I got Error when I install mmdetection3d on ubuntu 20.04 cuda 11.0 torch 1.7.0

Thanks for your great work!
I just follow the instruction to install, and successfully installed mmcv and mmdetection, when I do the last step,
I got an Error and don't know what to do.
pip install -v -e .
Using pip 20.2.4 from /home/jim/anaconda3/lib/python3.8/site-packages/pip (python 3.8)
Non-user install because site-packages writeable
Created temporary directory: /tmp/pip-ephem-wheel-cache-qsdbtpc5
Created temporary directory: /tmp/pip-req-tracker-xr10pfui
Initialized build tracking at /tmp/pip-req-tracker-xr10pfui
Created build tracker: /tmp/pip-req-tracker-xr10pfui
Entered build tracker: /tmp/pip-req-tracker-xr10pfui
Created temporary directory: /tmp/pip-install-c7x2mxra
Obtaining file:///home/jim/open-mmlab/mmdetection3d
Added file:///home/jim/open-mmlab/mmdetection3d to build tracker '/tmp/pip-req-tracker-xr10pfui'
Running setup.py (path:/home/jim/open-mmlab/mmdetection3d/setup.py) egg_info for package from file:///home/jim/open-mmlab/mmdetection3d
Created temporary directory: /tmp/pip-pip-egg-info-tlv67itt
Running command python setup.py egg_info
running egg_info
creating /tmp/pip-pip-egg-info-tlv67itt/mmdet3d.egg-info
writing /tmp/pip-pip-egg-info-tlv67itt/mmdet3d.egg-info/PKG-INFO
writing dependency_links to /tmp/pip-pip-egg-info-tlv67itt/mmdet3d.egg-info/dependency_links.txt
writing requirements to /tmp/pip-pip-egg-info-tlv67itt/mmdet3d.egg-info/requires.txt
writing top-level names to /tmp/pip-pip-egg-info-tlv67itt/mmdet3d.egg-info/top_level.txt
writing manifest file '/tmp/pip-pip-egg-info-tlv67itt/mmdet3d.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'mmdet3d/ops//*.cpp'
warning: no files found matching 'mmdet3d/ops//.cu'
warning: no files found matching 'mmdet3d/ops/**/.h'
warning: no files found matching 'mmdet3d/ops/**/*.cc'
warning: no files found matching 'mmdet3d/VERSION'
writing manifest file '/tmp/pip-pip-egg-info-tlv67itt/mmdet3d.egg-info/SOURCES.txt'
Source in /home/jim/open-mmlab/mmdetection3d has version 0.11.0, which satisfies requirement mmdet3d==0.11.0 from file:///home/jim/open-mmlab/mmdetection3d
Removed mmdet3d==0.11.0 from file:///home/jim/open-mmlab/mmdetection3d from build tracker '/tmp/pip-req-tracker-xr10pfui'
Requirement already satisfied: lyft_dataset_sdk in /home/jim/anaconda3/lib/python3.8/site-packages (from mmdet3d==0.11.0) (0.0.8)
Requirement already satisfied: networkx<2.3,>=2.2 in /home/jim/anaconda3/lib/python3.8/site-packages (from mmdet3d==0.11.0) (2.2)
Requirement already satisfied: numba==0.48.0 in /home/jim/anaconda3/lib/python3.8/site-packages (from mmdet3d==0.11.0) (0.48.0)
Requirement already satisfied: numpy<1.20.0 in /home/jim/.local/lib/python3.8/site-packages (from mmdet3d==0.11.0) (1.19.5)
Requirement already satisfied: nuscenes-devkit in /home/jim/anaconda3/lib/python3.8/site-packages (from mmdet3d==0.11.0) (1.1.2)
Requirement already satisfied: plyfile in /home/jim/anaconda3/lib/python3.8/site-packages (from mmdet3d==0.11.0) (0.7.3)
Requirement already satisfied: scikit-image in /home/jim/.local/lib/python3.8/site-packages (from mmdet3d==0.11.0) (0.18.0)
Requirement already satisfied: tensorboard in /home/jim/.local/lib/python3.8/site-packages (from mmdet3d==0.11.0) (2.4.1)
Requirement already satisfied: trimesh<2.35.40,>=2.35.39 in /home/jim/anaconda3/lib/python3.8/site-packages (from mmdet3d==0.11.0) (2.35.39)
Requirement already satisfied: cachetools>=3.1.0 in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (4.2.0)
Requirement already satisfied: plotly in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (4.14.3)
Requirement already satisfied: scipy>=1.1.0 in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (1.5.4)
Requirement already satisfied: opencv-python>=<IP_ADDRESS> in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (<IP_ADDRESS>)
Requirement already satisfied: tqdm>=4.25.0 in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (4.56.0)
Requirement already satisfied: pytest in /home/jim/anaconda3/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (6.2.2)
Requirement already satisfied: Pillow>=5.2.0 in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (8.1.0)
Requirement already satisfied: pandas in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (1.2.0)
Requirement already satisfied: scikit-learn>=0.19.2 in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (0.23.2)
Requirement already satisfied: pyquaternion>=0.9.5 in /home/jim/anaconda3/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (0.9.9)
Requirement already satisfied: matplotlib in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (3.3.3)
Requirement already satisfied: fire in /home/jim/anaconda3/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (0.4.0)
Requirement already satisfied: Shapely>=1.6.4.post2 in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (1.7.1)
Requirement already satisfied: black in /home/jim/anaconda3/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (20.8b1)
Requirement already satisfied: flake8 in /home/jim/.local/lib/python3.8/site-packages (from lyft_dataset_sdk->mmdet3d==0.11.0) (3.8.4)
Requirement already satisfied: decorator>=4.3.0 in /home/jim/.local/lib/python3.8/site-packages (from networkx<2.3,>=2.2->mmdet3d==0.11.0) (4.4.2)
Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /home/jim/anaconda3/lib/python3.8/site-packages (from numba==0.48.0->mmdet3d==0.11.0) (0.31.0)
Requirement already satisfied: setuptools in /home/jim/anaconda3/lib/python3.8/site-packages (from numba==0.48.0->mmdet3d==0.11.0) (50.3.1.post20201107)
1 location(s) to search for versions of pycocotools:

