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Bw-bestperf
HFM
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652c6426
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# https://github.com/maziarraissi/HFM
# HFM
## 论文
Application of Deep Learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM TTE Data
-
https://arxiv.org/abs/2303.00370
## 模型简介
## 模型简介
...
@@ -13,56 +8,75 @@ HFM是一种基于物理学的深度学习框架,能够对控制流体运动
...
@@ -13,56 +8,75 @@ HFM是一种基于物理学的深度学习框架,能够对控制流体运动
## 环境配置
## 环境配置
### Docker(方法一)
### 1. 拉取镜像
docker pull image.sourcefind.cn:5000/dcu/admin/base/tensorflow:2.13.1-py3.10-dtk24.04.3-ubuntu20.04
```
bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/tensorflow:2.18.0-ubuntu22.04-dtk25.04.2-py3.10
```
### 2. 创建容器
docker run --shm-size 50g --network=host --name=binary_distinguish_GRB_by_DL --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
```
bash
docker run
-it
\
--network
=
host
\
--hostname
=
localhost
\
--name
HFM
\
-v
/opt/hyhal:/opt/hyhal:ro
\
-v
$PWD
:/workspace
\
--ipc
=
host
\
--device
=
/dev/kfd
\
--device
=
/dev/mkfd
\
--device
=
/dev/dri
\
--shm-size
=
512G
\
--privileged
\
--group-add
video
\
--cap-add
=
SYS_PTRACE
\
--security-opt
seccomp
=
unconfined
\
image.sourcefind.cn:5000/dcu/admin/base/tensorflow:2.18.0-ubuntu22.04-dtk25.04.2-py3.10
\
/bin/bash
```
pip install scipy -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
---
## 测试步骤
###
Dockerfile(方法二)
###
1. 拉取代码
docker build -t <IMAGE_NAME>:<TAG> .
```
bash
git clone http://developer.sourcefind.cn/codes/bw-bestperf/hfm.git
cd
hfm/
```
docker run --shm-size 50g --network=host --name=binary_distinguish_GRB_by_DL --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
### 2. 安装依赖
pip install scipy -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```
bash
pip
install
scipy
-i
https://mirrors.aliyun.com/pypi/simple/
```
### 3. 获取优化包
curl -f -C - -o rocblas-install-1126-5bug.tar.gz https://ksefile.hpccube.com:65241/efile/s/d/emhhbmd3YjM=/7420623cbac23efd
#设置环境变量
CURRENT_DIR=$(pwd)
export LD_LIBRARY_PATH=$CURRENT_DIR/rocblas-install/lib:$LD_LIBRARY_PATH
#验证环境变量设置
echo $LD_LIBRARY_PATH | tr ':' '
\n
' | head -3
##
数据集
##
# 3. 下载
数据集
[
数据下载地址
](
https://o365coloradoedu-my.sharepoint.com/:f:/g/personal/mara4513_colorado_edu/EnMZcTjA6BVBjovGVJT3hk4BIemGxYF9lO6ry0SyizIjoQ?e=fuHKi2
)
[
数据下载地址
](
https://o365coloradoedu-my.sharepoint.com/:f:/g/personal/mara4513_colorado_edu/EnMZcTjA6BVBjovGVJT3hk4BIemGxYF9lO6ry0SyizIjoQ?e=fuHKi2
)
#仅下载Cylinder2D.mat即可
```
```
Data
Data
├── Aneurysm3D.mat
├── Cylinder2D.mat
├── Cylinder2D.mat
├── Cylinder2D_flower.mat
├── Cylinder2D_Re200Pec200_Dirichlet_Streaks_Forces.mat
├── Cylinder2D_Re200Pec200_Neumann_Streaks_Forces.mat
└── Cylinder2D_Re200Pec2000_Dirichlet_Streaks.mat
└── ......
```
```
## 训练
## 训练
-
DCU :K100 AI
```
bash
```
bash
cd
Source
cd
Source
python Cylinder2D.py
>
Cylinder2D_stdout.txt
export
TF_XLA_FLAGS
=
"--tf_xla_auto_jit=1"
python Stenosis2D.py
>
Stenosis2D_stdout.txt
numactl
--cpunodebind
=
3
--membind
=
3 python Cylinder2D.py
>
Cylinder2D_stdout.txt
python Aneurysm3D.py
>
Aneurysm3D_stdout.txt
python Cylinder2D_Pec_Re.py
>
Cylinder2D_Pec_Re_stdout.txt
python Stenosis2D_Pec_Re.py
>
Stenosis2D_Pec_Re_stdout.txt
python Davinci.py
>
Davinci_stdout.txt
python Cylinder3D.py
>
Cylinder3D_stdout.txt
python Cylinder2D_flower.py
>
Cylinder2D_flower_stdout.txt
python Cylinder2D_No_Slip.py
>
Cylinder2D_No_Slip_stdout.txt
python Cylinder2D_flower_convergence_plot.py
>
Cylinder2D_flower_convergence_plot_stdout.txt
python Aneurysm3D_Wall_Stresses.py
>
Aneurysm3D_Wall_Stresses_stdout.txt
```
```
注:多卡和其他相关训练参见Scripts
## 推理
## 推理
...
...
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