cuDNN in invalid state after OOM

System information

Have I written custom code
OS Platform and Distribution:  Linux Ubuntu 18.04
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): v1.13.1-0-g6612da8951
Python version: 3.6.9
CUDA/cuDNN version: CUDA 10.0.130-1, cuDNN 7.6.5
GPU model and memory: GeForce RTX 2070 super, 8 GiB

Describe the current behavior
After getting ResourceExhaustedError during an operation using cuDNN (for instance due to out of memory) it seems like cuDNN is left in a broken state and further calls using cuDNN fail even if they don't exceed the resources.
Describe the expected behavior
I would expect further calls to cuDNN to be ok. This is important since it seems that the only way to know if a computation will use too much resources (for instance when determining optimal batch size) is to actually try and fail.
Standalone code to reproduce the issue
import tensorflow as tf
import numpy as np
from tensorflow.python.framework.errors_impl import ResourceExhaustedError

x = tf.placeholder(tf.float32, shape=[None, None, None, None, 32])
fil = tf.zeros((3, 3, 3, 32, 32), dtype=tf.float32)
conv = tf.nn.conv3d(x, filter=fil, strides=(1, 1, 1, 1, 1), padding='VALID', name=None)

with tf.Session() as sess:
    try:
        c = np.zeros((1, 370, 370, 370, 32), np.float32)
        sess.run(conv, feed_dict={x: c})
        r1 = sess.run(conv)
    except ResourceExhaustedError:
        print('Resource Error')

with tf.Session() as sess:
    c = np.zeros((1, 100, 100, 100, 32), np.float32)
    sess.run(conv, feed_dict={x: c})

Other info / logs Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached.
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
2020-10-15 13:41:46.910748: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-10-15 13:41:47.076574: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-10-15 13:41:47.077067: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x1414860 executing computations on platform CUDA. Devices:
2020-10-15 13:41:47.077083: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2070 SUPER, Compute Capability 7.5
2020-10-15 13:41:47.096784: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-10-15 13:41:47.097210: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x158f0a0 executing computations on platform Host. Devices:
2020-10-15 13:41:47.097229: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-10-15 13:41:47.097351: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce RTX 2070 SUPER major: 7 minor: 5 memoryClockRate(GHz): 1.815
pciBusID: 0000:01:00.0
totalMemory: 7.79GiB freeMemory: 7.31GiB
2020-10-15 13:41:47.097368: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-10-15 13:41:47.098194: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-15 13:41:47.098208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-10-15 13:41:47.098215: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-10-15 13:41:47.098286: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7110 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-10-15 13:41:47.100978: W tensorflow/core/framework/allocator.cc:124] Allocation of<PHONE_NUMBER> exceeds 10% of system memory.
2020-10-15 13:42:01.641512: W tensorflow/core/common_runtime/bfc_allocator.cc:267] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.94GiB.  Current allocation summary follows.
2020-10-15 13:42:01.641594: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (256): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641616: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (512): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641638: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (1024): 	Total Chunks: 1, Chunks in use: 1. 1.2KiB allocated for chunks. 1.2KiB in use in bin. 1.0KiB client-requested in use in bin.
2020-10-15 13:42:01.641656: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (2048): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641672: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (4096): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641688: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (8192): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641704: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (16384): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641720: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (32768): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641742: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (65536): 	Total Chunks: 1, Chunks in use: 1. 108.0KiB allocated for chunks. 108.0KiB in use in bin. 108.0KiB client-requested in use in bin.
2020-10-15 13:42:01.641759: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (131072): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641775: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (262144): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641791: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (524288): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641807: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (1048576): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641823: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (2097152): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641839: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (4194304): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641856: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (8388608): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641872: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (16777216): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641888: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641904: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (67108864): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641924: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 13:42:01.641942: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (268435456): 	Total Chunks: 2, Chunks in use: 1. 6.94GiB allocated for chunks. 6.04GiB in use in bin. 6.04GiB client-requested in use in bin.
2020-10-15 13:42:01.641960: I tensorflow/core/common_runtime/bfc_allocator.cc:613] Bin for 5.94GiB was 256.00MiB, Chunk State:
2020-10-15 13:42:01.641986: I tensorflow/core/common_runtime/bfc_allocator.cc:619]   Size: 926.76MiB | Requested Size: 0B | in_use: 0, prev:   Size: 6.04GiB | Requested Size: 6.04GiB | in_use: 1
2020-10-15 13:42:01.642004: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7f86a6000000 of size 110592
2020-10-15 13:42:01.642017: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7f86a601b000 of size 1280
2020-10-15 13:42:01.642031: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7f86a601b500 of size<PHONE_NUMBER>
2020-10-15 13:42:01.642043: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7f8828755900 of size 971780864
2020-10-15 13:42:01.642055: I tensorflow/core/common_runtime/bfc_allocator.cc:638]      Summary of in-use Chunks by size:
2020-10-15 13:42:01.642071: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 1280 totalling 1.2KiB
2020-10-15 13:42:01.642087: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 110592 totalling 108.0KiB
2020-10-15 13:42:01.642101: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size<PHONE_NUMBER> totalling 6.04GiB
2020-10-15 13:42:01.642116: I tensorflow/core/common_runtime/bfc_allocator.cc:645] Sum Total of in-use chunks: 6.04GiB
2020-10-15 13:42:01.642136: I tensorflow/core/common_runtime/bfc_allocator.cc:647] Stats:
Limit:<PHONE_NUMBER>
InUse:<PHONE_NUMBER>
MaxInUse:<PHONE_NUMBER>
NumAllocs:                       3
MaxAllocSize:<PHONE_NUMBER>

2020-10-15 13:42:01.642153: W tensorflow/core/common_runtime/bfc_allocator.cc:271] ***************************************************************************************_____________
2020-10-15 13:42:01.642223: W tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at conv_ops_3d.cc:161 : Resource exhausted: OOM when allocating tensor with shape[1,368,368,368,32] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Resource Error
2020-10-15 13:42:01.801362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-10-15 13:42:01.801391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-15 13:42:01.801396: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-10-15 13:42:01.801400: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-10-15 13:42:01.801462: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7110 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-10-15 13:42:02.948064: E tensorflow/stream_executor/cuda/cuda_dnn.cc:334] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2020-10-15 13:42:02.967863: E tensorflow/stream_executor/cuda/cuda_dnn.cc:334] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
Traceback (most recent call last):
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call
    return fn(*args)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[{{node Conv3D}}]]
	 [[{{node Conv3D}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py", line 19, in <module>
    sess.run(conv, feed_dict={x: c})
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1328, in _do_run
    run_metadata)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[node Conv3D (defined at /home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py:7) ]]
	 [[node Conv3D (defined at /home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py:7) ]]

Caused by op 'Conv3D', defined at:
  File "/home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py", line 7, in <module>
    conv = tf.nn.conv3d(x, filter=fil, strides=(1, 1, 1, 1, 1), padding='VALID', name=None)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1440, in conv3d
    dilations=dilations, name=name)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1801, in __init__
    self._traceback = tf_stack.extract_stack()

