Missing functions from TF 2.4 vs TF 2.3 (tf.keras.backend)

System information

Used code from another repository : https://github.com/LeonLok/Deep-SORT-YOLOv4
Windows 10 Pro
pip install via anaconda
TensorFlow v2.4.0-rc4-71-g582c8d236cb 2.4.0
Python 3.6
CUDA 11.0 cuDNN 8.04
GeForce RTX 3060 Ti 8192 MB

I am trying to run the repository stated above on my 3060Ti and made some modifications to run it on tf 2.4. I noticed that alot of functions from tf.keras.backend for tf 2.4 (https://www.tensorflow.org/api_docs/python/tf/keras/backend) are missing compared to tf 2.3 (https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/backend) in the API docs . I tried to look for alternatives and managed to replace some of them. However, I am not able to find some of the functions mentioned below. Are they obsolete or are they any other alternatives.
K.arange
K.concatenate
K.dtype
K.min
K,gather
K.sum
K.switch
K.max
K.control_flow_ops.while_loop
K.learning_phase
Code
https://github.com/LeonLok/Deep-SORT-YOLOv4/blob/master/tensorflow2.0/deep-sort-yolov4/yolo.py
https://github.com/LeonLok/Deep-SORT-YOLOv4/blob/master/tensorflow2.0/deep-sort-yolov4/yolo4/model.py

@nch1997 Am I missing anything here. I am able to access K.arange etc without any problem. Please check the gist here. Thanks!
Please let us know know if you have any issues. Try with a simple code  and share it if you have any issues accessing different functions as you listed above. Thanks!

@nch1997 Am I missing anything here. I am able to access K.arange etc without any problem. Please check the gist here. Thanks!
Please let us know know if you have any issues. Try with a simple code  and share it if you have any issues accessing different functions as you listed above. Thanks!

Hi, thanks for the help. Not sure why it did not work the last time I tried it but now it works. However, I now encounter a 'failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED'. I attempted  to use the suggestions from here but it does not help https://stackoverflow.com/questions/41117740/tensorflow-crashes-with-cublas-status-alloc-failed. I attached the logs below if it is useful
2021-01-05 20:03:45.167619: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.474887: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-01-05 20:03:46.477817: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-01-05 20:03:46.499646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:46.499744: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.504933: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:46.505005: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:46.507991: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:46.509010: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:46.515565: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:46.517687: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:46.518316: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:46.518399: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:46.928907: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:46.929015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:46.929580: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:46.929933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:46.930555: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:46.937739: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:46.937822: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.938102: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:46.938254: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:46.938494: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:46.938687: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:46.938777: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:46.938964: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:46.939002: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:46.939233: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:46.939419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:46.939589: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:46.939767: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:46.940003: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:46.940141: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:46.954529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:46.954614: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.954684: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:46.955013: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:46.955251: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:46.955465: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:46.955665: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:46.955865: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:46.956071: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:46.956329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:46.956499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:46.956674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:46.956830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:46.957081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:46.957213: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:46.957565: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:46.957635: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:46.957820: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.957849: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:46.958045: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:46.958260: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:46.958469: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:46.958678: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:46.958890: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:46.959135: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:46.959325: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:47.227676: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:47.227750: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:47.228202: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:47.228228: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:47.228615: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:47.228827: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:47.228868: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:47.228894: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:47.229102: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:47.229162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:47.229373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:47.229792: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:47.230027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:47.230297: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:47.230434: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:51.665067: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
2021-01-05 20:03:55.109817: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:55.109906: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:55.110370: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:55.110397: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:55.110738: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:55.110987: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:55.111223: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:55.111519: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:55.111556: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:55.111796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:55.112025: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:55.112233: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:55.112255: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:55.112519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:55.112876: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:57.044600: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:58.065345: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0

2021-01-05 20:03:58.099300: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0

2021-01-05 20:03:58.142696: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:58.788922: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.817620: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.864186: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.895198: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.944997: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.955205: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.976647: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.008404: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.026395: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.054411: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.100066: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.124254: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.151229: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.166517: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.189911: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.229805: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.254890: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.271920: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.285410: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.306614: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.333288: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.373000: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.391758: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.406642: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.440534: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.469361: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.523541: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.556508: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.568339: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.599717: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.611995: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.696813: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.710269: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.756313: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.777329: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.797895: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.815704: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.466321: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.482469: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.505733: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.521207: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.526484: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.534348: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.548182: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.554963: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.562900: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.579876: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.581185: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.581238: W tensorflow/stream_executor/stream.cc:1455] attempting to perform BLAS operation using StreamExecutor without BLAS support

Hi, thanks for the help. Not sure why it did not work the last time I tried it but now it works. However, I now encounter a 'failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED'. I attempted  to use the suggestions from here but it does not help https://stackoverflow.com/questions/41117740/tensorflow-crashes-with-cublas-status-alloc-failed. I attached the logs below if it is useful
2021-01-05 20:03:45.167619: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.474887: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-01-05 20:03:46.477817: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-01-05 20:03:46.499646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:46.499744: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.504933: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:46.505005: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:46.507991: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:46.509010: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:46.515565: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:46.517687: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:46.518316: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:46.518399: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:46.928907: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:46.929015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:46.929580: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:46.929933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:46.930555: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:46.937739: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:46.937822: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.938102: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:46.938254: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:46.938494: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:46.938687: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:46.938777: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:46.938964: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:46.939002: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:46.939233: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:46.939419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:46.939589: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:46.939767: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:46.940003: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:46.940141: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:46.954529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:46.954614: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.954684: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:46.955013: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:46.955251: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:46.955465: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:46.955665: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:46.955865: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:46.956071: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:46.956329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:46.956499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:46.956674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:46.956830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:46.957081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:46.957213: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:46.957565: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:46.957635: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:46.957820: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:46.957849: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:46.958045: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:46.958260: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:46.958469: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:46.958678: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:46.958890: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:46.959135: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:46.959325: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:47.227676: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:47.227750: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:47.228202: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:47.228228: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:47.228615: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:47.228827: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:47.228868: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:47.228894: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:47.229102: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:47.229162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:47.229373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:47.229792: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:47.230027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:47.230297: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:47.230434: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:51.665067: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
2021-01-05 20:03:55.109817: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.665GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-05 20:03:55.109906: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-01-05 20:03:55.110370: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:55.110397: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-01-05 20:03:55.110738: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-01-05 20:03:55.110987: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-01-05 20:03:55.111223: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-01-05 20:03:55.111519: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-01-05 20:03:55.111556: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:55.111796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-01-05 20:03:55.112025: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-05 20:03:55.112233: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2021-01-05 20:03:55.112255: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2021-01-05 20:03:55.112519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6617 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-05 20:03:55.112876: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-05 20:03:57.044600: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-01-05 20:03:58.065345: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0

