Docker with GPU 2.3rc0 CUDA runtime implicit initialization on GPU:0 failed. Status: device kernel image is invalid

It seem that the Docker image tensorflow/tensorflow:2.3.0rc0-gpu won't work with my GPU   BUT on the other hand the image tensorflow/tensorflow:2.2.0rc0-gpu works fine
Or in other words, the solution to the present issue was to "downgrade" to  tensorflow/tensorflow:2.2.0rc0-gpu
tensorflow/tensorflow:2.3.0rc0-gpu also works fine with CPU only.
System information

Ubuntu 20.4
TensorFlow through Docker
TensorFlow version (use command below):
GPU model and memory: Geforce GTX 960M, coreClock: 1.176GHz coreCount: 5 deviceMemorySize: 1.96GiB deviceMemoryBandwidth: 74.65GiB/s
GPU drivers: 440.100

how to reproduce
> docker run -it --rm --gpus all  --entrypoint bash tensorflow/tensorflow:2.3.0rc0-gpu
> python
>>> import tensorflow as tf
>>> inputs = tf.keras.layers.Input(shape=(None,), name="input")
>>> embedded = tf.keras.layers.Embedding(100, 16)(inputs)

full stack trace:
2020-07-06 18:46:55.604377: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2020-07-06 18:46:55.608404: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-06 18:46:55.608911: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 960M computeCapability: 5.0
coreClock: 1.176GHz coreCount: 5 deviceMemorySize: 1.96GiB deviceMemoryBandwidth: 74.65GiB/s
2020-07-06 18:46:55.608943: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-06 18:46:55.610544: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-07-06 18:46:55.611696: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-07-06 18:46:55.611988: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-07-06 18:46:55.613589: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-07-06 18:46:55.614478: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-07-06 18:46:55.618025: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-07-06 18:46:55.618159: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-06 18:46:55.618734: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-06 18:46:55.619206: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-07-06 18:46:55.619480: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-07-06 18:46:55.643133: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-06 18:46:55.643781: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x44161a0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-07-06 18:46:55.643809: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-07-06 18:46:55.725002: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-06 18:46:55.725324: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x44aa610 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-07-06 18:46:55.725349: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 960M, Compute Capability 5.0
2020-07-06 18:46:55.725532: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-06 18:46:55.725767: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 960M computeCapability: 5.0
coreClock: 1.176GHz coreCount: 5 deviceMemorySize: 1.96GiB deviceMemoryBandwidth: 74.65GiB/s
2020-07-06 18:46:55.725796: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-06 18:46:55.725828: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-07-06 18:46:55.725854: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-07-06 18:46:55.725882: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-07-06 18:46:55.725908: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-07-06 18:46:55.725938: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-07-06 18:46:55.725988: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-07-06 18:46:55.726091: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-06 18:46:55.726485: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-06 18:46:55.726724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-07-06 18:46:55.726756: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 926, in __call__
    input_list)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 1098, in _functional_construction_call
    self._maybe_build(inputs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 2643, in _maybe_build
    self.build(input_shapes)  # pylint:disable=not-callable
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/tf_utils.py", line 323, in wrapper
    output_shape = fn(instance, input_shape)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/embeddings.py", line 135, in build
    if (context.executing_eagerly() and context.context().num_gpus() and
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/context.py", line 1082, in num_gpus
    self.ensure_initialized()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/context.py", line 539, in ensure_initialized
    context_handle = pywrap_tfe.TFE_NewContext(opts)
tensorflow.python.framework.errors_impl.InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: device kernel image is invalid

Hmm... doesn't have trouble on my machine in the same container. Thanks a bunch for the exact replication commands.
root@7b03d7e48af6:/# python
Python 3.6.9 (default, Apr 18 2020, 01:56:04)
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2020-07-06 21:27:04.584594: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
>>> inputs = tf.keras.layers.Input(shape=(None,), name="input")
>>> embedded = tf.keras.layers.Embedding(100, 16)(inputs)
2020-07-06 21:27:18.540476: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2020-07-06 21:27:18.569953: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:18:00.0 name: Quadro P1000 computeCapability: 6.1
coreClock: 1.4805GHz coreCount: 5 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 74.65GiB/s
2020-07-06 21:27:18.570001: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-06 21:27:18.985818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-07-06 21:27:19.579217: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-07-06 21:27:19.836486: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-07-06 21:27:20.597542: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-07-06 21:27:21.103251: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-07-06 21:27:22.960095: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-07-06 21:27:22.960782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-07-06 21:27:22.961151: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-07-06 21:27:23.000149: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-06 21:27:23.012170: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x45be680 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-07-06 21:27:23.012215: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-07-06 21:27:23.113028: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x462a940 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-07-06 21:27:23.113113: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Quadro P1000, Compute Capability 6.1
2020-07-06 21:27:23.114100: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:18:00.0 name: Quadro P1000 computeCapability: 6.1
coreClock: 1.4805GHz coreCount: 5 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 74.65GiB/s
2020-07-06 21:27:23.114167: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-06 21:27:23.114213: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-07-06 21:27:23.114240: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-07-06 21:27:23.114267: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-07-06 21:27:23.114293: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-07-06 21:27:23.114317: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-07-06 21:27:23.114344: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-07-06 21:27:23.115591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-07-06 21:27:23.115649: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-06 21:27:23.790561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-06 21:27:23.790610: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0
2020-07-06 21:27:23.790617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N
2020-07-06 21:27:23.791319: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3353 MB memory) -> physical GPU (device: 0, name: Quadro P1000, pci bus id: 0000:18:00.0, compute capability: 6.1)
>>>
root@7b03d7e48af6:/# nvidia-smi
Mon Jul  6 21:28:02 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.34       Driver Version: 430.34       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Quadro P1000        Off  | 00000000:18:00.0  On |                  N/A |
| 34%   36C    P0    N/A /  N/A |    223MiB /  4037MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+
root@7b03d7e48af6:/#

Can you include the output of nvidia-smi? This could be a case of old drivers on your host machine, an issue with compute capabilities, or something else.

Sure, here it is.
440.100 and CUDA 10.2 seem to be the default on Ubuntu 20.04 (fresh install).
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 960M    Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   56C    P0    N/A /  N/A |      0MiB /  2004MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Since it is mentioned in the TF docs that the NVIDIA® CUDA® Toolkit does not need to be installed I thought that the CUDA version on the host machine would not matter.
I also get the same behaviour as you with tensorflow/tensorflow:2.2.0rc0-gpu on the same machine.

CUDA Compute Capability is inherent to your graphics card. There were some size-reduction changes to our binaries in 2.3 that adjusted handling of old capabilities (such as 5.0), but I believe TensorFlow 2.3 should still support capabilities as old as 3.5.
Can you try to replicate this outside of a Docker container to see if it's related to your graphics card rather than with the container environment?
FYI @chsigg

We removed PTX for all but sm_70 from TF builds in cf1b6b3dfe9ba82e805fddf7f4462b2d92fe550a. We never shipped with kernels for sm_50, only sm_52. Apparently the driver was able to compile PTX for sm_52 to sm_50, even though it's not officially supported.
If you want to run on a sm_50 card, it would be best to build TF from source.

Forgive my ignorance here, but with the change on cf1b6b3, I don't see compute_75 listed in the compute capabilities. Would a GPU (the Tesla T4 in our case) at compute_75 have a similar problem here?

No, the driver will be able to JIT compute_70 and use it for any compute capabilities 7.x.
the startup may be slow, but it will work.

Moreover, I believe compute_70 implies sm_70 so we should not need to JIT PTX for T4, V100.  @chsigg Is this correct?
(It would indeed be a problem if we have to JIT for V100 since that would add startup latency for a popular GPU.)

