OOM when allocating tensor with shape[3,3,512,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Mul]

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System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):ubuntu Jetson Nano
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary):
TensorFlow version:2.4.0+nv21.2
Python version:3.6
Installed using virtualenv? pip? conda?:
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory:

Describe the problem
OOM when allocating tensor with shape[3,3,512,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Mul]

Provide the exact sequence of commands / steps that you executed before running into the problem
Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

/usr/bin/python3.6 /home/swag/Desktop/IGVC_ConeDetectionScript/detect_independent.py
2021-03-15 15:33:26.917255: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.2
2021-03-15 15:33:39.556381: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-03-15 15:33:39.691466: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-03-15 15:33:39.771661: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:33:39.793576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Found device 0 with properties:
pciBusID: 0000:00:00.0 name: NVIDIA Tegra X1 computeCapability: 5.3
coreClock: 0.9216GHz coreCount: 1 deviceMemorySize: 3.87GiB deviceMemoryBandwidth: 194.55MiB/s
2021-03-15 15:33:39.793916: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.2
2021-03-15 15:33:39.977313: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2021-03-15 15:33:39.977799: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10
2021-03-15 15:33:40.055171: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-03-15 15:33:40.164782: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-03-15 15:33:40.301700: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-03-15 15:33:40.377652: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2021-03-15 15:33:40.394881: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-03-15 15:33:40.395772: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:33:40.396638: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:33:40.396876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1888] Adding visible gpu devices: 0
GPUs [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
2021-03-15 15:33:40.573865: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-03-15 15:33:40.574405: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:33:40.574550: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Found device 0 with properties:
pciBusID: 0000:00:00.0 name: NVIDIA Tegra X1 computeCapability: 5.3
coreClock: 0.9216GHz coreCount: 1 deviceMemorySize: 3.87GiB deviceMemoryBandwidth: 194.55MiB/s
2021-03-15 15:33:40.574750: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.2
2021-03-15 15:33:40.574904: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2021-03-15 15:33:40.575010: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10
2021-03-15 15:33:40.575096: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-03-15 15:33:40.575171: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-03-15 15:33:40.575246: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-03-15 15:33:40.575320: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2021-03-15 15:33:40.575396: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-03-15 15:33:40.575675: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:33:40.575965: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:33:40.576181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1888] Adding visible gpu devices: 0
2021-03-15 15:33:40.589765: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.2
2021-03-15 15:35:04.102739: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1287] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-03-15 15:35:04.868559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1293]      0
2021-03-15 15:35:04.875308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 0:   N
2021-03-15 15:35:07.222793: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:35:07.698663: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:35:07.747358: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] ARM64 does not support NUMA - returning NUMA node zero
2021-03-15 15:35:08.060367: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 67 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3)
2021-03-15 17:16:57.309165: W tensorflow/core/common_runtime/bfc_allocator.cc:433] Allocator (GPU_0_bfc) ran out of memory trying to allocate 18.00MiB (rounded to 18874368)requested by op Mul
If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation.
Current allocation summary follows.
Current allocation summary follows.
2021-03-15 17:16:57.311009: I tensorflow/core/common_runtime/bfc_allocator.cc:975] BFCAllocator dump for GPU_0_bfc
2021-03-15 17:16:57.311451: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (256): 	Total Chunks: 64, Chunks in use: 64. 16.0KiB allocated for chunks. 16.0KiB in use in bin. 14.0KiB client-requested in use in bin.
2021-03-15 17:16:57.311711: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (512): 	Total Chunks: 84, Chunks in use: 84. 42.0KiB allocated for chunks. 42.0KiB in use in bin. 42.0KiB client-requested in use in bin.
2021-03-15 17:16:57.311908: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (1024): 	Total Chunks: 85, Chunks in use: 85. 85.8KiB allocated for chunks. 85.8KiB in use in bin. 85.0KiB client-requested in use in bin.
2021-03-15 17:16:57.312094: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (2048): 	Total Chunks: 9, Chunks in use: 9. 19.5KiB allocated for chunks. 19.5KiB in use in bin. 19.4KiB client-requested in use in bin.
2021-03-15 17:16:57.312260: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (4096): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-15 17:16:57.312452: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (8192): 	Total Chunks: 1, Chunks in use: 1. 8.0KiB allocated for chunks. 8.0KiB in use in bin. 8.0KiB client-requested in use in bin.
2021-03-15 17:16:57.312640: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (16384): 	Total Chunks: 6, Chunks in use: 6. 104.0KiB allocated for chunks. 104.0KiB in use in bin. 96.0KiB client-requested in use in bin.
2021-03-15 17:16:57.312826: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (32768): 	Total Chunks: 4, Chunks in use: 3. 161.0KiB allocated for chunks. 112.0KiB in use in bin. 96.0KiB client-requested in use in bin.
2021-03-15 17:16:57.313015: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (65536): 	Total Chunks: 12, Chunks in use: 12. 811.8KiB allocated for chunks. 811.8KiB in use in bin. 784.0KiB client-requested in use in bin.
2021-03-15 17:16:57.313200: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (131072): 	Total Chunks: 5, Chunks in use: 4. 736.0KiB allocated for chunks. 544.0KiB in use in bin. 544.0KiB client-requested in use in bin.
2021-03-15 17:16:57.313373: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (262144): 	Total Chunks: 13, Chunks in use: 11. 3.44MiB allocated for chunks. 2.88MiB in use in bin. 2.78MiB client-requested in use in bin.
2021-03-15 17:16:57.313720: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (524288): 	Total Chunks: 11, Chunks in use: 10. 6.50MiB allocated for chunks. 5.75MiB in use in bin. 5.50MiB client-requested in use in bin.
2021-03-15 17:16:57.313890: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (1048576): 	Total Chunks: 2, Chunks in use: 2. 2.12MiB allocated for chunks. 2.12MiB in use in bin. 2.12MiB client-requested in use in bin.
2021-03-15 17:16:57.313982: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (2097152): 	Total Chunks: 10, Chunks in use: 8. 23.75MiB allocated for chunks. 19.25MiB in use in bin. 18.00MiB client-requested in use in bin.
2021-03-15 17:16:57.314065: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (4194304): 	Total Chunks: 1, Chunks in use: 1. 4.50MiB allocated for chunks. 4.50MiB in use in bin. 4.50MiB client-requested in use in bin.
2021-03-15 17:16:57.314136: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (8388608): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-15 17:16:57.314225: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (16777216): 	Total Chunks: 1, Chunks in use: 1. 20.75MiB allocated for chunks. 20.75MiB in use in bin. 18.00MiB client-requested in use in bin.
2021-03-15 17:16:57.314309: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-15 17:16:57.314400: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (67108864): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-15 17:16:57.314494: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-15 17:16:57.314593: I tensorflow/core/common_runtime/bfc_allocator.cc:982] Bin (268435456): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2021-03-15 17:16:57.334694: I tensorflow/core/common_runtime/bfc_allocator.cc:998] Bin for 18.00MiB was 16.00MiB, Chunk State:
2021-03-15 17:16:57.334891: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Next region of size 1048576
2021-03-15 17:16:57.335299: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950000 of size 1280 next 1
2021-03-15 17:16:57.335496: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950500 of size 256 next 7
2021-03-15 17:16:57.335651: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950600 of size 256 next 8
2021-03-15 17:16:57.335798: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950700 of size 256 next 9
2021-03-15 17:16:57.335944: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950800 of size 256 next 10
2021-03-15 17:16:57.336090: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950900 of size 256 next 11
2021-03-15 17:16:57.336234: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950a00 of size 256 next 12
2021-03-15 17:16:57.336379: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950b00 of size 256 next 13
2021-03-15 17:16:57.336622: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950c00 of size 256 next 16
2021-03-15 17:16:57.336774: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950d00 of size 256 next 17
2021-03-15 17:16:57.336923: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950e00 of size 256 next 18
2021-03-15 17:16:57.337071: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00950f00 of size 256 next 19
2021-03-15 17:16:57.337220: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951000 of size 256 next 22
2021-03-15 17:16:57.337367: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951100 of size 256 next 23
2021-03-15 17:16:57.337688: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951200 of size 256 next 2
2021-03-15 17:16:57.337850: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951300 of size 256 next 3
2021-03-15 17:16:57.338003: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951400 of size 256 next 24
2021-03-15 17:16:57.338151: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951500 of size 256 next 26
2021-03-15 17:16:57.338297: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951600 of size 256 next 27
2021-03-15 17:16:57.338447: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951700 of size 256 next 28
2021-03-15 17:16:57.338653: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951800 of size 256 next 29
2021-03-15 17:16:57.338852: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951900 of size 256 next 32
2021-03-15 17:16:57.339011: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951a00 of size 256 next 33
2021-03-15 17:16:57.339158: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951b00 of size 256 next 34
2021-03-15 17:16:57.339304: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951c00 of size 256 next 37
2021-03-15 17:16:57.339450: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951d00 of size 256 next 38
2021-03-15 17:16:57.339595: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951e00 of size 256 next 39
2021-03-15 17:16:57.339741: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00951f00 of size 256 next 40
2021-03-15 17:16:57.339885: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00952000 of size 256 next 43
