Հրահանգների ճշգրտում FLAN T5 XL-ի համար Amazon SageMaker Jumpstart-ի հետ

!pip install nest-asyncio==1.5.5 --quiet
!pip install ipywidgets==8.0.4 --quiet
!pip install --upgrade sagemaker --quiet

import boto3
import sagemaker
# Get current region, role, and default bucket
aws_region = boto3.Session().region_name
aws_role = sagemaker.session.Session().get_caller_identity_arn()
output_bucket = sagemaker.Session().default_bucket()
# This will be useful for printing
newline, bold, unbold = "\n", "\033[1m", "\033[0m"
print(f"{bold}aws_region:{unbold} {aws_region}")
print(f"{bold}aws_role:{unbold} {aws_role}")
print(f"{bold}output_bucket:{unbold} {output_bucket}"

import IPython
from ipywidgets import Dropdown
from sagemaker.jumpstart.filters import And
from sagemaker.jumpstart.notebook_utils import list_jumpstart_models
# Default model choice
model_id = "huggingface-text2text-flan-t5-xl"
# Identify FLAN T5 models that support fine-tuning
filter_value = And(
"task == text2text", "framework == huggingface", "training_supported == true"
)
model_list = [m for m in list_jumpstart_models(filter=filter_value) if "flan-t5" in m]
# Display the model IDs in a dropdown, for user to select
dropdown = Dropdown(
value=model_id,
options=model_list,
description="FLAN T5 models available for fine-tuning:",
style={"description_width": "initial"},
layout={"width": "max-content"},
)
display(IPython.display.Markdown("### Select a pre-trained model from the dropdown below"))
display(dropdown)

from sagemaker.instance_types import retrieve_default
model_id, model_version = dropdown.value, "*"
# Instance types for training and inference
training_instance_type = retrieve_default(
model_id=model_id, model_version=model_version, scope="training"
)
inference_instance_type = retrieve_default(
model_id=model_id, model_version=model_version, scope="inference"
)
print(f"{bold}model_id:{unbold} {model_id}")
print(f"{bold}training_instance_type:{unbold} {training_instance_type}")
print(f"{bold}inference_instance_type:{unbold} {inference_instance_type}")

If you have chosen the FLAN T5 XL, you will see the following output:

model_id: huggingface-text2text-flan-t5-xl

training_instance_type: ml.p3.16xlarge

inference_instance_type: ml.g5.2xlarge

from sagemaker import image_uris, model_uris, script_uris
# Training instance will use this image
train_image_uri = image_uris.retrieve(
region=aws_region,
framework=None,  # automatically inferred from model_id
model_id=model_id,
model_version=model_version,
image_scope="training",
instance_type=training_instance_type,
)
# Pre-trained model
train_model_uri = model_uris.retrieve(
model_id=model_id, model_version=model_version, model_scope="training"
)
# Script to execute on the training instance
train_script_uri = script_uris.retrieve(
model_id=model_id, model_version=model_version, script_scope="training"
)
print(f"{bold}image uri:{unbold} {train_image_uri}")
print(f"{bold}model uri:{unbold} {train_model_uri}")
print(f"{bold}script uri:{unbold} {train_script_uri}")

from sagemaker.s3 import S3Downloader

# We will use the train split of SQuAD2.0
original_data_file = "train-v2.0.json"

# The data was mirrored in the following bucket
original_data_location = f"s3://sagemaker-sample-files/datasets/text/squad2.0/{original_data_file}"
S3Downloader.download(original_data_location, ".")

import json

local_data_file = "task-data.jsonl"  # any name with .jsonl extension

with open(original_data_file) as f:
data = json.load(f)

with open(local_data_file, "w") as f:
for article in data["data"]:
for paragraph in article["paragraphs"]:
# iterate over questions for a given paragraph
for qas in paragraph["qas"]:
if qas["is_impossible"]:
# the question is relevant, but cannot be answered
example = {"context": paragraph["context"], "question": qas["question"]}
json.dump(example, f)
f.write("\n")

template = {
"prompt": "Ask a question which is related to the following text, but cannot be answered based on the text. Text: {context}",
"completion": "{question}",
}
with open("template.json", "w") as f:
json.dump(template, f)

from sagemaker.s3 import S3Uploader

train_data_location = f"s3://{output_bucket}/train_data"
S3Uploader.upload(local_data_file, train_data_location)
S3Uploader.upload("template.json", train_data_location)
print(f"{bold}training data:{unbold} {train_data_location}")

from sagemaker import hyperparameters

# Retrieve the default hyper-parameters for fine-tuning the model
hyperparameters = hyperparameters.retrieve_default(model_id=model_id, model_version=model_version)