Actually, I have same error message after I run bazel tests with gpu benchmarks.
System info:
System: Ubuntu 18.04
Tensorflow version: 2.4.0-dev20200710
GPU: 2 x Tesla V100
Driver Version: 450.51.05
CUDA Version: 10.1.243, https://www.tensorflow.org/install/gpu#install_cuda_with_apt (Ubuntu 18.04, CUDA 10.1)
Bazel: 3.1.0 and 3.3.1
Input:
bazel run --config=cuda -c opt --copt="-mavx" eager_microbenchmarks_test -- --benchmarks=.
Output:
INFO: Options provided by the client:
  Inherited 'common' options: --isatty=0 --terminal_columns=80
INFO: Reading rc options for 'run' from /home/xingyulong/tensorflow/.bazelrc:
  Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'run' from /home/xingyulong/tensorflow/.bazelrc:
  Inherited 'build' options: --apple_platform_type=macos --define framework_shared_object=true --define open_source_build=true --java_toolchain=//third_party/toolchains/java:tf_java_toolchain --host_java_toolchain=//third_party/toolchains/java:tf_java_toolchain --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --noincompatible_prohibit_aapt1 --enable_platform_specific_config --config=v2
INFO: Found applicable config definition build:v2 in file /home/xingyulong/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file /home/xingyulong/tensorflow/.bazelrc: --config=using_cuda --define=using_cuda_nvcc=true
INFO: Found applicable config definition build:using_cuda in file /home/xingyulong/tensorflow/.bazelrc: --define=using_cuda=true --action_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain
INFO: Found applicable config definition build:linux in file /home/xingyulong/tensorflow/.bazelrc: --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --cxxopt=-std=c++14 --host_cxxopt=-std=c++14 --config=dynamic_kernels
INFO: Found applicable config definition build:dynamic_kernels in file /home/xingyulong/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
Loading:
Loading: 0 packages loaded
Loading: 0 packages loaded
Loading: 0 packages loaded
Loading: 0 packages loaded
DEBUG: /home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/external/bazel_tools/tools/cpp/lib_cc_configure.bzl:118:5:
Auto-Configuration Warning: 'TMP' environment variable is not set, using 'C:\Windows\Temp' as default
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (1 packages loaded, 0 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (12 packages loaded, 14 targets configured)
DEBUG: Rule 'io_bazel_rules_docker' indicated that a canonical reproducible form can be obtained by modifying arguments shallow_since = "1556410077 -0400"
DEBUG: Repository io_bazel_rules_docker instantiated at:
  no stack (--record_rule_instantiation_callstack not enabled)
Repository rule git_repository defined at:
  /home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/external/bazel_tools/tools/build_defs/repo/git.bzl:195:18: in <toplevel>
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (21 packages loaded, 35 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (85 packages loaded, 902 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (118 packages loaded, 1030 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (222 packages loaded, 5895 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (287 packages loaded, 8881 targets configured)
WARNING: /home/xingyulong/tensorflow/tensorflow/core/BUILD:1750:1: in linkstatic attribute of cc_library rule //tensorflow/core:lib_internal: setting 'linkstatic=1' is recommended if there are no object files. Since this rule was created by the macro 'cc_library', the error might have been caused by the macro implementation
WARNING: /home/xingyulong/tensorflow/tensorflow/core/BUILD:1775:1: in linkstatic attribute of cc_library rule //tensorflow/core:lib_headers_for_pybind: setting 'linkstatic=1' is recommended if there are no object files. Since this rule was created by the macro 'cc_library', the error might have been caused by the macro implementation
WARNING: /home/xingyulong/tensorflow/tensorflow/core/BUILD:2162:1: in linkstatic attribute of cc_library rule //tensorflow/core:framework_internal: setting 'linkstatic=1' is recommended if there are no object files. Since this rule was created by the macro 'tf_cuda_library', the error might have been caused by the macro implementation
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (306 packages loaded, 14342 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (348 packages loaded, 19532 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (351 packages loaded, 19917 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (356 packages loaded, 23062 targets configured)
Analyzing: target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (357 packages loaded, 23062 targets configured)
WARNING: /home/xingyulong/tensorflow/tensorflow/python/BUILD:4667:1: in py_library rule //tensorflow/python:standard_ops: target '//tensorflow/python:standard_ops' depends on deprecated target '//tensorflow/python/ops/distributions:distributions': TensorFlow Distributions has migrated to TensorFlow Probability (https://github.com/tensorflow/probability). Deprecated copies remaining in tf.distributions will not receive new features, and will be removed by early 2019. You should update all usage of `tf.distributions` to `tfp.distributions`.
WARNING: /home/xingyulong/tensorflow/tensorflow/python/BUILD:115:1: in py_library rule //tensorflow/python:no_contrib: target '//tensorflow/python:no_contrib' depends on deprecated target '//tensorflow/python/ops/distributions:distributions': TensorFlow Distributions has migrated to TensorFlow Probability (https://github.com/tensorflow/probability). Deprecated copies remaining in tf.distributions will not receive new features, and will be removed by early 2019. You should update all usage of `tf.distributions` to `tfp.distributions`.
INFO: Analyzed target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test (361 packages loaded, 30598 targets configured).
INFO: Found 1 target...
INFO: Deleting stale sandbox base /home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/sandbox
[0 / 226] [Prepa] BazelWorkspaceStatusAction stable-status.txt ... (2 actions, 0 running)
7,264 / 9,082] [Prepa] Writing file tensorflow/core/kernels/data/experimental/libignore_errors_dataset_op.pic.lo-2.params [for host]
[15,392 / 17,385] Executing genrule //tensorflow/python:framework/fast_tensor_util.pyx_cython_translation [for host]; 1s local
[22,092 / 23,859] Executing genrule //tensorflow/python:framework/fast_tensor_util.pyx_cython_translation; 1s local ... (2 actions running)
[30,382 / 30,594] [Prepa] Writing file tensorflow/python/gen_user_ops_py_wrappers_cc-2.params
INFO: From Executing genrule //tensorflow/python/keras/api:keras_python_api_gen:
2020-07-10 18:11:35.652291: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
INFO: From Executing genrule //tensorflow/python/keras/api:keras_python_api_gen_compat_v2:
2020-07-10 18:11:35.652292: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
INFO: From Executing genrule //tensorflow/python/keras/api:keras_python_api_gen_compat_v1:
2020-07-10 18:11:35.862417: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
INFO: From Executing genrule //tensorflow:tf_python_api_gen_v2:
2020-07-10 18:11:35.859993: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
Target //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test up-to-date:
  bazel-bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test
INFO: Elapsed time: 58.937s, Critical Path: 14.14s
INFO: 17 processes: 17 local.
INFO: Build completed successfully, 4672 total actions
INFO: Running command line: external/bazel_tools/tools/test/test-setup.sh tensorflow/python/keras/benchmarks/eager_microbenchmarks_test '--benchmarks=.'
INFO: Build completed successfully, 4672 total actions
exec ${PAGER:-/usr/bin/less} "$0" || exit 1
Executing tests from //tensorflow/python/keras/benchmarks:eager_microbenchmarks_test
-----------------------------------------------------------------------------
2020-07-10 18:11:41.266346: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-10 18:11:42.762645: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2020-07-10 18:11:42.840961: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:42.841808: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:00:04.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2020-07-10 18:11:42.841953: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:42.842708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2020-07-10 18:11:42.842750: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-10 18:11:42.844793: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-07-10 18:11:42.846855: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-07-10 18:11:42.847239: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-07-10 18:11:42.849488: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-07-10 18:11:42.850725: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-07-10 18:11:42.850894: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.8
2020-07-10 18:11:42.851032: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:42.851964: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:42.852842: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:42.853678: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:42.854423: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1
2020-07-10 18:11:42.854812: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-07-10 18:11:43.309452: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:43.310269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:00:04.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2020-07-10 18:11:43.310402: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:43.311151: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2020-07-10 18:11:43.311184: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-10 18:11:43.311210: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-07-10 18:11:43.311224: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-07-10 18:11:43.311234: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-07-10 18:11:43.311244: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-07-10 18:11:43.311254: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-07-10 18:11:43.311262: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.8
2020-07-10 18:11:43.311347: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:43.312197: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:43.313078: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:43.313907: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-10 18:11:43.314671: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1
2020-07-10 18:11:43.314714: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
Traceback (most recent call last):
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.py", line 326, in <module>
    test.main()
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/test.py", line 58, in main
    return _googletest.main(argv)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/googletest.py", line 66, in main
    benchmark.benchmarks_main(true_main=main_wrapper)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/benchmark.py", line 476, in benchmarks_main
    app.run(lambda _: _run_benchmarks(regex), argv=argv)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/absl_py/absl/app.py", line 299, in run
    _run_main(main, args)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/absl_py/absl/app.py", line 250, in _run_main
    sys.exit(main(argv))
File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/googletest.py", line 66, in main
    benchmark.benchmarks_main(true_main=main_wrapper)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/benchmark.py", line 476, in benchmarks_main
    app.run(lambda _: _run_benchmarks(regex), argv=argv)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/absl_py/absl/app.py", line 299, in run
    _run_main(main, args)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/absl_py/absl/app.py", line 250, in _run_main
    sys.exit(main(argv))
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/benchmark.py", line 476, in <lambda>
    app.run(lambda _: _run_benchmarks(regex), argv=argv)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/platform/benchmark.py", line 456, in _run_benchmarks
    instance_benchmark_fn()
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.py", line 156, in benchmark_layers_advanced_activations_elu_overhead
    x = tf.ones((1, 1))
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/util/dispatch.py", line 201, in wrapper
    return target(*args, **kwargs)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/ops/array_ops.py", line 3068, in ones
    tensor_shape.TensorShape(shape))
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/framework/constant_op.py", line 373, in _tensor_shape_tensor_conversion_function
    return constant(s_list, dtype=dtype, name=name)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/framework/constant_op.py", line 264, in constant
    allow_broadcast=True)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/framework/constant_op.py", line 275, in _constant_impl
    return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/framework/constant_op.py", line 300, in _constant_eager_impl
    t = convert_to_eager_tensor(value, ctx, dtype)
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/framework/constant_op.py", line 97, in convert_to_eager_tensor
    ctx.ensure_initialized()
  File "/home/xingyulong/.cache/bazel/_bazel_root/3240dade0c8746999ed99d7076f401e9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.runfiles/org_tensorflow/tensorflow/python/eager/context.py", line 549, in ensure_initialized
    context_handle = pywrap_tfe.TFE_NewContext(opts)
tensorflow.python.framework.errors_impl.InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: device kernel image is invalid

