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Copy file name to clipboardExpand all lines: articles/machine-learning/how-to-troubleshoot-environments.md
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@@ -51,7 +51,7 @@ It also caches the environments in Azure Container Registry associated with your
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Multiple environments with the same definition may result the same image, so the cached image will be reused.
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Running a training script remotely requires the creation of a Docker image.
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## Reproducibility and Vulnerabilities
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## Reproducibility and vulnerabilities
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Over time vulnerabilities are discovered and Docker images that correspond to AzureML environments may be flagged by the scanning tools.
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Updates for AzureML based images are released regularly, with a commitment of no unpatched vulnerabilities older than 30 days in the latest version of the image.
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> [!IMPORTANT]
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> There's no guarantee that the same set of python dependencies will be materialized with an image rebuild or for a new environment with the same set of Python dependencies.
- Consider using a newer version of Python as the specified version will eventually unsupported
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- See [Python versions](https://aka.ms/azureml/environment/python-versions) and [Python end-of-life dates](https://aka.ms/azureml/environment/python-end-of-life)
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#### **"Failed to validate python version"**
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#### **"Failed to validate Python version"**
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- The provided Python version may have been formatted improperly or specified with incorrect syntax
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- See [conda package pinning](https://aka.ms/azureml/environment/how-to-pin-conda-packages)
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experience using Azure Machine Learning CLI 2.0
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- See the [samples repository](https://aka.ms/azureml/environment/train-r-models-cli-v2) to get started with the Public Preview edition of the 2.0 CLI
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