TensorFlow can't communicate with GPU. "None of the algorithms provided by cuDNN frontend heuristics worked; trying fallback algorithms." "No algorithm worked!"

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Issue Type
Bug
Have you reproduced the bug with TF nightly?
Yes
Source
source
Tensorflow Version
tf 2.12
Custom Code
Yes
OS Platform and Distribution
Linux Ubuntu 22.04.2 LTS and Red Hat Enterprise Linux 9.2
Mobile device
No response
Python version
3.10
Bazel version
bazel 5.3.0
GCC/Compiler version
11.3.1
CUDA/cuDNN version
11.8
GPU model and memory
Nvidia Quadro K620 - 2048MiB of memory
Current Behaviour?
*This bug happens both in Linux Ubuntu 22.04.2 LTS and RHEL 9. I tested it on a live-usb Ubuntu installation, pip installing it with GPU support after getting tired of trying to solve it on rhel.
It is somewhat related to the convolutional (Conv2d) layer, because fitting a model on a pip install with the use of only dense layers works fine. With a source install this error happens independently of model time.
Using CLI or multi-user.target, leaving all the GPU resources to tf does not solve the issue.
Reducing the Conv2d filters does not solve the problem, nor the bach_size
The code should just have no errors. I can't exactly tell or understand the error output, so that's about all I can tell.
Standalone code to reproduce the issue
from keras.datasets import mnist
from keras.utils import to_categorical
from keras import layers, models

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=['accuracy'])

train_images = train_images.astype('float32') / 255
test_images = test_images.astype('float32') / 255

trains_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

model.fit(train_images, trains_labels, epochs=5, batch_size=64)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f'{test_acc=}')

Relevant log output
(tf) [lnapon@rhel ancient_ai]$ python3 lrnkeras/mnist_conv.py
2023-05-25 19:21:32.077966: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-05-25 19:21:33.793139: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:33.809742: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:33.809975: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:33.811051: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:33.811233: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:33.811384: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:34.315816: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:34.316045: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:34.316220: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:21:34.316380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1294 MB memory:  -> device: 0, name: Quadro K620, pci bus id: 0000:01:00.0, compute capability: 5.0
Epoch 1/5
2023-05-25 19:21:35.567154: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8600
2023-05-25 19:21:35.886005: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:219] failed to create cublas handle: the resource allocation failed
2023-05-25 19:21:35.886043: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:222] Failure to initialize cublas may be due to OOM (cublas needs some free memory when you initialize it, and your deep-learning framework may have preallocated more than its fair share), or may be because this binary was not built with support for the GPU in your machine.
2023-05-25 19:21:35.886349: W tensorflow/core/kernels/conv_ops_gpu.cc:143] None of the algorithms provided by cuDNN frontend heuristics worked; trying fallback algorithms.  Conv: batch: 1
in_depths: 1
out_depths: 32
in: 28
in: 28
data_format: 1
filter: 3
filter: 3
filter: 1
dilation: 1
dilation: 1
stride: 1
stride: 1
padding: 0
padding: 0
dtype: DT_FLOAT
group_count: 1
device_identifier: "sm_5.0 with<PHONE_NUMBER>B RAM, 3 cores, 1124000KHz clock, 900000KHz mem clock, 2097152B L2$"
fusion {
  activation_mode: kRelu
  conv_scale: 1
}
version: 3

2023-05-25 19:21:35.893161: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:219] failed to create cublas handle: the resource allocation failed
2023-05-25 19:21:35.893178: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:222] Failure to initialize cublas may be due to OOM (cublas needs some free memory when you initialize it, and your deep-learning framework may have preallocated more than its fair share), or may be because this binary was not built with support for the GPU in your machine.
2023-05-25 19:21:35.893443: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at conv_ops_fused_impl.h:625 : NOT_FOUND: No algorithm worked!  Error messages:
  Profiling failure on CUDNN engine eng11{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(4639): 'status'
  Profiling failure on CUDNN engine eng0{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(4639): 'status'
2023-05-25 19:21:35.893485: I tensorflow/core/common_runtime/executor.cc:1197] [/job:localhost/replica:0/task:0/device:GPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): NOT_FOUND: No algorithm worked!  Error messages:
  Profiling failure on CUDNN engine eng11{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(4639): 'status'
  Profiling failure on CUDNN engine eng0{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(4639): 'status'
	 [[{{node sequential/conv2d/Relu}}]]
Traceback (most recent call last):
  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 28, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)
  File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/tensorflow/python/eager/execute.py", line 52, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.NotFoundError: Graph execution error:

Detected at node 'sequential/conv2d/Relu' defined at (most recent call last):
    File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 28, in <module>
      model.fit(train_images, trains_labels, epochs=5, batch_size=1)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1685, in fit
      tmp_logs = self.train_function(iterator)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1284, in train_function
      return step_function(self, iterator)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1268, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1249, in run_step
      outputs = model.train_step(data)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1050, in train_step
      y_pred = self(x, training=True)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 558, in __call__
      return super().__call__(*args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/base_layer.py", line 1145, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/sequential.py", line 412, in call
      return super().call(inputs, training=training, mask=mask)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/functional.py", line 512, in call
      return self._run_internal_graph(inputs, training=training, mask=mask)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/functional.py", line 669, in _run_internal_graph
      outputs = node.layer(*args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/engine/base_layer.py", line 1145, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/layers/convolutional/base_conv.py", line 321, in call
      return self.activation(outputs)
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/activations.py", line 317, in relu
      return backend.relu(
    File "/home/lnapon/miniconda3/envs/tf/lib/python3.10/site-packages/keras/backend.py", line 5396, in relu
      x = tf.nn.relu(x)
Node: 'sequential/conv2d/Relu'
No algorithm worked!  Error messages:
  Profiling failure on CUDNN engine eng11{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(4639): 'status'
  Profiling failure on CUDNN engine eng0{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(4639): 'status'
	 [[{{node sequential/conv2d/Relu}}]] [Op:__inference_train_function_1058]

