version update to 1.0.0

This commit is contained in:
wuweikang 2020-09-21 16:07:11 +08:00
parent 71b5791aa1
commit 769a1316ad
6 changed files with 267 additions and 11 deletions

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@ -69,6 +69,8 @@ MindSpore offers build options across multiple backends:
| | Ubuntu-aarch64 | ✔️ |
| | EulerOS-x86 | ✔️ |
| | EulerOS-aarch64 | ✔️ |
| | CentOS-x86 | ✔️ |
| | CentOS-aarch64 | ✔️ |
| GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
| CPU | Ubuntu-x86 | ✔️ |
| | Ubuntu-aarch64 | ✔️ |
@ -79,7 +81,7 @@ For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an ex
1. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
```
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.7.0-beta/MindSpore/cpu/ubuntu_x86/mindspore-0.7.0-cp37-cp37m-linux_x86_64.whl
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.0/MindSpore/cpu/ubuntu_x86/mindspore-1.0.0-cp37-cp37m-linux_x86_64.whl
```
2. Run the following command to verify the install.
@ -136,8 +138,8 @@ currently the containerized build options are supported as follows:
For `CPU` backend, you can directly pull and run the latest stable image using the below command:
```
docker pull mindspore/mindspore-cpu:0.7.0-beta
docker run -it mindspore/mindspore-cpu:0.7.0-beta /bin/bash
docker pull mindspore/mindspore-cpu:1.0.0
docker run -it mindspore/mindspore-cpu:1.0.0 /bin/bash
```
* GPU
@ -170,8 +172,8 @@ currently the containerized build options are supported as follows:
```
Then you can pull and run the latest stable image using the below command:
```
docker pull mindspore/mindspore-gpu:0.7.0-beta
docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:0.7.0-beta /bin/bash
docker pull mindspore/mindspore-gpu:1.0.0
docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:1.0.0 /bin/bash
```
To test if the docker image works, please execute the python code below and check the output:

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@ -66,6 +66,8 @@ MindSpore提供跨多个后端的构建选项
| | Ubuntu-aarch64 | ✔️ |
| | EulerOS-x86 | ✔️ |
| | EulerOS-aarch64 | ✔️ |
| | CentOS-x86 | ✔️ |
| | CentOS-aarch64 | ✔️ |
| GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
| CPU | Ubuntu-x86 | ✔️ |
| | Ubuntu-aarch64 | ✔️ |
@ -76,7 +78,7 @@ MindSpore提供跨多个后端的构建选项
1. 请从[MindSpore下载页面](https://www.mindspore.cn/versions)下载并安装whl包。
```
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.7.0-beta/MindSpore/cpu/ubuntu_x86/mindspore-0.7.0-cp37-cp37m-linux_x86_64.whl
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.0/MindSpore/cpu/ubuntu_x86/mindspore-1.0.0-cp37-cp37m-linux_x86_64.whl
```
2. 执行以下命令,验证安装结果。
@ -132,8 +134,8 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore
对于`CPU`后端,可以直接使用以下命令获取并运行最新的稳定镜像:
```
docker pull mindspore/mindspore-cpu:0.7.0-beta
docker run -it mindspore/mindspore-cpu:0.7.0-beta /bin/bash
docker pull mindspore/mindspore-cpu:1.0.0
docker run -it mindspore/mindspore-cpu:1.0.0 /bin/bash
```
* GPU
@ -166,8 +168,8 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore
```
使用以下命令获取并运行最新的稳定镜像:
```
docker pull mindspore/mindspore-gpu:0.7.0-beta
docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:0.7.0-beta /bin/bash
docker pull mindspore/mindspore-gpu:1.0.0
docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:1.0.0 /bin/bash
```
要测试Docker是否正常工作请运行下面的Python代码并检查输出

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@ -1,3 +1,107 @@
# Release 1.0.0
## Major Features and Improvements
### MindSpore Training and Inference Framework
#### Ascend 910
* New models
* Mask-RCNN: a simple and flexible deep neural network for object instance segmentation on COCO 2014 dataset.
