问题描述
所以我刚刚进行了tune实验，并得到了以下输出：
+--------------------+------------+-------+-------------+----------------+--------+------------+
| Trial name | status | loc | lr | weight_decay | loss | accuracy |
|--------------------+------------+-------+-------------+----------------+--------+------------|
| trainable_13720f86 | TERMINATED | | 0.00116961 | 0.00371219 | 0.673 | 0.7977 |
| trainable_13792744 | TERMINATED | | 0.109529 | 0.0862344 | 0.373 | 0.8427 |
| trainable_137ecd98 | TERMINATED | | 4.35062e-06 | 0.0261442 | 0.6993 | 0.7837 |
| trainable_1383f9d0 | TERMINATED | | 1.37858e-05 | 0.0974182 | 0.4538 | 0.8428 |
| trainable_13892f72 | TERMINATED | | 0.0335583 | 0.0403495 | 0.3399 | 0.8618 |
| trainable_138dd720 | TERMINATED | | 0.00858623 | 0.0695453 | 0.3415 | 0.8612 |
| trainable_1395570c | TERMINATED | | 4.6309e-05 | 0.0172459 | 0.39 | 0.8283 |
| trainable_139ce148 | TERMINATED | | 2.32951e-05 | 0.0787076 | 0.3641 | 0.8512 |
| trainable_13a848ee | TERMINATED | | 0.00431763 | 0.0341105 | 0.3415 | 0.8611 |
| trainable_13ad0a78 | TERMINATED | | 0.0145063 | 0.050807 | 0.3668 | 0.8398 |
| trainable_13b3342a | TERMINATED | | 5.96148e-06 | 0.0110345 | 0.3418 | 0.8608 |
| trainable_13bd4d3e | TERMINATED | | 1.82617e-06 | 0.0655128 | 0.3667 | 0.8501 |
| trainable_13c45a2a | TERMINATED | | 0.0459573 | 0.0224991 | 0.3432 | 0.8516 |
| trainable_13d561d0 | TERMINATED | | 0.00060595 | 0.092522 | 0.3389 | 0.8623 |
| trainable_13dcb962 | TERMINATED | | 0.000171044 | 0.0449039 | 0.3429 | 0.8584 |
| trainable_13e6fd32 | TERMINATED | | 0.000104752 | 0.089106 | 0.3497 | 0.8571 |
| trainable_13ecd2ac | TERMINATED | | 0.000793432 | 0.0477341 | 0.6007 | 0.8051 |
| trainable_13f27464 | TERMINATED | | 0.0750381 | 0.0685323 | 0.3359 | 0.8616 |
| trainable_13f80b40 | TERMINATED | | 1.3946e-06 | 0.0192844 | 0.5615 | 0.8146 |
| trainable_13fdf6e0 | TERMINATED | | 9.4748e-06 | 0.0542356 | 0.3546 | 0.8493 |
+--------------------+------------+-------+-------------+----------------+--------+------------+
但是，当我查看单个结果时，我发现对于第三次试验（trainable_137ecd98），尽管最终精度较低，但其迭代精度却高于其他试验（89.8％）：
如果我要检查并报告给定试验达到的最高准确度（或最佳其他指标），是用户意图跟踪每个试验的best_metric并编写自定义报告best_metric更新时检查点？
我看到checkpoint_at_end中有一个tune.run选项，但是最常见的用例不是checkpoint_if_best，因为上一次试验的训练迭代很少是最好的？ / p>
谢谢！
解决方法
如果您只想为每个试验保留1个最佳检查点，则可以
tune.run(keep_checkpoints_num=1,checkpoint_score_attr="accuracy")
如果您想保留多个检查点，但想在实验结束后获得最佳检查点，则可以执行以下操作：
analysis = tune.run(...)
# Gets best trial based on max accuracy across all training iterations.
best_trial = analysis.get_best_trial(metric="accuracy",mode="max",scope="all")
# Gets best checkpoint for trial based on accuracy.
best_checkpoint = analysis.get_best_checkpoint(best_trial,metric="accuracy")