[autoscaler/tune] Optional YAML Fields + Fix Pretty Printing for Tune (#1541)

python/ray/tune/logger.py

@@ -6,6 +6,7 @@ import csv
 import json
 import numpy as np
 import os
+import yaml
 
 from ray.tune.result import TrainingResult
 from ray.tune.log_sync import get_syncer
@@ -176,3 +177,14 @@ class _CustomEncoder(json.JSONEncoder):
             return float(value)
         if np.issubdtype(value, int):
             return int(value)
+
+
+def pretty_print(result):
+    result = result._replace(config=None)  # drop config from pretty print
+    out = {}
+    for k, v in result._asdict().items():
+        if v is not None:
+            out[k] = v
+
+    cleaned = json.dumps(out, cls=_CustomEncoder)
+    return yaml.dump(json.loads(cleaned), default_flow_style=False)

python/ray/tune/result.py

@@ -3,14 +3,8 @@ from __future__ import division
 from __future__ import print_function
 
 from collections import namedtuple
-import json
 import os
 
-try:
-    import yaml
-except ImportError:
-    print("Could not import YAML module, falling back to JSON pretty-printing")
-    yaml = None
 
 """
 When using ray.tune with custom training scripts, you must periodically report
@@ -93,16 +87,4 @@ TrainingResult = namedtuple("TrainingResult", [
 ])
 
 
-def pretty_print(result):
-    result = result._replace(config=None)  # drop config from pretty print
-    out = {}
-    for k, v in result._asdict().items():
-        if v is not None:
-            out[k] = v
-    if yaml:
-        return yaml.safe_dump(out, default_flow_style=False)
-    else:
-        return json.dumps(out) + "\n"
-
-
 TrainingResult.__new__.__defaults__ = (None,) * len(TrainingResult._fields)

python/ray/tune/trial.py

@@ -11,9 +11,9 @@ import ray
 import os
 
 from ray.tune import TuneError
-from ray.tune.logger import NoopLogger, UnifiedLogger
+from ray.tune.logger import NoopLogger, UnifiedLogger, pretty_print
 from ray.tune.registry import _default_registry, get_registry, TRAINABLE_CLASS
-from ray.tune.result import TrainingResult, DEFAULT_RESULTS_DIR, pretty_print
+from ray.tune.result import TrainingResult, DEFAULT_RESULTS_DIR
 from ray.utils import random_string, binary_to_hex
 
 DEBUG_PRINT_INTERVAL = 5