change: Enable load_run without name args in Transform env

src/sagemaker/experiments/_environment.py

@@ -18,12 +18,13 @@
 import logging
 import os
 
+from sagemaker import Session
 from sagemaker.experiments import trial_component
 from sagemaker.utils import retry_with_backoff
 
 TRAINING_JOB_ARN_ENV = "TRAINING_JOB_ARN"
 PROCESSING_JOB_CONFIG_PATH = "/opt/ml/config/processingjobconfig.json"
-TRANSFORM_JOB_ENV_BATCH_VAR = "SAGEMAKER_BATCH"
+TRANSFORM_JOB_ARN_ENV = "TRANSFORM_JOB_ARN"
 MAX_RETRY_ATTEMPTS = 7
 
 logger = logging.getLogger(__name__)
@@ -40,7 +41,7 @@ class _EnvironmentType(enum.Enum):
 class _RunEnvironment(object):
     """Retrieves job specific data from the environment."""
 
-    def __init__(self, environment_type, source_arn):
+    def __init__(self, environment_type: _EnvironmentType, source_arn: str):
         """Init for _RunEnvironment.
 
         Args:
@@ -53,9 +54,9 @@ def __init__(self, environment_type, source_arn):
     @classmethod
     def load(
         cls,
-        training_job_arn_env=TRAINING_JOB_ARN_ENV,
-        processing_job_config_path=PROCESSING_JOB_CONFIG_PATH,
-        transform_job_batch_var=TRANSFORM_JOB_ENV_BATCH_VAR,
+        training_job_arn_env: str = TRAINING_JOB_ARN_ENV,
+        processing_job_config_path: str = PROCESSING_JOB_CONFIG_PATH,
+        transform_job_arn_env: str = TRANSFORM_JOB_ARN_ENV,
     ):
         """Loads source arn of current job from environment.
 
@@ -64,8 +65,8 @@ def load(
                 (default: `TRAINING_JOB_ARN`).
             processing_job_config_path (str): The processing job config path
                 (default: `/opt/ml/config/processingjobconfig.json`).
-            transform_job_batch_var (str): The environment variable indicating if
-                it is a transform job (default: `SAGEMAKER_BATCH`).
+            transform_job_arn_env (str): The environment key for transform job ARN
+                (default: `TRANSFORM_JOB_ARN_ENV`).
 
         Returns:
             _RunEnvironment: Job data loaded from the environment. None if config does not exist.
@@ -78,16 +79,15 @@ def load(
             environment_type = _EnvironmentType.SageMakerProcessingJob
             source_arn = json.loads(open(processing_job_config_path).read())["ProcessingJobArn"]
             return _RunEnvironment(environment_type, source_arn)
-        if transform_job_batch_var in os.environ and os.environ[transform_job_batch_var] == "true":
+        if transform_job_arn_env in os.environ:
             environment_type = _EnvironmentType.SageMakerTransformJob
-            # TODO: need to figure out how to get source_arn from job env
-            # with Transform team's help.
-            source_arn = ""
+            # TODO: need to update to get source_arn from config file once Transform side ready
+            source_arn = os.environ.get(transform_job_arn_env)
             return _RunEnvironment(environment_type, source_arn)
 
         return None
 
-    def get_trial_component(self, sagemaker_session):
+    def get_trial_component(self, sagemaker_session: Session):
         """Retrieves the trial component from the job in the environment.
 
