Getting ValueError: Attempt to convert a value (PerReplica ..) when starting training

Hi!
I am currently trying to start a simple training by following the instructions from the README.md. Everything works up to the point where I want to start the training.
Executing
python run_rnnt.py --mode train --data_dir /home/sfalk/pt/shards/

Throws
ValueError: Attempt to convert a value (PerReplica ..)

Click to expand full error log
/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: An import was requested from a module that has moved location.
Import requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.
  from numba.decorators import jit as optional_jit
/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: An import was requested from a module that has moved location.
Import of 'jit' requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.
  from numba.decorators import jit as optional_jit
2020-05-26 09:14:30.736191: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-05-26 09:14:30.748386: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.749173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.582GHz coreCount: 28 deviceMemorySize: 10.92GiB deviceMemoryBandwidth: 451.17GiB/s
2020-05-26 09:14:30.749232: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.750058: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 1 with properties:
pciBusID: 0000:02:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.582GHz coreCount: 28 deviceMemorySize: 10.92GiB deviceMemoryBandwidth: 451.17GiB/s
2020-05-26 09:14:30.750112: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.750888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 2 with properties:
pciBusID: 0000:03:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.582GHz coreCount: 28 deviceMemorySize: 10.92GiB deviceMemoryBandwidth: 451.17GiB/s
2020-05-26 09:14:30.750927: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.751427: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 3 with properties:
pciBusID: 0000:05:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.582GHz coreCount: 28 deviceMemorySize: 10.92GiB deviceMemoryBandwidth: 451.17GiB/s
2020-05-26 09:14:30.751570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-26 09:14:30.752638: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-05-26 09:14:30.753673: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-05-26 09:14:30.753866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-05-26 09:14:30.754997: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-05-26 09:14:30.755618: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-05-26 09:14:30.757804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-26 09:14:30.757899: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.759345: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.760097: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.760844: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.761589: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.762328: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.763068: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.763805: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:30.764521: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0, 1, 2, 3
2020-05-26 09:14:30.764770: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-05-26 09:14:30.770070: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency:<PHONE_NUMBER> Hz
2020-05-26 09:14:30.770492: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x560e6150fef0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-05-26 09:14:30.770507: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-05-26 09:14:31.020514: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.037961: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.041811: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.049635: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.050189: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x560e60e72d20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-05-26 09:14:31.050199: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1080 Ti, Compute Capability 6.1
2020-05-26 09:14:31.050203: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): GeForce GTX 1080 Ti, Compute Capability 6.1
2020-05-26 09:14:31.050206: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (2): GeForce GTX 1080 Ti, Compute Capability 6.1
2020-05-26 09:14:31.050209: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (3): GeForce GTX 1080 Ti, Compute Capability 6.1
2020-05-26 09:14:31.051527: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.051949: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.582GHz coreCount: 28 deviceMemorySize: 10.92GiB deviceMemoryBandwidth: 451.17GiB/s
2020-05-26 09:14:31.051989: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.052409: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 1 with properties:
pciBusID: 0000:02:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.582GHz coreCount: 28 deviceMemorySize: 10.92GiB deviceMemoryBandwidth: 451.17GiB/s
2020-05-26 09:14:31.052448: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.052867: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 2 with properties:
pciBusID: 0000:03:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.582GHz coreCount: 28 deviceMemorySize: 10.92GiB deviceMemoryBandwidth: 451.17GiB/s
2020-05-26 09:14:31.052904: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.053326: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 3 with properties:
pciBusID: 0000:05:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.582GHz coreCount: 28 deviceMemorySize: 10.92GiB deviceMemoryBandwidth: 451.17GiB/s
2020-05-26 09:14:31.053353: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-26 09:14:31.053366: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-05-26 09:14:31.053377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-05-26 09:14:31.053387: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-05-26 09:14:31.053397: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-05-26 09:14:31.053407: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-05-26 09:14:31.053418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-26 09:14:31.053452: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.053895: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.054339: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.054782: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.055227: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.055669: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.056126: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.056579: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.057003: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0, 1, 2, 3
2020-05-26 09:14:31.057025: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-26 09:14:31.059325: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-05-26 09:14:31.059335: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 1 2 3
2020-05-26 09:14:31.059340: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N Y Y Y
2020-05-26 09:14:31.059344: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 1:   Y N Y Y
2020-05-26 09:14:31.059347: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 2:   Y Y N Y
2020-05-26 09:14:31.059350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 3:   Y Y Y N
2020-05-26 09:14:31.060102: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.060567: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.061033: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.061486: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.061942: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.062368: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9449 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2020-05-26 09:14:31.062688: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.063131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10161 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1)
2020-05-26 09:14:31.063473: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.064690: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 10161 MB memory) -> physical GPU (device: 2, name: GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2020-05-26 09:14:31.065011: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-26 09:14:31.065455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 10161 MB memory) -> physical GPU (device: 3, name: GeForce GTX 1080 Ti, pci bus id: 0000:05:00.0, compute capability: 6.1)
4 Physical GPU, 4 Logical GPUs
