下载时出现using cached如何解决

最新推荐文章于 2024-02-29 14:25:43 发布
转载 最新推荐文章于 2024-02-29 14:25:43 发布 · 4.4k 阅读 · 5 ·
CC 4.0 BY-SA版权
原文链接：http://www.cnblogs.com/1a2a/p/9253596.html
文章标签：

#python #开发工具

本文介绍了一种使用pip3命令安装xlsxwriter Python模块的方法，通过添加--no-cache-dir参数来确保安装过程不使用缓存。
部署运行你感兴趣的模型镜像 
pip3 --no-cache-dir install xlsxwriter

这样就可以了

转载于:https://www.cnblogs.com/1a2a/p/9253596.html

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(ai_env) starying@LAPTOP-HT19I0UF:/mnt/f/Programmer/python/Ai_talk$ pip install -r requirements_core.txt Collecting torch==2.3.0 (from -r requirements_core.txt (line 1)) Using cached torch-2.3.0-cp312-cp312-manylinux1_x86_64.whl.metadata (26 kB) Collecting torchaudio (from -r requirements_core.txt (line 2)) Using cached torchaudio-2.9.1-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.9 kB) Collecting torchvision (from -r requirements_core.txt (line 3)) Using cached torchvision-0.24.1-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (5.9 kB) Collecting transformers (from -r requirements_core.txt (line 4)) Using cached transformers-4.57.3-py3-none-any.whl.metadata (43 kB) Collecting datasets (from -r requirements_core.txt (line 5)) Using cached datasets-4.4.1-py3-none-any.whl.metadata (19 kB) Collecting tokenizers (from -r requirements_core.txt (line 6)) Using cached tokenizers-0.22.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.8 kB) Collecting accelerate (from -r requirements_core.txt (line 7)) Using cached accelerate-1.12.0-py3-none-any.whl.metadata (19 kB) Collecting peft (from -r requirements_core.txt (line 8)) Using cached peft-0.18.0-py3-none-any.whl.metadata (14 kB) Collecting trl (from -r requirements_core.txt (line 9)) Using cached trl-0.25.1-py3-none-any.whl.metadata (11 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.11.2-cp38-abi3-manylinux1_x86_64.whl.metadata (18 kB) Collecting safetensors (from -r requirements_core.txt (line 11)) Using cached safetensors-0.7.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB) Collecting tiktoken (from -r requirements_core.txt (line 12)) Using cached tiktoken-0.12.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.7 kB) Collecting sentencepiece (from -r requirements_core.txt (line 13)) Using cached sentencepiece-0.2.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (10 kB) Collecting bitsandbytes (from -r requirements_core.txt (line 14)) Using cached bitsandbytes-0.48.2-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB) Collecting numpy (from -r requirements_core.txt (line 15)) Using cached numpy-2.3.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB) Collecting pandas (from -r requirements_core.txt (line 16)) Using cached pandas-2.3.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (91 kB) Collecting scikit-learn (from -r requirements_core.txt (line 17)) Using cached scikit_learn-1.7.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (11 kB) Collecting matplotlib (from -r requirements_core.txt (line 18)) Using cached matplotlib-3.10.7-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (11 kB) Collecting jupyter (from -r requirements_core.txt (line 19)) Using cached jupyter-1.1.1-py2.py3-none-any.whl.metadata (2.0 kB) Collecting ipykernel (from -r requirements_core.txt (line 20)) Using cached ipykernel-7.1.0-py3-none-any.whl.metadata (4.5 kB) Collecting python-dotenv (from -r requirements_core.txt (line 21)) Using cached python_dotenv-1.2.1-py3-none-any.whl.metadata (25 kB) Collecting requests (from -r requirements_core.txt (line 22)) Using cached requests-2.32.5-py3-none-any.whl.metadata (4.9 kB) Collecting tqdm (from -r requirements_core.txt (line 23)) Using cached tqdm-4.67.1-py3-none-any.whl.metadata (57 kB) Collecting typer (from -r requirements_core.txt (line 24)) Using cached typer-0.20.0-py3-none-any.whl.metadata (16 kB) Collecting fastapi (from -r requirements_core.txt (line 25)) Using cached fastapi-0.123.0-py3-none-any.whl.metadata (30 kB) Collecting filelock (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached filelock-3.20.0-py3-none-any.whl.metadata (2.1 kB) Collecting typing-extensions>=4.8.0 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached typing_extensions-4.15.0-py3-none-any.whl.metadata (3.3 kB) Collecting sympy (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached sympy-1.14.0-py3-none-any.whl.metadata (12 kB) Collecting networkx (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached networkx-3.6-py3-none-any.whl.metadata (6.8 kB) Collecting jinja2 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached jinja2-3.1.6-py3-none-any.whl.metadata (2.9 kB) Collecting fsspec (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached fsspec-2025.10.0-py3-none-any.whl.metadata (10 kB) Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB) WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)")': /simple/nvidia-cudnn-cu12/ WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)")': /simple/nvidia-cudnn-cu12/ Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB) Collecting nvidia-cublas-cu12==12.1.3.1 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cufft-cu12==11.0.2.54 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB) Collecting nvidia-curand-cu12==10.3.2.106 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB) Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB) Collecting nvidia-nccl-cu12==2.20.5 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB) Collecting nvidia-nvtx-cu12==12.1.105 (from torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB) Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch==2.3.0->-r requirements_core.txt (line 1)) Using cached nvidia_nvjitlink_cu12-12.9.86-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.7 kB) INFO: pip is looking at multiple versions of torchaudio to determine which version is compatible with other requirements. This could take a while. Collecting torchaudio (from -r requirements_core.txt (line 2)) WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out. (read timeout=15)")': /packages/f0/9c/58b8b49dfba2ae85e41ca86b0c52de45bbbea01987490de219c99c523a58/torchaudio-2.9.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata Downloading torchaudio-2.9.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.9 kB) Downloading torchaudio-2.8.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (7.2 kB) WARNING: Connection timed out while downloading. WARNING: Attempting to resume incomplete download (0 bytes/7.2 kB, attempt 1) Downloading torchaudio-2.8.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (7.2 kB) Downloading torchaudio-2.7.1-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.6 kB) Downloading torchaudio-2.7.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.6 kB) Downloading torchaudio-2.6.0-cp312-cp312-manylinux1_x86_64.whl.metadata (6.6 kB) WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out. (read timeout=15)")': /packages/34/1c/345d11bf492a1414dced70a9572ff1eb2c73013578d24fb4d728a91a09d1/torchaudio-2.5.1-cp312-cp312-manylinux1_x86_64.whl.metadata Downloading torchaudio-2.5.1-cp312-cp312-manylinux1_x86_64.whl.metadata (6.4 kB) Downloading torchaudio-2.5.0-cp312-cp312-manylinux1_x86_64.whl.metadata (6.4 kB) INFO: pip is still looking at multiple versions of torchaudio to determine which version is compatible with other requirements. This could take a while. Downloading torchaudio-2.4.1-cp312-cp312-manylinux1_x86_64.whl.metadata (6.4 kB) Downloading torchaudio-2.4.0-cp312-cp312-manylinux1_x86_64.whl.metadata (6.4 kB) Downloading torchaudio-2.3.1-cp312-cp312-manylinux1_x86_64.whl.metadata (6.4 kB) Downloading torchaudio-2.3.0-cp312-cp312-manylinux1_x86_64.whl.metadata (6.4 kB) INFO: pip is looking at multiple versions of torchvision to determine which version is compatible with other requirements. This could take a while. Collecting torchvision (from -r requirements_core.txt (line 3)) Downloading torchvision-0.24.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (5.9 kB) Downloading torchvision-0.23.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.1 kB) Downloading torchvision-0.22.1-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.1 kB) Downloading torchvision-0.22.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (6.1 kB) Downloading torchvision-0.21.0-cp312-cp312-manylinux1_x86_64.whl.metadata (6.1 kB) Downloading torchvision-0.20.1-cp312-cp312-manylinux1_x86_64.whl.metadata (6.1 kB) Downloading torchvision-0.20.0-cp312-cp312-manylinux1_x86_64.whl.metadata (6.1 kB) INFO: pip is still looking at multiple versions of torchvision to determine which version is compatible with other requirements. This could take a while. Downloading torchvision-0.19.1-cp312-cp312-manylinux1_x86_64.whl.metadata (6.0 kB) Downloading torchvision-0.19.0-cp312-cp312-manylinux1_x86_64.whl.metadata (6.0 kB) Downloading torchvision-0.18.1-cp312-cp312-manylinux1_x86_64.whl.metadata (6.6 kB) Downloading torchvision-0.18.0-cp312-cp312-manylinux1_x86_64.whl.metadata (6.6 kB) Collecting pillow!=8.3.*,>=5.3.0 (from torchvision->-r requirements_core.txt (line 3)) Using cached pillow-12.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (8.8 kB) Collecting huggingface-hub<1.0,>=0.34.0 (from transformers->-r requirements_core.txt (line 4)) Using cached huggingface_hub-0.36.0-py3-none-any.whl.metadata (14 kB) Collecting packaging>=20.0 (from transformers->-r requirements_core.txt (line 4)) Using cached packaging-25.0-py3-none-any.whl.metadata (3.3 kB) Collecting pyyaml>=5.1 (from transformers->-r requirements_core.txt (line 4)) Using cached pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (2.4 kB) Collecting regex!=2019.12.17 (from transformers->-r requirements_core.txt (line 4)) Using cached regex-2025.11.