2021-01-05 20:03:58.099300: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0

2021-01-05 20:03:58.142696: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-01-05 20:03:58.788922: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.817620: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.864186: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.895198: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.944997: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.955205: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:58.976647: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.008404: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.026395: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.054411: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.100066: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.124254: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.151229: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.166517: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.189911: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.229805: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.254890: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.271920: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.285410: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.306614: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.333288: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.373000: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.391758: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.406642: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.440534: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.469361: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.523541: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.556508: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.568339: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.599717: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.611995: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.696813: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.710269: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.756313: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.777329: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.797895: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:03:59.815704: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.466321: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.482469: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.505733: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.521207: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.526484: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.534348: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.548182: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.554963: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.562900: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.579876: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.581185: E tensorflow/stream_executor/cuda/cuda_blas.cc:226] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2021-01-05 20:04:00.581238: W tensorflow/stream_executor/stream.cc:1455] attempting to perform BLAS operation using StreamExecutor without BLAS support

@nch1997 Can you please close this issue and open another issue (with the above content) as the new issue is related to GPU. It will be better for the other users to follow and learn something from your issues. Thanks again.

@nch1997 Can you please close this issue and open another issue (with the above content) as the new issue is related to GPU. It will be better for the other users to follow and learn something from your issues. Thanks again.


Can MulticoreEngine retain process pool between runs?

The motivation for requesting than scene graph objects be picklable (#226) was that there is a large overhead involved in setting up a pool of worker processes each time MulticoreEngine.run() is called. In the use case described in that issue, this happend inside an inner loop which called TargettedPixel.observe(). The observation used 1000 rays each time, but was called ~750000 times, which meant setting up and destroying worker processes ~750000 times. This process management was comparable to the run time of each observation, so the overhead was massive. Working around this requires a significant redesign of the workflow, and switching to a SerialEngine instead.
However, the TargettedPixel concerned is instantiated only once, before the loop begins. Therefore, the render engine is also instantiated only once. Would it be possible to spawn the worker processes and task and result queues in MulticoreEngine.__init__, and destroy them in MulticoreEngine.__exit instead? Then the only overhead for each observation would be moving data through the queues, which I suspect is much quicker than spawning and destroying processes.
Note that this use case is applicable to other applications with pinhole cameras. Examples are bolometry and SXR within Cherab.

Unfortunately it isn't practically possible while retaining the ease of extension of the framework by users.
To perform a parallel calculation python must use processes to avoid the Global Interpreter Lock (GIL). Each of those processes must have exactly the same scenegraph state or the calculations from different processes would be inconsistent. When the worker processes are forked from the main process by the multicore engine, they each obtain a copy of the main process' memory (not an actual copy - see Copy on Write in linux, a logical copy so highly performant). The forking occurs as the first step in the observation loop - so we can therefore guarantee the scenegraph is identical across the processes before the observation is executed.
If we moved to a persistent worker process system, we would need to resync the state of the scenegraph. This would involve needing to serialise any changes between the independent processes. TO do this we would need to implement some for of object tracking/state delta. This would either involve a significant increase in code complexity for users (needing to implement state tracking in every scenegraph and dependent objects) or a large initialisation cost of each process before an observation (processes would need to report their current state, a comparison would need to be made with the main process state and then a resynchronisation of the new state to each worker would need to occur). The former is undesirable for obvious usability reasons. The later would almost certainly be more computationally expensive than forking due to the cost of interprocess communication.
A shared memory option would equally suffer a serialisation/deserialisation cost that would also compromise performance to a greater degree than the current forking system.
Unfortunately the specific use-case encountered by the user is better tackled by user partitioning of the work with the single core engine (avoiding the forking overhead) even if this does require more work for the user. The multicore engine is really intended for scenarios where the compute requirements of the observation greatly exceed the overhead of the process forking and ipc.
We'll have to add some documentation addressing this use case so user are aware when it is best to switch to the single core and self partitioning approach.


Downloadable PNG and VSDX

Please provide a downloadable PNG and VSDX file for this architecture.

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

ID: f26be54d-0f67-5098-7839-98f5bb064188
Version Independent ID: 187fa3ce-ade9-1b07-2af3-c874ec3d0aac
Content: Windows Virtual Desktop for the enterprise - Azure Example Scenarios
Content Source: docs/example-scenario/wvd/windows-virtual-desktop.md
Service: architecture-center
Sub-service: example-scenario
GitHub Login: @doodlemania2
Microsoft Alias: pnp

Thanks for reporting - this issue is under review.  This is a Microsoft Internal DevOps Bug ID AB#328838

@christiaanbrinkhoff - can you take a look at this one?

Fixed in next publish. Thanks!


Table reszie feature

Description
Much like the charts/subchart where you can resize the width and height on callbacks
Tables should implement the same feature
This will allow buttons/row/cell clicks to change what is rendered dynamically
Add Table.resize(height, width) with similar behavior as chart

Code example
No response
Method of implementation
No response

Hey @karunkrishna,
i'll look to implement in a future update.
Louis

Added in the latest version.
Louis


udf error

Steps to reproduce the behavior (Required)
select my_add4(10,20) as c from dual;
Expected behavior (Required)
30
Real behavior (Required)
CBO Optimizer don't support UDF function: my_add4
StarRocks version (Required)
starrocks-main, I build it on my machine, But It runs error use my udf
`package org.example.starrocksudf;
public class SimpleUDF {
public Integer evaluate(Integer a, Integer b) {
if (a != null && b != null) {
return a + b;
}
return null;
}
}`

you should set config enable_udf=true in fe/conf/fe/fe.conf

ok, it run well


Re-Enable the battery monitoring on Enviro! 🥳

As mentioned in the release note of v0.0.9 the battery voltage monitoring was disabled due to issues with the stability

One of the big causes of instability of past versions came from the introduction of battery monitoring. The pin to do this is shared with the Pico W's WiFi chip, and it seems there is no 100% safe way to read this without causing connection or other communication issues. Unfortunately the only way to get around this, for now, was to disable the battery monitoring on Enviro 😢

This pull request will solve this after I tested it as well as possible to be more or less 100% sure that the issue is gone.
Credit Note: The solution does (unfortunately) not origin in my own knowledge but was more or less inspired and then a partial C&P action from the following source(s) and super great work of Daniel Peron

Forum/Blog: https://forums.raspberrypi.com/viewtopic.php?t=339994
SourceCode: https://github.com/danjperron/PicoWSolar
All credit to him as he did the hard work or at least found a way with fiddling with the low-level documentation and configuration for the PicoW.