On the nightly version the error output is a bit different, maybe this is helpful:
(tf-nightly) [lnapon@rhel ancient_ai]$ python3 lrnkeras/mnist_conv.py
2023-05-25 19:41:31.002823: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:7704] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-05-25 19:41:31.002868: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-05-25 19:41:31.002884: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1520] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-05-25 19:41:31.847714: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-05-25 19:41:32.672538: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:32.688679: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:32.688911: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:32.689738: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:32.689894: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:32.690034: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:33.135930: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:33.136133: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:33.136290: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-05-25 19:41:33.136425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1318 MB memory:  -> device: 0, name: Quadro K620, pci bus id: 0000:01:00.0, compute capability: 5.0
Epoch 1/5
2023-05-25 19:41:34.396173: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:434] Loaded cuDNN version 8600
2023-05-25 19:41:34.653261: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:190] failed to create cublas handle: the resource allocation failed
2023-05-25 19:41:34.653298: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:193] Failure to initialize cublas may be due to OOM (cublas needs some free memory when you initialize it, and your deep-learning framework may have preallocated more than its fair share), or may be because this binary was not built with support for the GPU in your machine.
2023-05-25 19:41:34.653625: W tensorflow/core/kernels/conv_ops_gpu.cc:144] None of the algorithms provided by cuDNN frontend heuristics worked; trying fallback algorithms.  Conv: batch: 1
in_depths: 1
out_depths: 4
in: 28
in: 28
data_format: 1
filter: 3
filter: 3
filter: 1
dilation: 1
dilation: 1
stride: 1
stride: 1
padding: 0
padding: 0
dtype: DT_FLOAT
group_count: 1
device_identifier: "sm_5.0 with<PHONE_NUMBER>B RAM, 3 cores, 1124000KHz clock, 900000KHz mem clock, 2097152B L2$"
fusion {
  activation_mode: kRelu
  conv_scale: 1
}
version: 3

2023-05-25 19:41:34.660525: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:190] failed to create cublas handle: the resource allocation failed
2023-05-25 19:41:34.660542: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:193] Failure to initialize cublas may be due to OOM (cublas needs some free memory when you initialize it, and your deep-learning framework may have preallocated more than its fair share), or may be because this binary was not built with support for the GPU in your machine.
2023-05-25 19:41:34.660782: W tensorflow/core/framework/op_kernel.cc:1828] OP_REQUIRES failed at conv_ops_fused_impl.h:625 : NOT_FOUND: No algorithm worked!  Error messages:
  Profiling failure on CUDNN engine eng11{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5333): 'status'
  Profiling failure on CUDNN engine eng0{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5333): 'status'
Traceback (most recent call last):
  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)
  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.NotFoundError: Graph execution error:

Detected at node sequential/conv2d/Relu defined at (most recent call last):
  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 512, in call
    return self._run_internal_graph(inputs, training=training, mask=mask)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 512, in call
    return self._run_internal_graph(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph
    outputs = node.layer(*args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 512, in call
    return self._run_internal_graph(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph
    outputs = node.layer(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 512, in call
    return self._run_internal_graph(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph
    outputs = node.layer(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 512, in call
    return self._run_internal_graph(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph
    outputs = node.layer(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 512, in call
    return self._run_internal_graph(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph
    outputs = node.layer(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py", line 321, in call
    return self.activation(outputs)

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 512, in call
    return self._run_internal_graph(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph
    outputs = node.layer(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py", line 321, in call
    return self.activation(outputs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/activations.py", line 321, in relu
    return backend.relu(

  File "/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py", line 25, in <module>
    model.fit(train_images, trains_labels, epochs=5, batch_size=1)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1768, in fit
    tmp_logs = self.train_function(iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1364, in train_function
    return step_function(self, iterator)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1348, in step_function
    outputs = model.distribute_strategy.run(run_step, args=(data,))

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1329, in run_step
    outputs = model.train_step(data)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 1106, in train_step
    y_pred = self(x, training=True)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py", line 588, in __call__
    return super().__call__(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py", line 404, in call
    return super().call(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 512, in call
    return self._run_internal_graph(inputs, training=training, mask=mask)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py", line 669, in _run_internal_graph
    outputs = node.layer(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 1150, in __call__
    outputs = call_fn(inputs, *args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
    return fn(*args, **kwargs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py", line 321, in call
    return self.activation(outputs)

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/activations.py", line 321, in relu
    return backend.relu(

  File "/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/backend.py", line 5397, in relu
    x = tf.nn.relu(x)

No algorithm worked!  Error messages:
  Profiling failure on CUDNN engine eng11{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5333): 'status'
  Profiling failure on CUDNN engine eng0{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5333): 'status'
	 [[{{node sequential/conv2d/Relu}}]] [Op:__inference_train_function_1058]

@O-CLIPE,
I tried to execute the mentioned code by importing the keras from tensorflow and it was executed without any issues on tensorflow v2.12. Kindly find the gist, GPU-gist and below changes.

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import layers, models

Thank you!

@tilakrayal based on the error message, what could this error be about? I did get it is not a global tf issue, it's probably hardware specific. Also, with CPU only the code runs perfectly.

It is hardware specific. Tensorflow drowns my GPU with memory usage and fails itself. To solve this I just limited the GPU memory usage, to lower than what I can expend (I overflows the limit a bit — in my case a bit means 24% of GPU memory).
2048MiB of memory if just too little for most applications of machine learning.

