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[tune] Use public methods for trainable (#9184)

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This commit is contained in:
Richard Liaw
2020-07-01 11:00:00 -07:00
committed by GitHub
parent 1491508859
commit d35f0e40d0
40 changed files with 350 additions and 220 deletions
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python/ray/tune/examples/durable_trainable_example.py
+4 -4
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@@ -51,7 +51,7 @@ class OptimusFn(object):
def get_optimus_trainable(parent_cls):
class OptimusTrainable(parent_cls):
def _setup(self, config):
def setup(self, config):
self.iter = 0
if config.get("seed"):
np.random.seed(config["seed"])
@@ -61,7 +61,7 @@ def get_optimus_trainable(parent_cls):
self.initial_samples_per_step = 500
self.mock_data = open("/dev/urandom", "rb").read(1024)
def _train(self):
def step(self):
self.iter += 1
new_loss = self.func.eval(self.iter)
time.sleep(0.5)
@@ -71,7 +71,7 @@ def get_optimus_trainable(parent_cls):
"samples": self.initial_samples_per_step
}
def _save(self, checkpoint_dir):
def save_checkpoint(self, checkpoint_dir):
time.sleep(0.5)
return {
"func": cloudpickle.dumps(self.func),
@@ -80,7 +80,7 @@ def get_optimus_trainable(parent_cls):
"iter": self.iter
}
def _restore(self, checkpoint):
def load_checkpoint(self, checkpoint):
self.func = cloudpickle.loads(checkpoint["func"])
self.data = checkpoint["data"]
self.iter = checkpoint["iter"]