UnknownError (see above for traceback): Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[node Conv3D (defined at /home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py:7) ]]
	 [[node Conv3D (defined at /home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py:7) ]]

Process finished with exit code 1

I turned out that I reduced the minimal example above to much (it fails even if I remove the first run). Here is an update:
import tensorflow as tf
import numpy as np
from tensorflow.python.framework.errors_impl import ResourceExhaustedError

config = tf.ConfigProto()
config.gpu_options.allow_growth = True

x = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
fil = tf.zeros((3, 3, 3, 1, 32), dtype=tf.float32)
conv = tf.nn.convolution(x, filter=fil, padding='VALID', name=None)

with tf.Session(config=config) as sess:
    try:
        c = np.zeros((1, 370, 370, 370, 1), np.float32)
        sess.run(conv, feed_dict={x: c})
    except ResourceExhaustedError:
        print('Resource Error')

with tf.Session(config=config) as sess:
    c = np.zeros((1, 30, 30, 30, 1), np.float32)
    sess.run(conv, feed_dict={x: c})

Produces
/home/dbergh/.virtualenvs/boneseg/bin/python3.6 /home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
2020-10-15 17:15:28.759710: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-10-15 17:15:28.917081: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-10-15 17:15:28.917568: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0xf48540 executing computations on platform CUDA. Devices:
2020-10-15 17:15:28.917584: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2070 SUPER, Compute Capability 7.5
2020-10-15 17:15:28.936804: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-10-15 17:15:28.937281: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x10c0cc0 executing computations on platform Host. Devices:
2020-10-15 17:15:28.937311: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-10-15 17:15:28.937464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce RTX 2070 SUPER major: 7 minor: 5 memoryClockRate(GHz): 1.815
pciBusID: 0000:01:00.0
totalMemory: 7.79GiB freeMemory: 7.29GiB
2020-10-15 17:15:28.937484: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-10-15 17:15:28.938429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-15 17:15:28.938445: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-10-15 17:15:28.938453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-10-15 17:15:28.938536: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7096 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-10-15 17:15:39.476332: W tensorflow/core/common_runtime/bfc_allocator.cc:267] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.94GiB.  Current allocation summary follows.
2020-10-15 17:15:39.476412: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (256): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476452: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (512): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476476: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (1024): 	Total Chunks: 1, Chunks in use: 1. 1.2KiB allocated for chunks. 1.2KiB in use in bin. 1.0KiB client-requested in use in bin.
2020-10-15 17:15:39.476496: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (2048): 	Total Chunks: 2, Chunks in use: 2. 7.0KiB allocated for chunks. 7.0KiB in use in bin. 6.8KiB client-requested in use in bin.
2020-10-15 17:15:39.476515: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (4096): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476532: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (8192): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476550: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (16384): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476567: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (32768): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476609: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (65536): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476628: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (131072): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476645: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (262144): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476670: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (524288): 	Total Chunks: 1, Chunks in use: 0. 1015.8KiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476709: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (1048576): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476764: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (2097152): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476802: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (4194304): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476835: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (8388608): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476865: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (16777216): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476883: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476900: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (67108864): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476923: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-10-15 17:15:39.476946: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (268435456): 	Total Chunks: 3, Chunks in use: 2. 6.93GiB allocated for chunks. 6.19GiB in use in bin. 6.13GiB client-requested in use in bin.
2020-10-15 17:15:39.476968: I tensorflow/core/common_runtime/bfc_allocator.cc:613] Bin for 5.94GiB was 256.00MiB, Chunk State:
2020-10-15 17:15:39.476999: I tensorflow/core/common_runtime/bfc_allocator.cc:619]   Size: 756.47MiB | Requested Size: 0B | in_use: 0, prev:   Size: 5.94GiB | Requested Size: 5.94GiB | in_use: 1
2020-10-15 17:15:39.477020: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fafde000000 of size<PHONE_NUMBER>
2020-10-15 17:15:39.477036: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fb15a380000 of size 793221376
2020-10-15 17:15:39.477051: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fb18a000000 of size 268435456
2020-10-15 17:15:39.477066: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fb21f400000 of size 3584
2020-10-15 17:15:39.477081: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fb21f400e00 of size 1280
2020-10-15 17:15:39.477095: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fb21f401300 of size 3584
2020-10-15 17:15:39.477109: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fb21f402100 of size 1040128
2020-10-15 17:15:39.477122: I tensorflow/core/common_runtime/bfc_allocator.cc:638]      Summary of in-use Chunks by size:
2020-10-15 17:15:39.477140: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 1280 totalling 1.2KiB
2020-10-15 17:15:39.477157: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 2 Chunks of size 3584 totalling 7.0KiB
2020-10-15 17:15:39.477174: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 268435456 totalling 256.00MiB
2020-10-15 17:15:39.477190: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size<PHONE_NUMBER> totalling 5.94GiB
2020-10-15 17:15:39.477205: I tensorflow/core/common_runtime/bfc_allocator.cc:645] Sum Total of in-use chunks: 6.19GiB
2020-10-15 17:15:39.477227: I tensorflow/core/common_runtime/bfc_allocator.cc:647] Stats:
Limit:<PHONE_NUMBER>
InUse:<PHONE_NUMBER>
MaxInUse:<PHONE_NUMBER>
NumAllocs:                       5
MaxAllocSize:<PHONE_NUMBER>

2020-10-15 17:15:39.477247: W tensorflow/core/common_runtime/bfc_allocator.cc:271] **************************************************************************************__________****
2020-10-15 17:15:39.477328: W tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at conv_ops_3d.cc:399 : Resource exhausted: OOM when allocating tensor with shape[1,32,368,368,368] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Resource Error
2020-10-15 17:15:39.497736: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-10-15 17:15:39.497773: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-15 17:15:39.497779: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-10-15 17:15:39.497783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-10-15 17:15:39.497842: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7096 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-10-15 17:15:40.247329: E tensorflow/stream_executor/cuda/cuda_dnn.cc:334] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2020-10-15 17:15:40.251851: E tensorflow/stream_executor/cuda/cuda_dnn.cc:334] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
Traceback (most recent call last):
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call
    return fn(*args)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[{{node convolution}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py", line 21, in <module>
    sess.run(conv, feed_dict={x: c})
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1328, in _do_run
    run_metadata)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[node convolution (defined at /home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py:10) ]]

Caused by op 'convolution', defined at:
  File "/home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py", line 10, in <module>
    conv = tf.nn.convolution(x, filter=fil, padding='VALID', name=None)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 851, in convolution
    return op(input, filter)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 966, in __call__
    return self.conv_op(inp, filter)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 591, in __call__
    return self.call(inp, filter)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 208, in __call__
    name=self.name)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1440, in conv3d
    dilations=dilations, name=name)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1801, in __init__
    self._traceback = tf_stack.extract_stack()

UnknownError (see above for traceback): Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[node convolution (defined at /home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py:10) ]]

