# Can't find libdevice directory ${CUDA\_DIR}/nvvm/libdevice **URL:** \ **Category:** General Discussion\ **Tags:** build\ **Created:** [September 12, 2022, 3:45pm UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467 "2022-09-12T15:45:34Z")\ **Posts on this page:** 11\ **Page:** 1
**Author:** ![Charlie\_Talbot](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/charlie_talbot/32/10249_2.png) [@Charlie\_Talbot](https://discuss.ai.google.dev/u/Charlie_Talbot)\ **Post date:** [September 12, 2022, 3:45pm UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/1 "2022-09-12T15:45:34Z")
Hi, I have a related question to the topic in > > > \\Click to expand!\ > > ### Issue Type > > Bug > > \### Source… > > binary > > \### Tensorflow Version > > tf-nightly > > \### Custom Code > > Yes > > \### OS Platform and Distribution > > WSL Ubuntu > > \### Mobile device > > \_No response\_ > > \### Python version > > 3.9 > > \### Bazel version > > \_No response\_ > > \### GCC/Compiler version > > \_No response\_ > > \### CUDA/cuDNN version > > 11.2 > > \### GPU model and memory > > \_No response\_ > > \### Current Behaviour? > > \`\`\`shell > Can't find libdevice directory ${CUDA\_DIR}/nvvm/libdevice > > The error appears when setting groups \> 1 in tf.keras.layers.Conv2D with tf-nightly. > > The error does not appear in TensorFlow 2.8. > > Seems the previous TensorFlow build is packed with libdevice. > \`\`\` > > > \### Standalone code to reproduce the issue > > \`\`\`shell > tf.keras.layers.Conv2D( > 512, > 1, > use\_bias=False > groups=5, > ) > \`\`\` > > > \### Relevant log output > > \`\`\`shell > 2022-07-28 01:48:13.943787: W tensorflow/compiler/xla/service/gpu/nvptx\_helper.cc:56\] Can't find libdevice directory ${CUDA\_DIR}/nvvm/libdevice. This may result in compilation or runtime failures, if the program we try to run uses routines from libdevice. > Searched for CUDA in the following directories: > ./cuda\_sdk\_lib > /usr/local/cuda-11.2 > /usr/local/cuda > . > You can choose the search directory by setting xla\_gpu\_cuda\_data\_dir in HloModule's DebugOptions. For most apps, setting the environment variable XLA\_FLAGS=--xla\_gpu\_cuda\_data\_dir=/path/to/cuda will work. > 2022-07-28 01:48:13.986332: I tensorflow/core/platform/default/subprocess.cc:304\] Start cannot spawn child process: No such file or directory > 2022-07-28 01:48:13.986408: W tensorflow/stream\_executor/gpu/asm\_compiler.cc:80\] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version > 2022-07-28 01:48:14.018602: I tensorflow/core/platform/default/subprocess.cc:304\] Start cannot spawn child process: No such file or directory > 2022-07-28 01:48:14.018778: F tensorflow/compiler/xla/service/gpu/nvptx\_compiler.cc:453\] ptxas returned an error during compilation of ptx to sass: 'INTERNAL: Failed to launch ptxas' If the error message indicates that a file could not be written, please verify that sufficient filesystem space is provided. > Fatal Python error: Aborted > > Thread 0x00007f3abe7fc640 (most recent call first): > File "/home/edwardyehuang/miniconda3/envs/tf-nightly/lib/python3.9/threading.py", line 316 in wait > File "/home/edwardyehuang/miniconda3/envs/tf-nightly/lib/python3.9/threading.py", line 574 in wait > File "/home/edwardyehuang/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/\_vendored/pydevd/pydevd.py", line 257 in \_on\_run > File "/home/edwardyehuang/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/\_vendored/pydevd/\_pydevd\_bundle/pydevd\_daemon\_thread.py", line 49 in run > File "/home/edwardyehuang/miniconda3/envs/tf-nightly/lib/python3.9/threading.py", line 973 in \_bootstrap\_inner > File "/home/edwardyehuang/miniconda3/envs/tf-nightly/lib/python3.9/threading.py", line 930 in \_bootstrap > > Thread 0x00007f3abeffd640 (most recent call first): > File "/home/edwardyehuang/miniconda3/envs/tf-nightly/lib/python3.9/threading.py", line 316 in wait > File "/home/edwardyehuang/miniconda3/envs/tf-nightly/lib/python3.9/threading.py", line 574 in wait > File "/home/edwardyehuang/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/\_vendored/pydevd/pydevd.py", line 