The current solution was tested on my Enviro devices. Below is the list of when the patch was applied ie. since when the patch runs on each device(s). All the devices were using the pimoroni-picow_enviro-v1.19.10-micropython-v0.0.9.uf2 OS as the base.

02.01.2023	1x Weather
15.01.2023	1x Grow
20.01.2023	1x Indoor (one of four)
23.01.2023	3x Indoor (rest of them)
23.01.2023	1x Urban
Since I applied the patch to these devices on the given dates I never had any instability issues! 🤩

As this feature created a lot of issues in the past I added a feature toggle to the config so that the voltage monitoring can simply be deactivated in case one experience stability issues again with the feature enabled.
Important: In the config_template.py is the feature DISABLED by default!

Hi @PascalKern. Thanks for raising this. You're proposed solution is interesting, as your sources are the exact two things I tried but still had issues with. Perhaps I didn't implement them correctly??
I will need to set up my own tests for this code to check if it works, though I do very much appreciate the inclusion of the config parameter! That makes the risk of merging this much lower than would otherwise be the case. I just need to check how you are adding that parameter, as additions to config_template.py should be treated as optional, so users can safely upgrade without re provisioning.

Since I applied the patch to these devices on the given dates I never had any instability issues! 🤩

When you mention no instability issues, I assume you are still experiencing the other know lock-ups, just not any that are obviously attributed to vsys measurement?

Hi @ZodiusInfuser
First of all sorry for the late reply. Sometimes life (or just too much work) comes in the way when not expected.
Regarding the additions to the config_template.py, I took care of backward compatibility. I had the same issue with my other devices while developing and testing the solution on just one of them. :) But more checks are always better than fewer.
But thanks for appreciating the feature flag. As you said, I want that failing feature to be able to disable.
For the no instability issues I mentioned I can only tell about the issues I had before the implementation. I only had some in earlier versions of the FW but I'm also only using MQTT as a destination.
Additionally is my WiFi quite poorly configured/setup and some issues in the past were related to bad/weak connectivity. But since the dates provided in my comment, I never had any of the previous issues only once a connection loss but this was due to a battery pack on the lower end of the needed voltage ie. almost below what is needed AND this was the weather sensor ie. outdoor and with the cold(er) weather (here it was between -7 to 3°C) back in these days this was also not helpful. So I took this issue as the combination of the low battery voltage, cold weather and the sensor being outdoors which does also not help for my weak wifi. ;)
Anyway. I appreciate any further, most likely deeper and dedicated, testing for that feature.
I'm going to rebase the branch anyway to not lag to much behind the main branch in case of a future merge.
Best,
Pascal

Hope this gets merged into the main codebase soon!
I've just manually implemented the changes and it all seems to be working fine, fingers crossed this project hasn't been abandoned by Pimoroni!


Define release process

This pull request addresses #94 and introduces guidelines for project contributors and maintainers. It replaces the previous release workflow with two new workflows - one for beta releases, one for production releases.
Please study it and provide feedback on completeness and clarity.

It looks so great that I'm curious how it looks in practice. In the sense, I would like to see video how this workflow starts and what happens until the publication😉
Amazing work @embee8 👍

Ignoring version check before merging as it's not relevant for this change (docs and workflows only).


Callable Object Intersection with Function

In https://github.com/flowtype/flow-typed/pull/1635 I have tried to implement the flow types for https://github.com/albburtsev/bem-cn.
I have tried to implement the type of the main API through a callable object and another simple function as the function called without any parameters returns a string otherwise it returns a copy of itself with some configuration set.
As soon as I add the intersection between the callable object and the string-function all checks on the additional properties of the callable object are ignored. Did I do something wrong, is this intended or a bug?

Moved to repo of flow


Support Python build with external NetworKit core library

This is another PR to reduce Travis' build times (see #236); however, the functionality implemented in this PR might also be interesting on its own:
We extend the build system to allow building the Python extention without building the NetworKit core (i.e. C++) library. This is done by passing -DNETWORKIT_BUILD_CORE=OFF to cmake and requires an external prebuilt copy of the core library. Our Travis script is modified to take advantage of this feature.
I should remark that after this PR, NetworKit is built as a shared library by default. This can be deactivated using -DNETWORKIT_STATIC but is no longer coupled to -DNETWORKIT_PYTHON.
This PR reduces Travis build times from 3 h 50 min to 3 h 5 min; it also decreases latency and the maximal time for a single build considerably.
Pinging @manpen as he gave some comments in #236.

Apparently, Windows requires static builds. I will push a commit to add -DNETWORKIT_STATIC=ON to AppVeyor.

This has been open long enough. There are no comments, so I am going to merge it.


Should be allowed to have multiple OIDC issuers

I get this error when my WebID has two OIDC issuers
warn: Error verifying WebID via DPoP-bound access token: The access token issuer doesn't match its associated WebID's trusted OIDC issuers.

@pheyvaer Can't rep at the moment because your profile seems to have changed: https://rdf-play.rubensworks.net/#url=https%3A%2F%2Fpieterheyvaert.com%2F%23me

(Note that there is a 60 second delay, that might have been the issue: #12)

Yes I changed it, because I couldn't be blocked by this. But you can easily replicate by just adding a two issuers in a test WebID though.

@pheyvaer I tried reproducing with https://multiruben.solidcommunity.net/profile/card:
:me solid:oidcIssuer <https://example.com>, <https://solidcommunity.net>, <https://example.org>.

but it works:
2021-12-02T14:07:49.937Z [DPoPWebIdExtractor] info: Verified WebID via DPoP-bound access token: https://multiruben.solidcommunity.net/profile/card#me

@pheyvaer have you been able to reproduce?
If not, I will close the issue.

