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PR #10965: [GPU] Make xla_gpu_enable_nccl_per_stream_comms false by default
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PR #10965: [GPU] Make xla_gpu_enable_nccl_per_stream_comms false by default
Imported from GitHub PR https://github.com/openxla/xla/pull/10965
In #9845 I added a flag to toggle between per-stream communicators. The default option was true to preserve the current behavior. While per-stream comms can provide a speedup at the cost of additional memory, it doesn't seem to help for many common workloads which I have tested. I am proposing that we set the default value to false to reduce the amount of GPU memory used by XLA.
The table below shows how the total GPU memory usage changes (absolute value) and how the steps/sec changes (relative value) by changing --xla_gpu_enable_nccl_per_stream_comms **from true to false**. In all of these cases, we use less memory and the performance stays in general within 1%.
| Workload | Mem diff (MiB) | Speedup |
| ------------------------------------------------------------------------ | ------------- | --------- |
| t5x xxl pretrain 8xh100 fp8 bs-36 insize-512 outsize-128 tp-8 te_fmha-1 | \-2596 | 1.0045 |
| t5x xxl pretrain 8xh100 fp8 bs-36 insize-512 outsize-128 tp-8 te_fmha-0 | \-2594 | 0.9963 |
| t5x xxl pretrain 8xh100 bf16 bs-36 insize-512 outsize-128 tp-8 te_fmha-1 | \-2596 | 0.9996 |
| t5x xxl pretrain 8xh100 bf16 bs-36 insize-512 outsize-128 tp-8 te_fmha-0 | \-2596 | 1.0012 |
| t5x xxl pretrain 8xh100 fp8 bs-256 insize-512 outsize-128 tp-8 te_fmha-1 | \-2596 | 1.0040 |
| t5x xxl pretrain 8xh100 fp8 bs-256 insize-512 outsize-128 tp-8 te_fmha-0 | \-2596 | 1.0000 |
| paxml llama70b evaluate 16xh100 bfloat16 bs-4 ici--1-8-1 dcn--1-2-1 | \-2644 | 1.0192 |
| paxml GPT3 train 8xh100 bfl16 bs-4 ici--4-1-2 | \-2822 | 0.9929 |
| paxml GPT3 train 8xh100 bfl16 bs-8 ici--1-4-2 | \-5800 | 0.9990 |
| paxml GPT3pp train 16xh100 bf16 bs-8 ici--2-2-1-2 | \-6582 | 0.9968 |
Test settings
* T5X container: [ghcr.io/nvidia/jax:t5x-2024-03-15](http://ghcr.io/nvidia/jax:t5x-2024-03-15)
* PAX container: [ghcr.io/nvidia/jax:pax-2024-03-15](http://ghcr.io/nvidia/jax:pax-2024-03-15)
* Performed using DGXH100
* NCCL_NVLS_ENABLE=1
* XLA_PYTHON_CLIENT_MEM_FRACTION=0.8 (except paxml GPT3pp which uses 0.7 because it goes OOM otherwise when enable_nccl_per_stream_comms is true)
Copybara import of the project:
--
fed944b06a9fb830a79b874f68732bdd41a9cd75 by Trevor Morris <[email protected]>:
Make xla_gpu_enable_nccl_per_stream_comms false by default
Merging this change closes #10965
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10965 from trevor-m:tmorris-perstreamcomms-false fed944b06a9fb830a79b874f68732bdd41a9cd75
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PR_kwDOArmXAs5rZat4
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PR #10763: [XLA:GPU] Fix cuDNN FMHA fwd scale not passed into cuDNN
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PR #10763: [XLA:GPU] Fix cuDNN FMHA fwd scale not passed into cuDNN
Imported from GitHub PR https://github.com/openxla/xla/pull/10763
* FMHA fwd scale is not passed into cuDNN in pattern bmm1-scale-softmax-(dropout)-bmm2. Work correctly on other patterns (with bias/mask).
* A quick fix is that I simply copy what works in bwd scale setting.
* Weirdly e2e run does not capture this since we do have cases where fwd scale is not 1.
Copybara import of the project:
--
4a63497ba070f2b52c2942bccf8156f3a3bbd4ab by cjkkkk <[email protected]>:
fix scale is not passed into cudnn
--
b333fa3ce10712f6e9f4590478b6c053f331e00b by cjkkkk <[email protected]>:
increase scale in pattern bmm1-scale-softmax-bmm2 test
--
a6eb74b553b26d415be9d97265be0789a7bff47c by cjkkkk <[email protected]>:
use magic 37 as WAR
Merging this change closes #10763
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10763 from Cjkkkk:fix_fwd_scale_no_passed a6eb74b553b26d415be9d97265be0789a7bff47c
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Tensorflow not detecting GPU
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[
"Hi **@RetusRieben** ,\r\n- Make sure you have installed the appropriate NVIDIA drivers for your GPU. TensorFlow requires compatible NVIDIA drivers to communicate with the GPU.\r\n- Ensure that the user running TensorFlow has the necessary permissions to access the GPU. Sometimes, permissions issues can prevent GPU detection.\r\n- Sometimes, TensorFlow may be built without GPU support. You can check this by running.\r\n```\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n```\r\n\r\nThank you!",
"python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2024-04-04 14:42:07.986529: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.\r\n2024-04-04 14:42:08.027002: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-04-04 14:42:08.826422: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-04-04 14:42:09.350663: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\r\n2024-04-04 14:42:09.350711: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:134] retrieving CUDA diagnostic information for host: Host\r\n2024-04-04 14:42:09.350722: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:141] hostname: Host\r\n2024-04-04 14:42:09.350809: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:165] libcuda reported version is: 550.54.14\r\n2024-04-04 14:42:09.350834: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:169] kernel reported version is: 550.54.14\r\n2024-04-04 14:42:09.350845: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:248] kernel version seems to match DSO: 550.54.14\r\n\r\nthe following output is generated if I run\r\n\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n\r\nMy user has ROOT rights",
"I tried installing Tensorflow using both conda and pip. My Nvidia Gpu is not detected when I do print(tf.config.list_physical_devices('GPU')). I had installed using python3 -m pip install tensorflow[and-cuda]. Should i try Tensorflow version 2.15.1?",
"Same issue here. \r\nI have `Nvidia RTX 4060` Laptop, and command from above show nothing about GPU. \r\n\r\n```powershell\r\n# python -c \"import tensorflow as tf; print(tf.config.list_physical_devices())\"\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\r\n```\r\n",
"I experience same issue with TF 2.16.1, to be honest only workable fix for me was to rollback to 2.15.1 ",
"Apparently, TF has not been compiled with CUDA for windows, which is outrageous in this day and age. All documentation state to downgrade to some other version or recompile use WSL. Both ridiculous statements just showing laziness for wanting to dive into the issue. If you can do it before, and in WSL, it should be straight forward to do it also in MSYS/MinGW. \r\n\r\nPS. _I don't roll back, I only roll forward!_",
"> Apparently, TF has not been compiled with CUDA for windows, which is outrageous in this day and age. All documentation state to downgrade to some other version or recompile use WSL. Both ridiculous statements just showing laziness for wanting to dive into the issue. If you can do it before, and in WSL, it should be straight forward to do it also in MSYS/MinGW.\r\n> \r\n> PS. _I don't roll back, I only roll forward!_\r\n\r\nI absolutely agree. I now also installed it also on Win11 and the only way I managed to do so was via Conda. For someone that is not allowed to use conda that is a big issue, I did not find another way. But that is a indeed ridiculous. ",
"@RetusRieben or pip but install TF 2.15.1 (I’m not saying it’s great solution, but at least working somehow)\r\n\r\nAgreed with you both, that it’s just outrageous not compile libs properly. Hope it will be fixed soon",
"I wonder if we could extract the latest TF package binaries from Conda, as it seem to work there? Maybe install Conda in VM and lookup the packages. Sad way to have to fix this though.",
"Wonder why it is so difficult to install Tensor flow for GPU now. Tensor\r\nFlow 2.16.1 is so full of bugs and can only work on WSL and that too\r\ndoesn't detect my GPU. Tried on different machines with same issue of\r\nNvidia GPU not being detected. Only solution was to roll back to 2.15.1\r\nthat too after getting some weird warnings after command executions. Would\r\nprefer to use PyTorch instead of TensorFlow. PyTorch is so easy to install\r\n& use with minimum complications.\r\n\r\nOn Wed, May 15, 2024, 10:56 PM eabase ***@***.***> wrote:\r\n\r\n> I wonder if we could extract the latest TF package binaries from Conda, as\r\n> it seem to work there? Maybe install Conda in VM and lookup the packages.\r\n> Sad way to have to fix this though.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/64881#issuecomment-2113127123>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AIVENFB4235FWSAL5XFQHOLZCOOTRAVCNFSM6AAAAABFSYT73OVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCMJTGEZDOMJSGM>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"Unfortunately, like all you, I wasn't able to access my GPU as backend in TF. I have also tried all the way of installing it -pip, .deb, .tar... It's very hard to access GPU with other versions. \r\n\r\nAt last, I found a method for accessing GPU as backend, only by using python3.11 and TensorFlow 2.15.1, other than these versions nothing suits.\r\n\r\ntry this by creating a virtual environment:\r\n\r\n```\r\nconda create -n tmpenv python=3.11\r\nconda activate tmpenv\r\npip install tensorflow[and-cuda]==2.15.1\r\n```\r\n\r\nif all your GPU driver and cuda is installed, you should be able to run this command:\r\n```\r\nnvidia-smi\r\n```\r\nto check if GPU is detected by TF:\r\n```\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n```\r\nprobably you should get:\r\n\r\n```\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```\r\n\r\nLet me know, if this works for you all!\r\n",
"@MohanKrishnaGR \r\nWhat OS did you install this on? (Via WSL or native conda on Win?) \r\n\r\nThat is the annoying fact. \r\n\r\n1. You can install with Conda which is totally Python made. So Conda installs some (unknown, but working) CUDA packages.\r\n2. Python 11, is can't be *that* different from 12, so what changed and how to fix?\r\n\r\nIf possible, can you please help by:\r\n\r\n* Can you check to see if you can find the python packages installed by Conda? \r\n* What is the exact output of your `pip install tensorflow[and-cuda]==2.15.1`? \r\n A log would be great! Can you paste it here within tripple back quotes (```)?\r\n* Can you search you python installation directory for `*.whl`? \r\n* Try to install using: \r\n `python -m pip install --only-binary :all: tensorflow[and-cuda]==2.15.1` \r\n And take note of download URLs and output.\r\n\r\n\r\nReferences:\r\nhttps://packaging.python.org/en/latest/discussions/package-formats/\r\nhttps://realpython.com/python-wheels/\r\nhttps://opensource.com/article/23/1/packaging-python-modules-wheels\r\n",
"My Gpu is detected but I get a long cryptic warning when I use the relevant\r\ncommand for detecting gpu in 2.15.1. When I tried to do conda update my\r\ntensorflow version changed to cpu one ??\r\n\r\nOn Wed, 22 May 2024 at 10:24 PM, eabase ***@***.***> wrote:\r\n\r\n> @MohanKrishnaGR <https://github.com/MohanKrishnaGR>\r\n> What OS did you install this on? (Via WSL or native conda on Win?)\r\n>\r\n> That is the annoying fact.\r\n>\r\n> 1. You can install with Conda which is totally Python made. So Conda\r\n> installs some (unknow, but working) CUDA packages.\r\n> 2. Python 11, is can't be *that* different from 12, so what changed\r\n> and how to fix?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/64881#issuecomment-2125375814>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AIVENFAOSJ3NO7PDXJLZFMDZDTIDPAVCNFSM6AAAAABFSYT73OVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCMRVGM3TKOBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"@mhlhr There's no point of telling us it is cryptic, unless you also provide the message... :wink:",
"@learning-to-play ",
"This duplicates https://github.com/tensorflow/tensorflow/issues/63362 (edit: changed to right issue)",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64881\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64881\">No</a>\n"
] | 2024-04-02T06:42:10 | 2024-06-12T06:29:32 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