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (40 kB) Collecting hf-xet<2.0.0,>=1.1.3 (from huggingface-hub<1.0,>=0.34.0->transformers->-r requirements_core.txt (line 4)) Using cached hf_xet-1.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB) Collecting pyarrow>=21.0.0 (from datasets->-r requirements_core.txt (line 5)) Using cached pyarrow-22.0.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (3.2 kB) Collecting dill<0.4.1,>=0.3.0 (from datasets->-r requirements_core.txt (line 5)) Using cached dill-0.4.0-py3-none-any.whl.metadata (10 kB) Collecting httpx<1.0.0 (from datasets->-r requirements_core.txt (line 5)) Using cached httpx-0.28.1-py3-none-any.whl.metadata (7.1 kB) Collecting xxhash (from datasets->-r requirements_core.txt (line 5)) Using cached xxhash-3.6.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (13 kB) Collecting multiprocess<0.70.19 (from datasets->-r requirements_core.txt (line 5)) Using cached multiprocess-0.70.18-py312-none-any.whl.metadata (7.5 kB) Collecting aiohttp!=4.0.0a0,!=4.0.0a1 (from fsspec[http]<=2025.10.0,>=2023.1.0->datasets->-r requirements_core.txt (line 5)) Using cached aiohttp-3.13.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (8.1 kB) Collecting anyio (from httpx<1.0.0->datasets->-r requirements_core.txt (line 5)) Using cached anyio-4.12.0-py3-none-any.whl.metadata (4.3 kB) Collecting certifi (from httpx<1.0.0->datasets->-r requirements_core.txt (line 5)) Using cached certifi-2025.11.12-py3-none-any.whl.metadata (2.5 kB) Collecting httpcore==1.* (from httpx<1.0.0->datasets->-r requirements_core.txt (line 5)) Using cached httpcore-1.0.9-py3-none-any.whl.metadata (21 kB) Collecting idna (from httpx<1.0.0->datasets->-r requirements_core.txt (line 5)) Using cached idna-3.11-py3-none-any.whl.metadata (8.4 kB) Collecting h11>=0.16 (from httpcore==1.*->httpx<1.0.0->datasets->-r requirements_core.txt (line 5)) Using cached h11-0.16.0-py3-none-any.whl.metadata (8.3 kB) Collecting psutil (from accelerate->-r requirements_core.txt (line 7)) Using cached psutil-7.1.3-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB) Collecting cachetools (from vllm->-r requirements_core.txt (line 10)) Using cached cachetools-6.2.2-py3-none-any.whl.metadata (5.6 kB) Collecting blake3 (from vllm->-r requirements_core.txt (line 10)) Using cached blake3-1.0.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.6 kB) Collecting py-cpuinfo (from vllm->-r requirements_core.txt (line 10)) Using cached py_cpuinfo-9.0.0-py3-none-any.whl.metadata (794 bytes) Collecting protobuf (from vllm->-r requirements_core.txt (line 10)) Using cached protobuf-6.33.1-cp39-abi3-manylinux2014_x86_64.whl.metadata (593 bytes) Collecting openai>=1.99.1 (from vllm->-r requirements_core.txt (line 10)) Using cached openai-2.8.1-py3-none-any.whl.metadata (29 kB) Collecting pydantic>=2.12.0 (from vllm->-r requirements_core.txt (line 10)) Using cached pydantic-2.12.5-py3-none-any.whl.metadata (90 kB) Collecting prometheus_client>=0.18.0 (from vllm->-r requirements_core.txt (line 10)) Using cached prometheus_client-0.23.1-py3-none-any.whl.metadata (1.9 kB) Collecting prometheus-fastapi-instrumentator>=7.0.0 (from vllm->-r requirements_core.txt (line 10)) Using cached prometheus_fastapi_instrumentator-7.1.0-py3-none-any.whl.metadata (13 kB) Collecting lm-format-enforcer==0.11.3 (from vllm->-r requirements_core.txt (line 10)) Using cached lm_format_enforcer-0.11.3-py3-none-any.whl.metadata (17 kB) Collecting llguidance<1.4.0,>=1.3.0 (from vllm->-r requirements_core.txt (line 10)) Using cached llguidance-1.3.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB) Collecting outlines_core==0.2.11 (from vllm->-r requirements_core.txt (line 10)) Using cached outlines_core-0.2.11-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB) Collecting diskcache==5.6.3 (from vllm->-r requirements_core.txt (line 10)) Using cached diskcache-5.6.3-py3-none-any.whl.metadata (20 kB) Collecting lark==1.2.2 (from vllm->-r requirements_core.txt (line 10)) Using cached lark-1.2.2-py3-none-any.whl.metadata (1.8 kB) Collecting xgrammar==0.1.25 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.25-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.5 kB) Collecting partial-json-parser (from vllm->-r requirements_core.txt (line 10)) Using cached partial_json_parser-0.2.1.1.post7-py3-none-any.whl.metadata (6.1 kB) Collecting pyzmq>=25.0.0 (from vllm->-r requirements_core.txt (line 10)) Using cached pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl.metadata (6.0 kB) Collecting msgspec (from vllm->-r requirements_core.txt (line 10)) Using cached msgspec-0.20.