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Release 1.0.72

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@swapneils swapneils released this 23 Jan 18:49
· 80 commits to main since this release
v1.0.72
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Learn about vigilant mode.

Hey Nova builders! We have a bunch of features and Quality-of-Life enhancements for this release, and also improved some edge-case behaviors from the initial release.

New Features

MLflow Integration

  • Track training experiments with Amazon SageMaker MLflow tracking servers
  • Auto-discover DefaultMLFlowApp in your AWS account or specify a custom tracking URI
  • Log metrics, hyperparameters, and model artifacts automatically during training

Continued Pre-Training (CPT) Support

  • Pre-train Nova models on your own datasets

Data Mixing for SFT and CPT (Nova Forge customers only)

  • Blend your custom training data with Nova's high-quality curated datasets

OpenAI Messages Format Conversion

  • Transform datasets from OpenAI chat format to the Converse API format for use with Nova models

Multimodal SFT Dataset Validation

  • Validate image content in SFT datasets (PNG, JPEG, GIF formats)
  • Validate video content in SFT datasets (MOV, MKV, MP4 formats)
  • Validate document content for Nova 2.0 (PDF format)

Enhancements

Dataset Validation

  • Enhanced data-format validation for SFT, RFT, CPT, and Eval jobs, including message role ordering, content types, and tool specifications / uses.

IAM and Security

  • We now have more granular validations of whether the IAM calling role has the required permissions for a job. Where possible, we also validate the SMTJ execution role.
    • IAM validation can be disabled via the validation_config parameter of NovaModelCustomizer
  • New create_bedrock_execution_role() utility function generates scoped-down IAM roles for Bedrock model deployment with minimal required permissions
  • New VPC configuration parameters (subnets, security_group_ids) in SMTJRuntimeManager allow training jobs to run within your VPC for network isolation
  • New kms_key_id parameter encrypts training job output artifacts and inter-container traffic with your KMS key

Recipe Management

  • Recipes are now automatically pulled from SageMaker JumpStart, ensuring you always use the latest supported configurations
  • When starting evaluation, batch inference, or model deployment, we now automatically extract the model checkpoint from your most recent training job output unless explicitly specified.
    • You can also provide a TrainingResult object to extract the model checkpoint from it.

Documentation

  • Added SECURITY.md for vulnerability reporting

Bug Fixes

  • Fixed edge-case where evaluation jobs would fail if data_s3_path was set on the customizer but not needed for the evaluation task
  • Improved validation error messages with specific field locations
  • Improved README setup documentation and fixed some errors in the examples
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