Tracker.load() does not work when using sagemaker pipeline

[heizari]

Describe the bug
Create Experiment and Trial smexperiment. And configure PipelineExperimentConfig.
Run training Job using sagemaker pipeline. (When without sagemaker pipeline, this bug did not occur)
In training script, Tracker.load() return exception about this

Traceback (most recent call last):
  File "train.py", line 133, in <module>
    main()
  File "train.py", line 66, in main
    tracker = Tracker.load()
  File "/miniconda3/lib/python3.8/site-packages/smexperiments/tracker.py", line 161, in load
    _ArtifactUploader(tc.trial_component_name, artifact_bucket, artifact_prefix, boto3_session),
AttributeError: 'NoneType' object has no attribute 'trial_component_name'

Maybe this function return None.
But TrialComponentEnvironment.source_arn is defined.
So, I guess is this line wrong?
Because environment["TRAINING_JOB_ARN"] contains uppercase when using sagemaker pipeline.

Sorry for my poor English.

To Reproduce

1. configure Experiment, Trial and PipelineExperimentConfig
2. run training job using pipeline
3. Tracker.load() in training script

Expected behavior
Tracker.load() load trial_component in pipeline

Environment:
Framework (e.g. TensorFlow) / Algorithm (e.g. KMeans):
Framework Version:
Python Version: 3.9.11
CPU or GPU:CPU
Python SDK Version:

• sagemaker==2.116.0
• sagemaker-experiments==0.1.39 (in image: 0.1.41)

Are you using a custom image: yes

[joshcx]

I'm facing the same issue with Sagemaker Pipelines. When I remove the ".lower" in the code you mentioned here then it works. Is it necessary to convert the source arn to lowercase?