It's fine to close this 🙂


Debug dropdown: selecting an entry should not launch it

Steps to Reproduce:

make a selection change in the debug viewlet launch dropdown
the selection change seems to trigger a start?

From the looks of that widget, this is not what I would expect.

Dup of https://github.com/Microsoft/vscode/issues/16903


Autocomplete Field Where is it?

Can you please make a new component for autocomplete. Sometimes You want a user to select a value, but there are more than a reasonable amount of choices for a dropdown.

We do have this on our roadmap as a commonly-requested feature that we'd like to include. Unfortunately at this time there's no one assigned to work on it, but we do hear your request.


Need to add a VS .sln

Currently, one needs to have cmake tools installed on VS, and open the cloned dir on VS so it magically picks the cmake file.
CMake usage on VS isn't great and VS users are used to solution files, so need to make one of those one day.

I think I will hope Microsoft can just make CMake less painful in future versions of VS.


Using gts instead of eslint

I've been having some issues with eslint's configuration as it is configured as part of the gts module. Is there a specific reason for using gts instead of eslint, prettier, etc just like in the core repo?

As far as I remember gts was the tool of choice in the early OTel days. it was removed in core repo because of some dependency issues. There were other build setup changes done in core repo that time like using a single typescript project.
The idea was to do the same in contrib as followup but noone found the time to do this.

As far as I remember gts was the tool of choice in the early OTel days. it was removed in core repo because of some dependency issues. There were other build setup changes done in core repo that time like using a single typescript project.
The idea was to do the same in contrib as followup but noone found the time to do this.

I can create a PR for this. Is there anything else that should be done that was also changed in the core repo?

Can't remember in detail. I guess the only way is to compare the repos. Maybe browser tooling like webpack differ also. Maybe also typescript, lerna,...
But there is no need to create a single PR to cover everything. Usually such repo wide changes tend to be complicated/cumbersome even if you touch just a part.

Can't remember in detail. I guess the only way is to compare the repos. Maybe browser tooling like webpack differ also. Maybe also typescript, lerna,...
But there is no need to create a single PR to cover everything. Usually such repo wide changes tend to be complicated/cumbersome even if you touch just a part.

Agreed. Will start with a small PR for gts.


Improve Webmention Sending

Re-consider using Telegraph to send webmentions to others. The reasons why I removed telegraph were that:

Does not parse relative URLs
Does not allow to send webmentions for removed links/deleted posts.

However, Telegraph has the advantage of also sending pingbacks.

Idea:

Queue
If it fails through "normal sending", use Telegraph
Careful with sending lots of them at once to the same domain

I do not send that many notifications. There is no need for queuing.


Has BrPage become a client component in react-sdk?

I'm reading the code changes here: https://github.com/bloomreach/spa-sdk/commit/f7d9159503cdb206657f7bdc9e99cbff625391fc#diff-fc9893cb5040ad9f394c14325e50d32dc7328f7b9bdf9fa121776fbe61f53da5
Since page is set as 'undefined' in the constructor() of BrPage, and it is initialized in the async componentDidMount(),
has BrPage essentially become a client component with a useEffect() hook wherein page is initialized?
Related questions are:

What is the meaning of passing 'page' as props to BrPage?
Will BrPage be rendered if JavaScript has been disabled?

I created this issue because in our case (Next.js) we initialize the page in getServerSideProps() and then pass page as props to BrPage.
I see the new render() method of BrPage. It returns 'null' in the first render cycle because page is 'undefined'. I don't think an async componentDidMount() will be awaited where page in props is actually checked.
Does it mean that without JavaScript, BrPage will stay as 'null'? How does it work with SEO?
About design choice, what is the meaning of not using the initialized page in the first render cycle?

If there's no other way around, is it an idea to export not only 'BrPageContext' but also 'BrMappingContext' and 'BrNode' so that the users can at least build sort of their own BrPage?

Hello @cosieLq!
To answer some of your questions:

What is the meaning of passing 'page' as props to BrPage?
Page is an object constructed from the response of the Page Delivery Api, you can read more about that here:
https://documentation.bloomreach.com/content/reference/pages-json-representation

The BrPage component owns the responsibility of making the context of the Page available to all child components.

Will BrPage be rendered if JavaScript has been disabled?
It depends on how you build your SPA. You can build a CSR, SRR or Isomorphic app.
We recommend an Isomorphic approach where you send an already rendered Page to the client on first request, ensuring optimal first render time and SEO, with subsequent navigations to other pages being partial re-renders in the browser.

I see the new render() method of BrPage. It returns 'null' in the first render cycle because page is 'undefined'.
I am guessing it returns null since you are using getServerSideProps sync? It's hard to tell without seeing the code, there are many ways to implement this.
Providing us with a code sample or a repo is invaluable so that we can understand what issue you are facing.

About design choice, what is the meaning of not using the initialized page in the first render cycle?
There is a configuration property called non blocking render mode. This is useful if you want to render something like a loading spinner for part of an HTML page if you are pulling data/content from multiple sources (one of them being BloomReach content).

@beetlerom
Thanks a lot for your answers!
Unfortunately, I cannot show the code without permission, but our code resembles your example code for Next.js very much.
I have two more questions about your example code:

Is the example using an Isomorphic approach that you meant?
Will Page in the example be rendered when JavaScript is disabled? Have you tested it?

Hi, we also struggle with SSR issue using spa/react-sdk 19.0.1 (client app implemented in NextJS).
I reviewed the BrPage implementation and I think I've found the reason why SSR doesn't work.
This line was introduced in version 18.0.1
https://github.com/bloomreach/spa-sdk/blob/main/packages/react-sdk/src/page/BrPage.tsx#L63
state.page is set to undefined, thus no data is used to initially render the page. During SSR only constructor and render method are triggered. I can't see how it can possibly work.
I checked version 18.0.0 and SSR was working correctly, although I can see many errors in authoring (like the one reported in #10). The constructor implementation looked like this:
https://github.com/bloomreach/spa-sdk/blob/spa-sdk-18.0.0/packages/react-sdk/src/page/BrPage.tsx#L61-L65
For us, it's a critical (maybe even blocker) issue.

@cosieLq

Is the example using an Isomorphic approach that you meant?