Process finished with exit code 1

while the code
import tensorflow as tf
import numpy as np
from tensorflow.python.framework.errors_impl import ResourceExhaustedError

config = tf.ConfigProto()
config.gpu_options.allow_growth = True

x = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
fil = tf.zeros((3, 3, 3, 1, 32), dtype=tf.float32)
conv = tf.nn.convolution(x, filter=fil, padding='VALID', name=None)

# with tf.Session(config=config) as sess:
#     try:
#         c = np.zeros((1, 370, 370, 370, 1), np.float32)
#         sess.run(conv, feed_dict={x: c})
#     except ResourceExhaustedError:
#         print('Resource Error')

with tf.Session(config=config) as sess:
    c = np.zeros((1, 30, 30, 30, 1), np.float32)
    sess.run(conv, feed_dict={x: c})

produces the output
/home/dbergh/.virtualenvs/boneseg/bin/python3.6 /home/dbergh/.PyCharm2019.3/config/scratches/scratch_59.py
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/dbergh/.virtualenvs/boneseg/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
2020-10-15 17:16:25.418268: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-10-15 17:16:25.585102: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-10-15 17:16:25.585564: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x237e670 executing computations on platform CUDA. Devices:
2020-10-15 17:16:25.585579: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2070 SUPER, Compute Capability 7.5
2020-10-15 17:16:25.604750: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-10-15 17:16:25.605156: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x24f72f0 executing computations on platform Host. Devices:
2020-10-15 17:16:25.605177: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-10-15 17:16:25.605303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce RTX 2070 SUPER major: 7 minor: 5 memoryClockRate(GHz): 1.815
pciBusID: 0000:01:00.0
totalMemory: 7.79GiB freeMemory: 7.29GiB
2020-10-15 17:16:25.605321: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-10-15 17:16:25.606144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-15 17:16:25.606159: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-10-15 17:16:25.606166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-10-15 17:16:25.606249: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7096 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5)

Process finished with exit code 0

indicating success.

Ok. With 2.3.1 (over cuda 10.1) I get no error. The tensorflow 2 version of the code would be.
import tensorflow.compat.v1 as tf
import numpy as np
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
tf.disable_v2_behavior()

config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True

x = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
fil = tf.zeros((3, 3, 3, 1, 32), dtype=tf.float32)
conv = tf.nn.convolution(x, filter=fil, padding='VALID', name=None)

with tf.Session(config=config) as sess:
    try:
        c = np.zeros((1, 370, 370, 370, 1), np.float32)
        sess.run(conv, feed_dict={x: c})
    except ResourceExhaustedError:
        print('Resource Error')

with tf.Session(config=config) as sess:
    c = np.zeros((1, 30, 30, 30, 1), np.float32)
    sess.run(conv, feed_dict={x: c})

If someone else runs into this:
The error seems to subtly depend on the order of execution. The following code
import tensorflow as tf
import numpy as np
from tensorflow.python.framework.errors_impl import ResourceExhaustedError

config = tf.ConfigProto()
config.gpu_options.allow_growth = True

x = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
fil = tf.zeros((3, 3, 3, 1, 32), dtype=tf.float32)
conv = tf.nn.convolution(x, filter=fil, padding='VALID', name=None)

with tf.Session(config=config) as sess:
    c = np.zeros((1, 30, 30, 30, 1), np.float32)
    sess.run(conv, feed_dict={x: c})

with tf.Session(config=config) as sess:
    try:
        c = np.zeros((1, 370, 370, 370, 1), np.float32)
        sess.run(conv, feed_dict={x: c})
    except ResourceExhaustedError:
        print('Resource Error')

with tf.Session(config=config) as sess:
    c = np.zeros((1, 30, 30, 30, 1), np.float32)
    sess.run(conv, feed_dict={x: c})

produces no error.


New Bedford sections: Add more env flags for incremental deeployment

To enable quicker incremental changes as part of https://github.com/studentinsights/studentinsights/pull/2668.

selfie


[Feature] Create Disclaimer Page for the website (unclickable link present in footer)

Description

Create a  disclaimer page
Simple & User friendly layout
theme matching with website
same header and footer
improvement on further suggestions
@swaraj-das

Screenshots

Checklist

[X] I have checked the existing issues.
[X] I have read the Code Of Conduct
[X] The changes don't break the code
[X] Please attach the screenshots and video of the development work once it is done

@swaraj-das please assign me the task

create pr #628

@swaraj-das check this out.. i have created pr from so long

@swaraj-das


Add flag to throw exception for duplicate values in map

Adds flag velox_exception_on_duplicate_map_values to enable/disable the behavior of throwing exception when duplicate values are found in map.
Spark has two policies to deal with duplicates in map functions like CreateMap, MapFromArrays, MapFromEntries, StringToMap, MapConcat and TransformKeys.
Spark conf => spark.sql.mapKeyDedupPolicy
conf value => EXCEPTION, LAST_WIN

EXCEPTION => Spark throws exception whenever a duplicate is found in map if dedup policy is set as "Exception".
LAST_WIN => The map key,value that is inserted last takes the precedence if dedup policy is set as "LAST_WIN".

Velox by default treats duplicates values as "LAST_WIN" dedup policy. This patch adds flag to throw exception for spark scenarios to bring the parity between spark and velox behavior for map functions.
Currently, this flag is being utilised in MapConcat function. It will be reused in other map functions as well.

@rui-mo @PHILO-HE Can you please take a look at this PR?
This is in reference of Gluten PR:  [VL] Support map_concat spark function

@Surbhi-Vijay I noticed that there is already a util method checkDuplicateKeys in velox/functions/lib/CheckDuplicateKeys.cpp, should it be more reasonable to leverage it for this new config?


"Show on mouse" keybind does not properly clear.

Describe the bug
The "show on mouse" keybind does not get properly cleared, and furthermore causes the radial menu to appear and become permanently stuck on the screen.
To Reproduce
Steps to reproduce the behavior:

Click "set" and bind "show on mouse" to any key.
Click "set", then click "clear".
Observe that nothing is now showing as bound for "show on mouse", even if the options menu is closed and re-opened.
Press the key that was previously bound in step 1.
Observe that the radial menu still appears, and cannot be closed, and no options can be selected either.
Even after the game is restarted, upon opening the options menu, you can observe that the keybind set in step 1 reappeared, even though it was cleared in step 2.

Expected behavior
On step 4, pressing the key that was previously bound but then cleared should not open the radial menu.
Furthermore, even if the menu does appear, it should not become stuck permanently on the screen.
Screenshots
If applicable, add screenshots to help explain your problem.
System configuration (please fill in):

OS: Windows 10
GPU: RTX 2080 SUPER
Chainloading: arcdps -> GW2Radial

Additional context
This issue occurred on v2.1.0.
All other functionality of the addon appears to work correctly.

This seems to be a duplicate of the issue that was being tracked here: https://github.com/Friendly0Fire/GW2Radial/issues/229
A fix was committed and will probably come in the next release.