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (5.5 kB) Collecting gguf>=0.13.0 (from vllm->-r requirements_core.txt (line 10)) Using cached gguf-0.17.1-py3-none-any.whl.metadata (4.3 kB) Collecting mistral_common>=1.8.5 (from mistral_common[image]>=1.8.5->vllm->-r requirements_core.txt (line 10)) Using cached mistral_common-1.8.6-py3-none-any.whl.metadata (5.3 kB) Collecting opencv-python-headless>=4.11.0 (from vllm->-r requirements_core.txt (line 10)) Using cached opencv_python_headless-4.12.0.88-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (19 kB) Collecting six>=1.16.0 (from vllm->-r requirements_core.txt (line 10)) Using cached six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB) Collecting setuptools<81.0.0,>=77.0.3 (from vllm->-r requirements_core.txt (line 10)) Using cached setuptools-80.9.0-py3-none-any.whl.metadata (6.6 kB) Collecting einops (from vllm->-r requirements_core.txt (line 10)) Using cached einops-0.8.1-py3-none-any.whl.metadata (13 kB) Collecting compressed-tensors==0.12.2 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.12.2-py3-none-any.whl.metadata (7.0 kB) Collecting depyf==0.20.0 (from vllm->-r requirements_core.txt (line 10)) Using cached depyf-0.20.0-py3-none-any.whl.metadata (7.3 kB) Collecting cloudpickle (from vllm->-r requirements_core.txt (line 10)) Using cached cloudpickle-3.1.2-py3-none-any.whl.metadata (7.1 kB) Collecting watchfiles (from vllm->-r requirements_core.txt (line 10)) Using cached watchfiles-1.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB) Collecting python-json-logger (from vllm->-r requirements_core.txt (line 10)) Using cached python_json_logger-4.0.0-py3-none-any.whl.metadata (4.0 kB) Collecting scipy (from vllm->-r requirements_core.txt (line 10)) Using cached scipy-1.16.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (62 kB) Collecting ninja (from vllm->-r requirements_core.txt (line 10)) Using cached ninja-1.13.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (5.1 kB) Collecting pybase64 (from vllm->-r requirements_core.txt (line 10)) Using cached pybase64-1.4.2-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl.metadata (8.7 kB) Collecting cbor2 (from vllm->-r requirements_core.txt (line 10)) Using cached cbor2-5.7.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (5.4 kB) Collecting setproctitle (from vllm->-r requirements_core.txt (line 10)) Using cached setproctitle-1.3.7-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl.metadata (10 kB) Collecting openai-harmony>=0.0.3 (from vllm->-r requirements_core.txt (line 10)) Using cached openai_harmony-0.0.8-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (8.0 kB) Collecting anthropic==0.71.0 (from vllm->-r requirements_core.txt (line 10)) Using cached anthropic-0.71.0-py3-none-any.whl.metadata (28 kB) Collecting model-hosting-container-standards<1.0.0 (from vllm->-r requirements_core.txt (line 10)) Using cached model_hosting_container_standards-0.1.9-py3-none-any.whl.metadata (24 kB) Collecting numba==0.61.2 (from vllm->-r requirements_core.txt (line 10)) Using cached numba-0.61.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.8 kB) Collecting ray>=2.48.0 (from ray[cgraph]>=2.48.0->vllm->-r requirements_core.txt (line 10)) Using cached ray-2.52.1-cp312-cp312-manylinux2014_x86_64.whl.metadata (21 kB) INFO: pip is looking at multiple versions of vllm to determine which version is compatible with other requirements. This could take a while. Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.11.1-cp38-abi3-manylinux1_x86_64.whl.metadata (18 kB) Using cached vllm-0.11.0-cp38-abi3-manylinux1_x86_64.whl.metadata (17 kB) Collecting llguidance<0.8.0,>=0.7.11 (from vllm->-r requirements_core.txt (line 10)) Using cached llguidance-0.7.30-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB) Collecting setuptools<80,>=77.0.3 (from vllm->-r requirements_core.txt (line 10)) Using cached setuptools-79.0.1-py3-none-any.whl.metadata (6.5 kB) Collecting compressed-tensors==0.11.0 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.11.0-py3-none-any.whl.metadata (7.0 kB) Collecting depyf==0.19.0 (from vllm->-r requirements_core.txt (line 10)) Using cached depyf-0.19.0-py3-none-any.whl.metadata (7.3 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.10.2-cp38-abi3-manylinux1_x86_64.whl.metadata (16 kB) Collecting xgrammar==0.1.23 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.23-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.4 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.10.1.1-cp38-abi3-manylinux1_x86_64.whl.metadata (15 kB) Collecting lm-format-enforcer<0.11,>=0.10.11 (from vllm->-r requirements_core.txt (line 10)) Using cached lm_format_enforcer-0.10.12-py3-none-any.whl.metadata (17 kB) Collecting outlines_core==0.2.10 (from vllm->-r requirements_core.txt (line 10)) Using cached outlines_core-0.2.10-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB) Collecting xgrammar==0.1.21 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.21-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.3 