WARNING:tensorflow:From /home/sfalk/tmp/rnnt-speech-recognition/model.py:59: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
W0526 09:14:32.108052<PHONE_NUMBER>82080 deprecation.py:317] From /home/sfalk/tmp/rnnt-speech-recognition/model.py:59: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c9e97d820>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:32.108385<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c9e97d820>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:From /home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/ops/rnn_cell_impl.py:962: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.
W0526 09:14:32.109819<PHONE_NUMBER>82080 deprecation.py:317] From /home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/ops/rnn_cell_impl.py:962: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c9009e730>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:32.227335<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c9009e730>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c4811f9d0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:32.490125<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c4811f9d0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c48086070>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:32.669947<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c48086070>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c383bc4f0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:32.804272<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c383bc4f0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c383a7d00>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:32.951039<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c383a7d00>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c3830e190>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:33.074690<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c3830e190>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c382f8250>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:33.202479<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c382f8250>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c381bd820>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:33.890956<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c381bd820>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c086b71f0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0526 09:14:34.015121<PHONE_NUMBER>82080 rnn_cell_impl.py:909] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f6c086b71f0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
I0526 09:14:34.344151<PHONE_NUMBER>82080 run_rnnt.py:490] Using word-piece encoder with vocab size: 4341
Model: "encoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None, None, 240)]       0
_________________________________________________________________
batch_normalization (BatchNo (None, None, 240)         960
_________________________________________________________________
rnn (RNN)                    (None, None,<PHONE_NUMBER>
_________________________________________________________________
dropout (Dropout)            (None, None, 640)         0
_________________________________________________________________
layer_normalization (LayerNo (None, None, 640)         1280
_________________________________________________________________
rnn_1 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_1 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_1 (Layer (None, None, 640)         1280
_________________________________________________________________
time_reduction (TimeReductio (None, None, 1280)        0
_________________________________________________________________
rnn_2 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_2 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_2 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_3 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_3 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_3 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_4 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_4 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_4 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_5 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_5 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_5 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_6 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_6 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_6 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_7 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_7 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_7 (Layer (None, None, 640)         1280
=================================================================
Total params: 96,414,656
Trainable params: 96,414,176
Non-trainable params: 480
_________________________________________________________________
Model: "prediction_network"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_2 (InputLayer)         [(None, None)]            0
_________________________________________________________________
embedding (Embedding)        (None, None,<PHONE_NUMBER>
_________________________________________________________________
rnn_8 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_8 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_8 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_9 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
dropout_9 (Dropout)          (None, None, 640)         0
_________________________________________________________________
layer_normalization_9 (Layer (None, None, 640)         1280
=================================================================
Total params: 24,635,524
Trainable params: 24,635,524
Non-trainable params: 0
_________________________________________________________________
Model: "transducer"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
mel_specs (InputLayer)          [(None, None, 240)]  0
__________________________________________________________________________________________________
pred_inp (InputLayer)           [(None, None)]       0
__________________________________________________________________________________________________
encoder (Model)                 (None, None,<PHONE_NUMBER>6    mel_specs[0][0]
__________________________________________________________________________________________________
prediction_network (Model)      (None, None,<PHONE_NUMBER>4    pred_inp[0][0]
__________________________________________________________________________________________________
tf_op_layer_ExpandDims (TensorF [(None, None, 1, 640 0           encoder[1][0]
__________________________________________________________________________________________________
tf_op_layer_ExpandDims_1 (Tenso [(None, 1, None, 640 0           prediction_network[1][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2 (TensorFlowOp [(None, None, None,  0           tf_op_layer_ExpandDims[0][0]
                                                                 tf_op_layer_ExpandDims_1[0][0]
__________________________________________________________________________________________________
dense (Dense)                   (None, None, None, 6 410240      tf_op_layer_AddV2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, None, None, 4 2782581     dense[0][0]
==================================================================================================
Total params: 124,243,001
Trainable params: 124,242,521
Non-trainable params: 480
__________________________________________________________________________________________________
Starting training.
Performing evaluation.
Traceback (most recent call last):
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2292, in _convert_inputs_to_signature
    flatten_inputs[index] = ops.convert_to_tensor(
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 1341, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py", line 321, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py", line 261, in constant
    return _constant_impl(value, dtype, shape, name, verify_shape=False,
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py", line 270, in _constant_impl
    t = convert_to_eager_tensor(value, ctx, dtype)
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py", line 96, in convert_to_eager_tensor
    return ops.EagerTensor(value, ctx.device_name, dtype)
ValueError: Attempt to convert a value (PerReplica:{
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array([[[-9.8887777e+00, -9.5391264e+00, -9.2146311e+00, ...,
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        [  0.        ,   0.        ,   0.        , ...,   0.        ,
           0.        ,   0.        ]],