2021-03-15 17:16:57.340030: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00952100 of size 256 next 4
2021-03-15 17:16:57.340176: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00952200 of size 256 next 5
2021-03-15 17:16:57.340400: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00952300 of size 3584 next 6
2021-03-15 17:16:57.340549: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953100 of size 256 next 79
2021-03-15 17:16:57.340696: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953200 of size 256 next 80
2021-03-15 17:16:57.340844: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953300 of size 256 next 82
2021-03-15 17:16:57.340990: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953400 of size 256 next 83
2021-03-15 17:16:57.341136: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953500 of size 256 next 84
2021-03-15 17:16:57.341283: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953600 of size 256 next 85
2021-03-15 17:16:57.341430: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953700 of size 256 next 87
2021-03-15 17:16:57.341696: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953800 of size 256 next 88
2021-03-15 17:16:57.341825: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953900 of size 256 next 89
2021-03-15 17:16:57.341881: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953a00 of size 256 next 90
2021-03-15 17:16:57.341944: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953b00 of size 512 next 91
2021-03-15 17:16:57.341997: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953d00 of size 512 next 93
2021-03-15 17:16:57.342051: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00953f00 of size 512 next 94
2021-03-15 17:16:57.342102: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00954100 of size 512 next 95
2021-03-15 17:16:57.342157: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00954300 of size 1024 next 99
2021-03-15 17:16:57.342215: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00954700 of size 1024 next 100
2021-03-15 17:16:57.342278: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00954b00 of size 1024 next 101
2021-03-15 17:16:57.342342: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00954f00 of size 1024 next 102
2021-03-15 17:16:57.342408: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00955300 of size 512 next 105
2021-03-15 17:16:57.342477: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00955500 of size 512 next 106
2021-03-15 17:16:57.342549: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00955700 of size 512 next 107
2021-03-15 17:16:57.342623: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00955900 of size 512 next 108
2021-03-15 17:16:57.342697: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00955b00 of size 512 next 110
2021-03-15 17:16:57.342773: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00955d00 of size 512 next 111
2021-03-15 17:16:57.342848: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00955f00 of size 512 next 112
2021-03-15 17:16:57.342923: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00956100 of size 512 next 113
2021-03-15 17:16:57.342997: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00956300 of size 512 next 115
2021-03-15 17:16:57.343071: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00956500 of size 512 next 116
2021-03-15 17:16:57.343145: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00956700 of size 512 next 117
2021-03-15 17:16:57.343220: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00956900 of size 512 next 118
2021-03-15 17:16:57.343294: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00956b00 of size 512 next 121
2021-03-15 17:16:57.343369: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00956d00 of size 512 next 122
2021-03-15 17:16:57.343443: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00956f00 of size 512 next 30
2021-03-15 17:16:57.343519: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00957100 of size 8192 next 31
2021-03-15 17:16:57.343594: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00959100 of size 256 next 44
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2021-03-15 17:16:57.349528: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f009d2d00 of size 2048 next 303
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2021-03-15 17:16:57.349659: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] Free  at f009d4d00 of size 50176 next 81
2021-03-15 17:16:57.349683: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f009e1100 of size 147456 next 73
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2021-03-15 17:16:57.349725: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00a29100 of size 65536 next 92
2021-03-15 17:16:57.349746: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00a39100 of size 93952 next<PHONE_NUMBER>3709551615
2021-03-15 17:16:57.349767: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Next region of size 2097152
2021-03-15 17:16:57.349789: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00a50000 of size 294912 next 53
2021-03-15 17:16:57.349810: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00a98000 of size 65536 next 142
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2021-03-15 17:16:57.349926: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] Free  at f00af8000 of size 196608 next 119
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2021-03-15 17:16:57.350005: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00bf8000 of size 360448 next<PHONE_NUMBER>3709551615
2021-03-15 17:16:57.350027: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Next region of size 4194304
2021-03-15 17:16:57.350051: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c50000 of size 1024 next 197
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2021-03-15 17:16:57.350260: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c53000 of size 1024 next 211
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2021-03-15 17:16:57.350315: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c53800 of size 1024 next 213
2021-03-15 17:16:57.350342: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c53c00 of size 1024 next 215
2021-03-15 17:16:57.350371: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c54000 of size 1024 next 216
2021-03-15 17:16:57.350400: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c54400 of size 1024 next 217
2021-03-15 17:16:57.350428: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c54800 of size 1024 next 218
2021-03-15 17:16:57.350457: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c54c00 of size 1024 next 220
2021-03-15 17:16:57.350486: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c55000 of size 1024 next 221
2021-03-15 17:16:57.350515: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c55400 of size 1024 next 222
2021-03-15 17:16:57.350544: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c55800 of size 1024 next 223
2021-03-15 17:16:57.350573: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c55c00 of size 1024 next 225
2021-03-15 17:16:57.350602: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c56000 of size 1024 next 226
2021-03-15 17:16:57.350630: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c56400 of size 1024 next 227
2021-03-15 17:16:57.350659: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c56800 of size 1024 next 228
2021-03-15 17:16:57.350688: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c56c00 of size 1024 next 230
2021-03-15 17:16:57.350716: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c57000 of size 1024 next 231
2021-03-15 17:16:57.350745: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c57400 of size 1024 next 232
2021-03-15 17:16:57.350774: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c57800 of size 1024 next 233
2021-03-15 17:16:57.350803: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c57c00 of size 1024 next 235
2021-03-15 17:16:57.350832: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c58000 of size 1024 next 236
2021-03-15 17:16:57.350861: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c58400 of size 1024 next 237
2021-03-15 17:16:57.350890: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c58800 of size 1024 next 238
2021-03-15 17:16:57.350919: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c58c00 of size 1024 next 239
2021-03-15 17:16:57.350948: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c59000 of size 1024 next 240
2021-03-15 17:16:57.350976: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c59400 of size 1024 next 241
2021-03-15 17:16:57.351005: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c59800 of size 1024 next 242
2021-03-15 17:16:57.351034: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c59c00 of size 1024 next 244
2021-03-15 17:16:57.351063: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5a000 of size 1024 next 245
2021-03-15 17:16:57.351091: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5a400 of size 1024 next 246
2021-03-15 17:16:57.351120: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5a800 of size 1024 next 247
2021-03-15 17:16:57.351149: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5ac00 of size 1024 next 249
2021-03-15 17:16:57.351178: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5b000 of size 1024 next 250
2021-03-15 17:16:57.351206: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5b400 of size 1024 next 251
2021-03-15 17:16:57.351235: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5b800 of size 1024 next 252
2021-03-15 17:16:57.351264: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5bc00 of size 1024 next 255
2021-03-15 17:16:57.351293: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5c000 of size 1024 next 256
2021-03-15 17:16:57.351322: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5c400 of size 1024 next 257
2021-03-15 17:16:57.351352: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5c800 of size 1024 next 258
2021-03-15 17:16:57.351383: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5cc00 of size 1024 next 260
2021-03-15 17:16:57.351414: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5d000 of size 1024 next 261
2021-03-15 17:16:57.351444: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5d400 of size 1024 next 262
2021-03-15 17:16:57.351474: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5d800 of size 1024 next 263
2021-03-15 17:16:57.351504: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5dc00 of size 1024 next 265
2021-03-15 17:16:57.351533: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5e000 of size 1024 next 266
2021-03-15 17:16:57.351564: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5e400 of size 1024 next 267
2021-03-15 17:16:57.351594: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5e800 of size 1024 next 268
2021-03-15 17:16:57.351624: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5ec00 of size 1024 next 270
2021-03-15 17:16:57.351654: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5f000 of size 1024 next 271
2021-03-15 17:16:57.351683: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5f400 of size 1024 next 272
2021-03-15 17:16:57.351711: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5f800 of size 1024 next 273
2021-03-15 17:16:57.351740: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c5fc00 of size 1024 next 114
2021-03-15 17:16:57.351769: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c60000 of size 65536 next 103
2021-03-15 17:16:57.351799: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c70000 of size 131072 next 104
2021-03-15 17:16:57.351828: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00c90000 of size 131072 next 109
2021-03-15 17:16:57.351857: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00cb0000 of size 786432 next 97
2021-03-15 17:16:57.351887: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00d70000 of size 1179648 next 98
2021-03-15 17:16:57.351916: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00e90000 of size 589824 next 137
2021-03-15 17:16:57.351944: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00f20000 of size 262144 next 229