Yes, the Next.js example app is an Isomorphic app. You can see that by the fact that the first page render is always generated on the server and when you try to navigate to a different page, a new set of props is returned instead of a new HTML page.

Will Page in the example be rendered when JavaScript is disabled? Have you tested it?

Yes, Isomorphic means the first request is always rendered server side, meaning that regardless of JS being enabled or not the page will always be rendered. This is currently broken in v18+ for react, but you can see it work in v17.1.1 for example.

Is there by any chance an ETA on when this will be fixed?

Hi @mikroware ,
We had to wrap up the Vue3 SDK first but I'm working on this right now. We are reverting the changes we introduced to the async / sync behavior and finding different ways to optimize for the bundle size. My estimation is to release within a week if all passes review and Q&A. Thanks for your patience!

Hi @joerideg ,
Thank you for your updates!

Hi, we just released 21.0.0. This reverts the async behavior we introduced in 18. SSR is now fully supported again 👍🏻


Add support for configuration via YAML/JSON

Background
When deploying grafana-agent-flow on baremetal/vm to gather observability materials (metrics, logs, profiles...) the end users might want to use a configuration management tool such as Puppet or Ansible. The new configuration, River, is powerful but also introduce a fairly complex templating for configuration management. Below is an example around profiles scraping:
logging {
	level = "<%= $log_level %>"
}

pyroscope.write "default" {
	endpoint {
		url = "<%= $pyroscope_endpoint %>"

		basic_auth {
			username = "<%= $pyroscope_basic_auth_username %>"
			password = "<%= $pyroscope_basic_auth_password %>"
		}
	}

	<%- if $external_labels.length > 0 { -%>
	external_labels = {
		<%- $external_labels.each |$key, $value| { -%>
			"<%= $key %>" = "<%= $value %>",
		<%- } -%>
	}
	<%- } -%>
}

pyroscope.scrape "default" {
	targets = [
		<%- $scrape_targets.each |$service_name, $address| { -%>
		{ "__address__" = "<%= $address %>", "service_name" = "<%= $service_name %>" },
		<%- } -%>
	]
	forward_to = [pyroscope.write.default.receiver]

	scrape_interval = "<%= $scrape_interval %>"
	scheme = "<%= $scrape_scheme %>"

	<%- if $scrape_insecure_skip_verify { -%>
	tls_config {
		insecure_skip_verify = <%= String($scrape_insecure_skip_verify, "%t") %>
	}
	<% } -%>

	<% if $job_name { -%>
	job_name = "<%= $job_name %>"
	<% } -%>

	profiling_config {

		profile.goroutine {
			enabled = <%= String($goroutine_profiling_enabled, "%t") %>
			path = "<%= $goroutine_profiling_path %>"
			delta = false
		}

		profile.process_cpu {
			enabled = <%= String($process_cpu_profiling_enabled, "%t") %>
			path = "<%= $process_cpu_profiling_path %>"
			delta = true
		}

		profile.memory {
			enabled = <%= String($memory_profiling_enabled, "%t") %>
			path = "<%= $memory_profiling_path %>"
			delta = false
		}

		profile.mutex {
			enabled = <%= String($mutex_profiling_enabled, "%t") %>
			path = "<%= $mutex_profiling_path %>"
			delta = false
		}

		profile.block {
			enabled = <%= String($block_profiling_enabled, "%t") %>
			path = "<%= $block_profiling_path %>"
			delta = false
		}
	}
}

The associated Puppet code to deploy the template above could be similar to:
  file { '/etc/grafana-agent-flow.river':
    ensure  => 'file',
    content => epp('grafana_agent/config.river.epp',
      {
        'pyroscope_endpoint'            => $pyroscope_endpoint,
        'pyroscope_basic_auth_username' => $pyroscope_basic_auth_username,
        'pyroscope_basic_auth_password' => $pyroscope_basic_auth_password,
        'external_labels'               => $external_labels,
        'job_name'                      => $job_name,
        'log_level'                     => $log_level,
        'scrape_targets'                => $scrape_targets,
        'scrape_scheme'                 => $scrape_scheme,
        'scrape_interval'               => $scrape_interval,
        'scrape_insecure_skip_verify'   => $scrape_insecure_skip_verify,
        'goroutine_profiling_enabled'   => $goroutine_profiling_enabled,
        'goroutine_profiling_path'      => $goroutine_profiling_path,
        'process_cpu_profiling_enabled' => $process_cpu_profiling_enabled,
        'process_cpu_profiling_path'    => $process_cpu_profiling_path,
        'memory_profiling_enabled'      => $memory_profiling_enabled,
        'memory_profiling_path'         => $memory_profiling_path,
        'mutex_profiling_enabled'       => $mutex_profiling_enabled,
        'mutex_profiling_path'          => $mutex_profiling_path,
        'block_profiling_enabled'       => $block_profiling_enabled,
        'block_profiling_path'          => $block_profiling_path,
      }),
    owner   => $owner,
    group   => $group,
    mode    => '0640',
  }

The template can be optimized to be less verbose and more flexible but the general idea stays the same, we need to take into account possible cases before hand and maintain a complex template.
Proposal
One alternative to that would be to configuration grafana-agent-flow with either yaml or JSON. The proposal has already been discussed before here.
I don’t know the implied complexity of such request but it would facilitate everything from the configuration management point.
Puppet, but also Ansible, knows how to merge several configuration pieces but also render the result of this merge in a jsonor yaml file. This also apply to Terraform as well.
@rfratto examples in his proposal enable such Puppet code:
  file { '/etc/grafana-agent-flow.river':
    ensure       => 'file',
    content      => to_yaml($config_hash),
    owner        => $owner,
    group        => $group,
    mode         => '0640',
  }

The Ansible alternative could be similar to:
  - name: create grafana agent flow configuration
    ansible.builtin.copy:
      dest: /etc/grafana-agent-flow.river
      content: "{{ config_hash | to_nice_yaml }}"

The end users could benefits from both its configuration management tools feature and grafana-agent-flow. The code becomes less verbose and there is no need to maintain a template.

I'm going to remove the proposal label from this; I think this is a good enhancement request (and something currently on my radar), but at the moment it lacks sufficient detail for what YAML/JSON support would look like to be considered a proposal.