SUSE Linux Enterprise Server 15 SP3
### Mobile device
_No response_
### Python version
3.10.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 12.4
### GPU model and memory
NVIDIA A2, 16GB
### Current behavior?
TensorFlow is not recognising the NVIDIA A2 GPU or any other CUDA devices on my Server.
### Standalone code to reproduce the issue
```shell
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
### Relevant log output
```shell
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2024-04-02 08:37:38.517925: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-02 08:37:38.570940: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-02 08:37:39.624139: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-02 08:37:40.270665: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2024-04-02 08:37:40.270724: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:134] retrieving CUDA diagnostic information for host: HOST
2024-04-02 08:37:40.270738: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:141] hostname: HOST
2024-04-02 08:37:40.270865: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:165] libcuda reported version is: 550.54.14
2024-04-02 08:37:40.270887: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:169] kernel reported version is: 550.54.14
2024-04-02 08:37:40.270896: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:248] kernel version seems to match DSO: 550.54.14
```
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PR #63398: Fix checkfail in UnicodeEncode Op
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PR #63398: Fix checkfail in UnicodeEncode Op
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/63398
The Op UnicodeEncode segfaults when passed 2D tensor to `input_splits`.
It has the below check in `SetShapeFn` which supposed to raise exception if `rank !=1` AFAIk. This seems not working for reason unknown to me.
https://github.com/tensorflow/tensorflow/blob/6f64ad5d767a034df45a5eaab8b36fd688cd1217/tensorflow/core/ops/string_ops.cc#L316-L317
Same with `input_values` argument also.
Added an explicit check in Op.
Ref issue #63379
Copybara import of the project:
--
176048ad39a1d928208b92e1503e2d8d2cd35d28 by Surya <[email protected]>:
Fix checkfail in UnicodeEncode Op
The Op UnicodeEncode segfaults when passed 2D tensor to `input_splits`.
It has the below check in SetShapeFn which supposed to raise exception if rank !=1 AFAIk. This seems not working for reason unknown to me.
https://github.com/tensorflow/tensorflow/blob/6f64ad5d767a034df45a5eaab8b36fd688cd1217/tensorflow/core/ops/string_ops.cc#L316-L317
Same with input_values argument also.
Added an explicit check in Op.
Merging this change closes #63398
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63398 from tensorflow:Surya_UnicodeEncode 176048ad39a1d928208b92e1503e2d8d2cd35d28
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Too slow while fetching @llvm-raw repos while building tensorflow from Source
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"@vishwascm,\r\nCould you please confirm whether the build is failing due to the above issue or the build continued? and also it might be due to the vpn/network you are trying to use. Also try to clear the cache by calling **bazel clean –expunge**\r\nCommand prompt must be Run as Admin for the bazel build of TensorFlow to be successful. \r\n\r\n```\r\n\r\nbazel build --override_repository=\"llvm-raw=${LLVM_SRC}\" \\\r\n -c opt tensorflow/compiler/mlir:tf-opt\r\n```\r\n\r\nAs per Bazel [documentation](https://bazel.build/run/build#specifying-build-targets), the target patterns starting with // are resolved relative to the current workspace. The target patterns that do not begin with // are resolved relative to the current working directory.\r\n\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nThank you!",
"Hi @tilakrayal build will continue but only after around 25 mins of fetching llvm-raw repo. And later the build fails, the reason I think is it is downloading and building clang-17. Before building I have specifically mentioned path to use as clang-16 path, even then it is not using clang16. How to solve this issue and use clang16 to build because clang 17 has given build fails for others also.",
"@vishwascm,\r\nCould you please provide the complete error log which helps us to debug/analyse the issue in an effective way. Thank you!",
"@tilakrayal when I use clang 16, it will still take lot of time fetching llvm-raw repo (around 30 mins), but after that build will be successful.\r\nThe error i got when using clang 17 was :\r\n`ERROR: /home/vishwas/tensorflow/tensorflow/python/platform/BUILD:388:17: Linking` `tensorflow/python/platform/_pywrap_tf2.so failed: (Exit 1): clang-17 failed: error executing command (from target //tensorflow/python/platform:_pywrap_tf2.so) \r\n (cd /home/vishwas/.cache/bazel/_bazel_vishwas/e4f9045e13254a05e1721bc40c12e4c7/execroot/org_tensorflow && \\\r\n exec env - \\\r\n CLANG_COMPILER_PATH=/usr/bin/clang-17 \\\r\n PATH=/home/vishwas/.cache/bazelisk/downloads/sha256/7f7d2fe44570403c5c0c221259b5afc45f0a4176eeb0440bbaffbce76872599d/bin:/home/vishwas/venv_dir/tf_py311_original/bin:/home/vishwas/.vscode-server/cli/servers/Stable-863d2581ecda6849923a2118d93a088b0745d9d6/server/bin/remote-cli:/home/vishwas/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n PYTHON_BIN_PATH=/home/vishwas/venv_dir/tf216_perf23/bin/python3 \\\r\n PYTHON_LIB_PATH=/home/vishwas/venv_dir/tf216_perf23/lib/python3.10/site-packages \\\r\n TF2_BEHAVIOR=1 \\\r\n /usr/bin/clang-17 @bazel-out/aarch64-opt/bin/tensorflow/python/platform/_pywrap_tf2.so-2.params)\r\n Configuration: b7a52e65e2dd57fe1977152f59b9f737f0a4f8ef69a6d625a69816bca0a9acef\r\n Execution platform: @local_execution_config_platform//:platform\r\nld.lld: error: version script assignment of 'global' to symbol 'init_pywrap_tf2' failed: symbol not defined\r\nld.lld: error: version script assignment of 'global' to symbol 'init__pywrap_tf2' failed: symbol not defined\r\nclang-17: error: linker command failed with exit code 1 (use -v to see invocation)\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nINFO: Elapsed time: 4290.793s, Critical Path: 160.70s`",
"@vishwascm,\r\nI suspect the issue is that your tests are looking for a python3 binary. Bazel runs the tests in isolation, you need to make sure the PATH variable used there contains the path to Python & the Bazel you are trying . Also could you test with the python3.11 and let us know if you are facing the same. Thank you!"
] | 2024-04-02T06:27:58 | 2024-06-05T03:28:27 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
ubuntu 22.04.1 on aarch64
### Mobile device
_No response_
### Python version
python 3.10.12
### Bazel version
7.1.1
### GCC/compiler version
clang 17.0.2
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
It is taking too much time to fetch repo llvm-raw while building tensorflow from source on aarch64 machine with 32 cores. I have tried using all possible number of cores, from low to high. using ex: --local_cpu_resources=4. The process gets upto this point
`Loading:
Fetching repository @llvm-raw; starting 964s
Fetching ...c40c12e4c7/external/llvm-raw; Extracting 8697bbe2d4aed109520e83c6beab52196ec5b702.tar.gz 962s
`
and continues like this.
Please help me to solve this issue.
Commands used :
`export ONEDNN_VERBOSE=1`
`export TF_ENABLE_ONEDNN_OPTS=1`
`export CC="/usr/local/bin/clang"`
`export CXX="/usr/local/bin/clang++"`
`export TF_PYTHON_VERSION=3.10`
` bazel build -s --config=mkl_aarch64 --features=-layering_check --copt=-O3 --copt=-march=armv8-a+sve --copt=-msve-vector-bits=256 --local_cpu_resources=16 //tensorflow/tools/pip_package:build_pip_package --verbose_failures `
### Standalone code to reproduce the issue
```shell
Commands used :
`export ONEDNN_VERBOSE=1`
`export TF_ENABLE_ONEDNN_OPTS=1`
`export CC="/usr/local/bin/clang"`
`export CXX="/usr/local/bin/clang++"`
`export TF_PYTHON_VERSION=3.10`
` bazel build -s --config=mkl_aarch64 --features=-layering_check --copt=-O3 --copt=-march=armv8-a+sve --copt=-msve-vector-bits=256 --local_cpu_resources=16 //tensorflow/tools/pip_package:build_pip_package --verbose_failures `
```
### Relevant log output
```shell
`Loading:
Fetching repository @llvm-raw; starting 964s
Fetching ...c40c12e4c7/external/llvm-raw; Extracting 8697bbe2d4aed109520e83c6beab52196ec5b702.tar.gz 962s`
```
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Temporary fix for TensorFlow VS2019 breakage.
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Temporary fix for TensorFlow VS2019 breakage.
This is a duplicate of https://github.com/llvm/llvm-project/pull/87288.
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Fix the situation where the input tensor is an empty sparse tensor with empty indices.
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Fix the situation where the input tensor is an empty sparse tensor with empty indices.
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PR_kwDOArmXAs5rX4Hu
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Eliminate unused Compiler::AssignBuffers method and overloads.
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Eliminate unused Compiler::AssignBuffers method and overloads.
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PR_kwDOArmXAs5rX2_P
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Change ReserveDevice to return unique_ptr.
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Change ReserveDevice to return unique_ptr.
The returned DeviceRervation will be freed after its lifecycle ends, semantically it should be owned by the caller.
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xla_compile_lib: Also support modules in binary proto format.
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xla_compile_lib: Also support modules in binary proto format.
There's no reason not to support them, and it's a useful format.