Duplicate of #229


Update ecosystem map to include eCR

User story
As a ReportStream team member, I want to understand, and visually map how ELR and eCR data flows overlap and/or diverge on the ReportStream Ecosystem Map, so that I can build upon our teams' systems-level knowledge base and potentially chart areas for opportunity or exploration.
Background & context
Since conducting FQHC research, we've learned a little more about how senders conduct case reporting (and in some case, electronic case reporting). We'd like to continue to deepen our understanding of eCR as a data type and separate process from ReportStream through a systems-level lens. This ticket captures the work of conducting desk research and updating our existing artifact (RS Ecosystem Map) to reflect our learnings.
Risks/Impacts/Considerations

Unbalancing or disrupting the original intended flow of the ecosystem map
Creating a map that is too large or unwieldy to help make sense of different systems

Open questions

What are the critical difference(s) between ELR and eCR?
How is eCR collected? How is it sent?
Who are eCR Senders? Who are eCR Receivers?
How can we make space for different data systems/flows and pipelines?
How can we scale the Ecosystem Map to seamlessly and clearly add space for different data types?

Links

Create relationship map for the ecosystem ReportStream lives in #9191
[WIP] ReportStream Ecosystem Map
ReportStream Ecosystem Map (Clean version)

Acceptance criteria

[x] Ecosystem map is updated to include eCR data flows from Senders to Receivers

Out of scope

Formally establishing any other data types (i.e. vaccinations, ADT, etc.)

Began working on an updated ecosystem map version in the WIP space. cc @Andrey-Glazkv @jillian-hammer @jsutantio

Finished a draft of the updated ecosystem map within the WIP space after feedback from @jsutantio. For final review (and if needed, a successive "iteration" ticket), @jsutantio should probably again be approver. I can set a second meeting if that makes sense with her. cc @Andrey-Glazkv @jillian-hammer

I'll be discussing this work with @jsutantio on Thursday and bringing to Design Crit also on Thursday. So I am hoping to finalize and close this ticket by EoW.

cc @Andrey-Glazkv this ticket isn't finished quite yet - I'll put it back into "In Progress". Hoping to close out by EoW.

Review of eCR materials is complete! Thank you to @jsutantio for the final review. The eCR data flow artifact can be found in the ReportStream Ecosystem Map Mural, floating the right of the map. As such, this ticket can now be closed. cc @Andrey-Glazkv @jillian-hammer


[Bug/Feature] Allow msgpack as response type (de-serialize to JSON)

Currently, getting a response that is msgpack formatted results in the application window just going completely blank (at least that is what is happening for me). Likely a result of Bruno trying to parse the response as text.
It would be nice if it could detect msgpack (from Content-Type) or even an option to select the response format, and have it converted to JSON for readability.

can you add this as a hacktober? if you possible can you guide / elaborate issue more and provide some possible solution which you have in mind; and dont forget to assign me : )

can you add this as a hacktober?

you mean label as hacktober ?

yeah like it can count in my PR

can you add this as a hacktober? if you possible can you guide / elaborate issue more and provide some possible solution which you have in mind; and dont forget to assign me : )

To elaborate:
msgpack is a binary serialization format: "It's like JSON. but fast and small."
Currently bruno cannot handle a response in msgpack format, it essentially crashes. It doesn't actually crash, but the UI disappears, for me at least.
I see two possible solutions, though I don't know which one is better.
One solution is the ability to select a format for the received data (or at least a suggestion: parsing may fail if incorrect).
For example: you tell Bruno the received format is msgpack, but it displays it as JSON.
I suppose you should be able to see the raw binary data in some form as well without it crashing.
Another option would be automatic parsing if the response headers contain Content-Type: application/x-msgpack, although that may be a bit messy just because it is not an official registered MIME type.

I want to give it a try , can you assign it to me?

We don't have a process to assign issues. You can pick it up and post details as you make progress.
Once ready, please raise a PR.


Make FAB disappear on scroll

Here's what I tried: https://lambdasoup.com/post/fab_behavior_sync_appbarlayout/

Now fixed.


Add example - Keras Reproducibility Recipes

I am eager to write a tutorial for Keras Reproducibility Recipes. Here are some questions for this:

Do we always use the latest version of Keras in the examples? Because there will be some differences in reproducibility techniques between Keras versions (version <= Keras 2.7 and version > 2.7)

Does this fall under the category of quick recipes?i

When I have the confirmation, I can start writing this guide.

@Frightera ,

Yes, you should use the latest Keras. And 2.7 is really old now.

Can you provide more details on what you mean by Keras Reproducibility Recipes?

Thanks!

@hertschuh
I got the idea from: https://github.com/keras-team/keras-io/blob/master/call_for_contributions.md#keras-reproducibility-recipes
My goal is to provide a comprehensive guide that enables users to obtain consistent results in every run of their deep learning experiments. This issue is frequently raised in various Stackoverflow questions.

Ok, yeah, I think quick recipes makes sense, but we can see once it's ready if it fits better somewhere else.
Thank you for contributing!

Hi @hertschuh,
PR is ready.

Closing as PR is merged.


Not working on Android 11

Hi, thanks for your great plugin.
It working great on androids under 11, but it not working on android 11.
Can you help me to fix this on android 11?

Hi, same problem here. In my case, I actually observed this problem on some phones with Android 10, either.
Have you found any solution?

Same problem here. Just updated to Android 11. Seems like the MainActivity class is missing?
Here's a snapshot of the Android Logcat error that I'm getting at least.

Hey, I could actually found the solution in this thread:
https://stackoverflow.com/questions/45992420/speechrecognizer-bind-to-recognition-service-failed
Just add this lines to your unityLibrary/manifests/AndroidManifest.xml:
<queries>         <package android:name="com.google.android.googlequicksearchbox"/> </queries>

The update needed is in \SpeechToText_AppleAPI\Assets\Plugins\Android\AndroidManifest.xml
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
          package="com.starseed.speechtotext"
		  android:versionCode="1" android:versionName="1.0">
  <uses-permission android:name="android.permission.RECORD_AUDIO" />
  <uses-permission android:name="android.permission.INTERNET" />
  <uses-sdk android:minSdkVersion="16" android:targetSdkVersion="23" />
    <queries>
        <package android:name="com.google.android.googlequicksearchbox"/>
    </queries>
    <application
    android:icon="@drawable/app_icon"
    android:label="@string/app_name" android:debuggable="false"
    android:configChanges="orientation|screenSize|locale"
    android:theme="@style/UnityThemeSelector">
    <activity
      android:name="com.starseed.speechtotext.MainActivity"
	    android:configChanges="keyboard|keyboardHidden|orientation|screenLayout|uiMode|screenSize|smallestScreenSize"
      android:label="@string/app_name">
      <intent-filter>
        <action android:name="android.intent.action.MAIN" />
        <category android:name="android.intent.category.LAUNCHER" />
      </intent-filter>
    </activity>
  </application>
</manifest>