But, when I run models instead of running bazel test, it works fine.

when I run models

Are you running these models also using a TF built from source, or are you running them using a pip installed binary?

when I run models

Are you running these models also using a TF built from source, or are you running them using a pip installed binary?

Use TF built-in function.
import tensorflow as tf

inputs = tf.keras.xxxxx
xxxxx

I think it should be pip installed binary.

Ok, so that could explain the discrepancy -- the TF binary you built for tests probably does not include sm_70 or compute_70.
Can you grep for TF_CUDA_COMPUTE_CAPABILITIES in .tf_configure.bazelrc and share what you find there?

Can you grep for TF_CUDA_COMPUTE_CAPABILITIES in .tf_configure.bazelrc and share what you find there?

I searched .tf_configure.bazelrc in my tensorflow folder, it's not exist.
Let me clear my environment, I used pip to install tensorflow and download current tensorflow repo to run the bazel tests on tensorflow/tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.py and found the error msg like I posted and it works fine when I was training model.

It works for me after run ./configure in TF folder.

Hi all, I'm running into the same issue here. Both the Docker installation of Tensorflow and the local pip installation are giving me the same error:
tensorflow.python.framework.errors_impl.InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: device kernel image is invalid
System information

Ubuntu 16.04
TensorFlow through Docker, local TF installation with pip.
TensorFlow version: 2.3.0
GPU model and memory: Quadro M1000M coreClock: 1.0715 GHz coreCount: 4, deviceMemorySize: 3.95 GiB deviceMemoryBandwidth: 74.65GiB/s
computeCapability: 5.0
GPU drivers: 418.87.00
CUDA version: 10.1
cudNN version: 7.6.5

I was able to reproduce this error in the docker container using the steps listed above, but also just following the steps listed here lead to the same error. The GPU is detected ok using tf.config.experimental.list_physical_devices('GPU') but as soon as I try to define the tf.constant it fails immediately.
2.2.0 works fine without any issues - both locally and through Docker.

@navganti PTAL here https://github.com/tensorflow/tensorflow/issues/41892#issuecomment-667452483.

@sanjoy I see! Thank you for the update.

I'm seeing this error when I try to call TF_LoadSessionFromSavedModel (C API). It works correctly via the non-GPU docker image, but fails with the GPU docker image with the following error:
CUDA runtime implicit initialization on GPU:0 failed. Status: device kernel image is invalid
I'm using the latest docker image, i.e., tensorflow/tensorflow:latest-gpu built on 7/28/20.
My copy of the C API was downloaded a few days ago: libtensorflow-gpu-linux-x86_64-2.3.0
My GPU is a mobile Nvidia Geforce GTX 1660 Ti.
Nvidia driver version: 440.100
CUDA version 10.2
According to Wikipedia, my graphics chip has CUDA compute capability version 7.5, which seems to be the latest version of any chip that has been released, so I'm pretty sure the problem isn't my chip's CUDA capability.

CUDA 10.1 and CUDNN 7.4 and 7.5 is failed. (TF 2.3)

@motrek TF 2.3 should indeed work on compute capability 7.5.  However this suggests that it lacks tensor cores, it is possible that's why you get the error.

@sanjoy You're right, the chip (1660 Ti) absolutely does lack Tensor Cores, but that shouldn't be a problem at all. No GTX chip has tensor cores either and the C API works fine on those.
Also, training via Python is GPU-accelerated on my laptop with this chip.
It's just the C API that's giving me this "device kernel image is invalid" error, which is clearly a bug somewhere. :(

Hi everyone,
TF nightly starting from the latest nightly build as of now should include SASS for compute capability 5.0, so it should work on GPUs like GeForce GTX 960M.  Please give it a try and let us know how it goes!

@sanjoy I downloaded the nightly docker image but it seems to require CUDA 11. I'm not sure I'm prepared to switch to CUDA 11 yet... from a few cursory web searches, it seems like a lot of people are having a lot of problems with it.
But please note that my problem is that the C API isn't working ("device kernel image is invalid") and it's nothing to do with earlier versions of compute capability.
I have a GTX 1660 Ti, which has compute capability 7.5.

@motrek Actually I don't really understand of what is going on in your case (my previous message was mainly for folks running on GPUs with compute capability 5.0) since, as you've observed above, TF should work fine on compute capability 7.5.
Btw, how are you using the docker images?  If you're using the ones listed at https://www.tensorflow.org/install/docker then you should only need a recent enough GPU driver on your host system.

@sanjoy I've installed the "latest-gpu" docker image from that web page on my Ubuntu system.
GPU acceleration works well when I do training via a Python script.
But I also want to do inference via the C API.
I installed the C API library within my docker container via this page:
https://www.tensorflow.org/install/lang_c
But when I call TF_LoadSessionFromSavedModel, it gives me the "device kernel image is invalid" message.
If you can help me with this, I would really appreciate it.

@mihaimaruseac Could @motrek's issue indicate a bug in how we build libtensorflow.so?  I don't see the CUDA capabilities being configured here.
CC @angerson

Hmm, I don't recall having a distinction between building with or without GPU support for libtensorflow.so.

@mihaimaruseac On this page:
https://www.tensorflow.org/install/lang_c
There are two different versions of the C API library, one for CPU and one for GPU. The one for GPU is about twice the size as the CPU one. So there must be a difference somewhere.
On my system, I have also installed the docker image for TF without GPU support, and the C API library without GPU support, and this works fine with my code.

Oh, you are right, I missed that.
Then it seems we have a bug in the GPU CI.

@mihaimaruseac If there's anything I can do to help debug this situation, please let me know! I am eager to get inference working with GPU support for my project.

FYI @av8ramit, who IIRC set up the libtensorflow builds.
Our GPU libtensorflow builds use the default configure setting, revealed by our internal tensorflow/release/ubuntu_16/libtensorflow/gpu/nightly job):
 --action_env TF_CUDA_COMPUTE_CAPABILITIES=6.0,6.0,6.0,6.0

@angerson It seems there are two problems being discussed on this thread.
One is that some people have older graphics cards with compute capability < 6.0.
The problem I'm seeing isn't that, since my chip (1660 Ti) has compute capability 7.5.
One thing about the 1660 Ti is that it's a Turing chip but its Tensor Cores either don't exist or aren't enabled, so maybe that's tripping up TensorFlow somehow, if something in TensorFlow assumes that all Turing chips have Tensor Cores?

