adjusting sagemaker xgboost project to tensorflow (or even just different folder name)

I have sagemaker xgboost project template "build, train, deploy" working, but I'd like to modify if to use tensorflow instead of xgboost. First up I was just trying to change the abalone folder to topic to reflect the data we are working with.

I was experimenting with trying to change the topic/pipeline.py file like so

    image_uri = sagemaker.image_uris.retrieve(
        framework="tensorflow",
        region=region,
        version="1.0-1",
        py_version="py3",
        instance_type=training_instance_type,
    )

i.e. just changing the framework name from "xgboost" to "tensorflow", but then when I run the following from a notebook:

from pipelines.topic.pipeline import get_pipeline


pipeline = get_pipeline(
    region=region,
    role=role,
    default_bucket=default_bucket,
    model_package_group_name=model_package_group_name,
    pipeline_name=pipeline_name,
)

I get the following error

ValueError                                Traceback (most recent call last)
<ipython-input-5-6343f00c3471> in <module>
      7     default_bucket=default_bucket,
      8     model_package_group_name=model_package_group_name,
----> 9     pipeline_name=pipeline_name,
     10 )

~/topic-models-no-monitoring-p-rboparx6tdeg/sagemaker-topic-models-no-monitoring-p-rboparx6tdeg-modelbuild/pipelines/topic/pipeline.py in get_pipeline(region, sagemaker_project_arn, role, default_bucket, model_package_group_name, pipeline_name, base_job_prefix, processing_instance_type, training_instance_type)
    188         version="1.0-1",
    189         py_version="py3",
--> 190         instance_type=training_instance_type,
    191     )
    192     tf_train = Estimator(

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/utilities.py in wrapper(*args, **kwargs)
    197                 logger.warning(warning_msg_template, arg_name, func_name, type(value))
    198                 kwargs[arg_name] = value.default_value
--> 199         return func(*args, **kwargs)
    200 
    201     return wrapper

/opt/conda/lib/python3.7/site-packages/sagemaker/image_uris.py in retrieve(framework, region, version, py_version, instance_type, accelerator_type, image_scope, container_version, distribution, base_framework_version, training_compiler_config, model_id, model_version, tolerate_vulnerable_model, tolerate_deprecated_model, sdk_version, inference_tool, serverless_inference_config)
    152             if inference_tool == "neuron":
    153                 _framework = f"{framework}-{inference_tool}"
--> 154         config = _config_for_framework_and_scope(_framework, image_scope, accelerator_type)
    155 
    156     original_version = version

/opt/conda/lib/python3.7/site-packages/sagemaker/image_uris.py in _config_for_framework_and_scope(framework, image_scope, accelerator_type)
    277         image_scope = available_scopes[0]
    278 
--> 279     _validate_arg(image_scope, available_scopes, "image scope")
    280     return config if "scope" in config else config[image_scope]
    281 

/opt/conda/lib/python3.7/site-packages/sagemaker/image_uris.py in _validate_arg(arg, available_options, arg_name)
    443             "Unsupported {arg_name}: {arg}. You may need to upgrade your SDK version "
    444             "(pip install -U sagemaker) for newer {arg_name}s. Supported {arg_name}(s): "
--> 445             "{options}.".format(arg_name=arg_name, arg=arg, options=", ".join(available_options))
    446         )
    447 

ValueError: Unsupported image scope: None. You may need to upgrade your SDK version (pip install -U sagemaker) for newer image scopes. Supported image scope(s): eia, inference, training.

I was skeptical that the upgrade suggested by the error message would fix this, but gave it a try:

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pipelines 0.0.1 requires sagemaker==2.93.0, but you have sagemaker 2.110.0 which is incompatible.

So that seems like I can't upgrade sagemaker without changing pipelines, and it's not clear that's the right thing to do - like this project template may be all designed around those particular ealier libraries.

But so is it that the "framework" name should be different, e.g. "tf"? Or is there some other setting that needs changing in order to allow me to get a tensorflow pipeline ...?

However I find that if I use the existing abalone/pipeline.py file I can change the framework to "tensorflow" and there's no problem running that particular step in the notebook.

I've searched all the files in the project to try and find any dependency on the abalone folder name, and the closest I came was in codebuild-buildspec.yml but that hasn't helped.

Has anyone else successfully changed the folder name from abalone to something else, or am I stuck with abalone if I want to make progress?

Many thanks in advance

p.s. is there a slack community for sagemaker studio anywhere?

p.p.s. I have tried changing all instances of the term "Abalone" to "Topic" within the topic/pipeline.py file (matching case as appropriate) to no avail

p.p.p.s. I discovered that I can get an error free run of getting the pipeline from a unit test:

import pytest

from pipelines.topic.pipeline import *

region = 'eu-west-1'
role = 'arn:aws:iam::398371982844:role/SageMakerExecutionRole'
default_bucket = 'sagemaker-eu-west-1-398371982844'
model_package_group_name = 'TopicModelPackageGroup-Example'
pipeline_name = 'TopicPipeline-Example'

def test_pipeline():
    pipeline = get_pipeline(
        region=region,
        role=role,
        default_bucket=default_bucket,
        model_package_group_name=model_package_group_name,
        pipeline_name=pipeline_name,
    )

and strangely if I go to a different copy of the notebook, everything runs fine, there ... so I have two seemingly identical ipynb notebooks, and in one of them when I switch to trying to get a topic pipeline I get the above error, and in the other, I get no error at all, very strange

p.p.p.p.s. I also notice that conda list returns very different results depending on whether I run it in the notebook or the terminal ... but the conda list results are identical for the two notebooks ...