How to limit GPU memory usage: https://stackoverflow.com/a/60069601/20940588

I got the same issues and limit the GPU memory usage does not work. Any other clues?


Prerequisits for running quantities detection

I tried to get it running, using the test solr index ans test unit description.
But it did not work as described in the wiki (examples).
So:
how has the URL to be formated?
Some examples, which did not work as expected:
http://<IP_ADDRESS>:8983/solr/test/try?q=beer 25cl&debug=true&echoParams=all
http://<IP_ADDRESS>:8983/solr/test/try?q={!qty v='name:beer 25cl'}&debug=true&echoParams=all
http://<IP_ADDRESS>:8983/solr/test/select?debugQuery=on&q={!qty q='pils 33cl'}&rows=100
...

Hi @ChristophKohler sorry for such delay in response, could you please expand a bit your context?
I don't see anything wrong in your queries, at least at first sight, so probably that has something to do with your data or configuration.
I would be great if you paste some info about:

the configuration (schema, solrconfig), including the units.json
the data you're indexing
what you expect to see ("did not work" doesn't help me so much :) )

Hi, here are the requested Information:
For the Index, I use your test configuration:
https://github.com/SeaseLtd/solr-quantities-detection-qparsers/tree/master/src/test/resources/solr.home/test

schema.xml
solrconfig.xml
units.json

With one change to solrconfig.xml - the path of the libs points to a different folder

original-solr-quantities-detection-qparsers-1.0.jar
solr-quantities-detection-qparsers-1.0.jar

I Indexed your Data:
https://github.com/SeaseLtd/solr-quantities-detection-qparsers/blob/master/src/test/resources/docs.xml
For query http://<IP_ADDRESS>:8983/solr/test/try?q=IMPERIAL 65cl&debug=true&echoParams=all I get 4 Results
{
"responseHeader":{
"status":0,
"QTime":3,
"params":{
"q":"IMPERIAL 65cl",
"defaultBoost":"{!bfqty v=$q}",
"defType":"qty",
"df":"name",
"debug":"true",
"echoParams":"all",
"bq":"{!bqty v=$q}"}},
"response":{"numFound":4,"start":0,"docs":[
{
"id":"12",
"country":"Norway",
"capacity":25.0},
{
"id":"18",
"country":"USA",
"capacity":65.0},
{
"id":"19",
"country":"USA",
"capacity":65.0},
{
"id":"32",
"country":"USA",
"capacity":35.5}]
},
"debug":{
"rawquerystring":"IMPERIAL 65cl",
"querystring":"IMPERIAL 65cl",
"parsedquery":"+(DisjunctionMaxQuery((name:imperial)) DisjunctionMaxQuery((name:65cl))) MatchAllDocsQuery(:)",
"parsedquery_toString":"+((name:imperial) (name:65cl)) :",
"explain":{
...
"QParser":"ExtendedDismaxQParser",
"altquerystring":null,
"boost_queries":["{!bqty v=$q}"],
"parsed_boost_queries":["MatchAllDocsQuery(:)"],
I expected in accordance with (https://github.com/SeaseLtd/solr-quantities-detection-qparsers/wiki/Examples):
something like
"parsedquery_toString":"+((name:imperial)"
"parsed_boost_queries":["capacity:25"],

I've reproduced the issue, it is related to an error during the configuration file parsing.
I'll merge the fix asap
Thanks @ChristophKohler

Hi @ChristophKohler, the issue has been fixed: at the end it was just a matter of configuration file (units.json), please use this last version.
You will find the last commit also some other minor changes: they're not so relevant so you can skip then (I fixed the jar lib path, and declared as "stored" the product name).
Thanks again and sorry for such delay in response
Andrea


Reading Entity names too early in MobModifierComponent / Incompatibility with Hexbound

Got a bug report about a crash involving Eldritch Mobs when /summon-ing one of my entities, looks like this:
java.lang.NullPointerException: Cannot invoke "coffee.cypher.hexbound.util.DataTrackerDelegate.getValue(net.minecraft.class_1297, kotlin.reflect.KProperty)" because "this.isAltModelEnabled$delegate" is null
	at coffee.cypher.hexbound.feature.construct.entity.SpiderConstructEntity.isAltModelEnabled(SpiderConstructEntity.kt:43) ~[hexbound-0.1.0-beta.3+1.19.2.jar:?]
	at coffee.cypher.hexbound.util.UtilKt.redirectSpiderLang(Util.kt:70) ~[hexbound-0.1.0-beta.3+1.19.2.jar:?]
	at coffee.cypher.hexbound.feature.construct.entity.SpiderConstructEntity.method_23315(SpiderConstructEntity.kt:134) ~[hexbound-0.1.0-beta.3+1.19.2.jar:?]
	at net.minecraft.class_1297.method_5477(class_1297.java:2476) ~[client-intermediary.jar:?]
	at net.minecraft.class_1297.method_5476(class_1297.java:2719) ~[client-intermediary.jar:?]
	at net.hyper_pigeon.eldritch_mobs.component.MobModifierComponent.<init>(MobModifierComponent.java:44) ~[eldritch-mobs-1.12.10.jar:?]
	at dev.onyxstudios.cca._generated_.GeneratedComponentContainer_EntityImpl_class_1308_135614a3.<init>(Unknown Source) ~[?:?]
	at dev.onyxstudios.cca._generated_.GeneratedContainerFactory_EntityImpl_class_1308_135614a3.createContainer(Unknown Source) ~[?:?]
	at dev.onyxstudios.cca.internal.entity.CardinalEntityInternals.createEntityComponentContainer(CardinalEntityInternals.java:58) ~[cardinal-components-entity-5.0.2.jar-d23f46ae-afaf-4993-8664-1875d2a279eb-nested.jar:?]
	at net.minecraft.class_1297.handler$zoo000$initDataTracker(class_1297.java:5616) ~[client-intermediary.jar:?]