FWIW I don't see the value of river and the expressiveness of the config.  I'm deploying alloy from a configuration management tool orHelm, all of the logic you're trying to abstract in the config is part of my configuration management tool.  Please let us provide a vanilla YAML or JSON file without any fancy interpretation from Alloy!


Add makefile

What this PR does / why we need it:
Add makefile to build and run a development server
Which issue this PR fixes:
fixes #127
Special notes for your reviewer:
Release note:

LGTM


channels ID list

previous version managet created " channels ID list"
could you let me know how to make again please

Regarding channels ID name "channel id="57"" - could pe possible keep original channels names from enigma-bouquet instead "number";please
or have both option

`

4097:0:1:171:0:0:0:0:0:0
4097:0:1:1418:0:0:0:0:0:0
4097:0:1:869:0:0:0:0:0:0
4097:0:1:192:A:1:3520000:0:0:0
4097:0:1:1258:0:0:0:0:0:0
4097:0:1:982:0:0:0:0:0:0
4097:0:1:1580:0:0:0:0:0:0
`

would to have option for created "channels ID list" from bouquet or satelite.. like on the attached picture with "EPG id"
hope you know what i mean

Hi,
If you mean something like that:

That can be changed to the name of the channel. There is no big difference here. Additional options seem redundant to me.

Changed the numerical value to the name of the channel.

hope you know what i mean

As I already wrote about the additional option. It is not entirely clear what it is for and where to place it.

would like to get EPG in IPTV channels - coud be simple as possible and quick
scenario:
satelite possition astra 19.2E are scanned; or you have (lamedb file) if cannot be scanned (Astra UK 28E)
all programs-channels had EPG ID in (lamedb)- meaning you can downloading EPG from "epg import plugin" and all channels will showing EPG. does matter you have or not have signal.- we know this
m3u file are transfer to IPTV bouquet list.
IPTV Bouquet channels - if pairing with satelite channels (with same name of course); will transfer EPG ID(from lamedb) to IPTV EPG ID. thad mean all this channels on IPTV Bouquet) will have EPG too.
program could be:
two sides like now
left side list - have chosee satelite possition, and then channels - right side of choosen iptv bouquet
left side - showing list of channel with same or simular channel name as on the right side list
then confirm if channel is mach, if not had to showing other possibility (other channel close name)
after confirm just transfer/add  "EPG ID" to IPTV channel
something user friendly and quick,

Hi,
If you mean something like that:

could you point me how to get make it again, please.
do option to have epg id from current satelite channels what are had name on it

https://github.com/DYefremov/DemonEditor/issues/51

Here

have scanned whole orbit is over 12000 channels,
4000iptv  channels
know this options but is only for manually moving one channel from left side to (choosing) channel on right side
whithout more filtering like by (sattelite,then by channel name, and  authomatic options by program)- imposible to using pairing epg

in main is option filter
after click will make more filter setup like this
could you make this for EPG as well please

and still prefer to have program choose close chennel matching name automaticly

Look closely at this option in the EPG dialog. when this option is activated, the current epg.dat file will be loaded from the receiver and only those channels for which there are EPGs in this file will be displayed in the window on the right.

At the moment, this option needs to be worked out to support the newer format (ver 8.) . I am planning in the near future.

Channel names in IPTV lists (when importing from m3u) may differ from those in the main (scanned) one. For example, there may be Cyrillic names. Therefore, automatic assignment will not work this way. In addition, you may have duplicate channels (from different satellites but the same name)! What can really be useful is adding a filter by satellite positions to the EPG dialog for the panel on the left. In any case, the issue is not closed and we will need to experiment.

There is also an option similar to yours. It is also possible to load services in the panel on the left only those that are currently displayed in the main list (after the necessary filtering). Needs to experiment as well.

dont see any diference if filter by epg.dat activated or vs had channels list not scanned yet.
becouse channels from m3u list (iptv in bouquet) - never same and never know with channels are there.
best filter i gues could be:
iptv channel on right side - click on it and on the left side pop up same-very close channel name.
then choose (click) on it (left side) and epg assist
very simple might hard to do
anyway i leave it now for your decission and waiting new realese (win10)
all best in new year

Ok. Finally I understand what you want. :) Approximately the same as for assigning picons. I think it's quite feasible.

I've added small improvements to this feature, including auto filtering by name.

I hope it will be useful.

thank for another great uptade, filter work very vell
in my case (WIN10) bouth channels found (right and left side) then need make "copy refrece" on left side channel, then move to right side channel, click on and make assign
could you find easiet way (just one click) for assign?
like on screen both channels choosing - make pop up icon "assign refferency?" when click on it job done:)

Hi,

could you find easiet way (just one click) for assign?

Just drag and drop channel from the left side to the desired channel on the right. And there will be exactly one movement! :)

in small laptop screen, very hard to do.

Yes, may needs to enable font resizing not only for the main lists but for the rest too.

Maybe this is the same problem that I have? DemonEditor after upload settings doesn't write Channel name "<n/a>".
If I zap to channel AFTER write,

I don't think it has anything to do with this program. If this is an IPTV list, then it looks more like a WebIf problem (maybe there is a request to lamedb in which this service is missing) or the image itself. If you have any doubts, you can download the complete data set and send me a set of files from the 'data\profile name' folder. I can test.

Settings created with DreamEditor and send to STB with DreamEditor ; <n/a>
Settings created with DreamEditor and send to STB with DreamBoxEditor; <n/a>
Settings created with DreamBoxEditor and send to STB with DreamEditor ; <n/a>
Settings created with DreamBoxEditor and send to STB with DreamBoxEditor ; OK
DM7020HD.zip

I don't see any problems on my box.

Are you sure you are loading the full dataset, including the lamedb file?

If you see N/A in the bouquet for a regular channel, it means that it is missing in the lamedb file or the link to this channel in the bouquet file is incorrectly formed. How were the bouquets created? Are these services (which N/A) displayed in DemonEditor? Do you press 'save' before sending it to the receiver?

How were the bouquets created?
DE
Are these services (which N/A) displayed in DemonEditor?
No, only when I transfer to STB
Do you press 'save' before sending it to the receiver?
Yes

Settings created with DreamEditor and send to STB with DreamEditor: <n/a>
Settings created with DreamEditor and send to STB with DreamBoxEditor: <n/a>
Settings created with DreamBoxEditor and send to STB with DreamEditor: <n/a>
Settings created with DreamBoxEditor and send to STB with DreamBoxEditor: OK

Are these services (which N/A) displayed in DemonEditor?
No, only when I transfer to STB

This is the key to your problem!  I have a guess.
If possible, then:

Remove all bouquets.
Сreate new one.
Copy 2 channels to a bouquet from the main list. One is SD and the other is HD or UHD!
Save.
Download to the box.