The update needed is in \SpeechToText_AppleAPI\Assets\Plugins\Android\AndroidManifest.xml
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
          package="com.starseed.speechtotext"
		  android:versionCode="1" android:versionName="1.0">
  <uses-permission android:name="android.permission.RECORD_AUDIO" />
  <uses-permission android:name="android.permission.INTERNET" />
  <uses-sdk android:minSdkVersion="16" android:targetSdkVersion="23" />
    <queries>
        <package android:name="com.google.android.googlequicksearchbox"/>
    </queries>
    <application
    android:icon="@drawable/app_icon"
    android:label="@string/app_name" android:debuggable="false"
    android:configChanges="orientation|screenSize|locale"
    android:theme="@style/UnityThemeSelector">
    <activity
      android:name="com.starseed.speechtotext.MainActivity"
	    android:configChanges="keyboard|keyboardHidden|orientation|screenLayout|uiMode|screenSize|smallestScreenSize"
      android:label="@string/app_name">
      <intent-filter>
        <action android:name="android.intent.action.MAIN" />
        <category android:name="android.intent.category.LAUNCHER" />
      </intent-filter>
    </activity>
  </application>
</manifest>

Hey, I could actually found the solution in this thread:
https://stackoverflow.com/questions/45992420/speechrecognizer-bind-to-recognition-service-failed
Just add this lines to your unityLibrary/manifests/AndroidManifest.xml:
<queries>
 <package android:name="com.google.android.googlequicksearchbox"/>
</queries>

Hey, I could actually found the solution in this thread:
https://stackoverflow.com/questions/45992420/speechrecognizer-bind-to-recognition-service-failed
Just add this lines to your unityLibrary/manifests/AndroidManifest.xml:
<queries>
 <package android:name="com.google.android.googlequicksearchbox"/>
</queries>

Due to adding these lines There is an issue during build "package key is missing"

The update needed is in \SpeechToText_AppleAPI\Assets\Plugins\Android\AndroidManifest.xml
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
          package="com.starseed.speechtotext"
		  android:versionCode="1" android:versionName="1.0">
  <uses-permission android:name="android.permission.RECORD_AUDIO" />
  <uses-permission android:name="android.permission.INTERNET" />
  <uses-sdk android:minSdkVersion="16" android:targetSdkVersion="23" />
    <queries>
        <package android:name="com.google.android.googlequicksearchbox"/>
    </queries>
    <application
    android:icon="@drawable/app_icon"
    android:label="@string/app_name" android:debuggable="false"
    android:configChanges="orientation|screenSize|locale"
    android:theme="@style/UnityThemeSelector">
    <activity
      android:name="com.starseed.speechtotext.MainActivity"
	    android:configChanges="keyboard|keyboardHidden|orientation|screenLayout|uiMode|screenSize|smallestScreenSize"
      android:label="@string/app_name">
      <intent-filter>
        <action android:name="android.intent.action.MAIN" />
        <category android:name="android.intent.category.LAUNCHER" />
      </intent-filter>
    </activity>
  </application>
</manifest>

Hey, I could actually found the solution in this thread:
https://stackoverflow.com/questions/45992420/speechrecognizer-bind-to-recognition-service-failed
Just add this lines to your unityLibrary/manifests/AndroidManifest.xml:
<queries>
 <package android:name="com.google.android.googlequicksearchbox"/>
</queries>

Hey, I could actually found the solution in this thread:
https://stackoverflow.com/questions/45992420/speechrecognizer-bind-to-recognition-service-failed
Just add this lines to your unityLibrary/manifests/AndroidManifest.xml:
<queries>
 <package android:name="com.google.android.googlequicksearchbox"/>
</queries>

Due to adding these lines There is an issue during build "package key is missing"

how will i  solve this build error

Hi there, I seem to be having the same issues as everyone else, but the solutions don't seem to be doing anything.  Regarding upgrading the gradle version, the video says to upgrade it to 3.4.3, but on the latest version of unity it's already at 4.0.1, and going backwards doesn't seem to help.  I have also copy and pasted the exact xml code to AndroidManifest.xml, so I've really seemed to hit a roadblock.  Right now all I'm trying to do is to run the sample scene on my android phone and not get the  Error Unity java.lang.ClassCastException: com.unity3d.player.UnityPlayerActivity cannot be cast to com.starseed.speechtotext.MainActivity error.  Any help would go a long ways.  Thanks so much.
"

Hi there, I seem to be having the same issues as everyone else, but the solutions don't seem to be doing anything. Regarding upgrading the gradle version, the video says to upgrade it to 3.4.3, but on the latest version of unity it's already at 4.0.1, and going backwards doesn't seem to help. I have also copy and pasted the exact xml code to AndroidManifest.xml, so I've really seemed to hit a roadblock. Right now all I'm trying to do is to run the sample scene on my android phone and not get the Error Unity java.lang.ClassCastException: com.unity3d.player.UnityPlayerActivity cannot be cast to com.starseed.speechtotext.MainActivity error. Any help would go a long ways. Thanks so much. "

https://github.com/j1mmyto9/Speech-And-Text-Unity-iOS-Android/issues/61

After I unchecked the Minify option, it worked.

After adding

Hello All,
In android 11 we got this issue, after searching a lot we found that by adding the below lines it should work :
Error : Error SpeechRecognizer bind to recognition service failed
but our app was crashed & we got a new error please check the attached screenshot
Can anyone help us to fix it?

Hello All, In android 11 we got this issue, after searching a lot we found that by adding the below lines it should work : Error : Error SpeechRecognizer bind to recognition service failed
but our app was crashed & we got a new error please check the attached screenshot Can anyone help us to fix it?

hi, did you manage to resolve this one your end? i'm facing this exact same error

Hello All, In android 11 we got this issue, after searching a lot we found that by adding the below lines it should work : Error : Error SpeechRecognizer bind to recognition service failed
but our app was crashed & we got a new error please check the attached screenshot Can anyone help us to fix it?

hi, did you manage to resolve this one your end? i'm facing this exact same error

Hi, MaekellMina.
No unfortunately we haven't manage to resolve the issue from our end, we are exploring the other possible solutions at this stage. Will keep you posted if we find anything useful.

Hi,
I have found a fix for this issue. Reason for crash was in MainActivity.java
@Override
public void onResults(Bundle results) {
      ArrayList<String> text = results.getStringArrayList(SpeechRecognizer.RESULTS_RECOGNITION);
     /// Reason was  `text`  is null.
     UnityPlayer.UnitySendMessage("SpeechToText", "onResults", text.get(0));// this line was crashing.
}

so I added a null check like this and built a new SpeechToTextPlugin.jar
@Override
public void onResults(Bundle results) {
    ArrayList<String> text = results.getStringArrayList(SpeechRecognizer.RESULTS_RECOGNITION);
    if(text!=null && !text.isEmpty())
    {
         UnityPlayer.UnitySendMessage("SpeechToText", "onResults", text.get(0));
    }else {
        UnityPlayer.UnitySendMessage("SpeechToText", "onResults", "null recieved");
    }
}

Solution:

Download and extract following zip file.
SpeechToTextPlugin.zip
Replace new SpeechToTextPlugin.jar in 'Assets\Plugins\Android' folder.


