# Saving checkpoints with good custom\_metric using tune.run()

**URL:** <https://discuss.ray.io/t/saving-checkpoints-with-good-custom-metric-using-tune-run/2109>\
**Category:** Ray Tune\
**Created:** [May 9, 2021, 7:03am UTC](https://discuss.ray.io/t/saving-checkpoints-with-good-custom-metric-using-tune-run/2109 "2021-05-09T07:03:11Z")\
**Posts on this page:** 1\
**Showing post:** 12

<div class="post-metadata">

**Author:** ![mannyv](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/mannyv/32/606_2.png) [@mannyv](https://discuss.ray.io/u/mannyv)\
**Post date:** [May 13, 2021, 2:24am UTC](https://discuss.ray.io/t/saving-checkpoints-with-good-custom-metric-using-tune-run/2109/12 "2021-05-13T02:24:52Z")

</div>

At least for this example here using the json log does work with these changes applied.

```diff
--- orig/ray/tune/analysis/experiment_analysis.py	2021-05-12 22:18:02.195126415 -0400
+++ fixed/ray/tune/analysis/experiment_analysis.py	2021-05-12 22:15:36.774961258 -0400
@@ -17,7 +17,7 @@
 
 from ray.tune.error import TuneError
 from ray.tune.result import DEFAULT_METRIC, EXPR_PROGRESS_FILE, \
- EXPR_PARAM_FILE, CONFIG_PREFIX, TRAINING_ITERATION
+ EXPR_PARAM_FILE, CONFIG_PREFIX, TRAINING_ITERATION, EXPR_RESULT_FILE
 from ray.tune.trial import Trial
 from ray.tune.utils.trainable import TrainableUtil
 from ray.tune.utils.util import unflattened_lookup
@@ -172,9 +172,11 @@
         fail_count = 0
         for path in self._get_trial_paths():
             try:
- self.trial_dataframes[path] = pd.read_csv(
- os.path.join(path, EXPR_PROGRESS_FILE))
- except Exception:
+ data = [json.loads(line) for line in open(os.path.join(path, EXPR_RESULT_FILE), 'r').read().split('\n') if line]
+ self.trial_dataframes[path] = pd.json_normalize(data, sep="/")
+ except Exception as ex:
                 fail_count += 1
 
         if fail_count:
@@ -280,6 +282,8 @@
         assert mode is None or mode in ["max", "min"]
         rows = {}
         for path, df in self.trial_dataframes.items():
+ if metric not in df:
+ continue
             if mode == "max":
                 idx = df[metric].idxmax()
             elif mode == "min":

```

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_[View the full topic](https://discuss.ray.io/t/saving-checkpoints-with-good-custom-metric-using-tune-run/2109)._