Closed Zakk-Yang closed 8 months ago
Thanks for the detail in your report above. Most of your environment setup seems right. Did you install any CUDA packages into the WSL environment, outside of conda? Your WSL instance should not have a CUDA driver installed, or else it will cause problems. Only the Windows host system should provide the driver. Maybe post the output of apt list
?
Hi, I have the same problem (trying to use cudf on WSL2), however my numba -s
output is little bit different:
System info:
/home/nguyen/anaconda3/envs/rapids-23.10/lib/python3.10/site-packages/numba/np/ufunc/parallel.py:371: NumbaWarning: The TBB threading layer requires TBB version 2021 update 6 or later i.e., TBB_INTERFACE_VERSION >= 12060. Found TBB_INTERFACE_VERSION = 12050. The TBB threading layer is disabled.
warnings.warn(problem)
--------------------------------------------------------------------------------
__Time Stamp__
Report started (local time) : 2023-11-15 00:51:07.117163
UTC start time : 2023-11-14 17:51:07.117166
Running time (s) : 4.804779
__Hardware Information__
Machine : x86_64
CPU Name : skylake
CPU Count : 16
Number of accessible CPUs : 16
List of accessible CPUs cores : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CFS Restrictions (CPUs worth of runtime) : None
CPU Features : 64bit adx aes avx avx2 bmi bmi2
clflushopt cmov crc32 cx16 cx8
f16c fma fsgsbase fxsr invpcid
lzcnt mmx movbe pclmul popcnt
prfchw rdrnd rdseed sahf sse sse2
sse3 sse4.1 sse4.2 ssse3 xsave
xsavec xsaveopt xsaves
Memory Total (MB) : 32066
Memory Available (MB) : 26497
__OS Information__
Platform Name : Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.35
Platform Release : 5.10.16.3-microsoft-standard-WSL2
OS Name : Linux
OS Version : #1 SMP Fri Apr 2 22:23:49 UTC 2021
OS Specific Version : ?
Libc Version : glibc 2.35
__Python Information__
Python Compiler : GCC 12.3.0
Python Implementation : CPython
Python Version : 3.10.13
Python Locale : en_US.UTF-8
__Numba Toolchain Versions__
Numba Version : 0.57.1
llvmlite Version : 0.40.1
__LLVM Information__
LLVM Version : 14.0.6
__CUDA Information__
CUDA Device Initialized : True
CUDA Driver Version : 12.2
CUDA Runtime Version : 11.8
CUDA NVIDIA Bindings Available : True
CUDA NVIDIA Bindings In Use : False
CUDA Minor Version Compatibility Available : True
CUDA Minor Version Compatibility Needed : False
CUDA Minor Version Compatibility In Use : False
CUDA Detect Output:
Found 1 CUDA devices
id 0 b'NVIDIA GeForce RTX 2080 Ti' [SUPPORTED]
Compute Capability: 7.5
PCI Device ID: 0
PCI Bus ID: 1
UUID: GPU-01bc9f8e-d066-a5b1-bb43-007a711170ac
Watchdog: Enabled
FP32/FP64 Performance Ratio: 32
Summary:
1/1 devices are supported
CUDA Libraries Test Output:
Finding driver from candidates: /usr/lib/wsl/lib/libcuda.so.1...
Using loader <class 'ctypes.CDLL'>
trying to load driver... ok, loaded from /usr/lib/wsl/lib/libcuda.so.1
Finding nvvm from Conda environment
named libnvvm.so.4.0.0
trying to open library... ok
Finding cudart from Conda environment
named libcudart.so.11.8.89
trying to open library... ok
Finding cudadevrt from Conda environment
named libcudadevrt.a
Finding libdevice from Conda environment
trying to open library... ok
__NumPy Information__
NumPy Version : 1.24.4
NumPy Supported SIMD features : ('MMX', 'SSE', 'SSE2', 'SSE3', 'SSSE3', 'SSE41', 'POPCNT', 'SSE42', 'AVX', 'F16C', 'FMA3', 'AVX2')
NumPy Supported SIMD dispatch : ('SSSE3', 'SSE41', 'POPCNT', 'SSE42', 'AVX', 'F16C', 'FMA3', 'AVX2', 'AVX512F', 'AVX512CD', 'AVX512_SKX', 'AVX512_CLX', 'AVX512_CNL', 'AVX512_ICL')
NumPy Supported SIMD baseline : ('SSE', 'SSE2', 'SSE3')
NumPy AVX512_SKX support detected : False
__SVML Information__
SVML State, config.USING_SVML : False
SVML Library Loaded : False
llvmlite Using SVML Patched LLVM : False
SVML Operational : False
__Threading Layer Information__
TBB Threading Layer Available : False
+--> Disabled due to Unknown import problem.
