Comments (4)
It is ignored because you define load
in your inference.py
and not as documented here load_fn
here. That's why the toolkit uses the load
function of the HuggingFaceHandlerService
.
Additionally, I think the model
in your predict
might not be used since currently, the HuggingFaceHandlerService
uses self.model
in the predict
function.
from sagemaker-huggingface-inference-toolkit.
ok I'm lost :)
how should we name the functions in inference.py? (also asked here #6)
- load_fn, preprocess_fn, predict, postprocess (what's currently in the doc)
- load_fn, preprocess_fn, predict_fn, postprocess_fn
I understand that it will soon switch to model_fn, input_fn, predict_fn, output_fn ; but I'm curious about right now
from sagemaker-huggingface-inference-toolkit.
Here is the example we use for the tests. Yes, the documentation has an issue. It should be _fn
everywhere.
and here is it where they got loaded
from sagemaker-huggingface-inference-toolkit.
this is the final setup that worked ; as mentioned in the doc the contract now is model_fn
, input_fn
, predict_fn
, output_fn
import logging
import os
import tensorflow as tf
from transformers import TFAutoModelForQuestionAnswering, AutoTokenizer
logging.basicConfig(level=logging.INFO)
def model_fn(model_dir):
"""this function reads the model from disk"""
logging.info('model_fn dir view:')
logging.info(os.listdir())
# load model
transformer = TFAutoModelForQuestionAnswering.from_pretrained(model_dir)
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_dir)
return transformer, tokenizer
def predict_fn(processed_data, model):
"""this function runs inference"""
transformer, tokenizer = model
question, text = processed_data['question'], processed_data['context']
logging.info('processed_data received: {}'.format(processed_data))
# infer
input_dict = tokenizer(question, text, return_tensors='tf')
outputs = transformer(input_dict)
# post processing
start_logits = outputs.start_logits
end_logits = outputs.end_logits
all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0])
answer = ' '.join(all_tokens[tf.math.argmax(start_logits, 1)[0] : tf.math.argmax(end_logits, 1)[0]+1])
return answer
from sagemaker-huggingface-inference-toolkit.
Related Issues (20)
- Using custom inference script and models from Hub HOT 1
- get_pipeline function passes Path object rather than PretrainedTokenizer
- No support for multi-GPU HOT 3
- 🏷️ invalid
- Sagemaker endpoint inferencing error with HF model loading from s3bucket with new transformer update HOT 5
- Support multiple return sequences
- HF_TASK Enviournment Variable error HOT 1
- Endpoint creation completes before custom model_fn finishes loading resources
- ARCHITECTURES_2_TASK is limiting the tasks able to be deployed with HF DLC HOT 11
- Make DEFAULT_HF_HUB_MODEL_EXPORT_DIRECTORY configurable
- Sagemaker inference not loading model weight from s3 HOT 2
- Sagemaker endpoint doesn't use GPU (instance ml.g4dn.xlarge) HOT 1
- device kernel image is invalid
- Sagemaker endpoint inference Fails when following a tutorial
- Sagemaker HuggingfaceModel fails on phi3 model deployment HOT 2
- SageMaker deployment errors HOT 2
- Error on Sagemaker deployment for v1.0.1 HOT 1
- How can I delpoy a model with AWS S3 and without downloading model from hunggingface via TGI image on Sagemaker? HOT 2
- How to enable Batch inference on AWS deployed Serverless model from Hub? HOT 1
- Where is the logic for detecting custom inference.py? HOT 6
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from sagemaker-huggingface-inference-toolkit.