@angerson I'll send a fix internally that matches the pip package config.

https://github.com/tensorflow/tensorflow/commit/049fa9d21b28dbbb6780fd09852cde2f1a09b352 should fix this. I'll keep monitoring to see if there are any other issues.

@av8ramit Very sorry, I'm new to all of this. Will the fix eventually be available in the "latest" docker image and the C API libraries that are posted to the web page that I linked to (above)? Do you have any idea when that might happen? Thanks in advance.

Hello @motrek I'll trigger a new push to GCS. Hopefully this next one or by tomorrow's version we can have something working.

Unfortunately it appears my change did not work, digging into why today.

We're seeing the same issue as @motrek :
We can train a model using tf.keras without a problem.
Then on the same system using TF_LoadSessionFromSavedModel from the C API and libtensorflow installed from https://www.tensorflow.org/install/lang_c we get
CUDA runtime implicit initialization on GPU:0 failed. Status: device kernel image is invalid
If I hack our C++ application to link against _pywrap_tensorflow_internal.so instead then it seems to work. Although that's not a long term solution since the pywrap lib is considerably larger and we would also have to link our application against python.
OS: Ubuntu 20.04
GPU: RTX 2070
Driver: 440.100
Tensorflow: 2.3.0

Sorry I forgot to update this thread, but the latest GCS builds are built with the following computes:
sm_35,sm_50,sm_60,sm_70,sm_75,compute_80

If I hack our C++ application to link against _pywrap_tensorflow_internal.so instead then it seems to work. Although that's not a long term solution since the pywrap lib is considerably larger and we would also have to link our application against python.

Sorry for the [probably very basic] question but how do you "link against python"?  (As one would expect from reading your post.)

Sorry I forgot to update this thread, but the latest GCS builds are built with the following computes:
sm_35,sm_50,sm_60,sm_70,sm_75,compute_80

I'm working off of this page to get the libraries:
https://www.tensorflow.org/install/lang_c
There's a link to a "GCS bucket" but it goes to an XML file in my browser (Chrome) which seems like it's supposed to be read by a different piece of software that I don't have. I see that "GCS" is short for "Google Cloud Storage" but when I try to access (?) "Google Cloud Storage," I'm asked to sign up for a service (including entering payment/credit card details).
If somebody could point me in the right direction for accessing the files in this "GCS bucket" I would appreciate it.
(Also, looking at that XML file in my browser, all of the files that were modified on 09-15 are 'libtensorflow-cpu-linux-x86_64.tar.gz' ... I don't see any files that seem like they would have GPU support?)

So that link is a browser representation of the GCS bucket libtensorflow-nightly. I may be describing it badly, but regardless I think the file you're looking for is this which was built last night.

So that link is a browser representation of the GCS bucket libtensorflow-nightly. I may be describing it badly, but regardless I think the file you're looking for is this which was built last night.

Thanks for building this and posting this link. I appreciate it. I installed it and tried it--I no longer got the error about "device kernel image is invalid" but I did get a bunch of errors about not being able to load libraries that seem related to CUDA 11.
I will have to consider whether or not I want to switch to CUDA 11, seems like a big change and I'm reluctant to touch anything since my training with GPU acceleration is already working well with my current setup.

Glad we were able to solve the first issue, sorry about the new issues you are facing. Do you mind uploading some logs so I can see if that's something we can fix on our end? The package was built with our CUDA 11 toolchain.

If I hack our C++ application to link against _pywrap_tensorflow_internal.so instead then it seems to work. Although that's not a long term solution since the pywrap lib is considerably larger and we would also have to link our application against python.

Sorry for the [probably very basic] question but how do you "link against python"?  (As one would expect from reading your post.)

I used -l_pywrap_tensorflow -lpython3.8
If the issue has been fixed in the nightly which is TF 2.4 and not in the stable 2.3 release then I'm going to have the same dependency problem with CUDA that you are since the default CUDA version is 10.1 in the Ubuntu 20.04 package repos.

Glad we were able to solve the first issue, sorry about the new issues you are facing. Do you mind uploading some logs so I can see if that's something we can fix on our end? The package was built with our CUDA 11 toolchain.

Hi av8ramit, yeah, all the errors I'm seeing are to do with not being able to load CUDA 11 libraries, which I'm sure is expected if you built the libraries against CUDA 11 and I don't have CUDA 11 installed. I'm happy to post logs here but I can't imagine they would be useful to anybody. I will think about installing CUDA 11 later, but it might not be for a while, since I was able to get my code working by linking it against the Python libraries as parnham suggested.

Sorry for the [probably very basic] question but how do you "link against python"?  (As one would expect from reading your post.)

I used -l_pywrap_tensorflow -lpython3.8

@parnham Thanks so much for this. I don't have a good understanding of how the linker works with shared libraries but I was able to hack something together:

I found the absolute paths to these .so files and added them to the end of my link command, which worked
When I ran the executable, though, it said it couldn't find the .so files
I added the paths to the .so files to /etc/ld.so.conf and also added the paths to $LD_LIBRARY_PATH, I'm not sure if it was necessary to do both but there you go
The executable ran this time but failed to do inference with a CUDA error because the "allow growth" GPU option was not set to true (I don't know what "allow growth" does, but everybody seems to run into this problem, why isn't it set to true by default?!?!)
I found some code posted here to set "allow growth" to true via the C API:
https://github.com/neargye/hello_tf_c_api/issues/21

So everything seems pretty fragile but IT'S WORKING. Thanks so much. My C API inference workload is now ~5x as fast running on my GPU vs. the CPU. Great speedup. This is for a private project so I don't mind that I'm linking against those python libraries.
If you can share what you did to allow gcc to link nicely against those libraries (not use absolute paths, etc.) that would be great, really appreciated.
Thanks again!

Glad I could help a little @motrek
You should be able to link to python3.8 if you have the package `libpython3.8-dev" installed.
For linking the to the _pywrap_tensorflow library I just created a symlink to it in /usr/local/lib
lrwxrwxrwx 1 root root    93 Sep 16 11:16  lib_pywrap_tensorflow.so -> /home/dan/.local/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so

and then ran ldconfig. At which point it can be linked and also found at runtime.
There's a slightly cleaner solution to setting the "allow growth" option by including the experimental header
#include <tensorflow/c/c_api_experimental.h>

and then use the TF_CreateConfig helper.
auto options = TF_NewSessionOptions();
auto config  = TF_CreateConfig(true, true, 8);

TF_SetConfig(options, config->data, config->length, this->status);
TF_DeleteBuffer(config);

Use the session options as normal.

Apologies for adding more activity to this issue @av8ramit but we wanted to find out if there was going to be a point release of the TensorFlow C library v2.3 that has been patched with the correct CUDA capabilities?
I only ask because v2.3 is the current stable version, it works with the standard CUDA version in Ubuntu 20.04, and when installing tensorflow through python for training with keras it also uses the same version.

Looping in the release manager. @geetachavan1 would we be able to patch the fix for libtensorflow and release new binaries with the correct CUDA capabilities. Happy to help get this done internally.

For Nvidia 3090, Ubuntu 20.04, Cuda 10.1, Cudnn 7.6, Nvidia GPU driver 455 have the same isseu

Hi. We have uploaded the 2.3.1 libtensorflow binaries. Apologies for the delay, I missed them during the patch release

Hi @mihaimaruseac
Using the link from https://www.tensorflow.org/install/lang_c#download and assuming that the new version was in the same location, I downloaded https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.3.1.tar.gz
Unfortunately that build seems to have the same issue:
2020-10-03 18:26:42.084881: E tensorflow/core/common_runtime/session.cc:91] Failed to create session: Internal: CUDA runtime implicit initialization on GPU:0 failed. Status: device kernel image is invalid

In fact running a diff between the old and new libtensorflow.so.2.3.0 files shows them to be identical.

This is interesting. Even if our recent changes to the build script had no effects, I would have expected the binaries to differ based on the patch release changes.