# We will override some default hyperparameters with custom values
hyperparameters["epochs"] = "3"
# TODO
# hyperparameters["max_input_length"] = "300"  # data inputs will be truncated at this length
# hyperparameters["max_output_length"] = "40"  # data outputs will be truncated at this length
# hyperparameters["generation_max_length"] = "40"  # max length of generated output
print(hyperparameters)

from sagemaker.estimator import Estimator
from sagemaker.utils import name_from_base

model_name = "-".join(model_id.split("-")[2:])  # get the most informative part of ID
training_job_name = name_from_base(f"js-demo-{model_name}-{hyperparameters['epochs']}")
print(f"{bold}job name:{unbold} {training_job_name}")

training_metric_definitions = [
{"Name": "val_loss", "Regex": "'eval_loss': ([0-9\\.]+)"},
{"Name": "train_loss", "Regex": "'loss': ([0-9\\.]+)"},
{"Name": "epoch", "Regex": "'epoch': ([0-9\\.]+)"},
]

# Create SageMaker Estimator instance
sm_estimator = Estimator(
role=aws_role,
image_uri=train_image_uri,
model_uri=train_model_uri,
source_dir=train_script_uri,
entry_point="transfer_learning.py",
instance_count=1,
instance_type=training_instance_type,
volume_size=300,
max_run=360000,
hyperparameters=hyperparameters,
output_path=output_location,
metric_definitions=training_metric_definitions,
)

# Launch a SageMaker training job over data located in the given S3 path
# Training jobs can take hours, it is recommended to set wait=False,
# and monitor job status through SageMaker console
sm_estimator.fit({"training": train_data_location}, job_name=training_job_name, wait=False)

from sagemaker import TrainingJobAnalytics

# This can be called while the job is still running
df = TrainingJobAnalytics(training_job_name=training_job_name).dataframe()
df.head(10)

model uri: s3://sagemaker-us-west-2-802376408542/avkan/training-huggingface-text2text-huggingface-text2text-flan-t5-xl-repack.tar.gz
job name: jumpstart-demo-xl-3-2023-04-06-08-16-42-738
INFO:sagemaker:Creating training-job with name: jumpstart-demo-xl-3-2023-04-06-08-16-42-738

from sagemaker import image_uris

# Retrieve the inference docker image URI. This is the base HuggingFace container image
deploy_image_uri = image_uris.retrieve(
region=None,
framework=None,  # automatically inferred from model_id
model_id=model_id,
model_version=model_version,
image_scope="inference",
instance_type=inference_instance_type,
)

from sagemaker import model_uris, script_uris
from sagemaker.model import Model
from sagemaker.predictor import Predictor
from sagemaker.utils import name_from_base

# Retrieve the URI of the pre-trained model
pre_trained_model_uri = model_uris.retrieve(
model_id=model_id, model_version=model_version, model_scope="inference"
)

pre_trained_name = name_from_base(f"jumpstart-demo-pre-trained-{model_id}")

# Create the SageMaker model instance of the pre-trained model
if ("small" in model_id) or ("base" in model_id):
deploy_source_uri = script_uris.retrieve(
model_id=model_id, model_version=model_version, script_scope="inference"
)
pre_trained_model = Model(
image_uri=deploy_image_uri,
source_dir=deploy_source_uri,
entry_point="inference.py",
model_data=pre_trained_model_uri,
role=aws_role,
predictor_cls=Predictor,
name=pre_trained_name,
)
else:
# For those large models, we already repack the inference script and model
# artifacts for you, so the `source_dir` argument to Model is not required.
pre_trained_model = Model(
image_uri=deploy_image_uri,
model_data=pre_trained_model_uri,
role=aws_role,
predictor_cls=Predictor,
name=pre_trained_name,
)

print(f"{bold}image URI:{unbold}{newline} {deploy_image_uri}")
print(f"{bold}model URI:{unbold}{newline} {pre_trained_model_uri}")
print("Deploying an endpoint ...")

# Deploy the pre-trained model. Note that we need to pass Predictor class when we deploy model
# through Model class, for being able to run inference through the SageMaker API
pre_trained_predictor = pre_trained_model.deploy(
initial_instance_count=1,
instance_type=inference_instance_type,
predictor_cls=Predictor,
endpoint_name=pre_trained_name,
)
print(f"{newline}Deployed an endpoint {pre_trained_name}")

from sagemaker.model import Model
from sagemaker.predictor import Predictor
from sagemaker.utils import name_from_base

fine_tuned_name = name_from_base(f"jumpstart-demo-fine-tuned-{model_id}")
fine_tuned_model_uri = f"{output_location}{training_job_name}/output/model.tar.gz"

# Create the SageMaker model instance of the fine-tuned model
fine_tuned_model = Model(
image_uri=deploy_image_uri,
model_data=fine_tuned_model_uri,
role=aws_role,
predictor_cls=Predictor,
name=fine_tuned_name,
)

print(f"{bold}image URI:{unbold}{newline} {deploy_image_uri}")
print(f"{bold}model URI:{unbold}{newline} {fine_tuned_model_uri}")
print("Deploying an endpoint ...")