The problem here is the attempt over here to read the entity's display name, while creating the component. CCA components are created before the entity is fully finished running its constructor, so the $delegate synthetic variable I use in my getDisplayName is not populated yet.
Ultimately entities given by CCA should be considered volatile when the constructor is running, cause their internal state is all messed up by CCA passing it around mid-constructor, so while I can band-aid it on my end, it'd be best for Eldritch Mobs to create the boss bar at a later point, so as not to depend on "enough" of the entity being constructed in time.

Also seems to be the cause of #76 where a different component is required for the display name, but wasn't added yet


HTML imports or ES6 modules in polymer2

what is the current plan for polymer2? To use html imports (working via polyfill) or to use es6 modules? (when using es6 modules, how do i import the templates?). Problem is, debuging expirience is very bad, when HTML imports are not natively supported!.

We're currently only supporting HTML Imports for using Polymer 2. In the future, we will have tooling and best practices available for using ES6 Modules with Polymer 2, but we're still working that out.

Thanks for the info.
Would be nice if the debugging experience in browser not supporting html imports directly could be enhanced.


[AVM Module Issue]: Load balancer module not adding backend pool vms and also not giving any error

Check for previous/existing GitHub issues

[X] I have checked for previous/existing GitHub issues

Issue Type?
Bug
Module Name
avm/res/network/load-balancer
(Optional) Module Version
0.3.0
Description
Load balancer backendpools not adding the 2 vms i specify in my backend pool parameter file. below are my bicep and bicep param file
param lbName string
param frontendIPConfigurations array
param backendAddressPools array
param probes array
param loadBalancingRules array

module mylb 'br/public:avm/res/network/load-balancer:0.3.0' = {
  name: 'mydemo'
  params: {
    name: lbName
    frontendIPConfigurations: frontendIPConfigurations
    backendAddressPools: backendAddressPools
    probes: probes
    loadBalancingRules: loadBalancingRules
  }
}

using '../internalLb.bicep'
param lbName='mylbpoc'
param frontendIPConfigurations = [
      {
        name: 'sql-frontend'
        subnetId: '/subscriptions/<replace wih SubId>/resourceGroups/<replace with rg>/providers/Microsoft.Network/virtualNetworks/linux-sandbox-01VNET/subnets/agwsubnet'
        // privateIPAddress:'<IP_ADDRESS>'
        zones:[
              '1'
              '2'
              '3'
            ]

      }
      {
        name: 'wsfc-frontend'
        subnetId: '/subscriptions/<replace wih SubId>/resourceGroups/<replace with rg>/providers/Microsoft.Network/virtualNetworks/linux-sandbox-01VNET/subnets/agwsubnet'
        // privateIPAddress:'<IP_ADDRESS>'
        zones:[
              '1'
              '2'
              '3'
            ]
      }
    ]

param backendAddressPools = [
    {
        name: 'backendAddressPool1'
        properties: {
            loadBalancerBackendAddresses: [
                {
                    name: 'linux-sandbox-01'
                    properties: {
                         virtualNetwork: {
                             id: '/subscriptions/<replace wih SubId>/resourceGroups/<replace with rg>/providers/Microsoft.Network/virtualNetworks/linux-sandbox-01VNET'
                         }
                        ipAddress: '<IP_ADDRESS>'
                    }
                }
                {
                    name: 'linux-sandbox-02'
                    properties: {
                         virtualNetwork: {
                             id: '/subscriptions/<replace wih SubId>/resourceGroups/<replace with rg>/providers/Microsoft.Network/virtualNetworks/linux-sandbox-01VNET'
                         }
                        ipAddress: '<IP_ADDRESS>'
                    }
                }
            ]
        }
    }
]
param probes =  [
      {
        name: 'sql-probe'
        port: 5999
        protocol: 'tcp'
        intervalInSeconds: 5
        numberOfProbes: 1

      }
      {
        name: 'wsfc-probe'
        port: 5888
        protocol: 'tcp'
        intervalInSeconds: 5
        numberOfProbes: 1

      }

    ]

param loadBalancingRules = [
      {
        name: 'sql-lbRule'
        backendAddressPoolName: 'backendAddressPool1'
        backendPort: 5999
        frontendIPConfigurationName: 'sql-frontend'
        frontendPort: 5999
        idleTimeoutInMinutes: 4
        probeName: 'sql-probe'
        protocol: 'Tcp'
      }
      {
        name: 'wsfc-lbRule'
        backendAddressPoolName: 'backendAddressPool1'
        backendPort: 5888
        frontendIPConfigurationName: 'wsfc-frontend'
        frontendPort: 5888
        idleTimeoutInMinutes: 4
        probeName: 'wsfc-probe'
        protocol: 'Tcp'
      }
    ]

(Optional) Correlation Id
No response

I have the same issue albeit formulating the 'backendAddressPools' in a different way.

param vmNames = [
                                  { name: 'vmprd01', ip: '<IP_ADDRESS>' }
                                  { name: 'vmprd02', ip: '<IP_ADDRESS>' }
]
var loadBalancerBackendAddresses = [
  for vm in vmNames: {
    name: '${vm.name}-nic'
    properties: {
      virtualNetwork: {
        id: spokeVirtualNetwork.id
        }
        ipAddress: vm.ip
    }
  }
]

var ilbBackendAddressPools = [
  {
      name: ilbBackendpoolName
      properties: {
          loadBalancerBackendAddresses: loadBalancerBackendAddresses
    }
  }
]

module loadBalancer 'br/public:avm/res/network/load-balancer:0.4.0' = {
  name: 'loadBalancer-Deploy'
  params: {
    frontendIPConfigurations: [
      {
        name: frontEndName
        subnetId: subnetId
        ipAddress: ''
      }
    ]
    name: name
    backendAddressPools: ilbBackendAddressPools