       [[ -9.117433  ,  -8.756327  ,  -8.3021965 , ...,   2.6770406 ,
           2.9165506 ,   2.619464  ],
        [ -0.8334371 ,  -0.83099353,  -0.7541424 , ...,  -1.08567   ,
          -0.3741293 ,  -0.14404964],
        [ -1.1120342 ,  -1.2024816 ,  -1.1394764 , ...,  -1.3766332 ,
          -1.0821438 ,  -0.808341  ],
        ...,
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           0.        ,   0.        ],
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        [  0.        ,   0.        ,   0.        , ...,   0.        ,
           0.        ,   0.        ]],

       [[ -8.535436  ,  -8.446434  ,  -8.433824  , ...,   1.7634034 ,
           1.9151568 ,   2.234317  ],
        [ -0.73761785,  -1.0498412 ,  -1.1566993 , ...,  -1.9087753 ,
          -1.4135947 ,  -0.43615532],
        [ -3.1867118 ,  -1.3442845 ,  -0.6116022 , ...,  -1.2935882 ,
          -1.5357218 ,  -1.4339061 ],
        ...,
        [  0.        ,   0.        ,   0.        , ...,   0.        ,
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        [  0.        ,   0.        ,   0.        , ...,   0.        ,
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       ...,

       [[ -8.752668  ,  -8.795702  ,  -9.234339  , ...,   1.4543443 ,
           1.308625  ,   1.8872037 ],
        [ -0.8426125 ,  -0.5590738 ,  -0.64102423, ...,  -0.2699995 ,
          -0.34920692,  -0.23712015],
        [ -0.49331522,  -1.201171  ,  -1.1223062 , ...,  -1.4179659 ,
          -0.8049512 ,  -0.8301816 ],
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        [  0.        ,   0.        ,   0.        , ...,   0.        ,
           0.        ,   0.        ]],

       [[ -9.640016  ,  -9.385439  ,  -9.126853  , ...,   2.6549459 ,
           2.8241823 ,   3.1816008 ],
        [ -1.9176644 ,  -1.4502705 ,  -0.9663389 , ...,  -0.6905961 ,
          -0.6539016 ,  -0.5099206 ],
        [ -1.4909953 ,  -1.6704174 ,  -1.1156216 , ...,  -1.4263935 ,
          -0.81988144,  -1.1464152 ],
        ...,
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        [  0.        ,   0.        ,   0.        , ...,   0.        ,
           0.        ,   0.        ]],

