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+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Integration tests for SFT trainer on HyperPod (Nova Micro)"""
+from __future__ import absolute_import
+
+import time
+import random
+import pytest
+import boto3
+import logging
+
+from sagemaker.train import SFTTrainer
+from sagemaker.train.common import TrainingType
+from sagemaker.core.training.configs import HyperPodCompute
+from sagemaker.core.shapes import StoppingCondition
+
+logger = logging.getLogger(__name__)
+
+S3_BUCKET_PREFIX = "mc-flows-sdk-testing"
+S3_PREFIX = "input_data"
+
+
+@pytest.fixture(scope="module")
+def region():
+    """Resolve the AWS region for this test module."""
+    return "us-east-1"
+
+
+@pytest.fixture(scope="module")
+def account_id(region):
+    """Resolve the AWS account ID from the caller's credentials."""
+    return boto3.Session(region_name=region).client("sts").get_caller_identity()["Account"]
+
+
+@pytest.fixture(scope="module")
+def s3_bucket(region, account_id):
+    """Construct the S3 bucket name from region and account ID."""
+    return f"{S3_BUCKET_PREFIX}-{region}-{account_id}"
+
+
+@pytest.fixture(scope="module")
+def s3_client(region):
+    """Create an S3 client in the correct region."""
+    return boto3.client("s3", region_name=region)
+
+
+@pytest.fixture(scope="module")
+def verified_training_dataset(s3_client, s3_bucket):
+    """Verify the training dataset exists in S3 and return its path."""
+    s3_key = f"{S3_PREFIX}/sft-nova/sft_200_samples.jsonl"
+    s3_path = f"s3://{s3_bucket}/{s3_key}"
+    try:
+        bucket_region = s3_client.get_bucket_location(Bucket=s3_bucket)[
+            "LocationConstraint"
+        ] or "us-east-1"
+        s3_regional_client = boto3.client("s3", region_name=bucket_region)
+        s3_regional_client.head_object(Bucket=s3_bucket, Key=s3_key)
+    except s3_client.exceptions.ClientError as e:
+        if e.response["Error"]["Code"] in ("404", "NoSuchKey"):
+            pytest.fail(
+                f"Training file not found in S3: {s3_path}"
+            )
+        else:
+            raise
+    return s3_path
+
+
+@pytest.fixture(scope="module")
+def hyperpod_compute():
+    """Create HyperPod compute configuration."""
+    return HyperPodCompute(
+        cluster_name="pysdk-hp-integ-tests",
+        namespace="kubeflow",
+        instance_type="ml.g6.12xlarge",
+        node_count=1,
+    )
+
+
+@pytest.mark.gpu_intensive
+@pytest.mark.us_east_1
+def test_sft_trainer_nova_micro_hyperpod_lora(
+    verified_training_dataset, hyperpod_compute, s3_bucket
+):
+    """Test SFT training workflow with Nova Micro on HyperPod using LORA."""
+    unique_id = f"{int(time.time())}-{random.randint(1000, 9999)}"
+    s3_output_path = f"s3://{s3_bucket}/output"
+
+    stopping_condition = StoppingCondition(max_runtime_in_seconds=20800)
+
+    sft_trainer = SFTTrainer(
+        model="nova-textgeneration-micro",
+        training_type=TrainingType.LORA,
+        compute=hyperpod_compute,
+        base_job_name=f"hp-micro-sft-integ-{unique_id}",
+        training_dataset=verified_training_dataset,
+        s3_output_path=s3_output_path,
+        stopping_condition=stopping_condition,
+        overrides={"recipes.training_config.optim_config.lr": 5e-6},
+    )
+
+    sft_trainer.hyperparameters.max_epochs = 1
+
+    # Submit — SFTTrainer routes to HyperPod based on compute type
+    job_name = sft_trainer.train(wait=False)
+
+    logger.info(f"HyperPod SFT job submitted: {job_name}")
+
+    # Poll for job completion by checking for the manifest in S3
+    from sagemaker.train.base_trainer import BaseTrainer
+
+    max_wait_time = 21600  # 6 hour timeout (HyperPod jobs can take longer)
+    poll_interval = 60  # Check every 60 seconds
+    start_time = time.time()
+    checkpoint_path = None
+
+    while time.time() - start_time < max_wait_time:
+        checkpoint_path = BaseTrainer._resolve_checkpoint_from_manifest(
+            job_name=job_name,
+            output_s3_path=s3_output_path,
+        )
+        if checkpoint_path:
+            logger.info(f"Checkpoint resolved: {checkpoint_path}")
+            break
+
+        elapsed = int(time.time() - start_time)
+        logger.info(f"Waiting for manifest... ({elapsed}s elapsed)")
+        time.sleep(poll_interval)
+
+    assert checkpoint_path is not None, (
+        f"Job {job_name} did not produce a manifest within {max_wait_time}s"
+    )
+    logger.info(f"Training complete. Checkpoint: {checkpoint_path}")