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PR_kwDOArmXAs5rXhlW
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#tf-data-service Reduce error log frequency for the auto scaler.
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#tf-data-service Reduce error log frequency for the auto scaler.
Reverts fd5333cc3d6801cb5ce51d351ab20b0fd658f516
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11122 from ROCm:5.7-rccl-hang 2d196b645ccac046309d44f747d42777f00f1c9b
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Create test_gather_model test.
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Create test_gather_model test.
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PR_kwDOArmXAs5rXIRH
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Add replicate_on_last_tile_dims handling when validating tiled output sharding.
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Add replicate_on_last_tile_dims handling when validating tiled output sharding.
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Remove unnecessary Copybara transforms
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Remove unnecessary Copybara transforms
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[JAX] Update JAX CI dockerfiles to use NumPy 2.0.0rc1, SciPy 1.13.0rc1, and ml_dtypes 0.4.0.
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[JAX] Update JAX CI dockerfiles to use NumPy 2.0.0rc1, SciPy 1.13.0rc1, and ml_dtypes 0.4.0.
Change in preparation for releasing JAX with NumPy 2.0 support.
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[SavedModelBundleLite] Avoid copying the GraphDef during the load path.
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[SavedModelBundleLite] Avoid copying the GraphDef during the load path.
Previously, we reused the "legacy" SavedModelBundle loading path, then discarded the unused MetaGraphDef. An old TODO called out the opportunity to avoid copying the MetaGraphDef's GraphDef, and this change does that.
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Fix a cornercase in lowering form saved model, for the tf.BatchFunction op.
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Fix a cornercase in lowering form saved model, for the tf.BatchFunction op.
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PR_kwDOArmXAs5rWksR
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Make all MHLO ops not Pure to see if anything breaks
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Make all MHLO ops not Pure to see if anything breaks
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#tf-data-service Check that tf.data snapshot path supports atomic move.
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#tf-data-service Check that tf.data snapshot path supports atomic move.
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[XLA] Ignore channel id for all-reduce in spmd programs
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[XLA] Ignore channel id for all-reduce in spmd programs
Channel IDs are not used for all-reduce in spmd mode, so we shouldn't verify their correctness when generating the module group metadata in spmd mode.
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Add functional if definition
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Add functional if definition
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[xla:gpu] NFC: Remove AddressComputationFusion emitter
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[xla:gpu] NFC: Remove AddressComputationFusion emitter
After merging implementations always use DynamicAddressComputationFusion
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1. More comprehensive after-all handling. Specifically, the pass now handles after-alls with non-zero operands.
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1. More comprehensive after-all handling. Specifically, the pass now handles after-alls with non-zero operands.
2. Add a case for merging two TupleOrToken labels. This case can arise when merging labels for multple conditional branch computations as they often return tuples.
Added tests for both these fixes.
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[xla:gpu][NFC] No need for custom HloModuleConfigs in address_computation_fusion_test
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[xla:gpu][NFC] No need for custom HloModuleConfigs in address_computation_fusion_test
Since the rewriter is now in RunHloPasses, these configs do not do anything.
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[xla:gpu] No need for dynamic/static mode in AddressComputationFusionRewriter
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[xla:gpu] No need for dynamic/static mode in AddressComputationFusionRewriter
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PR_kwDOArmXAs5rVU2c
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Remove stray constant folding mutex.
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Remove stray constant folding mutex.
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poetry can't find the right installation candidate of tensorflow with python 3.11
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[
"Hi @SergeyHein ,\r\n\r\nCould you please try with TF 2.16.1 which is latest release and update us the outcome. This may be due to the Windows OS. On Windows GPU package won't be supported for `TF>= 2.11v` onwards. You may test with the CPU package `tensorflow_cpu==2.15.0.post1` and let us know the outcome. For GPU package you can install WSL2 and use it. Please refer more details [here](https://www.tensorflow.org/install/pip#windows-native).\r\n\r\nThanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64847\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64847\">No</a>\n"
] | 2024-04-01T15:34:48 | 2024-04-17T01:47:39 | 2024-04-17T01:47:36 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.15.0.post1)
### Custom code
No
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
[tool.poetry]
name = "test"
version = "0.1.0"
description = "test"
authors = ["test"]
readme = "README.md"
[tool.poetry.dependencies]
python = "~3.11"
[tool.poetry.group.dev.dependencies]
black = "^23.10.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
### Standalone code to reproduce the issue
```shell
(test-py3.11) root@a634e1891f96:/data/test# poetry add tensorflow
```
### Relevant log output
```shell
Using version ^2.15.0.post1 for tensorflow
Updating dependencies
Resolving dependencies... (1.3s)
Because no versions of tensorflow match >2.15.0.post1,<3.0.0
and tensorflow (2.15.0.post1) depends on tensorflow-intel (2.15.0.post1), tensorflow (>=2.15.0.post1,<3.0.0) requires tensorflow-intel (2.15.0.post1).
So, because no versions of tensorflow-intel match 2.15.0.post1
and test depends on tensorflow (^2.15.0.post1), version solving failed.
```
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I_kwDOArmXAs6ENXHR
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`SystemError` in `tf.constant` when `dtype` is `tf.uint64` and `value` is nagetive.
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[
"@xl1043237213 tf_value = tf.constant(-10, dtype=tf.uint64) tries to create a constant tensor named tf_value with the value -10 and data type tf.uint64. However, tf.uint64 can only represent non-negative integers. \r\nCould you please ensure you only provide non-negative values when using tf.constant with unsigned integer dtypes.\r\nThank you!",
"Thanks for your response. :)\r\n`tf_value = tf.constant(-10, dtype=tf.uint64)` triggers `SystemError` but `tf_value = tf.constant(-10, dtype=tf.uint32)` does not. I think it's a matter of insufficient processing of boundary inputs. If `value` of `tf.constant` must be a non-negative integer when using with unsigned integer dtypes, I think `tf.constant` should trigger `ValueError` when `value` is a negative integer.",
"@xl1043237213 You are right about the expected behavior. Could you explicitly check the value before passing it to tf.constant. You can use an if statement or a function like tf.math.greater_equal to ensure the value is non-negative. In the future TensorFlow versions will address this behavior and provide a more informative error message.\r\nThank you!",
"Thanks. I got it.",
"@xl1043237213 Could you please let us know if we can move this issue to closed status?\r\nThank you!",
"Yes, I hope tensorflow will fix the issue soon. I‘m waiting for the root cause.😊 I will close this issue.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64846\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64846\">No</a>\n"
] | 2024-04-01T11:38:41 | 2024-04-17T08:01:29 | 2024-04-17T07:50:54 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I know that negative number is an unexpected input, but when the `dtype` is `tf.uint32` and `tf.uint64`,`tf.constant` can map negative number to the corresponding unsigned integer. In fact,`tf.uint32` does exactly this.
Taking `tf.constant(-10, dtype=tf.uint64)` as an example, in eager mode, this API will trigger a `SystemError` error when it is called for the first time, but subsequent calls will no longer trigger the error. I think it is a bug.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/17EtoIjT5Ml7FOQnSh5PIxMcVQFB_eDNF#scrollTo=UBwqMQNy4OHb
```
### Relevant log output
```shell
SystemError: <class 'tensorflow.python.framework.ops.EagerTensor'> returned a result with an exception set
```
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I_kwDOArmXAs6ENW61
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What is the effect of TF_GUARDED_BY(mu) for variables like Tensor?
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[
"Hi **@Alok-Ranjan23** ,\r\n- TF_GUARDED_BY(mu) is a thread annotation used for indicating that a particular variable or resource is guarded by a mutex mu. This annotation helps the TensorFlow runtime and its tools to understand and enforce thread safety when accessing the variable or resource.\r\n- The variables cached_filter_data_ and cached_filter_md_ are annotated with TF_GUARDED_BY(mu_). This implies that access to these variables should be synchronized using the mutex mu.\r\n\r\nThank you!\r\n",
"Thanks for your response.\r\nPlease let me know the following.\r\nIs it preserving the memory of the node for the next iteration(#iteration = examples/batch_size) or memory of the same node resued in the same iteration?\r\n\r\nThanks,\r\nAlok.",
"Hi **@Alok-Ranjan23** ,\r\n- Sorry for the delay, The TF_GUARDED_BY(mu_) annotation itself does not dictate whether the memory of the node is preserved for the next iteration or reused within the same iteration. Instead, it ensures thread-safe access to the variable.\r\n\r\nThank you! "
] | 2024-04-01T11:38:02 | 2024-06-11T15:02:00 | null |
NONE
| null | null | null |
### Source
source
### TensorFlow version
TF2.14
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
What is the significance of TF_GUARDED_BY(mu) of Tensor in this file [mkl_conv_ops.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/mkl/mkl_conv_ops.cc#L1393)?
How is it affecting the memory use?
Is it preserving the memory of the node for the next iteration(#iteration = examples/batch_size) or memory of the same node resued in the same iteration?
### Standalone code to reproduce the issue
```shell
private:
std::shared_ptr<dnnl::memory> fuse_add_src_;
std::shared_ptr<dnnl::memory> fuse_add_dst_;
std::vector<int32> strides_;
std::vector<int32> dilations_;
std::vector<Tpadding> padding_list_;
bool is_filter_const_;
mutex mu_;
Padding padding_;
string data_format_str_;
TensorFormat data_format_;
Tensor cached_filter_data_ TF_GUARDED_BY(mu_);
#ifndef ENABLE_ONEDNN_V3
Tensor cached_filter_md_ TF_GUARDED_BY(mu_);
#else
FilterMemoryDesc cached_filter_md_ TF_GUARDED_BY(mu_);
#endif // !ENABLE_ONEDNN_V3
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5rTnyt
| 64,844 |
Transpose weights for hybrid quantized convolution
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Transpose weights for hybrid quantized convolution
Factored out function for matching input and kernel and function for transposing the weight values to share implementation between SRQ and weight-only convolution.
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PR_kwDOArmXAs5rTnqH
| 64,843 |
Verify WeightOnlyPtqPreset with VGG16
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Verify WeightOnlyPtqPreset with VGG16
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10965 from trevor-m:tmorris-perstreamcomms-false fed944b06a9fb830a79b874f68732bdd41a9cd75
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PR_kwDOArmXAs5rTcPr
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PR #11094: [ROCm] enable rocm for se_gpu_pjrt_compiler_aot_test
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PR #11094: [ROCm] enable rocm for se_gpu_pjrt_compiler_aot_test
Imported from GitHub PR https://github.com/openxla/xla/pull/11094
Copybara import of the project:
--
4f002765972dd0705a3c8fb2c7b374c59e5fdb49 by Chao Chen <[email protected]>:
enable rocm for pjrt_aot
--
f0199b1fbc5929f4c2e8b48857cb975b53279bcc by Chao Chen <[email protected]>:
add requires-gpu-nvidia tag to ensure this test runs with a GPU available
--
7f5d97f0ef9d2bdfe990120e2eee629f37f106ce by Chao Chen <[email protected]>:
add if_google tag for config-cuda-only
Merging this change closes #11094
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11094 from ROCm:ci_enable_se_gpu_pjrt_compiler_aot_test 7f5d97f0ef9d2bdfe990120e2eee629f37f106ce
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Register the op kernel of `GroupByWindowDataset` to make it work on mobile.
