升级magic pdf最新版本后出现Segmentation fault

randydl commented 1 week ago

Description of the bug | 错误描述

(data) (base) randy@3080Ti:/mnt/projects/settings$ magic-pdf -p small_ocr.pdf 2024-09-04 00:07:47.984	INFO	magic_pdf.libs.pdf_check:detect_invalid_chars:57 - cid_count: 0, text_len: 8, cid_chars_radio: 0.0 2024-09-04 00:07:47.985	WARNING	magic_pdf.filter.pdf_classify_by_type:classify:334 - pdf is not classified by area and text_len, by_image_area: False, by_text: False, by_avg_words: False, by_img_num: True, by_text_layout: False, by_img_narrow_strips: False, by_invalid_chars: True WARNING ⚠️ Ultralytics settings reset to default values. This may be due to a possible problem with your settings or a recent ultralytics package update. View settings with 'yolo settings' or at '/home/randy/.config/Ultralytics/settings.yaml' Update settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings. 2024-09-04 00:07:51.653	INFO	magic_pdf.model.pdf_extract_kit:init:121 - DocAnalysis init, this may take some times. apply_layout: True, apply_formula: True, apply_ocr: True, apply_table: False 2024-09-04 00:07:51.653	INFO	magic_pdf.model.pdf_extract_kit:init:129 - using device: cuda 2024-09-04 00:07:51.653	INFO	magic_pdf.model.pdf_extract_kit:init:131 - using models_dir: /mnt/models/PDF-Extract-Kit/models CustomVisionEncoderDecoderModel init CustomMBartForCausalLM init CustomMBartDecoder init [09/04 00:07:57 detectron2]: Rank of current process: 0. World size: 1 [09/04 00:07:57 detectron2]: Environment info:

sys.platform linux Python 3.10.14	packaged by conda-forge	(main, Mar 20 2024, 12:45:18) [GCC 12.3.0] numpy 1.26.4 detectron2 0.6 @/mnt/miniconda3/envs/data/lib/python3.10/site-packages/detectron2 Compiler GCC 11.4 CUDA compiler not available DETECTRON2_ENV_MODULE PyTorch 2.3.1+cu121 @/mnt/miniconda3/envs/data/lib/python3.10/site-packages/torch PyTorch debug build False torch._C._GLIBCXX_USE_CXX11_ABI False GPU available Yes GPU 0 NVIDIA GeForce RTX 3080 Ti (arch=8.6) Driver version 535.183.01 CUDA_HOME /usr/local/cuda Pillow 10.4.0 torchvision 0.18.1+cu121 @/mnt/miniconda3/envs/data/lib/python3.10/site-packages/torchvision torchvision arch flags 5.0, 6.0, 7.0, 7.5, 8.0, 8.6, 9.0 fvcore 0.1.5.post20221221 iopath 0.1.9 cv2 4.6.0

PyTorch built with:

GCC 9.3
C++ Version: 201703
Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v3.3.6 (Git Hash 86e6af5974177e513fd3fee58425e1063e7f1361)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 12.1
NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
CuDNN 8.9.7 (built against CUDA 12.2)
- Built with CuDNN 8.9.2
Magma 2.6.1
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.3.1, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,

[09/04 00:07:57 detectron2]: Command line arguments: {'config_file': '/mnt/miniconda3/envs/data/lib/python3.10/site-packages/magic_pdf/resources/model_config/layoutlmv3/layoutlmv3_base_inference.yaml', 'resume': False, 'eval_only': False, 'num_gpus': 1, 'num_machines': 1, 'machine_rank': 0, 'dist_url': 'tcp://127.0.0.1:57823', 'opts': ['MODEL.WEIGHTS', '/mnt/models/PDF-Extract-Kit/models/Layout/model_final.pth']} [09/04 00:07:57 detectron2]: Contents of args.config_file=/mnt/miniconda3/envs/data/lib/python3.10/site-packages/magic_pdf/resources/model_config/layoutlmv3/layoutlmv3_base_inference.yaml: AUG: DETR: true CACHE_DIR: ~/cache/huggingface CUDNN_BENCHMARK: false DATALOADER: ASPECT_RATIO_GROUPING: true FILTER_EMPTY_ANNOTATIONS: false NUM_WORKERS: 4 REPEAT_THRESHOLD: 0.0 SAMPLER_TRAIN: TrainingSampler DATASETS: PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 PROPOSAL_FILES_TEST: [] PROPOSAL_FILES_TRAIN: [] TEST:

