Update rules to exclude test set in scoring

RULES.md

@@ -50,7 +50,7 @@ For a description of how to submit a training algorithm to the AlgoPerf: Trainin
 
 ### Submissions
 
-A valid submission is a piece of code that defines all of the submission functions and is able to train all benchmark workloads on the [benchmarking hardware](#benchmarking-hardware) (defined in the [Scoring](#scoring) section). Both the validation set and the test set performance will be checked regularly during training (see the [Evaluation during training](#evaluation-during-training) section). Training halts when the workload-specific [target errors](#defining-target-performance) for the validation and test sets have been reached. For each workload, the training time to reach the *test* set target error is used as input to the [scoring process](#scoring) for the submission. Submissions using [external tuning](#external-tuning-ruleset) will be tuned independently for each workload using a single workload-agnostic search space for their specified hyperparameters. The tuning trials are selected based on the time to reach the *validation* target, but only their training times to reach the *test* target will be used for scoring. Submissions under either tuning ruleset may always self-tune while on the clock.
+A valid submission is a piece of code that defines all of the submission functions and is able to train all benchmark workloads on the [benchmarking hardware](#benchmarking-hardware) (defined in the [Scoring](#scoring) section). Both the validation set and the test set performance will be checked regularly during training (see the [Evaluation during training](#evaluation-during-training) section), however, only the validation performance is relevant for scoring. Training halts when the workload-specific [target errors](#defining-target-performance) for the validation and test sets have been reached. For each workload, only the training time to reach the *validation* set target error is used as input to the [scoring process](#scoring) for the submission. Submissions using [external tuning](#external-tuning-ruleset) will be tuned independently for each workload using a single workload-agnostic search space for their specified hyperparameters. The tuning trials are selected based on the time to reach the *validation* target. Submissions under either tuning ruleset may always self-tune while on the clock.
 
 #### Specification
 
@@ -356,17 +356,17 @@ Tuning will be substantially different for the [external](#external-tuning-rules
 
 For each workload, the hyperparameters are tuned using $O=20$ tuning **trials**. To estimate the variance of the results, this tuning will be repeated for $S=5$ **studies**, for a total of $S\cdot O = 100$ different hyperparameter settings. The submitters will provide a workload-agnostic search space and the working group will then return $100$ hyperparameters settings obtained using [(quasi)random search](https://arxiv.org/abs/1706.03200). The working group will also randomly partition these $100$ trials into $5$ studies of $20$ trials each. In lieu of independent samples from a search space, submissions can instead supply a fixed list of $20$ hyper-parameter points that will be sampled without replacement.
 
-In each trial, the tuning trial with the fastest training time to achieve the *validation target* is determined among the $O=20$ hyperparameter settings. For scoring, however, we use the training time to reach the *test targets* of those $5$ selected runs. The median of these $5$ per-study training times will be the final training time for the submission on this workload and is used in the scoring procedure (see the "[Scoring submissions](#scoring)" section). In other words: We use the *validation performance* for tuning and selecting the best hyperparameter but use the *test performance* when measuring the training speed. Runs that do not reach the target performance of the evaluation metric have an infinite time. Submissions are always free to perform additional self-tuning while being timed.
+In each trial, the tuning trial with the fastest training time to achieve the *validation target* is determined among the $O=20$ hyperparameter settings. For scoring, we use this required training time to reach the *validation targets* of those $5$ selected runs. The median of these $5$ per-study training times will be the final training time for the submission on this workload and is used in the scoring procedure (see the "[Scoring submissions](#scoring)" section). Runs that do not reach the target performance of the evaluation metric have an infinite time. Submissions are always free to perform additional self-tuning while being timed.
 
 #### Self-tuning ruleset
 
 Submissions to this ruleset are not allowed to have user-defined hyperparameters. This ruleset allows both submissions that use the same hyperparameters for all workloads, including the randomized ones (e.g. Adam with default parameters), as well as submissions that perform inner-loop tuning during their training run (e.g. SGD with line searches).
 
-Submissions will run on one instance of the [benchmarking hardware](#benchmarking-hardware). As always, submissions are allowed to perform inner-loop tuning (e.g. for their learning rate) but the tuning efforts will be part of their score. A submission will run *S=5* times and its score will be the median time to reach the target evaluation metric value on the test set. To account for the lack of external tuning, submissions have a longer time budget to reach the target performance. Compared to the [external tuning ruleset](#external-tuning-ruleset), the `max_runtime` is tripled. Runs that do not reach the target performance of the evaluation metric within this allotted time budget have an infinite time.
+Submissions will run on one instance of the [benchmarking hardware](#benchmarking-hardware). As always, submissions are allowed to perform inner-loop tuning (e.g. for their learning rate) but the tuning efforts will be part of their score. A submission will run *S=5* times and its score will be the median time to reach the target evaluation metric value on the validation set. To account for the lack of external tuning, submissions have a longer time budget to reach the target performance. Compared to the [external tuning ruleset](#external-tuning-ruleset), the `max_runtime` is tripled. Runs that do not reach the target performance of the evaluation metric within this allotted time budget have an infinite time.
 
 ### Workloads
 
-For the purposes of the Training Algorithm Track, we consider a workload the combination of a `dataset`, `model`, `loss_fn`, along with a target that is defined over some evaluation metric. E.g., ResNet50 on ImageNet using the cross-entropy loss until a target error of 34.6% on the test set has been reached, would constitute a workload. The evaluation metric, in this example the misclassification error rate, is directly implied by the dataset/task.
+For the purposes of the Training Algorithm Track, we consider a workload the combination of a `dataset`, `model`, `loss_fn`, along with a target that is defined over some evaluation metric. E.g., ResNet50 on ImageNet using the cross-entropy loss until a target error of 22.6% on the validation set has been reached, would constitute a workload. The evaluation metric, in this example the misclassification error rate, is directly implied by the dataset/task.
 