https://pypi.org/simple/pycocotools/
Fetching project page and analyzing links: https://pypi.org/simple/pycocotools/
Getting page https://pypi.org/simple/pycocotools/
Found index url https://pypi.org/simple
Getting credentials from keyring for https://pypi.org/simple
Getting credentials from keyring for pypi.org
Looking up "https://pypi.org/simple/pycocotools/" in the cache
Request header has "max_age" as 0, cache bypassed
Starting new HTTPS connection (1): pypi.org:443
https://pypi.org:443 "GET /simple/pycocotools/ HTTP/1.1" 304 0
Found link https://files.pythonhosted.org/packages/96/84/9a07b1095fd8555ba3f3d519517c8743c2554a245f9476e5e39869f948d2/pycocotools-2.0.0.tar.gz#sha256=cbb8c2fbab80450a67ee9879c63b0bc8a69e58dd9a0153d55de404c0d383a94b (from https://pypi.org/simple/pycocotools/), version: 2.0.0
Found link https://files.pythonhosted.org/packages/5c/82/bcaf4d21d7027fe5165b88e3aef1910a36ed02c3e99d3385d1322ea0ba29/pycocotools-2.0.1.tar.gz#sha256=1c06e73a85ed9874c1174d47064524b9fb2759b95a6997437775652f20c1711f (from https://pypi.org/simple/pycocotools/), version: 2.0.1
Found link https://files.pythonhosted.org/packages/66/45/36556573d509349a4a1a49bc96fdc3dd6046d691c6660fc0416d93fb1547/pycocotools-2.0.2a1.tar.gz#sha256=5138269c32d42772a7d1bd76815203680b1ca9f051929f6c0affa08e594c76fc (from https://pypi.org/simple/pycocotools/), version: 2.0.2a1
Found link https://files.pythonhosted.org/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz#sha256=24717a12799b4471c2e54aa210d642e6cd4028826a1d49fcc2b0e3497e041f1a (from https://pypi.org/simple/pycocotools/), version: 2.0.2
Given no hashes to check 2 links for project 'pycocotools': discarding no candidates
Using version 2.0.2 (newest of versions: 2.0.1, 2.0.2)
Using cached wheel link: file:///home/jim/.cache/pip/wheels/e7/77/b2/6f38b5bea571cd8f4689f91a7c1ed2eaecb2c2ce17f9945b17/pycocotools-2.0.2-cp38-cp38-linux_x86_64.whl
Processing /home/jim/.cache/pip/wheels/e7/77/b2/6f38b5bea571cd8f4689f91a7c1ed2eaecb2c2ce17f9945b17/pycocotools-2.0.2-cp38-cp38-linux_x86_64.whl
Added pycocotools>=2.0.1 from file:///home/jim/.cache/pip/wheels/e7/77/b2/6f38b5bea571cd8f4689f91a7c1ed2eaecb2c2ce17f9945b17/pycocotools-2.0.2-cp38-cp38-linux_x86_64.whl (from nuscenes-devkit->mmdet3d==0.11.0) to build tracker '/tmp/pip-req-tracker-xr10pfui'
Removed pycocotools>=2.0.1 from file:///home/jim/.cache/pip/wheels/e7/77/b2/6f38b5bea571cd8f4689f91a7c1ed2eaecb2c2ce17f9945b17/pycocotools-2.0.2-cp38-cp38-linux_x86_64.whl (from nuscenes-devkit->mmdet3d==0.11.0) from build tracker '/tmp/pip-req-tracker-xr10pfui'
Requirement already satisfied: descartes in /home/jim/.local/lib/python3.8/site-packages (from nuscenes-devkit->mmdet3d==0.11.0) (1.1.0)
Requirement already satisfied: jupyter in /home/jim/.local/lib/python3.8/site-packages (from nuscenes-devkit->mmdet3d==0.11.0) (1.0.0)
Requirement already satisfied: PyWavelets>=1.1.1 in /home/jim/.local/lib/python3.8/site-packages (from scikit-image->mmdet3d==0.11.0) (1.1.1)
Requirement already satisfied: imageio>=2.3.0 in /home/jim/.local/lib/python3.8/site-packages (from scikit-image->mmdet3d==0.11.0) (2.9.0)
Requirement already satisfied: tifffile>=2019.7.26 in /home/jim/.local/lib/python3.8/site-packages (from scikit-image->mmdet3d==0.11.0) (2020.12.8)
Requirement already satisfied: absl-py>=0.4 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (0.11.0)
Requirement already satisfied: google-auth<2,>=1.6.3 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (1.24.0)
Requirement already satisfied: werkzeug>=0.11.15 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (1.0.1)
Requirement already satisfied: requests<3,>=2.21.0 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (2.24.0)
Requirement already satisfied: markdown>=2.6.8 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (3.3.3)
Requirement already satisfied: wheel>=0.26; python_version >= "3" in /home/jim/anaconda3/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (0.35.1)
Requirement already satisfied: six>=1.10.0 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (1.15.0)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (0.4.2)
Requirement already satisfied: grpcio>=1.24.3 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (1.32.0)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (1.7.0)
Requirement already satisfied: protobuf>=3.6.0 in /home/jim/.local/lib/python3.8/site-packages (from tensorboard->mmdet3d==0.11.0) (3.14.0)
Requirement already satisfied: retrying>=1.3.3 in /home/jim/.local/lib/python3.8/site-packages (from plotly->lyft_dataset_sdk->mmdet3d==0.11.0) (1.3.3)
Requirement already satisfied: toml in /home/jim/.local/lib/python3.8/site-packages (from pytest->lyft_dataset_sdk->mmdet3d==0.11.0) (0.10.2)
Requirement already satisfied: py>=1.8.2 in /home/jim/anaconda3/lib/python3.8/site-packages (from pytest->lyft_dataset_sdk->mmdet3d==0.11.0) (1.9.0)
Requirement already satisfied: attrs>=19.2.0 in /home/jim/.local/lib/python3.8/site-packages (from pytest->lyft_dataset_sdk->mmdet3d==0.11.0) (20.3.0)
Requirement already satisfied: iniconfig in /home/jim/anaconda3/lib/python3.8/site-packages (from pytest->lyft_dataset_sdk->mmdet3d==0.11.0) (1.1.1)
Requirement already satisfied: pluggy<1.0.0a1,>=0.12 in /home/jim/anaconda3/lib/python3.8/site-packages (from pytest->lyft_dataset_sdk->mmdet3d==0.11.0) (0.13.1)
Requirement already satisfied: packaging in /home/jim/.local/lib/python3.8/site-packages (from pytest->lyft_dataset_sdk->mmdet3d==0.11.0) (20.8)
Requirement already satisfied: pytz>=2017.3 in /home/jim/anaconda3/lib/python3.8/site-packages (from pandas->lyft_dataset_sdk->mmdet3d==0.11.0) (2020.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /home/jim/anaconda3/lib/python3.8/site-packages (from pandas->lyft_dataset_sdk->mmdet3d==0.11.0) (2.8.1)
Requirement already satisfied: threadpoolctl>=2.0.0 in /home/jim/anaconda3/lib/python3.8/site-packages (from scikit-learn>=0.19.2->lyft_dataset_sdk->mmdet3d==0.11.0) (2.1.0)
Requirement already satisfied: joblib>=0.11 in /home/jim/.local/lib/python3.8/site-packages (from scikit-learn>=0.19.2->lyft_dataset_sdk->mmdet3d==0.11.0) (1.0.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /home/jim/.local/lib/python3.8/site-packages (from matplotlib->lyft_dataset_sdk->mmdet3d==0.11.0) (1.3.1)
Requirement already satisfied: cycler>=0.10 in /home/jim/.local/lib/python3.8/site-packages (from matplotlib->lyft_dataset_sdk->mmdet3d==0.11.0) (0.10.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/jim/.local/lib/python3.8/site-packages (from matplotlib->lyft_dataset_sdk->mmdet3d==0.11.0) (2.4.7)
Requirement already satisfied: termcolor in /home/jim/.local/lib/python3.8/site-packages (from fire->lyft_dataset_sdk->mmdet3d==0.11.0) (1.1.0)
Requirement already satisfied: appdirs in /home/jim/.local/lib/python3.8/site-packages (from black->lyft_dataset_sdk->mmdet3d==0.11.0) (1.4.4)
Requirement already satisfied: click>=7.1.2 in /home/jim/anaconda3/lib/python3.8/site-packages (from black->lyft_dataset_sdk->mmdet3d==0.11.0) (7.1.2)
Requirement already satisfied: pathspec<1,>=0.6 in /home/jim/anaconda3/lib/python3.8/site-packages (from black->lyft_dataset_sdk->mmdet3d==0.11.0) (0.8.1)
Requirement already satisfied: typed-ast>=1.4.0 in /home/jim/anaconda3/lib/python3.8/site-packages (from black->lyft_dataset_sdk->mmdet3d==0.11.0) (1.4.2)
Requirement already satisfied: mypy-extensions>=0.4.3 in /home/jim/anaconda3/lib/python3.8/site-packages (from black->lyft_dataset_sdk->mmdet3d==0.11.0) (0.4.3)
Requirement already satisfied: typing-extensions>=3.7.4 in /home/jim/.local/lib/python3.8/site-packages (from black->lyft_dataset_sdk->mmdet3d==0.11.0) (<IP_ADDRESS>)
Requirement already satisfied: regex>=2020.1.8 in /home/jim/.local/lib/python3.8/site-packages (from black->lyft_dataset_sdk->mmdet3d==0.11.0) (2020.11.13)
Requirement already satisfied: mccabe<0.7.0,>=0.6.0 in /home/jim/.local/lib/python3.8/site-packages (from flake8->lyft_dataset_sdk->mmdet3d==0.11.0) (0.6.1)
Requirement already satisfied: pycodestyle<2.7.0,>=2.6.0a1 in /home/jim/.local/lib/python3.8/site-packages (from flake8->lyft_dataset_sdk->mmdet3d==0.11.0) (2.6.0)
Requirement already satisfied: pyflakes<2.3.0,>=2.2.0 in /home/jim/.local/lib/python3.8/site-packages (from flake8->lyft_dataset_sdk->mmdet3d==0.11.0) (2.2.0)
Requirement already satisfied: cython>=0.27.3 in /home/jim/.local/lib/python3.8/site-packages (from pycocotools>=2.0.1->nuscenes-devkit->mmdet3d==0.11.0) (0.29.21)
Requirement already satisfied: ipykernel in /home/jim/.local/lib/python3.8/site-packages (from jupyter->nuscenes-devkit->mmdet3d==0.11.0) (5.4.2)
Requirement already satisfied: nbconvert in /home/jim/.local/lib/python3.8/site-packages (from jupyter->nuscenes-devkit->mmdet3d==0.11.0) (6.0.7)
Requirement already satisfied: qtconsole in /home/jim/.local/lib/python3.8/site-packages (from jupyter->nuscenes-devkit->mmdet3d==0.11.0) (5.0.1)
Requirement already satisfied: jupyter-console in /home/jim/.local/lib/python3.8/site-packages (from jupyter->nuscenes-devkit->mmdet3d==0.11.0) (6.2.0)
Requirement already satisfied: ipywidgets in /home/jim/.local/lib/python3.8/site-packages (from jupyter->nuscenes-devkit->mmdet3d==0.11.0) (7.6.3)
Requirement already satisfied: notebook in /home/jim/.local/lib/python3.8/site-packages (from jupyter->nuscenes-devkit->mmdet3d==0.11.0) (6.1.6)
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Requirement already satisfied: rsa<5,>=3.1.4; python_version >= "3.6" in /home/jim/.local/lib/python3.8/site-packages (from google-auth<2,>=1.6.3->tensorboard->mmdet3d==0.11.0) (4.6)
Requirement already satisfied: certifi>=2017.4.17 in /home/jim/anaconda3/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard->mmdet3d==0.11.0) (2020.6.20)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jim/anaconda3/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard->mmdet3d==0.11.0) (1.25.11)
Requirement already satisfied: chardet<4,>=3.0.2 in /home/jim/anaconda3/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard->mmdet3d==0.11.0) (3.0.4)
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Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/jim/.local/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmdet3d==0.11.0) (1.3.0)
Requirement already satisfied: ipython>=5.0.0 in /home/jim/.local/lib/python3.8/site-packages (from ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (7.19.0)
Requirement already satisfied: jupyter-client in /home/jim/.local/lib/python3.8/site-packages (from ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (6.1.7)
Requirement already satisfied: tornado>=4.2 in /home/jim/.local/lib/python3.8/site-packages (from ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (6.1)
Requirement already satisfied: traitlets>=4.1.0 in /home/jim/.local/lib/python3.8/site-packages (from ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (5.0.5)
Requirement already satisfied: pygments>=2.4.1 in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (2.7.3)
Requirement already satisfied: jinja2>=2.4 in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (2.11.2)
Requirement already satisfied: testpath in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.4.4)
Requirement already satisfied: mistune<2,>=0.8.1 in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.8.4)
Requirement already satisfied: jupyterlab-pygments in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.1.2)
Requirement already satisfied: pandocfilters>=1.4.1 in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (1.4.3)
Requirement already satisfied: bleach in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (3.2.1)
Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.5.1)
Requirement already satisfied: jupyter-core in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (4.7.0)
Requirement already satisfied: nbformat>=4.4 in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (5.0.8)
Requirement already satisfied: defusedxml in /home/jim/.local/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.6.0)
Requirement already satisfied: entrypoints>=0.2.2 in /home/jim/anaconda3/lib/python3.8/site-packages (from nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.3)
Requirement already satisfied: pyzmq>=17.1 in /home/jim/.local/lib/python3.8/site-packages (from qtconsole->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (20.0.0)
Requirement already satisfied: qtpy in /home/jim/.local/lib/python3.8/site-packages (from qtconsole->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (1.9.0)
Requirement already satisfied: ipython-genutils in /home/jim/.local/lib/python3.8/site-packages (from qtconsole->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.2.0)
Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /home/jim/.local/lib/python3.8/site-packages (from jupyter-console->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (3.0.8)
Requirement already satisfied: jupyterlab-widgets>=1.0.0; python_version >= "3.6" in /home/jim/.local/lib/python3.8/site-packages (from ipywidgets->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (1.0.0)
Requirement already satisfied: widgetsnbextension~=3.5.0 in /home/jim/.local/lib/python3.8/site-packages (from ipywidgets->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (3.5.1)
Requirement already satisfied: argon2-cffi in /home/jim/.local/lib/python3.8/site-packages (from notebook->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (20.1.0)
Requirement already satisfied: Send2Trash in /home/jim/.local/lib/python3.8/site-packages (from notebook->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (1.5.0)
Requirement already satisfied: prometheus-client in /home/jim/.local/lib/python3.8/site-packages (from notebook->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.9.0)
Requirement already satisfied: terminado>=0.8.3 in /home/jim/.local/lib/python3.8/site-packages (from notebook->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.9.1)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/jim/.local/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->mmdet3d==0.11.0) (0.4.8)