* DenseNet121: a dense convolutional neural network, which connects each layer to every other layer in a feed-forward fashion for object recognition on ImageNet dataset.
* PSENet: accurately detect arbitrary shape text instances and get better results on CTW1500, full text, ICDAR 2015, and ICDAR 2017 MLT datasets.
* UNet2D-Medical: Unet Medical model for 2D image segmentation, Convolutional Networks for Biomedical Image Segmentation on ISBI Challenge database.
* Frontend and user interface
* Second-Order Optimization
* Enable second-order optimization for Bert on Ascend 910, which can achieve a masked lm accuracy of 71.3% in 1000 seconds using 8 Ascend 910 (Bert-Large @MLPerf v0.7 dataset).
* New GNN model BGCF
* Bayesian Graph Convolutional Filtering network which naturally incorporate the uncertainty in the user-item interaction graph shows excellent recommendation performance on Amazon-Beauty dataset.
* Add append interface for SequentialCell.
* Add a level `auto` for AMP.
* Executor and performance optimization
* Support quantitative network (Resnet50 & YoloV3 & MobileNetV2).
* Project ease of use optimization: project compilation time optimization, CMakelist regularization, cudnn, cuda independent compilation and installation independent.
* Data processing, augmentation, and save format
* Support GeneratorDataset return string type
#### Other Hardware Support
* GPU platform
* New model supported: TinyBert, ShuffleNet, YoloV3-DarkNet53, EfficientNet-B0, NASNet-Mobile and Transformer.
* Enable second-order optimization for resnet50 on GPU, which achieve 20% improvement on training time compared to SGD with Momentum (Resnet50 @ImageNet).
* CPU platform
* ...
#### User interfaces change log
* Remove global object GradOperation in Autodiff([!5011](https://gitee.com/mindspore/mindspore/pulls/5011))
* Remove useless attribute 'name' in Autodiff([!5172](https://gitee.com/mindspore/mindspore/pulls/5172))
* Rectification distributed init([!5350](https://gitee.com/mindspore/mindspore/pulls/5350))
* Move the setting of ParalleMode from train.parallel_utils to context([!5351](https://gitee.com/mindspore/mindspore/pulls/5351))
* Modification of save_checkpoint([!5482](https://gitee.com/mindspore/mindspore/pulls/5482))
* Wrap numpy random seed into an api([!5634](https://gitee.com/mindspore/mindspore/pulls/5634))
* Delete enable_fused_layernorm in some modelzoo scripts([!5665](https://gitee.com/mindspore/mindspore/pulls/5665))
* Move 'multi-subgraphs' interface to internal([!5696](https://gitee.com/mindspore/mindspore/pulls/5696))
* Rename mirror_mean to gradient_mean([!5700](https://gitee.com/mindspore/mindspore/pulls/5700))
* Remove default value of 'group' of DepthWiseConv2d([!5865](https://gitee.com/mindspore/mindspore/pulls/5865))
* Modify interface for function and remove duplicated def([!5958](https://gitee.com/mindspore/mindspore/pulls/5958))
* Unify Conv2d and DepthwiseConv2d([!5916](https://gitee.com/mindspore/mindspore/pulls/5916))
* Modification of SoftmaxCrossEntropyWithLogits([!5502](https://gitee.com/mindspore/mindspore/pulls/5502))
* Change API set_strategy() to shard()([!5991](https://gitee.com/mindspore/mindspore/pulls/5991))
* Move batch_size from bert_cfg_cfg to cfg([!6233](https://gitee.com/mindspore/mindspore/pulls/6233))
* Remove unused parameters from SummaryRecord __init__([!5548](https://gitee.com/mindspore/mindspore/pulls/5548))
* remove sens parameter of TrainOneStepWithLossScaleCell([!5753](https://gitee.com/mindspore/mindspore/pulls/5753))
* optimize the TrainOneStepCell for user's define([!6159](https://gitee.com/mindspore/mindspore/pulls/6159))
* delete seed0 and seed1 of nn.Dropout([!5735](https://gitee.com/mindspore/mindspore/pulls/5735))
* delete DataWrapper([!6101](https://gitee.com/mindspore/mindspore/pulls/6101))
* LSTM API optimization([!6374](https://gitee.com/mindspore/mindspore/pulls/6374))
* Merge P\C\F of ops([!5645](https://gitee.com/mindspore/mindspore/pulls/5645))
* Log optimization([!5842](https://gitee.com/mindspore/mindspore/pulls/5842))
* Remove useless API dataset.set_dataset_size[!5806](https://gitee.com/mindspore/mindspore/pulls/5806))
* Some of Dataset API add usage parameter[!5605](https://gitee.com/mindspore/mindspore/pulls/5605))
* Change the import path, such as from mindspore.dataset.transforms.vision to mindspore.dataset.vision.transforms[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
* Rename ImageFolderDatasetV2 to ImageFolderDataset[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
* Dataset.map parameter optimization[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
* Add new api dataset.get_col_names[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
* Add new api dataset.get_col_names[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
* Remove useless API MindRecord finish[!5580](https://gitee.com/mindspore/mindspore/pulls/5580))
### MindSpore Lite
* Converter
* Add 6 TFLite op, 7 Caffe op, 1 ONNX op.
