inference performance reported as Total IPS (data + compute time taken into account). Reported mixed precision speedups are relative to FP32 numbers for corresponding configuration.

Batch size	Throughput (img/sec) - mixed precision	Throughput - speedup	Avg latency (ms) - mixed precision	Avg latency - speedup	50% latency (ms) - mixed precision	50% latency - speedup	90% latency (ms) - mixed precision	90% latency - speedup	95% latency (ms) - mixed precision	95% latency - speedup	99% latency (ms) - mixed precision	99% latency - speedup
1	131	1.11	7.61	1.17	7.10	0.97	10.28	0.92	11.35	0.95	15.05	0.96
2	277	1.48	7.20	1.53	7.30	1.19	7.74	1.48	8.82	1.49	12.09	1.58
4	374	1.47	10.67	1.50	10.20	1.40	13.51	1.09	14.82	1.03	22.36	0.74
8	672	2.21	11.90	2.23	11.21	2.21	14.54	1.74	17.24	1.48	28.65	0.92
16	1267	3.57	12.62	3.58	12.02	3.59	14.02	3.13	16.02	2.76	22.28	2.01
32	1473	3.85	21.71	3.86	21.67	3.76	22.63	3.64	22.98	3.60	23.85	3.52
64	1561	3.70	40.98	3.70	40.87	3.64	41.98	3.57	42.56	3.53	43.85	3.46
128	1555	3.60	82.26	3.60	81.86	3.57	83.87	3.51	84.63	3.49	96.56	3.09
192	1545	3.64	124.26	3.64	123.67	3.61	125.76	3.58	126.73	3.56	143.27	3.19
256	1559	3.71	164.15	3.71	163.97	3.71	166.28	3.70	167.01	3.70	168.54	3.69

Release notes

Changelog

1. Dec, 2018
   • Initial release (based on https://github.com/apache/incubator-mxnet/tree/v1.8.x/example/image-classification)
2. June, 2019
   • Code refactor
   • Label smoothing
   • Cosine LR schedule
   • MixUp regularization
   • Better configurations
3. February, 2021
   • DGX-A100 performance results
   • Container version upgraded to 20.12
4. December, 2022
   • Container version upgraded to 22.10
   • Updated the A100 performance results. V100 and T4 performance results reflect the performance using the 20.12 container
     

Known Issues

There are no known issues with this model.