Current version of linuxkit has no build option

Description
Invoking make produces the following error:
$ make all KUBE_RUNTIME=cri-containerd
curl -L -o kube-weave.yaml https://cloud.weave.works/k8s/v1.8/net?v=v2.0.5
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
Dload  Upload   Total   Spent    Left  Speed
100   274  100   274    0     0    254      0  0:00:01  0:00:01 --:--:--   254
100  6956  100  6956    0     0   5296      0  0:00:01  0:00:01 --:--:--  5296
linuxkit build -name kube-master -format iso-efi yml/kube.yml yml/cri-containerd.yml yml/cri-containerd-master.yml yml/weave.yml
"build" is not valid command.
USAGE: linuxkit [options] COMMAND
Commands:
metadata    Metadata utilities
push        Push a VM image to a cloud or image store
run         Run a VM image on a local hypervisor or remote cloud
version     Print version information
help        Print this message
Run 'linuxkit COMMAND --help' for more information on the command
Options:
-q	Quiet execution
-v	Verbose execution
make: *** [kube-master.iso] Error 1
$

Steps to reproduce the issue:
Just run make again
Describe the results you received:
See above
Describe the results you expected:
Successful build
Additional information you deem important (e.g. issue happens only occasionally):
$ linuxkit version
linuxkit version 0.0
commit: 92947c9c417d703c491711f23d00ceb9f53df5b0
$
I assume you now need buildkit to build images.

The current version does have build. How did you install LinuxKit?
On 27 Nov 2017 20:49, "Martin Borgman"<EMAIL_ADDRESS>wrote:

Description
Invoking make produces the following error:
$ make all KUBE_RUNTIME=cri-containerd
curl -L -o kube-weave.yaml https://cloud.weave.works/k8s/v1.8/net?v=v2.0.5
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 274 100 274 0 0 254 0 0:00:01 0:00:01 --:--:-- 254
100 6956 100 6956 0 0 5296 0 0:00:01 0:00:01 --:--:-- 5296
linuxkit build -name kube-master -format iso-efi yml/kube.yml
yml/cri-containerd.yml yml/cri-containerd-master.yml yml/weave.yml
"build" is not valid command.
USAGE: linuxkit [options] COMMAND
Commands:
metadata Metadata utilities
push Push a VM image to a cloud or image store
run Run a VM image on a local hypervisor or remote cloud
version Print version information
help Print this message
Run 'linuxkit COMMAND --help' for more information on the command
Options:
-q Quiet execution
-v Verbose execution
make: *** [kube-master.iso] Error 1
$
Steps to reproduce the issue:
Just run make again
Describe the results you received:
See above
Describe the results you expected:
Successful build
Additional information you deem important (e.g. issue happens only
occasionally):
$ linuxkit version
linuxkit version 0.0
commit: 92947c9c417d703c491711f23d00ceb9f53df5b0
$
I assume you now need buildkit to build images.
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/linuxkit/kubernetes/issues/12, or mute the thread
https://github.com/notifications/unsubscribe-auth/AAdcPGATI2s4OFniDeHb9TOON2i_bvWGks5s6yBjgaJpZM4QsRej
.

You have to upgrade your linuxkit cli because the script switched to linuxkit build which is missing in the usage output.

commit: 92947c9c417d703c491711f23d00ceb9f53df5b0

But:
$ pwd
/home/ijc/development/Docker/LinuxKit/gocode/src/github.com/linuxkit/linuxkit
$ git remote update -p
Fetching origin
Fetching ijc
$ git show 92947c9c417d703c491711f23d00ceb9f53df5b0
fatal: bad object 92947c9c417d703c491711f23d00ceb9f53df5b0

So I don't know what version this is or where it came from.
Please update to the latest linuxkit binary (newer than https://github.com/linuxkit/linuxkit/pull/2761) which does have the build subcommand.
Closing for now, if the upgrade doesn't help we can reopen and revisit.

The linuxkit version was installed using homebrew with --HEAD.
The installed version was linuxkit/HEAD-0df77e4.
However there is a rather annoying issue with homebrew. If you ever install something using --HEAD, it doesn't get updated using brew update && brew upgrade.
I reinstalled linuxkit and the current version is linuxkit/HEAD-c6fad9f or:
$ linuxkit version
linuxkit version 0.0
commit: c6fad9f76b9dd5208a3e9b70785de7d77a2a1713

This version of linuxkit has the build option so you can close this issue.

Yes, you need to do brew reinstall --HEAD linuxkit it is annoying. We will do a release version soon which should make this easier...


Model keeps output "there is no sound/ I can not hear anything" when there is actual sound

Thanks for the great work!
I tested different videos with sound (speech, music, noise etc) using the online demo, but the model keeps ignore the sound information no matter how I asked in the prompt explicitly or implicitly. Is it because the audio file of the video is not loaded properly? Can you help provide any hints here?  Anyone else has the same issue?

Sorry for the confusion. The currently available models do not include audio branch and therefore they will not take audio as input.

Thanks for your attention! You can switch to the audio_visual branch (https://github.com/DAMO-NLP-SG/VideoLLaMA2/tree/audio_visual) and clone the repository to run inference for audio related tasks.

Thanks for the great work!
I tested different videos with sound (speech, music, noise etc) using the online demo, but the model keeps ignore the sound information no matter how I asked in the prompt explicitly or implicitly. Is it because the audio file of the video is not loaded properly? Can you help provide any hints here? Anyone else has the same issue?

Hello,I'm a phD student from ZJU, I also use videollama2 to do my own research,we create a WeChat group to discuss some issues of videollama2 and help each other,could you join us? Please contact me: WeChat number == LiangMeng19357260600, phone number == +86<PHONE_NUMBER>0,e-mail ==<EMAIL_ADDRESS>


Fix typescript output, make build and dev faster by using esbuild/vitest

Summary
The typescript output was actually just es code output, not types. This fixes it so that the output is the actual declarations.
Key Changes

Change build system to use esbuild, which is significantly faster
Change test system to use vitest, which is significantly faster
Change playground to use vite
Fix typescript output
Bump package versions

@jonluca Could you solve the conflicts please?

Done

I'm not sure why it's failing, that commit isn't in this PR I believe?

Recreated it here with only one commit https://github.com/KarimMokhtar/react-drag-drop-files/pull/89


Add support to export code as PDF

I really like your website, it's straightforward and beautiful.
As a user,
I want to export the code to a PDF file,
So people who view the exported code in the PDF file, can also select the text and copy it easily (not just to view, as in a PNG file).
The PDF size should be the size of the
 element (and not a regular page size (e.g. A4, A3...)).

@chrisdola Thanks for the links to other code -> PDF tools, I wasn't aware of these.
However, I've checked a few out, but haven't found anything with quite the same styling as this site.
I really like the way the code looks here. Are there any tools with similar styling?
Because code -> PDF option isn't available here, I did it manually exporting
 to PDF, see:
Hands-on Generative AI.pdf
So something similar, but automated, would be amazing! Think it's possible?

One really simple option for now is to export it as a png with no background and then use an online png -> pdf converter.
It is definitely possible to add a feature to export as a PDF, though. Will play around with it.