211 in \_on\_run > File "/home/edwardyehuang/.vscode-server/extensions/ms-python.python-2022.10.1/pythonFiles/lib/python/debugpy/\_vendored/pydevd/\_pydevd\_bundle/pydevd\_daemon\_thread.py", line 49 in run > File "/home/edwardyehuang/miniconda3/envs/tf-nightly/lib/python3.9/threading.py", line 973 in \_bootstrap\_inner > File "/home/edwardyehuang/miniconda3/envs/tf-nightly/lib/python3.9/threading.py", line 930 in \_bootstrap > \`\`\` > \ but am using Ubuntu 22.04 After a fresh installation (I was using 18.04 until a cataclysmic event), I seem to be having a similar issue I am trying to use deepxde with tensorflow backend. (a lot of what follows is unnecessary background) I am trying to compile one of their examples and my attention is drawn to the portion where “nvvm” is mentioned. chaztikov@priority:~/git/deepxde/examples/pinn\_forward$ python3 Burgers\_RAR.py Using backend: tensorflow Enable just-in-time compilation with XLA. 2022-09-10 23:10:14.449892: I tensorflow/core/platform/cpu\_feature\_guard.cc:193] 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. 2022-09-10 23:10:15.157727: W tensorflow/core/common\_runtime/gpu/gpu\_bfc\_allocator.cc:42] Overriding orig\_value setting because the TF\_FORCE\_GPU\_ALLOW\_GROWTH environment variable is set. Original config value was 0. 2022-09-10 23:10:15.157784: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10113 MB memory: → device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1 Compiling model… ‘compile’ took 0.000428 s Training model… WARNING:tensorflow:AutoGraph could not transform \ and will run it as-is. Cause: could not parse the source code of \: no matching AST found among candidates: # coding=utf-8 lambda x, on: np.array([on\_boundary(x[i], on[i]) for i in range(len(x))]) To silence this warning, decorate the function with @tf.autograph.experimental.do\_not\_convert WARNING:tensorflow:AutoGraph could not transform \ and will run it as-is. Cause: could not parse the source code of \: no matching AST found among candidates: # coding=utf-8 lambda x, on: np.array([on\_boundary(x[i], on[i]) for i in range(len(x))]) To silence this warning, decorate the function with @tf.autograph.experimental.do\_not\_convert 2022-09-10 23:10:16.768843: I tensorflow/compiler/xla/service/service.cc:170] XLA service 0x55a2ebd8d3a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2022-09-10 23:10:16.768872: I tensorflow/compiler/xla/service/service.cc:178] StreamExecutor device (0): NVIDIA GeForce GTX 1080 Ti, Compute Capability 6.1 2022-09-10 23:10:16.797603: I tensorflow/compiler/mlir/tensorflow/utils/dump\_mlir\_util.cc:263] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. 2022-09-10 23:10:17.433963: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory 2022-09-10 23:10:17.434943: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory 2022-09-10 23:10:17.434971: W tensorflow/stream\_executor/gpu/asm\_compiler.cc:80] Couldn’t get ptxas version string: INTERNAL: Couldn’t invoke ptxas --version 2022-09-10 23:10:17.435713: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory 2022-09-10 23:10:17.435784: W tensorflow/stream\_executor/gpu/redzone\_allocator.cc:314] INTERNAL: Failed to launch ptxas Relying on driver to perform ptx compilation. Modify PATHtocustomizeptxaslocation.Thismessagewillbeonlyloggedonce.2022−09−1023:10:17.440252:Itensorflow/core/platform/default/subprocess.cc:304]Startcannotspawnchildprocess:Nosuchfileordirectory2022−09−1023:10:17.440280:Wtensorflow/streamexecutor/gpu/asmcompiler.cc:80]Couldn′tgetptxasversionstring:INTERNAL:Couldn′tinvokeptxas−−version2022−09−1023:10:17.441066:Itensorflow/core/platform/default/subprocess.cc:304]Startcannotspawnchildprocess:Nosuchfileordirectory2022−09−1023:10:17.441134:Wtensorflow/compiler/xla/service/gpu/buffercomparator.cc:640]INTERNAL:FailedtolaunchptxasRelyingondrivertoperformptxcompilation.SettingXLAFLAGS=−−xlagpucudadatadir=/path/to/cudaormodifyingPATHtocustomizeptxaslocation.Thismessagewillbeonlyloggedonce.2022−09−1023:10:17.440252:Itensorflow/core/platform/default/subprocess.cc:304]Startcannotspawnchildprocess:Nosuchfileordirectory2022−09−1023:10:17.440280:Wtensorflow/streamexecutor/gpu/asmcompiler.cc:80]Couldn′tgetptxasversionstring:INTERNAL:Couldn′tinvokeptxas−−version2022−09−1023:10:17.441066:Itensorflow/core/platform/default/subprocess.cc:304]Startcannotspawnchildprocess:Nosuchfileordirectory2022−09−1023:10:17.441134:Wtensorflow/compiler/xla/service/gpu/buffercomparator.cc:640]INTERNAL:FailedtolaunchptxasRelyingondrivertoperformptxcompilation.SettingXLAFLAGS=−−xlagpucudadatadir=/path/to/cudaormodifyingPATH