 Submissions will be scored based on their performance on the [fixed workload](#fixed-workloads). However, additionally submissions must also perform resonably well on a set of [held-out workloads](#randomized-workloads) in order for their score on the fixed workload to count (for full details see the [Scoring](#scoring) section). These held-out workloads will be generated after the submission deadline, but their randomized generating process is publicly available with the call for submissions (see "[Randomized workloads](#randomized-workloads)" section).  
 
@@ -409,9 +409,9 @@ For the [external tuning ruleset](#external-tuning-ruleset), we will only use $1
 
 ### Scoring
 
-Submissions will be scored based on their required training time to reach the target performance on the test set of each workload. This target performance metric can be the same as the loss function but might also be a different workload-specific metric such as the error rate or BLEU score. The target performance was defined using four standard training algorithms, see the "[Defining target performance](#defining-target-performance)" section for more details. The training time of a submission includes the compilation times for computation graphs and ops that could happen just-in-time during training; all our benchmarks should be fast enough to compile so as not to dramatically impact overall performance. The overall ranking is then determined by summarizing the performances across all [fixed workloads](#fixed-workloads), using [performance profiles](#benchmark-score-using-performance-profiles), as explained below.
+Submissions will be scored based on their required training time to reach the target performance on the validation set of each workload. This target performance metric can be the same as the loss function but might also be a different workload-specific metric such as the error rate or BLEU score. The target performance was defined using four standard training algorithms, see the "[Defining target performance](#defining-target-performance)" section for more details. The training time of a submission includes the compilation times for computation graphs and ops that could happen just-in-time during training; all our benchmarks should be fast enough to compile so as not to dramatically impact overall performance. The overall ranking is then determined by summarizing the performances across all [fixed workloads](#fixed-workloads), using [performance profiles](#benchmark-score-using-performance-profiles), as explained below.
 
-While the training time to the *test set* target is used for scoring, we use the training time to the *validation set* target for tuning. This is only relevant for submissions in the [external tuning ruleset](#external-tuning-ruleset) but is also enforced for self-reported results (i.e. submissions in the self-reported ruleset must also reach the validation target in time but only the time to the test target is used for scoring). Submitters must select the hyperparameter setting that reached the *validation* target the fastest, irrespective of its training time to achieve the *test* target. This ensures a fair and practical procedure.
+The training time until the target performance on the test set was reached is not used in the scoring procedure but might be used for additional analysis of the competition results.
 
 #### Benchmarking hardware
 
@@ -430,7 +430,7 @@ Both [tuning rulesets](#tuning) will use the same target performances. The runti
 
 We will aggregate the training times of a submission on all fixed workloads using [Performance Profiles](http://www.argmin.net/2018/03/26/performance-profiles/) (originally from [Dolan and Moré](https://arxiv.org/abs/cs/0102001)). Below we surface several relevant definitions from their work for easier readability, before explaining how we integrate the performance profiles to reach a scalar benchmark score that will be used for ranking submissions.
 
-*Notation:* We have a set $\mathcal{S} = \{s_1, s_2, \dots, s_k\}$ of in total $k$ submissions that we evaluate on a set of $n$ fixed workloads: $\mathcal{W} = \{w_1, w_2, \dots, w_n\}$. For each submission $s$ and each workload $w$ we have a training time score $t_{s,w} \in [0,\infty)$. This is the time it took the submission to reach the test target performance on this particular workload.
+*Notation:* We have a set $\mathcal{S} = \{s_1, s_2, \dots, s_k\}$ of in total $k$ submissions that we evaluate on a set of $n$ fixed workloads: $\mathcal{W} = \{w_1, w_2, \dots, w_n\}$. For each submission $s$ and each workload $w$ we have a training time score $t_{s,w} \in [0,\infty)$. This is the time it took the submission to reach the validation target performance on this particular workload.
 
 ##### Computing performance ratios
 
@@ -466,10 +466,10 @@ The integral is normalized by the total integration area, with higher benchmark
 
 For the benchmark score, we compute and integrate the performance profiles using the training times of only the fixed workloads. But we use the submission's performance on the held-out workloads to penalize submissions. Specifically, if a submission is unable to train a held-out workload, we score the submission on the corresponding fixed workload as if that submission did not reach the target. In other words, for a submission to receive a finite training time on a fixed workload, it needs to:
 
-- Reach the validation and test target on the fixed workload within the maximum runtime.
-- Reach the validation and test target fixed workload within 4x of the fastest submission.
-- Reach the validation and test target on the held-out workload (corresponding to the fixed workload) within the maximum runtime.
-- Reach the validation and test target on the held-out workload (corresponding to the fixed workload) within 4x of the fastest submission. To determine the fastest submission on a held-out workload, we only consider submissions that reached the target on the corresponding fixed workload. This protects us against extremely fast submissions that only work on a specific held-out workload and are useless as general algorithms.
+- Reach the validation target on the fixed workload within the maximum runtime.
+- Reach the validation target fixed workload within 4x of the fastest submission.
+- Reach the validation target on the held-out workload (corresponding to the fixed workload) within the maximum runtime.
+- Reach the validation target on the held-out workload (corresponding to the fixed workload) within 4x of the fastest submission. To determine the fastest submission on a held-out workload, we only consider submissions that reached the target on the corresponding fixed workload. This protects us against extremely fast submissions that only work on a specific held-out workload and are useless as general algorithms.
 
 Only if all four requirements are met, does the submission get a finite score. Otherwise, a submission will receive a training time of infinity.