In examples RTU server i'm only able to read nine registers not ten as expected

Describe the bug
During my test with your code exaple RTUserver on a ESP32 i can´t read register 00
Staring from register 01 works fine.
I can max read nine registers but i was expecting to be able reading ten registers.
please see my data log as bellow:
[RTU]>Tx > 16:45:48:762 - 01  03  00  01  00  09  D4  0C
[RTU]>Rx > 16:45:48:800 - 01  03  12  00  01  00  02  00  03  00  04  00  05  00  06  00  07  00  08  00  09  9C  B4
[RTU]>Tx > 16:46:55:237 - 01  03  00  00  00  09  85  CC
[RTU]>Rx > 16:46:55:268 - 01  83  02  C0  F1
Sys > 16:46:55:268 - Error : Illegal data address
RTU]>Tx > 17:13:29:646 - 01  03  00  00  00  0A  C5  CD
[RTU]>Rx > 17:13:29:666 - 01  83  02  C0  F1
Sys > 17:13:29:666 - Error : Illegal data address

This is according to the Modbus standard - address 1 is supposed to be the lowest possible address. This is guarded by the following code in the FC03 worker function in the example:
// Address and words valid? We assume 10 registers here for demo
  if (address && words && (address + words) <= 10) {
    // Looks okay. Set up message with serverID, FC and length of data

address needs to have a value != 0 for the if clause to be true.
In removing the very first condition you can change the behaviour.

Thank you for fast responce and making this powerful tool eMODBUS.


Support multicast streams through udpxy in the custom provider

Providers like init7 (www.init7.ch) have a multicast TV offering, and provide a m3u playlist with multicast groups in it (e.g.: https://api.init7.net/tvchannels.m3u).
this (set) of changes add support for generic multicast IPTV through the custom provider.

What's the configuration for this look like?

A bare minimum of
[[Source]]
Name = "test"
Provider = "Custom"
M3U = "https://api.init7.net/tvchannels.m3u"
Udpxy = "<udpxy_url>" (e.g.: http:localhost:6077)
would trigger the new behaviour.
A slight downside of using a single providers.Configuration is that every provider configuration will be able to specify a Udpxy entry like the above, but won't have any use for it.

Guys if this is accepted, let me know what is needed to integrate it to new docker architecture

What I don't understand about this is where that udpxy comes from.  Your example points to telly itself, but telly doesn't have any routes to /udp/.  Maybe I just don't understand some simple thing.

@chazlarson in this case udpxy it's exposed locally on the 6077 port
Anyway it would be configurable and allow any remote address.

So udpxy is a thing you set up separately, correct?  this thing? http://www.udpxy.com/
That's the missing piece.  What confused me here is that the configuration example given is using telly's own port [6077], and telly isn't publishing a udp proxy.  This pull request doesn't add anything to routes, so it seemed like maybe I was missing something obvious.
Also, the example given shows an invalid URL, which I'm assuming is a typo.  Looking at the code I'm thinking that setting should be localhost:6077 without the http:, since the example as shown would result in the URL http://http:localhost:6077/udp/@<IP_ADDRESS>:5000/. which seems wrong.

So udpxy is a thing you set up separately, correct? this thing? http://www.udpxy.com/

Yes, sorry, correct.

That's the missing piece. What confused me here is that the configuration example given is using telly's own port [6077], and telly isn't publishing a udp proxy. This pull request doesn't add anything to routes, so it seemed like maybe I was missing something obvious.
Also, the example given shows an invalid URL, which I'm assuming is a typo. Looking at the code I'm thinking that setting should be localhost:6077 without the http:, since the example as shown would result in the URL http://http:localhost:6077/udp/@<IP_ADDRESS>:5000/. which seems wrong.

correct again - I've fixed the snippets above (and used a less confusing port).

@chazlarson - so what are your thoughts on this?

Seems good; I’m just worried about documentation and support, as it’s not a thing I’m very familiar with.


Bump versions

Bumps dependabot versions

Codecov Report

Merging #203 (7b48f32) into main (c76d1f0) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main     #203   +/-   ##
=======================================
  Coverage   20.09%   20.09%
=======================================
  Files          12       12
  Lines        1612     1612
=======================================
  Hits          324      324
  Misses       1257     1257
  Partials       31       31

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General features inquiry

Wanted to ask a few questions about feature support:
-is there in-built tracking?
-native mobile support?
-is there a test runner like karma?

what's the bundle/library size?

what's the bundle/library size?

https://www.npmjs.com/package/plyr
Unpacked Size
7.2 MB
Total Files
122


Version bump the Google Python package to 2.3.1.

Current coverage is 98.36% (diff: 100%)

Merging #362 into development will not change coverage

@@           development       #362   diff @@
=============================================
  Files               38         38
  Lines             2989       2989
  Methods              0          0
  Messages             0          0
  Branches           146        146
=============================================
  Hits              2940       2940
  Misses              43         43
  Partials             6          6

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Error: listen EADDRINUSE

hey @arunoda, this is a new issue that just started happening. Seeing this in the mup logs

Error: listen EADDRINUSE
at errnoException (net.js:905:11)
at Server._listen2 (net.js:1043:14)
at listen (net.js:1065:10)
at net.js:1147:9
at dns.js:72:18
at process._tickCallback (node.js:442:13)
error: Forever detected script exited with code: 7
error: Script restart attempt #54

Looks like there are multiple node processes. One by root and another by meteorus

The only way to stop it is to power cycle the droplet (digital ocean).
Not sure what's going on here. Any ideas? Here's my mup.json
{
    // Server authentication info
    "servers": [{
        "host": "<ip>",
        "username": "root",
        "password": "blah"
        // or pem file (ssh based authentication)
        //"pem": "~/.ssh/id_rsa"
    }],

    // Install MongoDB in the server, does not destroy local MongoDB on future setup
    "setupMongo": true,

    // WARNING: Node.js is required! Only skip if you already have Node.js installed on server.
    "setupNode": true,

    // WARNING: If nodeVersion omitted will setup 0.10.31 by default. Do not use v, only version number.
    "nodeVersion": "0.10.36",

    // Install PhantomJS in the server
    "setupPhantom": true,

    // Application name (No spaces)
    "appName": "aoscale1",

    // Location of app (local directory)
    "app": "/Users/landland/Documents/development/ao",

    // Configure environment
    "env": {
        "PORT": 80,
        // "ROOT_URL": "<ip>",
        "MONGO_URL": "<mongourl>",
        "MONGO_OPLOG_URL": "<oplog>",
        "MAIL_URL": "<mail>",
        "DISABLE_WEBSOCKETS": 1
    },

    // Meteor Up checks if the app comes online just after the deployment
    // before mup checks that, it will wait for no. of seconds configured below
    "deployCheckWaitTime": 15
}

I upgraded to latest mup version but same thing.

I am getting the same error after upgrade Meteor to the latest version (<IP_ADDRESS>).  Then I tried upgrading to the latest mup version and node.js (0.10.36) and still no luck.  Not sure how I can debug this further.

Okay, my error is resolved.  Apparently, I had problem trying to deploy and run the app, and had to change the port to 80 (thinking that was the problem).  Then I upgraded mup and node.js to 0.10.36 and didn't change the port back to 3001.  Once I did that, it worked.  My EADDRINUSE problem was simply because it was set to port 80 (used by Nginx of course).  Duh.

