That’s right, loader is not instantiated. This is DataLoader1
class DataLoader1():

    def __init__(self, trg_file_path, emb_indices, batch_size, max_seq_len, label_signature=None,
                sample_per_word=False, one_hot_labels=False, update_emb_indices=False,
                infinite_batches=False, intents_to_use=None, device=None, num_samples_key='num_samples',
                get_raw_samples_while_iter=False, debug_mode=False):
        """
        Iterator to feed trg data
        args:;
            labels = [
                ('intent',[None, 'greet', 'direction']),
                ('action',[None, 'enquire', 'navigate']),
                ('subject',[None, 'thank', 'hello'])
                ]
            or
            labels = [None, 'person','location','day', 'time']
            batches_to_produce:
                -1 - feeds indefinitely
                0 - Feeds till the file exhausts
                <int value> - feeds <value> batches and exits
            intents_to_use - if a list is provided, excludes samples not included in this list
        """
        if debug_mode:
            torch.set_printoptions(threshold=10000)
        else:
            torch.set_printoptions(threshold=500)
        self.emb_indices = emb_indices
        self.batch_size = batch_size
        self.max_seq_len = max_seq_len
        self.sample_per_word = sample_per_word
        self.one_hot_labels = one_hot_labels
        self.update_emb_indices = update_emb_indices
        self.get_raw_samples_while_iter = get_raw_samples_while_iter
        self.sample_ptr = 0
        self.infinite_batches = infinite_batches
        self.num_samples_key = num_samples_key

        print ('Preparing data from file = {}'.format(trg_file_path))
        self.phrase_match, self.num_samples, self.intents_to_train, labels_from_trg_file = getClasses(
                                            trg_file_path,intents_to_get=intents_to_use,
                                            num_samples_key=num_samples_key)
        self.labels = labels_from_trg_file if label_signature is None else label_signature

        if device:
            self.device = device
        else:
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

        if get_raw_samples_while_iter:
            self.samples = []
        # calc feature vec size
        try:
            self.features_vec = torch.zeros((self.num_samples, self.max_seq_len), dtype=torch.long, device=self.device)
        except RuntimeError as e:
            torch.cuda.empty_cache()
            raise e

As you can see self.features_vec is the 1st tensor that is created, all others are trivial and small python variables. I tried clearing cache with a try block, but that didnt help.
Yes I’m getting OOM in DataLoader2 just after DataLoader1 fails to instantiate. If I run just DataLoader2 it works fine (That’s how I am training now)