       [[-11.273719  , -10.959808  , -10.497703  , ...,   1.7455099 ,
           1.9854834 ,   2.464494  ],
        [ -2.0011683 ,  -1.4769925 ,  -0.9517286 , ...,  -0.25176716,
          -0.5848155 ,  -0.5404253 ],
        [ -1.4961203 ,  -1.4866172 ,  -1.359714  , ...,  -1.1029644 ,
          -1.4994144 ,  -0.89810133],
        ...,
        [  0.        ,   0.        ,   0.        , ...,   0.        ,
           0.        ,   0.        ],
        [  0.        ,   0.        ,   0.        , ...,   0.        ,
           0.        ,   0.        ],
        [  0.        ,   0.        ,   0.        , ...,   0.        ,
           0.        ,   0.        ]]], dtype=float32)>
}) with an unsupported type (<class 'tensorflow.python.distribute.values.PerReplica'>) to a Tensor.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "run_rnnt.py", line 586, in <module>
    app.run(main)
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/absl/app.py", line 299, in run
    _run_main(main, args)
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/absl/app.py", line 250, in _run_main
    sys.exit(main(argv))
  File "run_rnnt.py", line 532, in main
    run_training(
  File "run_rnnt.py", line 347, in run_training
    checkpoint_model()
  File "run_rnnt.py", line 304, in checkpoint_model
    eval_loss, eval_metrics_results = run_evaluate(
  File "run_rnnt.py", line 433, in run_evaluate
    loss, metrics_results = eval_step(inputs)
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 580, in __call__
    result = self._call(*args, **kwds)
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 647, in _call
    self._stateful_fn._function_spec.canonicalize_function_inputs(  # pylint: disable=protected-access
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2235, in canonicalize_function_inputs
    inputs = _convert_inputs_to_signature(
  File "/home/sfalk/miniconda3/envs/rnnt/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2296, in _convert_inputs_to_signature
    raise ValueError("When input_signature is provided, all inputs to "
ValueError: When input_signature is provided, all inputs to the Python function must be convertible to tensors:
  inputs: (
    (PerReplica:{
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array([[[-9.8887777e+00, -9.5391264e+00, -9.2146311e+00, ...,
          1.4807711e+00,  1.4137149e+00,  1.5833356e+00],
        [-2.5297828e+00, -1.0314496e+00, -4.4551528e-01, ...,
         -8.5550594e-01, -3.8671780e-01, -6.2595654e-01],
        [-9.2890608e-01, -9.3925929e-01, -1.0737282e+00, ...,
         -4.6040058e-01, -1.3226795e-01, -4.6705770e-01],
        ...,
        [-8.9524627e-02, -1.3095784e-01,  4.4763446e-02, ...,
         -7.6179504e-03,  3.1356859e-01,  1.3805485e-01],
        [-1.0855615e-01, -3.6668968e-01, -3.5269606e-01, ...,
         -1.6952515e-01, -4.3339968e-01, -2.3297167e-01],
        [-3.4607446e-01, -4.6576285e-01, -2.4114418e-01, ...,
         -3.9931583e-01, -6.5470409e-01, -5.9117317e-02]],

       [[-1.0188073e+01, -9.7674351e+00, -9.2495003e+00, ...,
          2.7845190e+00,  3.0497322e+00,  2.9723659e+00],
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         -1.2318707e+00, -1.2984281e+00, -1.2217040e+00],
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         -1.1765385e+00, -1.4464822e+00, -6.3945484e-01],
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          0.0000000e+00,  0.0000000e+00,  0.0000000e+00],
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          0.0000000e+00,  0.0000000e+00,  0.0000000e+00]],

       [[-9.7931051e+00, -9.2576466e+00, -8.7187033e+00, ...,
          2.6441813e+00,  2.5935564e+00,  2.7019000e+00],
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       ...,

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       [[-9.4380770e+00, -9.2610064e+00, -9.3578424e+00, ...,
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       [[-9.2918978e+00, -9.2315750e+00, -8.9223146e+00, ...,
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  1: <tf.Tensor: shape=(8, 267, 240), dtype=float32, numpy=
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  3: <tf.Tensor: shape=(8, 267, 240), dtype=float32, numpy=
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Any idea what could be the issue here?
I have the following setup:
$ pip freeze | grep tensor
tensorboard==2.2.1
tensorboard-plugin-wit==1.6.0.post3
tensorflow-datasets==3.1.0
tensorflow-estimator==2.2.0
tensorflow-gpu==2.2.0
tensorflow-metadata==0.22.0
warprnnt-tensorflow==0.1

I noticed that FLAGS.gpus  is None which leads to MirroredStrategy(devices=gpu_names) where gpu_names is None. I am not sure if this has something to do with the issue.

@stefan-falk Were you able to resolve this issue?

Using only on GPU resolves this issue (see https://github.com/noahchalifour/rnnt-speech-recognition/issues/18#issuecomment-633862788). But I wouldn't call it "resolved" yet.

same error

@BuaaAlban @prajwaljpj Were you able to run it on multiple GPUs yet?

@BuaaAlban @prajwaljpj Were you able to run it on multiple GPUs yet?

No, and it does't converge

I'm able to run it on 8 v100 GPU in single instance, using TF2.2.0 according to https://github.com/tensorflow/tensorflow/issues/29911 .
The problem now is that it does't converge, the Loss is around 100 after 40 epochs, the batch size is 64, training data is common_voice_data/cv-corpus-6.1-2020-12-11_zh-CN.