can be used to set the location of ptxas This message will only be logged once. 2022-09-10 23:10:17.534116: W tensorflow/compiler/xla/service/gpu/nvptx\_helper.cc:56] Can’t find libdevice directory ${CUDA\_DIR}/nvvm/libdevice. This may result in compilation or runtime failures, if the program we try to run uses routines from libdevice. Searched for CUDA in the following directories: ./cuda\_sdk\_lib /usr/local/cuda-11.2 /usr/local/cuda . You can choose the search directory by setting xla\_gpu\_cuda\_data\_dir in HloModule’s DebugOptions. For most apps, setting the environment variable XLA\_FLAGS=–xla\_gpu\_cuda\_data\_dir=/path/to/cuda will work. 2022-09-10 23:10:17.721031: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory 2022-09-10 23:10:17.721072: W tensorflow/stream\_executor/gpu/asm\_compiler.cc:80] Couldn’t get ptxas version string: INTERNAL: Couldn’t invoke ptxas --version 2022-09-10 23:10:17.721699: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory 2022-09-10 23:10:17.722104: F tensorflow/compiler/xla/service/gpu/nvptx\_compiler.cc:456] ptxas returned an error during compilation of ptx to sass: ‘INTERNAL: Failed to launch ptxas’ If the error message indicates that a file could not be written, please verify that sufficient filesystem space is provided. Aborted (core dumped) chaztikov@priority:~~/git/deepxde/examples/pinn\_forwardCO COLORTERMCOLORTERMCOLUMNS $COMP\_WORDBREAKS chaztikov@priority:~~/git/deepxde/examples/pinn\_forwardCO COLORTERMCOLORTERMCOLUMNS COMPWORDBREAKSchaztikov@priority: /git/deepxde/examples/pinnforwardCOMPWORDBREAKSchaztikov@priority: /git/deepxde/examples/pinnforward COCOCOLORTERM COLUMNSCOLUMNSCOMP\_WORDBREAKS I did not “conda init bashrc” because I found that doing so interfered with an otherwise successful installation of nvidia cuda cudatoolkit etc. (though I am afraid to break anything, I welcome suggestions on this point and on all points) I see that I do have nvvm as indicated below chaztikov@priority:~~/git/deepxde/examples/pinn\_forward$ locate /nvvm/libdevice /home/chaztikov/anaconda3/nvvm/libdevice /home/chaztikov/anaconda3/nvvm/libdevice/libdevice.10.bc /home/chaztikov/anaconda3/pkgs/cuda-nvcc-11.7.99-0/nvvm/libdevice /home/chaztikov/anaconda3/pkgs/cuda-nvcc-11.7.99-0/nvvm/libdevice/libdevice.10.bc chaztikov@priority:~~/git/deepxde/examples/pinn\_forward$ so I will set in ~/.bashrc export CUDA\_DIR=“/home/chaztikov/anaconda3/pkgs/cuda-nvcc-11.7.99-0/” I tried the above, and it didn’t work, still getting the same error message, **Note** : before and after that change (export CUDA\_DIR etc) to my ~/.bashrc, I still seem to have tensorflow locating the GPU. chaztikov@priority:~/git/deepxde/examples/pinn\_forward$ python3 Python 3.10.4 (main, Jun 29 2022, 12:14:53) [GCC 11.2.0] on linux Type “help”, “copyright”, “credits” or “license” for more information. > > > import tensorflow > > > tensorflow.device(‘GPU’) > > > 2022-09-11 21:07:57.423925: I tensorflow/core/platform/cpu\_feature\_guard.cc:193] 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. > > > 2022-09-11 21:07:57.966877: I tensorflow/core/common\_runtime/gpu/gpu\_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9858 MB memory: → device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1 > > > \ torch also seems to work as a side note chaztikov@priority:~/git/deepxde/examples/pinn\_forward$ python3 Python 3.10.4 (main, Jun 29 2022, 12:14:53) [GCC 11.2.0] on linux Type “help”, “copyright”, “credits” or “license” for more information. > > > import torch;torch.cuda.is\_available() > > > True EDIT: I re-read the output when importing tensorflow, is this indicating that the GPU is found but somehow not using the GPU? It doesn’t seem that way, it seems to be indicating that tensorflow was compiled on my system without the proper optimization flags. ---
