MindSpore-tsc

mindspore-tsc@mindspore.cn

July 2020

3 participants
2 discussions

MindSpore TSC Monthly Meeting Jul 16th
by Zhipeng Huang 16 Jul '20

16 Jul '20

Dear TSC Members, This is a kind reminder for today's TSC meeting, please access the meeting via ZOOM link provided in the calendar invitation. Given the current limitation of ZOOM subscription restriction, we have 40mins of discussion time. As usual, US-Asia friendly meeting time is about in 1 hour (Beijing time 12:00), and EU-Asia friendly meeting time will start on UTC0800 (Beijing time 16:00) The initial agenda is as follow: Roll Call Approval of previous minute Community Progress Update (with 0.5.0 release) Release Plan Peview (0.6.0 release preview) Operational Matters (MindSpore Study Group) -- Zhipeng (Howard) Huang Principle Engineer OpenStack, Kubernetes, CNCF, LF Edge, ONNX, Kubeflow, OpenSDS, Open Service Broker API, OCP, Hyperledger, ETSI, SNIA, DMTF, W3C

2 1

[Release]Plan For 0.6.0-beta
by ljllujiale 08 Jul '20

08 Jul '20

Please find below the release plan for our upcoming release 0.6.0 beta targeting the end of July. We've also documented the release management related info at https://gitee.com/mindspore/mindspore/wikis/July%202020?sort_id=2378784 Please also note that this is not the final release note, which will contain additional information on bug fixes and so forth. Feel free to ask any questions or provide any feedback through the list! Planned Features and Improvements For 0.6.0-beta release MindSpore Ascend 910 Training and Inference Framework New models MaskRCNN: a simple and flexible deep neural network for object instance segmentation on COCO 2014 dataset. TinyBERT: a smaller version of the base BERT model for natural language understanding using transformer distillation and two-stage learning framework. Frontend and user interface Supports user side operator compilation and graph execution error rendering. Uniform definition dynamic learning rate behavior in optimizers. Support ps and allreduce mixing in optimizers. Support IndexSlice in sparse expression. Support mixed precision in pynative mode. Support the process of forward execution, dynamically constructing reverse graph. Support use parent construct method during construct. Support asynchronous execution save checkpoint file. Support implicit type conversion in pynative mode. Executor and performance optimization Decouple C++ and python, so make the architecture more extensible. Parameter Server for distributed deep learning supported, and is verified in Wide&Deep model. Quantitative training of YoloV3 on Ascend-910 is supported, and quantitative inference on Ascend-310 is also supported. Serving：a flexible service deployment framework for deep learning models. Memory reuse is enhanced, and the batch size of Bert large model is increased from 96 to 160 on a single server. Data processing, augmentation, and save format Support single cache after data processing Support MindRecord save operator after date processing Support automatic fusion operator, such as decode/resize/crop Support CSV dataset loading Other Hardware Support GPU platform New model supported: ShuffleNet, NASNet, RetinaFace + MobileNetV2. Support hyperparametric search and data enhanced automl on GPU. Support Resnet50 automatic parallel in GPU backend. GraphEngine GE supports function control operators such as If/Case/While/For. In a single operator call scenario, GE supports recording the correspondence between operators and tasks for performance commissioning. GE supports new operator overflow positioning solution. MindArmour Differential privacy model training Optimizers with differential privacy Differential privacy model training now supports some new policies. Adaptive Norm policy is supported. Adaptive Noise policy with expontional decrease is supported. Differential Privacy Training Monitor A new monitor is supported using zCDP as its asmpotetic budget estimator. MindInsight Provide monitoring capabilities for each of Ascend AI processor and other hardware resources,including CPU, memory and disk. Visualization of weight, gradient and other tensor data in model training Provide tabular form presentation of tensor data. Provide histogram to show the distribution of tensor data and its change over time. Thanks Lujiale

1 0