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[RLlib] Issue 15724: Breaking example script in docs due to outdated eager config flag (use framework='tf2|tfe' instead). (#15736)

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Sven Mika 2021-05-18 11:34:46 +02:00 • committed by GitHub
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4
doc/source/rllib-concepts.rst
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@ -423,8 +423,8 @@ Building Policies in TensorFlow Eager
Policies built with ``build_tf_policy`` (most of the reference algorithms are) Policies built with ``build_tf_policy`` (most of the reference algorithms are)
can be run in eager mode by setting can be run in eager mode by setting
the ``"eager": True`` / ``"eager_tracing": True`` config options or the ``"framework": "tf2"`` / ``"eager_tracing": True`` config options or
using ``rllib train --eager [--trace]``. using ``rllib train '{"framework": "tf2", "eager_tracing": True}'``.
This will tell RLlib to execute the model forward pass, action distribution, This will tell RLlib to execute the model forward pass, action distribution,
loss, and stats functions in eager mode. loss, and stats functions in eager mode.

2
doc/source/rllib-toc.rst
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@ -222,7 +222,7 @@ references in the cluster.
TensorFlow 2.0 TensorFlow 2.0
~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~
RLlib currently runs in ``tf.compat.v1`` mode. This means eager execution is disabled by default, and RLlib imports TF with ``import tensorflow.compat.v1 as tf; tf.disable_v2_behaviour()``. Eager execution can be enabled manually by calling ``tf.enable_eager_execution()`` or setting the ``"eager": True`` trainer config. RLlib currently runs in ``tf.compat.v1`` mode. This means eager execution is disabled by default, and RLlib imports TF with ``import tensorflow.compat.v1 as tf; tf.disable_v2_behaviour()``. Eager execution can be enabled manually by calling ``tf.enable_eager_execution()`` or setting the ``"framework": "tf2"`` trainer config.
.. |tensorflow| image:: tensorflow.png .. |tensorflow| image:: tensorflow.png
:class: inline-figure :class: inline-figure

4
doc/source/rllib-training.rst
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@ -14,7 +14,7 @@ You can train a simple DQN trainer with the following command:
.. code-block:: bash .. code-block:: bash
rllib train --run DQN --env CartPole-v0 # --eager [--trace] for eager execution rllib train --run DQN --env CartPole-v0 # --config '{"framework": "tf2", "eager_tracing": True}' for eager execution
By default, the results will be logged to a subdirectory of ``~/ray_results``. By default, the results will be logged to a subdirectory of ``~/ray_results``.
This subdirectory will contain a file ``params.json`` which contains the This subdirectory will contain a file ``params.json`` which contains the
@ -906,7 +906,7 @@ Eager Mode
Policies built with ``build_tf_policy`` (most of the reference algorithms are) Policies built with ``build_tf_policy`` (most of the reference algorithms are)
can be run in eager mode by setting the can be run in eager mode by setting the
``"framework": "[tf2|tfe]"`` / ``"eager_tracing": True`` config options or using ``"framework": "[tf2|tfe]"`` / ``"eager_tracing": True`` config options or using
``rllib train --eager [--trace]``. ``rllib train --config '{"framework": "tf2"}' [--trace]``.
This will tell RLlib to execute the model forward pass, action distribution, This will tell RLlib to execute the model forward pass, action distribution,
loss, and stats functions in eager mode. loss, and stats functions in eager mode.

4
doc/source/rllib.rst
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@ -28,8 +28,8 @@ Then, you can try out training in the following equivalent ways:
.. code-block:: bash .. code-block:: bash
rllib train --run=PPO --env=CartPole-v0 # -v [-vv] for verbose, rllib train --run=PPO --env=CartPole-v0 # -v [-vv] for verbose,
# --eager [--trace] for eager execution, # --config='{"framework": "tf2", "eager_tracing": True}' for eager,
# --torch to use PyTorch # --torch to use PyTorch OR --config='{"framework": "torch"}'
.. code-block:: python .. code-block:: python