add missing file

examples/OpenAIGym/README.md

@@ -11,6 +11,9 @@ Training with a significant slower speed (e.g. on CPU) will give bad performance
 probably because of async issues.
 The pre-trained models are all trained with 4 GPUs for about 2 days.
 
+Occasionally processes may not get terminated completely, therefore it is suggested to use systemd-run to run any
+multiprocess Python program to get a cgroup dedicated for the task.
+
 ### To run a pretrained Atari model for 100 episodes:
 
 1. Download models from [model zoo](https://goo.gl/9yIol2)

tensorpack/predict/config.py

+# -*- coding: UTF-8 -*-
+# File: config.py
+# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+
+import six
+
+from tensorpack.models import ModelDesc
+from ..tfutils import get_default_sess_config
+from ..tfutils.sessinit import SessionInit, JustCurrentSession
+
+__all__ = ['PredictConfig']
+
+
+class PredictConfig(object):
+    def __init__(self, **kwargs):
+        """
+        Args:
+            session_init (SessionInit): how to initialize variables of the session.
+            model (ModelDesc): the model to use.
+            input_names (list): a list of input tensor names.
+            output_names (list): a list of names of the output tensors to predict, the
+                tensors can be any computable tensor in the graph.
+            return_input: same as in :attr:`PredictorBase.return_input`.
+        """
+        # TODO use the name "tensor" instead of "variable"
+        def assert_type(v, tp):
+            assert isinstance(v, tp), v.__class__
+        # XXX does it work? start with minimal memory, but allow growth.
+        # allow_growth doesn't seem to work very well in TF.
+        self.session_config = kwargs.pop('session_config', get_default_sess_config(0.4))
+        self.session_init = kwargs.pop('session_init', JustCurrentSession())
+        assert_type(self.session_init, SessionInit)
+        self.model = kwargs.pop('model')
+        assert_type(self.model, ModelDesc)
+
+        # inputs & outputs
+        # TODO add deprecated warning later
+        self.input_names = kwargs.pop('input_names', None)
+        if self.input_names is None:
+            self.input_names = kwargs.pop('input_var_names', None)
+            if self.input_names is not None:
+                pass
+                # logger.warn("[Deprecated] input_var_names is deprecated in PredictConfig. Use input_names instead!")
+        if self.input_names is None:
+            # neither options is set, assume all inputs
+            raw_vars = self.model.get_input_vars_desc()
+            self.input_names = [k.name for k in raw_vars]
+        self.output_names = kwargs.pop('output_names', None)
+        if self.output_names is None:
+            self.output_names = kwargs.pop('output_var_names')
+            # logger.warn("[Deprecated] output_var_names is deprecated in PredictConfig. Use output_names instead!")
+        assert len(self.input_names), self.input_names
+        for v in self.input_names:
+            assert_type(v, six.string_types)
+        assert len(self.output_names), self.output_names
+
+        self.return_input = kwargs.pop('return_input', False)
+        assert len(kwargs) == 0, 'Unknown arguments: {}'.format(str(kwargs.keys()))