I have been working with multiple individuals on the load balancer product group related to backend address pools. I was able to identify how their provided sample ARM templates are not idempotent and would cause failures on subsequent deployments.  Attached is a working example that creates the NICs and assigns them to the backend address pool without error. You can add additional NICs without error.
The key is in how you are specifying the subnet on the NIC. If I use the Bicep resourceId function to specify the subnets any subsequent deployments would fail. You need to specify the virtualNetwork property instead (virtualNetwork.properties.subnets[0].id)
@description('Location for all resources.')
param location string = resourceGroup().location

var virtualNetworkName = 'vNet'
var subnetName = 'backendSubnet'
var loadBalancerName = 'ilb'
var networkInterfaceName = 'nic'
var numberOfInstances = 3

resource virtualNetwork 'Microsoft.Network/virtualNetworks@2023-09-01' = {
  name: virtualNetworkName
  location: location
  properties: {
    addressSpace: {
      addressPrefixes: [
        '<IP_ADDRESS>/16'
      ]
    }
    subnets: [
      {
        name: subnetName
        properties: {
          addressPrefix: '<IP_ADDRESS>/24'
        }
      }
    ]
  }
}

module loadBalancer 'br/public:avm/res/network/load-balancer:0.4.0' = {
  name: loadBalancerName
  params: {
    name: loadBalancerName
    location: location
    skuName: 'Standard'
    frontendIPConfigurations: [
      {
        subnetId: virtualNetwork.properties.subnets[0].id
        privateIPAddress: '<IP_ADDRESS>'
        privateIPAllocationMethod: 'Static'
        name: 'LoadBalancerFrontend'
      }
    ]
    backendAddressPools: [
      {
        name: 'BackendPool1'
      }
    ]
  }
}

resource networkInterface 'Microsoft.Network/networkInterfaces@2023-09-01' = [
  for i in range(0, numberOfInstances): {
    name: '${networkInterfaceName}${i}'
    location: location
    properties: {
      ipConfigurations: [
        {
          name: 'ipconfig1'
          properties: {
            privateIPAllocationMethod: 'Dynamic'
            subnet: {
              id: virtualNetwork.properties.subnets[0].id
            }
            loadBalancerBackendAddressPools: [
              {
                id: resourceId('Microsoft.Network/loadBalancers/backendAddressPools', loadBalancerName, 'BackendPool1')
              }
            ]
          }
        }
      ]
    }
    dependsOn: [
      loadBalancer
    ]
  }
]

I am working on some additional changes to the AVM module that should help with backend deployment issues.

Thanks @arnoldna
I can deploy successfully using the Virtual Machine NIC as follows. This works fine for the initial deployment but subsequent deployments fails with the 'ModificationOfNICIpConfigBackendPoolNotSupported' are you saying that i need to define the NIC seperately and not as a AVM?
@description('Deploying the Virtual Machines')
module vm 'br/public:avm/res/compute/virtual-machine:0.8.0' = [ for (vmName, i) in vmNames: if (vmName.updateIfExist) {
name: '${vmName.name}-Deploy'
params: {
enableTelemetry: false
location: location
name: vmName.name
nicConfigurations: [
{
name: '${vmName.name}-ni01'
ipConfigurations: [
{
name: 'ipconfig1'
subnetResourceId: subnetMgmtId
privateIPAllocationMethod: 'Static'
privateIPAddress: vmName.ip
loadBalancerBackendAddressPools: !empty(loadBalancerBackendAddressPoolsId) ? [
{
id: loadBalancerBackendAddressPoolsId
}
] : []
}
]
}
]
'''

What I am saying is that your "subnetMgmtID" should reflect "virtualNetwork.properties.subnets[0].id".

Hi @arnoldna,
To be honest when i hit the issue with the NICs being attached error on subsequent deployments 'ModificationOfNICIpConfigBackendPoolNotSupported' i was then of the opinion to deploy the load balancer with IPs so independent of the VM NIC. I still can get that working as per @abhiramani-git  original post. Do we have a working solution for that yet?


Error: Connection refused for URI

I'm new to the DocuSign API. Trying the sample code, I have it running on my localhost, and when I try to authenticate:

And then the callback gives me a 500 error:

When I check my PHP server logs, I get this:
Next GuzzleHttp\Exception\ConnectException: Connection refused for URI https://account-d.docusign.com/oauth/token in C:\MAMP\htdocs\docusign\vendor\guzzlehttp\guzzle\src\Handler\StreamHandler.php:72
Has anyone encountered the same thing? Or any ideas?

This is the expected behavior, you only really care about the code that's being passed back and as you can see it's passed successfully in the URL that you're getting.

The expected behavior is a 500 error? What do I do with that code? I'm not seeing anything in the Readme about that. I thought the sample code would have methods to use the API with the included demo_documents - not just provide an authorization token which you could use Postman to do...

Did you run this from quickstart?

No, this 500 error is not expected for the PHP code if it's configured and run correctly

Found this in the error logs as well:
[19-Jul-2022 17:30:09 UTC] PHP Fatal error:  Uncaught GuzzleHttp\Exception\RequestException: cURL error 60: SSL certificate problem: unable to get local issuer certificate (see https://curl.haxx.se/libcurl/c/libcurl-errors.html) for https://account-d.docusign.com/oauth/token in C:\MAMP\htdocs\docusign\vendor\guzzlehttp\guzzle\src\Handler\CurlFactory.php:211
Stack trace:
From the CURL docs:
CURLE_PEER_FAILED_VERIFICATION (60)
The remote server's SSL certificate or SSH md5 fingerprint was deemed not OK. This error code has been unified with CURLE_SSL_CACERT since 7.62.0. Its previous value was 51.
I'm not sure if this is an issue with my localhost config, or an issue with DocuSign's certificate...