Requirement already satisfied: oauthlib>=3.0.0 in /home/jim/anaconda3/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmdet3d==0.11.0) (3.1.0)
Requirement already satisfied: backcall in /home/jim/.local/lib/python3.8/site-packages (from ipython>=5.0.0->ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.2.0)
Requirement already satisfied: pickleshare in /home/jim/.local/lib/python3.8/site-packages (from ipython>=5.0.0->ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.7.5)
Requirement already satisfied: jedi>=0.10 in /home/jim/.local/lib/python3.8/site-packages (from ipython>=5.0.0->ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.17.2)
Requirement already satisfied: pexpect>4.3; sys_platform != "win32" in /home/jim/anaconda3/lib/python3.8/site-packages (from ipython>=5.0.0->ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (4.8.0)
Requirement already satisfied: MarkupSafe>=0.23 in /home/jim/anaconda3/lib/python3.8/site-packages (from jinja2>=2.4->nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (1.1.1)
Requirement already satisfied: webencodings in /home/jim/.local/lib/python3.8/site-packages (from bleach->nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.5.1)
Requirement already satisfied: nest-asyncio in /home/jim/.local/lib/python3.8/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (1.4.3)
Requirement already satisfied: async-generator in /home/jim/.local/lib/python3.8/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (1.10)
Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /home/jim/.local/lib/python3.8/site-packages (from nbformat>=4.4->nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (3.2.0)
Requirement already satisfied: wcwidth in /home/jim/.local/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->jupyter-console->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.2.5)
Requirement already satisfied: cffi>=1.0.0 in /home/jim/.local/lib/python3.8/site-packages (from argon2-cffi->notebook->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (1.14.4)
Requirement already satisfied: ptyprocess; os_name != "nt" in /home/jim/.local/lib/python3.8/site-packages (from terminado>=0.8.3->notebook->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.6.0)
Requirement already satisfied: parso<0.8.0,>=0.7.0 in /home/jim/.local/lib/python3.8/site-packages (from jedi>=0.10->ipython>=5.0.0->ipykernel->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.7.1)
Requirement already satisfied: pyrsistent>=0.14.0 in /home/jim/.local/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (0.17.3)
Requirement already satisfied: pycparser in /home/jim/.local/lib/python3.8/site-packages (from cffi>=1.0.0->argon2-cffi->notebook->jupyter->nuscenes-devkit->mmdet3d==0.11.0) (2.20)
Installing collected packages: mmdet3d, pycocotools
Running setup.py develop for mmdet3d
Running command /home/jim/anaconda3/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/home/jim/open-mmlab/mmdetection3d/setup.py'"'"'; file='"'"'/home/jim/open-mmlab/mmdetection3d/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' develop --no-deps
running develop
running egg_info
writing mmdet3d.egg-info/PKG-INFO
writing dependency_links to mmdet3d.egg-info/dependency_links.txt
writing requirements to mmdet3d.egg-info/requires.txt
writing top-level names to mmdet3d.egg-info/top_level.txt
reading manifest template 'MANIFEST.in'
warning: no files found matching 'mmdet3d/ops//*.cpp'
warning: no files found matching 'mmdet3d/ops//.cu'
warning: no files found matching 'mmdet3d/ops/**/.h'
warning: no files found matching 'mmdet3d/ops/**/*.cc'
warning: no files found matching 'mmdet3d/VERSION'
writing manifest file 'mmdet3d.egg-info/SOURCES.txt'
running build_ext
building 'mmdet3d.ops.spconv.sparse_conv_ext' extension
Emitting ninja build file /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/build.ninja...
Compiling objects...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
ninja: no work to do.
g++ -pthread -shared -B /home/jim/anaconda3/compiler_compat -L/home/jim/anaconda3/lib -Wl,-rpath=/home/jim/anaconda3/lib -Wl,--no-as-needed -Wl,--sysroot=/ /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/spconv/src/all.o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/spconv/src/reordering.o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/spconv/src/reordering_cuda.o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/spconv/src/indice.o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/spconv/src/indice_cuda.o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/spconv/src/maxpool.o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/spconv/src/maxpool_cuda.o -L/home/jim/anaconda3/lib/python3.8/site-packages/torch/lib -L/usr/local/cuda-11.0/lib64 -lc10 -ltorch -ltorch_cpu -ltorch_python -lcudart -lc10_cuda -ltorch_cuda -o build/lib.linux-x86_64-3.8/mmdet3d/ops/spconv/sparse_conv_ext.cpython-38-x86_64-linux-gnu.so
building 'mmdet3d.ops.iou3d.iou3d_cuda' extension
Emitting ninja build file /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/build.ninja...
Compiling objects...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
ninja: no work to do.
g++ -pthread -shared -B /home/jim/anaconda3/compiler_compat -L/home/jim/anaconda3/lib -Wl,-rpath=/home/jim/anaconda3/lib -Wl,--no-as-needed -Wl,--sysroot=/ /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/iou3d/src/iou3d.o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/iou3d/src/iou3d_kernel.o -L/home/jim/anaconda3/lib/python3.8/site-packages/torch/lib -L/usr/local/cuda-11.0/lib64 -lc10 -ltorch -ltorch_cpu -ltorch_python -lcudart -lc10_cuda -ltorch_cuda -o build/lib.linux-x86_64-3.8/mmdet3d/ops/iou3d/iou3d_cuda.cpython-38-x86_64-linux-gnu.so
building 'mmdet3d.ops.voxel.voxel_layer' extension
Emitting ninja build file /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/build.ninja...
Compiling objects...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
[1/1] /usr/local/cuda-11.0/bin/nvcc -DWITH_CUDA -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/include/python3.8 -c -c /home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu -o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/voxel/src/scatter_points_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=voxel_layer -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=sm_75 -std=c++14
FAILED: /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/voxel/src/scatter_points_cuda.o
/usr/local/cuda-11.0/bin/nvcc -DWITH_CUDA -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/TH -I/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/jim/anaconda3/include/python3.8 -c -c /home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu -o /home/jim/open-mmlab/mmdetection3d/build/temp.linux-x86_64-3.8/mmdet3d/ops/voxel/src/scatter_points_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=voxel_layer -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=sm_75 -std=c++14
/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/ATen/core/boxing/impl/boxing.h(100): warning: integer conversion resulted in a change of sign
/home/jim/anaconda3/lib/python3.8/site-packages/torch/include/ATen/core/op_registration/op_whitelist.h(39): warning: integer conversion resulted in a change of sign
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(242): error: class "at::Tensor" has no member "max_values"
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: type name is not allowed
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: expected an expression
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: no instance of function template "coors_id_kernel" matches the argument list
argument types are: (uint8_t *, , int64_t *, const int, const int)
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: type name is not allowed
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: expected an expression
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: no instance of function template "coors_id_kernel" matches the argument list
argument types are: (int8_t *, , int64_t *, const int, const int)
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: type name is not allowed
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: expected an expression
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: no instance of function template "coors_id_kernel" matches the argument list
argument types are: (int32_t *, , int64_t *, const int, const int)
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: type name is not allowed
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: expected an expression
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: no instance of function template "coors_id_kernel" matches the argument list
argument types are: (int64_t *, , int64_t *, const int, const int)
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: type name is not allowed
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: expected an expression
/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu(249): error: no instance of function template "coors_id_kernel" matches the argument list
argument types are: (int16_t *, , int64_t *, const int, const int)
16 errors detected in the compilation of "/home/jim/open-mmlab/mmdetection3d/mmdet3d/ops/voxel/src/scatter_points_cuda.cu".
ninja: build stopped: subcommand failed.
Traceback (most recent call last):
File "/home/jim/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1516, in _run_ninja_build
subprocess.run(
File "/home/jim/anaconda3/lib/python3.8/subprocess.py", line 512, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "", line 1, in
File "/home/jim/open-mmlab/mmdetection3d/setup.py", line 139, in
setup(
File "/home/jim/anaconda3/lib/python3.8/site-packages/setuptools/init.py", line 153, in setup
return distutils.core.setup(**attrs)
File "/home/jim/anaconda3/lib/python3.8/distutils/core.py", line 148, in setup
dist.run_commands()
File "/home/jim/anaconda3/lib/python3.8/distutils/dist.py", line 966, in run_commands
self.run_command(cmd)
File "/home/jim/anaconda3/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/home/jim/anaconda3/lib/python3.8/site-packages/setuptools/command/develop.py", line 34, in run
self.install_for_development()
File "/home/jim/anaconda3/lib/python3.8/site-packages/setuptools/command/develop.py", line 136, in install_for_development
self.run_command('build_ext')
File "/home/jim/anaconda3/lib/python3.8/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/home/jim/anaconda3/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/home/jim/anaconda3/lib/python3.8/site-packages/setuptools/command/build_ext.py", line 79, in run
_build_ext.run(self)
File "/home/jim/.local/lib/python3.8/site-packages/Cython/Distutils/old_build_ext.py", line 186, in run
_build_ext.build_ext.run(self)
File "/home/jim/anaconda3/lib/python3.8/distutils/command/build_ext.py", line 340, in run
self.build_extensions()
File "/home/jim/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 653, in build_extensions
build_ext.build_extensions(self)
File "/home/jim/.local/lib/python3.8/site-packages/Cython/Distutils/old_build_ext.py", line 195, in build_extensions
_build_ext.build_ext.build_extensions(self)
File "/home/jim/anaconda3/lib/python3.8/distutils/command/build_ext.py", line 449, in build_extensions
self._build_extensions_serial()
File "/home/jim/anaconda3/lib/python3.8/distutils/command/build_ext.py", line 474, in _build_extensions_serial
self.build_extension(ext)
File "/home/jim/anaconda3/lib/python3.8/site-packages/setuptools/command/build_ext.py", line 196, in build_extension
_build_ext.build_extension(self, ext)
File "/home/jim/anaconda3/lib/python3.8/distutils/command/build_ext.py", line 528, in build_extension
objects = self.compiler.compile(sources,
File "/home/jim/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 473, in unix_wrap_ninja_compile
_write_ninja_file_and_compile_objects(
File "/home/jim/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1233, in _write_ninja_file_and_compile_objects
_run_ninja_build(
File "/home/jim/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1538, in _run_ninja_build
raise RuntimeError(message) from e
RuntimeError: Error compiling objects for extension
ERROR: Command errored out with exit status 1: /home/jim/anaconda3/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/home/jim/open-mmlab/mmdetection3d/setup.py'"'"'; file='"'"'/home/jim/open-mmlab/mmdetection3d/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' develop --no-deps Check the logs for full command output.
Exception information:
Traceback (most recent call last):
File "/home/jim/anaconda3/lib/python3.8/site-packages/pip/_internal/cli/base_command.py", line 228, in _main
status = self.run(options, args)
File "/home/jim/anaconda3/lib/python3.8/site-packages/pip/_internal/cli/req_command.py", line 182, in wrapper
return func(self, options, args)
File "/home/jim/anaconda3/lib/python3.8/site-packages/pip/_internal/commands/install.py", line 397, in run
installed = install_given_reqs(
File "/home/jim/anaconda3/lib/python3.8/site-packages/pip/_internal/req/init.py", line 82, in install_given_reqs
requirement.install(
File "/home/jim/anaconda3/lib/python3.8/site-packages/pip/_internal/req/req_install.py", line 790, in install
install_editable_legacy(
File "/home/jim/anaconda3/lib/python3.8/site-packages/pip/_internal/operations/install/editable_legacy.py", line 49, in install_editable
call_subprocess(
File "/home/jim/anaconda3/lib/python3.8/site-packages/pip/_internal/utils/subprocess.py", line 242, in call_subprocess
raise InstallationError(exc_msg)
pip._internal.exceptions.InstallationError: Command errored out with exit status 1: /home/jim/anaconda3/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/home/jim/open-mmlab/mmdetection3d/setup.py'"'"'; file='"'"'/home/jim/open-mmlab/mmdetection3d/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' develop --no-deps Check the logs for full command output.
Removed build tracker: '/tmp/pip-req-tracker-xr10pfui'

