add run_in_docker

Dockerfile.mlu

@@ -9,4 +9,4 @@ RUN pip install open_clip_torch==2.24.0 sortedcontainers modelscope av==11.0.0
 RUN sed -i 's|source /torch/venv3/pytorch/bin/activate|source /torch/venv3/pytorch_infer/bin/activate|' /root/.bashrc
 
 COPY . /workspace/
-RUN pip install whls/cambricon_pytorch_lightning-2.5.0+mlu0.7.0-py3-none-any.whl 
+RUN pip install whls.mlu/cambricon_pytorch_lightning-2.5.0+mlu0.7.0-py3-none-any.whl 

README.md

@@ -0,0 +1,23 @@
+## Quickstart
+
+### 构建镜像
+```bash
+docker build -t text2video:v0.1 .
+```
+
+### 模型下载
+模型地址：https://modelscope.cn/models/iic/text-to-video-synthesis
+并放到目录：`/mnt/contest_ceph/zhanghao/models/iic/text-to-video-synthesis`（如更改目录，请修改后面的执行脚本中的模型路径）
+
+### 测试程序
+1. 准备输入数据集，可以参考示例`dataset.json`
+2. 在docker镜像里运行测试程序，会根据`dataset.json`内容，在`output`目录下生成视频文件。
+```bash
+./run_in_docker.sh
+```
+
+## 测试结果
+|      | A100 平均生成时间（秒） | MLU-x4 平均生成时间（秒） |   MLU-x8 平均生成时间（秒）|
+|------|-------------------------|----------------------------|---------------------------|
+| 时间 | 12                  | 37                     |   45
+

clear.sh

@@ -0,0 +1,16 @@
+
+own=mlu370
+for device in a100 ascend bi100 mlu370 r200
+do
+   git rm run_in_docker_"$device".sh
+   if [[ $device == $own ]]; then
+     continue
+   fi
+   git rm Dockerfile.$device
+   git rm patch.$device
+done
+git rm diffusers_video.py
+git rm iic.py
+git rm test_ms.sh
+
+git mv Dockerfile.$own Dockerfile

dataset.json

@@ -1,4 +1,4 @@
 [
-    "An image of a squirrel in Picasso style",
+    "A panda is eating burgers and french fries",
-    "A cozy cabin in the woods, watercolor painting"
+    "A sheep is walking and eating in the grass"
-]
+]

run_in_docker.sh

@@ -0,0 +1,5 @@
+#! /usr/bin/env bash
+# cnmon
+image=text2video:v0.1
+device_id=0
+docker run -v `pwd`:/workspace -v /mnt:/mnt --device=/dev/cambricon_dev$device_id:/dev/cambricon_dev0 --device=/dev/cambricon_ctl:/dev/cambricon_ctl $image ./test.sh