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Title: Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints

Paper content:
In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence.
However, the growing complexity of machine learning datasets and models may violate such assumptions.
Indeed, current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints.
Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training.
We analyze the following problem: "given a dataset, algorithm, and fixed resource budget, what is the best achievable performance?"
We focus on the number of optimization iterations as the representative resource.
Under such a setting, we show that it is critical to adjust the learning rate schedule according to the given budget.
Among budget-aware learning schedules, we find simple linear decay to be both robust and high-performing.
We support our claim through extensive experiments with state-of-the-art models on ImageNet (image classification), Kinetics (video classification), MS COCO (object detection and instance segmentation), and Cityscapes (semantic segmentation).
We also analyze our results and find that the key to a good schedule is budgeted convergence, a phenomenon whereby the gradient vanishes at the end of each allowed budget.
We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.
Deep neural networks have made an undeniable impact in advancing the state-of-the-art for many machine learning tasks.
Improvements have been particularly transformative in computer vision (Huang et al., 2017b; He et al., 2017) .
Much of these performance improvements were enabled by an ever-increasing amount of labeled visual data (Russakovsky et al., 2015; Kuznetsova et al., 2018) and innovations in training architectures (Krizhevsky et al., 2012; He et al., 2016) .
However, as training datasets continue to grow in size, we argue that an additional limiting factor is that of resource constraints for training.
Conservative prognostications of dataset sizes -particularly for practical endeavors such as self-driving cars (Bojarski et al., 2016) , assistive medical robots (Taylor et al., 2008) , and medical analysis (Fatima & Pasha, 2017) -suggest one will train on datasets orders of magnitude larger than those that are publicly available today.
Such planning efforts will become more and more crucial, because in the limit, it might not even be practical to visit every training example before running out of resources (Bottou, 1998; Rai et al., 2009 ).
We note that resource-constrained training already is implicitly widespread, as the vast majority of practitioners have access to limited compute.
This is particularly true for those pursuing research directions that require a massive number of training runs, such as hyper-parameter tuning (Li et al., 2017) and neural architecture search (Zoph & Le, 2017; Cao et al., 2019; Figure 1 : We formalize the problem of budgeted training, in which one maximizes performance subject to a fixed training budget.
We find that a simple and effective solution is to adjust the learning rate schedule accordingly and anneal it to 0 at the end of the training budget.
This significantly outperforms off-the-shelf schedules, particularly for small budgets.
This plot shows several training schemes (solid curves) for ResNet-18 on ImageNet.
The vertical axis in the right plot is normalized by the validation accuracy achieved by the full budget training.
The dotted green curve indicates an efficient way of trading off computation with performance.
Instead of asking "what is the best performance one can achieve given this data and algorithm?", which has been the primary focus in the field so far, we decorate this question with budgeted training constraints as follows: "what is the best performance one can achieve given this data and algorithm within the allowed budget?".
Here, the allowed budget refers to a limitation on the total time, compute, or cost spent on training.
More specifically, we focus on limiting the number of iterations.
This allows us to abstract out the specific constraint without loss of generality since any one of the aforementioned constraints could be converted to a finite iteration limit.
We make the underlying assumption that the network architecture is constant throughout training, though it may be interesting to entertain changes in architecture during training (Rusu et al., 2016; Wang et al., 2017) .
Much of the theoretical analysis of optimization algorithms focuses on asymptotic convergence and optimality (Robbins & Monro, 1951; Nemirovski et al., 2009; Bottou et al., 2018) , which implicitly makes use of an infinite compute budget.
That said, there exists a wide body of work (Zinkevich, 2003; Kingma & Ba, 2015; Reddi et al., 2018; Luo et al., 2019) that provide performance bounds which depend on the iteration number, which apply even in the non-asymptotic regime.
Our work differs in its exploration of maximizing performance for a fixed number of iterations.
Importantly, the globally optimal solution may not even be achievable in our budgeted setting.
Given a limited budget, one obvious strategy might be data subsampling (Bachem et al., 2017; Sener & Savarese, 2018) .
However, we discover that a much more effective, simpler, and under-explored strategy is adopting budget-aware learning rate schedules -if we know that we are limited to a single epoch, one should tune the learning schedule accordingly.
Such budget-aware schedules have been proposed in previous work (Feyzmahdavian et al., 2016; Lian et al., 2017) , but often for a fixed learning rate that depends on dataset statistics.
