DAWNBench: An End-to-End Deep Learning
Benchmark and Competition

Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi,Peter Bailis, Kunle Olukotun, Chris Ré, Matei Zaharia
Stanford DAWN Project
http://dawn.cs.stanford.edu/benchmark

Abstract
Despite considerable research on systems, algorithms and hardware to speed up deep learning
workloads, there is no standard means of evaluating end-to-end deep learning performance. Existing
benchmarks measure proxy metrics, such as time to process one minibatch of data, that do notindicate whether the system as a whole will produce a high-quality result. In this work, we introduce
DAWNBench, a benchmark and competition focused on end-to-end training time to achieve astate-of-the-art accuracy level, as well as inference time with that accuracy. Using time to accuracy
as a target metric, we explore how different optimizations, including choice of optimizer, stochastic
depth, and multi-GPU training, affect end-to-end training performance. Our results demonstrate that
optimizations can interact in non-trivial ways when used in conjunction, producing lower speed-ups
and less accurate models. We believe DAWNBench will provide a useful, reproducible means ofevaluating the many trade-offs in deep learning systems.

1

Introduction

Deep learning methods are effective but computationally expensive, leading to a great deal ofwork to optimize their computational performance. Researchers have proposed new softwaresystems [29, 1, 8, 12, 9, 46], training algorithms [33, 48, 45, 28, 44, 18, 27, 50, 42, 43, 14],communication methods [39, 49, 11, 9, 12, 22] and hardware [20, 30, 37, 19, 7, 21] to decrease thiscost. However, despite these significant advances, it is difficult to measure or compare the utilityof these results due to a lack of standard evaluation criteria. Most existing benchmarks for deeplearning performance [16, 10, 5, 2, 41, 8, 3] only measure proxy metrics such as the time to process
one minibatch of data. In reality, deep learning performance is far more complex. In some settings,
techniques such as using larger batch sizes [18, 30], reduced precision [21, 9, 25, 11] and asynchronous
updates [39, 12, 9, 49] can stop an algorithm from converging to a good result, or increase the time todo so. Moreover, these approaches interact in nontrivial ways and may require updating the underlying
optimization algorithm [33, 18, 36], further affecting end-to-end performance.
This lack of standard evaluation criteria results in a set of poorly-understood trade-offs. For example,
minimal effort back propagation delivers a 3.1x speed up over back propagation on MNIST [43].
Using 8-bit precision gives a 3x speed up on MNIST [11]. Does combining minimal effort backpropagation with 8-bit precision give a 9.3x speed up? Would that speed translate to a larger model
on a dataset like ImageNet, and combine with accurate, large minibatch SGD [18] to train an ImageNetmodel in 7 minutes? Currently, these questions can only be answered via tedious and time-consuming
experimentation. Moreover, which previous techniques should one build on when evaluating theefficiency of a new optimization?
To provide an objective means of quantifying end-to-end deep learning performance, we introduce
DAWNBench, an open benchmark and competition for end-to-end deep learning training and inference.
Instead of simply measuring time per iteration or throughput, DAWNBench measures end-to-end
31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

TasksMetrics
Datasets
Training time
ImageNet
Image classification
Training cost
CIFAR10
Inference latency
Question answering
SQuAD
Inference cost
Table 1: Dimensions evaluated in the first version of DAWNBench. All metrics are for a near-state-of-the-art
accuracy.

performance of training (e.g., time, cost) and inference (e.g., latency, cost) at a specified accuracy
level. This provides an objective means of normalizing across differences in computation frameworks,
hardware, optimization algorithms, hyperparameter settings, and other factors that affect real-world
performance. Our initial release of DAWNBench provides end-to-end learning and inference tasks
including image classification on CIFAR10 [34] and ImageNet [40], and question answering on
SQuAD [38], and reference implementations for each task.
In this paper, we present DAWNBench’s benchmark specification and goals, and show how one can use
DAWNBench to perform a simple performance study that measures the impact of choice of optimizer,
batch size, multi-GPU training, and stochastic depth [26]. Over time, and with community input, weplan to expand DAWNBench’s scope to include both additional benchmark tasks (e.g., segmentation,
machine translation, video classification) and metrics (e.g., energy, sample complexity).