Do the same in another program (in your case, dreamboxEDIT). For the same channels!  They sent me the settings saved in both programs.
Best

Done.
setting_test.zip
I think the problem is DE that doesn't write correct transponders section of lamedb. Services section is the same, but DE lamedb is more big than lamedb dbE.
Regards

It's even more interesting.

I think the problem is DE that doesn't write correct transponders section of lamedb.

This section is fine. Only the order is slightly different, but this does not affect.
But there is another interesting point.
DemonEditor settings:
39d2:00820000:1fa4:013e:1:0:0
Revival TV
dreamboxEDIT settings:
39d2:00820000:1fa4:013e:1:0
Revival TV
We have an extra zero at the end in the DemonEditor settings. If you open in the DemonEditor the settings created by the dreamboxEDIT via File -> Open and then save, the settings (lamedb format) will remain unchanged! Therefore, everything should work.
Since the DemonEditor tries not to change the format of the channel strings, I would like to understand how the lamedb file was created.

Every services in lamedb dreamboxEDIT settings ended with ":0" plus

I wrote about this above. Try to open the settings without the extra zero through File -> Open and hit save!

Try this:

Open wiith DE
Send to STB: it's OK
Save file with DE (overwrite)
Send to STB: NO WORK <n/a>

DM7020HD.zip

I HAVE FOUND THE PROBLEM!!!!
Simply all files that create (or change) DE is formatted with Windows end line; change to UNIX and it works!

I correctly understood that all this time you used the build for Windows? But I also tried the Windows version. Still need to test again.

I correctly understood that all this time you used the build for Windows?
Yes.
A small cosmetic thing: why don't add Bouquet tree on the right panel that open when load settings?

Yes, I use Windows.

Yes, you were right. Added end of the line in Unix format for lamedb writing. Thanks for your persistence. Somewhat surprised that my experiment with the Windows version is interesting for users of this OS. If it continues like this, then I think that it will be possible to bring it to a completely working state. :)
If there is time, I will try to release a new version in the near future.

A small cosmetic thing: why don't add Bouquet tree on the right panel that open when load settings?

Personally, I don't really like this option, but I'll think about it ...


typescript toolset build could fit better for FE builds

This is related to https://github.com/opendevstack/ods-pipeline/pull/343#issuecomment-998579788
The build-typescript.sh script seems to be more target to backend node builds.
For FE builds one would not need node_modules in the docker context.
Perhaps we could introduce a boolean argument to skip copying node_modules or make their presence optional.
Also at least for one existing project I am working with the npm run build output does not go to dist but to build (in full build/public/static/js/*).
This could perhaps be another boolean parameter assume-fe-build or so which of course could control both behaviors.
Let's discuss whether some FE oriented parameters make sense

Thanks for starting this discussion.
I believe the task should target FE builds.
I do not want to encourage Node.js backends. I'm OK if the task can also be used for this purpose by specifying parameters but the default should be FE I think.
Seems we need some help on this task to make that happen! FYI @henninggross @netzartist @m-apsolon

Wouldn't it make more sense to have a separate BE task as it could be a lot leaner?
My initial sentiment would be that ods-pipeline should provide this. I agree that this should not be misunderstood as ods-pipeline pushing for folks jumping to implement node backends.

Wouldn't it make more sense to have a separate BE task as it could be a lot leaner?

What difference do you see?
I'd be fine with two tasks as well but currently I do not see that much of a difference. I think we could introduce two parameters which could provide the required changes between FE (default) and BE:

build-dir: the directory into which npm run build places generated files. This directory will be copied to output-dir.
copy-node-modules: whether node_modules is copied or not, defaults to false

I would also vote for introducing parameters that handle the separation of BE and FE builds, because currently I see both cases to be pretty similar except for those minor changes that @michaelsauter mentioned.
If in the future we notice that there is a bigger difference than we currently imagine and the build-typescript script becomes full of if/else statements we could still refactor it and move them into separate tasks.
If you like I could give this a try.

@henninggross Great! Please go ahead.


Add new midstream branches and update pipelines

Discover new branches and update Konflux pipelines using openshift-knative/hack.

/hold

/unhold


pull request for e2e test at 01/10/2017 00:56:13

:white_check_mark: Validation status: passed
For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


ISPN-12824 JBoss Marshalling classloader fixes

https://issues.redhat.com/browse/ISPN-12824

Retriggering CI since all WildFly integration tests failed

Thanks @diegolovison


buf.length !== len error when using ipfs.dag.get

This condition buf.length !== len is true for me when calling ipfs.dag.get('zdpuAyt3vivTZGVtNvoaS2PG6ffdHynokfWA3v52mNVDbjng9') and the hash is correct, or at least ipfs handles it and it gets pinned and can be retrieved.
Some probably relevant background: I am deconstructing the hashes, storing just the 32 content bytes in Ethereum to save mucho ether gas costs. I deconstruct and reconstruct the hashes using this code, and again, the ipfs hashes seem to be valid so I'm not sure if this is a bug or what's the matter here.  Thoughts on what I may be doing wrong or is this a bug?  Oh, I should add that the len is 31 and the buf.length is 32 when this error throws.
Happy to move this to js-ipfs if that's where it should be -- wasn't sure.

feel free to re open this if this still happens.