It seems like there will be a compilation error for scatter_points_cuda with cuda11 or pt1.7. I am not sure whether it is from the recent related PRs (#318 , #326 ). We will check and fix it ASAP. For now, you can try to compile v0.10.0 or use cuda10/pt1.3-1.6.

It may be inconvenient for us to debug it now because both cuda11 and pt1.7 are not ready on our machines. It would be appreciated if you can provide more information about your attempts. For example, is it ok for v0.10.0?
Looking forward to more voice from the community.

The latest feedback indicates that it seems like a compatibility problem introduced by #318 and #326 . We will try to fix it soon. For now, you can use v0.10.0 or lower version cuda and pytorch as a workaround.
Should you find the problem, PRs are definitely welcome.

First fix is removing "max_values" as it is not supporterd in pytorch 1.8
It is changed to "amax"
pytorch/pytorch#44069
That leaves you with this error:
scatter_points_cuda.cu(274): error: no instance of overloaded function "at::Tensor::index_put_" matches the argument list
argument types are: (at::Tensor, at::Tensor)
object type is: at::Tensor
Couldn't solve this so rolled back to previous version of the ops/voxel folder and that worked
https://github.com/open-mmlab/mmdetection3d/tree/d2816ed9240773f25fda0fc7a526b4e8bd5a4912/mmdet3d/ops/voxel

It seems like there will be a compilation error for scatter_points_cuda with cuda11 or pt1.7. I am not sure whether it is from the recent related PRs (#318 , #326 ). We will check and fix it ASAP. For now, you can try to compile v0.10.0 or use cuda10/pt1.3-1.6.

Thanks a lot

Hi,
I had he same issue with Cuda 11.0 and Torch 1.7.0 and I tried to install MMdetection v0.10.0 and fail on the same issue.
Can you give more detilles how to install it right and aviod this bug?
I also didnt understand what I should do with Torch bug on max value.

I have reproduce the compilation error with torch 1.7.0. It seems that there are some incompatibility between the cuda code and torch 1.7.

It seems installed sucessfully with the changes setup.py
if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':
define_macros += [('WITH_CUDA', None)]
extension = CUDAExtension
extra_compile_args['nvcc'] = extra_args + [
'-D__CUDA_NO_HALF_OPERATORS__',
'-D__CUDA_NO_HALF_CONVERSIONS__',
'-D__CUDA_NO_HALF2_OPERATORS__',
] + ['-gencode=arch=compute_80,code=sm_80']
add the ['-gencode=arch=compute_80,code=sm_80'], my enviroment cuda 11.0 pytorch 1.7.1

I met same problem, error is as below:
mmdet3d/ops/voxel/src/scatter_points_cuda.cu(272): error: no instance of overloaded function "at::Tensor::index_put_" matches the argument list
            argument types are: (at::Tensor, at::Tensor)
            object type is: at::Tensor

1 error detected in the compilation of "mmdet3d/ops/voxel/src/scatter_points_cuda.cu".

I solved it as below:
diff --git a/mmdet3d/ops/voxel/src/scatter_points_cuda.cu b/mmdet3d/ops/voxel/src/scatter_points_cuda.cu
index 8d888b0..a70aa49 100644
--- a/mmdet3d/ops/voxel/src/scatter_points_cuda.cu
+++ b/mmdet3d/ops/voxel/src/scatter_points_cuda.cu
@@ -269,7 +269,7 @@ std::vector<at::Tensor> dynamic_point_to_voxel_forward_gpu(
   AT_CUDA_CHECK(cudaGetLastError());

   coors_map_sorted = coors_map_sorted.cumsum(0, torch::kInt32);
-  coors_map.index_put_(coors_id_argsort, coors_map_sorted);
+  coors_map.index_put_({coors_id_argsort}, coors_map_sorted);

   const int num_coors_cpu =
       coors_map_sorted[-1].cpu().data_ptr<int32_t>()[0] + 1;


Cannot SSH into lab Apple Silicon DTK

Cannot SSH into lab flutter-devicelab-mac-15, times out.

Machine was rebooted and is back online. Ssh is working again.

I'm in, thank you!


Testing Dashboard Connectivity to New DB

Need to improve database from SQLite to Postrgres or something in AWS (no Mongo since I hate writing Mongo queries)
Database Connectivity
Goto settings.py of main directory in each dashboard application and update below settings to correspond to our Postgres DB
    DATABASES = {
        'default': {
            'ENGINE': 'django.db.backends.#databaseservername#',
            'NAME': 'Your Database Name',
            'USER' : 'Database User Name',
            'PASSWORD' : 'Your Password',
            'HOST' : 'Write down Host',
            'PORT' : 'Write down port',
        }
    }

root./projectRocket/settings.py

Settings connection updated for the client dash (NOT THE MAIN DASH). Currently hosted on railway since I like their UI. Likely to be moved to AWS since they have a free tier postgre option

Want to have this hosted at client.rocketpsa.com before closing this out


Custom timeout per request

At the moment we can configure a global timeout, but not a custom timeout per request, which would be quite handy.
Is there a reason for this?
 const { Client } = require('undici')
 const client = new Client(`http://localhost:3000`)

 client.request({
   path: '/',
   method: 'GET',
+  timeout: 10000
 },  function (err, data) {
   //  handle response
 })

Related: #160

I'm not quite sure how that would work. The current timeout is on the socket.
So you could never have a per request timeout that is larger than the socket timeout. Allowing to change the socket timeout per requests makes things complicated, especially with pipelining.
I guess we could add it per request as long as it's less than the socket timeout. Also does it apply on the request body (i.e read) or just socket activity (i.e. write/read)?

I think we are talking about different things. The timeout on the socket is an activity timeout, e.g. the timeout between two chunk of data. I think @delvedor is asking about a request timeout.

I guess we could add it per request as long as it's less than the socket timeout.

It can be bigger than the socket timeout as a single request is composed of multiple chunks. Essentially it's a matter of creating a setTimeout with that value, and calling the callback if it triggers earlier.
I agree this will add overhead when used. However this is a common feature which would have similar overhead both here and externally, so if it does not cost us overhead when it is disabled, I would be ok in adding it.

But it would incur a performance penalty when used.

Ah, so it's a timeout for the entire request regardless of activity. @delvedor is this what you mean? Also please not that this is different from e.g. timeout on core http.request which is based on socket activity.

I think @delvedor is asking about a request timeout.

Correct :)
I would expect it to work in the same way as core's http.request.

Correct :)

I would expect it to work in the same way as core's http.request.

That's contradictory, the timeout argument for http.request is not the same thing as a request timeout.

Essentially I would like to achieve something like this:
'use strict'

const http = require('http')
const server = http.createServer(handler)

server.listen(0, () => {
  const request = http.request(
    `http://localhost:${server.address().port}`,
    { timeout: 500 }
  )

  request.on('response', response => {
    console.log(response.statusCode)
  })

  request.on('timeout', () => {
    request.abort()
    console.log('timeout')
  })

  request.on('abort', () => {
    console.log('abort')
  })

  request.on('error', err => {
    console.log(err)
  })

  request.end()
})

function handler (req, res) {
  setTimeout(() => {
    res.end('ok')
  }, 1000)
}

The log is:
timeout
abort
Error: socket hang up
    at connResetException (internal/errors.js:604:14)
    at Socket.socketCloseListener (_http_client.js:400:25)
    at Socket.emit (events.js:323:22)
    at TCP.<anonymous> (net.js:668:12) {
  code: 'ECONNRESET'
}

The error is caused by request.abort.

That's not enough to determine how we implement this. Notice that undici is fundamentally different due to pipelining so that example cannot be directly applied. So how would you expect that timeout to work?
e.g. you might have requests that are being served ahead in the pipeline. Should the waiting time for other responses ahead in the pipeline count towards timeout or not?
client.request({ ...opts }, (err) => {
 // This is a large requests downloading lots of data.
})

client.request({ ...opts, timeout: 100 }, (err) => {
  // Should this timeout if there is no activity on this specific request since it's waiting for the previous large request. Or of if there is no activity on the socket.
})

That's a good question. As an external user, I don't really care about why the timeout has been reached, I would expect that if set a timeout of 1000 milliseconds, once the timeout has passed, I get an error back, even if the request has never been sent.
In such case, I know that I would need to increase the number of connections.

In such case, I know that I would need to increase the number of connections.

So your expectation would be that if the request has not completed within the timeout time counted from when the request was created, it should timeout.
So essentially it's the same as:
const timeout = setTimeout(() => {
  throw new Error('timeout')
}
const { body } = await client.request(...opts)
finished(body, () => {
  clearTimeout(timeout)
})

Not that this is different than what http.request does.

So your expectation would be that if the request has not completed within the timeout time counted from when the request was created, it should timeout.

I think it is what a user without the knowledge of the internals would expect.
I don't recall how http.request is handling this, I'll take a look at node internals :)

I think it is what a user without the knowledge of the internals would expect.