OpenMP Threading Layer Available : True
+-->Vendor: GNU
Workqueue Threading Layer Available : True
+-->Workqueue imported successfully.
__Numba Environment Variable Information__
NUMBA_CUDA_DRIVER : /usr/lib/wsl/lib/libcuda.so.1
__Conda Information__
Conda Build : 3.27.0
Conda Env : 23.10.0
Conda Platform : linux-64
Conda Python Version : 3.9.18.final.0
Conda Root Writable : True
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multidict 6.0.4 py310h2372a71_1 conda-forge
multipledispatch 0.6.0 py_0 conda-forge
munch 4.0.0 pyhd8ed1ab_0 conda-forge
munkres 1.1.4 pyh9f0ad1d_0 conda-forge
mysql-common 8.0.33 hf1915f5_6 conda-forge
mysql-libs 8.0.33 hca2cd23_6 conda-forge
nbclient 0.8.0 pyhd8ed1ab_0 conda-forge
nbconvert 7.11.0 pyhd8ed1ab_0 conda-forge
nbconvert-core 7.11.0 pyhd8ed1ab_0 conda-forge
nbconvert-pandoc 7.11.0 pyhd8ed1ab_0 conda-forge
nbformat 5.9.2 pyhd8ed1ab_0 conda-forge
nccl 2.19.3.1 h6103f9b_0 conda-forge
ncurses 6.4 h59595ed_2 conda-forge
nest-asyncio 1.5.6 py310h06a4308_0
networkx 3.2.1 pyhd8ed1ab_0 conda-forge
nodejs 20.8.1 h1990674_0 conda-forge
notebook 7.0.6 pyhd8ed1ab_0 conda-forge
notebook-shim 0.2.3 py310h06a4308_0
nspr 4.35 h27087fc_0 conda-forge
nss 3.94 h1d7d5a4_0 conda-forge
numba 0.57.1 py310h0f6aa51_0 conda-forge
numpy 1.24.4 py310ha4c1d20_0 conda-forge
nvcomp 2.6.1 h838ba91_3 conda-forge
nvtx 0.2.8 py310h2372a71_1 conda-forge
openjpeg 2.5.0 h488ebb8_3 conda-forge
openslide 3.4.1 hcb9e6c7_11 conda-forge
openssl 3.1.4 hd590300_0 conda-forge
orc 1.9.0 h52d3b3c_2 conda-forge
overrides 7.4.0 pyhd8ed1ab_0 conda-forge
packaging 23.2 pyhd8ed1ab_0 conda-forge
pandas 1.5.3 py310h9b08913_1 conda-forge
pandoc 3.1.3 h32600fe_0 conda-forge
pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge
panel 1.3.1 pyhd8ed1ab_0 conda-forge
param 2.0.1 pyhca7485f_0 conda-forge
parso 0.8.3 pyhd3eb1b0_0
partd 1.4.1 pyhd8ed1ab_0 conda-forge
pcre2 10.40 hc3806b6_0 conda-forge
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pillow 10.1.0 py310h01dd4db_0 conda-forge
pip 23.3.1 pyhd8ed1ab_0 conda-forge
pixman 0.42.2 h59595ed_0 conda-forge
pkgutil-resolve-name 1.3.10 pyhd8ed1ab_1 conda-forge
platformdirs 4.0.0 pyhd8ed1ab_0 conda-forge
ply 3.11 py310h06a4308_0
poppler 23.10.0 h590f24d_0 conda-forge
poppler-data 0.4.12 hd8ed1ab_0 conda-forge
postgresql 16.1 h8972f4a_0 conda-forge
proj 9.3.0 h1d62c97_2 conda-forge
prometheus_client 0.18.0 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.36 py310h06a4308_0
prompt_toolkit 3.0.36 hd3eb1b0_0
protobuf 4.23.4 py310h620c231_3 conda-forge
psutil 5.9.5 py310h2372a71_1 conda-forge
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
ptxcompiler 0.8.1 py310h70a93da_2 conda-forge
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
