Release 1.7 - 7/8/2020

Major Features

Training Service

• Support AML(Azure Machine Learning) platform as NNI training service.
• OpenPAI job can be reusable. When a trial is completed, the OpenPAI job won't stop, and wait next trial. refer to reuse flag in OpenPAI config.
• Support ignoring files and folders in code directory with .nniignore when uploading code directory to training service.

Neural Architecture Search (NAS)

• Provide NAS Open Benchmarks (NasBench101, NasBench201, NDS) with friendly APIs.

• Support Classic NAS (i.e., non-weight-sharing mode) on TensorFlow 2.X.

Model Compression

• Improve Model Speedup: track more dependencies among layers and automatically resolve mask conflict, support the speedup of pruned resnet.

• Added new pruners, including three auto model pruning algorithms: NetAdapt Pruner, SimulatedAnnealing Pruner, AutoCompress Pruner, and ADMM Pruner.

• Added model sensitivity analysis tool to help users find the sensitivity of each layer to the pruning.

• Easy flops calculation for model compression and NAS.

• Update lottery ticket pruner to export winning ticket.

Examples

• Automatically optimize tensor operators on NNI with a new customized tuner OpEvo.

Built-in tuners/assessors/advisors

• Allow customized tuners/assessor/advisors to be installed as built-in algorithms.

WebUI

• Support visualizing nested search space more friendly.
• Show trial's dict keys in hyper-parameter graph.
• Enhancements to trial duration display.

Others

• Provide utility function to merge parameters received from NNI
• Support setting paiStorageConfigName in pai mode

Documentation

• Improve documentation for model compression
• Improve documentation
  and examples for NAS benchmarks.
• Improve documentation for AzureML training service
• Homepage migration to readthedoc.

Bug Fixes

• Fix bug for model graph with shared nn.Module
• Fix nodejs OOM when make build
• Fix NASUI bugs
• Fix duration and intermediate results pictures update issue.
• Fix minor WebUI table style issues.