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

Before Change

            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
                var = tf.contrib.framework.load_variable(checkpoint_dir, var_name)

                // Set the new name
                new_name = var_name
                new_name = new_name.replace("global/", "online/")
                variables[new_name] = var

            for v in self.variables_to_restore:
                self.sess.run(v.assign(variables[v.name.split(":")[0]]))

    def occasionally_save_checkpoint(self):

After Change

        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 \

Instances