kB) Collecting compressed-tensors==0.10.2 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.10.2-py3-none-any.whl.metadata (7.0 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.10.1-cp38-abi3-manylinux1_x86_64.whl.metadata (15 kB) Using cached vllm-0.10.0-cp38-abi3-manylinux1_x86_64.whl.metadata (14 kB) Collecting openai<=1.90.0,>=1.87.0 (from vllm->-r requirements_core.txt (line 10)) Using cached openai-1.90.0-py3-none-any.whl.metadata (26 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.9.2-cp38-abi3-manylinux1_x86_64.whl.metadata (15 kB) Collecting outlines==0.1.11 (from vllm->-r requirements_core.txt (line 10)) Using cached outlines-0.1.11-py3-none-any.whl.metadata (17 kB) Collecting xgrammar==0.1.19 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.19-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.6 kB) Collecting depyf==0.18.0 (from vllm->-r requirements_core.txt (line 10)) Using cached depyf-0.18.0-py3-none-any.whl.metadata (7.1 kB) INFO: pip is still looking at multiple versions of vllm to determine which version is compatible with other requirements. This could take a while. Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.9.1-cp38-abi3-manylinux1_x86_64.whl.metadata (15 kB) Collecting compressed-tensors==0.10.1 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.10.1-py3-none-any.whl.metadata (7.0 kB) Collecting opentelemetry-sdk>=1.26.0 (from vllm->-r requirements_core.txt (line 10)) Using cached opentelemetry_sdk-1.38.0-py3-none-any.whl.metadata (1.5 kB) Collecting opentelemetry-api>=1.26.0 (from vllm->-r requirements_core.txt (line 10)) Using cached opentelemetry_api-1.38.0-py3-none-any.whl.metadata (1.5 kB) Collecting opentelemetry-exporter-otlp>=1.26.0 (from vllm->-r requirements_core.txt (line 10)) Using cached opentelemetry_exporter_otlp-1.38.0-py3-none-any.whl.metadata (2.4 kB) Collecting opentelemetry-semantic-conventions-ai>=0.4.1 (from vllm->-r requirements_core.txt (line 10)) Using cached opentelemetry_semantic_conventions_ai-0.4.13-py3-none-any.whl.metadata (1.1 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.9.0.1-cp38-abi3-manylinux1_x86_64.whl.metadata (15 kB) Collecting compressed-tensors==0.9.4 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.9.4-py3-none-any.whl.metadata (7.0 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.9.0-cp38-abi3-manylinux1_x86_64.whl.metadata (15 kB) Using cached vllm-0.8.5.post1-cp38-abi3-manylinux1_x86_64.whl.metadata (14 kB) Collecting xgrammar==0.1.18 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.18-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.6 kB) Collecting importlib_metadata (from vllm->-r requirements_core.txt (line 10)) Using cached importlib_metadata-8.7.0-py3-none-any.whl.metadata (4.8 kB) Collecting compressed-tensors==0.9.3 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.9.3-py3-none-any.whl.metadata (7.0 kB) Collecting opentelemetry-sdk<1.27.0,>=1.26.0 (from vllm->-r requirements_core.txt (line 10)) Using cached opentelemetry_sdk-1.26.0-py3-none-any.whl.metadata (1.5 kB) Collecting opentelemetry-api<1.27.0,>=1.26.0 (from vllm->-r requirements_core.txt (line 10)) Using cached opentelemetry_api-1.26.0-py3-none-any.whl.metadata (1.4 kB) Collecting opentelemetry-exporter-otlp<1.27.0,>=1.26.0 (from vllm->-r requirements_core.txt (line 10)) Using cached opentelemetry_exporter_otlp-1.26.0-py3-none-any.whl.metadata (2.3 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.8.5-cp38-abi3-manylinux1_x86_64.whl.metadata (14 kB) INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C. Using cached vllm-0.8.4-cp38-abi3-manylinux1_x86_64.whl.metadata (27 kB) Using cached vllm-0.8.3-cp38-abi3-manylinux1_x86_64.whl.metadata (27 kB) Collecting xgrammar==0.1.17 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.17-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.5 kB) Collecting gguf==0.10.0 (from vllm->-r requirements_core.txt (line 10)) Using cached gguf-0.10.0-py3-none-any.whl.metadata (3.5 kB) Collecting compressed-tensors==0.9.2 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.9.2-py3-none-any.whl.metadata (7.0 kB) Collecting numba==0.61 (from vllm->-r requirements_core.txt (line 10)) Using cached numba-0.61.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.8 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.8.2-cp38-abi3-manylinux1_x86_64.whl.metadata (27 kB) Collecting numpy (from -r requirements_core.txt (line 15)) Using cached numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB) Collecting xgrammar==0.1.16 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.16-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.3 kB) Collecting numba==0.60.0 (from vllm->-r requirements_core.txt (line 10)) Using cached numba-0.60.