c0be03672484e706c890bef199ad7db76cdaf4d2,tgen/parallel_percrank_train.py,RankerTrainingService,exposed_training_pass,#RankerTrainingService#,237

Before Change

        ranker.train_order = all_train_order
        // return the result of the computation
        log_info("Training pass %d / %d / %d done." % (pass_no, data_offset, data_len))
        return pickle.dumps((ranker.get_weights(), ranker.get_diagnostics()), pickle.HIGHEST_PROTOCOL)


def run_worker(head_host, head_port, debug_out=None):
    // setup debugging output, if applicable

After Change

        ranker = self.ranker_inst
        tstart = time.time()
        ranker.set_weights(pickle.loads(w))
        log_info("Weights loading: %f secs." % (time.time() - tstart))
        // save rest of the training data to temporary variables, set just the
        // required portion for computation
        all_train_das = ranker.train_das
        ranker.train_das = ranker.train_das[data_offset:data_offset + data_len]
        all_train_trees = ranker.train_trees
        ranker.train_trees = ranker.train_trees[data_offset:data_offset + data_len]
        all_train_feats = ranker.train_feats
        ranker.train_feats = ranker.train_feats[data_offset:data_offset + data_len]
        all_train_sents = ranker.train_sents
        ranker.train_sents = ranker.train_sents[data_offset:data_offset + data_len]
        all_train_order = ranker.train_order
        ranker.train_order = range(len(ranker.train_trees))
        if ranker.randomize:
            rnd.seed(rnd_seed)
            rnd.shuffle(ranker.train_order)
        // do the actual computation (update w)
        ranker._training_pass(pass_no)
        // return the rest of the training data to member variables
        ranker.train_das = all_train_das
        ranker.train_trees = all_train_trees
        ranker.train_feats = all_train_feats
        ranker.train_sents = all_train_sents
        ranker.train_order = all_train_order
        // return the result of the computation
        log_info("Training pass %d / %d / %d done." % (pass_no, data_offset, data_len))
        tstart = time.time()
        dump = pickle.dumps((ranker.get_weights(), ranker.get_diagnostics()), pickle.HIGHEST_PROTOCOL)
        log_info("Weights saving: %f secs." % (time.time() - tstart))
        return dump

Instances