Source code for tests.system.providers.amazon.aws.example_bedrock

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from __future__ import annotations

import json
from datetime import datetime

from botocore.exceptions import ClientError

from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.models.dag import DAG
from airflow.providers.amazon.aws.hooks.bedrock import BedrockHook
from airflow.providers.amazon.aws.operators.bedrock import (
    BedrockCustomizeModelOperator,
    BedrockInvokeModelOperator,
)
from airflow.providers.amazon.aws.operators.s3 import (
    S3CreateBucketOperator,
    S3CreateObjectOperator,
    S3DeleteBucketOperator,
)
from airflow.providers.amazon.aws.sensors.bedrock import BedrockCustomizeModelCompletedSensor
from airflow.utils.trigger_rule import TriggerRule
from tests.system.providers.amazon.aws.utils import SystemTestContextBuilder

# Externally fetched variables:

[docs]ROLE_ARN_KEY = "ROLE_ARN"


[docs]sys_test_context_task = SystemTestContextBuilder().add_variable(ROLE_ARN_KEY).build()



[docs]DAG_ID = "example_bedrock"


# Creating a custom model takes nearly two hours. If SKIP_LONG_TASKS is True then set
# the trigger rule to an improbable state.  This way we can still have the code snippets
# for docs, and we can manually run the full tests occasionally.

[docs]SKIP_LONG_TASKS = True



[docs]LLAMA_MODEL_ID = "meta.llama2-13b-chat-v1"


[docs]PROMPT = "What color is an orange?"


[docs]TITAN_MODEL_ID = "amazon.titan-text-express-v1"


[docs]TRAIN_DATA = {"prompt": "what is AWS", "completion": "it's Amazon Web Services"}


[docs]HYPERPARAMETERS = {
    "epochCount": "1",
    "batchSize": "1",
    "learningRate": ".0005",
    "learningRateWarmupSteps": "0",
}



@task

[docs]def delete_custom_model(model_name: str):
    try:
        BedrockHook().conn.delete_custom_model(modelIdentifier=model_name)
    except ClientError as e:
        if SKIP_LONG_TASKS and (e.response["Error"]["Code"] == "ValidationException"):
            # There is no model to delete.  Since we skipped making one, that's fine.
            return
        raise e



with DAG(
    dag_id=DAG_ID,
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    tags=["example"],
    catchup=False,
) as dag:

[docs]    test_context = sys_test_context_task()

    env_id = test_context["ENV_ID"]
    bucket_name = f"{env_id}-bedrock"
    input_data_s3_key = f"{env_id}/train.jsonl"
    training_data_uri = f"s3://{bucket_name}/{input_data_s3_key}"
    custom_model_name = f"CustomModel{env_id}"
    custom_model_job_name = f"CustomizeModelJob{env_id}"

    create_bucket = S3CreateBucketOperator(
        task_id="create_bucket",
        bucket_name=bucket_name,
    )

    upload_training_data = S3CreateObjectOperator(
        task_id="upload_data",
        s3_bucket=bucket_name,
        s3_key=training_data_uri,
        data=json.dumps(TRAIN_DATA),
    )

    # [START howto_operator_invoke_llama_model]
    invoke_llama_model = BedrockInvokeModelOperator(
        task_id="invoke_llama",
        model_id=LLAMA_MODEL_ID,
        input_data={"prompt": PROMPT},
    )
    # [END howto_operator_invoke_llama_model]

    # [START howto_operator_invoke_titan_model]
    invoke_titan_model = BedrockInvokeModelOperator(
        task_id="invoke_titan",
        model_id=TITAN_MODEL_ID,
        input_data={"inputText": PROMPT},
    )
    # [END howto_operator_invoke_titan_model]

    # [START howto_operator_customize_model]
    customize_model = BedrockCustomizeModelOperator(
        task_id="customize_model",
        job_name=custom_model_job_name,
        custom_model_name=custom_model_name,
        role_arn=test_context[ROLE_ARN_KEY],
        base_model_id=f"arn:aws:bedrock:us-east-1::foundation-model/{TITAN_MODEL_ID}",
        hyperparameters=HYPERPARAMETERS,
        training_data_uri=training_data_uri,
        output_data_uri=f"s3://{bucket_name}/myOutputData",
    )
    # [END howto_operator_customize_model]

    # [START howto_sensor_customize_model]
    await_custom_model_job = BedrockCustomizeModelCompletedSensor(
        task_id="await_custom_model_job",
        job_name=custom_model_job_name,
    )
    # [END howto_sensor_customize_model]

    if SKIP_LONG_TASKS:
        customize_model.trigger_rule = TriggerRule.ALL_SKIPPED
        await_custom_model_job.trigger_rule = TriggerRule.ALL_SKIPPED

    delete_bucket = S3DeleteBucketOperator(
        task_id="delete_bucket",
        trigger_rule=TriggerRule.ALL_DONE,
        bucket_name=bucket_name,
        force_delete=True,
    )

    chain(
        # TEST SETUP
        test_context,
        create_bucket,
        upload_training_data,
        # TEST BODY
        invoke_llama_model,
        invoke_titan_model,
        customize_model,
        await_custom_model_job,
        # TEST TEARDOWN
        delete_custom_model(custom_model_name),
        delete_bucket,
    )

    from tests.system.utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when "tearDown" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()


from tests.system.utils import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)

[docs]test_run = get_test_run(dag)

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