2

Benchmark Overview and Goals

DAWNBench evaluates deep learning systems on different tasks based on several metrics, using
multiple datasets. The benchmark allows innovation in software, algorithms, communication methods,
etc. By only specifying the task, DAWNBench also allows experimentation of new model architecturesand hardware. In the initial release, we seed entries for two tasks: image classification on CIFAR10
and ImageNet, and question answering on SQuAD, and evaluate on four metrics: training time toa specified validation accuracy, cost (in USD) of training to a specified validation accuracy using
public cloud instances, average latency of performing inference on a single item (image or question),and average cost of inference for 10,000 items (Table 1). We also provide reference implementations
and seed entries, implemented in two popular deep learning frameworks, PyTorch and TensorFlow.
These reference implementations were collected and adapted from official repositories on Github,and produce accuracy numbers on par with those reported in the original research papers [24], while
also conforming to the various performance recommendations published with these frameworks [17].
DAWNBench is not the first benchmark to compare and evaluate deep learning systems on standard
tasks; previous efforts include Baidu DeepBench [5], Fathom [2], and TensorFlow Benchmark [16].
Our benchmark differs in two key aspects. First, DAWNBench focuses on end-to-end performance.
Many prior benchmarks use the time needed to train on a single minibatch of data as the key metric,
while disregarding the resulting accuracy of the trained model [2, 16, 10, 41, 4]. Other benchmarks [2, 5] focus on timing individual low-level operations (e.g. convolutions, matrix multiplications)
utilized in deep learning computations. DAWNBench, instead, measures time to a pre-specified (andhigh) level of accuracy, taking into account both hardware and statistical performance.
Second, we view DAWNBench as open and evolving. Prior benchmarks were often defined at asnapshot in time, using fixed model architectures and tasks. However, advances in deep learning
have quickly invalidated architectures. For example, recent approaches in machine translation using
recurrent layers have been quickly superseded by convolutional and attention layers [13, 47]. Deeplearning is a rapidly evolving field, and needs benchmarks that adapt at the same pace. We plan toallow DAWNBench to grow and evolve, and hope to foster a community discussion around advancesin the field, including new tasks.

3

Early Results

To demonstrate the utility of an end-to-end benchmark, we present DAWNBench results that aim
to answer three questions: (1) Is training time to a specified validation accuracy a useful metric toevaluate deep learning systems? (2) Do different optimizations in deep learning actually compose? (3)Using DAWNBench’s seed entries, what trade-offs between training time, training cost, and inference
latency do different software frameworks and hardware present?
2

Validation Acc. (%)

94
93
92
91
90

Throughput

Convergence Rate

2500
2000

0

50

100

150

200

Number of epochs

250

End-to-end Training Time

Images / second

Validation Acc. (%)

94
93
92
91
90

1500
1000
500

0.0

0.5

1.0

Batch size = 32

1.5

2.0

2.5

Time (in hours)

3.0

3.5

4.0

Batch size = 256

0

Batch size

Batch size = 2048

Figure 1: Effect of minibatch size on convergence rate, throughput, and end-to-end training time of a ResNet56
CIFAR10 model on a P100. Learning rates are tuned as per [18].

We follow the same basic training procedure as the original ResNet paper [24] for all experimentsin this section. For a batch size of 128, we use SGD with a weight decay of 0.0005 and momentumof 0.9. We use the weight initialization procedure from [23] and use Batch Normalization [28] without
dropout. We warm-up training with a learning rate of 0.01 for five epochs, then proceed with an
initial learning rate of 0.1 for 90 epochs; we decay the learning rate by a factor of 10 every 45 epochs
thereafter, and terminate training after 185 epochs. We follow the same data augmentation process
as [24]. For larger batch sizes, we linearly scale the initial learning rate according to [18], using a baselearning rate of 0.1 (corresponding to a batch size of 128).
Unless stated otherwise, the K80 machines used in this section are Google Cloud Engine instances
based on the n1-standard-8 instance type (8 vCPUs, 30 GB memory), with Haswell CPUs andGoogle’s standard HDD for persistent storage. The P100 machines used have 512 GB of memoryand 28 CPU cores, and are in a private cluster. We used TensorFlow 1.2 and PyTorch 0.1.12, compiled
from source with CUDA 8.0 and CuDNN 5.1. DAWNBench does not currently include distributed
training results, but we plan to explore this as future work, and also welcome outside submissions. All
experiments in this section compute validation accuracy on the CIFAR10 test set (10,000 images) [34].
3.1