2021-03-15 17:16:57.351973: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] Free  at f00f60000 of size 327680 next 147
2021-03-15 17:16:57.352002: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f00fb0000 of size 655360 next<PHONE_NUMBER>3709551615
2021-03-15 17:16:57.352030: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Next region of size 8388608
2021-03-15 17:16:57.352059: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f01050000 of size 589824 next 158
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2021-03-15 17:16:57.352145: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f01200000 of size 589824 next 187
2021-03-15 17:16:57.352174: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f01290000 of size 262144 next 248
2021-03-15 17:16:57.352203: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f012d0000 of size 262144 next 259
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2021-03-15 17:16:57.352260: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f01350000 of size 262144 next 278
2021-03-15 17:16:57.352289: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f01390000 of size 262144 next 288
2021-03-15 17:16:57.352317: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f013d0000 of size 262144 next 298
2021-03-15 17:16:57.352346: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] Free  at f01410000 of size 786432 next 224
2021-03-15 17:16:57.352376: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f014d0000 of size 3670016 next<PHONE_NUMBER>3709551615
2021-03-15 17:16:57.352403: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Next region of size 16777216
2021-03-15 17:16:57.352432: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] Free  at f01850000 of size 262144 next 219
2021-03-15 17:16:57.352462: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f01890000 of size 262144 next 208
2021-03-15 17:16:57.352492: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f018d0000 of size 524288 next 209
2021-03-15 17:16:57.352672: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f01950000 of size 524288 next 214
2021-03-15 17:16:57.352704: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f019d0000 of size 1048576 next 304
2021-03-15 17:16:57.352734: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] Free  at f01ad0000 of size 2097152 next 202
2021-03-15 17:16:57.352763: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f01cd0000 of size 4718592 next 203
2021-03-15 17:16:57.352793: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f02150000 of size 2359296 next 234
2021-03-15 17:16:57.352823: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f02390000 of size 2359296 next 243
2021-03-15 17:16:57.352853: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] Free  at f025d0000 of size 2621440 next<PHONE_NUMBER>3709551615
2021-03-15 17:16:57.352881: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Next region of size 33554432
2021-03-15 17:16:57.352911: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f02850000 of size 2359296 next 254
2021-03-15 17:16:57.352942: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f02a90000 of size 2359296 next 264
2021-03-15 17:16:57.352971: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f02cd0000 of size 2359296 next 274
2021-03-15 17:16:57.353003: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f02f10000 of size 2359296 next 283
2021-03-15 17:16:57.353033: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f03150000 of size 2359296 next 293
2021-03-15 17:16:57.353065: I tensorflow/core/common_runtime/bfc_allocator.cc:1031] InUse at f03390000 of size 21757952 next<PHONE_NUMBER>3709551615
2021-03-15 17:16:57.353093: I tensorflow/core/common_runtime/bfc_allocator.cc:1036]      Summary of in-use Chunks by size:
2021-03-15 17:16:57.353132: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 64 Chunks of size 256 totalling 16.0KiB
2021-03-15 17:16:57.353170: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 84 Chunks of size 512 totalling 42.0KiB
2021-03-15 17:16:57.353202: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 83 Chunks of size 1024 totalling 83.0KiB
2021-03-15 17:16:57.353235: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 1280 totalling 1.2KiB
2021-03-15 17:16:57.353266: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 1536 totalling 1.5KiB
2021-03-15 17:16:57.353298: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 8 Chunks of size 2048 totalling 16.0KiB
2021-03-15 17:16:57.353330: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 3584 totalling 3.5KiB
2021-03-15 17:16:57.353361: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 8192 totalling 8.0KiB
2021-03-15 17:16:57.353393: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 5 Chunks of size 16384 totalling 80.0KiB
2021-03-15 17:16:57.353426: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 24576 totalling 24.0KiB
2021-03-15 17:16:57.353459: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 2 Chunks of size 32768 totalling 64.0KiB
2021-03-15 17:16:57.353527: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 49152 totalling 48.0KiB
2021-03-15 17:16:57.353563: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 9 Chunks of size 65536 totalling 576.0KiB
2021-03-15 17:16:57.353600: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 2 Chunks of size 73728 totalling 144.0KiB
2021-03-15 17:16:57.353627: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 93952 totalling 91.8KiB
2021-03-15 17:16:57.353649: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 2 Chunks of size 131072 totalling 256.0KiB
2021-03-15 17:16:57.353670: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 2 Chunks of size 147456 totalling 288.0KiB
2021-03-15 17:16:57.353690: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 9 Chunks of size 262144 totalling 2.25MiB
2021-03-15 17:16:57.353710: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 294912 totalling 288.0KiB
2021-03-15 17:16:57.353730: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 360448 totalling 352.0KiB
2021-03-15 17:16:57.353750: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 2 Chunks of size 524288 totalling 1.00MiB
2021-03-15 17:16:57.353769: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 6 Chunks of size 589824 totalling 3.38MiB
2021-03-15 17:16:57.353788: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 655360 totalling 640.0KiB
2021-03-15 17:16:57.353807: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 786432 totalling 768.0KiB
2021-03-15 17:16:57.353826: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 1048576 totalling 1.00MiB
2021-03-15 17:16:57.353845: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 1179648 totalling 1.12MiB
2021-03-15 17:16:57.353864: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 7 Chunks of size 2359296 totalling 15.75MiB
2021-03-15 17:16:57.353882: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 3670016 totalling 3.50MiB
2021-03-15 17:16:57.353901: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 4718592 totalling 4.50MiB
2021-03-15 17:16:57.353920: I tensorflow/core/common_runtime/bfc_allocator.cc:1039] 1 Chunks of size 21757952 totalling 20.75MiB
2021-03-15 17:16:57.353940: I tensorflow/core/common_runtime/bfc_allocator.cc:1043] Sum Total of in-use chunks: 56.95MiB
2021-03-15 17:16:57.353957: I tensorflow/core/common_runtime/bfc_allocator.cc:1045] total_region_allocated_bytes_: 66060288 memory_limit_: 70307840 available bytes: 4247552 curr_region_allocation_bytes_: 67108864
2021-03-15 17:16:57.394864: I tensorflow/core/common_runtime/bfc_allocator.cc:1051] Stats:
Limit:                        70307840
InUse:                        59718656
MaxInUse:                     59718656
NumAllocs:                         419
MaxAllocSize:                 21757952
Reserved:                            0
PeakReserved:                        0
LargestFreeBlock:                    0
2021-03-15 17:16:57.395156: W tensorflow/core/common_runtime/bfc_allocator.cc:444] **xx_____***********************************************xxxx
2021-03-15 17:16:57.584017: W tensorflow/core/framework/op_kernel.cc:1763] OP_REQUIRES failed at cwise_ops_common.h:128 : Resource exhausted: OOM when allocating tensor with shape[3,3,512,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
File "/home/swag/Desktop/IGVC_ConeDetectionScript/detect_independent.py", line 17, in
yolo = Load_Yolo_model()
File "/home/swag/Desktop/IGVC_ConeDetectionScript/utils.py", line 41, in Load_Yolo_model
yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES="classes.txt")
File "/home/swag/Desktop/IGVC_ConeDetectionScript/utils.py", line 18, in Create_Yolo
conv_tensors = YOLOv4(input_layer, NUM_CLASS)
File "/home/swag/Desktop/IGVC_ConeDetectionScript/yolov4.py", line 289, in YOLOv4
route_1, route_2, conv = cspdarknet53(input_layer)
File "/home/swag/Desktop/IGVC_ConeDetectionScript/yolov4.py", line 155, in cspdarknet53
input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True, activate_type="mish")
File "/home/swag/Desktop/IGVC_ConeDetectionScript/yolov4.py", line 44, in convolutional
bias_initializer=tf.constant_initializer(0.))(input_layer)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 952, in call
input_list)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 1091, in _functional_construction_call
inputs, input_masks, args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 822, in _keras_tensor_symbolic_call
return self._infer_output_signature(inputs, args, kwargs, input_masks)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 862, in _infer_output_signature
self._maybe_build(inputs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 2710, in _maybe_build
self.build(input_shapes)  # pylint:disable=not-callable
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 205, in build
dtype=self.dtype)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 639, in add_weight
caching_device=caching_device)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py", line 810, in _add_variable_with_custom_getter
**kwargs_for_getter)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py", line 142, in make_variable
shape=variable_shape if variable_shape else None)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py", line 260, in call
return cls._variable_v1_call(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py", line 221, in _variable_v1_call
shape=shape)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py", line 199, in
previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py", line 2618, in default_variable_creator
shape=shape)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py", line 264, in call
return super(VariableMetaclass, cls).call(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 1585, in init
distribute_strategy=distribute_strategy)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 1712, in _init_from_args
initial_value = initial_value()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops_v2.py", line 420, in call
dtype)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops_v2.py", line 1073, in random_normal
shape=shape, mean=mean, stddev=stddev, dtype=dtype, seed=self.seed)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
return target(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/random_ops.py", line 96, in random_normal
mul = rnd * stddev_tensor
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py", line 1164, in binary_op_wrapper
return func(x, y, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py", line 1496, in _mul_dispatch
return multiply(x, y, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
return target(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py", line 518, in multiply
return gen_math_ops.mul(x, y, name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 6068, in mul
_ops.raise_from_not_ok_status(e, name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 6862, in raise_from_not_ok_status
six.raise_from(core._status_to_exception(e.code, message), None)
File "", line 3, in raise_from
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[3,3,512,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Mul]
Process finished with exit code 1

