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I have a pretrained Keras model (tensorflow backend) - `model.json` and `model-weight.hdf5` which I was able to deploy using [this tutorial](https://aws.amazon.com/blogs/machine-learning/deploy-traine…
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**Describe the bug**
The logic to repack model artifact (notably used in MXnet) uses a temp dir under `/tmp`. However, on SageMaker notebook instance classic, this partition is limited in size and …
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Hello,
I am using AWS EKS cluster and trying to use sagemaker API using Kale. I have created a variable called as `tf_estimator` using sagemaker api for training and it works fine when I run the p…
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### Description
At the moment, in the `aws` provider, there is a resource `aws_sagemaker_model` but there is no resource available for creating model registry in sagemaker.
Reference: https://docs.…
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Starting a Sagemaker [Pytorch Estimator][1] based on a custom docker image stored in AWS ECR
```python
from sagemaker.pytorch.estimator import PyTorch
role = "arn:..."
estimator = Py…
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I'm using an Azure virtual machine. In jupyter lab I downloaded the git extension with:
`pip install jupyterlab-git `
and downloaded @jupyterlab/git from the extension manager.
After refresh I ge…
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hey,
can you please provide an example (documentation) of using `content_type = 'x-recordio-protobuf'`, when invoking a sagemaker endpoint? thanks
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## Paste the link of the GitHub organisation below and submit
https://github.com/aws
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https://speakerdeck.com/takapy/komiyuniteisabisuniokerurekomendesiyonfalsebian-qian-tomlpaipurainnituite?slide=40
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I wasn't able to call `fullSummary` method.
> NameError: name 'fullSummary' is not defined
vkorf updated
2 years ago