**Author:** ![Charlie\_Talbot](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/charlie_talbot/32/10249_2.png) [@Charlie\_Talbot](https://discuss.ai.google.dev/u/Charlie_Talbot)\ **Post date:** [September 13, 2022, 2:57pm UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/3 "2022-09-13T14:57:41Z")
Please help, I’d really appreciate it, as this is holding up progress on a time-sensitive project 😬 Thanks! ---
**Author:** ![chunduriv](https://avatars.discourse-cdn.com/v4/letter/c/bbe5ce/32.png) [@chunduriv](https://discuss.ai.google.dev/u/chunduriv)\ **Post date:** [September 14, 2022, 5:47am UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/5 "2022-09-14T05:47:09Z")
Could you share the output of the snippet ? ```auto import tensorflow as tf tf.config.list_physical_devices('GPU') ``` ---
**Author:** ![torlarse](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/torlarse/32/65793_2.png) [@torlarse](https://discuss.ai.google.dev/u/torlarse)\ **Post date:** [November 28, 2022, 1:37am UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/7 "2022-11-28T01:37:38Z")
For what it’s worth, I solved the same problem by first creating the `nvvm/libdevice` folder in my Conda environment `lib` folder. Thereafter copying the "libdevice.10.bc` file to that directory. Next, I set > export XLA\_FLAGS=–xla\_gpu\_cuda\_data\_dir=/home//miniconda3/envs//lib in Bash, within my activated Conda environment. Based on the error message snip `Can't find libdevice directory ${CUDA_DIR}/nvvm/libdevice.`, it is not sufficient to make the XLA\_FLAGS point to the file. The folder structure must match. This procedure is more or less described in numerous other similar issues. ---
**Author:** ![torlarse](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/torlarse/32/65793_2.png) [@torlarse](https://discuss.ai.google.dev/u/torlarse)\ **Post date:** [November 28, 2022, 1:38am UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/8 "2022-11-28T01:38:52Z")
After fixing my libdevice issue, I ran into the `Couldn't invoke ptxas --version` issue. Still using Conda, as recommended by the [official docs](https://www.tensorflow.org/install/pip). I solved that by installing > conda install -c nvidia cuda-nvcc in my activated Conda/ Tensorflow environment. I got that recipe from this Github issue: > **[FIx for "Couldn't invoke ptxas --version" with cuda-11.3 and jaxlib...](https://github.com/jax-ml/jax/discussions/6843)** > > Hi all, Just wanted to share my solution to the "Couldn't invoke ptxas --version" error that I got after a recent install of jax using cuda-11.3. TLDR, I needed to install nvidia-cuda-toolkit, whic... ---
**Author:** ![Steven\_Cohen](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/steven_cohen/32/15070_2.png) [@Steven\_Cohen](https://discuss.ai.google.dev/u/Steven_Cohen)\ **Post date:** [January 10, 2023, 11:04am UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/10 "2023-01-10T11:04:23Z")
Hi guys, i’ve done two fresh installs ([Install TensorFlow with pip](https://www.tensorflow.org/install/pip#windows-wsl2)) on two laptops RTX (ubuntu/anaconda/jupyter) and GTX (ubuntu/miniconda/jupyter) . Both have the same issue. nvidia-smi works but shows CUDA 12.0. import tensorflow as tf; print(tf.config.list\_physical\_devices(‘GPU’)) → works. At model.fit(x\_train, y\_train, epochs=5) i get the same issue → tensorflow/compiler/xla/service/gpu/nvptx\_helper.cc:56] Can’t find libdevice directory ${CUDA\_DIR}/nvvm/libdevice. This may result in compilation or runtime failures, if the program we try to run uses routines from libdevice. Searched for CUDA in the following directories: ./cuda\_sdk\_lib /usr/local/cuda-11.2 /usr/local/cuda . You can choose the search directory by setting xla\_gpu\_cuda\_data\_dir in HloModule’s DebugOptions. For most apps, setting the environment variable XLA\_FLAGS=–xla\_gpu\_cuda\_data\_dir=/path/to/cuda will work. I’v run both sudo cp -r /home/steve/anaconda3.2202.10/pkgs/cudatoolkit-11.2.2-hbe64b41\_10/lib/ /usr/local/cuda and export XLA\_FLAGS=–xla\_gpu\_cuda\_data\_dir=/anaconda3.2202.10/pkgs/cudatoolkit-11.2.2-hbe64b41\_10/lib/libdevice.10.bc and still getting the same error message. tensorflow 2.10 native works great still. I really would like to progress on to the linux platform to continue to use tf latest features any advise? ---