         Args:
@@ -99,14 +99,6 @@ def get_trial_component(self, sagemaker_session):
         Returns:
             _TrialComponent: The trial component created from the job. None if not found.
         """
-        # TODO: Remove this condition check once we have a way to retrieve source ARN
-        # from transform job env
-        if self.environment_type == _EnvironmentType.SageMakerTransformJob:
-            logger.error(
-                "Currently getting the job trial component from the transform job environment "
-                "is not supported. Returning None."
-            )
-            return None
 
         def _get_trial_component():
             summaries = list(

src/sagemaker/experiments/_metrics.py

@@ -14,7 +14,6 @@
 from __future__ import absolute_import
 
 import datetime
-import json
 import logging
 import os
 import time
@@ -35,85 +34,6 @@
 logger = logging.getLogger(__name__)
 
 
-# TODO: remove this _SageMakerFileMetricsWriter class
-# when _MetricsManager is fully ready
-class _SageMakerFileMetricsWriter(object):
-    """Write metric data to file."""
-
-    def __init__(self, metrics_file_path=None):
-        """Construct a `_SageMakerFileMetricsWriter` object"""
-        self._metrics_file_path = metrics_file_path
-        self._file = None
-        self._closed = False
-
-    def log_metric(self, metric_name, value, timestamp=None, step=None):
-        """Write a metric to file.
-
-        Args:
-            metric_name (str): The name of the metric.
-            value (float): The value of the metric.
-            timestamp (datetime.datetime): Timestamp of the metric.
-                If not specified, the current UTC time will be used.
-            step (int):  Iteration number of the metric (default: None).
-
-        Raises:
-            SageMakerMetricsWriterException: If the metrics file is closed.
-            AttributeError: If file has been initialized and the writer hasn't been closed.
-        """
-        raw_metric_data = _RawMetricData(
-            metric_name=metric_name, value=value, timestamp=timestamp, step=step
-        )
-        try:
-            logger.debug("Writing metric: %s", raw_metric_data)
-            self._file.write(json.dumps(raw_metric_data.to_record()))
-            self._file.write("\n")
-        except AttributeError as attr_err:
-            if self._closed:
-                raise SageMakerMetricsWriterException("log_metric called on a closed writer")
-            if not self._file:
-                self._file = open(self._get_metrics_file_path(), "a", buffering=1)
-                self._file.write(json.dumps(raw_metric_data.to_record()))
-                self._file.write("\n")
-            else:
-                raise attr_err
-
-    def close(self):
-        """Closes the metric file."""
-        if not self._closed and self._file:
-            self._file.close()
-            self._file = None  # invalidate reference, causing subsequent log_metric to fail.
-        self._closed = True
-
-    def __enter__(self):
-        """Return self"""
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        """Execute self.close()"""
-        self.close()
-
-    def __del__(self):
-        """Execute self.close()"""
-        self.close()
-
-    def _get_metrics_file_path(self):
-        """Get file path to store metrics"""
-        pid_filename = "{}.json".format(str(os.getpid()))
-        metrics_file_path = self._metrics_file_path or os.path.join(METRICS_DIR, pid_filename)
-        logger.debug("metrics_file_path = %s", metrics_file_path)
-        return metrics_file_path
-
-
-class SageMakerMetricsWriterException(Exception):
-    """SageMakerMetricsWriterException"""
-
-    def __init__(self, message, errors=None):
-        """Construct a `SageMakerMetricsWriterException` instance"""
-        super().__init__(message)
-        if errors:
-            self.errors = errors
-
-
 class _RawMetricData(object):
     """A Raw Metric Data Object"""
 

src/sagemaker/experiments/_utils.py

@@ -127,11 +127,9 @@ def get_tc_and_exp_config_from_job_env(
             num_attempts=4,
         )
     else:  # environment.environment_type == _EnvironmentType.SageMakerTransformJob
-        raise RuntimeError(
-            "Failed to load the Run as loading experiment config "
-            "from transform job environment is not currently supported. "
-            "As a workaround, please explicitly pass in "
-            "the experiment_name and run_name in load_run."
+        job_response = retry_with_backoff(
+            callable_func=lambda: sagemaker_session.describe_transform_job(job_name),
+            num_attempts=4,
         )
 
     job_exp_config = job_response.get("ExperimentConfig", dict())

src/sagemaker/experiments/run.py

@@ -120,19 +120,18 @@ def __init__(
                 estimator.fit(job_name="my-job")  # Create a training job
 
         In order to reuse an existing run to log extra data, ``load_run`` is recommended.
+        For example, instead of the ``Run`` constructor, the ``load_run`` is recommended to use
+        in a job script to load the existing run created before the job launch.
+        Otherwise, a new run may be created each time you launch a job.
+
         The code snippet below displays how to load the run initialized above
         in a custom training job script, where no ``run_name`` or ``experiment_name``
         is presented as they are automatically retrieved from the experiment config
         in the job environment.
 
