wassname/ray
1
0
Fork 0
mirror of https://github.com/wassname/ray.git synced 2026-07-25 13:30:52 +08:00
Code Issues Packages Projects Releases Wiki Activity

[tune] PBT replay utility scheduler (#9953)

Browse Source 
Co-authored-by: Kai Fricke <kai@anyscale.com>
This commit is contained in:
krfricke
2020-08-07 12:41:49 -07:00
committed by GitHub
co-authored by Kai Fricke
parent 326a470bc2
commit 0ef8224446
6 changed files with 390 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/source/tune/_tutorials/tune-advanced-tutorial.rst
+45 -13
View File
@@ -77,19 +77,19 @@ During the training, we can constantly check the status of the models from conso
.. code-block:: bash
== Status ==
Memory usage on this node: 10.4/16.0 GiB
PopulationBasedTraining: 4 checkpoints, 1 perturbs
Resources requested: 4/12 CPUs, 0/0 GPUs, 0.0/3.42 GiB heap, 0.0/1.17 GiB objects
Number of trials: 4 ({'RUNNING': 4})
Result logdir: /Users/yuhao.yang/ray_results/pbt_test
+--------------------------+----------+---------------------+----------+------------+--------+------------------+----------+
| Trial name | status | loc | lr | momentum | iter | total time (s) | acc |
|--------------------------+----------+---------------------+----------+------------+--------+------------------+----------|
| PytorchTrainble_3b42d914 | RUNNING | 30.57.180.224:49840 | 0.122032 | 0.302176 | 18 | 3.8689 | 0.8875 |
| PytorchTrainble_3b45091e | RUNNING | 30.57.180.224:49835 | 0.505325 | 0.628559 | 18 | 3.90404 | 0.134375 |
| PytorchTrainble_3b454c46 | RUNNING | 30.57.180.224:49843 | 0.490228 | 0.969013 | 17 | 3.72111 | 0.0875 |
| PytorchTrainble_3b458a9c | RUNNING | 30.57.180.224:49833 | 0.961861 | 0.169701 | 13 | 2.72594 | 0.1125 |
+--------------------------+----------+---------------------+----------+------------+--------+------------------+----------+
Memory usage on this node: 11.6/16.0 GiB
PopulationBasedTraining: 5 checkpoints, 4 perturbs
Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/3.96 GiB heap, 0.0/1.37 GiB objects
Result logdir: /Users/foo/ray_results/pbt_test
Number of trials: 4 (4 TERMINATED)
+------------------------------+------------+-------+-----------+------------+----------+--------+------------------+
| Trial name | status | loc | lr | momentum | acc | iter | total time (s) |
|------------------------------+------------+-------+-----------+------------+----------+--------+------------------|
| PytorchTrainable_ba982_00000 | TERMINATED | | 0.0457501 | 0.99 | 0.6375 | 25 | 5.35712 |
| PytorchTrainable_ba982_00001 | TERMINATED | | 0.175808 | 0.0667043 | 0.909375 | 29 | 6.18802 |
| PytorchTrainable_ba982_00002 | TERMINATED | | 0.21097 | 0.99 | 0.040625 | 29 | 6.19634 |
| PytorchTrainable_ba982_00003 | TERMINATED | | 0.0571876 | 0.852088 | 0.96875 | 30 | 6.37298 |
+------------------------------+------------+-------+-----------+------------+----------+--------+------------------+
In {LOG_DIR}/{MY_EXPERIMENT_NAME}/, all mutations are logged in pbt_global.txt
and individual policy perturbations are recorded in pbt_policy_{i}.txt. Tune logs:
@@ -114,6 +114,38 @@ Checking the accuracy:
.. image:: /images/tune_advanced_plot1.png
.. _tune-advanced-tutorial-pbt-replay:
Replaying a PBT run
-------------------
A run of Population Based Training ends with fully trained models. However, sometimes
you might like to train the model from scratch, but use the same hyperparameter
schedule as obtained from PBT. Ray Tune offers a replay utility for this.
All you need to do is pass the policy log file for the trial you want to replay.
This is usually stored in the experiment directory, for instance
``~/ray_results/pbt_test/pbt_policy_ba982_00000.txt``.
The replay utility reads the original configuration for the trial and updates it
each time when it was originally perturbed. You can (and should)
thus just use the same ``Trainable`` for the replay run.
.. code-block:: python
from ray import tune
from ray.tune.examples.pbt_convnet_example import PytorchTrainable
from ray.tune.schedulers import PopulationBasedTrainingReplay
replay = PopulationBasedTrainingReplay(
"~/ray_results/pbt_test/pbt_policy_ba982_00003.txt")
tune.run(
PytorchTrainable,
scheduler=replay,
stop={"training_iteration": 100})
DCGAN with Trainable and PBT
----------------------------
doc/source/tune/api_docs/schedulers.rst
+24
View File
@@ -148,6 +148,30 @@ You can run this :doc:`toy PBT example </tune/examples/pbt_function>` to get an
.. autoclass:: ray.tune.schedulers.PopulationBasedTraining
.. _tune-scheduler-pbt-replay:
Population Based Training Replay (tune.schedulers.PopulationBasedTrainingReplay)
--------------------------------------------------------------------------------
Tune includes a utility to replay hyperparameter schedules of Population Based Training runs.
You just specify an existing experiment directory and the ID of the trial you would
like to replay. The scheduler accepts only one trial, and it will update its
config according to the obtained schedule.
.. code-block:: python
replay = PopulationBasedTrainingReplay(
experiment_dir="~/ray_results/pbt_experiment/",
trial_id="XXXXX_00001")
tune.run(
...,
scheduler=replay)
See :ref:`here for an example <tune-advanced-tutorial-pbt-replay>` on how to use the
replay utility in practice.
.. autoclass:: ray.tune.schedulers.PopulationBasedTrainingReplay
.. _tune-scheduler-bohb:
BOHB (tune.schedulers.HyperBandForBOHB)