@AbhisekOmkar,
In order to expedite the trouble-shooting process, could you please provide the following information

CUDA/cuDNN version:
GPU model and memory:
the complete code and the dataset you are using

Also try limiting GPU memory growth using any of the methods listed here and check if it helps. Thanks!

@AbhisekOmkar,
In order to expedite the trouble-shooting process, could you please provide the following information

CUDA/cuDNN version:
GPU model and memory:
the complete code and the dataset you are using

Also try limiting GPU memory growth using any of the methods listed here and check if it helps. Thanks!

GPU: Nvidia Jetson Nano NVIDIA Maxwell architecture with 128 NVIDIA CUDA® cores
CUDA: 11.2
CODE: https://github.com/Project-SwaG/IGVC_ConeDetectionScript

Did you build TF from sources to support cuda 11.2?
Also have you tried limiting gpu memory growth? Make sure you close all python sessions and exit python interpreter to free up memory and try running script as:
#On top of your script
import tensorflow as tf
gpu = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpu[0], True) #limits gpu memory
# Rest of your code

Issues has been resolved (Jetson Nano has 4gb CPU and Tensorflow was using more than 4gb of CPU during Inference. So, we finally Shift from Tensorflow to Tensorflow RT or You can Switch to Higher versions of Jetson).

@gsoc

Issues has been resolved (Jetson Nano has 4gb CPU and Tensorflow was using more than 4gb of CPU during Inference. So, we finally Shift from Tensorflow to Tensorflow RT or You can Switch to Higher versions of Jetson).


Docs - White space & code snippet formatting clean up

Overview
Some clean up of our white space and code snippet formatting. We use 4 space indents in most other CSS/JS code snippets so just trying to be consistent here.
Fix for syntax highlights
This should also improve the formatting of the code snippets in https://docs.wagtail.org/en/stable/advanced_topics/documents/overview.html
Observe below, the top screenshot has no Django template formatting but the bottom of the screenshot does. This is because we should be using html+django in the template markup.

Now, see on the PR deployed version, there is nicer syntax highlighting of the documentation snippets.

Rebasing & adding changelog for the small fix, then will merge.


Fix infinite scroll when "Upcoming livestream" appears

Issue
When the "Upcoming livestream" appears in a list, infinite scroll stops working.
Cause
The difference between mainEl.getBoundingClientRect().bottom and window.innerHeight became slightly greater than 0.5, so it was deemed as "haven't reached the bottom".
Change
Coincidently, I've been wanting to make the inf scroll load earlier (instead of after reaching the absolute bottom) to make the experience smoother, so added a 200px threshold, which is roughly the height of a tile. This gets us the new behavior while also fixes the original problem.

THANK YOU!


package_bin: backport interface embedding support

Support interface embedding. Also, add the test case. Backport of golang/tools@b08393c.
Current Go tip will reach p.int() != 0 becasue has been supported interface embedding on golang/go@ee272bb.
We don't have type.Named and type.Interface.embeddeds method, So I fixed the code to corresponds to the gocode. just append the *ast.Field.
And add test case from the golang/tools@b08393c#diff. That is if we have not merged this fix, will PANIC with tip.(but another test also fail)
I tested tip and go1.8. But I don't know it is correct fix, and there is no guarantee that backward compatibility is maintained. Please code review if you have time.

Is if embeddeds != nil are needs...?

if embeddeds != nil is not necessary indeed. Go will not panic when iterating over nil slice. Just like len(s) for nil slice returns zero.


Jeanette/scoot176 ref daemon client impl

reminding user to install docopt

Current coverage is 64.19% (diff: 100%)

Merging #177 into master will not change coverage

@@             master       #177   diff @@
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  Files            58         58
  Lines          2843       2843
  Methods           0          0
  Messages          0          0
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==========================================
  Hits           1825       1825
  Misses          895        895
  Partials        123        123

Powered by Codecov. Last update 52929d8...02aa58c


minor text mistake

Thank you! Could you run ./devtools/format_md.sh to format the text file?