Mode Quality of Life improvements

Use array initializers to ensure that each mode's data is in the index to which its enum corresponds.  This can help catch errors since the changes are across multiple files.
Also update the mode enumeration to have a common BP_MODE_ prefix.  It would be better is C supported scoped enumerations, but this is the standard practice to reduce the chance of name collisions in the global namespace.
typedef the bytecode structure so don't need to keep typing struct _bytecode everywhere.
And mark the modes as const.  I had hoped the compiler would place this into a ROM section directly, and thus save a few extra bytes of RAM, but ... unfortunately this is not the case.  Still, it is good to mark things as const whenever possible.

Merged into single PR https://github.com/DangerousPrototypes/BusPirate5-firmware/pull/70


Wonder 2.5

Theory, models  and algorithms
WONDERv2.5.zip

new release, with basic elements of SAXS


tests/subsys/settings/fcb_int: add storage preparation

The test assumes that storage is clear before it runs. This
might be not true as lot of devices are only partially erased by
'west flash'.
Patch introduce procedure for testing whether test runs the first
time and clear storage if so.
fixes #16463
Signed-off-by: Andrzej Puzdrowski<EMAIL_ADDRESS>

@ioannisg typo fixed

@hakehuang can you please try the patch and confirm the fix?

@nvlsianpu it is not clear to me (from the description in the commit) why the patches written are under CONFIG_ARM. This does not seem to be architecture-specific, so we need to elaborate a bit more on that.

@ioannisg
Yes - solution is architecture specific - It will work on most of ARM SoC.
Yes, it is kind of temp-hack here to make stuff testable.
Will add description on that in commit message and code

@ioannisg
Yes - solution is architecture specific - It will work on most of ARM SoC.
Yes, it is kind of temp-hack here to make stuff testable.
Will add description on that in commit message and code

I think we need something better here. CONFIG_ARM is really misleading; shows that something is required because of the Cortex-M architecture. We better have #ifdefs that are more close to reality, i.e. platform, SOC, or SOC-family specific (or Driver specific)

@ioannisg reworked

@ioannisg updated


Document structure improvements

This page - https://accessibility-manual.dwp.gov.uk/best-practice/document-structure - like the rest of the manual contains some really great stuff.  Lots to build on and learn from.
Language
Could be improved by explaining about the language of parts. It is a bit technical here, and many WYSIWYG editors may not have an easy way to insert a language attribute into it.  We have built it into Drupal, but even there it isn't enabled by default, and not something that a generic editor can enable.  More examples here will be useful, as most folks won't know how to hard code the HTML for Je Ne Sais Quoi.  This isn't something that people are trained to do in my experience:

If you have more than one language in your document, for example you have an English document which contains a French quote, then you should make sure you set the language correctly for each part.

It is common to say, "You should try to write in plain English and for a reading age of 9." but if you put the line above into https://hemingwayapp.com/ it shows as Grade 14.  We all do this. Plain English is hard to write. It is important to have some automated process to check.
I also like to remind folks that we're writing for real world situations.  People are reading work email after have a drink or two with friends at the pub, while on their cell phone.  We can't assume that people reading this content are at the top of their game and have already have their 2nd cup of coffee in the morning.  Real world, especially in a pandemic, is more complicated than that.
It was unclear to me what type of documents you were writing..
The only office tool I know that has accessibility checkers built-in is the one from Microsoft, so you might as well say that. If government uses other software that has it, it would be good to include it.
Also, on PDFs, can you be clear and consistent and tell people not to? We know they will do it anyways, but ya, a warning like this one would be good.
Plus, why not link to the great work over here https://www.gov.uk/guidance/publishing-accessible-documents

All valid points. Have made updates. Thanks!

Updated in v1.1.0


fix: releasing

use outreach version of orb publishing semantic release plugin

What this PR does / why we need it

Jira ID
[XX-XX]

Notes for your reviewers

I hope this doesn't require auth, or that if it does it just works


Smoketest failure

https://dev.azure.com/monacotools/Monaco/_build/results?buildId=133609&view=logs&j=3792f238-f35e-5f82-0dbc-272432d9a0fb&t=00d0ae10-f5a4-5438-a3b8-c58076933cab

fyi - pushed change to skip this test

@sandy081 There is a trace attached that you can open with npx playwright show-trace trace.zip
The last of the traces in  https://dev.azure.com/monacotools/Monaco/_build/results?buildId=133609&view=artifacts&pathAsName=false&type=publishedArtifacts

Seems to me it is just not clicking the "Install" button:

@bpasero Thanks for the tooling support. It helped to figure out what's going wrong.