pulseaudio-client 16.1 hb77b528_5 conda-forge
pure_eval 0.2.2 pyhd3eb1b0_0
py-xgboost 1.7.6 rapidsai_py310h1395376_2310 rapidsai
pyarrow 12.0.1 py310hf9e7431_14_cpu conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pyct 0.4.6 py_0 conda-forge
pyct-core 0.4.6 py_0 conda-forge
pyee 8.1.0 pyhd8ed1ab_0 conda-forge
pygments 2.16.1 pyhd8ed1ab_0 conda-forge
pylibcugraph 23.10.00 cuda11_py310_231011_gc3dc860d_0 rapidsai
pylibraft 23.10.00 cuda11_py310_231011_gafdddfb3_0 rapidsai
pynvml 11.4.1 pyhd8ed1ab_0 conda-forge
pyparsing 3.1.1 pyhd8ed1ab_0 conda-forge
pyppeteer 1.0.2 pyhd8ed1ab_0 conda-forge
pyproj 3.6.1 py310h32c33b7_4 conda-forge
pyqt 5.15.10 py310h6a678d5_0
pyqt5-sip 12.13.0 py310h5eee18b_0
pysocks 1.7.1 pyha2e5f31_6 conda-forge
python 3.10.13 hd12c33a_0_cpython conda-forge
python-confluent-kafka 1.9.2 py310h5764c6d_2 conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-fastjsonschema 2.18.1 pyhd8ed1ab_0 conda-forge
python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge
python_abi 3.10 4_cp310 conda-forge
pytz 2023.3.post1 pyhd8ed1ab_0 conda-forge
pyviz_comms 3.0.0 pyhd8ed1ab_0 conda-forge
pywavelets 1.4.1 py310h1f7b6fc_1 conda-forge
pyyaml 6.0.1 py310h2372a71_1 conda-forge
pyzmq 25.1.1 py310h795f18f_2 conda-forge
qt-main 5.15.8 h82b777d_17 conda-forge
qtconsole 5.4.2 py310h06a4308_0
qtpy 2.2.0 py310h06a4308_0
raft-dask 23.10.00 cuda11_py310_231011_gafdddfb3_0 rapidsai
rapids 23.10.00 cuda11_py310_231011_gd3958fe_0 rapidsai
rapids-xgboost 23.10.00 cuda11_py310_231011_gd3958fe_0 rapidsai
rav1e 0.6.6 he8a937b_2 conda-forge
rdma-core 28.9 h59595ed_1 conda-forge
re2 2023.03.02 h8c504da_0 conda-forge
readline 8.2 h8228510_1 conda-forge
referencing 0.30.2 pyhd8ed1ab_0 conda-forge
requests 2.31.0 pyhd8ed1ab_0 conda-forge
rfc3339-validator 0.1.4 pyhd8ed1ab_0 conda-forge
rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge
rich 13.6.0 pyhd8ed1ab_0 conda-forge
rmm 23.10.00 cuda11_py310_231011_gf8ac6f8e_0 rapidsai
rpds-py 0.12.0 py310hcb5633a_0 conda-forge
rtree 1.1.0 py310hbdcdc62_0 conda-forge
s2n 1.3.54 h06160fa_0 conda-forge
scikit-image 0.21.0 py310hc6cd4ac_0 conda-forge
scikit-learn 1.3.2 py310h1fdf081_1 conda-forge
scipy 1.11.3 py310hb13e2d6_1 conda-forge
send2trash 1.8.2 pyh41d4057_0 conda-forge
setuptools 68.2.2 pyhd8ed1ab_0 conda-forge
shapely 2.0.2 py310h7dcad9a_0 conda-forge
simpervisor 1.0.0 pyhd8ed1ab_0 conda-forge
sip 6.7.12 py310h6a678d5_0
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 h9fff704_0 conda-forge
sniffio 1.3.0 pyhd8ed1ab_0 conda-forge
sortedcontainers 2.4.0 pyhd8ed1ab_0 conda-forge
soupsieve 2.5 pyhd8ed1ab_1 conda-forge
spdlog 1.11.0 h9b3ece8_1 conda-forge
sqlite 3.44.0 h2c6b66d_0 conda-forge
stack_data 0.2.0 pyhd3eb1b0_0
