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

Developer Tools

Host code, build, test, and deploy your applications quickly and effectively with AWS Developer Tools. Leverage core tools like software development kits (SDKs), code editors, and continuous integration and delivery (CI/CD) services for DevOps software development. Use machine learning (ML)-guided best practices and abstractions to improve agility, security, velocity, and code quality.

Recent questions

see all
1/18

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' ``` 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 ...
0
answers
0
votes
9
views
asked 15 hours 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 ...
0
answers
0
votes
8
views
asked 16 hours ago

Unable to execute HTTP request: Connect to sts.us-east-1.amazonaws.com:443 [sts.us-east-1.amazonaws.com/209.54.177.185] failed: Connect timed out

Sometimes I am getting the below error from sts while API call. I am not able to find the root cause of this error. ``` Unable to execute HTTP request: Connect to sts.us-east-1.amazonaws.com:443 [sts.us-east-1.amazonaws.com/209.54.177.185] failed: Connect timed out ``` Stack Trace JSON ``` { "message": "Unable to execute HTTP request: Connect to sts.us-east-1.amazonaws.com:443 [sts.us-east-1.amazonaws.com/209.54.177.185] failed: Connect timed out", "source": "JavaSDK", "stackTrace": "software.amazon.awssdk.core.exception.SdkClientException$BuilderImpl.build(SdkClientException.java:102)", "cause": { "message": "Connect to sts.us-east-1.amazonaws.com:443 [sts.us-east-1.amazonaws.com/209.54.177.185] failed: Connect timed out", "source": "JavaSDK", "stackTrace": "org.apache.http.impl.conn.DefaultHttpClientConnectionOperator.connect(DefaultHttpClientConnectionOperator.java:151)", "cause": { "message": "Connect timed out", "source": "JavaSDK", "stackTrace": "java.base/sun.nio.ch.NioSocketImpl.timedFinishConnect(NioSocketImpl.java:546)\njava.base/sun.nio.ch.NioSocketImpl.connect(NioSocketImpl.java:597)", "cause": null, "applicationFailureInfo": { "type": "java.net.SocketTimeoutException", "nonRetryable": false, "details": null } }, "applicationFailureInfo": { "type": "org.apache.http.conn.ConnectTimeoutException", "nonRetryable": false, "details": null } }, "applicationFailureInfo": { "type": "software.amazon.awssdk.core.exception.SdkClientException", "nonRetryable": false, "details": null } } ```
0
answers
0
votes
14
views
asked 2 days ago

SDK and ChainableTemporaryCredentials

Hi, I already posted my problem in: https://stackoverflow.com/questions/73702466/chainabletemporarycredentials-getpromise-and-missing-credentials-in-config-if-u Basically it is the following. When I use ``` const credentials = new ChainableTemporaryCredentials({ params: { RoleArn: 'arn:aws:iam::${this.accountId}:role/${this.targetRoleName}', RoleSessionName: this.targetRoleName, }, masterCredentials: new WebIdentityCredentials({ RoleArn: 'arn:aws:iam::<proxyAccountId>:role/<proxyRoleName>', RoleSessionName: this.proxyRoleName, WebIdentityToken: token, }), }) await credentials.getPromise() ``` with `token` a a token received from GCP-cloud do I still need some kind of AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY in my environment? I don't think so, since the idea of the token is to grant access exactly without such credentials. Right? (In the codeblock above I had to manipulate some charaters because the code-template here in the forum had some difficulties withe original 1:1 code...) At runtime I get always an error message: `Missing credentials in config, if using AWS_CONFIG_FILE, set AWS_SDK_LOAD_CONFIG=1` I think I have not to use AWS_CONFIG_FILE: My application runs in GCP and just want access AWS via STS. My token looks good so far as I would assess: ``` { "aud": <here my email address of the service account in GCP>, "azp": "21 digit number", "email": <same email as under "aud">, "email_verified": true, "exp": <10 digit number>, "iat": <10 digit number>, "iss": "https://accounts.google.com", "sub": "<same number as under azp>" } ``` Are my expectations wrong? What is the reason for the error message? Best regards Thomas
2
answers
0
votes
13
views
asked 2 days ago
1
answers
0
votes
10
views
asked 4 days ago

Recent articles

see all
1/2

Popular users

see all
1/18

Learn AWS faster by following popular topics

1/1