scihub_train TRAIN:
scihub_train GLOBAL: HACK: 1.0 ICDAR_DATA_DIR_TEST: '' ICDAR_DATA_DIR_TRAIN: '' INPUT: CROP: ENABLED: true SIZE:
- 384
- 600 TYPE: absolute_range FORMAT: RGB MASK_FORMAT: polygon MAX_SIZE_TEST: 1333 MAX_SIZE_TRAIN: 1333 MIN_SIZE_TEST: 800 MIN_SIZE_TRAIN:
480
512
544
576
608
640
672
704
736
768
800 MIN_SIZE_TRAIN_SAMPLING: choice RANDOM_FLIP: horizontal MODEL: ANCHOR_GENERATOR: ANGLES:
- - -90
  - 0
  - 90 ASPECT_RATIOS:
- - 0.5
  - 1.0
  - 2.0 NAME: DefaultAnchorGenerator OFFSET: 0.0 SIZES:
- - 32
- - 64
- - 128
- - 256
- - 512 BACKBONE: FREEZE_AT: 2 NAME: build_vit_fpn_backbone CONFIG_PATH: '' DEVICE: cuda FPN: FUSE_TYPE: sum IN_FEATURES:
- layer3
- layer5
- layer7
- layer11 NORM: '' OUT_CHANNELS: 256 IMAGE_ONLY: true KEYPOINT_ON: false LOAD_PROPOSALS: false MASK_ON: true META_ARCHITECTURE: VLGeneralizedRCNN PANOPTIC_FPN: COMBINE: ENABLED: true INSTANCES_CONFIDENCE_THRESH: 0.5 OVERLAP_THRESH: 0.5 STUFF_AREA_LIMIT: 4096 INSTANCE_LOSS_WEIGHT: 1.0 PIXEL_MEAN:
127.5
127.5
127.5 PIXEL_STD:
127.5
127.5
127.5 PROPOSAL_GENERATOR: MIN_SIZE: 0 NAME: RPN RESNETS: DEFORM_MODULATED: false DEFORM_NUM_GROUPS: 1 DEFORM_ON_PER_STAGE:
- false
- false
- false
- false DEPTH: 50 NORM: FrozenBN NUM_GROUPS: 1 OUT_FEATURES:
- res4 RES2_OUT_CHANNELS: 256 RES5_DILATION: 1 STEM_OUT_CHANNELS: 64 STRIDE_IN_1X1: true WIDTH_PER_GROUP: 64 RETINANET: BBOX_REG_LOSS_TYPE: smooth_l1 BBOX_REG_WEIGHTS:
- 1.0
- 1.0
- 1.0
- 1.0 FOCAL_LOSS_ALPHA: 0.25 FOCAL_LOSS_GAMMA: 2.0 IN_FEATURES:
- p3
- p4
- p5
- p6
- p7 IOU_LABELS:
- 0
- -1
- 1 IOU_THRESHOLDS:
- 0.4
- 0.5 NMS_THRESH_TEST: 0.5 NORM: '' NUM_CLASSES: 10 NUM_CONVS: 4 PRIOR_PROB: 0.01 SCORE_THRESH_TEST: 0.05 SMOOTH_L1_LOSS_BETA: 0.1 TOPK_CANDIDATES_TEST: 1000 ROI_BOX_CASCADE_HEAD: BBOX_REG_WEIGHTS:
- - 10.0
  - 10.0
  - 5.0
  - 5.0
- - 20.0
  - 20.0
  - 10.0
  - 10.0
- - 30.0
  - 30.0
  - 15.0
  - 15.0 IOUS:
- 0.5
- 0.6
- 0.7 ROI_BOX_HEAD: BBOX_REG_LOSS_TYPE: smooth_l1 BBOX_REG_LOSS_WEIGHT: 1.0 BBOX_REG_WEIGHTS:
- 10.0
- 10.0
- 5.0
- 5.0 CLS_AGNOSTIC_BBOX_REG: true CONV_DIM: 256 FC_DIM: 1024 NAME: FastRCNNConvFCHead NORM: '' NUM_CONV: 0 NUM_FC: 2 POOLER_RESOLUTION: 7 POOLER_SAMPLING_RATIO: 0 POOLER_TYPE: ROIAlignV2 SMOOTH_L1_BETA: 0.0 TRAIN_ON_PRED_BOXES: false ROI_HEADS: BATCH_SIZE_PER_IMAGE: 512 IN_FEATURES:
- p2