I think that fits @mcollina's suggestion and should be rather simple to implement.

I don't recall how http.request is handling this, I'll take a look at node internals :)

It just invokes socket.setTimeout

It just invokes socket.setTimeout once it has received a socket.

Oooh, now I've understood what you mean. We can't use the same approach here because if we are using pipelining > 1, then there might be more than one request per socket, making the timeout unreliable.
What do you think about optimizing this case, and using socket.setTimeout when pipelining = 1 and use a setTimeout when pipelining > 1?

socket.setTimeout just use setTimeout internally, so there is no point on optimizing it.

Oks! So if we would like to compare Node.js core http.request and undici, we can summarize it like this:

Node.js core
Undici

Agent keep alive timeout
undici global timeout

http.request timeout
undici internal request setTimeout

Did I git this right?

Did I git this right?

I don't really think they can be compared in a meaningful way. 😕

Suggestion on what we can do:

Before we have received response headers, timeout is relative to when request was enqueued
After we have received response headers, timeout is relative to most recent response chunk.

I would simply do

Before we have received response headers, timeout is relative to when request was enqueued.

So we can offer a strong guarantee to our users.

This has been added


Allow constructing BxcanInstance outside HAL

I'm using bxcan traits to implement my own can driver and #842 made them impossible to use outside the HAL. This PR just adds pub methods to construct BxcanInstance.

What're you using this for exactly? you're writing your own CAN driver and need to access the value of REGISTERS, NUM_FILTER_BANKS?

So the main reason is that I want to construct my own embassy_stm32::can::Can driver, which also takes IRQs, extra buffers, etc. Inside it I use the bxcan crate to access peripheral like embassy does with Deref, but for it to work I need the bxcan::Instantce and bxcan::FilterOwner to be implemented by embassy-stm32. I can't implement those traits myself, becauase macro table macros are private. Moreover, bxcan::FilterOwner implementation has some custom logic for each family, so I rather have it implemented by embassy-stm32 instead of duplicating all conditions.

If it's something widely-applicable it should Ideally be in-tree though.
If it's not widely applicable (specific to a project) then lack of macrotables shouldn't be an issue because the project will be using one particular chip anyway.

It's an async CAN driver, however, I don't think I can upstream it into embassy-stm32, because:

CAN uses four interrupts (RX0, RX1, TX, SCE) so PeripheralMutex does not work and I had to hack around that with more unsafe than usual. Ideally PeripheralMutex could support multiple IRQs, but that requires some Rust generic and macro gymnastics to implement.
I also needed RX and TX loopback timestamping, which pulls in dependency on Instant::now()
Async driver requires additional queue to store frames between RX IRQ and when user consumes it

TLDR: I think the driver gets too beefy to suit everyone's needs. Unless it is implemented as separate driver (like BufferedUart).
What if BxcanInstance is moved to low_level module, like other peripherals under unstable-pac feature?


Expose DefaultContentLanguage in Site variables

Currently, there's no way to get the default language of the site (other than assuming it has the smallest weight in the list of AllTranslations, with some caveats). This variable would be useful for creating conditions, for example (in my case) to check if a localized documentation is up-to-date with the original page.

Though not very intuitive, .Sites.First.Language will give you what you're looking for (.Sites.First.Language.Lang returns the language code)

Is there any documentation for .Sites.First or .Sites? I can't seem to find it in Hugo Docs.

https://gohugo.io/variables/page/#page-variables


BUGFIX: Highlight Reconciled Rows

If you highlighted a reconciled row, the styling of that row would go away, this makes sure it doesn't.

Thank you!


Release v3.12.1

Description
This PR is to release version 3.12.1 to fix compatibility with kedro==0.17.0
Development notes
This will not be merge into main since main already contains the necessary commit. The commit was cherry-picked into this branch because main already contains kedro-telemetry, which we don't want to release just yet. The release will be made from this branch. This PR is for review purpose only.
QA notes
Please ignore the CI failure. It's due to a mypy upgrade that's already fixed in main.
Checklist

[x] Read the contributing guidelines
[ ] Opened this PR as a 'Draft Pull Request' if it is work-in-progress
[ ] Updated the documentation to reflect the code changes
[x] Added new entries to the RELEASE.md file
[ ] Added tests to cover my changes

Legal notice

[x] I acknowledge and agree that, by checking this box and clicking "Submit Pull Request":

I submit this contribution under the Apache 2.0 license and represent that I am entitled to do so on behalf of myself, my employer, or relevant third parties, as applicable.

I certify that (a) this contribution is my original creation and / or (b) to the extent it is not my original creation, I am authorised to submit this contribution on behalf of the original creator(s) or their licensees.

I certify that the use of this contribution as authorised by the Apache 2.0 license does not violate the intellectual property rights of anyone else.

This is now released.


Update viabackrewards

Hey,
I saw that today viabackrewards was updated on jenkins with a new build, and working with viaversion?  Or should I wait for another build?

This isn't related to ViaVersion. Nor is VB updated (read change log).


CI Actions Setup, Directory structure change to Poetry

Changed directory format to Poetry
Added Github Actions configs and requirements to pyproject.toml
Edited template Readme for team name and some badges

rip


[examples/remix-with-typescript] React 18 version example is required.

Duplicates

[X] I have searched the existing issues

Latest version

[X] I have tested the latest version

Current behavior 😯
A function called renderToPipeableStream() is used in entry.server.ts of remix 1.7.0 using react 18.2.0. However, this part is not reflected in the remix example of mui. Can you please reflect it?
// npx create-remix@latest (at 2022-09-02)
// app/entry.server.tsx

import { PassThrough } from "stream";
import type { EntryContext } from "@remix-run/node";
import { Response } from "@remix-run/node";
import { RemixServer } from "@remix-run/react";
import { renderToPipeableStream } from "react-dom/server";

const ABORT_DELAY = 5000;

export default function handleRequest(
  request: Request,
  responseStatusCode: number,
  responseHeaders: Headers,
  remixContext: EntryContext
) {
  return new Promise((resolve, reject) => {
    let didError = false;

    const { pipe, abort } = renderToPipeableStream(
      <RemixServer context={remixContext} url={request.url} />,
      {
        onShellReady: () => {
          const body = new PassThrough();

          responseHeaders.set("Content-Type", "text/html");

          resolve(
            new Response(body, {
              headers: responseHeaders,
              status: didError ? 500 : responseStatusCode,
            })
          );

          pipe(body);
        },
        onShellError: (err) => {
          reject(err);
        },
        onError: (error) => {
          didError = true;

          console.error(error);
        },
      }
    );

    setTimeout(abort, ABORT_DELAY);
  });
}

// https://github.com/mui/material-ui/blob/master/examples/remix-with-typescript/app/entry.server.tsx

import * as React from 'react';
import { renderToString } from 'react-dom/server';
import { RemixServer } from 'remix';
import type { EntryContext } from 'remix';
import createEmotionCache from './src/createEmotionCache';
import theme from './src/theme';
import CssBaseline from '@mui/material/CssBaseline';
import { ThemeProvider } from '@mui/material/styles';
import { CacheProvider } from '@emotion/react';
import createEmotionServer from '@emotion/server/create-instance';

export default function handleRequest(
  request: Request,
  responseStatusCode: number,
  responseHeaders: Headers,
  remixContext: EntryContext,
) {
  const cache = createEmotionCache();
  const { extractCriticalToChunks } = createEmotionServer(cache);

  const MuiRemixServer = () => (
    <CacheProvider value={cache}>
      <ThemeProvider theme={theme}>
        {/* CssBaseline kickstart an elegant, consistent, and simple baseline to build upon. */}
        <CssBaseline />
        <RemixServer context={remixContext} url={request.url} />
      </ThemeProvider>
    </CacheProvider>
  );

  // Render the component to a string.
  const html = renderToString(<MuiRemixServer />);

  // Grab the CSS from emotion
  const { styles } = extractCriticalToChunks(html);

  let stylesHTML = '';

  styles.forEach(({ key, ids, css }) => {
    const emotionKey = `${key} ${ids.join(' ')}`;
    const newStyleTag = `<style data-emotion="${emotionKey}">${css}</style>`;
    stylesHTML = `${stylesHTML}${newStyleTag}`;
  });

  // Add the Emotion style tags after the insertion point meta tag
  const markup = html.replace(
    /<meta(\s)*name="emotion-insertion-point"(\s)*content="emotion-insertion-point"(\s)*\/>/,
    `<meta name="emotion-insertion-point" content="emotion-insertion-point"/>${stylesHTML}`,
  );

  responseHeaders.set('Content-Type', 'text/html');

  return new Response(`<!DOCTYPE html>${markup}`, {
    status: responseStatusCode,
    headers: responseHeaders,
  });
}

Expected behavior 🤔
I hope that the contents mentioned in 'Current behavior' are reflected.
Steps to reproduce 🕹
There are no special reproduction steps.
Context 🔦
As of now (2022-09-02) I want to use mui in the remix of the latest version (1.7.0).
Your environment 🌎

npx @mui/envinfo
  System:
    OS: Linux 5.15 Ubuntu 20.04.4 LTS (Focal Fossa)
  Binaries:
    Node: 14.20.0 - ~/.nvm/versions/node/v14.20.0/bin/node
    Yarn: Not Found
    npm: 6.14.17 - ~/.nvm/versions/node/v14.20.0/bin/npm
  Browsers:
    Chrome: Not Found
    Firefox: Not Found
  npmPackages:
    @emotion/react: 11.10.4 => 11.10.4
    @emotion/styled: 11.10.4 => 11.10.4
    @mui/base:  5.0.0-alpha.95
    @mui/core-downloads-tracker:  5.10.3
    @mui/icons-material: 5.10.3 => 5.10.3
    @mui/material: 5.10.3 => 5.10.3
    @mui/private-theming:  5.10.3
    @mui/styled-engine:  5.10.3
    @mui/system: 5.10.3 => 5.10.3
    @mui/types:  7.2.0
    @mui/utils:  5.10.3
    @types/react: 18.0.15 => 18.0.15
    react: 18.2.0 => 18.2.0
    react-dom: 18.2.0 => 18.2.0
    typescript: 4.7.4 => 4.7.4

@hiuny Thanks for the feedback. Do you want to submit a PR to update the example?

@siriwatknp
I'd love to, but I don't know how the renderToPipeableStream() function works, so it's going to be hard right now. I think it would be quick if someone who knows the 18 version of react helped.

Hi! @siriwatknp, can I pick this up?

I would like to take the work on it!

Agree, the example should be updated according to the latest template from Remix. Would be great if someone from the community, especially if that someone uses Remix can handle it.

