I am using SageMaker to train a model with multiple GBs of data. My data is loaded using huggingface’s datasets.load_dataset method. Since data is huge and I want to re-use it, I want to store it in S3 bucket. I tried below: from datasets import load_dataset dataset = load_dataset(‘s3://bucket_name/some_dir/data’, ‘oscar’, ‘unshuffled_deduplicated_en’) but this results in: ..
I’m currently testing sagemaker reinforcement learning, specifically dueling double deep q network. My model prediction (endpoint deployment) is totally different from evaluation step taken by the agent. The operation that I used for output nodes is ‘main_level/agent/main/online/network_0/dueling_q_values_head_0/output’ type=Add`. Any help would be appreciated. Source: Python..
I am trying to trigger the AWS sagemaker jupyter notebook from AWS lambda function using websockets. The Jupyter notebook gets initiated fine. In the notebook I try to read the other python files I have in the same folder as the Jupyter Notebook. For example in the sagemaker notebook there is a folder called "Data" ..
I am currently working on setting up a pipeline in Amazon Sagemaker. For that I set up an xgboost-estimator and trained it on my dataset. The training job runs as expected and the freshly trained model is saved to the specified output bucket. Later I want to reimport the model, which is done by getting ..
I am running a JupyterLab on AWS SageMaker. Kernel: conda_amazonei_mxnet_p27 The number of fields found: saw 9 increments by 1, each run. Error: ParserError: Error tokenizing data. C error: Expected 2 fields in line 50, saw 9 Code: Invocation (Error doesn’t appear when running all cells before this but does when this is ran): train ..
I need to import function from different python scripts, which will used inside preprocessing.py file. I was not able to find a way to pass the dependent files to SKLearnProcessor Object, due to which I am getting ModuleNotFoundError. Code: from sagemaker.sklearn.processing import SKLearnProcessor from sagemaker.processing import ProcessingInput, ProcessingOutput sklearn_processor = SKLearnProcessor(framework_version=’0.20.0′, role=role, instance_type=’ml.m5.xlarge’, instance_count=1) sklearn_processor.run(code=’preprocessing.py’, ..
Running into issue loading the model. I have defined my model and am trying to deploy it on a non GPU instance. Not sure where the error is since i’m specifying it to load it on CPU. Should this not be the case ? I have tried passing on torch.device to map to location as ..
I have a SageMaker endpoint, which works just as expected from Postman. And I have tried to send the same HTTP POST request from python3, but always get some sort of error, while Postman seems to work fine. I would like to know what I am missing with signature in my Python code and what ..
I use Brave browser for my jupyter notebook (not jupyterlab) works under Amazon Sagemaker, but I also use Chrome for my Internet activities, so last week the update symbol appeared on Chrome, so I updated it, and inmediately my notebooks on Brave stopped rendering well the following tag <hr style="border:3px solid black"> </hr>, which shows ..
I use Brave browser for my jupyter notebook (not jupyterlab) works under Amazon Sagemaker, but I also use Chrome for my Internet activities, so last week the update symbol appeared on Chrome, so I updated it, and immediately my notebooks on Brave stopped rendering well the following tag <hr style="border:3px solid black"> </hr>, which shows ..