DarkMode intitialTheme prop only effects theme after DarkMode component mounted - White theme flash on reload

Describe the bug
When using the initialTheme = 'dark' in +layout.svelte page in route, I still see a flash of white theme in  background and component backgrounds on page reloads.
I'm presuming, since bg-white is set in the  in the app.html file. The logic to switch the mode to dark in the DarkMode component happens only on the component mount, which  would be after.
My workaround at the moment is to use this in the head tag in the app.html:

<!DOCTYPE html>

	<meta charset="utf-8" />
	<link rel="icon"
		  href="%sveltekit.assets%/favicon.png" />
	<meta name="viewport"
		  content="width=device-width" />

	%sveltekit.head%

%sveltekit.body%

Reproduction
https://stackblitz.com/edit/sveltejs-kit-template-default-aitbwc?file=src%2Fapp.html
Flowbite version and System Info
System:
    OS: Windows 10 10.0.22621
    CPU: (16) x64 12th Gen Intel(R) Core(TM) i5-12600K
    Memory: 18.09 GB / 31.79 GB
  Binaries:
    Node: 18.15.0 - C:\Program Files\nodejs\node.EXE
    npm: 9.5.0 - C:\Program Files\nodejs\npm.CMD
  Browsers:
    Edge: Spartan (44.22621.1702.0), Chromium (113.0.1774.42)
    Internet Explorer: 11.0.22621.1
  npmPackages:
    @sveltejs/kit: ^1.18.0 => 1.18.0
    flowbite-svelte: ^0.36.2 => 0.36.2
    svelte: ^3.54.0 => 3.59.0
    vite: ^4.3.0 => 4.3.5

Please see this comment (chain) for a solution: https://github.com/themesberg/flowbite-svelte/issues/770#issuecomment-1554232385

@Destaq well I'm not concerned by the elements flickering, I just wanted the dark theme to persist across reload.
Adding the below workaround for me is good enough. Since the state of the theme is stored in localStorage of the browser, every time the page reloads it is checking that state from there but it hits the default light them first before the DarkMode onMount() so for the initialTheme prop setting in DarkMode component to make sense, it can't show light them before the component mounts if we want dark mode by default.

...

...

@Maxiviper117 I do see the validity of your solution — just wanted to point out that if you're looking for a more idiomatic solution, then I found one. If you look at the specific comment / video that I referenced above, you'll see that it addresses this issue of dark mode flashing.

@Destaq agreed, seems like some combination of using cookies instead of localStorage to then modify in the hook. But like you said, would need rewriting how the Darkmode is handled.

Yes, and rewriting the document as well.

Adding #776 initalTheme was not a good idea and it needs a better solution.

Is that repaired with #811? Can we close the issue?

@jjagielka
Essentially #811 resolves the issue.


Play 2.6.20 sbt 1.2.1 issue

Doesn't  work with sbt 1.2.1 and play-2.6.20
Manifest not found exception

Hi :). Sorry for the late reply.
If it's still relevant

how did you configure the path to the manifest file?
What are you using to generate the manifest?

Thank you.

Doesn't work with sbt 1.2.6 and play 2.6.0, (play-webpack-plugin 0.1.19: not found)

Thank you. I will check.


Why doesn't signxml produce tag ds:Transforms in the detached method?

I've asked this question in Stackoverflow, can someone help?
https://stackoverflow.com/questions/55885345/why-doesnt-signxml-produce-tag-dstransforms-in-the-detached-method

This should be fixed in v2.7.0, please test.


fix: fix removeImage metric logic

Signed-off-by: KevinBetterQ<EMAIL_ADDRESS>
Ⅰ. Describe what this PR did
fixing the statistical logic of ImageActionsCounter metric when removing image
Ⅱ. Does this pull request fix one issue?

fixes #2923
Ⅲ. Why don't you add test cases (unit test/integration test)? (你真的觉得不需要加测试吗？)
added
Ⅳ. Describe how to verify it
Ⅴ. Special notes for reviews

LGTM, @KevinBetterQ could you make sure is this feature still be wanted?

LGTM, @KevinBetterQ could you make sure is this feature still be wanted?

For status monitoring, it is indeed necessary

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

Hi, thank you very much for your submission. I probably understand your logic, which means that the status monitoring in the original defer may lead to unnecessary statistics of the number of actions, because it may not be returned when the image operation is performed. But I think the logic is understandable, when you call the remove operation, it should cause the operation count to be incremented by 1, not what you changed.


Generic lazy data handling.

DO NOT MERGE
Still in development..
This is a starting point for replacing all Iris' explicit use of biggus concepts with something more detached from the implementation.
( Which I think should be better than explicitly referencing dask everywhere instead. )
At present there is no recognition here that realisation with 'as_concrete_array' might need to make some distinction whether the result should be a masked array (as biggus API does).
For the cube.data property (getter), this is the existing Iris behaviour anyway.

It seems to me that this PR would lead us down a route which is gonna cause problems later on. I think that if we want to be agnostic about the underlying implementation of the lazy data provider (i.e. Biggus or Dask), then having the Cube._mydata attribute set to an instance of that provider weds us to the implementation. For example, currently if you do cube1 + cube2, that leads to calling cube1._mydata + cube2._mydata, so you have an implicit dependency on the type of _mydata having an __add__ method. Obviously it's highly like that the implementation will have that method, but there are surely other methods which won't be present in all implementations.
I would suggest having a wrapper class that implements a documented interface, which we could initially derive from the current Biggus interface in addition to methods based on the functions in this PR (except please can we avoid attribute checking? Personally I find it ugly, but maybe that's just me). Most of the methods would be boilerplate that would just pass the operation down to the underlying provider (e.g. add), but for methods not available on the provider we could provide our own implementation.

I would suggest having a wrapper class that implements a documented interface, which we could initially derive from the current Biggus interface in addition to methods based on the functions in this PR

I don't think that this PR makes a decision on this matter.  I think there there is a key design decision that we have to consider
In my view, these helper functions do not take us down a particular path.  Perhaps they help to highlight different paths we could follow
I'm content to take these as is and have that discussion as part of a follow on activity


Revisit the --no-wallet option

This option is really confusing--at least three or four people have stumbled over this already.
One possible approach would be to change the default to make the wallet a controller by default. People could then choose to remove it from the controller list or run with --no-wallet to explicitly run commands using their current identity. This would also address the related issue https://github.com/dfinity/wallet-rs/issues/66.
Another possibility might be to change the name of the option to something like --use-current-identity.

The flag was changed to only apply to canister creation, and canister creation changed to set both the caller and wallet as controllers. Is this still an issue?


SVD training code?

Hi, thanks for your great work.
When will it be released.

Hello! Thanks for your interest to our work!
We will consider to release the training code after finishing the paper.

Hello! Thanks for your interest to our work!
We will consider to release the training code after finishing the paper.

Based on your work, segment training code based on sd1.5, would you consider open-sourcing it? ~


Don't send RESET on READY (clean) connections

Waiting for https://github.com/neo4j-drivers/testkit/pull/195 to re-run the TestKit suite.