Evaluating Time to Accuracy for Various Minibatch Sizes

To illustrate the value of DAWNBench’s end-to-end performance metric, we study how minibatch size
impacts both the convergence rate and hardware performance (FLOPS) of a deep learning workload.
Prior work [32, 18, 15, 6, 35] has shown that picking a minibatch size too small or too large can leadto poor convergence, i.e. minibatch size affects convergence. Additionally, larger minibatch sizes
better saturate hardware execution units [6, 15]. In choosing the minibatch size that minimizes total
time to a target accuracy, we must balance these two factors.
As we show in Figure 1, for a ResNet56 model trained on the CIFAR10 dataset on a Nvidia P100 GPU,a minibatch size of 32 produces the best convergence rate (least number of epochs to highest accuracy),
and a minibatch size of 2048 produces the best throughput (number of images processed divided bytotal time taken). A minibatch size of 256 represents a reasonable trade-off between convergence rate
and throughput. A minibatch size of 256 reaches an accuracy of 93.38%, which is only 0.43% lessthan the maximum accuracy achieved with a minibatch size of 32, in 1.9x less time. Benchmarks that
focus exclusively on convergence rate and throughput are unable to surface these practical trade-offs
for optimizations even as simple as modifying the minibatch size.
3.2

Composing Optimizations

To show how DAWNBench can be used for performance studies in practice, we run a small study that
evaluates the effect of composing several different optimizations for training Convolutional Neural
Networks (CNNs) on the CIFAR10 dataset. Evaluating deep learning systems is challenging, in part
because it’s unclear how different optimizations interact with each other. Given the complexity of deeplearning systems and plethora of hyperparameters involved, seemingly complementary optimizations
can interact in non-trivial ways and stop an algorithm from converging to a good result, or increase
the time to do so.
3Adam (LR = 0.01)

100
2
80

Loss

Validation Acc. (%)

SGD (LR = 0.1)

1

60
0

2

0

4

Time (in hours)

0

50

100

Epoch

150

Training time (in hours)

Figure 2: Impact of different optimizers while training a ResNet56 model on CIFAR10. The graph on the leftplots the top-1 validation accuracy with respect to time, and the graph on the right plots a rolling average of theloss with respect to the epoch. We see that Adam initially outperforms SGD with momentum, but falls shortaround epoch 100 (~2 hours in the graph on the left), and does not reach a validation accuracy of 93%.
1.00x 1.00x

6
5
4
3
2
1
0

93% Validation Acc.
94% Validation Acc.

1.02x
1.21x
1.72x 1.62x

1.72x 1.62x

1.71x

5.10x

4.95x

5.10x
DNC

DNC

DNC

DNC

Base
+SD
+4xK80 +BS=512
All
no SD no 4xK80 BS=128
Figure 3: Factor analysis for training a ResNet110 model on CIFAR10 with stochastic depth (SD), 4 Nvidia
K80 GPUs on a single node (4xK80), and a minibatch size (BS) of 512. Cumulatively enabling each optimization
reduces the time to 93% top-1 accuracy, but combined, the model does not converge (DNC) to the 94% accuracy
threshold. By removing the larger minibatch size, the model reaches the higher accuracy target.

Here, we demonstrate that different optimizations in fact do not compose while training a ResNet110
model on CIFAR10. Our goal here is not to provide blanket statements about any particular training
method. Instead, we showcase the trade-offs and interactions between methods and their non-trivial
impact on end-to-end accuracy and performance. We consider the following three optimizations,• Adam, an adaptive optimizer for gradient descent, which reports considerable speed-ups
over other adaptive optimizers when training CNNs on CIFAR10 [33].
• Single-node multi-GPU training, using 4 GPUs in two different settings: 1) With the same
minibatch size (128) as that used in the baseline approach, but distributed across the 4 GPUs
2) With effectively a minibatch size multiplied by 4, where every GPU is given a minibatch
size of 128. For the larger minibatch size, we tune the learning rate as per [18].
• Stochastic Depth [26], a technique that can be thought of as a form of regularization similar
to dropout. Entire layers are randomly dropped during training, helping to regularize against
co-adaptation, while still using the full network to perform inference. Stochastic Depth
reports improvements in both training time and accuracy.
Adam. After evaluating each optimization in isolation, we discovered Adam with multiple initial
learning rates (0.0001, 0.001, 0.01, 0.1) was unable to come close to the maximum validation accuracy
we were able to achieve with our baseline. Figure 2 shows the best performance using Adam (learning
rate = 0.01) compared to SGD with momentum using the standard learning rate schedule from [24].
Despite the better training loss and faster initial convergence on both the training and validation dataset,
Adam’s promising performance plateaued well below SGD. Because Adam failed to reach a reasonablevalidation accuracy in isolation, we eliminated it from the remainder of our factor analysis. Thisshows that training loss is only a proxy for accuracy and generalization, and that “time to an accuracy
threshold” is a valuable metric when evaluating deep learning systems.
Single-node Multi-GPU Training. We observe that the other two optimizations each provide a reasonable speed-up, but the net speed-up from applying all the optimizations at once is less than the productof the individual speed-ups as shown Figure 3. All speed-ups and accuracy thresholds are relative to theperformance of ResNet110 on 1 Nvidia K80 GPU with a minibatch size of 128 and initial learning rateof 0.1. The top accuracy ResNet110 achieved was 94.33%. Using this as a baseline, we chose two threshold points, 93% and 94% top-1 accuracy, representing different points in the trade-off space between
4Validation Acc. (%)