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Register the op kernel of `GroupByWindowDataset` to make it work on mobile.
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tensorflow:Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001F58F9464C8> could not be transformed and will be executed as-is.
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[
"@ShayanDodge,\r\nTensorflow 2.0 which you are using is pretty old. I suggest trying to use tensorflow latest stable versions 2.15 or 2.16 where the warning was not displayed while executing the above mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/c9b3cc5ad4ca88cffedac481e43bc27a/untitled1789.ipynb) and also screenshot of the windows for the reference. Thank you!\r\n\r\n<details><summary>Windows ScreeShot</summary> \r\n\r\n\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64840\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64840\">No</a>\n"
] | 2024-04-01T08:57:14 | 2024-04-17T01:47:42 | 2024-04-17T01:47:38 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.0.0
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.7.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The code should be executed without any warnings.
### Standalone code to reproduce the issue
```shell
# Training a neural network on Fashion MNIST using ℓ2 Regularization
# Importing essential package
import numpy as np
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
# Loading Fashion MNIST dataset and preprocessing
(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()
# Scaling pixel values to the range [0, 1]
X_train_full = X_train_full / 255.0
X_test = X_test / 255.0
# Splitting training set into validation and training subsets
X_valid, X_train = X_train_full[:5000], X_train_full[5000:]
y_valid, y_train = y_train_full[:5000], y_train_full[5000:]
# Calculating mean and standard deviation of training pixel values
pixel_means = X_train.mean(axis=0, keepdims=True)
pixel_stds = X_train.std(axis=0, keepdims=True)
# Standardizing the input features using mean and standard deviation
X_train_scaled = (X_train - pixel_means) / pixel_stds
X_valid_scaled = (X_valid - pixel_means) / pixel_stds
X_test_scaled = (X_test - pixel_means) / pixel_stds
# Setting random seeds for reproducibility
tf.random.set_seed(42)
np.random.seed(42)
# Creating a Sequential model
model = keras.models.Sequential([
# Input layer: Flatten layer to convert 2D input (28x28) to 1D array
keras.layers.Flatten(input_shape=[28, 28]),
# Hidden layer 1: Dense layer with 300 neurons, ELU activation function,
# He initialization, and ℓ2 regularization with a regularization strength of 0.01
keras.layers.Dense(300, activation="elu",
kernel_initializer="he_normal",
kernel_regularizer=keras.regularizers.l2(0.01)),
# Hidden layer 2: Dense layer with 100 neurons, ELU activation function,
# He initialization, and ℓ2 regularization with a regularization strength of 0.01
keras.layers.Dense(100, activation="elu",
kernel_initializer="he_normal",
kernel_regularizer=keras.regularizers.l2(0.01)),
# Output layer: Dense layer with 10 neurons (for 10 classes), softmax activation function,
# and ℓ2 regularization with a regularization strength of 0.01
keras.layers.Dense(10, activation="softmax",
kernel_regularizer=keras.regularizers.l2(0.01))
])
# Compiling the model
model.compile(loss="sparse_categorical_crossentropy", optimizer="nadam", metrics=["accuracy"])
# Defining number of epochs
n_epochs = 2
# Training the model
history = model.fit(X_train_scaled, y_train, epochs=n_epochs,
validation_data=(X_valid_scaled, y_valid))
```
### Relevant log output
```shell
Train on 55000 samples, validate on 5000 samples
Epoch 1/2
WARNING:tensorflow:Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001F58F9464C8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
WARNING: Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000001F58F9464C8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
55000/55000 [==============================] - 5s 95us/sample - loss: 1.6073 - accuracy: 0.8114 - val_loss: 0.7551 - val_accuracy: 0.8054
Epoch 2/2
55000/55000 [==============================] - 4s 80us/sample - loss: 0.7145 - accuracy: 0.8279 - val_loss: 0.7187 - val_accuracy: 0.8230
```
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Automated Code Change
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Calculate min/max and histogram inside the CustomAggregator op
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This cl is the first part of removing the Calibration singleton:
- Part 1 (this cl): Move the min/max and histogram calculation inside CustomAggregator op.
- Part 2 (follow-up cl): Aggregate the statistics inside StatisticsSaver op and remove the singleton.
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Preserve `stablehlo.gather` requantize pattern to meet operand and result type constraint.
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Preserve `stablehlo.gather` requantize pattern to meet operand and result type constraint.
github.com/openxla/stablehlo/blob/main/docs/spec.md#gatherix states `(C14) element_type(operand) = element_type(result)`.
This should be kept for `stablehlo.gather`, `stablehlo.slice`, and `stablehlo.gather_slice`.
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Adding reduce-scatter to the list of known collectives in collective_ops_utils.cc
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Adding reduce-scatter to the list of known collectives in collective_ops_utils.cc
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[xla:gpu] No need for dynamic/static mode in AddressComputationFusionRewriter
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[xla:gpu][NFC] No need for custom HloModuleConfigs in address_computation_fusion_test
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[xla:gpu][NFC] No need for custom HloModuleConfigs in address_computation_fusion_test
Since the rewriter is now in RunHloPasses, these configs do not do anything.
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[xla:gpu] DUS support for generic custom call emission in DynamicAddressComputationFusion emitter
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[XLA] Make shape util fuzzer happy
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[XLA] Make shape util fuzzer happy
Handle the following cases:
1. Don't call ShapeUtil::ByteSizeOf() on a shape that isn't a dense array.
2. Don't call ShapeUtil::ByteSizeOf() on a shape with unbounded dim(s).
3. Handle negative element_size_in_bits field in the Layout proto. Change Layout::element_size_in_bits_ to match the proto type to avoid narrowing conversion and sign loss. Then check if the field is negative and return an error during validation.
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[xla:gpu] Generic custom call emission for DynamicAddressComputationFusion emitter
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I_kwDOArmXAs6EJcSl
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JUCE LNK2001 - LNK2019: UNRESOLVED EXTERNAL SYMBOL
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"Hi @Ikerlandarech,\r\n\r\nUpon inspecting the screenshot, these errors indicate that the code is referencing functions or variables that are not defined or available in the linked libraries. Check library versions for compatibility, make sure necessary headers are included. In order to reproduce the issue, please fill the [template](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=&projects=&template=tensorflow_issue_template.yaml) and elaborate more the issue and the environment which you are working. \r\n\r\nThank You"
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Hey there and thank you for reading this.
I've been trying to work on a VST for my final thesis project which consists on timbre transfer voice to instruments, I've been using JUCE and all source files are loaded from the Projuicer, the problem I'm facing is those Linker Errors that I think have to do with reading/accessing the Tensorflow files. I have no idea on how to solve it since I tried changing all the "includes" so that filepath were accessible and also tried importing all TF into the Projuicer but then it crashes.
If anyone can help with this I would really appreciate some help, thank u so very much in advance and have a beautiful day whoever is reading this :)
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efficient_serving.ipynb has some bugs
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/bc883716e434c4cb51cfc6d125c08710/efficient_serving.ipynb#scrollTo=if-ujOZN-Par).\r\nThank you!",
"Hi @paya-alavi77 ,\r\n\r\nThis needs to be addressed at [recommenders](https://github.com/tensorflow/recommenders/issues) repo. Please file an issue there. Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64817\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64817\">No</a>\n"
] | 2024-03-31T16:02:35 | 2024-05-03T01:48:50 | 2024-05-03T01:48:43 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi. I was trying to run [this](https://www.tensorflow.org/recommenders/examples/efficient_serving) guide on tensorflow site and got error when tried to create the Movielensmodel. I didn't change any code and just ran it on a google colab server.
The code that I'm getting error is on 10th cell:
`model = MovielensModel()`
which is supposed to create the retrieval model however I get below error I tried to work it out but I couldn't fix it. I found out that the installation of scann brings this problem. If we don't install scann the project will run just fine.:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-10-0536416ac940>](https://localhost:8080/#) in <cell line: 1>()
----> 1 model = MovielensModel()
2 model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))
6 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/backend/common/variables.py](https://localhost:8080/#) in standardize_shape(shape)
460 continue
461 if not is_int_dtype(type(e)):
--> 462 raise ValueError(
463 f"Cannot convert '{shape}' to a shape. "
464 f"Found invalid entry '{e}' of type '{type(e)}'. "
ValueError: Cannot convert '('c', 'o', 'u', 'n', 't', 'e', 'r')' to a shape. Found invalid entry 'c' of type '<class 'str'>'.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/efficient_serving.ipynb
```
### Relevant log output
_No response_
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"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64814\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64814\">No</a>\n",
"Oh sorry! That was my fault"
] | 2024-03-31T03:52:32 | 2024-03-31T04:05:10 | 2024-03-31T04:04:40 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Windows
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Referring to tf.keras.layers.Normalization
In the documentation, I read that adapt-data is used so that tensor-flow can use the adapt data for computing means and variance corresponding to each axes. And axes can be specified as None if you want to compute the overall mean and variance of adapt data. It was mentioned either u can use the adapt data or explicitly specify mean and variance . Explicitly specifiying mean and variance doesnt work until or unless data is of the format np.array([[6.], [7.], [3.],[4.],[5.]] But if it is of np.array([[6,7,3,4,5]])/ A little difference in shape, This explicit will not work
Although It used to work in the back.
