I’m trying to run a TensorRT Tiny YOLOv4 model on a 4GB Nano development board.
I’m running the Nano headless, so should have enough memory (details in jtop output below). And, the model inference definitely works fine on my MacBook CPU.
But, whenever I try to run the inference script on the Nano (available here), I get the following too many resources requested error:
2020-12-16 20:01:04.688877: F tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc:493] Non-OK-status: GpuLaunchKernel(kernel, config.block_count, config.thread_per_block, 0, d.stream(), config.virtual_thread_count, images.data(), height_scale, width_scale, batch, in_height, in_width, channels, out_height, out_width, output.data()) status: Internal: too many resources requested for launch
Fatal Python error: Aborted
Thread 0x0000007f876e6010 (most recent call first):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py", line 60 in quick_execute
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 550 in call
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1924 in _call_flat
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py", line 106 in _call_flat
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1722 in _call_with_flat_signature
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1673 in _call_impl
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1655 in __call__
File "detect_video.py", line 92 in main
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 251 in _run_main
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 303 in run
File "detect_video.py", line 124 in <module>
Aborted (core dumped)
I’ve read elsewhere (e.g. here) that this error might relate to CUDA’s maximum number of threads per block being too large. According to deviceQuery (see below) it’s set to 1024. But I’m not sure
a) whether this is the problem, or
b) how to go about reducing the max threads per block (e.g. to 512) if it is the problem.
Device 0: "NVIDIA Tegra X1"
CUDA Driver Version / Runtime Version 10.2 / 10.2
CUDA Capability Major/Minor version number: 5.3
Total amount of global memory: 3964 MBytes (4156682240 bytes)
( 1) Multiprocessors, (128) CUDA Cores/MP: 128 CUDA Cores
GPU Max Clock rate: 922 MHz (0.92 GHz)
Memory Clock rate: 13 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 262144 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: Yes
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: No
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS
I’m very much a beginner with Python/Tensorflow/Jetson, so would really appreciate some help to get my inference running.