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upgrade pytorch container to 2.x
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| "local_dir = \"data\"\n", | ||
| "MNIST.mirrors = [\n", | ||
| " f\"https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/\"\n", | ||
| "base_url = f\"https://sagemaker-example-files-prod-{region}.s3.amazonaws.com/datasets/image/MNIST/\"\n", |
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Potential S3 bucket sniping vulnerability detected. This rule has identified S3 bucket references that could be vulnerable to bucket sniping attacks. Bucket sniping occurs when an attacker registers an S3 bucket name after finding it referenced in code but not yet created. This can lead to data exposure, malicious content hosting, or service disruption.
Recommendations:
- Create all referenced S3 buckets immediately
- Use organization-specific prefixes for bucket names
- Verify bucket ownership before use
- Consider using AWS Organizations S3 bucket naming rules
Discovered: datasets
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upgrade pytorch container to 2.x
Issue #, if available:
Description of changes:
upgrade sagemaker from v2 to v3 and upgrade pytorch container image used in training and inference
Testing done:
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