Huawei Noah Vega Versions Save

• Feature enhancement:

  • Fixed some bugs in distributed training.
  • Some networks support PyTorch + Ascend 910.
  • The Vega-process, Vega-progress, and vega-verify-cluster commands provide JSON format information.

• Feature enhancement:

  • Support TensorFlow and Mindpore distributed training in the search phase.
  • The classification and detection joint training with same backend.
  • Dynamically setting transformers.
  • Added tools such as vega-process and vega-progress.
  • New trainer ScriptRunner that directly invoke user scripts during the HPO process.
  • The BO of the BOHB can use HEBO.

• New algorithm:

  • PBT：Population Based Training of Neural Networks
  • Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation

Vega ver1.3.0 released:

• Feature enhancement:
  • Ascend platform, search and training on the Ascend 910 (TensorFlow and MindSpore), and model evaluation on the Ascend 310.
  • Model evaluation is supported on the Kirin 990.
  • Allows user datasets to be FineTune on DNet pretrained models and surpass SOTA on Ascend 910/310.
  • Support the pruning capability of user datasets and ResNet models. For the Cifar100 data set, the precision changes slightly (+– 0.5), the latency decreases by 15%, and the model size decreases by 30%.
• New algorithm:
  • ModularNAS: Towards Modularized and Reusable Neural Architecture Search, A code library for various neural architecture search methods including weight sharing and network morphism.
  • DNet: Network architecture search algorithms and Model Zoo that are affinity with Davinci chips.
  • MF-ASC: Multi-Fidelity neural Architecture Search with Co-kriging.

Install:

pip3 install --user --upgrade noah-vega

or

pip3 install --user --upgrade noah_vega-1.3.0-py3-none-any.whl

pip3 install --user noah_vega-1.2.0-py3-none-any.whl
python3 -m vega.tools.install_pkgs

release ver1.0.0