**Author:** ![Steven\_Cohen](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/steven_cohen/32/15070_2.png) [@Steven\_Cohen](https://discuss.ai.google.dev/u/Steven_Cohen)\ **Post date:** [February 20, 2023, 9:27am UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/12 "2023-02-20T09:27:56Z")
ok guys for wsl2 tf==2.10 on works. using code import tensorflow as tf tf.config.list\_physical\_devices(‘GPU’) sys\_details = tf.sysconfig.get\_build\_info() cuda = sys\_details[“cuda\_version”] cudnn = sys\_details[“cudnn\_version”] print(cuda, cudnn) confirms “11.2 8” where as for tf==2.11 it gives “64\_112 64\_8” lets hope this is not a monopolistic attack by google against microsoft ---
**Author:** ![Steven\_Cohen](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/steven_cohen/32/15070_2.png) [@Steven\_Cohen](https://discuss.ai.google.dev/u/Steven_Cohen)\ **Post date:** [February 21, 2023, 7:25am UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/14 "2023-02-21T07:25:00Z")
ok found this solution for tf==2.11 optimizer=tf.keras.optimizers.legacy.Adam() NOT optimizer=“adam” so as self taught coder that is interesting to see what part of tensorflow calls the gpu ---
**Author:** ![sravan953](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/sravan953/32/15225_2.png) [@sravan953](https://discuss.ai.google.dev/u/sravan953)\ **Post date:** [March 24, 2023, 3:56am UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/16 "2023-03-24T03:56:26Z")
@Steven_Cohen this fix worked! I was trying to reproduce the [official DDPM example on the Keras website](https://keras.io/examples/generative/ddpm/) on TF 2.11/Python 3.10 using Miniconda3. Do you have any insights as to why legacy worked? ---
**Author:** ![Steven\_Cohen](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/steven_cohen/32/15070_2.png) [@Steven\_Cohen](https://discuss.ai.google.dev/u/Steven_Cohen)\ **Post date:** [April 10, 2023, 1:51pm UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/18 "2023-04-10T13:51:18Z")
i just followed the installation instructions for tf12\* [Install TensorFlow with pip](https://www.tensorflow.org/install/pip#windows-wsl2) and when i run and a simple optimizers.Adam() I get, 2023-04-09 18:02:22.272960: W tensorflow/compiler/xla/service/gpu/llvm\_gpu\_backend/gpu\_backend\_lib.cc:530] Can’t find libdevice directory ${CUDA\_DIR}/nvvm/libdevice. This may result in compilation or runtime failures, if the program we try to run uses routines from libdevice. Also when i look in the directory there is no nvvc file downloaded by nvidia. I’ve tried looking though “\wsl.localhost\Ubuntu\home\steve\anaconda3.23.03tf12\lib\python3.10\site-packages\keras\optimizers\adam.py” for a solution but its a bit above my paygrade. I have also noticed that for windows native, tf.keras.optimizers.experimental.Adam(), the same error occurs InternalError: Graph execution error: … Node: ‘StatefulPartitionedCall\_2’ libdevice not found at ./libdevice.10.bc [[{{node StatefulPartitionedCall\_2}}]] [Op:\_\_inference\_train\_function\_739]. But at least i can find “C:\Users\sjc52\anaconda3.2022.10\pkgs\cuda-nvcc-11.7.99-0” ---
**Author:** ![sravan953](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/sravan953/32/15225_2.png) [@sravan953](https://discuss.ai.google.dev/u/sravan953)\ **Post date:** [April 10, 2023, 2:07pm UTC](https://discuss.ai.google.dev/t/cant-find-libdevice-directory-cuda-dir-nvvm-libdevice/20467/19 "2023-04-10T14:07:25Z")
Thank you for your response! If you do figure out why please share!