Please update the example. I have an issue with remix.js

There is an ongoing PR for udpating the Remix example - https://github.com/mui/material-ui/pull/34407

this is stuck or any one knows how is it going ?

Install remix with JS instead of TSX would work.

I found this example works well https://stackblitz.com/edit/github-ut9vkm

Is this still stuck?

Give this a shot, worked for me: https://github.com/remix-run/examples/blob/main/chakra-ui/app/entry.server.tsx

Give this a shot, worked for me: https://github.com/remix-run/examples/blob/main/chakra-ui/app/entry.server.tsx

For me this creates a NodeJS.ReadWriteStream that is not compatible with the body parameter of the Response constructor. Does anyone have a suggestion how to convert a NodeJS.ReadWriteStream to a ReadableStream?

@tmo207

Give this a shot, worked for me: https://github.com/remix-run/examples/blob/main/chakra-ui/app/entry.server.tsx

Does MUI also use Emotion?


[BUG]: Remove LD_LIBRARY hack in DFP visualization

Version
23.07
Which installation method(s) does this occur on?
Docker
Describe the bug.
This was a last-minute fix in 828 for 23.03
Likely this is due to the fact that nodejs was installed via apt, and instead we should look to installing nodejs via conda, which is what we do for the dev env, but this example is based upon the release container which doesn't contain node.
Minimum reproducible example
No response
Relevant log output
No response
Full env printout
No response
Other/Misc.
No response
Code of Conduct

[X] I agree to follow Morpheus' Code of Conduct
[X] I have searched the open bugs and have found no duplicates for this bug report

cc: @efajardo-nv

@dagardner-nv thanks. i'll add the nodejs conda install to PR #873.

@dagardner-nv using nodejs conda install still doesn't fix this error:
error - Error: libnvrtc.so.11.2: cannot open shared object file: No such file or directory
    at Object.Module._extensions..node (node:internal/modules/cjs/loader:1275:18)
    at Module.load (node:internal/modules/cjs/loader:1069:32)
    at Function.Module._load (node:internal/modules/cjs/loader:904:12)
    at Module.require (node:internal/modules/cjs/loader:1093:19)
    at require (node:internal/modules/cjs/helpers:108:18)
    at bindings (/workspace/external/morpheus-visualizations/DFP/node_modules/bindings/bindings.js:112:48)
    at Object.<anonymous> (/workspace/external/morpheus-visualizations/DFP/node_modules/@rapidsai/cuda/build/js/addon.js:20:25)
    at Module._compile (node:internal/modules/cjs/loader:1191:14)
    at Object.Module._extensions..js (node:internal/modules/cjs/loader:1245:10)
    at Module.load (node:internal/modules/cjs/loader:1069:32) {
  code: 'ERR_DLOPEN_FAILED'

@efajardo-nv Yeah... I tried that as well https://github.com/dagardner-nv/Morpheus/tree/david-dfp-viz
We'll probably need to reach out to the nodejs rapids folks, ask them if they've encountered this before mixing nodejs/yarn packages inside of a conda env.

@dagardner-nv confirmed this is an issue with node-rapids. fix is included in latest version which will be published soon.


Feature/images

Searching images across the web is now added.

Hm.. Command Line says its already up to date when i resolved the conflicts and tried to merge. Closing for now.


Install libcudnn8 and libcusolver-11 in Docker

See Issues #11 and #26. There are no lines to install libcudnn8 and libcusolver11.
I specified CUDA=11.1 because Tensorflow 2.5.0 requires libcusolver-11-1. libcusolver-11-0 and libcusolver-11-0-dev packages will install libcusolver.so.10, not .so.11.

@googlebot I signed it!

For me this results in the following error:

I believe cudatoolkit==${CUDA}.3 should be cudatoolkit==${CUDA}.1 which will yield 11.1.1 instead of 11.1.3.

I believe cudatoolkit==${CUDA}.3 should be cudatoolkit==${CUDA}.1 which will yield 11.1.1 instead of 11.1.3.
Sorry, I made a mistake. My suggestion in Issue #11 is correct.

This matrix indicates tensorflow-2.5.0 needs CUDA 11.2, not 11.1.
In addition, I believe this installs cudnn 8.2, whereas the matrix indicates tensorflow-2.5.0 requires cudnn8.1.

Another option might be to use the following base image which has cuda 11.2, cudnn 8.1, and cusolver-11-2 already bundled:
nvidia/cuda:11.2.2-cudnn8-runtime-ubuntu20.04

Another option might be to use the following base image which has cuda 11.2, cudnn 8.1, and cusolver-11-2 already bundled:
nvidia/cuda:11.2.2-cudnn8-runtime-ubuntu20.04

This is what we do as well. It's best to keep the Dockerfile simple and use the Nvidia primitives like this!

Thanks for the PR. In acf25fc87964cab56b79cc5f5a4929c3805ecbeb we've now moved to the cudnn base image. Please see if that fixes this issue.

Thanks for the PR. In acf25fc we've now moved to the cudnn base image. Please see if that fixes this issue.

@tfgg This does not actually fix the issue. You can easily verify it, by building the image and then running the following command:
docker run --gpus=all --rm --entrypoint=python3  alphafold -c 'import tensorflow as tf; tf.constant("Hello")'

This is my output:
2021-07-30 13:26:56.418725: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-30 13:26:57.204547: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-30 13:26:57.241695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2021-07-30 13:26:57.241755: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 13:26:57.243000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 1 with properties:
pciBusID: 0000:21:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2021-07-30 13:26:57.243054: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 13:26:57.255770: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 2 with properties:
pciBusID: 0000:4c:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2021-07-30 13:26:57.255792: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-30 13:26:57.257848: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2021-07-30 13:26:57.257878: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2021-07-30 13:26:57.265618: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10
2021-07-30 13:26:57.265796: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10
2021-07-30 13:26:57.265907: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
2021-07-30 13:26:57.266343: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11
2021-07-30 13:26:57.266452: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2021-07-30 13:26:57.266463: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-07-30 13:26:57.266760: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-07-30 13:26:57.268994: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-30 13:26:57.269009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]

The relevant section is this:
2021-07-30 13:26:57.265907: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64

I don't know if this is a required library, because it runs without it as well. But it sure does look suspicous.

Thanks for the comment. That's an non-fatal warning - we don't actually need GPU support in Tensorflow since we're just using it for feature pre-processing. In b3ed8603e8b5f085342c50259da0ba9fe485ef94 I've flipped requirements.txt to use tensorflow-cpu to make this clear.

we don't actually need GPU support in Tensorflow since we're just using it for feature pre-processing. In b3ed860 I've flipped requirements.txt to use tensorflow-cpu to make this clear.

If we don't actually need GPU support, then why is the docker image based on an nvidia image and why do we have to install and run it in the nvidia docker runtime?

Tensorflow is only used for feature pre-processing, JAX is used to run the model itself and uses the GPU.


Multiline match + join lines

How can we get the description text out of this?
+-----------------------------+
|            Slime            |
+-----------------------------+
| Slimes are amazing. I don't |
| know what you mean when you |
| say that they're furry,     |
| though.                     |
+-----------------------------+

I think something like this might do well:
match: # regex to get text inbetween lines
group: # ^^
lines: # lines to use match/group on
  begin: 4 # slimes are amazing..
  end: 7 # ..though
join: ' ' # join each line by spaces

PS Here's the match/group:
match: '\|\s(.+?(?= \|))'
group: 1


Footer template duplicating  ui-grid 3.0

When using a custom footer and using the ui-selection directive for ui-grid 3.0  the footer is being duplicated inside of the selection column.

I believe this is no longer occurring - could you confirm?  My plunker: http://plnkr.co/edit/yYLCBBmBB30ljOaxsdst?p=preview

Believed to be fixed in recent changes, no updates from information request, assuming is resolved and closing.


Weird build failure

The exception was:
user error ('/opt/ghc/bin/ghc-pkg-8.0.2' exited with an error:
distributive-0.5.2: Warning: Unrecognized field indefinite on line 16
distributive-0.5.2: Warning: haddock-interfaces:
/home/travis/.cabal/store/ghc-8.0.2/distributive-0.5.2-31ccea362b2d345e62f260a7f6d05bcc42f7dc320e187a15128f6a668dd10a08/share/doc/html/distributive.haddock
doesn't exist or isn't a file
distributive-0.5.2: Warning: haddock-html:
/home/travis/.cabal/store/ghc-8.0.2/distributive-0.5.2-31ccea362b2d345e62f260a7f6d05bcc42f7dc320e187a15128f6a668dd10a08/share/doc/html
doesn't exist or isn't a directory
distributive-0.5.2: installed package info from too old version of Cabal (key
field does not match id field)

with cabal-head and ghc-8.0.2...

that's probably were related to Backpack. I blame myself or not writing more stuff.


folder_block_ops: don't fail fast-forwarding if there are 0 nodes

That just means there's nothing to be done.
Issue: KBFS-1865

lgtm, but can you please write a unit test that'll catch this? (in a separate PR)

lgtm, but can you please write a unit test that'll catch this? (in a separate PR)

The fast forward stuff is very hard to unit test in libkbfs, because so many moving pieces are required.
To test this in the DSL tests, I'd need a way to access the TLF via GetTLFCryptKeys, without going through a mountpoint, which doesn't seem like a good fit for DSL tests and is probably more trouble than it's worth.
So with sadness and regret I'm going to punt on a test for now.


canutils: changed print_usage function to static

Summary
Global print_usage function in canutils/candump/ was causing double definition error while compiling NuttX with some extern apps, like pysimCoder for example. Therefore the following change to static function. To be on safe side, the same change was also done in cansend.
Testing
Tested on Teensy 4.1 with pysimCoder.

Oh, wasn´t expecting that much nx style errors. Gonna have to get some sleep before fixing this, I´ll push nx style changes tomorrow.

Oh, wasn´t expecting that much nx style errors. Gonna have to get some sleep before fixing this, I´ll push nx style changes tomorrow.

I think for canutils we have in the past just left it as is to closely match the upstream and make it easier to backport.

@btashton Well I definitely wouldn´t mind if we just merge it as it is.

So, let's ignore the style issue and merge the correct fix.


Change Architecture source file location toa allow inclusion of architecture doc in any project through a profile

Because I would like to have both architecture and code in the same folder

No, in fact it's a bad idea: putting the architecture in the standard folder allow considering it as a part of the application, making the architecture tghtly linked to the application.


Can not created additional child accounts and the Seed account appears twice.

