5fadb9c18e3de16cc5633175199f9e9e2c381102,rl_coach/graph_managers/graph_manager.py,GraphManager,restore_checkpoint,#GraphManager#,493

Before Change

        if hasattr(self.task_parameters, "checkpoint_restore_dir") and self.task_parameters.checkpoint_restore_dir:
            import tensorflow as tf
            checkpoint_dir = self.task_parameters.checkpoint_restore_dir
            checkpoint = tf.train.get_checkpoint_state(checkpoint_dir)
            screen.log_title("Loading checkpoint: {}".format(checkpoint.model_checkpoint_path))
            variables = {}
            for var_name, _ in tf.contrib.framework.list_variables(checkpoint_dir):
                // Load the variable

After Change

        // TODO: find better way to load checkpoints that were saved with a global network into the online network
        if hasattr(self.task_parameters, "checkpoint_restore_dir") and self.task_parameters.checkpoint_restore_dir:
            if self.task_parameters.framework_type == Frameworks.tensorflow:
                self._restore_checkpoint_tf(self.task_parameters.checkpoint_restore_dir)
            elif self.task_parameters.framework_type == Frameworks.mxnet:
                // TODO implement checkpoint restore
                pass
            else:
                raise ValueError("Invalid framework {}".format(self.task_parameters.framework_type))

    def occasionally_save_checkpoint(self):
        // only the chief process saves checkpoints
        if self.task_parameters.checkpoint_save_secs \
                and time.time() - self.last_checkpoint_saving_time >= self.task_parameters.checkpoint_save_secs \

Instances