95

Baseline
Stoch. Depth
GPUs = 4 (BS = 128)
GPUs = 4 (BS = 512)
(GPUs = 4 [BS = 128]) + Stoch. Depth(GPUs = 4 [BS = 512]) + Stoch. Depth

94
93

0

2

4

6

Training time (in hours)

8

Figure 4: Comparison of maximum top-1 validation accuracy and training times of different combination of
optimizations. While reducing training time, each optimization decreases the maximum accuracy.

10

2

10

1

10

ResNet164 (S)ResNet164 (B)
1 K80 (EC2)
1 P10016 vCPUs (GC)

TensorFlow

0

10

1

Training time (hours; log10)

Inf. time (ms; log10)

Inf. time (ms; log10)

ResNet56
1 K80 (GC)

10

2

10

2

10

1

10

PyTorch0101Training time (hours; log10)

102Figure 5: Inference time vs. training time to 93% validation accuracy, for different hardware, frameworks, andmodel architectures in DAWNBench’s seed entries. ResNet164 (S) uses a simple building block, while (B) uses
a bottleneck building block. Amazon EC2 instances use a p2.xlarge instance type (4vCPUs, 61 GB memory).

training time and accuracy. 94% represents human-level accuracy on the CIFAR10 dataset [31]. 93%,
while considerably lower, helps demonstrate both the magnitude of speed-up possible when accuracy
constraints are relaxed, and the difficulty of achieving human-level or super human-levels of accuracy.
Scaling the number of GPUs while keeping the minibatch size the same produces a 1.68x and 1.59x
speed-up to reach 93% and 94% accuracy respectively, compared to the baseline. A larger batch sizeof 512 reached an accuracy of 93% much faster (speed-up of 4.95x over baseline), but failed to reachthe higher threshold. Interestingly, the speed-up in terms of time to 93% is larger than the increasein throughput alone (3.57x). In addition to scaling the learning rate according to [18], we also trieda batch size of 512 with multiple initial learning rates (0.1, 0.2, 0.3) with the same learning rate decay
schedule. Surprisingly, the learning rate of 0.1 outperformed the linearly scaled learning rate of 0.4
on CIFAR10 in terms of maximum accuracy. We thus used a learning rate of 0.1 for all experimentswith a batch size of 512 in Figure 3.
Stochastic Depth. Adding stochastic depth to ResNet110 provided a speed-up for both thresholds.
Stochastic depth was much faster at reaching the lower threshold (1.21x speed-up) than the higher
threshold (1.02x speed-up). While the total training time speed-up was close to the 1.25x speed-up
reported in [26], the number of epochs to reach the higher threshold was larger for stochastic depth
than the baseline, which led to the decreased speed-up in terms of time to accuracy.
Multi-GPU Training and Stochastic Depth Combined. By combining stochastic depth andmulti-GPU training, we see that the maximum accuracy reached by applying all the optimizations isabout a percent lower than the maximum accuracy reached by the baseline (Figure 4). We note that there
are many possible ways to tune learning rates, batch size and optimization algorithms that may achieve
better performance here. For this experiment, we chose to follow the standard recommendations in
the literature [24, 18, 26] to illustrate what a practitioner following these approaches may achieve, andwe performed parameter sweeps where possible to explore alternative settings. Without standard ways
to run and tune different optimizations jointly, composing optimizations may affect the convergence
rate of the algorithm, making performance hard to reason about.
3.3

Comparing Hardware and Software Frameworks

We seeded DAWNBench with single-GPU and CPU results for TensorFlow and PyTorch, using reference implementations of models when possible. We show some of the variability present across DAWNBench’s metrics even from simple factors such as the model, software framework, and hardware type, in
Figure 5. This figure presents training time to 93% validation accuracy, and single-image inference la5