Yes no problem. I’m not sure the text is now completely linguistically
correct though. Can you confirm?
On Wed, 19 Jan 2022 at 11:10, Simon Warta @.***> wrote:

Thank you! Could you run ./devtools/format_md.sh to format the text file?
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AAP | Network bandwidth

Hello,
Would be great to know what is network bandwidth when use Azure Application Proxy,
Does AAP limit speed somehow?
We have internal WEB server and AAP in same VM cluster, this network is 1Gbps (internal&external);
On WEB server we have "speed test tool":
Internally we got average: 251821 kbit/s;
From public network (100Mbs) via AAP average: 10670 kbit/s;
Does this means that AAP limit to xxxx kbit/s somehow?
Also, how speed is controlled, when multiple users connect to AAP, example:

Connector group has only one AAP server
2x or more users connect to APP -> all will get 10670 kbit/s? or it will be splitted out?
If possible, then would be great to have this info documented..

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: a760d39c-1eae-e617-3aa8-df57d4bf57e8
Version Independent ID: 4a3da9d1-997c-0920-72f3-ea98d24d958d
Content: Network topology considerations when using Azure Active Directory Application Proxy
Content Source: articles/active-directory/manage-apps/application-proxy-network-topology.md
Service: active-directory
GitHub Login: @CelesteDG
Microsoft Alias: celested

@lightupdifire Thanks for the feedback! We are currently investigating and will update you shortly.

@lightupdifire I believe there might be chances when you experience latency due to any kind of proxy solutions you introduce into your network connection and you might need to do some network topology tweaks to improve the speed.  Please refer to Network topology considerations when using Azure Active Directory Application Proxy for the evaluation of different typologies as there are currently no further recommendations for performance tuning.  Sometimes a closer data center proximity might help with the latency.  You can check for latency for a list of data centers using latency test page.

@SaurabhSharma-MSFT
Thanks, the AAP server and WEB server are in same network and in same VM cluster.
Question is if AAP has some limitations, if yes, what kind of...

@lightupdifire I am checking up this and let you know once I have any updates.

@lightupdifire We are working on improved documentation around how this specifically works. For now we have the following documentation that outlines our performance and scale considerations https://docs.microsoft.com/en-us/azure/active-directory/manage-apps/application-proxy-connectors#performance-and-scalability. We also recommend having multiple connectors to assist with serving multiple connections. The capacity planning section can help with planning this https://docs.microsoft.com/en-us/azure/active-directory/manage-apps/application-proxy-connectors#capacity-planning.

@jaszperez Thank you.
@lightupdifire Please let us know if this helps or if you have any other queries.

@jaszperez @SaurabhSharma-MSFT
Our setup use multiple connectors, our setup is already set, that WEB server and AAP server are in same Data center, same VM cluster, so network and servers location are as much as possible close to each other, looking eye to eye :)
Question is if AAP has any limitation in network capacity, like what could be the maximum throughput, does it limit, yes or no, and if yes, then what limits could be, I try to find in all articles but didn't saw it, so just added a note to this article, which is about "Network topology considerations", for advise to check if there is any limits (as we see there are some by doing tests).
From my point of view, it is just an advise, to this article, to add info about speed limits of AAP if there any, if not, then maybe you can also add info that there is no limits, AAP use maximum bandwidth and maybe add example, like if AAP is installed on WEB server, WEB server has 100Mbps network, then from public internet you should get approx. same speed when accessing server via AAP if client network located in same region and has same or higher network speed etc.

@lightupdifire Thank you for your valuable feedback.  As mentioned by @jaszperez that we are working on improving the documentation and so your feedback will definitely be considered by the authors and the product team while creating the improved version of the documentation.

@lightupdifire We will now proceed to close this thread. If there are further questions regarding this matter, please reopen it and we will gladly continue the discussion.


[Docs] Dataset release

📚 The doc issue
Is the full dataset used for pre-training and fine-tuning released publicly? If not, is there any plan to do?
Suggest a potential alternative/fix
Add more details about the type of instructions used during training. This has a direct impact on prompt tuning.

Thank you for your suggestion. The datasets used have already been specified in our paper, and we will add various data details in our user manual shortly.


Add main front-end components

Hi @beltranrengifo ! I have some doubts:

I try to access the "description" variable doing: eventDetail.data.description but it doesn't work. How do you think I can do that?
After adding the youtube link to the iframe it displays this:

Hey @Stephy6 !
To use the YT vídeo in an iframe you should build an URL like this:
src="https://www.youtube.com/embed/JnjxpmPjRTw"

instead of
src="https://www.youtube.com/watch?v=JnjxpmPjRTw"

There are other ways of doing this using the YT API, is nice, take a look if you want:
https://developers.google.com/youtube/iframe_api_reference?hl=es

Regarding the description issue @Stephy6 it's a common issue you'll face a lot using JS frameworks...
Basically the JSX is trying to render this:
<p>{eventDetail.data.description}</p>

before the eventDetail object is available, because fetch is not finished. The template render part of the component is not clever enough to know when the data is ready, so we should add a check before rendering the JSX. Something like
  if (!eventDetail.data) {
    return null
  }

However, lucky us, we have many info available from the fetching process thanks to useQuery
So we know exactly in which point the fetch is at every moment.
This way we can nicely play with a loader component to avoid this error and show feedback to the user.
Take a look a this component, it's a good example you can follow almost exactly I think.
https://github.com/lugspain/portal/blob/ecf5c1be823a86fb86e63fb96efa335e6235567e/src/components/Events/PastEvents/Main/PastEvents.tsx#L37
Let me know if it works!!

Hi @beltranrengifo ! You were right, now looks like is working fine:

Let me know what changes should I make to the code to work and look smoothly!

Merging draft branch!


Introduce a feature for @_inheritActorContext

Add a feature for this new attribute, and make sure we use the feature
guard for functions that use it, e.g., the new async.
Finishes rdar://76927008.

@swift-ci please smoke test and merge


tests:chore - move Mock implementations to testutil pkg

Previously each mock implementation of structs that is used by tests was
implemented on your own package, which add some unnecessary testing
dependencies on main packages.
This commit move all mocks implementation to testutil package and change
their references.
Note that the internal/utils/mock package was also removed and their
code was moved to testutil package to centralize all testing stuffs
only on testutil package.
Signed-off-by: Matheus Alcantara<EMAIL_ADDRESS>
- What I did
- How to verify it
- Description for the changelog

to not get too confused, what do you think of having a file split of "mock", "e2e tests" "unit tests" and others? or doesn't it make sense?

Are you saying for example use testutil/requirements_mock.go instead? I think that would be good.
About unit tests and e2e I don't think soo. Some funcs and const can be used in both tests.

to not get too confused, what do you think of having a file split of "mock", "e2e tests" "unit tests" and others? or doesn't it make sense?

Are you saying for example use testutil/requirements_mock.go instead? I think that would be good.
About unit tests and e2e I don't think soo. Some funcs and const can be used in both tests.

oh great :)

sync your branch with main and you can merge this pr :)


Add halo character costumes from Kerbal Stuff

This pull request was automatically generated by Kerbal Stuff on behalf of amankduffers, to add halo character costumes to CKAN.
Please direct questions about this pull request to amankduffers.

