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---
layout: post
title: "Python Woes (TensorFlow)"
date: 2024-10-03 18:31 +0200
lang: en
categories: ["tech"]
---
In the last few days, I was experimenting with
[ZenithO-o/fursuit-detection](https://github.com/ZenithO-o/fursuit-detection), after sorting some photos from
a fursuit walk. I couldn't get it to work/run, no matter how hard I tried, with
the system packages. (Debian stable is somewhat old, granted, which leads to
some problems).
So since venv wouldn't do it's job, I thought about (ana|mini)conda again. Only
to find out now there's also miniforge. And apparently a project with a faster
dependency resolver, mamba, split up. Its dependency resolver already
re-integrated into conda (?), and then there's micromamba which is a standalone
executable compiled from C++ (?)… I already wasted some hours researching that
rabbit hole. And apparently you can't use that stuff without putting some stuff
into your `.bashrc`, "activate only when needed" doesn't seem to be a supported
usecase? (I didn't want to put even more time into this, but yes, looking at
what's inside `.bashrc', I could simply do this manually…).
So anyways. Next step was searching for the required packages in conda-forge. I
found some very outdated guides on the internet, which installed some things
manually. I simply went with `micromamba install tensorflow-gpu` - and hey, it
works! Or so I thought…
Running the `run_on_images` script gave me a
```
tensorflow.python.framework.errors_impl.InternalError: Graph execution error:
Detected at node MultiscaleGridAnchorGenerator/GridAnchorGenerator/mul_3 defined at (most recent call last):
<stack traces unavailable>
Detected at node MultiscaleGridAnchorGenerator/GridAnchorGenerator/mul_3 defined at (most recent call last):
<stack traces unavailable>
2 root error(s) found.
(0) INTERNAL: 'cuLaunchKernel(function, gridX, gridY, gridZ, blockX, blockY, blockZ, 0, reinterpret_cast<CUstream>(stream), params, nullptr)' failed with 'CUDA_ERROR_INVALID_HANDLE'
[[ { {node MultiscaleGridAnchorGenerator/GridAnchorGenerator/mul_3 } } ]]
[[StatefulPartitionedCall/map/while/loop_body_control/_430/_23]]
(1) INTERNAL: 'cuLaunchKernel(function, gridX, gridY, gridZ, blockX, blockY, blockZ, 0, reinterpret_cast<CUstream>(stream), params, nullptr)' failed with 'CUDA_ERROR_INVALID_HANDLE'
[[ { {node MultiscaleGridAnchorGenerator/GridAnchorGenerator/mul_3 } } ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_restored_function_body_41075]
```
A very useless error message. I did a lot of fruitless internet searches. I then
noticed the author of the script limited logging. So I removed that line. With that, I
suddenly got a more promising
```
2024-10-03 16:04:42.816559: W tensorflow/compiler/mlir/tools/kernel_gen/tf_gpu_runtime_wrappers.cc:40] 'cuModuleLoadData(&module, data)' failed with 'CUDA_ERROR_NO_BINARY_FOR_GPU'
2024-10-03 16:04:42.816603: W tensorflow/compiler/mlir/tools/kernel_gen/tf_gpu_runtime_wrappers.cc:40] 'cuModuleGetFunction(&function, module, kernel_name)' failed with 'CUDA_ERROR_INVALID_HANDLE'
```
Which didn't help me much either itself. But then I spotted this at the beginning of
the script
```
2024-10-03 16:04:36.034469: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2432] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 5.2. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
```
So… my GPU was too old. And apparently *something* went wrong with the JIT
compilation. So I step-by-step installed older `tensorflow-gpu` packages until I
arrived at `tensorflow-gpu~=2.14.0`. Mind you, this whole process took half a
day. And even then, I wasn't spared:
```
2024-10-03 18:16:03.567311: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:559] libdevice is required by this HLO module but was not found at ./libdevice.10.bc
error: libdevice not found at ./libdevice.10.bc
2024-10-03 18:16:03.568777: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: JIT compilation failed.
2024-10-03 18:16:03.568846: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: JIT compilation failed.
```
Luckily, for that one, I found a solition pretty quickly: You have to copy a
file to a subdirectory in the execution directory:
```
cp ${PUT_ENV_PATH_HERE}/lib/libdevice.10.bc ./cuda_sdk_lib/nvvm/libdevice/
```
And then, FINALLY!!!, this sh** works. Half a day wasted, and a lot of angry
shouts were emitted.
|