Validation Acc. (%)

ResNet20

ResNet56

ResNet164 (S)ResNet164 (B)94

Accuracy
threshold
91.8%
93%

Time tothreshold1h 43m4h 42m92

94%

10h 6m96

90

Fastest model
ResNet20
ResNet56
ResNet164(simple)ResNet164(bottleneck)

94.4%
11h 42m
6
8
10
12
14
Training time (in hours)
Figure 6: Validation accuracy vs. training time for different ResNet architectures on CIFAR10. Horizontal lines
indicate accuracy thresholds of 91.8%, 93%, 94%, and 94.4%. ResNet20, ResNet56, ResNet164 (with simple building blocks), and ResNet164 (with bottleneck building blocks) are fastest to the corresponding accuracy thresholds.
2

4

Training cost ($)

0

8
6
4
2
0

0

1

2

3

4

Training time (in hours)

GPUs = 1 (BS = 128)
GPUs = 2 (BS = 256)
GPUs = 4 (BS = 512)
GPUs = 8 (BS = 1024)
GPUs = 8 (BS = 512)

Figure 7: Training cost vs. training time for ResNet56 on the CIFAR10 dataset, using different numbers of GPUs,
with an accuracy threshold of 92.5%. The cost of training stays roughly the same, regardless of the number ofGPUs used, until 8 GPUs. Training time scales almost linearly with the inverse of the number of GPUs.

tency for various ResNet architectures for the CIFAR10 dataset, on different hardware platforms (1 K80
GPU on two cloud providers [Google Compute Engine and Amazon EC2], 1 P100 GPU on a private
cluster, and a 16vCPU machine on Google Cloud with Broadwell microarchitecture) and frameworks.
As the figure illustrates, TensorFlow is faster than PyTorch on CPUs, but slightly slower on GPUs,
both for training and inference. This is partly due to data format: TensorFlow supports both NCHW
and NHWC layouts (N: Number of Samples, C: Number of Channels, H: Height, W: Width), whichgive better performance on GPUs and CPUs respectively, while PyTorch only supports NCHW. K80
performance is similar on both cloud providers, but with spot pricing for GPU instances, Amazon
is cheaper. Training and inference time are proportional to the depth of the model, as expected.
We also see that for different target validation accuracies, different ResNet architectures are “optimal”(that is, fastest to reach target validation accuracy). Figure 6 shows how the top-1 validation accuracy
varies with training time for different ResNets for CIFAR10 on a Nvidia K80 GPU. For lower accuracy
thresholds, shallower architectures reach the threshold faster.
We also demonstrate how training cost relates to training time for ResNet56 on CIFAR10 as the number
of GPUs used changes (Figure 7). Prior to running this experiment, we expected training cost to
increase as the number of GPUs increases (and training time decreases), leading to a Pareto frontier
between cost and time. In practice, we did not observe this to be the case for CIFAR10 experiments
run on 1 to 4 GPUs – training time scales perfectly linearly with the inverse of the number of GPUs
used, and hence cost remains constant despite training time going down. However, when scaling thiscomputation to 8 GPUs, we did see an increase in the cost of training, because training time does notdecrease enough to counter the doubling in instance cost per unit time.

4

Conclusion

DAWNBench is a benchmark that measures the end-to-end time to train a model with state-of-the-art
accuracy, and inference time with that accuracy. By focusing on time to accuracy, we show how
different optimizations can interact; using different optimizations in conjunction can prevent models
from converging, or increase the time to do so. Reasoning about deep learning systems in this way
exposes valuable trade-offs between training time, training cost, and inference time. We intend tokeep DAWNBench up to date with new tasks and goals to foster a quantitatively-informed dialogue
around progress in deep learning systems.
6

Acknowledgments
We thank Toby Boyd, Doug Burger, Soumith Chintala, Ramesh Illikal, Mario Srouji, Jian Zhang, BillZhao, Xiaoqiang Zheng, the anonymous reviewers, and the many members of the Stanford InfoLab
for their valuable feedback on this work. We also thank Amazon and Google for cloud credits. This
research was supported in part by affiliate members and other supporters of the Stanford DAWN
project – Intel, Microsoft, Teradata, and VMware – as well as industrial gifts and support from Toyota
Research Institute, Juniper Networks, Keysight Technologies, Hitachi, Facebook, Northrop Grumman,
NetApp, and the NSF under grants DGE-1656518, DGE-114747, and CNS-1651570.

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