In this paper, we specifically point out linearly decaying the learning rate to 0 at the end of the budget, may be more robust than more complicated strategies suggested in prior work.
Though we are motivated by budget-aware training, we find that a linear schedule is quite competitive for general learning settings as well.
We verify our findings with state-of-the-art models on ImageNet (image classification), Kinetics (video classification), MS COCO (object detection and instance segmentation), and Cityscapes (semantic segmentation).
We conduct several diagnostic experiments that analyze learning rate decays under the budgeted setting.
We first observe a statistical correlation between the learning rate and the full gradient magnitude (over the entire dataset).
Decreasing the learning rate empirically results in a decrease in the full gradient magnitude.
Eventually, as the former goes to zero, the latter vanishes as well, suggesting that the optimization has reached a critical point, if not a local minimum 1 .
We call this phenomenon budgeted convergence and we find it generalizes across budgets.
On one hand, it implies that one should decay the learning rate to zero at the end of the training, even given a small budget.
On the other hand, it implies one should not aggressively decay the learning rate early in the optimization (such as the case with an exponential schedule) since this may slow down later progress.
Finally, we show that linear budget-aware schedules outperform recently-proposed fast-converging methods that make use of adaptive learning rates and restarts.
Our main contributions are as follows:
• We introduce a formal setting for budgeted training based on training iterations and provide an alternative perspective for existing learning rate schedules.
• We discover that budget-aware schedules are handy solutions to budgeted training.
Specifically, our proposed linear schedule is more simple, robust, and effective than prior approaches, for both budgeted and general training.
• We provide an empirical justification of the effectiveness of learning rate decay based on the correlation between the learning rate and the full gradient norm.
In this section, we summarize our empirical analysis with a desiderata of properties for effective budget-aware learning schedules.
We highlight those are inconsistent with conventional wisdom and follow the experimental setup in Sec 4.1 unless otherwise stated.
∇F (x i , y i ).
We empirically find that the dynamics of ||g * t || over time highly correlates with the learning rate α t (Fig 3) .
As the learning rate vanishes for budget-aware schedules, so does the gradient magnitude.
We call this "vanishing gradient" phenomenon budgeted convergence.
This correlation suggests that decaying schedules to near-zero rates (and using BAC) may be more effective than early stopping.
As a side note, budgeted convergence resonates with classic literature that argues that SGD behaves similar to simulated annealing (Bottou, 1991) .
Given that α t and ||g * t || decrease, the overall update ||−α t g t || also decreases 4 .
In other words, large moves are more likely given large learning rates in the beginning, while small moves are more likely given small learning rates in the end.
However, the exact mechanism by which the learning rate influences the gradient magnitude remains unclear.
Desideratum: don't waste the budget.
Common machine learning practise often produces multiple checkpointed models during a training run, where a validation set is used to select the best one.
Such additional optimization is wasteful in our budgeted setting.
Tab 4 summarizes the progress point at which the best model tends to be found.
Step decay produces an optimal model somewhat towards the end of the training, while linear and poly are almost always optimal at the precise end of the training.
This is especially helpful for state-of-the-art models where evaluation can be expensive.
For example, validation for Kinetics video classification takes several hours.
Budget-aware schedules require validation on only the last few epochs, saving additional compute.
Table 4 : Where does one expect to find the model with the highest validation accuracy within the training progress?
Here we show the best checkpoint location measured in training progress p and averaged for each schedule across budgets greater or equal than 10% and 3 different runs.
Aggressive early descent.
Guided by asymptotic convergence analysis, faster descent of the objective might be an apparent desideratum of an optimizer.
Many prior optimization methods explicitly call for faster decrease of the objective (Kingma & Ba, 2015; Clevert et al., 2016; Reddi et al., 2018) .
In contrast, we find that one should not employ aggressive early descent because large learning rates can prevent budgeted convergence.
Consider AMSGrad (Reddi et al., 2018) , an adaptive learning rate that addresses a convergence issue with the widely-used Adam optimizer (Kingma & Ba, 2015) .
Fig 4 shows that while AMSGrad does quickly descend over the training objective, it still underperforms budget-aware linear schedules over any given training budget.
To examine why, we derive the equivalent rate β t for AMSGrad (Appendix B) and show that it is dramatically larger than our defaults, suggesting the optimizer is too aggressive.
We include more adaptive methods for evaluation in Appendix E.
