By using AWS re:Post, you agree to the Terms of Use

Questions tagged with AWS CodePipeline

Sort by most recent

Browse through the questions and answers listed below or filter and sort to narrow down your results.

how to deploy an ecs service with a task definition that has 2 images with blue green deployment?

I had configured CodePipeline with CodeBuild and ECS blue green as an action provider to deploy my ECS service. In my buildspec.yml I created imageDetail.json like this ``` {"ImageURI": "imageid"}. ``` This setup was working fine when my task definition had only one image. Now my task definition has two images where one image depends from the other so I changed my buildspec.yml to create an imageDetail.json like this: ``` [{"ImageURI":"image1"}, {"ImageURI":"image2"}] ``` When configuring the pipeline with codebuild and ECS blue green deploy with this new task definition and imageDetail.json that has 2 images it is throwing the following error: "Exception while trying to parse the image URI file from the artifact: BuildArtifact." Then I tried doing this same setup but with ECS (rolling update) as an action provider instead of ECS blue green and it worked. With ECS (rolling update) as an action provider I needed to create an imagedefinitions.json instead of an imageDetail.json. The imagedefinitions.json created in buildspec.yml looks like this: ``` [{"name":"name1","imageUri":"image1"}, {"name":"name2","imageUri":"image2"}] ``` However, I want to use ECS blue green as an action provider where I need to create an imageDetail.json in the buildspec.yml file. So, can I create an imageDetail.json with two images like in imagedefinitions.json? I also made the same question here: https://stackoverflow.com/questions/73947923/how-to-deploy-an-ecs-service-with-a-task-definition-that-has-2-images-with-blue
0
answers
0
votes
2
views
asked 35 minutes ago

using transformers module with sagemaker studio project: ModuleNotFoundError: No module named 'transformers'