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.7 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.8.1-cp38-abi3-manylinux1_x86_64.whl.metadata (26 kB) Using cached vllm-0.8.0-cp38-abi3-manylinux1_x86_64.whl.metadata (26 kB) Using cached vllm-0.7.3-cp38-abi3-manylinux1_x86_64.whl.metadata (25 kB) Collecting xgrammar==0.1.11 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.11-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (2.0 kB) Collecting compressed-tensors==0.9.1 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.9.1-py3-none-any.whl.metadata (6.8 kB) Collecting ray==2.40.0 (from ray[adag]==2.40.0->vllm->-r requirements_core.txt (line 10)) Using cached ray-2.40.0-cp312-cp312-manylinux2014_x86_64.whl.metadata (17 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.7.2-cp38-abi3-manylinux1_x86_64.whl.metadata (12 kB) Collecting uvicorn[standard] (from vllm->-r requirements_core.txt (line 10)) Using cached uvicorn-0.38.0-py3-none-any.whl.metadata (6.8 kB) Collecting xgrammar>=0.1.6 (from vllm->-r requirements_core.txt (line 10)) Using cached xgrammar-0.1.27-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.8 kB) Collecting nvidia-ml-py>=12.560.30 (from vllm->-r requirements_core.txt (line 10)) Using cached nvidia_ml_py-13.580.82-py3-none-any.whl.metadata (9.6 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.7.1-cp38-abi3-manylinux1_x86_64.whl.metadata (12 kB) Collecting compressed-tensors==0.9.0 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.9.0-py3-none-any.whl.metadata (6.8 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.7.0-cp38-abi3-manylinux1_x86_64.whl.metadata (12 kB) Using cached vllm-0.6.6.post1-cp38-abi3-manylinux1_x86_64.whl.metadata (11 kB) Collecting compressed-tensors==0.8.1 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.8.1-py3-none-any.whl.metadata (6.8 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.6.6-cp38-abi3-manylinux1_x86_64.whl.metadata (11 kB) Using cached vllm-0.6.5-cp38-abi3-manylinux1_x86_64.whl.metadata (11 kB) Using cached vllm-0.6.4.post1-cp38-abi3-manylinux1_x86_64.whl.metadata (10 kB) Collecting outlines<0.1,>=0.0.43 (from vllm->-r requirements_core.txt (line 10)) Using cached outlines-0.0.46-py3-none-any.whl.metadata (15 kB) Collecting compressed-tensors==0.8.0 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.8.0-py3-none-any.whl.metadata (6.8 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.6.4-cp38-abi3-manylinux1_x86_64.whl.metadata (10 kB) Collecting lm-format-enforcer==0.10.6 (from vllm->-r requirements_core.txt (line 10)) Using cached lm_format_enforcer-0.10.6-py3-none-any.whl.metadata (16 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.6.3.post1-cp38-abi3-manylinux1_x86_64.whl.metadata (10 kB) Collecting compressed-tensors==0.6.0 (from vllm->-r requirements_core.txt (line 10)) Using cached compressed_tensors-0.6.0-py3-none-any.whl.metadata (6.8 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.6.3-cp38-abi3-manylinux1_x86_64.whl.metadata (10 kB) Using cached vllm-0.6.2-cp38-abi3-manylinux1_x86_64.whl.metadata (2.4 kB) Using cached vllm-0.6.1.post2-cp38-abi3-manylinux1_x86_64.whl.metadata (2.4 kB) Collecting gguf==0.9.1 (from vllm->-r requirements_core.txt (line 10)) Using cached gguf-0.9.1-py3-none-any.whl.metadata (3.3 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.6.1.post1-cp38-abi3-manylinux1_x86_64.whl.metadata (2.3 kB) Using cached vllm-0.6.1-cp38-abi3-manylinux1_x86_64.whl.metadata (2.3 kB) Using cached vllm-0.6.0-cp38-abi3-manylinux1_x86_64.whl.metadata (2.2 kB) Using cached vllm-0.5.5-cp38-abi3-manylinux1_x86_64.whl.metadata (2.0 kB) Collecting librosa (from vllm->-r requirements_core.txt (line 10)) Using cached librosa-0.11.0-py3-none-any.whl.metadata (8.7 kB) Collecting soundfile (from vllm->-r requirements_core.txt (line 10)) Using cached soundfile-0.13.1-py2.py3-none-manylinux_2_28_x86_64.whl.metadata (16 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.5.4-cp38-abi3-manylinux1_x86_64.whl.metadata (1.8 kB) Collecting cmake>=3.21 (from vllm->-r requirements_core.txt (line 10)) Using cached cmake-4.2.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (6.5 kB) Collecting lm-format-enforcer==0.10.3 (from vllm->-r requirements_core.txt (line 10)) Using cached lm_format_enforcer-0.10.3-py3-none-any.whl.metadata (16 kB) Collecting vllm (from -r requirements_core.txt (line 10)) Using cached vllm-0.5.3.post1-cp38-abi3-manylinux1_x86_64.whl.metadata (1.8 kB) Using cached vllm-0.5.3-cp38-abi3-manylinux1_x86_64.whl.metadata (1.8 kB) Using cached vllm-0.5.2-cp38-abi3-manylinux1_x86_64.whl.metadata (1.8 kB) Using cached vllm-0.5.1.tar.gz (790 kB) Installing build dependencies ... |
最新发布
12-02
Using cached vllm-0.5.1.tar.gz (790 kB) Installing build dependencies ... ``` - 源码编译需要： - 安装 `cmake`, `ninja`, `pybind11`, `cuda-toolkit` 等 - 极高内存（>8GB 推荐） - 长时间运行（可能...