I make a passphrase, go to my wallet, try to create an additional account multiple times in a row. I end up creating an extra seed address. Also after the 2nd address was created on the 3rd try, I can't create anymore.
Here is a screen shot.
I'm using a Mac build of Version: 0.13.8 and I've actually had this problem before in the past too.

Not sure if it is related to the passphrase or not or any other issues with account creation
Seed: pyramid opinion silly month order envelope artwork saddle black since fiber guitar
notable belt certain device toe retire quick vacuum behave hire palace trap

May be related with: https://github.com/nemgrouplimited/symbol-desktop-wallet/issues/1246

looks like the same error more or less as #1246

@jabo38 nope these issues are different, but I'm thinking how could you reproduce it, as that was a result of the submit button not having a loader for the async operation which is not the case now.

@jabo38 @bassemmagdy @cryptoBeliever I can not reproduce it.

I also could not reproduce this issue. FYI: There was pretty the same issue which was fixed in February: 949

Because nobody is able to reproduce, I'm closing the issue.


Replacing nn.Linear w/ te.Linear FP8 convergence issue

Hi,
I'm seeing higher losses using te.Linear over nn.Linear directly in transformer models such as Llama which I assume is expected due to the nature of FP8.
However, I don't see a loss increase of the same magnitude replacing the Transformer Layer with te.TransformerLayer. I took a look, and it seems like among the various improvements te.TransformerLayer implements, one looks like a reuse of a singular FP8 scaling factor across multiple gemm invocations within the fused layernorm_mlp. Was this implemented to reduce such cascading precision losses, and are there other notable changes to mitigate this?
I'm seeing in sufficient size models (e.g. Llama 70b), this loss difference is causing a divergence in validation loss leading to convergence issues, whereas without FP8, the validation loss continues to decrease.
I'm wondering if you all have any insight into this or pointers to any references discussing this problem and solutions more in-depth, that would be greatly appreciated.
Thanks!

Hi @viclzhu. There is no recipe difference between using te.Linear alone vs using te.TransformerLayer and so having different behavior in loss curves is not expected. This suggests to me that your problems come from incorrect usage somehow. Could you provide some more information on the software you are using and the specific of the training (like, are you seeing the higher loss issue from the very beginning or after some number of iterations)? One of the typical integration issues we saw was not passing the correct distributed group as fp8_group  when running under fp8_autocast context manager.
In general, we do not expect seeing higher losses when using FP8 compared to BF16, loss curves should overlap.

Hmmm, those settings look ok. Are you able to provide some reproduction script for this issue so that we can investigate?

Yes, here's a script reproducing the issue comparing the output of nn.Linear BF16 to te.Linear FP8 for a single gpu.
Please let me know if you see anything wrong w/ my script or need any more information!
Environment:

PT 2.2.0
TE v1.2.1
H100 GPU
Python 3.10.8

Run command: torchrun --standalone --nnodes=1 --nproc-per-node=1 test_te_linear.py.
# Filename: test_te_linear.py
import os
import torch
import torch.nn as nn
import torch.distributed as dist
import transformer_engine.pytorch as te
from torch.distributed.distributed_c10d import _get_default_group
from transformer_engine.common.recipe import Format, DelayedScaling

def setup():
    seed = 7
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    local_rank = int(os.environ["LOCAL_RANK"])
    dist.init_process_group(backend="nccl")
    torch.cuda.set_device(local_rank)

def check_tol(t1, t2, atol=5e-2, rtol=1e-8):
    # atol from https://github.com/NVIDIA/TransformerEngine/blob/v1.2.1/tests/pytorch/test_numerics.py#L888
    result = torch.allclose(t1, t2, atol=atol, rtol=rtol)
    if not result:
        diff = torch.abs(t1 - t2).flatten()
        m = torch.argmax(diff)
        msg = (
            f"Outputs not close enough in tensor. "
            f"Location of the maximum difference: {m.item()} "
            f"with {t1.flatten()[m].item()} vs {t2.flatten()[m].item()} "
            f"(diff {diff[m].item()})."
        )
        print(msg)

def main():
    setup()

    all_gpus = dist.new_group(backend="nccl")
    all_gpus_ranks = dist.get_process_group_ranks(all_gpus)
    default_pg_ranks = dist.get_process_group_ranks(
        dist.distributed_c10d._get_default_group()
    )
    print(f"all_ranks: {dist.get_process_group_ranks(all_gpus)}")
    print(
        f"default_pg: {dist.get_process_group_ranks(dist.distributed_c10d._get_default_group())}"
    )
    assert all_gpus_ranks == default_pg_ranks

    # Set base dtype used
    dtype = torch.bfloat16  # [torch.float32, torch.bfloat16]
    device = "cuda"

    # Setup FP8 recipe
    amax_history_len = 32
    fp8_format = Format.HYBRID  # E4M3 during forward pass, E5M2 during backward pass
    fp8_recipe = DelayedScaling(
        fp8_format=fp8_format,
        amax_history_len=amax_history_len,
        amax_compute_algo="max",
    )

    # FP8 context manager
    ctx_mgr = te.fp8_autocast
    ctx_mgr_kwargs = {
        "enabled": True,
        "fp8_recipe": fp8_recipe,
        "fp8_group": all_gpus,
    }

    hidden_size = 16  # needs to be divisible by 16

    # Instantiate nn Linear layer
    nn_layer = nn.Linear(hidden_size, hidden_size, dtype=dtype, device=device)
    # Instantiate TE Linear layer
    te_layer = te.Linear(hidden_size, hidden_size, params_dtype=dtype, device=device)

    # Copy nn Layer weights into TE Layer
    with torch.no_grad():
        te_layer.weight.copy_(nn_layer.weight)

    assert torch.equal(te_layer.weight, nn_layer.weight)

    print(f"nn_layer.weight: {nn_layer.weight}")
    print(f"te_layer.weight: {te_layer.weight}")

    bs = 8
    x = torch.rand((bs, hidden_size), dtype=dtype).to(device)

    output_nn = nn_layer(x)
    loss_nn = output_nn.sum()
    print(f"nn loss 0: {loss_nn}")

    output_nn = nn_layer(x)
    loss_nn = output_nn.sum()
    print(f"nn loss 1: {loss_nn}")

    # Run forward for FP8 TE layer multiple times
    # Note: looks like 1st forward is different
    # likely due to initial scaling factor
    for i in range(amax_history_len + 2):
        with ctx_mgr(**ctx_mgr_kwargs):
            output_te = te_layer(x)
        loss_te = output_te.sum()
        if i == amax_history_len - 1:
            print(f"***TE loss {i}: {loss_te}")
        else:
            print(f"TE loss {i}: {loss_te}")

    check_tol(output_te, output_nn)

if __name__ == "__main__":
    main()