Consolidated into #2282


Missing SLF4J Dependency Causing Runtime Error

Description
First of all, thank you for your work on this project.
I encountered an error when running ./startAgent.sh,The error was caused by a missing SLF4J dependency:
ERROR: java.lang.NoClassDefFoundError: org/slf4j/LoggerFactory

Perfmon-Server-Agent version: 2.2.3
Operating System: Ubuntu 20.04.6 LTS
Java version:  openjdk 11.0.20 2023-07-18
I was able to resolve the issue by manually downloading the SLF4J API JAR file from the Central Repository and placing it in the lib directory of the project. However, this information was not mentioned in the project's documentation. It would be immensely helpful to update the documentation or to include all necessary dependencies.

@Moh-Snoussi Feel free to create a PR with your view of documentation improvement

@Moh-Snoussi
Hi, I am trying to use this plugin in Jmeter for resource monitoring. For that I am trying to start this Agent but it is not recognizing SLF4J. It shows this error :
SLF4J: No SLF4J providers were found.
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See https://www.slf4j.org/codes.html#noProviders for further details.
I have performed these steps :

Downloaded slf4j-api-2.0.9.jar from https://repo1.maven.org/maven2/org/slf4j/slf4j-api/2.0.9/
Pasted this file in lib folder of ServerAgent.

Specs :
Server-Agent Version : 2.2.3
Operating System : Windows
Java version : java version "1.8.0_401"
Java(TM) SE Runtime Environment (build 1.8.0_401-b10)
Java HotSpot(TM) 64-Bit Server VM (build 25.401-b10, mixed mode)
Do you know of any other step that I am missing ? Is there any pre-requisite or something that needs to be done in order to use SLF4J ?

@mayankyadavy29 please try slf4j-api-2.0.0.jar


Docker Swarm Elassandra cluster

Is there an example on how to build a Elassandra cluster for use on on a Docker Swarm cluster?  I see a helm chart for Kubernetes and is mentioned in the documentation as well but didn't see much for docker swarm.

No sorry, we don’t have a deployment guide on swarm, mainly becauses warm does not provide something equivalent to k8s statefulset.
Thanks.

On 14 Oct 2019, at 23:14, weworksonam<EMAIL_ADDRESS>wrote:
Is there an example on how to build a Elassandra cluster for use on on a Docker Swarm cluster? I see a helm chart for Kubernetes and is mentioned in the documentation as well but didn't see much for docker swarm.
Describe the feature:
Elassandra version:
Plugins installed: []
JVM version (java -version):
OS version (uname -a if on a Unix-like system):
Description of the problem including expected versus actual behavior:
Steps to reproduce:
Please include a minimal but complete recreation of the problem, including
(e.g.) index creation, mappings, settings, query etc. The easier you make for
us to reproduce it, the more likely that somebody will take the time to look at it.
Please provide the following information:
elassandra logs (logs/system.logs or /var/lib/cassandra/system.log)
elasticsearch cluster state (curl http://localhost:9200/_cluster/state http://localhost:9200/_cluster/state)
cassandra schema (cqlsh>DESC KEYSPACE <your_keyspace>)
cassandra gossip state (run: nodetool gossipinfo)
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You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub https://github.com/strapdata/elassandra/issues/310?email_source=notifications&email_token=ACOMPGNFYYHFS2BPYMNQU7DQOTONBA5CNFSM4JAUDIBKYY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4HRWK64Q, or unsubscribe https://github.com/notifications/unsubscribe-auth/ACOMPGKQE75F5J3JFYNFMKLQOTONBANCNFSM4JAUDIBA.

I want to create a Elassandra cluster manually within one datacenter.  I saw some mentions of it some time ago but can't find it anymore.  Can you point me to where I can find it?  It would be of much help.

Please see quick start guide using docker-compose.


What can be better next year?

What worked well this time? What could've gone better? What missed the mark? Is there anything we could've used more of or less of?
Any and all feedback and ideas are welcome.
@elainewong, @meli-lewis, @chrislkeller, @aboutaaron, @zufanka, @robroc, @esagara, @thejqs?
Any PyCAR students have advice or reactions?

One quick thought: we could update the HTML table used in the scraping portion. That 2015 table hosted on S3 is looking pretty dated.

@robroc, this is a good idea. I think we can fairly easily freshen that data with more recent films from the same source.

Little bit of feedback I got was that we spent a lot of time explaining at the expense of helping them build up some muscle memory. (I'm paraphrasing.)
Almost all of us have helped with PyCAR more than once, and I found myself wondering, too, whether I spent too much energy anticipating problems and trying to pre-explain them out of existence instead of letting things happen more as learning-by-doing moments. It felt like they raised their hands for help less this year and that that might be a byproduct of less typing.


onClick within a View seems not to work

The onClick function seems not to be triggered on a View.
import React from 'react';
import View from '@allthings/elements/atoms/View';
import { css } from 'glamor';

const styles =
  css({
    width: '100px',
    height: '100px',
    backgroundColor: 'black'
});

export class TestView extends React.Component {

  constructor(props) {
    super(props);
    this.click = this.click.bind(this);
  }

  click() {
    alert("click");
  }

  render() {
    return(
        <View {...styles} onClick={this.click}>
        </View>
    );
  }
}

I cannot reproduce this issue in current version. Works fine here. If you continue having trouble with this, you could submit fiddle or a test that shows this.


Replace Field Error

Replace field no longer works due to refactor in Roboscheduler.scheduler.designsNext.

Fixed.


GraphitePickleHandler drops queued data on socket error

%subj
GraphiteHandler work well at the same time

This repo has moved to https://github.com/python-diamond/Diamond
Please open a new ticket there if this issue is not resolved with the current code there.
Thanks!


This library produces a different result from a different base58 converter library. Which is correct?

This library's .decode function produces a hex code, which when converter to an integer does not match this library:
https://github.com/jimeh/node-base58#readme
Which library is producing the correct hex code?

Can you provide code for reproducing? Thanks.

const bs58 = require('bs58');
const Base58 = require('base58');

// Creating Int with bs58 lib
const bytes = bs58.decode("9iS1fj");
const hex = bytes.toString('hex');
result1 =  parseInt(hex, 16);

// Creating Int with base58 lib
result2 = Base58.base58_to_int('9iS1fj');

// This is false
console.log(result1 === result2);

Base58.int_to_base58(6857269519); // 'brXijP'
Base58.base58_to_int('brXijP');

bs58 use bitcoin alphabet, while base58 use Flickr alphabet.
In README: https://github.com/jimeh/node-base58#what
In code: https://github.com/cryptocoinjs/bs58/blob/master/index.js#L2 / https://github.com/jimeh/node-base58/blob/master/src/base58.js#L1
PR in base58: https://github.com/jimeh/node-base58/pull/10

@fanatid So it's just that the capital letters are reversed? Thank you for your quick response. I work with crypto so I will go with the bitcoin version. Thanks.


PhReInitializeTheme: runtime theme switching on ImmersiveColorSet message

General

Theme can be changed at runtime w/o restart by EnableThemeSupport and EnableThemeUseWindowsTheme. All PH/PV windows and dialogs will be redrawed automatically.
If EnableThemeUseWindowsTheme enabled theme will be switched automatically when Windows theme is changing.
Streamer mode, acrylic, theme custom colors settings are applied immediately on all windows w/o restart.
New option EnableThemeUseWindowsTheme - Use Windows app color mode. On default settings PH will use current Windows theme.

phlib and other UI fixes

Moved delayhook to phlib.
phlib PhIsThemeSupportEnabled, PhReInitializeTheme, PhReInitializeStreamerMode. Exported for plugins.
New option EnableThemeTabBorders (default 0).
TreeNew: fix custom ForeColor for dark mode.

