Now also available via Tor
http://iwccx6ebuapto7dlkd4m5fooud6nqn2z7go7aass5c4q6vl6gzs5htad.onion/
Einträge
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Python Woes (TensorFlow)
In the last few days, I was experimenting with 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 I found some very outdated guides on the internet, which installed some things manually.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_imagesscript gave me atensorflow.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-gpupackages until I arrived attensorflow-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.