def handle()

in dynalab/handler.py [0:0]

• 27 lines of code
• 7 McCabe index (conditional complexity)

def handle(torchserve_data, context):
    if not _service.initialized:
        _service.initialize(context)
    if torchserve_data is None:
        return None

    start_time = time.time()
    all_samples = deserialize(torchserve_data)
    n = len(all_samples)
    logger.info(
        f"Deserialized a batch of size {n} ({n/(time.time()-start_time):.2f} samples / s)"
    )
    # Adapt this to your model. The GPU has 16Gb of RAM.
    batch_size = 128
    results = []
    samples = []
    for i, sample in enumerate(all_samples):
        samples.append(sample)
        if len(samples) < batch_size and i + 1 < n:
            continue

        results.extend(handle_mini_batch(_service, samples))
        samples = []

    assert len(results)
    start_time = time.time()
    response = "\n".join(json.dumps(r, indent=None, ensure_ascii=False) for r in results)
    logger.info(
        f"Serialized a batch of size {n} ({n/(time.time()-start_time):.2f} samples / s)"
    )
    return [response]