[tune] Tune Documentation and expose better API (#1681)

2026-08-09 12:20:09 +08:00 · 2018-03-19 12:55:10 -07:00
parent 7b493aa4a1
commit 23954e7ce2
9 changed files with 315 additions and 89 deletions
@@ -0,0 +1,56 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from ray.tune.variant_generator import generate_trials
+from ray.tune.result import DEFAULT_RESULTS_DIR
+
+
+class Experiment(object):
+    """Tracks experiment specifications.
+
+    Parameters:
+        name (str): Name of experiment.
+        run (str): The algorithm or model to train. This may refer to the
+            name of a built-on algorithm (e.g. RLLib's DQN or PPO), or a
+            user-defined trainable function or class
+            registered in the tune registry.
+        stop (dict): The stopping criteria. The keys may be any field in
+            TrainingResult, whichever is reached first.  Defaults to
+            empty dict.
+        config (dict): Algorithm-specific configuration
+            (e.g. env, hyperparams). Defaults to empty dict.
+        resources (dict): Machine resources to allocate per trial,
+            e.g. ``{"cpu": 64, "gpu": 8}``. Note that GPUs will not be
+            assigned unless you specify them here. Defaults to 1 CPU and 0
+            GPUs.
+        repeat (int): Number of times to repeat each trial. Defaults to 1.
+        local_dir (str): Local dir to save training results to.
+            Defaults to ``~/ray_results``.
+        upload_dir (str): Optional URI to sync training results
+            to (e.g. ``s3://bucket``).
+        checkpoint_freq (int): How many training iterations between
+            checkpoints. A value of 0 (default) disables checkpointing.
+        max_failures (int): Try to recover a trial from its last
+            checkpoint at least this many times. Only applies if
+            checkpointing is enabled. Defaults to 3.
+    """
+    def __init__(self, name, run, stop=None, config=None,
+                 resources=None, repeat=1, local_dir=None,
+                 upload_dir="", checkpoint_freq=0, max_failures=3):
+        spec = {
+            "run": run,
+            "stop": stop or {},
+            "config": config or {},
+            "resources": resources or {"cpu": 1, "gpu": 0},
+            "repeat": repeat,
+            "local_dir": local_dir or DEFAULT_RESULTS_DIR,
+            "upload_dir": upload_dir,
+            "checkpoint_freq": checkpoint_freq,
+            "max_failures": max_failures
+        }
+        self._trials = generate_trials(spec, name)
+
+    def trials(self):
+        for trial in self._trials:
+            yield trial