![]() Sequence_batch = np.code(sequence_batch.astype("bytes"), "utf-8") # need to convert dtype=object to bytes first Tokenizer = om_pretrained("bert-base-uncased") ![]() Logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(name)s: %(message)s") Logger = logging.getLogger("examples.huggingface_bert_jax.server") import loggingįrom transformers import BertTokenizer, FlaxBertModelįrom pytriton.model_config import ModelConfig, Tensor For the full code, see the HuggingFace BERT JAX Model. ![]() An example deployment of a HuggingFace text classification pipeline using PyTriton is shown below. PyTriton provides the simplicity of Flask and the benefits of Triton in Python. Triton Inference Server includes built-in support for features like those listed above, and many more.
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