Collecting scaler Using cached scaler-1.9.5-py3-none-any.whl.metadata (15 kB) Collecting bidict (from scaler) Using cached bidict-0.23.1-py3-none-any.whl.metadata (8.7 kB) Collecting cloudpickle (from scaler) Using cached cloudpickle-3.1.1-py3-none-any.whl.metadata (7.1 kB) INFO: pip is looking at multiple versions of scaler to determine which version is compatible with other requirements. This could take a while. Collecting scaler Using cached scaler-1.9.4-py3-none-any.whl.metadata (14 kB) Using cached scaler-1.9.3-py3-none-any.whl.metadata (14 kB) Using cached scaler-1.9.2-py3-none-any.whl.metadata (14 kB) Using cached scaler-1.9.1-py3-none-any.whl.metadata (14 kB) Using cached scaler-1.9.0-py3-none-any.whl.metadata (14 kB) Using cached scaler-1.8.17-py3-none-any.whl.metadata (11 kB) Using cached scaler-1.8.14-py3-none-any.whl.metadata (11 kB) INFO: pip is still looking at multiple versions of scaler to determine which version is compatible with other requirements
05-02
好的，我需要帮助用户解决关于Scaler的pip依赖项及其版本兼容性的问题。首先，用户提到“scaler”，这可能指的是数据预处理中的缩放器（如StandardScaler或MinMaxScaler）或者是某个具体的Python库。根据引用中的...
以下命令完成，并出现错误: D:\python\python.exe C:/Program Files/JetBrains/PyCharm 2025.2.3/plugins/python-ce/helpers/packaging_tool.py install scale 输出: Collecting scale Using cached scale-1.1.2-py3-none-any.whl.metadata (1.0 kB) Collecting numpy>=1.14.2 (from scale) Using cached numpy-2.3.4-cp314-cp314-win_amd64.whl.metadata (60 kB) Collecting pandas>=0.22.0 (from scale) Using cached pandas-2.3.3-cp314-cp314-win_amd64.whl.metadata (19 kB) Collecting scipy>=0.19.1 (from scale) Using cached scipy-1.16.2-cp314-cp314-win_amd64.whl.metadata (60 kB) Collecting scikit-learn>=0.19.1 (from scale) Using cached scikit_learn-1.7.2-cp314-cp314-win_amd64.whl.metadata (11 kB) Collecting torch>=1.0.0 (from scale) Using cached torch-2.9.0-cp314-cp314-win_amd64.whl.metadata (30 kB) Collecting tqdm>=4.28.1 (from scale) Using cached tqdm-4.67.1-py3-none-any.whl.metadata (57 kB) Collecting matplotlib>=3.0.2 (from scale) Using cached matplotlib-3.10.7-cp314-cp314-win_amd64.whl.metadata (11 kB) Collecting seaborn>=0.9.0 (from scale) Using cached seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB) Collecting umap-learn (from scale) Using cached umap_learn-0.5.9.post2-py3-none-any.whl.metadata (25 kB) Collecting scanpy>=1.6.1 (from scale) Using cached scanpy-1.11.5-py3-none-any.whl.metadata (9.3 kB) Collecting contourpy>=1.0.1 (from matplotlib>=3.0.2->scale) Using cached contourpy-1.3.3-cp314-cp314-win_amd64.whl.metadata (5.5 kB) Collecting cycler>=0.10 (from matplotlib>=3.0.2->scale) Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB) Collecting fonttools>=4.22.0 (from matplotlib>=3.0.2->scale) Using cached fonttools-4.60.1-cp314-cp314-win_amd64.whl.metadata (114 kB) Collecting kiwisolver>=1.3.1 (from matplotlib>=3.0.2->scale) Using cached kiwisolver-1.4.9-cp314-cp314-win_amd64.whl.metadata (6.4 kB) Collecting packaging>=20.0 (from matplotlib>=3.0.2->scale) Using cached packaging-25.0-py3-none-any.whl.metadata (3.3 kB) Collecting pillow>=8 (from matplotlib>=3.0.2->scale) Using cached pillow
10-25
在使用 `D:\python\python.exe C:/Program Files/JetBrains/PyCharm 2025.2.3/plugins/python-ce/helpers/packaging_tool.py install scale` 命令安装 `scale` 包时出现错误，可尝试以下解决办法： ### 1. 检查路径...