Example log:
all_ranks: [0]
default_pg: [0]
nn_layer.weight: Parameter containing:
tensor([[ 0.2275, -0.0025,  0.2207,  0.0464,  0.0996, -0.1455,  0.0874, -0.0095,
          0.0669,  0.0366,  0.2061,  0.1104,  0.1270, -0.0354, -0.0116,  0.0493],
        [ 0.1963, -0.0898, -0.1631, -0.0222, -0.0757,  0.2256,  0.0938, -0.1514,
         -0.1416, -0.2109, -0.1768,  0.2246,  0.0359, -0.0145,  0.0204,  0.0977],
        [ 0.0728,  0.0771, -0.1289,  0.0273, -0.1562,  0.1660, -0.1270,  0.1377,
          0.1030, -0.2412, -0.1738, -0.1836, -0.1602, -0.0232,  0.0452, -0.1436],
        [-0.1260,  0.2490,  0.0767, -0.2344,  0.0889,  0.0884, -0.2256,  0.1367,
         -0.0713,  0.1074, -0.2041,  0.2344, -0.0938, -0.0086,  0.0845,  0.2275],
        [-0.2383, -0.0510, -0.2227,  0.0579, -0.0317, -0.1206, -0.1455, -0.1758,
         -0.1123, -0.1865,  0.2256, -0.1494, -0.0112, -0.2217,  0.0003,  0.1924],
        [-0.1621, -0.2021,  0.0869,  0.1982,  0.0679,  0.2246, -0.2100,  0.1826,
         -0.2070,  0.0173, -0.1885, -0.0688, -0.1445, -0.2139,  0.1582, -0.0162],
        [-0.1011, -0.2383,  0.2002,  0.0300,  0.2432, -0.0161,  0.2051, -0.2314,
          0.1621,  0.0544, -0.0603, -0.2344, -0.2461,  0.2451, -0.2324,  0.1582],
        [ 0.1348, -0.0496, -0.1123, -0.0199, -0.0349, -0.0859,  0.2061, -0.1592,
          0.2461,  0.0986,  0.1045, -0.0693, -0.2207,  0.1982, -0.1377, -0.2422],
        [ 0.0153, -0.2295, -0.0315, -0.2402,  0.0630,  0.1729,  0.1787,  0.1270,
         -0.1641, -0.2344,  0.0579, -0.1514, -0.2441, -0.2236,  0.0309, -0.0505],
        [-0.1484,  0.1797, -0.2080, -0.0664, -0.1318, -0.1406,  0.2109,  0.0947,
          0.2021, -0.2080, -0.2266,  0.0214,  0.2334, -0.1426,  0.2031,  0.0903],
        [-0.1719,  0.1836, -0.0933, -0.0220, -0.2178,  0.0942,  0.1138, -0.1235,
         -0.2256,  0.2090, -0.1670, -0.0981,  0.1226, -0.2285,  0.0476, -0.0388],
        [ 0.2480, -0.0618,  0.1289, -0.2393,  0.1187,  0.2217,  0.0776,  0.2061,
          0.1494, -0.0996,  0.1924,  0.1523,  0.0474, -0.0141, -0.0334, -0.1553],
        [ 0.0284, -0.0481, -0.1758,  0.2197,  0.1475, -0.0654, -0.0557,  0.1357,
          0.0273, -0.1328, -0.0231, -0.2217, -0.0598,  0.0962,  0.0986, -0.1211],
        [ 0.2168,  0.1885,  0.2227,  0.0415,  0.1299, -0.0229,  0.1094, -0.0928,
          0.0796,  0.0815, -0.0615, -0.2012,  0.0986,  0.0854, -0.2188, -0.1187],
        [ 0.1050, -0.2109,  0.0532, -0.2188, -0.2256,  0.1572,  0.1533,  0.1348,
          0.1318, -0.1602, -0.0591,  0.2275,  0.1670, -0.0635,  0.1641, -0.1011],
        [ 0.2217, -0.0188,  0.2090, -0.0354, -0.0032, -0.0933,  0.0664,  0.1553,
          0.0425, -0.0913,  0.1167,  0.2217, -0.2031, -0.1436,  0.1729, -0.0330]],
       device='cuda:0', dtype=torch.bfloat16, requires_grad=True)
te_layer.weight: Parameter containing:
tensor([[ 0.2275, -0.0025,  0.2207,  0.0464,  0.0996, -0.1455,  0.0874, -0.0095,
          0.0669,  0.0366,  0.2061,  0.1104,  0.1270, -0.0354, -0.0116,  0.0493],
        [ 0.1963, -0.0898, -0.1631, -0.0222, -0.0757,  0.2256,  0.0938, -0.1514,
         -0.1416, -0.2109, -0.1768,  0.2246,  0.0359, -0.0145,  0.0204,  0.0977],
        [ 0.0728,  0.0771, -0.1289,  0.0273, -0.1562,  0.1660, -0.1270,  0.1377,
          0.1030, -0.2412, -0.1738, -0.1836, -0.1602, -0.0232,  0.0452, -0.1436],
        [-0.1260,  0.2490,  0.0767, -0.2344,  0.0889,  0.0884, -0.2256,  0.1367,
         -0.0713,  0.1074, -0.2041,  0.2344, -0.0938, -0.0086,  0.0845,  0.2275],
        [-0.2383, -0.0510, -0.2227,  0.0579, -0.0317, -0.1206, -0.1455, -0.1758,
         -0.1123, -0.1865,  0.2256, -0.1494, -0.0112, -0.2217,  0.0003,  0.1924],
        [-0.1621, -0.2021,  0.0869,  0.1982,  0.0679,  0.2246, -0.2100,  0.1826,
         -0.2070,  0.0173, -0.1885, -0.0688, -0.1445, -0.2139,  0.1582, -0.0162],
        [-0.1011, -0.2383,  0.2002,  0.0300,  0.2432, -0.0161,  0.2051, -0.2314,
          0.1621,  0.0544, -0.0603, -0.2344, -0.2461,  0.2451, -0.2324,  0.1582],
        [ 0.1348, -0.0496, -0.1123, -0.0199, -0.0349, -0.0859,  0.2061, -0.1592,
          0.2461,  0.0986,  0.1045, -0.0693, -0.2207,  0.1982, -0.1377, -0.2422],
        [ 0.0153, -0.2295, -0.0315, -0.2402,  0.0630,  0.1729,  0.1787,  0.1270,
         -0.1641, -0.2344,  0.0579, -0.1514, -0.2441, -0.2236,  0.0309, -0.0505],
        [-0.1484,  0.1797, -0.2080, -0.0664, -0.1318, -0.1406,  0.2109,  0.0947,
          0.2021, -0.2080, -0.2266,  0.0214,  0.2334, -0.1426,  0.2031,  0.0903],
        [-0.1719,  0.1836, -0.0933, -0.0220, -0.2178,  0.0942,  0.1138, -0.1235,
         -0.2256,  0.2090, -0.1670, -0.0981,  0.1226, -0.2285,  0.0476, -0.0388],
        [ 0.2480, -0.0618,  0.1289, -0.2393,  0.1187,  0.2217,  0.0776,  0.2061,
          0.1494, -0.0996,  0.1924,  0.1523,  0.0474, -0.0141, -0.0334, -0.1553],
        [ 0.0284, -0.0481, -0.1758,  0.2197,  0.1475, -0.0654, -0.0557,  0.1357,
          0.0273, -0.1328, -0.0231, -0.2217, -0.0598,  0.0962,  0.0986, -0.1211],
        [ 0.2168,  0.1885,  0.2227,  0.0415,  0.1299, -0.0229,  0.1094, -0.0928,
          0.0796,  0.0815, -0.0615, -0.2012,  0.0986,  0.0854, -0.2188, -0.1187],
        [ 0.1050, -0.2109,  0.0532, -0.2188, -0.2256,  0.1572,  0.1533,  0.1348,
          0.1318, -0.1602, -0.0591,  0.2275,  0.1670, -0.0635,  0.1641, -0.1011],
        [ 0.2217, -0.0188,  0.2090, -0.0354, -0.0032, -0.0933,  0.0664,  0.1553,
          0.0425, -0.0913,  0.1167,  0.2217, -0.2031, -0.1436,  0.1729, -0.0330]],
       device='cuda:0', dtype=torch.bfloat16, requires_grad=True)
nn loss 0: 10.375
nn loss 1: 10.375
TE loss 0: -1.484375
TE loss 1: -1.578125
TE loss 2: -1.578125
TE loss 3: -1.578125
TE loss 4: -1.578125
TE loss 5: -1.578125
TE loss 6: -1.578125
TE loss 7: -1.578125
TE loss 8: -1.578125
TE loss 9: -1.578125
TE loss 10: -1.578125
TE loss 11: -1.578125
TE loss 12: -1.578125
TE loss 13: -1.578125
TE loss 14: -1.578125
TE loss 15: -1.578125
TE loss 16: -1.578125
TE loss 17: -1.578125
TE loss 18: -1.578125
TE loss 19: -1.578125
TE loss 20: -1.578125
TE loss 21: -1.578125
TE loss 22: -1.578125
TE loss 23: -1.578125
TE loss 24: -1.578125
TE loss 25: -1.578125
TE loss 26: -1.578125
TE loss 27: -1.578125
TE loss 28: -1.578125
TE loss 29: -1.578125
TE loss 30: -1.578125
***TE loss 31: -1.578125
TE loss 32: -1.578125
TE loss 33: -1.578125
Outputs not close enough in tensor. Location of the maximum difference: 43 with 0.52734375 vs 0.7890625 (diff 0.26171875).

@ptrendx Did you get a chance to review the reproducible script?

Oh awesome, thanks for the catch and sanity check!
I'll look closer in my implementation then, something else must be going wrong.

I actually just re-ran the script with your bias fix in my environment, but for some reason I still see a greater difference in loss than the set 5e-2 tolerance. Could there be an issue w/ my environment?
Additionally, I see the losses are still different in your output just smaller 10.625-10.375=0.25.
 Location of the maximum difference: 8 with -1.203125 vs -1.1171875 (diff 0.0859375).

Note: not sure why the loss is different here than previous run. I had changed containers though in this test, but looking into why this is different.

@ptrendx Any updates on this issue? We are observing similar loss differences across different model scales for llama.


drawio ignors pgw in grouping mode

see example from ziv

@zivnevo
@kyorav
@adisos
hi all
in example iks_workers_large, we have the following grouped connectivity:
vsi1,vsi2 -> "Public Internet"
both vsi1 and vsi2 have a floating IP.
so, what sould I do with the FIP? ignore both of them? ungroup  the connection?

I am not sure there is a "right answer" here, but my feeling is that routing information is less important than readability, so I vote for ignoring the FIPs.

@kyorav , but still draw the fip icon. right?


update logging levels in printer.py to match the intent

See https://github.com/fishtown-analytics/dbt/issues/1818

This CLA bot is new! I'm going to need to get an internal review before proceeding...

I've gotten approval to sign the agreement, and have signed it.  Is there anything else I need to do to kick off the cls-signed check again?

Thanks @heisencoder! You should be all set now -- I'll kick off the tests :)
@cla-bot check


Small mistake in box predictor weight initalization

🐛 Bug
It's probably just a nitpick since it works well enough as it is, but in the roy_box_predictors weight initalization there is a mistake probably caused by some copy-pasting https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py#L19-L23 .
As you can see, self.cls_score.weight and self.bbox_pred.weight get initialized twice, which makes no sense.
It should be something like this unofficial implementation of Detectron https://github.com/roytseng-tw/Detectron.pytorch/blob/master/lib/modeling/fast_rcnn_heads.py#L24-L27 in which the bias is the one initialized to 0 (as per the paper).

Oh, right, that's definitely a bug!
Can you send a PR fixing this?
Thanks!

I'm actually sending a PR to fix this, don't worry.


don't allow closing of modal?

is it possible to not allow the modal to be closed (hide the close button, don't allow clicking on bg, etc).
PS: Nice lib!

Hi,
Indeed, that could be useful. I will add it in the next release
Thanks for your feedback!


HelicsBool handling and comments modification

first attempt at adding % in front of all function comments to make use of help feature in MATLAB.
also modified HelicsBool arguments to accept
logcials or int/float values of 0 and 1.

@afisher1  is this change included in the 3.3 stuff you did or is this separate?

It looks like the changes here were not included in PR #10

reading the MATLAB documentation the help text needs to be just below the function definition in the .m files so I'll need to change that.

I made the requested changes to the function help comments. removed the multi comment brackets and removed all occurences of param[in,out] err lines from the generated functions.


Feature request: Ogg (or equivalent) compression for AudioSummary with TensorBoard

When logging an AudioSummary every couple of epochs, the logs grow quickly (as in several gigabytes), and checking TensorBoard remotely (as in not on the same LAN) also becomes very slow. Would it be possible to have builtin support for Ogg compression or an equivalent lossy format? There are multiple use cases where this would be beneficial.

Or in TensorBoard at least, maybe just download an audio file upon hitting play (or just the most recent AudioSummary per default), instead of loading everything?

This is certainly an issue -- for those hitting it here are some workarounds until we get some better codecs in TensorFlow:

Use tf.summary.audio's max_outputs argument to limit the number of WAVs you're writing to your events file per summary run.
If using tf.Supervisor, increase your save_summaries_secs to write summaries less frequently.

IMHO a lossless codec like FLAC would greatly improve the situation without degrading the quality of the synthesized audio.

This is certainly a reasonable suggestion. I've migrated it to our new repository at https://github.com/tensorflow/tensorboard/issues/64. Please feel free to submit PRs there, too!


Incompatibility with newly release pymatgen==5.31.2024

FYI that there is an incompatability with pymatgen==5.31.2024:
from doped.vasp import DefectDictSet

    from pymatgen.io.vasp.sets import DictSet, UserPotcarFunctional
E   ImportError: cannot import name 'UserPotcarFunctional' from 'pymatgen.io.vasp.sets' (/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/pymatgen/io/vasp/sets.py)

This also, indirectly, effects shakenbreak via from shakenbreak.input import Distortions.

Thanks for flagging Andrew! Will update now. The new update has also broken the CI as there's a misplaced typing_extensions again (https://github.com/materialsproject/pymatgen/pull/3752)

Sorry that I also keep posting on shakenbreak when I mean to post on doped!

Haha no worries, the end justifies the means!