# Deploy the fine-tuned model.
fine_tuned_predictor = fine_tuned_model.deploy(
initial_instance_count=1,
instance_type=inference_instance_type,
predictor_cls=Predictor,
endpoint_name=fine_tuned_name,
)
print(f"{newline}Deployed an endpoint {fine_tuned_name}")

import boto3
import json

# Parameters of (output) text generation. A great introduction to generation
# parameters can be found at https://huggingface.co/blog/how-to-generate
parameters = {
"max_length": 40,  # restrict the length of the generated text
"num_return_sequences": 5,  # we will inspect several model outputs
"num_beams": 10,  # use beam search
}

# Helper functions for running inference queries
def query_endpoint_with_json_payload(payload, endpoint_name):
encoded_json = json.dumps(payload).encode("utf-8")
client = boto3.client("runtime.sagemaker")
response = client.invoke_endpoint(
EndpointName=endpoint_name, ContentType="application/json", Body=encoded_json
)
return response

def parse_response_multiple_texts(query_response):
model_predictions = json.loads(query_response["Body"].read())
generated_text = model_predictions["generated_texts"]
return generated_text

def generate_questions(endpoint_name, text):
expanded_prompt = prompt.replace("{context}", text)
payload = {"text_inputs": expanded_prompt, **parameters}
query_response = query_endpoint_with_json_payload(payload, endpoint_name=endpoint_name)
generated_texts = parse_response_multiple_texts(query_response)
for i, generated_text in enumerate(generated_texts):
print(f"Response {i}: {generated_text}{newline}")

print(f"{bold}Prompt:{unbold} {repr(prompt)}")
for paragraph in test_paragraphs:
print("-" * 80)
print(paragraph)
print("-" * 80)
print(f"{bold}pre-trained{unbold}")
generate_questions(pre_trained_name, paragraph)
print(f"{bold}fine-tuned{unbold}")
generate_questions(fine_tuned_name, paragraph)

delaide is the capital city of South Australia, the state's largest city and the fifth-most populous city in Australia.
"Adelaide" may refer to either Greater Adelaide (including the Adelaide Hills) or the Adelaide city centre.
The demonym Adelaidean is used to denote the city and the residents of Adelaide. The Traditional Owners of the Adelaide
region are the Kaurna people. The area of the city centre and surrounding parklands is called Tarndanya in the Kaurna language.

Adelaide is situated on the Adelaide Plains north of the Fleurieu Peninsula, between the Gulf St Vincent in the west and
the Mount Lofty Ranges in the east. Its metropolitan area extends 20 km (12 mi) from the coast to the foothills of
the Mount Lofty Ranges, and stretches 96 km (60 mi) from Gawler in the north to Sellicks Beach in the south.

Response 0: What is the area of the city centre and surrounding parklands called in the Kaurna language?
Response 1: What is the area of the city centre and surrounding parklands is called Tarndanya in the Kaurna language?
Response 2: What is the area of the city centre and surrounding parklands called in Kaurna?
Response 3: What is the capital city of South Australia?
Response 4: What is the area of the city centre and surrounding parklands known as in the Kaurna language?

Response 0: What is the second most populous city in Australia?
Response 1: What is the fourth most populous city in Australia?
Response 2: What is the population of Gawler?
Response 3: What is the largest city in Australia?
Response 4: What is the fifth most populous city in the world?

esponse 0: What is the difference between Amazon EBS and Amazon Elastic Block Store (Amazon EBS)?
Response 1: What is the difference between Amazon EBS and Amazon Elastic Block Store?
Response 2: What is the difference between Amazon EBS and Amazon Simple Storage Service (Amazon S3)?
Response 3: What is Amazon Elastic Block Store (Amazon EBS)?
Response 4: What is the difference between Amazon EBS and a hard drive?

Response 0: What type of applications are not well suited to Amazon EBS?
Response 1: What behaves like formatted block devices?
Response 2: What type of applications are not suited to Amazon EBS?
Response 3: What type of applications are not well suited for Amazon EBS?
Response 4: What type of applications are not suited for Amazon EBS?

Response 0: What does Amazon Comprehend use to extract insights about the content of documents?
Response 1: How does Amazon Comprehend extract insights about the content of documents?
Response 2: What does Amazon Comprehend use to develop insights about the content of documents?
Response 3: How does Amazon Comprehend develop insights about the content of documents?
Response 4: What does Amazon Comprehend use to extract insights about the content of a document?

Response 0: What does Amazon Comprehend use to extract insights about the structure of documents?
Response 1: How does Amazon Comprehend recognize sentiments in a document?
Response 2: What does Amazon Comprehend use to extract insights about the content of social networking feeds?
Response 3: What does Amazon Comprehend use to extract insights about the content of documents?
Response 4: What type of files does Amazon Comprehend reject as input?

# Delete resources
pre_trained_predictor.delete_model()
pre_trained_predictor.delete_endpoint()
fine_tuned_predictor.delete_model()
fine_tuned_predictor.delete_endpoint()