-        Note:
-            Instead of the ``Run`` constructor, the ``load_run`` is recommended to use
-            in a job script to load the existing run created before the job launch.
-            Otherwise, a new run may be created each time you launch a job.
-
         .. code:: python
 
-            with load_run() as run:
+            with load_run(sagemaker_session=sagemaker_session) as run:
                 run.log_metric(...)
                 ...
 

tests/data/experiment/inference.py

@@ -46,6 +46,9 @@ def model_fn(model_dir):
         run.log_parameters({"p3": 3.0, "p4": 4.0})
         run.log_metric("test-job-load-log-metric", 0.1)
 
+    with load_run(sagemaker_session=sagemaker_session) as run:
+        run.log_parameters({"p5": 5.0, "p6": 6})
+
     model_file = "xgboost-model"
     booster = pkl.load(open(os.path.join(model_dir, model_file), "rb"))
     return booster

tests/integ/sagemaker/experiments/test_metrics.py

@@ -30,8 +30,7 @@ def verify_metrics():
             sagemaker_session=sagemaker_session,
         )
         metrics = updated_tc.metrics
-        # TODO: revert to len(metrics) == 2 once backend fix reaches prod
-        assert len(metrics) > 0
+        assert len(metrics) == 2
         assert list(filter(lambda x: x.metric_name == "test-x-step", metrics))
         assert list(filter(lambda x: x.metric_name == "test-x-timestamp", metrics))
 

tests/integ/sagemaker/experiments/test_run.py

@@ -444,10 +444,9 @@ def test_run_from_processing_job_and_override_default_exp_config(
 def test_run_from_transform_job(sagemaker_session, dev_sdk_tar, xgboost_latest_version):
     # Notes:
     # 1. The 1st Run (run) created locally
-    # 2. In the inference script running in a transform job, load the 1st Run
-    # via explicitly passing the experiment_name and run_name of the 1st Run
-    # TODO: once we're able to retrieve exp config from the transform job env,
-    # we should expand this test and add the load_run() without explicitly supplying the names
+    # 2. In the inference script running in a transform job, load the 1st Run twice and log data
+    # 1) via explicitly passing the experiment_name and run_name of the 1st Run
+    # 2) use load_run() without explicitly supplying the names
     # 3. All data are logged in the Run either locally or in the transform job
     exp_name = unique_name_from_base(_EXP_NAME_BASE_IN_SCRIPT)
     xgb_model_data_s3 = sagemaker_session.upload_data(
@@ -494,6 +493,7 @@ def test_run_from_transform_job(sagemaker_session, dev_sdk_tar, xgboost_latest_v
                 content_type="text/libsvm",
                 split_type="Line",
                 wait=True,
+                logs=False,
                 job_name=f"transform-job-{name()}",
             )
 
@@ -506,7 +506,7 @@ def test_run_from_transform_job(sagemaker_session, dev_sdk_tar, xgboost_latest_v
             experiment_name=run.experiment_name, run_name=run.run_name
         )
         _check_run_from_job_result(
-            tc_name=tc_name, sagemaker_session=sagemaker_session, is_init=False
+            tc_name=tc_name, sagemaker_session=sagemaker_session, is_init=False, has_extra_load=True
         )
 