Google-oauth is not working inside Jupyter Notebook

When importing modele with recomended code:
flags = argparser.parse_args()
credentials = run_flow(flow, storage, flags) inside Jupyter Notebook got exception.
Simple
from oauth2client.tools import argparser
argparser.parse_args()
Failing with:
usage: main.py [--auth_host_name AUTH_HOST_NAME]
[--noauth_local_webserver]
[--auth_host_port [AUTH_HOST_PORT [AUTH_HOST_PORT ...]]]
[--logging_level {DEBUG,INFO,WARNING,ERROR,CRITICAL}]
main.py: error: unrecognized arguments: -f /run/user/1000/jupyter/kernel-c9aa5199-fcea-4884-8e5f-a004c66a733e.json
SystemExit                                Traceback (most recent call last)
 in ()
----> 1 argparser.parse_args()
/usr/lib/python3.5/argparse.py in parse_args(self, args, namespace)
1736         if argv:
1737             msg = _('unrecognized arguments: %s')
-> 1738             self.error(msg % ' '.join(argv))
1739         return args
1740
/usr/lib/python3.5/argparse.py in error(self, message)
2392         self.print_usage(_sys.stderr)
2393         args = {'prog': self.prog, 'message': message}
-> 2394         self.exit(2, _('%(prog)s: error: %(message)s\n') % args)
/usr/lib/python3.5/argparse.py in exit(self, status, message)
2379         if message:
2380             self._print_message(message, _sys.stderr)
-> 2381         _sys.exit(status)
2382
2383     def error(self, message):
SystemExit: 2

@snarb Could you provide more information about what you're trying to do and what the source of the recommended code you're running is?
At first glace it looks like what you tried to do resulted in parsing the Jupyter command line arguments.

Hi Snarb. Have you worked it to run  argparser.parse_args() in jupyter notebook. I also run into this problem today.

The issue here is just what @ncouture guessed -- you're not passing any args to parse_args, so it's trying to parse sys.argv.
Try calling argparser.parse_args([]), or add additional arguments there as you'd like.

Thank you for creating this issue, however, this project is deprecatedand we will only be addressing critical security issues. You can read moreabout this deprecation here.
If you need support or help using this library, we recommend that you ask yourquestion on StackOverflow.
If you still think this issue is relevant and should be addressed, pleasecomment and let us know!


Background is checkered and buttons are not showing up

I do have resource loader. where the highlighted place is there is supposed to be a button.
Here is my config: https://pastebin.com/LHaiSagn
Here is my menu:

Ok but why did you create another identical issue?
Could you upload your textures please? Did you check the example button texture? They have to have both a normal and "hovered" version in them.

I'm sorry for making another one but i fixed the button problem but my background is still checkered, I would appreciate it if someone helps. :D

Its simple, you just need to put the image folder in the mod pack and use their way on the code, like the examples.. you just figured out yourself now :P

Thank you guys so much for your help!!!!!!


edit Course Outline autogenerated English subs

I've edited English subs(autogenerated) for 'Course Outline' video. @aneagoie

You're amazing as always! I'm adding this on udemy now as well.


Pulling updated changes from parent project

Pulling in the latest changes from the parent project repository into my project.

Merged changes from the remote parent project into this project.


Rewrite GTK theme detection to also get correct DE theme.

This rewrite of the theme detection fixes cases where the gtk theme is
incorrectly displayed due to the DE not writing .gtkrc files etc.
I've opened this PR to see if anyone has any ideas/queries about this before
it gets pushed to master.
This is pretty much done, all that's missing is KDE support  and
some error detection which I'm working on as we speak.

all that's missing is KDE support

Images are not showing properly in Konsole at all :cry:.  Image starts in the menubar and even with that off it won't output the image if there's text on the same line.  I can't even get a picture of the bug because the image disappears when Konsole becomes an inactive window.  Works fine in UXTerm though.

@Wyn10, do images in ranger work fine in Konsole?

@Wyn10, I just installed Konsole and I don't think images will work with it. (Images act the same in ranger)
The only way I can see us fixing this is if we add an option to draw the image in a loop. (This is how ranger manages to at least display it.)

@dylanaraps
Looks like it, it opens the image with feh.
Here's a screenshot.  Ranger through konsole on left, Ranger through Xterm on right, and what fetch shows through konsole on the bottom-right.

You need to enable inline images inside ranger.
https://github.com/hut/ranger/wiki/Image-Previews


The latest updates on your projects. Learn more about Vercel for Git ↗

    **The latest updates on your projects**. Learn more about [Vercel for Git ↗](https://vercel.link/github-learn-more)

Name
Status
Preview
Updated

snapshot
✅ Ready (Inspect)
Visit Preview
Oct 4, 2022 at 10:48AM (UTC)

Originally posted by @vercel[bot] in https://github.com/snapshot-labs/snapshot/issues/3202#issuecomment-1266759206

@Dependabot rebase


http proxy from HTTP_PROXY env variable?

Hello,
With the default transport we can configure http proxy using the HTTP_PROXY env variable, but it does not seem to work with httpcontrol transport?
thanx

You can do this by configuring a transport like such:
t := &httpcontrol.Transport{
  Proxy: http.ProxyFromEnvironment,
}

This is how the http.DefaultTransport works as well: https://golang.org/pkg/net/http/#DefaultTransport


Create a new Font

does not work anymore since
Remove yAdvance guessing, parse max glyph height inc. offsets

Good catch.
Does https://github.com/tchapi/Adafruit-GFX-Font-Customiser/commit/8d111c6abe2a996d734a7c0403b420c38d43234f fix it? (it's in master)

Thank you very much - now it works again!


