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Deploying a Random Forest Model on Amazon Sagemaker always getting a UnexpectedStatusException with Reason: AlgorithmError

Hey I am trying to deploy my RandomForest Classifier on Amazon Sagemaker but get a StatusException Error even though the script worked fine before: The script runs fine and prints out the confusion matrix and accuracy as expected. When I try to deploy the model to amazon Sagemaker using the script it does not work. >>! python --n-estimators 100 \ --max_depth 2 \ --model-dir ./ \ --train ./ \ --test ./ \ Confusion Matrix: [[13 8] [ 1 17]] Accuracy: 0.7692307692307693 I used the Estimator from Sagemaker Python SDK >>from sagemaker.sklearn.estimator import SKLearn >>sklearn_estimator = SKLearn( entry_point='', role = get_execution_role(), instance_count=1, instance_type='ml.m4.xlarge', framework_version='0.20.0', base_job_name='rf-scikit') I launched the training job as follows >>{'train':trainpath, 'test': testpath}, wait=False) Here I am trying to deploy the model which leads to the StatusExceptionError that I cannot seem to fix >>sklearn_estimator.latest_training_job.wait(logs='None') >>artifact = m_boto3.describe_training_job(['ModelArtifacts'['S3ModelArtifacts'] >>print('Model artifact persisted at ' + artifact) >>2022-08-25 12:03:27 Starting - Starting the training job.... >>2022-08-25 12:03:52 Starting - Preparing the instances for training............ >>2022-08-25 12:04:55 Downloading - Downloading input data...... >>2022-08-25 12:05:31 Training - Downloading the training image......... >>2022-08-25 12:06:22 Training - Training image download completed. Training in progress.. >>2022-08-25 12:06:32 Uploading - Uploading generated training model. >>2022-08-25 12:06:43 Failed - Training job failed --------------------------------------------------------------------------- UnexpectedStatusException Traceback (most recent call last) <ipython-input-37-628f942a78d3> in <module> ----> 1 sklearn_estimator.latest_training_job.wait(logs='None') 2 artifact = m_boto3.describe_training_job( 3['ModelArtifacts']['S3ModelArtifacts'] 4 5 print('Model artifact persisted at ' + artifact) ~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/ in wait(self, logs) 2109 self.sagemaker_session.logs_for_job(self.job_name, wait=True, log_type=logs) 2110 else: -> 2111 self.sagemaker_session.wait_for_job(self.job_name) 2112 2113 def describe(self): ~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/ in wait_for_job(self, job, poll) 3226 lambda last_desc: _train_done(self.sagemaker_client, job, last_desc), None, poll 3227 ) -> 3228 self._check_job_status(job, desc, "TrainingJobStatus") 3229 return desc 3230 ~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/ in _check_job_status(self, job, desc, status_key_name) 3390 message=message, 3391 allowed_statuses=["Completed", "Stopped"], -> 3392 actual_status=status, 3393 ) 3394 UnexpectedStatusException: Error for Training job rf-scikit-2022-08-25-12-03-25-931: Failed. Reason: AlgorithmError: framework error: Traceback (most recent call last): File "/miniconda3/lib/python3.7/site-packages/sagemaker_containers/", line 84, in train entrypoint() File "/miniconda3/lib/python3.7/site-packages/sagemaker_sklearn_container/", line 39, in main train(environment.Environment()) File "/miniconda3/lib/python3.7/site-packages/sagemaker_sklearn_container/", line 35, in train runner_type=runner.ProcessRunnerType) File "/miniconda3/lib/python3.7/site-packages/sagemaker_training/", line 100, in run wait, capture_error File "/miniconda3/lib/python3.7/site-packages/sagemaker_training/", line 291, in run cwd=environment.code_dir, File "/miniconda3/lib/python3.7/site-packages/sagemaker_training/", line 208, in check_error info=extra_info, sagemaker_training.errors.ExecuteUserScriptError: ExecuteUserScriptError: ExitCode 1 ErrorMessage "" Command "/miniconda3/bin/python" ExecuteUserScriptErr > I am happy for some help
asked a month ago