streamz 0.6.4 pyh6c4a22f_0 conda-forge
svt-av1 1.7.0 h59595ed_0 conda-forge
tblib 2.0.0 pyhd8ed1ab_0 conda-forge
terminado 0.18.0 pyh0d859eb_0 conda-forge
threadpoolctl 3.2.0 pyha21a80b_0 conda-forge
tifffile 2023.9.26 pyhd8ed1ab_0 conda-forge
tiledb 2.16.3 h8c794c1_3 conda-forge
tinycss2 1.2.1 pyhd8ed1ab_0 conda-forge
tk 8.6.13 noxft_h4845f30_101 conda-forge
tomli 2.0.1 py310h06a4308_0
toolz 0.12.0 pyhd8ed1ab_0 conda-forge
tornado 6.3.3 py310h2372a71_1 conda-forge
tqdm 4.66.1 pyhd8ed1ab_0 conda-forge
traitlets 5.13.0 pyhd8ed1ab_0 conda-forge
treelite 3.9.1 py310h4a6579d_0 conda-forge
treelite-runtime 3.9.1 pypi_0 pypi
types-python-dateutil 2.8.19.14 pyhd8ed1ab_0 conda-forge
typing-extensions 4.8.0 hd8ed1ab_0 conda-forge
typing_extensions 4.8.0 pyha770c72_0 conda-forge
typing_utils 0.1.0 pyhd8ed1ab_0 conda-forge
tzcode 2023c h0b41bf4_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
uc-micro-py 1.0.1 pyhd8ed1ab_0 conda-forge
ucx 1.14.1 h64cca9d_5 conda-forge
ucx-proc 1.0.0 gpu rapidsai
ucx-py 0.34.00 py310_231011_g17dceab_0 rapidsai
unicodedata2 15.1.0 py310h2372a71_0 conda-forge
uri-template 1.3.0 pyhd8ed1ab_0 conda-forge
uriparser 0.9.7 hcb278e6_1 conda-forge
urllib3 1.26.18 pyhd8ed1ab_0 conda-forge
wcwidth 0.2.5 pyhd3eb1b0_0
webcolors 1.13 pyhd8ed1ab_0 conda-forge
webencodings 0.5.1 pyhd8ed1ab_2 conda-forge
websocket-client 1.6.4 pyhd8ed1ab_0 conda-forge
websockets 10.4 py310h5764c6d_1 conda-forge
wheel 0.41.3 pyhd8ed1ab_0 conda-forge
widgetsnbextension 4.0.5 py310h06a4308_0
xarray 2023.10.1 pyhd8ed1ab_0 conda-forge
xcb-util 0.4.0 hd590300_1 conda-forge
xcb-util-image 0.4.0 h8ee46fc_1 conda-forge
xcb-util-keysyms 0.4.0 h8ee46fc_1 conda-forge
xcb-util-renderutil 0.3.9 hd590300_1 conda-forge
xcb-util-wm 0.4.1 h8ee46fc_1 conda-forge
xerces-c 3.2.4 hac6953d_3 conda-forge
xgboost 1.7.6 rapidsai_py310h1395376_2310 rapidsai
xkeyboard-config 2.40 hd590300_0 conda-forge
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.1.1 hd590300_0 conda-forge
xorg-libsm 1.2.4 h7391055_0 conda-forge
xorg-libx11 1.8.7 h8ee46fc_0 conda-forge
xorg-libxau 1.0.11 hd590300_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h0b41bf4_2 conda-forge
xorg-libxrender 0.9.11 hd590300_0 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge
xorg-xf86vidmodeproto 2.3.1 h7f98852_1002 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xyzservices 2023.10.1 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
yarl 1.9.2 py310h2372a71_1 conda-forge
zeromq 4.3.5 h59595ed_0 conda-forge
zfp 1.0.0 h59595ed_4 conda-forge
zict 3.0.0 pyhd8ed1ab_0 conda-forge
zipp 3.17.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hd590300_5 conda-forge
zlib-ng 2.0.7 h0b41bf4_0 conda-forge
zstd 1.5.5 hfc55251_0 conda-forge
No errors reported.
