Description of problem: 镜像缺少moviepy依赖，导致自带用例执行失败 Version-Release number of selected component (if applicable): 容器镜像信息： [root@localhost modelscope]# docker images REPOSITORY TAG IMAGE ID CREATED SIZE registry.openanolis.cn/openanolis/modelscope 1.10.0-an8 736efcaf9a7c 5 weeks ago 25.3GB How reproducible: 以gpu的方式创建并启动容器 docker create --gpus all -it -v /tmp:/tmp 736efcaf9a7c 进入容器后clone modelscope社区代码 git clone https://github.com/modelscope/modelscope.git cd modelscope 执行测试用例： [root@1a186991ac23 modelscope]# python3 /tmp/modelscope/tests/pipelines/test_face_reconstruction.py Traceback (most recent call last): File "/tmp/modelscope/tests/pipelines/test_face_reconstruction.py", line 9, in <module> from moviepy.editor import ImageSequenceClip ModuleNotFoundError: No module named 'moviepy' [root@1a186991ac23 modelscope]# pip3 list | grep moviepy [root@1a186991ac23 modelscope]# Steps to Reproduce: 同上 Actual results: 没有moviepy whl包，用例执行失败 Expected results: 用例执行成功 Additional info: 对比ubuntu： root@ee8906738fa2:/tmp/modelscope# pip3 list | grep moviepy moviepy 1.0.2 root@ee8906738fa2:/tmp/modelscope# python3 /tmp/modelscope/tests/pipelines/test_face_reconstruction.py 2024-01-29 11:21:24,157 - modelscope - INFO - PyTorch version 2.1.0+cu118 Found. 2024-01-29 11:21:24,159 - modelscope - INFO - TensorFlow version 2.14.0 Found. 2024-01-29 11:21:24,159 - modelscope - INFO - Loading ast index from /mnt/workspace/.cache/modelscope/ast_indexer 2024-01-29 11:21:24,205 - modelscope - INFO - Loading done! Current index file version is 1.10.0, with md5 44f0b88effe82ceea94a98cf99709694 and a total number of 946 components indexed 2024-01-29 11:21:31,443 - modelscope - INFO - Use user-specified model revision: v2.0.0-HRN Downloading: 100%|██████████████████████████████████████████| 6.00k/6.00k [00:00<00:00, 833kB/s] Downloading: 100%|█████████████████████████████████████████| 91.9M/91.9M [00:06<00:00, 14.3MB/s] Downloading: 100%|█████████████████████████████████████████| 21.5k/21.5k [00:00<00:00, 3.90MB/s] Downloading: 100%|███████████████████████████████████████████| 121M/121M [00:09<00:00, 13.3MB/s] Downloading: 100%|█████████████████████████████████████████| 3.00M/3.00M [00:00<00:00, 11.8MB/s] Downloading: 100%|███████████████████████████████████████████| 240M/240M [00:19<00:00, 13.1MB/s] Downloading: 100%|███████████████████████████████████████████| 558k/558k [00:00<00:00, 9.83MB/s] Downloading: 100%|█████████████████████████████████████████████| 195/195 [00:00<00:00, 1.84MB/s] Downloading: 100%|█████████████████████████████████████████| 51.2M/51.2M [00:03<00:00, 16.0MB/s] Downloading: 100%|███████████████████████████████████████████| 224M/224M [00:18<00:00, 12.8MB/s] Downloading: 100%|█████████████████████████████████████████| 26.5M/26.5M [00:01<00:00, 22.1MB/s] Downloading: 100%|███████████████████████████████████████████| 353M/353M [00:29<00:00, 12.6MB/s] Downloading: 100%|██████████████████████████████████████████| 4.00k/4.00k [00:00<00:00, 392kB/s] Downloading: 100%|█████████████████████████████████████████| 33.3M/33.3M [00:01<00:00, 18.6MB/s] Downloading: 100%|█████████████████████████████████████████| 86.2k/86.2k [00:00<00:00, 7.31MB/s] Downloading: 100%|███████████████████████████████████████████| 104M/104M [00:08<00:00, 13.2MB/s] Downloading: 100%|█████████████████████████████████████████| 85.7M/85.7M [00:06<00:00, 13.3MB/s] Downloading: 100%|█████████████████████████████████████████| 33.4M/33.4M [00:02<00:00, 15.8MB/s] Downloading: 100%|█████████████████████████████████████████████| 994/994 [00:00<00:00, 8.99MB/s] Downloading: 100%|█████████████████████████████████████████| 2.51M/2.51M [00:00<00:00, 15.5MB/s]