- p3
- p4
- p5 IOU_LABELS:
- 0
- 1 IOU_THRESHOLDS:
- 0.5 NAME: CascadeROIHeads NMS_THRESH_TEST: 0.5 NUM_CLASSES: 10 POSITIVE_FRACTION: 0.25 PROPOSAL_APPEND_GT: true SCORE_THRESH_TEST: 0.05 ROI_KEYPOINT_HEAD: CONV_DIMS:
- 512
- 512
- 512
- 512
- 512
- 512
- 512
- 512 LOSS_WEIGHT: 1.0 MIN_KEYPOINTS_PER_IMAGE: 1 NAME: KRCNNConvDeconvUpsampleHead NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true NUM_KEYPOINTS: 17 POOLER_RESOLUTION: 14 POOLER_SAMPLING_RATIO: 0 POOLER_TYPE: ROIAlignV2 ROI_MASK_HEAD: CLS_AGNOSTIC_MASK: false CONV_DIM: 256 NAME: MaskRCNNConvUpsampleHead NORM: '' NUM_CONV: 4 POOLER_RESOLUTION: 14 POOLER_SAMPLING_RATIO: 0 POOLER_TYPE: ROIAlignV2 RPN: BATCH_SIZE_PER_IMAGE: 256 BBOX_REG_LOSS_TYPE: smooth_l1 BBOX_REG_LOSS_WEIGHT: 1.0 BBOX_REG_WEIGHTS:
- 1.0
- 1.0
- 1.0
- 1.0 BOUNDARY_THRESH: -1 CONV_DIMS:
- -1 HEAD_NAME: StandardRPNHead IN_FEATURES:
- p2
- p3
- p4
- p5
- p6 IOU_LABELS:
- 0
- -1
- 1 IOU_THRESHOLDS:
- 0.3
- 0.7 LOSS_WEIGHT: 1.0 NMS_THRESH: 0.7 POSITIVE_FRACTION: 0.5 POST_NMS_TOPK_TEST: 1000 POST_NMS_TOPK_TRAIN: 2000 PRE_NMS_TOPK_TEST: 1000 PRE_NMS_TOPK_TRAIN: 2000 SMOOTH_L1_BETA: 0.0 SEM_SEG_HEAD: COMMON_STRIDE: 4 CONVS_DIM: 128 IGNORE_VALUE: 255 IN_FEATURES:
- p2
- p3
- p4
- p5 LOSS_WEIGHT: 1.0 NAME: SemSegFPNHead NORM: GN NUM_CLASSES: 10 VIT: DROP_PATH: 0.1 IMG_SIZE:
- 224
- 224 NAME: layoutlmv3_base OUT_FEATURES:
- layer3
- layer5
- layer7
- layer11 POS_TYPE: abs WEIGHTS: OUTPUT_DIR: SCIHUB_DATA_DIR_TRAIN: ~/publaynet/layout_scihub/train SEED: 42 SOLVER: AMP: ENABLED: true BACKBONE_MULTIPLIER: 1.0 BASE_LR: 0.0002 BIAS_LR_FACTOR: 1.0 CHECKPOINT_PERIOD: 2000 CLIP_GRADIENTS: CLIP_TYPE: full_model CLIP_VALUE: 1.0 ENABLED: true NORM_TYPE: 2.0 GAMMA: 0.1 GRADIENT_ACCUMULATION_STEPS: 1 IMS_PER_BATCH: 32 LR_SCHEDULER_NAME: WarmupCosineLR MAX_ITER: 20000 MOMENTUM: 0.9 NESTEROV: false OPTIMIZER: ADAMW REFERENCE_WORLD_SIZE: 0 STEPS:
10000 WARMUP_FACTOR: 0.01 WARMUP_ITERS: 333 WARMUP_METHOD: linear WEIGHT_DECAY: 0.05 WEIGHT_DECAY_BIAS: null WEIGHT_DECAY_NORM: 0.0 TEST: AUG: ENABLED: false FLIP: true MAX_SIZE: 4000 MIN_SIZES:
- 400
- 500
- 600
- 700
- 800
- 900
- 1000
- 1100
- 1200 DETECTIONS_PER_IMAGE: 100 EVAL_PERIOD: 1000 EXPECTED_RESULTS: [] KEYPOINT_OKS_SIGMAS: [] PRECISE_BN: ENABLED: false NUM_ITER: 200 VERSION: 2 VIS_PERIOD: 0