So as mentioned in my [other recent post](https://repost.aws/questions/QUAL9Vn9abQ6KKCs2ASwwmzg/adjusting-sagemaker-xgboost-project-to-tensorflow-or-even-just-different-folder-name), I'm trying to modify the sagemaker example abalone xgboost template to use tensorfow. My current problem is that running the pipeline I get a failure and in the logs I see: ``` ModuleNotFoundError: No module named 'transformers' ``` NOTE: I am importing 'transformers' in `preprocess.py` not in `pipeline.py` Now I have 'transformers' listed in various places as a dependency including: * `setup.py` - `required_packages = ["sagemaker==2.93.0", "sklearn", "transformers", "openpyxl"]` * `pipelines.egg-info/requires.txt` - `transformers` (auto-generated from setup.py?) but so I'm keen to understand, how can I ensure that additional dependencies are available in the pipline itself? Many thanks in advance ------------ ------------ ------------ ADDITIONAL DETAILS ON HOW I ENCOUNTERED THE ERROR From one particular notebook (see [previous post](https://repost.aws/questions/QUAL9Vn9abQ6KKCs2ASwwmzg/adjusting-sagemaker-xgboost-project-to-tensorflow-or-even-just-different-folder-name) for more details) I have succesfully constructed the new topic/tensorflow pipeline and run the following steps: ``` pipeline.upsert(role_arn=role) execution = pipeline.start() execution.describe() ``` the `describe()` method gives this output: ``` {'PipelineArn': 'arn:aws:sagemaker:eu-west-1:398371982844:pipeline/topicpipeline-example', 'PipelineExecutionArn': 'arn:aws:sagemaker:eu-west-1:398371982844:pipeline/topicpipeline-example/execution/0aiczulkjoaw', 'PipelineExecutionDisplayName': 'execution-1664394415255', 'PipelineExecutionStatus': 'Executing', 'PipelineExperimentConfig': {'ExperimentName': 'topicpipeline-example', 'TrialName': '0aiczulkjoaw'}, 'CreationTime': datetime.datetime(2022, 9, 28, 19, 46, 55, 147000, tzinfo=tzlocal()), 'LastModifiedTime': datetime.datetime(2022, 9, 28, 19, 46, 55, 147000, tzinfo=tzlocal()), 'CreatedBy': {'UserProfileArn': 'arn:aws:sagemaker:eu-west-1:398371982844:user-profile/d-5qgy6ubxlbdq/sjoseph-reg-genome-com-273', 'UserProfileName': 'sjoseph-reg-genome-com-273', 'DomainId': 'd-5qgy6ubxlbdq'}, 'LastModifiedBy': {'UserProfileArn': 'arn:aws:sagemaker:eu-west-1:398371982844:user-profile/d-5qgy6ubxlbdq/sjoseph-reg-genome-com-273', 'UserProfileName': 'sjoseph-reg-genome-com-273', 'DomainId': 'd-5qgy6ubxlbdq'}, 'ResponseMetadata': {'RequestId': 'f949d6f4-1865-4a01-b7a2-a96c42304071', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'f949d6f4-1865-4a01-b7a2-a96c42304071', 'content-type': 'application/x-amz-json-1.1', 'content-length': '882', 'date': 'Wed, 28 Sep 2022 19:47:02 GMT'}, 'RetryAttempts': 0}} ``` Waiting for the execution I get: ``` --------------------------------------------------------------------------- WaiterError Traceback (most recent call last) <ipython-input-14-72be0c8b7085> in <module> ----> 1 execution.wait() /opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in wait(self, delay, max_attempts) 581 waiter_id, model, self.sagemaker_session.sagemaker_client 582 ) --> 583 waiter.wait(PipelineExecutionArn=self.arn) 584 585 /opt/conda/lib/python3.7/site-packages/botocore/waiter.py in wait(self, **kwargs) 53 # method. 54 def wait(self, **kwargs): ---> 55 Waiter.wait(self, **kwargs) 56 57 wait.__doc__ = WaiterDocstring( /opt/conda/lib/python3.7/site-packages/botocore/waiter.py in wait(self, **kwargs) 376 name=self.name, 377 reason=reason, --> 378 last_response=response, 379 ) 380 if num_attempts >= max_attempts: WaiterError: Waiter PipelineExecutionComplete failed: Waiter encountered a terminal failure state: For expression "PipelineExecutionStatus" we matched expected path: "Failed" ``` Which I assume is corresponding to the failure I see in the logs: ![buildl pipeline error message on preprocessing step](/media/postImages/original/IMMpF6LeI6TgWxp20TnPZbUw) I did also run `python setup.py build` to ensure my build directory was up to date ... here's the terminal output of that command: ``` sagemaker-user@studio$ python setup.py build /opt/conda/lib/python3.9/site-packages/setuptools/dist.py:771: UserWarning: Usage of dash-separated 'description-file' will not be supported in future versions. Please use the underscore name 'description_file' instead warnings.warn( /opt/conda/lib/python3.9/site-packages/setuptools/config/setupcfg.py:508: SetuptoolsDeprecationWarning: The license_file parameter is deprecated, use license_files instead. warnings.warn(msg, warning_class) running build running build_py copying pipelines/topic/pipeline.py -> build/lib/pipelines/topic running egg_info writing pipelines.egg-info/PKG-INFO writing dependency_links to pipelines.egg-info/dependency_links.txt writing entry points to pipelines.egg-info/entry_points.txt writing requirements to pipelines.egg-info/requires.txt writing top-level names to pipelines.egg-info/top_level.txt reading manifest file 'pipelines.egg-info/SOURCES.txt' adding license file 'LICENSE' writing manifest file 'pipelines.egg-info/SOURCES.txt' ``` It seems like the dependencies are being written to `pipelines.egg-info/requires.txt` but are these not being picked up by the pipeline?
1
answers
0
votes
46
views
asked 6 days ago

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 ...
1
answers
0
votes
24
views
asked 6 days ago
1
answers
0
votes
14
views
asked 9 days ago