Non UI fixes

Public hndlmenu.h APIs for plugins.
memsrcht show memory editor on double click.
@jxy-s new memory strings dialog is amazing :rocket:
Improve PhUiCloseHandles for protected handles

If AppMode is set to PreferredAppModeDarkOnDark, PhShouldAppsUseDarkMode should return current Windows app color mode.
It's weird that sometimes it can return incorrect inverted value (idk why).
In this case use PhGetAppsUseLightTheme() to retrieve raw value from registry.
When app mode set to always dark/light all subsequent calls to PhShouldAppsUseDarkMode always ignore
the current Windows app color mode. Possible return values are:

PreferredAppMode
PhShouldAppsUseDarkMode return
PhGetAppsUseLightTheme return

PreferredAppModeDisabled
current Windows app color mode
current Windows app color mode

PreferredAppModeDarkOnDark
current Windows app color mode
current Windows app color mode

PreferredAppModeDarkAlways
always TRUE
current Windows app color mode

PreferredAppModeLightAlways
always FALSE
current Windows app color mode

https://github.com/user-attachments/assets/cbe024b8-4c02-41bb-85f7-a07ceed5d051

I think this PR is ready to review. 😊

long-awaited feature for me!


Change grant loader dockerfile

To use entrypoint so env vars can properly expand on docker run.

Merging this now since there will be one final PR for all data loaders with final docker config.


demo vertex kfp pipeline fails with "Invalid Cloud Storage URI for gcs_output_directory"

Expected Behavior
the pipeline job has to start
Actual Behavior
the pipeline job run fails
Steps to Reproduce the Problem

python3 -m pip install google-cloud-aiplatform==1.7.1
google-cloud-pipeline-components==0.2.0
kfp==1.8.9

python3 pipellines_intro_kfp.py (obtained by converting https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)

Specifications

Version:
Platform:

@ra312
Hello. Can you be more specific? What do you mean by: obtained by converting https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)

Hi @andrewferlitsch
Thanks for getting back so quickly! To be more specific, I used Jupiter nbconvert --to script pipelines_intro_kfp.ipynb and edited the script a bit to adjust for the ML workflow I am attempting to productionize
'''
from kfp.v2.dsl import component
from kfp.v2 import compiler
from kfp import dsl
from typing import NamedTuple
import google.cloud.aiplatform as aip
from datetime import datetime
import os
import google
PROJECT_ID = "ds-ml-development"
os.system(f"gcloud config set project ${PROJECT_ID}")
REGION = "europe-west1"  # Change
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")
BUCKET_NAME = "ml-bucket-dev"
BUCKET_URI = f"gs://{BUCKET_NAME}"
SERVICE_ACCOUNT = (
f"my-service-account"
<EMAIL_ADDRESS>)
API_ENDPOINT = f"{REGION}-aiplatform.googleapis.com"  # API service endpoint
PIPELINE_ROOT = "{BUCKET_URI}/pipeline_root/intro/url.csv"
aip.init(
project=PROJECT_ID,
staging_bucket=BUCKET_URI
)
@component(output_component_file="hw.yaml", base_image="python:3.9")
def hello_world(text: str) -> str:
print(text)
return text
@component(packages_to_install=["google-cloud-storage"])
def two_outputs(
text: str,
) -> NamedTuple(
"Outputs",
[
("output_one", str),  # Return parameters
("output_two", str),
],
):
# the import is not actually used for this simple example, but the import
# is successful, as it was included in the packages_to_install list.
from google.cloud import storage  # noqa: F401
o1 = f"output one from text: {text}"
o2 = f"output two from text: {text}"
print("output one: {}; output_two: {}".format(o1, o2))
return (o1, o2)

@component
def consumer(text1: str, text2: str, text3: str):
print(f"text1: {text1}; text2: {text2}; text3: {text3}")
@dsl.pipeline(
name="hello-world-v2",
description="A simple intro pipeline",
pipeline_root=PIPELINE_ROOT,
)
def pipeline(text: str = "hi there"):
hw_task = hello_world(text)
two_outputs_task = two_outputs(text)
consumer_task = consumer(  # noqa: F841
hw_task.output,
two_outputs_task.outputs["output_one"],
two_outputs_task.outputs["output_two"],
)
compiler.Compiler().compile(pipeline_func=pipeline,
package_path="intro_pipeline.json")
DISPLAY_NAME = "intro_" + TIMESTAMP
job = aip.PipelineJob(
display_name=DISPLAY_NAME,
template_path="intro_pipeline.json",
pipeline_root=PIPELINE_ROOT,
)
runtime_config = google.cloud.aiplatform_v1.types.PipelineJob.RuntimeConfig(gcs_output_directory=BUCKET_URI)
job.runtime_config = runtime_config
job.run(
service_account=SERVICE_ACCOUNT
)
job.delete()
'''
I debug this script to observe pipeline in vertex AI UI, but get this exception instead

I added runtime_config and specified service_account to resolve the Exception

@ra312 Does the bucket exist? Otherwise you need to create it first.
BUCKET_NAME = "ml-bucket-dev"
BUCKET_URI = f"gs://{BUCKET_NAME}"

Hi @andrewferlitsch,
Yes, I made sure the bucket exists

Any updates on this?

Hi,
I would be happy to look further into the issue (maybe a PR), but we are moving fast to MVP with Vertex, so currently I am using the same PipelineJob with different versions. So if it helps anyone else, here are the versions I am using right now
google-api-core==1.31.5
google-api-python-client==1.12.11
google-auth==1.35.0
google-cloud-aiplatform==1.11.0
google-cloud-storage==1.44.0
google-cloud-bigquery==2.9.0
oauth2client==4.1.3
kfp==1.8.12
requests==2.27.1
requests-oauthlib==1.3.1
requests-toolbelt==0.9.1
typing==<IP_ADDRESS>
typing-extensions==<IP_ADDRESS>
So it seems we could close the issue for now.
Thanks for getting back to me.

@ra312 I see what might be a typo. PIPELINE_ROOT should be a folder not a file.
PIPELINE_ROOT = "{BUCKET_URI}/pipeline_root/intro/url.csv"

Thanks, @andrewferlitsch for your help! . It did actually work!


should dispatch "slideend" on click

When clicking on the slider, the slider moves to the new position but does not dispatch a "slideend" event.

I wanted this feature as well. I don't know that everybody would want it, but it was definitely awkward having the data update when the slider is done being dragged, but not when clicked to a new position. You can get this if you dispatch "slide", but in my case it didn't make sense to update the data as frequently as that - I have a comparatively small step compared to the range of the slider, so it is essentially continuous.
For others who may want this, I did get the functionality I wanted by adding:
dispatch.slideend(d3.event, value);
to the end of the onClickHorizontal() and onClickVertical() functions.
I could see this as an option to turn on or off, and I could also see making the click event itself optional. It depends on how you want to direct the user to interact with the data.


A number of changes

Hi there,
I've been working on my own fork of this plugin for a while, and think I've made some improvements which should be integrated into the main branch. Here's a list of the changes:

Make the README.md clearer
Cleanup and streamline index.js
Added a standalone HTTP function for simplicity (also removed the response code check)
Added a timeout duration to prevent Homebridge from getting stuck if the thermostat is not responding
Solved #32, allowing you to control the temperature via Siri (previously unsupported)
Allows you to specify a custom manufacturer, serial and model
Makes the humidity fields optional (can be enabled individually or together in the config.json as per README.md)

Hopefully you approve of these changes and agree that they improve on the current version. If you have any question, quibbles or suggestions then please feel free to let me know!
Kind regards, Tom

Thx @Tommrodrigues for your time. As I do not have much time to invest and since you look to care about this plugin, I both merge your code and thank you a lot for your contribution.
Best,
PJCzx


indentation format should be configurable (or default to 4 spaces)

If I'm reading this code correctly, it looks like a tab (\t) is hard-coded as the additional indentation format, even though PEP 8 says, "Spaces are the preferred indentation method." Can this be made configurable or maybe default to 4 spaces instead of a tab character, since this causes errors if the next line of code at the same level uses spaces instead of tabs?