[09/04 00:07:58 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from /mnt/models/PDF-Extract-Kit/models/Layout/model_final.pth ... [09/04 00:07:58 fvcore.common.checkpoint]: [Checkpointer] Loading from /mnt/models/PDF-Extract-Kit/models/Layout/model_final.pth ... 2024-09-04 00:07:59.048 | INFO | magic_pdf.model.pdf_extract_kit:init:159 - DocAnalysis init done! 2024-09-04 00:07:59.049 | INFO | magic_pdf.model.doc_analyze_by_custom_model:custom_model_init:98 - model init cost: 11.06343150138855

C++ Traceback (most recent call last):

0 at::_ops::conv2d::call(at::Tensor const&, at::Tensor const&, std::optional const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, c10::SymInt) 1 at::native::conv2d_symint(at::Tensor const&, at::Tensor const&, std::optional const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, c10::SymInt) 2 at::_ops::convolution::call(at::Tensor const&, at::Tensor const&, std::optional const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, bool, c10::ArrayRef, c10::SymInt) 3 at::_ops::convolution::redispatch(c10::DispatchKeySet, at::Tensor const&, at::Tensor const&, std::optional const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, bool, c10::ArrayRef, c10::SymInt) 4 at::native::convolution(at::Tensor const&, at::Tensor const&, std::optional const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, bool, c10::ArrayRef, long) 5 at::_ops::_convolution::call(at::Tensor const&, at::Tensor const&, std::optional const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, bool, c10::ArrayRef, c10::SymInt, bool, bool, bool, bool) 6 at::native::_convolution(at::Tensor const&, at::Tensor const&, std::optional const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, bool, c10::ArrayRef, long, bool, bool, bool, bool) 7 at::_ops::cudnn_convolution::call(at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, c10::SymInt, bool, bool, bool) 8 at::native::cudnn_convolution(at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, long, bool, bool, bool)

Error Message Summary:

FatalError: Segmentation fault is detected by the operating system. [TimeInfo: Aborted at 1725379679 (unix time) try "date -d @1725379679" if you are using GNU date ] [SignalInfo: SIGSEGV (@0x20000002ef4) received by PID 13766 (TID 0x7fcbb9641740) from PID 12020 ]

Segmentation fault

How to reproduce the bug | 如何复现

环境完全按照教程提示配置，依然报错 (data) (base) randy@3080Ti:/mnt/projects/settings$ pip list Package Version

