Huawei Noah Vega Versions Save

AutoML tools chain

v1.8.5

1 year ago
  • Bug Fixed:
    • Fixed a bug when the SPNAS algorithm cluster training fails.
    • Fixed bugs such as model copy failure in safe mode.

v1.8.4

1 year ago
  • Bug Fixed:

    • Fixed bug that ASHA failed to update data.
    • Fixed bug that loss is not updated on HCCL+Apex.
    • Add dictionary metrics.
    • Update the security configuration document.
    • Not Allowed Horovod and TensorFlow in safe mode.
    • Python 3.9 or later is required in the security model.

v1.8.3

2 years ago

Bug Fixed:

  • Fixed bug of updating report.
  • Fixed bug of Horovod training in multi-nodes.
  • Fixed docs error of vega-kill.

V1.8.2

2 years ago
  • Bug Fixed:

    • Fixed bad document links.
    • The model to be evaluated supports multiple imputs.
    • Fixed bug when using Apex on the NPU.

v1.8.1

2 years ago
  • Bug fix:

    • The shared directory was not set when the evaluation service was started.
    • Failed to load the pre-trained model.

v1.8.0

2 years ago
  • Feature enhancement:

    • Security enhancement: Security protocols communication.
    • Provide evaluation service release package.
    • Update the auto-lane model and provide auto-lane inference sample code.

v1.7.1

2 years ago
  • Bug fixes:

    • Maximum number of evaluation service attempts.
    • Use SafeLoader to load the YAML file.
    • Catch evaluation service input parameter exceptions.

v1.7.0

2 years ago
  • Feature enhancement:

    • Releases Ascend MindStudio version.
    • Provides data parallel training capabilities for Horovod (GPU) and HCCL (NPU).
    • Fixed bug: The BOHB algorithm may not automatically stop after more than three rounds.

v1.6.1

2 years ago
  • Bug Fixes:
    • Evaluation time error in log.
    • Updating error model description while updating record.

v1.6.0

2 years ago
  • Feature enhancement:

    • Supports simple quota settings, for example, quota: flops < 11.2 and params in [34.0, 56.0].
    • Supports running Vega in a Python virtual environment.
    • Supported running environments: Python 3.8 and PyTorch 1.9.
    • Fixed some bugs with parallel training and distributed search.