Removing a friend and refreshing is going into an endless loop

I had Hexa dev. I upgraded it. I had Anant as friend already ( from the previous version of the app). I removed him as contact and refreshed this screen. The app stopped responding apart from the refresh icon going in circles.

done


Install-DbaWhoIsActive with -LocalFile parameter deletes the local file

Summary of new feature
I was testing out the Install-DbaWhoIsActive cmdlet and using the -LocalFile parameter.  The stored procedure already existed on the target server, so it was updated, but the local file was then deleted.  I understand why you would want to delete it if it was the downloaded zip file.  Is there a way to have it not delete the local file if you use the -LocalFile parameter?
Thank you
Latest version of dbatools as of writing
I'm using version 1.0.50 of dbatools module.

Working on making this cmdlet Cross Platform, and could easily add this feature change in on my next PR. I honestly don't know why we are cleaning the script up at the end. I looked at similar Install- functions and they are only cleaning up .zip and not the uncompressed files.


stuck on: Generating anchor boxes for training images and annotation...

Hi dear team,
trying to train new model and proses stuck on this step:
Generating anchor boxes for training images and annotation...
And nothing happens. It's just hanging there.
in  folder cache I have only one file:
detection_train_data.pkl
What can be the issue?
Is there a way to see full log?
Training set made same way as other models, and they worked.
Thanks
V.
my config:
from imageai.Detection.Custom import DetectionModelTrainer

trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="20210205")
trainer.setTrainConfig(object_names_array=["object"], batch_size=4, num_experiments=200, train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()

My bad, min max issue.
closing ticket.
thank you.

Hi @vladzilla
I'm having this same issue, how did you solve it?

Hi @vladzilla
I'm having this same issue, how did you solve it?

hey  @jillcolebourn  - check your top and bottom coordinates - lowest value at the bottom etc.


Xnero: Watch (?-i:SMARTCHOICEWEB)(?#Skype)

Xnero requests the watch of the watch_keyword (?-i:SMARTCHOICEWEB)(?#Skype). See the MS search here and the Stack Exchange search in text, in URLs, and in code.

(?-i:SMARTCHOICEWEB)(?#Skype) has been seen in 1 true positive, 0 false positives, and 0 NAAs.

Approved by cigien in Charcoal HQ


Make miniflux compatible with secrets

Inside docker for example, you can create a secret that its then available inside the container as a file like /var/run/database_url.
The convention is to have environment variables with _FILE appended, like DATABASE_URL_FILE or ADMIN_PASSWORD_FILE.
This will make miniflux more secure when deployed via docker swarm or similar.

This feature has been implemented in this commit: https://github.com/miniflux/miniflux/commit/d2f4ed93df5e1866c5389aa2a687a6bc3c944b1d

Thanks for implementing it so fast!


create github actions to update pypi package

[x] create secret in the CloudDrift account for pypitest and pypi API tokens
[x] create a rule to build (this should work):

on:
  release:
    types:
      - published
  push:
    tags:
      - 'v*'

that triggers on new release, if the tags starts with v.

[x] send to pypi test
[x] send to pypi
[ ] validate that the package is properly upload to pypi on a new version released

Reference (https://github.com/OceanParcels/parcels/blob/master/.github/workflows/pypi-release.yaml)

I've tested most of it, but let's see when we create a new release if everything works fine. There might be things to modify in the last two steps of the workflow.
You can see previous execution of the workflow.

@milancurcic just tagging you here so you can see that this has been implemented.

Thanks, let's release 0.5.0 after #88 and #93 are implemented.


Opponent text in opponent health panel overlaps border for opponents with long names

Example: (and zoomed version for more detail)

fixed in 82cef9329663833ee800575d32b52c97fda34e4a


Add handling of interrupting signals to the UI

This PR fixes the problem where "Ctrl+C-ing" out of Syft would leave your terminal in a state where the cursor was hidden.
It adds a mechanism to the UI's for/select loop to listen for particular signals of interest and respond by breaking the loop. This allows the execution of the deferred statement that re-enables the user's cursor.
Currently, there are two signals being subscribed to, but this list is easily amended if anyone has suggestions.

SIGINT (e.g. hitting Ctrl+C after running a syft command)
SIGTERM (e.g. terminating a running syft process via kill -TERM <syft-command>)

@wagoodman Good thinking — I'll add that


Docker setup in dockerfile

Hi,
After removing build cache from previous docker layers, I notice that the only thing it does in docker_setup.sh is yarn build.
Perhaps it's better to put it in the Dockerfile, so that all docker related setup can be found in a single folder.

Thanks @wuhanstudio. Let me merge it.


add script to get efi file base address and offset for gdb usage.

add script to get efi file base address and offset for gdb usage.

added in 95837549b504bd3b451eafbc382014cf97038410


Conditionally set cache driver

Is there a option to conditionally change the cache driver depending on the runtime config.
I'm currently using the nitro route cache and there I can mount different drivers depending on my runtime config:
export default defineNitroPlugin(() => {
  const {
    useRedisCache,
  } = useRuntimeConfig();

  const storage = useStorage();
  if (!!useRedisCache ) {
    storage.mount(
      'cache',
      redisDriver({
        ...
      }),
    );
  } else {
    ...
  }
});

Is there a way to achieve this with your package? As far as I can see there is no option to pass a function instead of an config object to defineMultiCacheOptions.