### Standalone code to reproduce the issue
```shell
normalizer=keras.layers.Normalization(mean=5,variance=4,axis=-1)
x_normalized=tf.constant([3,4,5,6,7],dtype='float32')
normalizer.adapt(x_normalized)
normalizer(x_normalized)
Whether axis is -1 or None this code won't work
But the below one would do
input_data=tf.constant([[3.],[4.],[5.],[6.],[7.]],dtype='float32')
layer = keras.layers.Normalization(mean=5., variance=4.)
layer(input_data)
Now, another thing if I specify the adapt layer
adapt_data=np.array([[1.,2.,3.,4.,5.],
[3.,4.,5.,6.,7.],
[4.,5.,6.,7.,8.]],dtype='float32')
input_data=np.array([[3.,4.,5.,6.,7.]],dtype='float32')
layer = keras.layers.Normalization(axis=-1)
layer.adapt(adapt_data)
layer(input_data)
The above code of adapt layer will work but the same if I try to specify the same mean and variance corr to each axes or as an whole overall, it won't
Although, this shoudln't be the case since explici specifying is allowed here
EVEN AFTER USING RESHAPE , IT WON'T WORK
```
### Relevant log output
```shell
{{function_node __wrapped_}}
```
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[xla:gpu][NFC] Use the same helpers to get slices for GEMM and generic custom call emissions
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[xla:gpu][NFC] Explicitly rewrite AddressComputationFusion in custom call tests
AddressComputationFusionRewriter is now part of `RunHloPasses`, we need to explicitly call it to transform the HLO in order to keep tests meaningful.
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"@janeon,\r\nThe model Generating Images with BigGAN which you are trying to execute is compatible with the 1.x version. There are multiple changes that happened from 1.x to 2.x and also this is related to tf-hub. \r\nPlease try to connect with the tf-hub repo for the quick solution. \r\nhttps://github.com/tensorflow/hub/issues\r\nThank you! ",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64810\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64810\">No</a>\n"
] | 2024-03-30T19:45:58 | 2024-04-17T01:47:45 | 2024-04-17T01:47:39 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 1.7.0
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.7-11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When first running demo in docs, the following bug was encountered when running cell under section: Load a BigGAN generator module from TF Hub
`AttributeError: module 'tensorflow_hub' has no attribute 'Module'`
Following [Johnny Tang's solution](https://stackoverflow.com/questions/78059929/module-tensorflow-hub-has-no-attribute-module) to change `hub.Module` to `hub.load`, the below error is met:
`AttributeError: 'AutoTrackable' object has no attribute 'get_input_info_dict'`
This persists after trying multiple versions of tf, tensorflow_hub as well as Python
### Standalone code to reproduce the issue
```shell
The demo notebook: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/biggan_generation_with_tf_hub.ipynb
```
### Relevant log output
```shell
AttributeError: 'AutoTrackable' object has no attribute 'get_input_info_dict'
```
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[
"I have same issue on python 3.8 image on docker\r\n\r\n\" tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\"",
"I'm having the same problem, manage to solve it?\r\n\r\ntensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT",
"i think pip install tensorflow[cuda] will solve the issue but i am not quite sure",
"Did you install TensorRT? You can install from here:\r\nhttps://developer.nvidia.com/tensorrt",
"Hi @MachinesOnTheRise ,\r\n\r\nYou might need to install compatible TensorRT version from Nvidia. Since this is a optimization library for Inferences and developed by Nvidia for their specific GPUs, it may not come bundled with TF package. You can use `tensorflow[and-cuda] `package for installing TF with required cuda/cudnn packages as below.But they seems don't have tensorrt bundled.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/ced28148be8cec1f234886f21c7e528c374032b8/tensorflow/tools/pip_package/setup.py#L151-L166\r\n\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nI'm getting this error while running dreambooth lora on colab .\r\npip install tensorflow[and-cuda] -- This also not solved the problem.\r\nAny solutions ?",
"You need to install TensorRT separately. It's not bundled with tensorflow.",
" W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nI get this error in docker",
"@MachinesOnTheRise @vinay-colaco @SuryanarayanaY @Dheeraj2002 \r\n\r\nAny update? Sadly been getting the same issue for about a month now.\r\n\r\nThis what i am getting:\r\n\r\n```\r\n2024-04-21 08:00:13.414353: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-04-21 08:00:13.552935: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-04-21 08:00:14.655097: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-04-21 08:00:16.735268: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:17:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-04-21 08:00:16.828498: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] 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.\r\n```\r\n\r\nI have manually installed TensorRT and still have the same error\r\n\r\nHere is my Dockerfile for reference:\r\n\r\n```\r\nFROM nvidia/cuda:12.0.0-devel-ubuntu20.04\r\n\r\nENV DEBIAN_FRONTEND noninteractive\r\n\r\nRUN apt-get update && \\\r\n apt-get install -y git ffmpeg libssl-dev lsb-release wget curl\r\n\r\n\r\nENV PATH=\"/root/miniconda3/bin:${PATH}\"\r\nARG PATH=\"/root/miniconda3/bin:${PATH}\"\r\n\r\nRUN wget \\\r\n https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \\\r\n && mkdir /root/.conda \\\r\n && bash Miniconda3-latest-Linux-x86_64.sh -b \\\r\n && rm -f Miniconda3-latest-Linux-x86_64.sh s\r\n\r\nRUN conda --version\r\n# conda 24.1.2\r\n\r\n\r\n# CUDNN\r\nRUN apt clean\r\nRUN apt update \r\nRUN apt autoremove\r\n\r\nRUN wget https://developer.download.nvidia.com/compute/cudnn/9.1.0/local_installers/cudnn-local-repo-ubuntu2004-9.1.0_1.0-1_amd64.deb\r\nRUN dpkg -i cudnn-local-repo-ubuntu2004-9.1.0_1.0-1_amd64.deb\r\nRUN cp /var/cudnn-local-repo-ubuntu2004-9.1.0/cudnn-*-keyring.gpg /usr/share/keyrings/\r\nRUN apt-get update\r\nRUN apt-get -y install cudnn-cuda-12\r\n\r\n\r\n# CUBLASS\r\nRUN curl https://developer.download.nvidia.com/hpc-sdk/ubuntu/DEB-GPG-KEY-NVIDIA-HPC-SDK | gpg --dearmor -o /usr/share/keyrings/nvidia-hpcsdk-archive-keyring.gpg\r\nRUN echo 'deb [signed-by=/usr/share/keyrings/nvidia-hpcsdk-archive-keyring.gpg] https://developer.download.nvidia.com/hpc-sdk/ubuntu/amd64 /' | tee /etc/apt/sources.list.d/nvhpc.list\r\nRUN apt-get update -y\r\nRUN apt-get install -y nvhpc-24-3\r\nRUN apt install -f\r\n\r\n#TENSORRT\r\nRUN wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/local_repos/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb\r\nRUN dpkg -i nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb\r\nRUN cp /var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/*-keyring.gpg /usr/share/keyrings/\r\nRUN apt-get update\r\nRUN apt-get install -y tensorrt\r\nRUN apt-get install -y libnvinfer-lean8\r\nRUN apt-get install -y libnvinfer-vc-plugin8\r\nRUN python3 -m pip install numpy\r\n\r\nRUN dpkg-query -W tensorrt\r\n#tensorrt\t10.0.0.6-1+cuda12.4\r\n\r\nRUN conda install python=3.9.18\r\n\r\nRUN pip install boto3 spleeter tensorrt cuda-python\r\nRUN pip install git+https://github.com/facebookresearch/audiocraft.git\r\nRUN pip install torch==2.1.0+cu121 --index-url https://download.pytorch.org/whl/cu121\r\n\r\nRUN conda install -c nvidia cuda-python cudatoolkit\r\n\r\nRUN export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-12.0/targets/x86_64-linux/lib\r\n\r\nRUN find / -name '*libcudart*'\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart_static.a\r\n/root/miniconda3/lib/python3.9/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12\r\n/root/miniconda3/lib/python3.9/site-packages/torch/lib/libcudart-9335f6a2.so.12\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12.0.107\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart_static.a\r\n\r\nRUN find / -name '*tensorrt*'\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart_static.a\r\n/root/miniconda3/lib/python3.9/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12\r\n/root/miniconda3/lib/python3.9/site-packages/torch/lib/libcudart-9335f6a2.so.12\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12.0.107\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart_static.a\r\nExpand\r\n\r\n=> Step 35: RUN find / -name '*tensorrt*'\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings/tensorrt.so\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings-8.6.1.dist-info\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_libs\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_libs-8.6.1.dist-info\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt-8.6.1.post1.dist-info\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/python/compiler/tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v2/experimental/tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/_api/v2/experimental/tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/compiler/tf2tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/include/tensorflow/compiler/tf2tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/torch/_dynamo/backends/tensorrt.py\r\n/root/miniconda3/lib/python3.9/site-packages/torch/_dynamo/backends/__pycache__/tensorrt.cpython-39.pyc\r\n/root/miniconda3/lib/python3.9/site-packages/torch/ao/quantization/backend_config/tensorrt.py\r\n/root/miniconda3/lib/python3.9/site-packages/torch/ao/quantization/backend_config/__pycache__/tensorrt.cpython-39.pyc\r\n/usr/share/doc/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0\r\n/usr/share/doc/tensorrt-10.0.0.6\r\n/usr/share/doc/tensorrt\r\n/usr/share/keyrings/nv-tensorrt-local-9A1EDFBA-keyring.gpg\r\n/usr/lib/python3.8/dist-packages/tensorrt\r\n/usr/lib/python3.8/dist-packages/tensorrt/tensorrt.so\r\n/usr/lib/python3.8/dist-packages/tensorrt-10.0.0b6.dist-info\r\n/usr/lib/python3.8/dist-packages/tensorrt_lean\r\n/usr/lib/python3.8/dist-packages/tensorrt_lean/tensorrt_lean.so\r\n/usr/lib/python3.8/dist-packages/tensorrt_lean-10.0.0b6.dist-info\r\n/usr/lib/python3.8/dist-packages/tensorrt_dispatch\r\n/usr/lib/python3.8/dist-packages/tensorrt_dispatch/tensorrt_dispatch.so\r\n/usr/lib/python3.8/dist-packages/tensorrt_dispatch-10.0.0b6.dist-info\r\n/usr/src/tensorrt\r\n/usr/src/tensorrt/samples/python/yolov3_onnx/onnx_to_tensorrt.py\r\n/etc/apt/sources.list.d/nv-tensorrt-local-ubuntu2004-8.6.1-cuda-12.0.list\r\n/var/lib/apt/lists/_var_nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_InRelease\r\n/var/lib/apt/lists/_var_nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_Packages.lz4\r\n/var/lib/dpkg/info/tensorrt.md5sums\r\n/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.list\r\n/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.conffiles\r\n/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.md5sums\r\n/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.postinst\r\n/var/lib/dpkg/info/tensorrt.list\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/nv-tensorrt-local-9A1EDFBA-keyring.gpg\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt-dev_8.6.1.6-1+cuda12.0_amd64.deb\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt-libs_8.6.1.6-1+cuda12.0_amd64.deb\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt_8.6.1.6-1+cuda12.0_amd64.deb\r\n/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb\r\n\r\n\r\nRUN python3 -c \"import tensorrt; print(tensorrt.