👏


Defining Open Source Software

Open source software is a type of software that is publicly accessible and anyone has the right to inspect, edit, and run in their own environment.

https://opensource.com/resources/what-open-source
here is a link to explore more about open source software.
Open source refers to a type of software whose source code is available to the public, meaning anyone can view, modify, and distribute the code. This allows for collaboration and customization of the software, and encourages a community-driven development process.


cryptowatch-desktop: 0.5.0 -> 0.7.1

Description of changes
change log: https://docs.cryptowat.ch/cryptowatch-docs/desktop/desktop-changelog (doesn't actually list 0.7.1)
changed sha256 yo hash in src
added alsa-lib buildInput as libasound.so.2 is required and found in alsa-lib
added myself as a maintainer to cryptowatch-desktop

Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[x] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[x] Tested basic functionality of all binary files (usually in ./result/bin/)
23.11 Release Notes (or backporting 23.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[x] Fits CONTRIBUTING.md.

Result of nixpkgs-review pr 253108 run on x86_64-linux 1

1 package built:

cryptowatch-desktop


Recall@1 does not return the same results as micro recall (on single-prediction classification)?

Hi,
First of all, thank you so much for this useful library!
I have been doing a few very simple tests, to compare the output of this library with the output of scikit-learn's micro averaged metrics (precision and recall). Based on my understanding, with k=1, the results of precision@1 and recall@1 should be the same of micro precision and micro recall, if we have just one label predicted by the model.
This holds for precision@1, but recall@1 returns different results. Here is a minimal example:
import numpy as np
from scipy.sparse import csr_matrix
from xclib.evaluation import xc_metrics
from sklearn.metrics import precision_recall_fscore_support

gold_vecs = np.array([[1, 1, 0, 0], [0, 1, 1, 0], [0, 0, 1, 0]])
pred_vecs = np.array([[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0]])

print("sklearn micro:", precision_recall_fscore_support(y_true=gold_vecs, y_pred=pred_vecs, average='micro', zero_division=0))

print("xclib precision@1:", xc_metrics.precision(csr_matrix(pred_vecs), csr_matrix(gold_vecs), k=1)[0])
print("xclib recall@1:", xc_metrics.recall(csr_matrix(pred_vecs), csr_matrix(gold_vecs), k=1)[0])

this outputs:
sklearn micro: (0.6666666666666666, 0.4, 0.5, None)  # the first two values are precision and recall, respectively
xclib precision@1: 0.66666667
xclib recall@1: 0.5

By doing manual verification, I get:
3 fn, 1 fp, 2 tp
recall = 2/(2+3) = 0.4
Am I missing something?

Hi,
Thanks for checking out the library.
In XC setup, we compute the metric for each test point and then take the average - this would correspond to average='samples' in scikit-learn library. The recall@1 for individual samples (in the given example) is 0, 0.5, and 1 respectively and the overall recall@1 is 0.5 as computed by pyxclib.
output as per scikit-learn is:
sklearn: (0.6666666666666666, 0.5, 0.5555555555555555, None)


Alpha 6 : Shutdown menu keypress has no action.
Are the settings saved?
Menu screen will blank out after a few seconds. Missed that on the recording.

Progress comment from developer in 'Messenger Chat' :- "it does save before whatever it doesn't do"
So presumably its a screen menu glitch and is safe to power down after execution.


python: provide hasCxxDistutils attribute for pythonPackages.numpy

Patching numpy.distutils used to be required for pythonPackages.cython
to build on darwin. It was later accidentally disabled during one of the
refactorings, but that did not break cython. This change reinstantiates
the patch. It still applies, so it should be low maintenance and it can
still be useful.

Motivation for this change
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS)
Built on platform(s)

[ ] NixOS
[x] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nix-review --run "nix-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Assured whether relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

How come it did not break the build, and in what way is it then still useful?

How come it did not break the build, and in what way is it then still useful?

Probably because the pythonPackages.cython  tests were disabled at some point
https://github.com/NixOS/nixpkgs/blob/8c881a1bb56926f2aaf2d413c93be9b821e40534/pkgs/development/python-modules/Cython/default.nix#L55-L57
Though the test was disabled (bca81442744ddf18b5721a397a29ea1146d74fd2) before the patch was taken out (efbe87f3ef769aac5e95512609b4759a43109307)
This should be useful for the original purpose of fixing compilation of the C++ code for modules that use numpy.distutils. I don't expect that there are too many such instances. It might be still useful for pythonPackages.cython tests and maybe for building pythonPackages.xgboost on macOS, but I did not check that extensively.


Add RX/TX placement support on memif interface

This PR introduces handling of interface events from VPP. It is used for setting RX/TX queues on workers for memif interface, because the interface must have link state up before this.