Apolgies. It seems our CI uploaded the wrong package under the new name after we refactored parts of the CI. I think it should be fixed now, can you give it a try please?
mihaimaruseac@ankh:/tmp$ sha256sum libtensorflow-gpu-linux-x86_64-2.3.*
5e4d934fd7602b6d002a89b972371151aa478ea99bf1a70987833e11c34c8875  libtensorflow-gpu-linux-x86_64-2.3.0.tar.gz
bdfb52677cf9793dcd7b66254b647c885c417967629f58af4a19b386fa7e7e0f  libtensorflow-gpu-linux-x86_64-2.3.1.tar.gz

Apolgies. It seems our CI uploaded the wrong package under the new name after we refactored parts of the CI. I think it should be fixed now, can you give it a try please?

This now works for me. Thanks for sorting this out. Might want to update the links on the C API page to point to the new versions.
GPU: mobile GeForce GTX 1660 Ti

Thank you so much @av8ramit and @mihaimaruseac - v2.3.1 is now working for us also!

No problem! Hats off to @geetachavan1 and @mihaimaruseac who did the heavy lifting!


kube does not allow underscores

Updating README to reflect that Kubernetes regex is strict against _:
Mar 15 09:29:31 my_openshift_2-openshift-master-1 atomic-openshift-node: I0315 09:29:31.323146    7484 kubelet.go:972] Attempting to register node my_openshift_2-openshift-master-1.example.com
Mar 15 09:29:31 my_openshift_2-openshift-master-1 atomic-openshift-node: I0315 09:29:31.325224    7484 kubelet.go:975] Unable to register my_openshift_2-openshift-master-1.example.com with the apiserver: Node "my_openshift_2-openshift-master-1.example.com" is invalid: metadata.name: invalid value 'my_openshift_2-openshift-master-1.example.com', Details: must be a DNS subdomain (at most 253 characters, matching regex [a-z0-9]([-a-z0-9]*[a-z0-9])?(\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*): e.g. "example.com"

Thanks for this patch !
I can definitely live with my-openshift too :-) Could you please replace the other occurences of my_openshift in the README ?

Hi Judd, I'm closing this one in favor of #64 which replaces all occurrences.


[Bug] 邦布记录不全

Checklist

[X] My issue was not mentioned by others, and it is not a duplicate issue.

Description
邦布的抽取记录只能读取到一部分

17页，应该是81条抽取记录，但软件只能读到最新的30条记录

Reproduction Steps

Expected Behavior
No response
Screenshots
No response
Starward Version
0.11.7-preview.7
Windows Version
Windows 11 22631.3737
Log
不会

Additional Context
No response

我一开始也是，点了更新全部记录之后就全了

请重新点击 更新(全部) 按键


Mobile Officer Boxes

There is a problem with the contents of the officer boxes on mobile devices. The contents overflow over the edges of the boxes.

No longer relevant


Sensor fusion

Hello, I have been using this git a lot for my research and got to the question: The slam algorithms are using more than one sensor?
I mean I know that they use the RPlidar and the scan topic, but I want to know if he uses odom as well.
How do I know if he uses more than one sensor?

Hi @claudiosouzabrito
Please use this issue thread for reporting bugs and issues as there is a ROS Answers community where you can get abundant information.
In short, SLAM algorithms may use other sensors such as IMU or encoders to compensate the disadvantages of Lidar.
For example, Gmapping requires odometry from the wheel.
Any required inputs(Subscribed Topics) for a specific SLAM algorithm will be described in its documentation.
Thank you.


Support multiple allow ip for kms connection(Triage on ostracon#692)

cosmos-sdk also need to apply Finschia/finschia-sdk#821


LocationSettingsRequest says Ble is allowed, but we still can't scan

Summary
For API > 23, with location services turned off, we are required to turn them on in order to continue. However,  following this guide https://developers.google.com/android/reference/com/google/android/gms/location/SettingsClient  and setting the builder to  builder.setNeedBle(true); I am getting results back that ble scan is allowed, however the library prevents it.  Is it possible that the library should be using the SettingsClient to determine if locationServices are required to be turned on?
Library version
1.8.2

Hello,
What is the exact error you get? I expect it is LOCATION_SERVICES_DISABLED?
From the docs of LocationSettingsRequest:

public LocationSettingsRequest.Builder setNeedBle (boolean needBle)
Sets whether the client wants BLE scan to be enabled. When this flag is set to true, if the platform supports BLE scan mode and Bluetooth is off, the dialog will prompt the user to enable BLE scan. If the platform doesn't support BLE scan mode, the dialog will prompt to enable Bluetooth.

It seems that it only handles turning the Bluetooth ON/OFF, not the Location Services?

Correct, for LOCATION_SERVICES_DISABLED

It seems that it only handles turning the Bluetooth ON/OFF, not the Location Services?

You're correct.
After digging in a bit more, LocationSettingsRequest will return successfully if you:

Don't have a specific locationRequest added
or
Have a location request added with PRIORITY_NO_POWER as the priority setting.

However, if you add a location request with PRIORITY_LOW_POWER you are able to confirm that location settings are off, and follow the provided documentation to request that the user turns it on. (It provides a system dialog).  I think this issue can be considered closed, but might be helpful for people who want address when LOCATION_SERVICES_DISABLED is the Client Status


Tumblr Publish Agent undefined method `[]' for nil:NilClass

Not sure what would be useful for debugging this, but stack trace below:
                        undefined method `[]' for nil:NilClass
/pub/lin/huginn/app/models/agents/tumblr_publish_agent.rb:131:in `publish_post'
/pub/lin/huginn/app/models/agents/tumblr_publish_agent.rb:111:in `block in receive'
/usr/lib/ruby/gems/2.5.0/gems/activerecord-5.1.5/lib/active_record/relation/delegation.rb:39:in `each'
/usr/lib/ruby/gems/2.5.0/gems/activerecord-5.1.5/lib/active_record/relation/delegation.rb:39:in `each'
/pub/lin/huginn/app/models/agents/tumblr_publish_agent.rb:104:in `receive'
/pub/lin/huginn/app/jobs/agent_receive_job.rb:8:in `perform'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/execution.rb:37:in `block in perform_now'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:108:in `block in run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/i18n-0.9.5/lib/i18n.rb:268:in `with_locale'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/translation.rb:7:in `block (2 levels) in <module:Translation>'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:117:in `instance_exec'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:117:in `block in run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/logging.rb:24:in `block (4 levels) in <module:Logging>'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/notifications.rb:166:in `block in instrument'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/notifications/instrumenter.rb:21:in `instrument'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/notifications.rb:166:in `instrument'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/logging.rb:23:in `block (3 levels) in <module:Logging>'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/logging.rb:44:in `block in tag_logger'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/tagged_logging.rb:69:in `block in tagged'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/tagged_logging.rb:26:in `tagged'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/tagged_logging.rb:69:in `tagged'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/logging.rb:44:in `tag_logger'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/logging.rb:20:in `block (2 levels) in <module:Logging>'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:117:in `instance_exec'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:117:in `block in run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:135:in `run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/execution.rb:33:in `perform_now'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/execution.rb:22:in `block in execute'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:108:in `block in run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/railtie.rb:26:in `block (4 levels) in <class:Railtie>'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/execution_wrapper.rb:85:in `wrap'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/reloader.rb:68:in `block in wrap'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/execution_wrapper.rb:85:in `wrap'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/reloader.rb:67:in `wrap'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/railtie.rb:25:in `block (3 levels) in <class:Railtie>'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:117:in `instance_exec'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:117:in `block in run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/activesupport-5.1.5/lib/active_support/callbacks.rb:135:in `run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/execution.rb:20:in `execute'
/usr/lib/ruby/gems/2.5.0/gems/activejob-5.1.5/lib/active_job/queue_adapters/delayed_job_adapter.rb:36:in `perform'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/backend/base.rb:81:in `block in invoke_job'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:61:in `block in initialize'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:66:in `execute'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:40:in `run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/backend/base.rb:78:in `invoke_job'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:230:in `block (2 levels) in run'
/usr/lib/ruby/2.5.0/timeout.rb:93:in `block in timeout'
/usr/lib/ruby/2.5.0/timeout.rb:103:in `timeout'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:230:in `block in run'
/usr/lib/ruby/2.5.0/benchmark.rb:308:in `realtime'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:229:in `run'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:312:in `block in reserve_and_run_one_job'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:61:in `block in initialize'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:66:in `execute'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:40:in `run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:312:in `reserve_and_run_one_job'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:213:in `block in work_off'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:212:in `times'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:212:in `work_off'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:175:in `block (4 levels) in start'
/usr/lib/ruby/2.5.0/benchmark.rb:308:in `realtime'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:174:in `block (3 levels) in start'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:61:in `block in initialize'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:66:in `execute'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:40:in `run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:173:in `block (2 levels) in start'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:172:in `loop'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:172:in `block in start'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/plugins/clear_locks.rb:7:in `block (2 levels) in <class:ClearLocks>'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:79:in `block (2 levels) in add'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:61:in `block in initialize'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:79:in `block in add'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:66:in `execute'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/lifecycle.rb:40:in `run_callbacks'
/usr/lib/ruby/gems/2.5.0/gems/delayed_job-4.1.4/lib/delayed/worker.rb:171:in `start'
/pub/lin/huginn/lib/delayed_job_worker.rb:6:in `run'
/pub/lin/huginn/app/concerns/long_runnable.rb:71:in `block in run!'