__path__)\"\r\nRUN python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\nRUN python -c \"import tensorflow.compiler as tf_cc; print(tf_cc.tf2tensorrt._pywrap_py_utils.get_linked_tensorrt_version())\"\r\nRUN pip list\r\n\r\nRUN nvcc --version\r\n#nvcc: NVIDIA (R) Cuda compiler driver\r\n#Copyright (c) 2005-2023 NVIDIA Corporation\r\n#Built on Fri_Jan__6_16:45:21_PST_2023\r\n#Cuda compilation tools, release 12.0, V12.0.140\r\n#Build cuda_12.0.r12.0/compiler.32267302_0\r\n\r\nRUN lsb_release -a\r\n# No LSB modules are available.\r\n# Distributor ID: Ubuntu\r\n# Description: Ubuntu 20.04.5 LTS\r\n# Release: 20.04\r\n# Codename: focal\r\n```",
"Hi @MachinesOnTheRise ,\r\n\r\nCan you confirm the output of this code that is in your docker file?\r\n\r\npython3 -c \"import tensorrt; print(tensorrt.__path__)\"",
"Exact same issue as OP.\r\n\r\nResults of attempting to print tensorrt.path results in the following:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\nAttributeError: module 'tensorrt' has no attribute 'path'\r\n```",
"> python3 -c \"import tensorrt; print(tensorrt.**path**)\"\r\n\r\nyou should better run\r\n\r\npython3 -c \"import tensorrt; print(tensorrt.\\_\\_file\\_\\_)\"",
"That works. Here's what I get:\r\n\r\n`\r\n/home/azureuser/miniconda3/envs/tensorflow-2.16.1/lib/python3.12/site-packages/tensorrt/__init__.py\r\n`",
"I am having the same issues and have tried all the listed remedies to no avail. I followed nvidia instructions and installed from tar.gz, as per the latest above I do verify the import does see tensorrt. I am trying to run it in a simple anaconda virtual environment. This is the only library I am having issues with, no matter what I do, no change in outcome, I am wondering if we have a real bug here. ",
"Just commenting to say I am experiencing the exact same issue. Some follow up information.\r\n\r\n```\r\nwhich python3\r\n>>> /home/bplaster/anaconda3/bin/python3\r\n```\r\n\r\n```\r\npython3 -c \"import tensorrt; print(tensorrt.__file__)\"\r\n>>> /home/bplaster/anaconda3/lib/python3.11/site-packages/tensorrt/__init__.py\r\n```\r\n\r\n```\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices())\"\r\n>>> 2024-05-02 15:35:07.243819: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-05-02 15:35:07.269922: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-05-02 15:35:07.628229: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-05-02 15:35:08.153905: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-05-02 15:35:08.156012: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] 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.\r\nSkipping registering GPU devices...\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\r\n```\r\n\r\n```\r\nnvcc --version\r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2021 NVIDIA Corporation\r\nBuilt on Thu_Nov_18_09:45:30_PST_2021\r\nCuda compilation tools, release 11.5, V11.5.119\r\nBuild cuda_11.5.r11.5/compiler.30672275_0\r\n```\r\n\r\n```\r\n nvidia-smi\r\nThu May 2 15:46:11 2024\r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 550.76.01 Driver Version: 552.22 CUDA Version: 12.4 |\r\n|-----------------------------------------+------------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+========================+======================|\r\n| 0 NVIDIA RTX A4000 On | 00000000:01:00.0 Off | Off |\r\n| 41% 37C P8 6W / 140W | 10MiB / 16376MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+------------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=========================================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------------------+\r\n```",
"Some more information for the devs maintaining Tensorflow.\r\n\r\nWhen attempting to run the following in terminal:\r\n`python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"`\r\n\r\nI get the following error:\r\n```\r\n2024-05-03 14:51:51.684995: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-05-03 14:51:52.375372: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] 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.\r\nSkipping registering GPU devices...\r\n[]\r\n```\r\n\r\nTensorRT is installed and I'm quite certain that I have all required nvidia drivers, CUDA, cuDNN toolkits, etc. installed...\r\n\r\n`nvidia-smi` results in:\r\n\r\n```\r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 |\r\n|-----------------------------------------+------------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+========================+======================|\r\n| 0 Tesla V100-PCIE-16GB Off | 00000001:00:00.0 Off | Off |\r\n| N/A 29C P0 25W / 250W | 102MiB / 16384MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+------------------------+----------------------+\r\n \r\n+-----------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=========================================================================================|\r\n| 0 N/A N/A 926 G /usr/lib/xorg/Xorg 87MiB |\r\n| 0 N/A N/A 1009 G /usr/bin/gnome-shell 13MiB |\r\n+-----------------------------------------------------------------------------------------+\r\n```\r\n\r\nI am able to run other libraries on the GPU (e.g. XGBoost) with no problem. Consequently, this is a Tensorflow issue.\r\n\r\nHere's my very basic dev environment:\r\n```\r\n# packages in environment at /home/azureuser/miniconda3/envs/tensorflow-2.16.1:\r\n#\r\n# Name Version Build Channel\r\n_libgcc_mutex 0.1 conda_forge conda-forge\r\n_openmp_mutex 4.5 2_gnu conda-forge\r\nabsl-py 2.1.0 pypi_0 pypi\r\nastunparse 1.6.3 pypi_0 pypi\r\nbzip2 1.0.8 hd590300_5 conda-forge\r\nca-certificates 2024.2.2 hbcca054_0 conda-forge\r\ncertifi 2024.2.2 pypi_0 pypi\r\ncharset-normalizer 3.3.2 pypi_0 pypi\r\nflatbuffers 24.3.25 pypi_0 pypi\r\ngast 0.5.4 pypi_0 pypi\r\ngoogle-pasta 0.2.0 pypi_0 pypi\r\ngrpcio 1.63.0 pypi_0 pypi\r\nh5py 3.11.0 pypi_0 pypi\r\nidna 3.7 pypi_0 pypi\r\nkeras 3.3.3 pypi_0 pypi\r\nld_impl_linux-64 2.40 h55db66e_0 conda-forge\r\nlibclang 18.1.1 pypi_0 pypi\r\nlibexpat 2.6.2 h59595ed_0 conda-forge\r\nlibffi 3.4.2 h7f98852_5 conda-forge\r\nlibgcc-ng 13.2.0 h77fa898_6 conda-forge\r\nlibgomp 13.2.0 h77fa898_6 conda-forge\r\nlibnsl 2.0.1 hd590300_0 conda-forge\r\nlibsqlite 3.45.3 h2797004_0 conda-forge\r\nlibuuid 2.38.1 h0b41bf4_0 conda-forge\r\nlibxcrypt 4.4.36 hd590300_1 conda-forge\r\nlibzlib 1.2.13 hd590300_5 conda-forge\r\nmarkdown 3.6 pypi_0 pypi\r\nmarkdown-it-py 3.0.0 pypi_0 pypi\r\nmarkupsafe 2.1.5 pypi_0 pypi\r\nmdurl 0.1.2 pypi_0 pypi\r\nml-dtypes 0.3.2 pypi_0 pypi\r\nnamex 0.0.8 pypi_0 pypi\r\nncurses 6.4.20240210 h59595ed_0 conda-forge\r\nnumpy 1.26.4 pypi_0 pypi\r\nnvidia-cublas-cu12 12.3.4.1 pypi_0 pypi\r\nnvidia-cuda-cupti-cu12 12.3.101 pypi_0 pypi\r\nnvidia-cuda-nvcc-cu12 12.3.107 pypi_0 pypi\r\nnvidia-cuda-nvrtc-cu12 12.3.107 pypi_0 pypi\r\nnvidia-cuda-runtime-cu12 12.3.101 pypi_0 pypi\r\nnvidia-cudnn-cu12 8.9.7.29 pypi_0 pypi\r\nnvidia-cufft-cu12 11.0.12.1 pypi_0 pypi\r\nnvidia-curand-cu12 10.3.4.107 pypi_0 pypi\r\nnvidia-cusolver-cu12 11.5.4.101 pypi_0 pypi\r\nnvidia-cusparse-cu12 12.2.0.103 pypi_0 pypi\r\nnvidia-nccl-cu12 2.19.3 pypi_0 pypi\r\nnvidia-nvjitlink-cu12 12.3.101 pypi_0 pypi\r\nopenssl 3.3.0 hd590300_0 conda-forge\r\nopt-einsum 3.3.0 pypi_0 pypi\r\noptree 0.11.0 pypi_0 pypi\r\npackaging 24.0 pypi_0 pypi\r\npip 24.0 pyhd8ed1ab_0 conda-forge\r\nprotobuf 4.25.3 pypi_0 pypi\r\npygments 2.17.2 pypi_0 pypi\r\npython 3.11.9 hb806964_0_cpython conda-forge\r\nreadline 8.2 h8228510_1 conda-forge\r\nrequests 2.31.0 pypi_0 pypi\r\nrich 13.7.1 pypi_0 pypi\r\nsetuptools 69.5.1 pyhd8ed1ab_0 conda-forge\r\nsix 1.16.0 pypi_0 pypi\r\ntensorboard 2.16.2 pypi_0 pypi\r\ntensorboard-data-server 0.7.2 pypi_0 pypi\r\ntensorflow 2.16.1 pypi_0 pypi\r\ntensorflow-io-gcs-filesystem 0.37.0 pypi_0 pypi\r\ntensorrt 10.0.1 pypi_0 pypi\r\ntensorrt-cu12 10.0.1 pypi_0 pypi\r\ntermcolor 2.4.0 pypi_0 pypi\r\ntk 8.6.13 noxft_h4845f30_101 conda-forge\r\ntyping-extensions 4.11.0 pypi_0 pypi\r\ntzdata 2024a h0c530f3_0 conda-forge\r\nurllib3 2.2.1 pypi_0 pypi\r\nwerkzeug 3.0.2 pypi_0 pypi\r\nwheel 0.43.0 pyhd8ed1ab_1 conda-forge\r\nwrapt 1.16.0 pypi_0 pypi\r\nxz 5.2.6 h166bdaf_0 conda-forge\r\n```\n\nNote: I'm on Linux (MSFT Azure VM)\r\n\r\nAny luck in replicating the issue?",
