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[tune/sgd] Document func_trainable and add checkpoint context (#9739)

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Co-authored-by: krfricke <krfricke@users.noreply.github.com>
Co-authored-by: Amog Kamsetty <amogkam@users.noreply.github.com>
This commit is contained in:
Richard Liaw
2020-07-30 09:46:37 -07:00
committed by GitHub
parent e540e425e4
commit 0c3b9ebeef
23 changed files with 619 additions and 452 deletions
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python/ray/tune/session.py
+40 -51
View File
@@ -1,3 +1,4 @@
from contextlib import contextmanager
import os
import logging
@@ -75,48 +76,34 @@ def report(**kwargs):
def make_checkpoint_dir(step=None):
"""Gets the next checkpoint dir.
.. code-block:: python
import time
from ray import tune
def func(config, checkpoint=None):
start = 0
if checkpoint:
with open(checkpoint) as f:
state = json.loads(f.read())
start = state["step"] + 1
for iter in range(start, 100):
time.sleep(1)
checkpoint_dir = tune.make_checkpoint_dir(step=step)
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "w") as f:
f.write(json.dumps({"step": start}))
tune.save_checkpoint(path)
tune.report(hello="world", ray="tune")
.. warning:: Do not call this function within the Trainable Class API.
Args:
step (int): Current training iteration - used for setting
an index to uniquely identify the checkpoint.
.. versionadded:: 0.8.6
.. deprecated:: 0.8.7
Use tune.checkpoint_dir instead.
"""
_session = get_session()
if _session:
return _session.make_checkpoint_dir(step=step)
else:
return os.path.abspath("./")
raise DeprecationWarning(
"Deprecated method. Use `tune.checkpoint_dir` instead.")
def save_checkpoint(checkpoint):
"""Register the given checkpoint.
.. versionadded:: 0.8.6
.. deprecated:: 0.8.7
Use tune.checkpoint_dir instead.
"""
raise DeprecationWarning(
"Deprecated method. Use `tune.checkpoint_dir` instead.")
@contextmanager
def checkpoint_dir(step=None):
"""Returns a checkpoint dir inside a context.
Store any files related to restoring state within the
provided checkpoint dir.
.. code-block:: python
import os
@@ -124,10 +111,10 @@ def save_checkpoint(checkpoint):
import time
from ray import tune
def func(config, checkpoint=None):
def func(config, checkpoint_dir=None):
start = 0
if checkpoint:
with open(checkpoint) as f:
if checkpoint_dir:
with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
state = json.loads(f.read())
accuracy = state["acc"]
start = state["step"] + 1
@@ -135,27 +122,29 @@ def save_checkpoint(checkpoint):
for iter in range(start, 10):
time.sleep(1)
checkpoint_dir = tune.make_checkpoint_dir(step=iter)
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "w") as f:
f.write(json.dumps({"step": start}))
tune.save_checkpoint(path)
with tune.checkpoint_dir(step=iter) as checkpoint_dir:
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "w") as f:
f.write(json.dumps({"step": start}))
tune.report(hello="world", ray="tune")
analysis = tune.run(run_me)
Yields:
checkpoint_dir (str): Directory for checkpointing.
.. warning:: Do not call this function within the Trainable Class API.
Args:
**kwargs: Any key value pair to be logged by Tune. Any of these
metrics can be used for early stopping or optimization.
.. versionadded:: 0.8.6
.. versionadded:: 0.8.7
"""
_session = get_session()
if _session:
return _session.save_checkpoint(checkpoint)
_checkpoint_dir = _session.make_checkpoint_dir(step=step)
else:
_checkpoint_dir = os.path.abspath("./")
yield _checkpoint_dir
if _session:
_session.save_checkpoint(_checkpoint_dir)
def get_trial_dir():