SageMaker Pipeline creation stuck at pipeline.upsert() without error

Hi, I'm working on to create an SageMaker Pipeline with two processing phases in a jupyter notebook. In the phases, my goal is to process files with an generative ai model. When I run my pipeline creation code, the process gets stuck indefinitely at two lines:

definition = json.loads(pipeline.definition())
print(definition)

or

pipeline.upsert(role_arn=role)

Here's my code (without sensitive information):

!pip install sagemaker
!pip install boto3

import sagemaker
from sagemaker.processing import ScriptProcessor, ProcessingInput, ProcessingOutput
from sagemaker.workflow.steps import ProcessingStep
from sagemaker.workflow.pipeline import Pipeline
from sagemaker.workflow.parameters import ParameterString
from sagemaker.workflow.pipeline_context import PipelineSession
import boto3

sagemaker_session = sagemaker.Session()
role = sagemaker.get_execution_role()
pipeline_session = PipelineSession()

S3_BUCKET_NAME = "my-s3-bucket"

PHASE1_INSTANCE_TYPE = "ml.g5.2xlarge"
PHASE1_IMAGE_URI = "<PHASE1_IMAGE_URI>"
PHASE1_SCRIPT_FILE_PATH = "phase1_script.py"

PHASE2_INSTANCE_TYPE = "ml.t3.medium"
PHASE2_IMAGE_URI = "phase2_image_uri"
PHASE2_SCRIPT_FILE_PATH = "phase2_script.py"

input_data = ParameterString(
    name="InputDataUri",
    default_value=f's3://{S3_BUCKET_NAME}/raw_pdf_files/'
)
phase1_output = ParameterString(
    name="Phase1OutputUri",
    default_value=f's3://my-s3-bucket/phase1_md_outputs/'
)
phase2_output = ParameterString(
    name="Phase2OutputUri", 
    default_value=f's3://my-s3-bucket/phase2_json_outputs/'
)

# Phase 1:
phase1_processor = ScriptProcessor(
    image_uri=PHASE1_IMAGE_URI,  
    command=["python3"],
    instance_type=PHASE1_INSTANCE_TYPE,
    instance_count=1,
    role=role,
    sagemaker_session=pipeline_session
)

phase1_job_args = phase1_processor.run(
    code=PHASE1_SCRIPT_FILE_PATH,
    inputs=[
        ProcessingInput(
            source=input_data,
            destination="/opt/ml/processing/input",
            s3_data_type="S3Prefix",
            s3_input_mode="File"
        )
    ],
    outputs=[
        ProcessingOutput(
            output_name="phase1_output",
            source="/opt/ml/processing/output",
            destination=phase1_output,
            s3_upload_mode="EndOfJob"
        )
    ],
    wait=False
)

phase1_processing_step = ProcessingStep(
    name="Phase1",
    step_args=phase1_job_args
)

# Phase 2
phase2_processor = ScriptProcessor(
    image_uri=PHASE2_IMAGE_URI,
    command=["python3"],
    instance_type=PHASE2_INSTANCE_TYPE,
    instance_count=1,
    role=role,
    sagemaker_session=pipeline_session
)

phase2_job_args = phase2_processor.run(
    code=PHASE2_SCRIPT_FILE_PATH, 
    inputs=[
        ProcessingInput(
            source=phase1_output,
            destination="/opt/ml/processing/input",
            s3_data_type="S3Prefix",
            s3_input_mode="File"
        )
    ],
    outputs=[
        ProcessingOutput(
            output_name="phase2_output",
            source="/opt/ml/processing/output",
            destination=phase2_output,
            s3_upload_mode="EndOfJob"
        )
    ],
    wait=False
)

phase2_processing_step = ProcessingStep(
    name="Phase2",
    step_args=phase2_job_args
)

pipeline = Pipeline(
    name="My_Pipeline_Name",
    parameters=[input_data, phase1_output, phase2_output],
    steps=[phase1_processing_step, phase2_processing_step],
    sagemaker_session=pipeline_session,
)

import json
definition = json.loads(pipeline.definition())
print(definition)

pipeline.upsert(role_arn=role)

Thanks for your time.