"Wanted to add more onto this, I spent a day trying various WSL Tensorflow installation methods and still no luck.\r\n\r\nFor context, I'm on Windows 11, with an i7-13700 and an RTX A4000 Series GPU.\r\n\r\nHere are my printouts:\r\n\r\n```\r\nnvidia-smi\r\nFri May 3 09:50:02 2024\r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 550.76.01 Driver Version: 552.22 CUDA Version: 12.4 |\r\n|-----------------------------------------+------------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+========================+======================|\r\n| 0 NVIDIA RTX A4000 On | 00000000:01:00.0 Off | Off |\r\n| 45% 37C P0 35W / 140W | 0MiB / 16376MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+------------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=========================================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------------------+\r\n```\r\n\r\n```\r\nnvcc --version\r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2022 NVIDIA Corporation\r\nBuilt on Wed_Sep_21_10:33:58_PDT_2022\r\nCuda compilation tools, release 11.8, V11.8.89\r\nBuild cuda_11.8.r11.8/compiler.31833905_0\r\n```\r\n\r\n```\r\n which python\r\n/home/bplaster/anaconda3/bin/python\r\n```\r\n\r\ninterestingly, after using `pip install tensorflow[and-cuda]` within the WSL environment, I was getting this:\r\n```\r\npython3 -c \"import tensorrt; print(tensorrt.__file__)\"\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\nModuleNotFoundError: No module named 'tensorrt'\r\n```\r\n\r\nI attempted to resolve this with: `pip install tensorrt`\r\n\r\n```\r\nCollecting tensorrt\r\n Downloading tensorrt-10.0.1.tar.gz (16 kB)\r\n Preparing metadata (setup.py) ... done\r\nCollecting tensorrt-cu12 (from tensorrt)\r\n Downloading tensorrt-cu12-10.0.1.tar.gz (18 kB)\r\n Preparing metadata (setup.py) ... done\r\nBuilding wheels for collected packages: tensorrt, tensorrt-cu12\r\n Building wheel for tensorrt (setup.py) ... done\r\n Created wheel for tensorrt: filename=tensorrt-10.0.1-py2.py3-none-any.whl size=16333 sha256=19d103fecc3bfb19836fe815d369e65171978f61bb476e89ae84b857953f64e7\r\n Stored in directory: /home/bplaster/.cache/pip/wheels/31/90/ef/53ad98d9a1bd660c0177aa1ea91bde288edfbe4d15621ca472\r\n Building wheel for tensorrt-cu12 (setup.py) ... done\r\n Created wheel for tensorrt-cu12: filename=tensorrt_cu12-10.0.1-py2.py3-none-any.whl size=17549 sha256=5877c62938e16384e9c969b00a94dea5bda1bea7de2db0249bcfd8520d5082fd\r\n Stored in directory: /home/bplaster/.cache/pip/wheels/c5/08/30/a058eaf14eeeabb3799e3abd3a2a534649123824dcd9695876\r\nSuccessfully built tensorrt tensorrt-cu12\r\nInstalling collected packages: tensorrt-cu12, tensorrt\r\nSuccessfully installed tensorrt-10.0.1 tensorrt-cu12-10.0.1\r\n```\r\n\r\nChecking the nvidia compatibility matrrix, this should be good to go:\r\n\r\n\r\nNow, we see that TensorRT is there when called:\r\n```\r\npython3 -c \"import tensorrt; print(tensorrt.__file__)\"\r\n/home/bplaster/anaconda3/lib/python3.11/site-packages/tensorrt/__init__.py\r\n```\r\n\r\nYet we still get the following message when checking the Tensorflow version:\r\n\r\n```\r\npython -c \"import tensorflow as tf; print(tf.__version__)\"\r\n2024-05-03 09:51:40.606740: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-05-03 09:51:40.632018: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-05-03 09:51:41.004536: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2.16.1\r\n```\r\n\r\nFinally, confirming that the GPU cannot be seen by Tensorflow for some reason:\r\n\r\n```\r\npython -c \"import tensorflow as tf; print(tf.config.list_physical_devices())\"\r\n2024-05-03 10:01:27.648034: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-05-03 10:01:27.673662: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-05-03 10:01:28.043090: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-05-03 10:01:30.488133: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-05-03 10:01:30.501591: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] 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.\r\nSkipping registering GPU devices...\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\r\n```\r\n\r\nThis is all done on a completely fresh WSL2 Ubuntu environment, all done in the (base) conda environment. Hope this is helpful.\r\n\r\n",
"Same issue:\r\n```\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\" \r\n2024-05-08 12:14:54.731695: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-05-08 12:14:55.226452: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-05-08 12:14:55.882997: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-05-08 12:14:55.911562: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-05-08 12:14:55.911640: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n``` \r\nMy nvidia drivers and cuda version:\r\n```\r\nnvidia-smi\r\nWed May 8 12:06:07 2024 \r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 550.76.01 Driver Version: 552.22 CUDA Version: 12.4 |\r\n|-----------------------------------------+------------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+========================+======================|\r\n| 0 NVIDIA GeForce RTX 3060 Ti On | 00000000:2B:00.0 On | N/A |\r\n| 30% 31C P0 42W / 200W | 979MiB / 8192MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+------------------------+----------------------+\r\n``` \r\n```\r\nnvcc --version\r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2021 NVIDIA Corporation\r\nBuilt on Thu_Nov_18_09:45:30_PST_2021\r\nCuda compilation tools, release 11.5, V11.5.119\r\nBuild cuda_11.5.r11.5/compiler.30672275_0\r\n```\r\nI've installed tensorflow and tensorrt through pip:\r\n```\r\npip install tensorflow[and-cuda]\r\npip install tensorrt\r\n```\r\n\r\nI have follow the guide: [Install TensorFlow with pip](https://www.tensorflow.org/install/pip) and added path as in [Install Tensorflow & Pytorch with CUDA [Linux | WSL2]](https://gist.github.com/mantasu/d79d23b58d822d675274f87c46eb7aca) but I'm still getting the same warning (TF-TRT Warning: Could not find TensorRT).\r\n\r\nDoes any anyone managed to find a solution?",
"Any solution?",
"Same issue here, installed tensorrt with `pip install --extra-index-url https://pypi.nvidia.com tensorrt-libs`,\r\n followed by `pip install tensorflow[and-cuda]==2.15.0`. It successfully updated the 'nvidia-...' packages.\r\n\r\nYet, I get the following when running it in python (3.10.8):\r\n\r\n```\r\n>>> import tensorflow\r\n2024-05-14 17:25:38.312524: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-05-14 17:25:38.312576: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-05-14 17:25:38.314278: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2024-05-14 17:25:38.322356: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-05-14 17:25:39.497691: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n```\r\n```\r\n>> import tensorrt\r\n>> print(tensorrt.__file__)\r\n/home/bariller/.conda/envs/myenv/lib/python3.10/site-packages/tensorrt/__init__.py\r\n```\r\n\r\nHas anyone managed to make it work ?\r\n\r\n\r\n```\r\n$ nvidia-smi\r\nTue May 14 17:40:12 2024 \r\n+---------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 535.161.08 Driver Version: 535.161.08 CUDA Version: 12.2 |\r\n|-----------------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+======================+======================|\r\n| 0 NVIDIA TITAN RTX On | 00000000:3B:00.0 Off | N/A |\r\n| 41% 30C P8 15W / 280W | 1MiB / 24576MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+----------------------+----------------------+\r\n \r\n+---------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=======================================================================================|\r\n| No running processes found |\r\n+---------------------------------------------------------------------------------------+\r\n```",
"I think that the only way is to find the compatible versions for python, tensorflow, nvidia driver, cuda, cudnn and tensorrt. I'll try the tested build configurations from [here](https://www.tensorflow.org/install/source#setup_for_linux_and_macos) in WSL and Ubuntu. Hope it works 🙏 ",
"> I think that the only way is to find the compatible versions for python, tensorflow, nvidia driver, cuda, cudnn and tensorrt. I'll try the tested build configurations from [here](https://www.tensorflow.org/install/source#setup_for_linux_and_macos) in WSL and Ubuntu. Hope it works 🙏\r\n\r\nSo, after trying out pretty much everything else, I can confirm that this worked out and my GPU is now also detected. I am on Ubuntu 22.0.4.4 running Tensorflow 2.16. \r\n\r\nMany thanks @BigKhalPablo, saved my day ;)\r\n\r\nCheers,\r\nEric",
"Could you explain how you managed to make it work @eric86y? Did you follow the \"build from source\" guide for Linux or the \"install with pip\"? I'm trying in WSL2 but I haven't made any progress yet 😞 ",
"**Couldn't resolve TF-TRT Warning: Could not find TensorRT**\r\n\r\nI'm using wsl2 in windows 11.\r\n\r\n```\r\nDistributor ID: Ubuntu\r\nDescription: Ubuntu 22.04.4 LTS\r\nRelease: 22.04\r\nCodename: jammy\r\n```\r\nPreviously, I wasn't able to have GPU as the backend, I had tried all the methods in installing tensorflow in wsl. \r\n\r\nBut, trying with python3.11 and tensorflow 2.15.1, got me access to the GPU backend. \r\n\r\n```\r\nconda create -n tmpenv python=3.11\r\nconda activate tmpenv\r\npip install tensorflow[and-cuda]==2.15.1\r\n```\r\n\r\nversions:\r\n```\r\ntensorflow 2.15.1\r\ntensorrt 10.0.1\r\n```\r\nnvidia-smi:\r\n```\r\nTue May 21 15:45:23 2024\r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 550.76.01 Driver Version: 552.44 CUDA Version: 12.4 |\r\n|-----------------------------------------+------------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+========================+======================|\r\n| 0 NVIDIA GeForce RTX 4080 ... On | 00000000:01:00.0 Off | N/A |\r\n| N/A 38C P4 17W / 55W | 0MiB / 12282MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+------------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=========================================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------------------+\r\n```\r\n```\r\n2024-05-21 15:46:26.487553: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fcc3f7b2c00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2024-05-21 15:46:26.487663: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 4080 Laptop GPU, Compute Capability 8.9\r\n```\r\nI've tried all ways of installing tensorrt - pip, .deb, .tar;\r\nupdated path variable, etc.\r\nIts importing in python shell\r\n\r\n```\r\n>>> import tensorrt as trt\r\n>>> trt.__version__\r\n'10.0.1'\r\n>>> trt.