Massively Parallel Hyperparameter Tuning
Modern machine learning models are characterized by large hyperparameter search spaces and prohibitively expensive training costs. For such models, we cannot afford to train candidate models sequentially and wait months before finding a suitable hyperparameter configuration. Hence, we introduce the large-scale regime for parallel hyperparameter tuning, where we need to evaluate orders of magnitude more configurations than available parallel workers in a small multiple of the wall-clock time needed to train a single model. We propose a novel hyperparameter tuning algorithm for this setting that exploits both parallelism and aggressive early-stopping techniques, building on the insights of the Hyperband algorithm. Finally, we conduct a thorough empirical study of our algorithm on several benchmarks, including large-scale experiments with up to 500 workers. Our results show that our proposed algorithm finds good hyperparameter settings nearly an order of magnitude faster than random search.
 This paper adapts the sequential halving algorithm that underpins Hyperband to run across multiple workers in a compute cluster. This represents a very practical scenario where a user of this algorithm would like to trade off computational efficiency for a reduction in wall time. The paper*s empirical results confirm that indeed significant reductions in wall time come with modest increases in overall computation, it*s a practical improvement. The paper is crisply written, the extension is a natural one, the experiment protocols and choice of baselines are appropriate. The left panel of figure 3 is blurry, compared with the right one.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Massively Parallel Hyperparameter Tuning
Modern machine learning models are characterized by large hyperparameter search spaces and prohibitively expensive training costs. For such models, we cannot afford to train candidate models sequentially and wait months before finding a suitable hyperparameter configuration. Hence, we introduce the large-scale regime for parallel hyperparameter tuning, where we need to evaluate orders of magnitude more configurations than available parallel workers in a small multiple of the wall-clock time needed to train a single model. We propose a novel hyperparameter tuning algorithm for this setting that exploits both parallelism and aggressive early-stopping techniques, building on the insights of the Hyperband algorithm. Finally, we conduct a thorough empirical study of our algorithm on several benchmarks, including large-scale experiments with up to 500 workers. Our results show that our proposed algorithm finds good hyperparameter settings nearly an order of magnitude faster than random search.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper adapts the sequential halving algorithm that underpins Hyperband to run across multiple workers in a compute cluster. This represents a very practical scenario where a user of this algorithm would like to trade off computational efficiency for a reduction in wall time. The paper*s empirical results confirm that indeed significant reductions in wall time come with modest increases in overall computation, it*s a practical improvement. The paper is crisply written, the extension is a natural one, the experiment protocols and choice of baselines are appropriate. The left panel of figure 3 is blurry, compared with the right one.
assistant
 This paper adapts the sequential halving algorithm that underpins Hyperband to run across multiple workers in a compute cluster. This represents a very practical scenario where a user of this algorithm would like to trade off computational efficiency for a reduction in wall time. The paper*s empirical results confirm that indeed significant reductions in wall time come with modest increases in overall computation, it*s a practical improvement. The paper is crisply written, the extension is a natural one, the experiment protocols and choice of baselines are appropriate. The left panel of figure 3 is blurry, compared with the right one.