* Add support for Windows.
* Support parallel inference of multiple sessions to adapt to more scenarios
* Support 8bits only weight-quantization, most main-stream models has small accuracy loss (less than 0.5%) when compared to non-qunantized fp32 model.
* CPU & GPU
* Add 20 CPU opsinclude FP32, int8/uint8, FP16 and int32 ops.
* Add supporting FP16 for GPU, add 14 GPU ops include FP32/FP16.
* Add Buffer/Image2D transform op for GPU
* Performance optimization for CPU ops focus on ARM32.
* Performance optimization for GPU Convolution using winograd.
* Tool & example
* Add object detection Android Demo.
## Bugfixes
* Models
* Python API
* fix semi auto parallel parameter of reshape has another user([!5722](https://gitee.com/mindspore/mindspore/pulls/5722))
* raise ValueError when call hook function in graph mode([!5831](https://gitee.com/mindspore/mindspore/pulls/5831))
* Executor
* Bugfix pynative mode to build temporary nn objects.[!6189](https://gitee.com/mindspore/mindspore/pulls/6189))
* Bugfix the accuracy problem of multiple inputs of multi-card communication operator broadcast.([!6522](https://gitee.com/mindspore/mindspore/pulls/5622))
* Bugfix the problem that the sample distribution interface categorical does not support graph mode.([!5772](https://gitee.com/mindspore/mindspore/pulls/5772))
* Bugfix the random seed failure problem of the polynomial downsampling distribution operator.([!5948](https://gitee.com/mindspore/mindspore/pulls/5948))
* Bugfix unnecessary address binding issues in GPU heterogeneous scenarios.([!6232](https://gitee.com/mindspore/mindspore/pulls/6232))
* GPU platform
* Bugfix for kernel resource leak([!5315](https://gitee.com/mindspore/mindspore/pulls/5315))
* Bugfix for insufficient memory for continuous unit test running([!5617](https://gitee.com/mindspore/mindspore/pulls/5617))
* Bugfix for the memory leak in the sparse slicer([!5578](https://gitee.com/mindspore/mindspore/pulls/5578))
* Data processing and Pro
* ...
## Contributors
Thanks goes to these wonderful people:
Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, danish, Danish, dayschan, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, gongdaguo, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, root, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, Zichun, Zirui, Ziyan, zjun, ZPaC
Contributions of any kind are welcome!