I figured it out. You actually have access to useRuntimeConfig in the config file and you can just build the config object before passing it to the config method.


ruby-build not utilizing all cores by default

Steps to reproduce the behavior
❯ rbenv --version
rbenv 1.2.0
❯ ruby-build --version
ruby-build 20220726

❯ env - PATH="/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin" RBENV_ROOT="slow" rbenv install 2.7.2
# Finishes in 4m 8s
# CPU usage seems to hover around 60%-70% utilization for 3-4 CPUs and less for others

❯ env - PATH="/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin" MAKEOPTS="-j 10" RBENV_ROOT="fast" rbenv install 2.7.2
# Finishes in 2m 44s
# CPU usage is maxed out during build steps

This is on a 10 core Macbook Pro on a slow-ish network (otherwise the % speed difference would be more dramatic).
The sysctl in that code does work:
❯ sysctl -n hw.ncpu
10

Expected vs. actual behavior
It looks like a patch about 9 years ago did add support for detecting number of CPUs. https://github.com/rbenv/ruby-build/commit/6c08c56d3aa0add92c60653744324e7cdb3b24c3
Is it not getting plumbed through correctly? Or is the CPU usage being limited deliberately?
Logs
n/a

Hi, in cases when ruby-build needs to compile OpenSSL (which happens before compiling Ruby), it does that explicitly using -j 1 https://github.com/rbenv/ruby-build/blob/a4ac4be47f9db57eb5d39916f11a51f3ad442091/bin/ruby-build#L1195-L1197
But unless MAKEOPTS is defined, the default will always be to use sysctl -n hw.ncpu cores https://github.com/rbenv/ruby-build/blob/a4ac4be47f9db57eb5d39916f11a51f3ad442091/bin/ruby-build#L565-L569

Slightly confused by the wording... could you clarify if you mean 1 or 2:

By default, OpenSSL is compiled with -j 1 and Ruby itself should be compiled with NCPUs. ← If this is the case, it's unclear to me why defaulting to compile OpenSSL with -j 1 is desirable. As the numbers show, there is a big difference in build times on a modern machine with the defaults vs explicit MAKEOPTS.
By default, both OpenSSL and Ruby should be compiled with NCPUs. ← If this is the case, are you implying that there must be something wrong with my config causing a CPU usage difference?

@varungandhi-src You can skip the openssl build step with ruby-build:
brew install<EMAIL_ADDRESS>RUBY_CONFIGURE_OPTS="--with-openssl-dir=$(brew --prefix openssl@1.1)" rbenv install 2.7.2

Now, if all CPU cores still aren't utilized for the duration of the build, but they get utilized when you add MAKEOPTS="-j 10", then you are right that the CPU core number is not getting plumbed through correctly and we could investigate.

@varungandhi-src Were you able to confirm that ruby-build doesn't utilize all available CPU cores, except in the case of building openssl?

Closing due to lack of information.


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.search_type and .preferences always error

I have a query like this, which works just fine...
UsersIndex.query(match: {name: 'test'}).object_hash

Following the docs,  I tried something like this to use the user ID to guarantee the same shards respond for a give user...
UsersIndex.query(match: {name: 'test'}).preference('MY_USER_ID_STRING').object_hash

... this throws this error...
Elasticsearch::Transport::Transport::Errors::BadRequest: [400] {"error":{"root_cause":[{"type":"parsing_exception","reason":"Unknown key for a VALUE_STRING in [preference].","line":1,"col":15}],"type":"parsing_exception","reason":"Unknown key for a VALUE_STRING in [preference].","line":1,"col":15},"status":400}
from /home/vagrant/.rbenv/versions/2.3.6/lib/ruby/gems/2.3.0/gems/elasticsearch-transport-5.0.4/lib/elasticsearch/transport/transport/base.rb:202:in `__raise_transport_error'

... which is not that surprising, it looks like it's putting it in the body. This can be seen inspecting with binding.pry...
[42] pry(main)> UsersIndex.query(match: {name: 'test'}).preference('MY_USER_ID_STRING')
=> <UsersIndex::Query {:index=>["users"], :type=>["user"], :body=>{:preference=>"MY_USER_ID_STRING", :query=>{:match=>{:name=>"test"}}}}>

... same thing happens when I try to set a search_type like this according to the docs...
UsersIndex.query(match: {name: 'test'}).search_type(:dfs_query_then_fetch).object_hash

... it gives a similar error ...
Elasticsearch::Transport::Transport::Errors::BadRequest: [400] {"error":{"root_cause":[{"type":"parsing_exception","reason":"Unknown key for a VALUE_STRING in [search_type].","line":1,"col":16}],"type":"parsing_exception","reason":"Unknown key for a VALUE_STRING in [search_type].","line":1,"col":16},"status":400}
from /home/vagrant/.rbenv/versions/2.3.6/lib/ruby/gems/2.3.0/gems/elasticsearch-transport-5.0.4/lib/elasticsearch/transport/transport/base.rb:202:in `__raise_transport_error'

... also because it's putting search_type in the query body.
Elasticsearch docs say both of those should be in the query string instead.
I looked around and tried to find where to fix this, but haven't had time yet to really figure out the Chewy code base. Hopefully this is an easy quick fix for someone that knows their way around? Thanks to anyone who can help with this!

Any solution to this ? I am having the same problem

It actually looks like an elasticsearch-ruby issue. Its API accepts everything and should decide where to put particular params. Try to update it. Or downgrade.

I've been having the same issue attempting to set the search type to dfs_query_then_fetch.
I'm on elasticsearch-ruby version 5.0.5. When Chewy calls elasticsearch-ruby's search method,
https://github.com/elastic/elasticsearch-ruby/blob/86a36ec0db704b2a62dd4d5fe9edf887625b1826/elasticsearch-api/lib/elasticsearch/api/actions/search.rb#L173
the __validate_and_extract_params method will only parse the keys at the first level of the hash passed in. search_type is being missed because it's within the body key of the hash.
I think chewy should not be putting search_type within the body, but at a level above

Hey, try the latest master