Pull requests are welcome!

I'm not familiar at all with this code and just searched for what would be adding the \t indent. Do you have a suggestion for what should be done to make it configurable (although, I'm not sure if/when I'll get a chance to implement the changes)? Thanks!


Updated entry for COMSOL

Fixes https://github.com/KhronosGroup/glTF-Project-Explorer/issues/31

When I look at the page, the entry for COMSOL has not changed as of 2/6/20. I have force-refreshed (Ctrl+R).
Do I need to do something more? Or will the updates appear later without me doing anything further?

That's true - and I wonder why:
@weegeekps Are you aware of any update processes that mus be triggered so that the new data file is deployed there? I thought that this happened automagically - at least it did during the initial deployment. I now also noticed that the change from https://github.com/KhronosGroup/glTF-Project-Explorer/issues/29 also does not appear in the deployed state - I'll reopen that issue, that will also trigger the webmaster, maybe he has some insights...

Yes. Unfortunately we can't seem to use Actions yet with this repo, so the process is still semi manual.
I've been meaning to write something up so you know how. I'll try to do that today. In the meantime I'm actually working on the layout bug with Chrome and will do a deploy when I'm done.


update dependencies version

Signed-off-by: kpango<EMAIL_ADDRESS>

Codecov Report

Merging #27 into master will not change coverage.
The diff coverage is 100%.

@@           Coverage Diff           @@
##           master      #27   +/-   ##
=======================================
  Coverage   87.13%   87.13%
=======================================
  Files          14       14
  Lines         692      692
=======================================
  Hits          603      603
  Misses         60       60
  Partials       29       29

Impacted Files
Coverage Δ

log/log.go
71.42% <100%> (ø)
:arrow_up:

Continue to review full report at Codecov.

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Enemy spawners need implementing next

there is also the possibility that i may need to rethink /  redesign how the enemy system works
possible questions that will need answering before I continue:

how will the user create their own custom enemy
how does the user give that enemy custom behaviour (overrides?)
a lot of prefabs now need access to the player, should I create a static reference or keep using the inspector?

I'll spend most of tomorrow planning around these points until I have a plan in place on how I want to tackle them

fixed and implemented in the latest commit
enemies now have working spawners and I have planned solutions to the problems brought up in this issue.


stereo on rp2040 using I2S PCM5102 Stereo DAC Decoder

Hi Mike,
Thanks for your work on the Micropython I2S class!
Playing mono wav files works fine but when I play a stereo file it sounds like it alternatingly fills the buffer with zeroes. So I hear one buffer full of sound and then for the same length silence.
Do you have any idea what could cause this? I am using code very similar to your example.
Thanks,
Guustaaf

Hi Guustaaf,
Thanks for reporting this issue.  I can take a look, but I will need some additional background:

what stereo file are you using?
can you provide MicroPython code that shows the problem?
what PCM5102 hardware are you using?
Mike

Mike,
I tried two different DACs both with the same result. I tried wav files generated with Audacity as well as with Reaper.
Thanks for looking into this. Let me know if you need any more information.
Guustaaf
Code:
`from machine import Pin, SPI, I2S
import sdcard
import uos
cs = machine.Pin(17, machine.Pin.OUT,value=1)
spi = machine.SPI(0,
baudrate=1000000,
polarity=0,
phase=0,
bits=8,
firstbit=machine.SPI.MSB,
sck=machine.Pin(18),
mosi=machine.Pin(19),
miso=machine.Pin(16))
sd = sdcard.SDCard( spi, cs)
vfs = uos.VfsFat(sd)
uos.mount(vfs, "/sd")
files = uos.listdir("/sd")
for fi in files:
print(fi)
sck_pin = Pin(12)
ws_pin = Pin(13)
sdout_pin = Pin(10)
audio_out = I2S(
1,
sck=sck_pin,
ws=ws_pin,
sd=sdout_pin,
mode=I2S.TX,
bits=16,
format=I2S.MONO,
rate=44100,
ibuf=40000
)
samples = bytearray(44100)
with open("/sd/1khz-mono.wav", "rb") as file:
while True:
samples_read = file.readinto(samples)
if samples_read == 0:
break
else:
audio_out.write(samples[:samples_read])
audio_out.deinit()
file.close()
uos.umount("/sd")
spi.deinit()
`

Hi Guustaaf,
Good news.  I was able to reproduce the exact problem you are seeing.  The problem is caused by a SPI bus that is running at a slow BAUD rate of 1000000.  With this SPI configuration, audio sample data comes out of the SD card at a low rate.  The I2S driver runs out of samples and silence is heard.
There are two ways to correct this problem:

initialize the SPI bus to a faster BAUD rate by calling sd.init_spi(25_000_000) as shown below.

sd = sdcard.SDCard( spi, cs)
sd.init_spi(25_000_000)  # increase SPI bus speed to SD card

OR

If you are using the latest sdcard.py version you can change your code to:
sd = sdcard.SDCard(spi, cs, baudrate=25_000_000)

Either of those fixes should allow audio samples to be read from the SD card at a fast enough rate to avoid audio gaps.
Mike

Hi Mike,
Yes! That is the fix! Thanks so much Mike. I really appreciate it!
Guustaaf

Great !


Component: Badge

We have a need for a component that can be used to label or tag small bits of information. There are effectively two variants of this component: a non-interactive one, and an interactive one. It appears in a lot of design systems under the names "badge," "chip," "label," and "tag."

Material Chips - interactive
Ant Design Tag - non-interactive (the CheckableTag variant is interactive)
USWDS Tag - non-interactive
Lightning Badge - non-interactive
Atlassian DS Badge - non-interactive
Atlassian DS Tab - interactive
Carbon Tag - hybrid (adding the filter prop makes it interactive)
GitHub Label - non-interactive

Splitting this into two components: Badge (this issue) and Tag (#135).

Issue linked to JIRA Story: [NDS-3]


Added stacked area charts

Coverage increased (+0.01%) to 93.0% when pulling 79776cfe1cdf052f8b460d9f28d40e6fd2624305 on stacked_area_chart into cabfc95c93b547b8e44e3b33d9411205149a7aed on fireant1.0.


DartEditor x64 on windows tryes to start with x86 jre

This issue was originally filed by<EMAIL_ADDRESS>
What steps will reproduce the problem?
1.install both 32 bit and 64 bit JDK
2.set java_home to 32 bit
3.run darteditor x64

DartEditor

Failed to load the JNI shared library "C:\Program Files (x86)\Java\jdk1.8.0\bin..\jre\bin\client\jvm.dll".

ОК

so even java launchers with bat file are smart enaught to detect correct JDK, why can't your exe detect that "C:\Program Files (x86)" isn't correct for 64bit app and try something another  (java locations are pretty standard) or at least allow  to edit java location in some config file (I don't want to change my global environmnt settings)

Stale.


Replace magic number in CFE_SB_IncrBufUseCnt

Is your feature request related to a problem? Please describe.
The 0x7FFF limit on an uint16 doesn't seem to make sense, looks like int16 max instead of uint16 max?
https://github.com/nasa/cFE/blob/c8b5e00bb722d8f59787ae8f82634feba5d39f60/modules/sb/fsw/src/cfe_sb_buf.c#L200
Describe the solution you'd like
Use a defined max, add a comment if the max choice isn't obvious.
Describe alternatives you've considered
None
Additional context
Code review
Requester Info
Jacob Hageman - NASA/GSFC

Given that no event is generated, nor an error returned, the check is almost pointless (no one will ever know if it was triggered). Also, as you mention, it seems unlikely/impossible for it to be called given the limitations set by CFE_PLATFORM_SB_MAX_DEST_PER_PKT anyway.