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chemprop example code use gpu

Thomas McFadden answered on August 21, 2023 Popularity 1/10 Helpfulness 1/10

answer chemprop example code use gpu

chemprop example code use gpu

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num_iters = len(data) if args.last_batch else len(data) // args.batch_size * args.batch_size

    if args.parallel_featurization:
        batch_queue = Queue(args.batch_queue_max_size)
        exit_queue = Queue(1)
        batch_process = Process(target=async_mol2graph, args=(batch_queue, data, args, num_iters, args.batch_size, exit_queue, args.last_batch))
        batch_process.start()
        currently_loaded_batches = []

    iter_size = 1 if args.maml else args.batch_size

    for i in trange(0, num_iters, iter_size):
        if args.moe:
            if not args.batch_domain_encs:
                model.compute_domain_encs(train_smiles)  # want to recompute every batch
            mol_batch = [MoleculeDataset(d[i:i + args.batch_size]) for d in data]
            train_batch, train_targets = [], []
            for b in mol_batch:
                tb, tt = b.smiles(), b.targets()
                train_batch.append(tb)
                train_targets.append(tt)
            test_batch = test_smiles[i:i + args.batch_size]
            loss = model.compute_loss(train_batch, train_targets, test_batch)
            model.zero_grad()

            loss_sum += loss.item()
            iter_count += len(mol_batch)
        elif args.maml:
            task_train_data, task_test_data, task_idx = data.sample_maml_task(args)
            mol_batch = task_test_data
            smiles_batch, features_batch, target_batch = task_train_data.smiles(), task_train_data.features(), task_train_data.targets(task_idx)
            # no mask since we only picked data points that have the desired target

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Source: snyk.io

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Contributed on Aug 21 2023

Thomas McFadden

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chemprop example code use gpu

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