Sagemaker endpoint inferencing error with HF model loading from s3bucket with new transformer update

[miteshkotak]

Since the transformer update (4.35.0). model outputs model.safetensor by default instead of pytorch.bin

endpoint created in sagamaker with model saved in s3bucket is throwing error. asking to define "HF_TASK".

As per documentation of toolkit we do not need to define env variables when loading model from s3 Bucket, just a customer inference.py is needed.

Does someone know what's going wrong here ? @philschmid any idea ?

[miteshkotak]

is it possible to pass env "HF_TASK"?, when creating an endpoint in sagemaker, loading model from s3 bucket

[miteshkotak]

I got it working. but i believe it's bug,

endpoint returns error ( ask to define HF_TASK) even though i deployed the model form s3 bucket. Also if i generate zip from colab or local OSx , endpoint still return error (ask to define HF_TASK). which doesn't sense to me.

How it worked for me:

• After the training i pushed my model to Huggingface hub,
• Git cloned the model in sagemaker environment
• Generated zip file, moved to s3 bucket
• Deployed the model from s3 bucket to sagemaker endpoint

I will keep this thread open incase someone can point out my mistake or correct me. which might be helpful for other

[aduverger]

@miteshkotak This is likely an issue with how you generated the archive file and/or your inference.py.

This message probably means that Sagemaker is using the default load method from the HuggingFace base image, instead of the custom model_fn you wrote. See here

How does your archive file look like? How do you create your endpoint: boto3 or Cfn/Cdk?

[jeremyarancio]

Just got the same error It seems the custom code/inference.py script is no longer recognized in the s3 artifact.

# inference.py
import io
import logging
import sys
import json
from PIL import Image
import requests
import os

from transformers import LayoutLMForTokenClassification, AutoProcessor
import torch

os.system('chmod 777 /tmp') # https://discuss.huggingface.co/t/how-to-install-tesseract-ocr-in-a-training-dlc-of-hf-via-a-script/19251/6
os.system('apt-get update -y')
os.system('apt-get install tesseract-ocr -y')

logging.basicConfig(
    level=logging.getLevelName("INFO"),
    handlers=[
        logging.StreamHandler(sys.stdout)
    ],
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)

def model_fn(model_dir):
    logging.info("Start loading model.")
    processor = AutoProcessor.from_pretrained(
        "microsoft/layoutlmv2-base-uncased", apply_ocr=True
    )
    model = LayoutLMForTokenClassification.from_pretrained(model_dir)
    logging.info("Model and tokenizer loaded.")
    return processor, model

def input_fn(input_data, content_type):
    if content_type == "application/json":
        data = json.loads(input_data)
        urls = data.pop("urls", None)
        if urls:
            images = [get_image_from_url(url) for url in urls]
            return images
        else:
            raise ValueError("No document url was provided.")
    else:
        raise ValueError("Content type {} is not supported.".format(content_type))

def predict_fn(images, model):
    logging.info("Start predict_fn.")
    processor, model = model
    encoding = processor(
        images, 
        return_tensors="pt", 
        padding="max_length", 
        truncation=True
    )
    del encoding["image"]
    outputs = model(**encoding)
    results = process_outputs(
        outputs, encoding=encoding, 
        images=images, model=model, 
        processor=processor
    )
    return results

def get_image_from_url(url: str) -> Image:
    response = requests.get(url)
    f = io.BytesIO(response.content)
    return Image.open(f).convert("RGB")

# Handle by processor
# def normalize_bbox(bbox, width, height):
#     return [
#         int(1000 * (bbox[0] / width)),
#         int(1000 * (bbox[1] / height)),
#         int(1000 * (bbox[2] / width)),
#         int(1000 * (bbox[3] / height)),
#     ]

def unnormalize_box(bbox, width, height):
    return [
        int(width * (bbox[0] / 1000)),
        int(height * (bbox[1] / 1000)),
        int(width * (bbox[2] / 1000)),
        int(height * (bbox[3] / 1000)),
    ]

def _process_outputs(encoding, tokenizer, labels, scores, images):
    results = []
    for batch_idx, input_ids in enumerate(encoding["input_ids"]):
        width, height = images[batch_idx].size
        entities = []
        previous_word_idx = 0
        tokens = tokenizer.convert_ids_to_tokens(input_ids)
        word_ids = encoding.word_ids(batch_index=batch_idx)
        word = ""
        for i, word_idx in enumerate(word_ids):
            if word_idx is None:
                continue
            elif previous_word_idx != word_idx:
                # +1 because of [CLS] token
                entities.append(
                    {
                        "word": word,
                        "label": labels[batch_idx][previous_word_idx + 1],
                        "score": scores[batch_idx][previous_word_idx + 1],
                        "bbox": unnormalize_box(
                            encoding["bbox"][batch_idx][previous_word_idx + 1],
                            width=width,
                            height=height,
                        ),
                    }
                )
                word = tokens[i]
            else:
                word += tokens[i]
            previous_word_idx = word_idx
        for entity in entities:
            entity["word"] = entity["word"].replace("##", "")
        results.append(entities)
    return results

def process_outputs(outputs, encoding, images, model, processor):
    scores, _ = torch.max(outputs.logits.softmax(axis=-1), dim=-1)
    scores = scores.tolist()
    predictions = outputs.logits.argmax(-1)
    labels = [[model.config.id2label[pred.item()] for pred in prediction] for prediction in predictions]
    results = _process_outputs(
        encoding=encoding,
        tokenizer=processor.tokenizer,
        labels=labels,
        scores=scores,
        images=images,
    )
    return results

if __name__ == "__main__":

    import boto3

    BUCKET = "invoice-reader-project"
    KEYS = ["data/training/documents/invoice_0.png", "data/training/documents/invoice_1.png"]
    s3 = boto3.client("s3")

    urls = []
    for key in KEYS:
        urls.append(
            s3.generate_presigned_url(
                ClientMethod="get_object",
                Params={"Bucket": BUCKET, "Key": key},
                ExpiresIn=3600,
            )
        )
    payload = {"urls": urls}

    results = predict_fn(
        input_fn(input_data=json.dumps(payload), content_type="application/json"),
        model_fn(model_dir="artifact")
    )
    print(results)

[jeremyarancio]

Got it! The way I repacked model.tar.gz was wrong The correct approach:

• Go to the folder
• tar -czvf model.tar.gz *

Stupid mistake, but mistake