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Amazon SageMaker deploying from model artifacts - what object do we load from archive?


Amazon SageMaker deploying from model artifacts - what object do we load from archive?

By : J.Pride
Date : October 25 2020, 07:10 AM
I think the issue was by ths following , The model.tar.gz is the SavedModel or the output of the training stage. You can generate this file from every source that is able to train a TF model, including SageMaker training jobs (see here for example), Colab etc.
code :


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how to deploy a xgboost model on amazon sagemaker?

how to deploy a xgboost model on amazon sagemaker?


By : Optxx
Date : March 29 2020, 07:55 AM
it helps some times This example notebook is good starting point showing how to use a pre-existing scikit-learn xgboost model with the Amazon SageMaker to create a hosted endpoint for that model.
Pros and Cons of Amazon SageMaker VS. Amazon EMR, for deploying TensorFlow-based deep learning models?

Pros and Cons of Amazon SageMaker VS. Amazon EMR, for deploying TensorFlow-based deep learning models?


By : mohamed
Date : March 29 2020, 07:55 AM
it fixes the issue In general terms, they serve different purposes.
EMR is when you need to process massive amounts of data and heavily rely on Spark, Hadoop, and MapReduce (EMR = Elastic MapReduce). Essentially, if your data is in large enough volume to make use of the efficiencies of Spark, Hadoop, Hive, HDFS, HBase and Pig stack then go with EMR.
Sagemaker custom model : "The model data archive is too large" error when creation endpoint

Sagemaker custom model : "The model data archive is too large" error when creation endpoint


By : XCoder
Date : March 29 2020, 07:55 AM
it fixes the issue When an Endpoint is created with SageMaker, the model data artifact is downloaded and uncompressed onto the associated disk/EBS volume on the instance. This volume size is proportional[1] to the instance type that you chose.
Please make sure that the instance type that you picked has enough disk space to accommodate the uncompressed .tar.gz file. (It does not matter if it is fully or partially loaded onto memory later on, it has to fit in the disk uncompressed).
How to solve the error with deploying a model in aws sagemaker?

How to solve the error with deploying a model in aws sagemaker?


By : user3387909
Date : March 29 2020, 07:55 AM
With these it helps Using Sagemaker script mode can be much simpler than dealing with container and nginx low-level stuff like you're trying to do, have you considered that?
You only need to provide the keras script:
How can I deploy a model that i trained on amazon sagemaker locally?

How can I deploy a model that i trained on amazon sagemaker locally?


By : Виталий Черненко
Date : October 09 2020, 05:00 PM
hope this fix your issue SageMaker BlazingText comes in 2 flavors:
a supervised version, that learns to classify variable-length token sequences and an unsupervised version, that learns token embeddings.
code :
import fasttext

# bin file is found in the model.tar.gz produced by Sagemaker
model = fasttext.load_model('model_filename.bin')

# inference for unsupervised version
model['king']

# inference for supervised version
model.predict('Which baking dish is best to bake a banana bread ?')
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