The update version now handles enable/disable procedures of event processing automatically.

when the first watcher starts - subscribes to interface events and enables them (sends WantInterfaceEvents{Enable: 1} message to VPP)
and when the last watcher stops - disables interface events (sends WantInterfaceEvents{Enable: 0} message to VPP) and unsubscribes from interface events

CC @hedibouattour


Indication not working

Hi. I'm using your great bglib library (c#) to create a react native module for windows wpf/uwp.
I'm starting to implement notification and indication. Notifications are working perfectly but I'm unable to work with indication. This is my piece of code:
        [ReactMethod()]
        public async void monitorCharacteristicForService(JObject device, string serviceIdentifier, string characteristicUUID, IPromise promise)
        {
            Device dev = device.ToObject<Device>();
            TaskCompletionSource<byte[]> task = new TaskCompletionSource<byte[]>();
            ushort? notify_chrhandle, chrhandle;

            // get notify chrhandle (0x29, 0x02)
            notify_chrhandle = dev.GetCharacteristicHandle(serviceIdentifier, "2902");
            if (notify_chrhandle == null)
            {
                promise.Reject(new NotSupportedException("The requested service is not notificable."));
                return;
            }
            // get characteristic chrhandle
            chrhandle = dev.GetCharacteristicHandle(serviceIdentifier, characteristicUUID);
            if (chrhandle == null)
            {
                promise.Reject(new KeyNotFoundException("Service or characteristic not found for this device."));
                return;
            }

            //Byte[] cmdNotify = bglib.BLECommandATTClientAttributeWrite(dev.connectionHandle.Value, notify_chrhandle.Value, new Byte[] { 0x01, 0x00 });
            Byte[] cmdIndicate = bglib.BLECommandATTClientAttributeWrite(dev.connectionHandle.Value, notify_chrhandle.Value, new Byte[] { 0x02, 0x00 });

            Bluegiga.BLE.Events.ATTClient.AttributeValueEventHandler avEventHandler = new Bluegiga.BLE.Events.ATTClient.AttributeValueEventHandler((sender, e) => {
                if (e.connection == dev.connectionHandle && e.atthandle == chrhandle)
                {
                    task.TrySetResult(e.value);
                }
            });

            bglib.BLEEventATTClientAttributeValue += avEventHandler;
            //bglib.SendCommand(serialAPI, cmdNotify);
            bglib.SendCommand(serialAPI, cmdIndicate);

            await task.Task;

            bglib.BLEEventATTClientAttributeValue -= avEventHandler;

            promise.Resolve(task.Task.Result);
        }

But the AttributeValueEvent is never fired.
Am I missing something?

The code here looks reasonable. Are you certain that the characteristic you're testing with actually supports indications, and not just notifications? For example, a correctly built Health Thermometer service has the right permissions on the temperature characteristic.

Yes, I'm successfully testing the device with an iOS app.
Anyway, the next week I'll have a meeting with the device manufacturer. He wrote a driver like mine using the C bgapi.
I'll check my code with him and I'll let you know ;)
Thanks!

Ok, solved ... even if I cannot figure why this change solves the problem:
Byte[] cmdIndicate = bglib.BLECommandATTClientAttributeWrite(dev.connectionHandle.Value, (ushort)(chrhandle.Value + 1), new Byte[] { 0x02, 0x00 });

instead of:
Byte[] cmdIndicate = bglib.BLECommandATTClientAttributeWrite(dev.connectionHandle.Value, notify_chrhandle.Value, new Byte[] { 0x02, 0x00 });

Just searching for 2902 directly may give you the 'wrong one' if your service has multiple characteristics which each have their own 2902. You need to find the 'next' 2902 that occurs after the desired chrhandle


enhancement: Use SpatialBundle inside our bundles

Currently we have transforms, global transforms, visibility, etc. inlined into each bundle (VelloSceneBundle, VelloAssetBundle, VelloTextBundle).
We should take the approach of splined and perhaps inline the SpatialBundle instead.

I go back and forth on it -- it's nice to simplify the code but the UX suffers a little bit needing to now nest into SpatialBundle for customizing its components.

Wondering if there's a macro to flatten it, like #[serde(flatten)]?

By the way I asked this on the Bevy discord a while ago: https://discord.com/channels/691052431525675048/742569353878437978/1145869833729216583
Looks like the conclusion I came to there is that inlining the components is nice to mirror native bundles like SpriteBundle.