# Release 0.7.0-beta
## Major Features and Improvements

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@ -0,0 +1,67 @@
FROM ubuntu:18.04
MAINTAINER leonwanghui <leon.wanghui@huawei.com>
# Set env
ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
ENV PATH /usr/local/bin:$PATH
# Install base tools
RUN apt update \
&& DEBIAN_FRONTEND=noninteractive apt install -y \
vim \
wget \
curl \
xz-utils \
net-tools \
openssh-client \
git \
ntpdate \
tzdata \
tcl \
sudo \
bash-completion
# Install compile tools
RUN DEBIAN_FRONTEND=noninteractive apt install -y \
gcc \
g++ \
zlibc \
make \
libgmp-dev \
patch \
autoconf \
libtool \
automake \
flex
# Set bash
RUN echo "dash dash/sh boolean false" | debconf-set-selections
RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
# Install python (v3.7.5)
RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
&& cd /tmp \
&& wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
&& tar -xvf v3.7.5.tar.gz \
&& cd /tmp/cpython-3.7.5 \
&& mkdir -p ${PYTHON_ROOT_PATH} \
&& ./configure --prefix=${PYTHON_ROOT_PATH} \
&& make -j4 \
&& make install -j4 \
&& rm -f /usr/local/bin/python \
&& rm -f /usr/local/bin/pip \
&& ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
&& ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
&& rm -rf /tmp/cpython-3.7.5 \
&& rm -f /tmp/v3.7.5.tar.gz
# Set pip source
RUN mkdir -pv /root/.pip \
&& echo "[global]" > /root/.pip/pip.conf \
&& echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
&& echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
# Install MindSpore cpu whl package
RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.0/MindSpore/cpu/ubuntu_x86/mindspore-1.0.0-cp37-cp37m-linux_x86_64.whl

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@ -0,0 +1,81 @@
FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
MAINTAINER leonwanghui <leon.wanghui@huawei.com>
# Set env
ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
ENV OMPI_ROOT_PATH /usr/local/openmpi-3.1.5
ENV PATH ${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
ENV LD_LIBRARY_PATH ${OMPI_ROOT_PATH}/lib:$LD_LIBRARY_PATH
# Install base tools
RUN apt update \
&& DEBIAN_FRONTEND=noninteractive apt install -y \
vim \
wget \
curl \
xz-utils \
net-tools \
openssh-client \
git \
ntpdate \
tzdata \
tcl \
sudo \
bash-completion
# Install compile tools
RUN DEBIAN_FRONTEND=noninteractive apt install -y \
gcc \
g++ \
zlibc \
make \
libgmp-dev \
patch \
autoconf \
libtool \
automake \
flex
# Set bash
RUN echo "dash dash/sh boolean false" | debconf-set-selections
RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
# Install python (v3.7.5)
RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
&& cd /tmp \
&& wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
&& tar -xvf v3.7.5.tar.gz \
&& cd /tmp/cpython-3.7.5 \
&& mkdir -p ${PYTHON_ROOT_PATH} \
&& ./configure --prefix=${PYTHON_ROOT_PATH} \
&& make -j4 \
&& make install -j4 \
&& rm -f /usr/local/bin/python \
&& rm -f /usr/local/bin/pip \
&& ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
&& ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
&& rm -rf /tmp/cpython-3.7.5 \
&& rm -f /tmp/v3.7.5.tar.gz
# Set pip source
RUN mkdir -pv /root/.pip \
&& echo "[global]" > /root/.pip/pip.conf \
&& echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
&& echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
# Install openmpi (v3.1.5)
RUN cd /tmp \
&& wget https://download.open-mpi.org/release/open-mpi/v3.1/openmpi-3.1.5.tar.gz \
&& tar -xvf openmpi-3.1.5.tar.gz \
&& cd /tmp/openmpi-3.1.5 \
&& mkdir -p ${OMPI_ROOT_PATH} \
&& ./configure --prefix=${OMPI_ROOT_PATH} \
&& make -j4 \
&& make install -j4 \
&& rm -rf /tmp/openmpi-3.1.5 \
&& rm -f /tmp/openmpi-3.1.5.tar.gz
# Install MindSpore cuda-10.1 whl package
RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.0/MindSpore/gpu/ubuntu_x86/cuda-10.1/mindspore_gpu-1.0.0-cp37-cp37m-linux_x86_64.whl

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@ -23,7 +23,7 @@ from setuptools import setup, find_packages
from setuptools.command.egg_info import egg_info
from setuptools.command.build_py import build_py
version = '0.7.0'
version = '1.0.0'
backend_policy = os.getenv('BACKEND_POLICY')
device_target = os.getenv('BACKEND_TARGET')