That looks like that either the options key of the Agent options is empty or gets overridden by the incoming Event. Can you share your Agent configuration and the Event the Agent received?

Agent config:
{
  "state": "queue",
  "id": "{{tumblr_post_id}}",
  "reblog_key": "{{reblog_key}}",
  "tags": "originaltags, {{ categories | join: ', ' }}",
  "expected_update_period_in_days": "2",
  "blog_name": "ourwitching",
  "post_type": "reblog"
}

And the event
{
  "id": "tag:rss.noblejury.com,2018-03-22:/1222215",
  "url": "http://cognitohazard.tumblr.com/post/172141195299",
  "urls": [
    "http://cognitohazard.tumblr.com/post/172141195299",
    "http://cognitohazard.tumblr.com/"
  ],
  "links": [
    {
      "href": "http://cognitohazard.tumblr.com/post/172141195299",
      "rel": "alternate",
      "type": "text/html"
    },
    {
      "href": "http://cognitohazard.tumblr.com/",
      "rel": "self"
    }
  ],
  "title": "picsthatmakeyougohmm:\nhmmm",
  "description": "
picsthatmakeyougohmm:\nhmmm<\/p>",
  "content": "

picsthatmakeyougohmm<\/a>:<\/p>\n

hmmm<\/p><\/blockquote>",
  "image": null,
  "enclosure": null,
  "authors": [

  ],
  "categories": [
    "hmmm"
  ],
  "date_published": null,
  "last_updated": "2018-03-22T17:36:34+00:00",
  "message": "Its a tumblr link",
  "reblog_key": "55fnHgu8",
  "blog_name": "cognitohazard",
  "tumblr_post_id":<PHONE_NUMBER>99,
  "tumblr_post_type": "photo",
  "tumblr_source_url": "https://picsthatmakeyougohmm.tumblr.com/post/170394645293/hmmm"
}

That Agent config will not work, you can change the values of the default configuration, but the key and nested options of the configuration have to be identical to the default configuration.
{
  "expected_update_period_in_days": "10",
  "blog_name": "{{blog_name}}",
  "post_type": "{{post_type}}",
  "options": {
    "state": "{{state}}",
    "tags": "{{tags}}",
    "tweet": "{{tweet}}",
    "date": "{{date}}",
    "format": "{{format}}",
    "slug": "{{slug}}",
    "title": "{{title}}",
    "body": "{{body}}",
    "caption": "{{caption}}",
    "link": "{{link}}",
    "source": "{{source}}",
    "quote": "{{quote}}",
    "url": "{{url}}",
    "description": "{{description}}",
    "conversation": "{{conversation}}",
    "external_url": "{{external_url}}",
    "embed": "{{embed}}",
    "id": "{{id}}",
    "reblog_key": "{{reblog_key}}",
    "comment": "{{comment}}"
  }
}

GOTCHA. I had some kind of corruption of the config that messed up the web interface and this was how I restored it, thank you so much!!


Freecam only works with the controller in Port 1

Maybe a selection could be added to choose which controller to use?

Oh good find. Handling this like input display does would probably be simpler - adding all controllers' inputs together


Wasi.Sdk upgrade to 0.1.3-preview.10012

This set of changes upgrades Wasi.Sdk dependency version to be 0.1.3-preview.10012. It also upgrades wit-bindgen version in make bootstrap to match what is currently used in the Spin v0.7.1 SDK. Additionally I upgraded wizer to the latest commit on main. I'm not sure if the wizer / wit-bindgen versions are correct, I just grabbed the latest revisions.
Additionally it comments out a section of code that is failing to initialize with wizer because the expected header isn't present.

Source Issue: #17

Closing this one as stale.


Display only shows bluetooth turned off - yellow triangle when pressed

I recently got a new laptop and installed a backup of my stream deck for use on the new machine. The bluetooth icon only ever shows that bluetooth is turned off:

And tapping the button shows a yellow triangle with an exclamation point/bang inside. The plugin still works on my old device (M2 Pro) with no issues at all. Here are the troubleshooting steps I've tried on the new device:

full restart
press button when bluetooth is in either on or off state
uninstall and reinstall bluetooth toggle plugin
close and reopen stream deck software

Changing the bluetooth setting on my laptop has no effect on the status of the plugin icon - it continues to show that bluetooth is off regardless of its actual setting. My new device is an M3 Pro, if that matters.

Thanks for the report! Did you try to reinstall the plugin?

Thanks for the quick response! Yes, I uninstalled and reinstalled the plugin. I also wrote a list of troubleshooting steps in my initial post. I'm wondering if there's a permissions request I never received for this plugin. For example, pasting text requires accessibility permissions to be enabled in Settings. Is there anything similar required to view and change the bluetooth on/off status?

Is there anything similar required to view and change the bluetooth on/off status?

I don't think so, I've never seen anything like this.
The plugin works for me on mac m2 14.5 Sonoma.
But, I don't have m3 available to test. It might have a bit different API or something 🤔


Feature Request: Add Eyedropper

Chrome and Edge support a very cool new EyeDropper API. Could you add it to Coloris?
https://developer.mozilla.org/en-US/docs/Web/API/EyeDropper

This has already been requested in #95.
My position on it hasn't changed, the EyeDropper interface is still experimental and is only supported in Chromium/Blink browsers. I will consider adding it when it matures and is supported by all modern browsers.

I didn't check the closed issues, sorry. Thanks for you reply!


Style guide for diagrams in docs

Now that we're adding diagrams to our docs, we should work with Design ( @kuanluo) to come up with a consistent style and approach.
Current docs with diagrams:

Parallel Statement Execution
Remove Nodes

Thank you for flagging, @jseldess! I will take a stab at this next week.

The parallel statement execution diagrams will also be used in the corresponding blog post, which is slated for release in a few weeks. I'm really excited to see what @kuanluo comes up with!

Now that's a lot of pressure, @nvanbenschoten. I will put some time on the calendar for us to discuss how to improve the diagrams.

@kuanluo, 2 questions:

Would you be willing to redesign the existing images in the docs (removing nodes and parallel statement execution)?
Are we doing more work on design templates for 1.2, or should I close this issue?

To your questions,

There are some component library style update work underway, so I won’t update the diagrams just yet. We will hopefully be able to update existing styles before end of Q4.

And yes, feel free to close this issue!

On Nov 19, 2017, at 5:40 AM, Jesse Seldess<EMAIL_ADDRESS>wrote:
@kuanluo, 2 questions:
Would you be willing to redesign the existing images in the docs (removing nodes and parallel statement execution)?
Are we doing more work on design templates for 1.2, or should I close this issue?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

Thanks, @kuanluo!