Warm restarts.
SGDR (Loshchilov & Hutter, 2017 ) explores periodic schedules, in which each period is a cosine scaling.
The schedule is intended to escape local minima, but its effectiveness has been questioned (Gotmare et al., 2019).
Fig 5 shows that SDGR has faster descent but is inferior to budget-aware schedules for any budget (similar to the adaptive optimizers above).
Additional comparisons can be found in Appendix F. Whether there exists a method that achieves promising anytime performance and budgeted performance at the same time remains an open question.
(Loshchilov & Hutter, 2017 ) with linear schedules.
(a) SGDR makes slightly faster initial descent of the training loss, but is surpassed at each given budget by the linear schedule.
(b) for SGDR, the correlation between full gradient norm ||g * t || and learning rate αt is also observed.
Warm restart does not help to achieve better budgeted performance.
This paper introduces a formal setting for budgeted training.
Under this setup, we observe that a simple linear schedule, or any other smooth-decaying schedules can achieve much better performance.
Moreover, the linear schedule even offers comparable performance on existing visual recognition tasks for the typical full budget case.
In addition, we analyze the intriguing properties of learning rate schedules under budgeted training.
We find that the learning rate schedule controls the gradient magnitude regardless of training stage.
This further suggests that SGD behaves like simulated annealing and the purpose of a learning rate schedule is to control the stage of optimization.
In the main text, we list neural architecture search as an application of budgeted training.
Due to resource constraint, these methods usually train models with a small budget (10-25 epochs) to evaluate their relative performance (Cao et al., 2019; Cai et al., 2018; Real et al., 2019) .
Under this setting, the goal is to rank the performance of different architectures instead of obtaining the best possible accuracy as in the regular case of budgeted training.
Then one could ask the question that whether budgeted training techniques help in better predicting the relative rank.
Unfortunately, budgeted training has not been studied or discussed in the neural architecture search literature, it is unknown how well models only trained with 10 epochs can tell the relative performance of the same ones that are trained with 200 epochs.
Here we conduct a controlled experiment and show that proper adjustment of learning schedule, specifically the linear schedule, indeed improves the accuracy of rank prediction.
We adapt the code in (Cao et al., 2019) to generate 100 random architectures, which are obtained by random modifications (adding skip connection, removing layer, changing filter numbers) on top of ResNet-18 (He et al., 2017) .
First, we train these architectures on CIFAR-10 given full budget (200 epochs), following the setting described in Sec 4.1.
This produces a relative rank between all pairs of random architectures based on the validation accuracy and this rank is considered as the target to predict given limited budget.
Next, every random architecture is trained with various learning schedules under various small budgets.
For each schedule and each budget, this generates a complete rank.
We treat this rank as the prediction and compare it with the target full-budget rank.
The metric we adopt is Kendall's rank correlation coefficient (τ ), a standard statistics metric for measuring rank similarity.
It is based on counting the inversion pairs in the two ranks and (τ + 1)/2 is approximately the probability of estimating the rank correctly for a pair.
We consider the following schedules: (1) constant, it might be possible that no learning rate schedule is required if only the relative performance is considered.
(2) step decay (γ = 0.1, decay at p ∈ { The results suggest that with more budget, we can better estimate the full-budget rank between architectures.
And even if only relative performance is considered, learning rate decay should be applied.
Specifically, smooth-decaying schedule, such as linear or cosine, are preferred over step decay.
We list some additional details about the experiment.
To reduce stochastic noise, each configuration under both the small and full budget is repeated 3 times and the median accuracy is taken.
The fullbudget model is trained with linear schedule, similar results are expected with other schedules as evidenced by the CIFAR-10 results in the main text (Tab 2).
Among the 100 random architectures, 21 cannot be trained, the rest of 79 models have validation accuracy spanning from 0.37 to 0.94, with the distribution mass centered at 0.91.
Such skewed and widespread distribution is the typical case in neural architecture search.
We remove the 21 models that cannot be trained for our experiments.
We take the epoch with the best validation accuracy for each configuration, so the drawback of constant or step decay not having the best model at the very end does not affect this experiment (see Sec 5).
Table C : Tab B normalized by the full-budget accuracy and then averaged across architectures.
Linear schedule achieves solutions closer to their full-budget performance than the rest of schedules under small budgets.

assistant: text: This work presents a technique for tuning the learning rate for Neural Network training when under a fixed number of epochs.