__file__\r\n'/home/mohan/miniconda3/envs/tmpenv/lib/python3.11/site-packages/tensorrt/__init__.py'\r\n```\r\nAll the files are available (.tar installation method)\r\n```\r\n(tmpenv) mohan@LAPTOP-INPO8147:~$ ls TensorRT-10.0.1.6/\r\nbin data doc include lib onnx_graphsurgeon python samples targets\r\n\r\n(tmpenv) mohan@LAPTOP-INPO8147:~$ ls TensorRT-10.0.1.6/lib/\r\nlibnvinfer.so libnvinfer_plugin.so.10.0.1\r\nlibnvinfer.so.10 libnvinfer_plugin_static.a\r\nlibnvinfer.so.10.0.1 libnvinfer_static.a\r\nlibnvinfer_builder_resource.so.10.0.1 libnvinfer_vc_plugin.so\r\nlibnvinfer_dispatch.so libnvinfer_vc_plugin.so.10\r\nlibnvinfer_dispatch.so.10 libnvinfer_vc_plugin.so.10.0.1\r\nlibnvinfer_dispatch.so.10.0.1 libnvinfer_vc_plugin_static.a\r\nlibnvinfer_dispatch_static.a libnvonnxparser.so\r\nlibnvinfer_lean.so libnvonnxparser.so.10\r\nlibnvinfer_lean.so.10 libnvonnxparser.so.10.0.1\r\nlibnvinfer_lean.so.10.0.1 libnvonnxparser_static.a\r\nlibnvinfer_lean_static.a libonnx_proto.a\r\nlibnvinfer_plugin.so stubs\r\nlibnvinfer_plugin.so.10\r\n\r\n(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-10.0.1.6/python$ ls\r\ntensorrt-10.0.1-cp310-none-linux_x86_64.whl\r\ntensorrt-10.0.1-cp311-none-linux_x86_64.whl\r\ntensorrt-10.0.1-cp312-none-linux_x86_64.whl\r\ntensorrt-10.0.1-cp38-none-linux_x86_64.whl\r\ntensorrt-10.0.1-cp39-none-linux_x86_64.whl\r\ntensorrt_dispatch-10.0.1-cp310-none-linux_x86_64.whl\r\ntensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl\r\ntensorrt_dispatch-10.0.1-cp312-none-linux_x86_64.whl\r\ntensorrt_dispatch-10.0.1-cp38-none-linux_x86_64.whl\r\ntensorrt_dispatch-10.0.1-cp39-none-linux_x86_64.whl\r\ntensorrt_lean-10.0.1-cp310-none-linux_x86_64.whl\r\ntensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl\r\ntensorrt_lean-10.0.1-cp312-none-linux_x86_64.whl\r\ntensorrt_lean-10.0.1-cp38-none-linux_x86_64.whl\r\ntensorrt_lean-10.0.1-cp39-none-linux_x86_64.whl\r\n```\r\n\r\nI've also used the files from .tar method to install using pip:\r\n```\r\n(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-10.0.1.6/python$ python3 -m pip install tensorrt-10.0.1-cp311-none-linux_x86_64.whl\r\nLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\r\nProcessing ./tensorrt-10.0.1-cp311-none-linux_x86_64.whl\r\ntensorrt is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n\r\n(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-10.0.1.6/python$ python3 -m pip install tensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl\r\nLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\r\nProcessing ./tensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl\r\ntensorrt-dispatch is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n\r\n(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-10.0.1.6/python$ python3 -m pip install tensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl\r\nLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\r\nProcessing ./tensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl\r\ntensorrt-lean is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n```\r\nBut still I get this error\r\n\r\n```\r\n2024-05-21 15:46:19.659564: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n```",
"> W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT I'm getting this error while running dreambooth lora on colab . pip install tensorflow[and-cuda] -- This also not solved the problem. Any solutions ?\r\n\r\nI got exactly the same question like you, any ideas?\r\n",
"> > W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT I'm getting this error while running dreambooth lora on colab . pip install tensorflow[and-cuda] -- This also not solved the problem. Any solutions ?\r\n> \r\n> I got exactly the same question like you, any ideas?\r\n\r\nstill no",
"Setting TF_CPP_MAX_VLOG_LEVEL=3 in my environment (WSL2 Ubuntu) gives the following specific error:\r\n\r\n```\r\n2024-06-04 15:42:11.291621: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer.so.8.6.1'; dlerror: libnvinfer.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/zilla/miniconda3/lib/:/home/zilla/miniconda3/lib/python3.12/site-packages/nvidia/cudnn/lib\r\n2024-06-04 15:42:11.291675: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer_plugin.so.8.6.1'; dlerror: libnvinfer_plugin.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/zilla/miniconda3/lib/:/home/zilla/miniconda3/lib/python3.12/site-packages/nvidia/cudnn/lib\r\n2024-06-04 15:42:11.291687: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n```\r\n\r\nWhile I have TensorRT, and thus libnvinfer, installed, the actual libraries are named libnvinfer.so.10 and libnvinfer_plugin.so.10\r\nJust as a test, I did the following:\r\n\r\n```\r\n~/miniconda3/lib$ ln -s python3.12/site-packages/tensorrt_libs/libnvinfer.so.10 libnvinfer.so.8.6.1\r\n~/miniconda3/lib$ ln -s python3.12/site-packages/tensorrt_libs/libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1\r\n```\r\n\r\nAnd the warning about not finding TensorRT diapered.\r\nOf course, if the libraries are not binary compatible, this might lead to further issues down the line, but at least it hints at what the problem is.\r\n\r\n",
"@tilakrayal This issue seems to be related to TensorRT and GPU. Could you inform them?",
"> Setting TF_CPP_MAX_VLOG_LEVEL=3 in my environment (WSL2 Ubuntu) gives the following specific error:\r\n> \r\n> ```\r\n> 2024-06-04 15:42:11.291621: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer.so.8.6.1'; dlerror: libnvinfer.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/zilla/miniconda3/lib/:/home/zilla/miniconda3/lib/python3.12/site-packages/nvidia/cudnn/lib\r\n> 2024-06-04 15:42:11.291675: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer_plugin.so.8.6.1'; dlerror: libnvinfer_plugin.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/zilla/miniconda3/lib/:/home/zilla/miniconda3/lib/python3.12/site-packages/nvidia/cudnn/lib\r\n> 2024-06-04 15:42:11.291687: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n> ```\r\n> \r\n> While I have TensorRT, and thus libnvinfer, installed, the actual libraries are named libnvinfer.so.10 and libnvinfer_plugin.so.10 Just as a test, I did the following:\r\n> \r\n> ```\r\n> ~/miniconda3/lib$ ln -s python3.12/site-packages/tensorrt_libs/libnvinfer.so.10 libnvinfer.so.8.6.1\r\n> ~/miniconda3/lib$ ln -s python3.12/site-packages/tensorrt_libs/libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1\r\n> ```\r\n> \r\n> And the warning about not finding TensorRT diapered. Of course, if the libraries are not binary compatible, this might lead to further issues down the line, but at least it hints at what the problem is.\r\n\r\nYes. It works.\r\nI have the miniconda env \"cuda12\", cuda 12, tensorrt 10 on Ubuntu 22, WSL2.\r\n\r\nThat is my commands:\r\n```\r\ncd ~/miniconda3/envs/cuda12/lib/python3.11/site-packages/tensorrt_libs/\r\nln -s libnvinfer.so.10 libnvinfer.so.8.6.1\r\nln -s libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1\r\n\r\n```\r\n"
] | 2024-03-30T19:41:17 | 2024-06-10T05:41:20 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
11.2
### CUDA/cuDNN version
12.4
### GPU model and memory
RTX 3050 TI
### Current behavior?
Hey everybody,
I have tried installing tensorflow from my ubuntu 22.04 terminal with anaconda environment. I am getting Can not find TensorRT. I have a 4 monitor setup with Ubuntu and the 550 NVIDIA driver for the RTX 3050 TI does not work on my machine. I have to run the 535 driver, and I think 12.4 CUDA is supported. It installed the 550 driver automatically and I revert back to the 535 driver. But after all that I still ended up with the error below.
I have tried uninstalling, reinstalling, and can't get it to work. I am a graduate student taking a Machine Learning course. I am spending more time debugging this issue then actually coding.
Could somebody help me resolve?
### Standalone code to reproduce the issue
```shell
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.161.08 Driver Version: 535.161.08 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3050 ... Off | 00000000:01:00.0 On | N/A |
| N/A 66C P8 8W / 35W | 1011MiB / 4096MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 2812 G /usr/lib/xorg/Xorg 648MiB |
| 0 N/A N/A 3023 G /usr/bin/gnome-shell 148MiB |
| 0 N/A N/A 78082 G ...az/anaconda3/envs/skynet/bin/python 1MiB |
| 0 N/A N/A 78475 G ...seed-version=20240329-165146.919000 96MiB |
| 0 N/A N/A 79970 G ...gnu/webkit2gtk-4.0/WebKitWebProcess 68MiB |
+---------------------------------------------------------------------------------------+
(base) thenaz@Skynet:~/Downloads$ lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation GA107M [GeForce RTX 3050 Ti Mobile] (rev a1)
01:00.1 Audio device: NVIDIA Corporation Device 2291 (rev a1)
(base) thenaz@Skynet:~/Downloads$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Feb_27_16:19:38_PST_2024
Cuda compilation tools, release 12.4, V12.4.99
Build cuda_12.4.r12.4/compiler.33961263_0
```
### Relevant log output
```shell
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2024-03-30 14:29:58.668656: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-30 14:29:58.691384: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-03-30 14:29:58.691405: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-03-30 14:29:58.692006: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-03-30 14:29:58.695703: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-30 14:29:59.119119: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-03-30 14:29:59.660586: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-30 14:29:59.675221: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-30 14:29:59.675340: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
```
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Remove code preventing TPU function from being hoisted.
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Tf_nightly fails to install in docker
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[
"It looks like it would be due to the nightly getting a version flagged as a release version when we switched over how we built the pip package for 2.16's release. We will need to fix that but in the mean time you should be able to get around this by changing the install to \r\n`pip install tf-nightly --pre`",
"I have pulled the bad release from the tf-nightly repo. The replication code provided now correctly pulls the latest nightly for me on the first attempt.\r\n",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64807\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64807\">No</a>\n"
] | 2024-03-30T18:23:47 | 2024-04-26T16:41:05 | 2024-04-26T16:41:02 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf-nightly
### Custom code
No
### OS platform and distribution
Linux
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
Nvidia 3090 ti
### Current behavior?
Actual Behavior: When trying to install tf_nightly in a docker container, pip starts iterating backwards in time, downloading previous days' tensorflow nightly versions. I stopped the process after it made it back to March 9th...
Expected behavior: tf_nightly installs or produces an error
Note that I can install tf_nightly_cpu just fine. IIRC, GPUs are not available during build-time for docker by default, so maybe the error is related to the build environment silently failing a check for some gpu-related support? If so, (1) this shouldn't happen silently and (2) it's unlikely to be something that downloading a previous version would fix?
### Standalone code to reproduce the issue
```shell
from nvidia/cuda:12.3.2-cudnn9-devel-ubuntu22.04
RUN apt-get update && apt-get install python3 python3-pip python3-dev --yes
RUN pip install tf_nightly
```
### Relevant log output
_No response_
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