Open files in new windows BUT reveal (existing window) if already open

Does this issue occur when all extensions are disabled?: Yes

VS Code Version: 1.88.1
OS Version: macos 14.2.1

Steps to Reproduce:

In the settings, set

"workbench.editor.revealIfOpen": true,
"window.openFilesInNewWindow": "on",

Double-click on a file associated with VSCode in the Finder (i.e. OS file browser)
File should be opened in a new Window (do not close that window)
Double-click on the same file in the Finder
Bug:

Expected behaviour: The  already opened Window should be revealed
Actual behaviour: The file is opened again in another Window

I would consider this a bug as the behaviour ignores "workbench.editor.revealIfOpen": true,. I know (so we do not need to discuss that) that there are two concepts: Window and Workbench. That is, I assume that workbench.editor.revealIfOpen only effects the behaviour when files are opened from the Explorer view within VSCode, while window.openFilesInNewWindow only effects the behaviour when files are opened from outside of VSCode -- and from this point of view the actual behaviour would be correct. So you may give this report another label feature request for a new setting "window.revealIfOpen": "on".
There are some related issues:

"workbench.editor.revealIfOpen": true Not Work When "window.openFilesInNewWindow": "on"
And some more specific issues (which are also not solved as far as I understand the problems):

New window.openFilesInNewWindow option: onlyIfOutsideOpenFolders
Files are not in same folder being opened in new window

Note: I'm well aware of the possibility of just setting
"window.openFilesInNewWindow": "off",

But in that case, all files are just opened in the same Window (in a new tab). Since I usually open (software project) folders, I do not want other, completely unrelated files, to be opened in that window as well.
I'm a little bit surprised to not find more complains about this behaviour, as it has some consequences:

I cannot use VScode as my default editor for arbitrary files AND as my (kind of ) IDE, since it would open all the files in one single window (this would be the case with "window.openFilesInNewWindow": "off") -- but hey, I want to use multiple windows!
I have often opened files accidentally in two Windows. Since the files are not synchronised, I have already lost my work since I saved the wrong file (or I have two versions of the file with different content as I mixed up the windows). This is the case for "window.openFilesInNewWindow": "on"

Thanks for creating this issue! It looks like you may be using an old version of VS Code, the latest stable release is 1.89.1. Please try upgrading to the latest version and checking whether this issue remains.
Happy Coding!

This feature request is now a candidate for our backlog. The community has 60 days to upvote the issue. If it receives 20 upvotes we will move it to our backlog. If not, we will close it. To learn more about how we handle feature requests, please see our documentation.
Happy Coding!

Maybe one addition:
"window.openFilesInNewWindow": "default",

does not solve the issue.
That is, setting window.openFilesInNewWindow to on or default behave similar on macos, when opening a file via macos Finder.

And: The behaviour is similar on Microsoft Windows.

I had a look in the source code... it seems as if there currently is no way of retrieving a list of opened files in the main process (not in the render process, of course), is there? That is, in particular a window which only shows simple files (and no workspace or folder) basically is a black box for the main process.
When implementing the feature request, one would need a way of retrieving that list. Do you have a hint of how to best implement that? By adding some new channels? Or by keeping a list of opened files in the main process (although I did not find a mechanism to detect when a file, i.e. text editor, is closed).

:slightly_smiling_face: This feature request received a sufficient number of community upvotes and we moved it to our backlog. To learn more about how we handle feature requests, please see our documentation.
Happy Coding!


Update to 0.4 3

Alternative to #11

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.


Updating Azure key vault Rules [Part 1 Update activity]

Required items, please complete.
Change(s):

Updated Analytics rules of azure key vault

Reason for Change(s):

decommissioned commands, improper handling of variable has been handled in this PR

Version Updated:

N/A

Testing Completed:

Yes

Checked that the validations are passing and have addressed any issues that are present:

N/A

Hello @manishkumar1991 looking into this

Hi @devikamehra / @vakohl Could you please check the Analytic Rules in this, thanks!

Hello Team ,
I just have came to know about new thing in which full name entity mapping is going to deprecate in near future, so please allow some time, I will update those changes and confirm once done ,
@anki-narravula @petebryan FYI

Hello there,
Any updates @manishkumar1991

It doesn't make much sense to do this PR and not do the entity mapping changes at the same time.

@shainw Mappings are done, please review the rules

Got the review from Shain , He has suggested some changes


fix: consolidate batch errors when using GraphQL

Problem

GraphQL calls return an array of errors due to the APIs ability to batch requests. As a result, errors come back in an unexpected format and those messages are not sent to the customer's onError callback
Solution

Add an extra step to map and join error messages when using GraphQL and pass this to the customer's onError callback.
Additional Notes

Links
Ticket

GitHub issue:
Other links
Verification
Manual tests

Automated tests

[x] Unit tests added/updated
[ ] E2E tests added/updated
[ ] N/A - (provide a reason)
[ ] deferred - (provide GitHub issue for tracking)

Housekeeping

[x] No non-essential console logs
[x] All new files contain license notice

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Codecov Report

Merging #1064 (a219f3c) into main (7ab5bc3) will increase coverage by 0.00%.
The diff coverage is 100.00%.

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@@           Coverage Diff           @@
##             main    #1064   +/-   ##
=======================================
  Coverage   93.87%   93.87%
=======================================
  Files         124      124
  Lines        5826     5829    +3
  Branches     1765     1767    +2
=======================================
+ Hits         5469     5472    +3
  Misses        339      339
  Partials       18       18

Files Changed
Coverage Δ

.../codegen-ui-react/lib/amplify-ui-renderers/form.ts
95.38% <100.00%> (+0.22%)
:arrow_up:

Continue to review full report in Codecov by Sentry.

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No index schema validation when creating an index

I got the error when I wanted to index some data.

This issue seems to be a duplicate of #757. Closing.


Invalid version: '3.0.0-a44284e-torch1.12.1' (package: pytorch-nndct)

When I use 'pip install -v -e .' to package my code (which depends on pytorch-nndct)，errors like below
      File "/home/zq/anaconda3/envs/sxcv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 2631, in hashcmp
        self.parsed_version,
      File "/home/zq/anaconda3/envs/sxcv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 2685, in parsed_version
        raise packaging.version.InvalidVersion(f"{str(ex)} {info}") from None
    pkg_resources.extern.packaging.version.InvalidVersion: Invalid version: '3.0.0-a44284e-torch1.12.1' (package: pytorch-nndct)
    error: subprocess-exited-with-error

it seems '3.0.0-a44284e-torch1.12.1' is a invalid version, how to fix it ?

Hi. I cannot get your error with VAI 3.0 and pytorch 1.12.1. Does it work with installing pytorch-nndct by python setup.py develop or python setup.py install?

Hi @Alexiazzf , I use ‘conda install pytorch_nndct=3.0.0 xcompiler -c pytorch -c conda-forge -c file:///local/zq/vitis/3.0/conda-channel’ (https://www.xilinx.com/bin/public/openDownload?filename=conda-channel-3.0.tar.gz)
pip list info:
...
pytorch-nndct      3.0.0-a44284e-torch1.12.1
...
torch              1.12.1
...

Could you show your pip list info?

Hi @ZQ-zz , My pip info is the same as yours. Maybe your could delete this file nndct/pytorch_binding/version.txt then try again. Or you could reinstall pytorch-nndct.


Fixes empty "properties" collection.

When using an event-context variable, the properties collection was
empty, even when property items where explicitly given. This function
fixes this.
For more information, see this stackoverflow question (and answer!) on how and why this small patch was created:
http://stackoverflow.com/questions/23272439/nlog-event-contextitem-xxxx-not-writing-in-logging-database

We are doing this exact same thing, this bug appears to not be fixed. We are on the latest via NuGet, have disassembled the code using DotNet Peek and the supposed fix is in there, however, none of our properties for these items are being saved to the database, always blank:

This is just not working and makes it almost useless to try to log information to SQL Server as you get almost no useful information


Change default icons por expand and colapse

Hi, how can i change de expand/colapse icons. Thnks in advance

Hello @alphapyme,
You can check this code sample and do the same
https://github.com/AmrDeveloper/TreeView/blob/9ac184a43e0c96c6062e54560ccd3475d80a6b0d/app/src/main/java/com/amrdeveloper/treeviewlib/logtree/LogViewHolder.java#L44
Thank you,
Amr Hesham

Amr, excelent component congrats
Saludos
De: Amr Hesham @.>
Enviado el: sábado, 27 de abril de 2024 09:23 a. m.
Para: AmrDeveloper/TreeView @.>
CC: Edwin José Hernández Pinto @.>; Mention @.>
Asunto: Re: [AmrDeveloper/TreeView] Change default icons por expand and colapse (Issue #19)
Hello @alphapymehttps://github.com/alphapyme,
You can check this code sample and do the same
https://github.com/AmrDeveloper/TreeView/blob/9ac184a43e0c96c6062e54560ccd3475d80a6b0d/app/src/main/java/com/amrdeveloper/treeviewlib/logtree/LogViewHolder.java#L44
Thank you,
Amr Hesham
—
Reply to this email directly, view it on GitHubhttps://github.com/AmrDeveloper/TreeView/issues/19#issuecomment-2080895277, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AT7GRW4BT3MP272UCTIN5BTY7PGGZAVCNFSM6AAAAABG3GRR7SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAOBQHA4TKMRXG4.
You are receiving this because you were mentioned.Message ID<EMAIL_ADDRESS>


