Nguyễn Thanh Sang - 19133048
Lê Thị Nhung - 19133043
- AWS S3:
- Store training data
- Store pre-trained model
- AWS SageMaker:
- Data Wrangling
- Training
- Deploy Endpoint
- AWS EC2:
- Deploy streamlit webapp
- VIHSD
- VLSP
- Xử lý tên cột
- Balancing dữ liệu
- Visualize đơn giản
Trong training notebook
trainer.push_to_hub()
from sagemaker.huggingface import HuggingFaceModel
import sagemaker
role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
'HF_MODEL_ID':'tsdocode/phobert-finetune-hatespeech',
'HF_TASK':'text-classification'
}
# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
transformers_version='4.17.0',
pytorch_version='1.10.2',
py_version='py38',
env=hub,
role=role,
)
# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
initial_instance_count=1, # number of instances
instance_type='ml.m5.xlarge' # ec2 instance type
)
predictor.predict({
'inputs': "ngu vcl",
'return_all_scores' : True
})
- Start một EC2 instance
- Thêm AWS credentials vào ~/.aws/credentials
- Thay đổi trong file .env:
DISCORD_TOKEN=""
DISCORD_GUILD=""
SAGEMAKER_ENDPOINT=""
- Sử dụng Boto3 để invoke Sagemaker enpoint
Code invoke SageMaker Endpoint
@st.cache(allow_output_mutation=True)
def load_endpoint():
sagemaker_session = Session(boto_session=boto3.session.Session())
predictor = HuggingFacePredictor(
endpoint_name='huggingface-pytorch-inference-2022-05-07-04-03-22-044',
sagemaker_session=sagemaker_session
)
return predictor
Start Web App and Discord bot
./run.sh
Start web APP
streamlit run app.py
python Bot/bot.py