absl-py 2.1.0 aiohappyeyeballs 2.4.0 aiohttp 3.10.5 aiosignal 1.3.1 albucore 0.0.14 albumentations 1.4.14 annotated-types 0.7.0 antlr4-python3-runtime 4.9.3 anyio 4.4.0 astor 0.8.1 async-timeout 4.0.3 attrdict 2.0.1 attrs 24.2.0 babel 2.16.0 bce-python-sdk 0.9.21 beautifulsoup4 4.12.3 black 24.8.0 blinker 1.8.2 boto3 1.35.10 botocore 1.35.10 braceexpand 0.1.7 Brotli 1.1.0 cachetools 5.5.0 certifi 2024.8.30 cffi 1.17.0 charset-normalizer 3.3.2 click 8.1.7 cloudpickle 3.0.0 colorlog 6.8.2 contourpy 1.3.0 cryptography 43.0.0 cssselect 1.2.0 cssutils 2.11.1 cycler 0.12.1 Cython 3.0.11 datasets 2.21.0 decorator 5.1.1 detectron2 0.6 dill 0.3.8 et-xmlfile 1.1.0 eva-decord 0.6.1 eval_type_backport 0.2.0 evaluate 0.4.2 exceptiongroup 1.2.2 fairscale 0.4.13 fast-langdetect 0.2.0 fasttext-wheel 0.9.2 filelock 3.15.4 fire 0.6.0 Flask 3.0.3 flask-babel 4.0.0 fonttools 4.53.1 frozenlist 1.4.1 fsspec 2024.6.1 ftfy 6.2.3 future 1.0.0 fvcore 0.1.5.post20221221 grpcio 1.66.1 h11 0.14.0 httpcore 1.0.5 httpx 0.27.2 huggingface-hub 0.24.6 hydra-core 1.3.2 idna 3.8 imageio 2.35.1 imgaug 0.4.0 iopath 0.1.9 itsdangerous 2.2.0 Jinja2 3.1.4 jmespath 1.0.1 joblib 1.4.2 kiwisolver 1.4.5 langdetect 1.0.9 lazy_loader 0.4 lmdb 1.5.1 loguru 0.7.2 lxml 5.3.0 magic-pdf 0.7.1 Markdown 3.7 MarkupSafe 2.1.5 matplotlib 3.9.2 more-itertools 10.4.0 mpmath 1.3.0 multidict 6.0.5 multiprocess 0.70.16 mypy-extensions 1.0.0 networkx 3.3 numpy 1.26.4 nvidia-cublas-cu11 11.11.3.6 nvidia-cublas-cu12 12.1.3.1 nvidia-cuda-cupti-cu11 11.8.87 nvidia-cuda-cupti-cu12 12.1.105 nvidia-cuda-nvrtc-cu11 11.8.89 nvidia-cuda-nvrtc-cu12 12.1.105 nvidia-cuda-runtime-cu11 11.8.89 nvidia-cuda-runtime-cu12 12.1.105 nvidia-cudnn-cu11 8.7.0.84 nvidia-cudnn-cu12 8.9.2.26 nvidia-cufft-cu11 10.9.0.58 nvidia-cufft-cu12 11.0.2.54 nvidia-curand-cu11 10.3.0.86 nvidia-curand-cu12 10.3.2.106 nvidia-cusolver-cu11 11.4.1.48 nvidia-cusolver-cu12 11.4.5.107 nvidia-cusparse-cu11 11.7.5.86 nvidia-cusparse-cu12 12.1.0.106 nvidia-nccl-cu11 2.19.3 nvidia-nccl-cu12 2.20.5 nvidia-nvjitlink-cu12 12.6.68 nvidia-nvtx-cu11 11.8.86 nvidia-nvtx-cu12 12.1.105 omegaconf 2.3.0 opencv-contrib-python 4.6.0.66 opencv-python 4.6.0.66 opencv-python-headless 4.10.0.84 openpyxl 3.1.5 opt-einsum 3.3.0 packaging 24.1 paddleocr 2.7.3 paddlepaddle 3.0.0b1 paddlepaddle-gpu 3.0.0b1 pandas 2.2.2 pathspec 0.12.1 pdf2docx 0.5.8 pdfminer.six 20231228 pillow 10.4.0 pip 24.2 platformdirs 4.2.2 portalocker 2.10.1 premailer 3.10.0 protobuf 5.28.0 psutil 6.0.0 py-cpuinfo 9.0.0 pyarrow 17.0.0 pybind11 2.13.5 pyclipper 1.3.0.post5 pycocotools 2.0.8 pycparser 2.22 pycryptodome 3.20.0 pydantic 2.8.2 pydantic_core 2.20.1 PyMuPDF 1.24.10 PyMuPDFb 1.24.10 pypandoc 1.13 pyparsing 3.1.4 python-dateutil 2.9.0.post0 python-docx 1.1.2 pytz 2024.1 PyYAML 6.0.2 rapidfuzz 3.9.7 rarfile 4.2 regex 2024.7.24 requests 2.32.3 robust-downloader 0.0.2 s3transfer 0.10.2 safetensors 0.4.4 scikit-image 0.24.0 scikit-learn 1.5.1 scipy 1.14.1 seaborn 0.13.2 setuptools 73.0.1 shapely 2.0.6 six 1.16.0 sniffio 1.3.1 soupsieve 2.6 struct-eqtable 0.1.0 sympy 1.13.2 tabulate 0.9.0 tensorboard 2.17.1 tensorboard-data-server 0.7.2 termcolor 2.4.0 threadpoolctl 3.5.0 tifffile 2024.8.30 timm 0.9.16 tokenizers 0.19.1 tomli 2.0.1 torch 2.3.1 torchtext 0.18.0 torchvision 0.18.1 tqdm 4.66.5 transformers 4.40.0 triton 2.3.1 typing_extensions 4.12.2 tzdata 2024.1 ultralytics 8.2.87 ultralytics-thop 2.0.6 unimernet 0.1.6 urllib3 2.2.2 visualdl 2.5.3 Wand 0.6.13 wcwidth 0.2.13 webdataset 0.2.100 Werkzeug 3.0.4 wheel 0.44.0 wordninja 2.0.0 xxhash 3.5.0 yacs 0.1.8 yarl 1.9.7

Operating system | 操作系统

Linux

Python version | Python 版本

3.10

Software version | 软件版本 (magic-pdf --version)

0.7.x

Device mode | 设备模式

cuda

randydl commented 1 week ago

pip install -U magic-pdf[full] \ "/nas_data/userdata/tools/detectron2-0.6-cp310-cp310-linux_x86_64.whl" \ "/nas_data/userdata/tools/paddlepaddle_gpu-3.0.0b1-cp310-cp310-linux_x86_64.whl"

whl包是从安装教程中的网站下载的

randydl commented 1 week ago

+-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA A40 On | 00000000:34:00.0 Off | 0 | | 0% 40C P0 79W / 300W | 3370MiB / 46068MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ | 1 NVIDIA A40 On | 00000000:35:00.0 Off | 0 | | 0% 40C P0 80W / 300W | 3370MiB / 46068MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+

bingyunsky commented 1 week ago

同样的问题，执行完python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ 出现

opendatalab / MinerU