MultiSelect Permit and add legacy PROD RESERV

Intent:
Adding templates for legacy PROD and RESERV reports (for use in internal lodgement portal).
Restricting multi-select of permits for well based reports (e.g. WCR, WAR) and reports that may pertain to a well-set in a single permit (e.g. Well Prod Testing, Geothem Inj Testing).
I don't trust my own JSON skills. please review carefully
Changes:
version
modified date
updated by
multiSelect restricted from:

Hydraulic Fracturing Activity Report
Well or Bore Completion Report
Well Production Testing Report
Well or Bore Abandonment Report
Geothermal Report - Injection Testing
Geothermal Report - Production Testing
PA-42 Notice of intention to convert a petroleum well to a water observation bore or water supply bore
WRA-05A Notice of completion of conversion of petroleum well to water supply bore or water observation bore
MMOL-44 Notice of decommissioning a well, water observation bore, water monitoring bore or water supply bore

Add (for internal use only)

Legacy Petroleum Production Report
Legacy Petroleum Reserves Report
Permit Report - Six Month

Easiest way to compare seems to be using something like https://jsoneditoronline.org/#right=local.nizazu&left=local.yicaki

Checked with JSON validator as well, syntax appears fine. Confirmed multiselect for all 9 reports is restricted.
Is openFIleDateConfig going to be updated at a later date?


Allow admins to create events

Feature
Why is this feature being added?

What should your feature do?

Closed per discussion regarding events.  Working plan is to not allow administrators to create events via the operation code website.  Primary event platform will be meet up.  Events will be pulled from meetup and displayed on the operation code website.


OK you forece me to debug

1.There is no such place om my disk  'G:\Delphi Projects\rathlev-home_de\innounpack-s\InnoUnpack.exe'
UnpProg:=ReadString(CfgSekt,iniUnp,'');
You have to take care of this
That why this  Execute(UnpProg+' -v',cbFile.Text,'','');
dose not work.
and what kind of a program is this that execute it self ?
This is why i see in task manager 2 instances.
https://winmerge.org/ they have inno extractor works great.
I am willing to check your program once it works on one instance and loads reasonably well.
www.limelect.com you have on my site lots of free utilities all done with Delphi
Just found that if I stop debug under Delphi the 2 instances stay in memory.
Good luck.

If you would read the "readme.md" on the GitHub project page, you would have seen that "innunp.exe" is a separate program, see Innounp on SourceForge.net
You can download the exe file from here:
https://www.rathlev-home.de/tools/download/innounp050.zip
My GUI provides a button to select the path to this program if it is not installed together with the InnoUnpack.exe


fix: rich text editor not working on airgapped instance

Pull Request Template
Use this template to quickly create a well written pull request. Delete all quotes before creating the pull request.

Description

Please include a summary of the changes and which issue has been fixed. Please also include relevant motivation
and context. List any dependencies that are required for this change
Links to Notion, Figma or any other documents that might be relevant to the PR

Add a TL;DR when description is extra long (helps content team)

Fixes # (issue)

if no issue exists, please create an issue and ask the maintainers about this first

Media

A video or a GIF is preferred. when using Loom, don’t embed because it looks like it’s a GIF. instead, just link to the video

Type of change

Please delete options that are not relevant.

Bug fix (non-breaking change which fixes an issue)
New feature (non-breaking change which adds functionality)
Breaking change (fix or feature that would cause existing functionality to not work as expected)
Chore (housekeeping or task changes that don't impact user perception)
This change requires a documentation update

How Has This Been Tested?

Please describe the tests that you ran to verify your changes. Provide instructions, so we can reproduce.
Please also list any relevant details for your test configuration.
Delete anything that is not important

Manual
Jest
Cypress

Test Plan

Add Testsmith test cases links that relate to this PR

Issues raised during DP testing

Link issues raised during DP testing for better visiblity and tracking (copy link from comments dropped on this PR)

Checklist:
Dev activity

[ ] My code follows the style guidelines of this project
[ ] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes
[ ] PR is being merged under a feature flag

QA activity:

[ ] Test plan has been approved by relevant developers
[ ] Test plan has been peer reviewed by QA
[ ] Cypress test cases have been added and approved by either SDET or manual QA
[ ] Organized project review call with relevant stakeholders after Round 1/2 of QA
[ ] Added Test Plan Approved label after reveiwing all Cypress test

/ok-to-test sha=6874bbf

/build-deploy-preview env=release

@RakshaKShetty  @btsgh  can you check this DP url and see if rich text editor works as expected.

/ok-to-test sha=bebb464

/ok-to-test sha=2d7baa4

/ok-to-test sha=1ec76d8

/ci-merge-check


Increases coverage, adds ruff linting and splits predict() functionality from simulate()

This PR adds the following:

[x] Ruff linted changes
[ ] pre-commit with ruff hook (#58)
[x] Splits model.simulate() and model.predict() (#59)
[x] Tests for priors.py, parameter_set.py
[x] Working maximum likelihood example using pints (#52)

This is currently open, once the above is completed this will be good for review.

Merging this without #58 complete. Will leave that issue open and close it in a future PR.


ComboBox.SelectedItem only returns the typename and not the selected Value When using a Datatemplate

I have a combobox configured like so
      <ComboBox SelectedItem="{Binding SelectedModule, Mode=OneWayToSource}" Grid.Column="0" BorderBrush="Black" Grid.Row="2" Height="25" Margin="5" Items="{Binding SubCommands}">
        <ComboBox.ItemTemplate>
          <DataTemplate>
            <TextBlock Text="{Binding DisplayName}" ToolTip.Tip="{Binding Description}"/>
          </DataTemplate>
        </ComboBox.ItemTemplate>
      </ComboBox>

And the viewmodel is
        public string SelectedModule
        {
            get => selectedModule;
            private set
            {
                var res = value;
                this.RaiseAndSetIfChanged(ref selectedModule, value);
            }
        }

I am getting back the typename of the Items property instead of the value of the textblock text. How can I get that WPF mentions DisplayMemberName but that doesn't seem to exist in Avalonia.
I also tried
SelectionBoxItem="{Binding SelectedModule, Mode=OneWayToSource}"
but that didn't work either

This is the expected behavior and matches what the Wpf version does. As you noted we are missing the DisplayMemberPath feature and the SelectedValue property. This needs to be added. For now you could use a converter.

How is SubCommands defined in the view model?

Hi @grokys @Gillibald,
It is defined like
public class SubCommandModel : CommandModelBase
    {
        public List<DiagnosticTestModel> DiagnosticTests { get; set; }
        public SubCommandModel()
        {
            DiagnosticTests = new List<DiagnosticTestModel>();
        }
    }

and commandbase is
    public class CommandModelBase
    {
        public string Description { get; set; }
        public string DisplayName { get; set; }
        public string CommandName { get; set; }
    }

It displays fine just returns "Cedar.Model.SubCommand" as a string

Thanks... Now I'm confused:

It displays fine just returns "Cedar.Model.SubCommand" as a string

What do you mean by this? It displays fine? So the problem isn't how it's displayed?

@grokys Thank you, I did not realize it was just calling ToString() on subcommand module, it makes a lot of sense in hindsight. I did just as you suggested and it works great.


Added jonesangga.md

I added my site info. Currently I don't have feed and I did not use any ssg. Thanks.

Thanks!!


W3C changes

Description

Motivation and Context

Types of changes

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] Documentation fix (typos, incorrect content, missing content, etc.)

Checklist

[ ] I have read the contributing document.
[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly.
[ ] I have added tests to cover my changes.
[ ] All new and existing tests passed.

Hi @diemol , had one additional question: do you think we should add examples in other languages? Right now, it's pretty much all Java. Or, I could point users to the Selenium doc.

Hi @diemol , I've made changes -- could you pls take a look?