Since it appears to be the convention... we'll keep as is. Won't do.


improve cargo watch

Add start and stop commands
Cleanup trypescript code to avoid definite assignment assertions (! after possibly undefined value)
Recover rustc-watch problem matcher because it's still useful, can be used with any command, for example cargo test

Looks good to me, would you mind to update the doc/user.md too?

bors r+
Thanks!


Fix semantic error

At the end and in the end. Similar but not the same.
For more info check this very descriptive video: https://youtu.be/2_JpHDfQFFE

My second pull request :)


Will-change memory consumption is too high

I'm working at a wordpress theme built with Roots Sage starter one, using the following front-end packages:
sage
├─┬ bootstrap-sass#3.3.6
│ └── jquery#3.1.1
├─┬ magnific-popup#1.1.0
│ └── jquery#3.1.1
├── smooth-scrollbar#7.2.9
├── wow#1.1.2
└── wp-bootstrap-navwalker#2.0.4
Issue
Would like to use smooth-scrollbar UI in the main scrollbar of a single-page WP theme; the scroll simply don't work.
Only way to scroll is to drag the scrollbar with mouse pointer. No imput from mousewheel, nor trackpad or touch-imput device.
Firefox 51 console say to me:
Will-change memory consumption is too high. Budget limit is the document surface area multiplied by 3 (1535100 px). Occurrences of will-change over the budget will be ignored.
Chrome: silent fail
IE11: silent fail
Steps to Reproduce
Init:
var scrollOptions = {
  speed: 1,
  damping: 0.1,
  thumbMinSize: 20,
  renderByPixels: true,
  continuousScrolling: 'auto',
  alwaysShowTracks: false,
  overscrollEffect: false,
  overscrollEffectColor: '#fff'
};
Scrollbar.initAll(scrollOptions);

Markup:
<main class="main" data-scrollbar>
  <!-- get the content -->
  <?php include Wrapper\template_path(); ?>
</main>

Css;
main {
  height: 100%;
  width: 100%;
}

Environment

Version of smooth-scrollbar: 7.2.9

Notes
In my custom JS i have some scroll-related script, mainly to show/hide menu bars, i have not understand if this plugin affect the ordinary scroll event.

Actually it not fails in chrome or IE, just being ignored XD.
Thanks for reporting, will-change property has already been removed in next version (WIP).


URLs in CSS not save GET params

If I have in my css sprites with get params, for caching. After process over minimalcss url`s remain without get params. It caused double resourse load if I also async include all css.
Example, in full css I have css property:
background: transparent url("../_img/sprite.svg?1518693909462") -294px -542px no-repeat;
After minimalcss I receive:
background: transparent url("/_img/sprite.svg") -294px -542px no-repeat;
Expected:
background: transparent url("/_img/sprite.svg?1518693909462") -294px -542px no-repeat;

Fixed as of https://github.com/peterbe/minimalcss/pull/148
And version 0.7.1 now released.
Thank you so much @usebaz ! I hope you find more things to contribute like this.


How to use with SwiftUI

I am new to the whole iOS ecosystem. Sorry if this is a basic question. But how do you use Flare within SwiftUI? The example sadly doesn't use SwiftUI.

Hey there, I think you might be interested in the new version of our app, with new improved runtimes for Swift.
https://beta.rive.app is our new animation tool and you can see the new Swift runtime, with SwiftUI example, at https://github.com/rive-app/rive-ios


RFM - FileOptimizer

https://chocolatey.org/packages/FileOptimizer
Current Maintainer

[ ] I am the maintainer of the package and wish to pass it to someone else;

I DON'T Want To Become The Maintainer

[ ] I have followed the Package Triage Process and I do NOT want to become maintainer of the package;
[x] There is no existing open maintainer request for this package;

Checklist

[x] Issue title starts with 'RFM - '

Existing Package Details
Package URL: https://chocolatey.org/packages/FileOptimizer
Package source URL:https://github.com/Spunkie/chocolatey-packages/tree/master/automatic/FileOptimizer

@akkiogra Did you contact the current maintainers and if so how and when? (as per the email I responded back to you with).

As the maintainers have not been contacted and this has just been done now I'm going to close this. We can always re-open it later if needed.

Ya sorry, maintainer here. I think I have been contacted about this by others recently but I've been mostly off grid for the past 6 months.
Guess at some point in the last couple of months my new release script broke. I can take a look at it in a few days.


Not enough image to showcase projects on the site

This feature request is to add more images to enhance the presentation and improve the visual appeal of the projects section.
Acceptance Criteria:
Source high-quality images on the site and add links with proper layout

just added!