 
@@ -636,8 +636,7 @@ def _check_run_from_local_end_result(sagemaker_session, tc, is_complete_log=True
     assert "s3://Input" == tc.input_artifacts[artifact_name].value
     assert not tc.input_artifacts[artifact_name].media_type
 
-    # TODO: revert to len(tc.metrics) == 1 once backend fix reaches prod
-    assert len(tc.metrics) > 0
+    assert len(tc.metrics) == 1
     metric_summary = tc.metrics[0]
     assert metric_summary.metric_name == metric_name
     assert metric_summary.max == 9.0
@@ -651,9 +650,7 @@ def validate_tc_updated_in_init():
         assert tc.status.primary_status == _TrialComponentStatusType.Completed.value
         assert tc.parameters["p1"] == 1.0
         assert tc.parameters["p2"] == 2.0
-        # TODO: revert to assert len(tc.metrics) == 5 once
-        # backend fix hits prod
-        assert len(tc.metrics) > 0
+        assert len(tc.metrics) == 5
         for metric_summary in tc.metrics:
             # metrics deletion is not supported at this point
             # so its count would accumulate

tests/unit/sagemaker/experiments/test_environment.py

@@ -21,6 +21,7 @@
 import pytest
 
 from sagemaker.experiments import _environment
+from sagemaker.experiments._environment import TRANSFORM_JOB_ARN_ENV, TRAINING_JOB_ARN_ENV
 from sagemaker.utils import retry_with_backoff
 
 
@@ -33,22 +34,22 @@ def tempdir():
 
 @pytest.fixture
 def training_job_env():
-    old_value = os.environ.get("TRAINING_JOB_ARN")
-    os.environ["TRAINING_JOB_ARN"] = "arn:1234aBcDe"
+    old_value = os.environ.get(TRAINING_JOB_ARN_ENV)
+    os.environ[TRAINING_JOB_ARN_ENV] = "arn:1234aBcDe"
     yield os.environ
-    del os.environ["TRAINING_JOB_ARN"]
+    del os.environ[TRAINING_JOB_ARN_ENV]
     if old_value:
-        os.environ["TRAINING_JOB_ARN"] = old_value
+        os.environ[TRAINING_JOB_ARN_ENV] = old_value
 
 
 @pytest.fixture
 def transform_job_env():
-    old_value = os.environ.get("SAGEMAKER_BATCH")
-    os.environ["SAGEMAKER_BATCH"] = "true"
+    old_value = os.environ.get(TRANSFORM_JOB_ARN_ENV)
+    os.environ[TRANSFORM_JOB_ARN_ENV] = "arn:1234aBcDe"
     yield os.environ
-    del os.environ["SAGEMAKER_BATCH"]
+    del os.environ[TRANSFORM_JOB_ARN_ENV]
     if old_value:
-        os.environ["SAGEMAKER_BATCH"] = old_value
+        os.environ[TRANSFORM_JOB_ARN_ENV] = old_value
 
 
 def test_processing_job_environment(tempdir):
@@ -70,8 +71,7 @@ def test_training_job_environment(training_job_env):
 def test_transform_job_environment(transform_job_env):
     environment = _environment._RunEnvironment.load()
     assert _environment._EnvironmentType.SageMakerTransformJob == environment.environment_type
-    # TODO: update if we figure out how to get source_arn from the transform job
-    assert not environment.source_arn
+    assert "arn:1234aBcDe" == environment.source_arn
 
 
 def test_no_environment():
@@ -100,8 +100,3 @@ def test_resolve_trial_component_fails(mock_retry, sagemaker_session, training_j
     client.list_trial_components.side_effect = Exception("Failed test")
     environment = _environment._RunEnvironment.load()
     assert environment.get_trial_component(sagemaker_session) is None
-
-
-def test_resolve_transform_job_trial_component_fail(transform_job_env, sagemaker_session):
-    environment = _environment._RunEnvironment.load()
-    assert environment.get_trial_component(sagemaker_session) is None