pip/conda安装包出错：Using cached http://mirrors.aliyun......
qq_38230414的博客
07-23 3855
pip/conda 安装包出问题：Using cached ......
pip安装过程中出现Using cached
keyboard专栏
02-29 6063
正在使用之前下载的包的缓存版本，而不是从指定的索引URL重新下载包。不使用缓存，而是重新从指定的URL下载包。这可能会使安装过程变慢，尤其是对于像PyTorch这样大型的包。就会直接使用这个缓存的文件进行安装，而不会从PyPI或其他配置的额外索引URL下载。会检查本地缓存目录看是否已经存在你想要安装的包的下载文件。如果存在，并且与你请求安装的版本匹配，这些包的缓存版本已经存在于你的系统中，因此。直接使用了这些缓存的版本进行安装，而没有从。这个额外的索引URL下载新的包。安装Python包时，
anaconda使用pip安装包时出现：Using cached https:XXX
qq_42006303的博客
03-03 7486
参考：https://blog.youkuaiyun.com/zys351148/article/details/80916717 向大神致敬！！！ 如果不使用缓存则删除pip目录下的cache下的文件就行
TMS320C64x+ DSP------Using Cache（转）
haiou0的专栏
02-27 1197
一、配置cache >配置L1 Cache：      CACHE_L1pSetSize();                           CACHE_L1dSetSize(); >配置L2 cache：     启动时默认状态下L2 cache被disabled, 全部L2为SRAM。如果启用了DSP/BIOS，则L2 cache被自动使能；否则，可以通过调用CSL命令：C
pip现Using cached的解决方案
pxyyoona的博客
04-11 3153
pip3 --no-cache-dir install -r requirements.txt
docker build高效利用cache
jeorch的博客
11-23 5550
原文链接：docker build高效利用cache 前言 Dockerfile 可以通过docker build命令构建为一个新的镜像，Dockerfile 中每一条命令都会构建出一个新的镜像层。当你重新build相同的Docker时，Docker会逐条语句check自身的cache镜像层，如果命中相同的，就使用cache而不执行这条语句继续往下逐条check直至build完成。Docker...
Vivado IP Status显示为“Using cached IP results“，变更回“synth_design Complete“方法
李正军的博客
10-22 1万+
Vivado 中IP status显示"Using cached IP results"，如图1所示，该情况多出现在直接复制IP核文件到工程目录中时出现，可改可不改； 图1 若要将状态改回成“synth_design Complete”，可进行如下操作： 1.清空IP缓存区数据：在Tcl Console窗口中输入“config_ip_ca...
如何利用好cache写出高性能的代码（Make your programs run faster by better using cache ）
DayDayUp
10-12 3901
笔者在学习cache时总结了一些在软件层面充分利用cache，来写出高性能代码方法。 如何利用好cache写出高性能的C代码 一，当线性访问数据（需要遍历数据结构）时，使用数组（arrays）而不是链表（Linked lists） 二，经常使用的变量应该放在一起声明定义，(Variables you access often together should be close to one-another in memory) 三，内存对齐：优化访问结构体或者类数组 四，高效地访问矩阵（二维数组）Access
关于basicsr==1.4.2安装失败的终极解决方案
lckj2009的专栏
10-19 1万+
看看这个pip，应该是torch这个组件没有安装吧。网上的答案有很多种，有人说是setuptools的问题，甚至有人说是numpy的问题，还有不少人说是pip源的问题。WINDOWS系统一般是不会出这个错误的。按照网上的这些做法，这个错误是不会消失的。之后再 pip install basicsr==1.4.2。相信最近大家在做一些东西的时候，要用到basicsr插件（pip安装），在某些ubuntu机子上面。pip install basicsr==1.4.2是安装失败的。慢慢分析解决问题是一种思路。
Vivado使用与注意事项
黑客三遍猪
09-27 2万+
Verilog语法检查能力差 变量声明必须在处理之前，否则，vivado不报错，直接把process reg a部分优化掉，所以建议所以变量都声明在文件顶部， always @(...) begin process reg a end reg a; axi stream data fifo 如果你不在block design中使用这个IP，那么GUI中设置的DATA宽度不会适配到生成的ver...
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