__Warning log__
Warning (no file): /sys/fs/cgroup/cpuacct/cpu.cfs_quota_us
Warning (no file): /sys/fs/cgroup/cpuacct/cpu.cfs_period_us
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I followed this installation guide from NVIDIA: https://docs.nvidia.com/cuda/wsl-user-guide/index.html. Other applications using CUDA such as stable diffusion are working well.
Please comment on how I can provide more information if needed to troubleshoot this.
@NTNguyen13 Can you also post the result of apt list
? Also take a look at this issue from Numba: https://github.com/numba/numba/issues/6777
my apt list
is quite long, could you suggest some grep
keyword to filter it?
I tried the command NUMBA_CUDA_LOG_LEVEL=DEBUG python -c "import cudf; cudf.Series([1,2,3])"
the output is:
== CUDA (ptxcompiler) [653] DEBUG -- CUDA Driver version 12.2
== CUDA (ptxcompiler) [653] DEBUG -- CUDA Runtime version 11.8
== CUDA [803] DEBUG -- call runtime api: cudaRuntimeGetVersion
== CUDA [2490] INFO -- init
== CUDA [2491] DEBUG -- call driver api: cuInit
== CUDA [2491] DEBUG -- call driver api: cuCtxGetCurrent
== CUDA [2491] DEBUG -- call driver api: cuDeviceGetCount
== CUDA [2492] DEBUG -- call driver api: cuDeviceGet
== CUDA [2492] DEBUG -- call driver api: cuDeviceGetAttribute
== CUDA [2492] DEBUG -- call driver api: cuDeviceGetAttribute
== CUDA [2492] DEBUG -- call driver api: cuDeviceGetName
== CUDA [2492] DEBUG -- call driver api: cuDeviceGetUuid_v2
== CUDA [2492] DEBUG -- call driver api: cuDevicePrimaryCtxRetain
== CUDA [2963] DEBUG -- call driver api: cuCtxPushCurrent_v2
== CUDA [2963] DEBUG -- call driver api: cuMemGetInfo_v2
Edit: After adding the export to bashrc AND restart my VScode runtime, it works fine now
Hi, Problem solved by following: under the wsl instance nano ~/.bashrc
export LD_LIBRARY_PATH="/usr/lib/wsl/lib/"
export NUMBA_CUDA_DRIVER="/usr/lib/wsl/lib/libcuda.so.1"
export LD_LIBRARY_PATH=/usr/local/cuda-12.3/targets/x86_64-linux/lib:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda-12.3/bin:$PATH
@Zakk-Yang thanks for sharing your fix!
Closing as resolved.
I know this is closed, but I must say that installing CUDA and cuDNN is a nightmare on WSL, and the likelihood of this issue is that CUDF can not find your CUDA. If anyone is facing this issue, best of luck in resolving it.
Setup:
Installed RAPIDS in a WSL2 environment. Error when importing cudf:
Full error code:
nvidia-smi has the following info in wsl:
Installation command used:
Command numba -s in WSL environment with the following info:
Objective: Seeking help in resolving the CUDA initialization error when trying to import cudf in the WSL2 environment.