@benlwong can you elaborate on how you resolved this? Do you mean you changed the env.PORT in mup.json to 3001 and that fixed your problem? That doesn't seem to fix it over here for me.

Sure, but honestly, I am a little fuzzy what all happened.  Basically, after I upgraded Meteor to <IP_ADDRESS> and tried to deploy the app with mup, I started getting an error that said "Meteor requires Node v0.10.36 or later.  I then tried to upgrade node.js using mup but it was giving some error about port 3001 being used.  That's when I tried to change the port to 80 in the mup.json file.  The other problem I was struggling with was mup doesn't appear to able to upgrade node.js.    After I finally got it upgraded to 0.10.36 and make sure there is no duplicate node.js install on the box, I started getting the EADDRINUSE error.  I finally realized that I switched the port 80 to 3001 earlier and it was conflicting with Nginx since it is using port 80 to serve the web.  After I switched it back to 3001, everything was working again.
Are you using Nginx as the web server?  If so, then I think your mup.json file should have 3001 as the port (not 80).

This is happening on all of my servers, even the ones without nginx so I don't think it is related. But I did change the port on all of them to 3001 and other values to no avail.
But I did follow similar steps as you. Upgraded Meteor, then upgraded node to v0.10.36 and deployed. I have done this in the past without any issues though, so I'm not sure what's different this time.

@benlwong if you deploy a second time, is everything ok? It is pretty consistent over here that if I deploy one time using a port (say 3001), everything is fine. If I deploy again on port 3001, I get the same issue.

Yes, I redeployed a couple times yesterday and all seem to be fine.  I noticed you have "setupNode" as true, not sure that might be an issue.  Probably not, but just throwing some ideas out there.

Ah good find, but that wasn't it. Rats! Back to the drawing board. @arunoda any clues?

Sorry, I tried!  Let us know when you figured it out.  BTW, have you try running the deploy in debug mode (e.g. DEBUG=* mup deploy)?  Good luck!

right, I should have added that

Meteor Up: Production Quality Meteor Deployments
------------------------------------------------

Building Started: /Users/landland/Documents/development/aoprocessor

Started TaskList: Deploy app 'aoprocessor' (linux)
[ip] - Uploading bundle
  nodemiral:sess:ip copy file - src: /tmp/7301eade-62c8-4da8-865f-a447244ef04f/bundle.tar.gz, dest: /opt/aoprocessor/tmp/bundle.tar.gz, vars: undefined +0ms
  nodemiral:sess:ip spawning command- sshpass -p pw scp -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null /tmp/7301eade-62c8-4da8-865f-a447244ef04f/bundle.tar.gz root@ip:/opt/aoprocessor/tmp/bundle.tar.gz +1ms
  nodemiral:sess:ip spawn completed - code: 0 -
	stdout:  		stderr +30s Warning: Permanently added 'ip' (RSA) to the list of known hosts.

[ip] ✔ Uploading bundle: SUCCESS
[ip] - Setting up Environment Variables
  nodemiral:sess:ip copy file - src: /usr/local/lib/node_modules/mup/templates/linux/env.sh, dest: /opt/aoprocessor/config/env.sh, vars: {"env":{"PORT":3000,"ROOT_URL":"http://blah","MONGO_URL":"mongodb://private","MONGO_OPLOG_URL":"mongodb://oplog","MAIL_URL":"smtp://mail/","METEOR_SETTINGS":"{\"public\":{}}","CLUSTER_ENDPOINT_URL":"http://ip:3000"},"appName":"aoprocessor"} +2ms
  nodemiral:sess:ip spawning command- sshpass -p pw scp -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null /tmp/axUMvzwrWXTLYeKxR root@ip:/opt/aoprocessor/config/env.sh +4ms
  nodemiral:sess:ip spawn completed - code: 0 -
	stdout:  		stderr +25s Warning: Permanently added 'ip' (RSA) to the list of known hosts.

[ip] ✔ Setting up Environment Variables: SUCCESS
[ip] - Invoking deployment process
  nodemiral:sess:ip execute - command: #!/bin/bash

# utilities
gyp_rebuild_inside_node_modules () {
  for npmModule in ./*; do
    cd $npmModule

    isBinaryModule="no"
    # recursively rebuild npm modules inside node_modules
    check_for_binary_modules () {
      if [ -f binding.gyp ]; then
        isBinaryModule="yes"
      fi

      if [ $isBinaryModule != "yes" ]; then
        if [ -d ./node_modules ]; then
          cd ./node_modules
          for module in ./*; do
            cd $module
            check_for_binary_modules
            cd ..
          done
          cd ../
        fi
      fi
    }

    check_for_binary_modules

    if [ $isBinaryModule == "yes" ]; then
      echo " > $npmModule: npm install due to binary npm modules"
      rm -rf node_modules
      if [ -f binding.gyp ]; then
        sudo npm install
        sudo node-gyp rebuild || :
      else
        sudo npm install
      fi
    fi

    cd ..
  done
}

rebuild_binary_npm_modules () {
  for package in ./*; do
    if [ -d $package/node_modules ]; then
      cd $package/node_modules
        gyp_rebuild_inside_node_modules
      cd ../../
    elif [ -d $package/main/node_module ]; then
      cd $package/node_modules
        gyp_rebuild_inside_node_modules
      cd ../../../
    fi
  done
}

revert_app (){
  if [[ -d old_app ]]; then
    sudo rm -rf app
    sudo mv old_app app
    sudo stop aoprocessor || :
    sudo start aoprocessor || :

    echo "Latest deployment failed! Reverted back to the previous version." 1>&2
    exit 1
  else
    echo "App did not pick up! Please check app logs." 1>&2
    exit 1
  fi
}

# logic
set -e

TMP_DIR=/opt/aoprocessor/tmp
BUNDLE_DIR=${TMP_DIR}/bundle

cd ${TMP_DIR}
sudo rm -rf bundle
sudo tar xvzf bundle.tar.gz > /dev/null
sudo chown -R ${USER} ${BUNDLE_DIR}

# rebuilding fibers
cd ${BUNDLE_DIR}/programs/server

if [ -d ./npm ]; then
  cd npm
  rebuild_binary_npm_modules
  cd ../
fi

if [ -d ./node_modules ]; then
  cd ./node_modules
  gyp_rebuild_inside_node_modules
  cd ../
fi

if [ -f package.json ]; then
  # support for 0.9
  sudo npm install
else
  # support for older versions
  sudo npm install fibers
  sudo npm install bcrypt
fi

cd /opt/aoprocessor/

# remove old app, if it exists
if [ -d old_app ]; then
  sudo rm -rf old_app
fi

## backup current version
if [[ -d app ]]; then
  sudo mv app old_app
fi

sudo mv tmp/bundle app

#wait and check
echo "Waiting for MongoDB to initialize. (5 minutes)"
. /opt/aoprocessor/config/env.sh
wait-for-mongo ${MONGO_URL} 300000

# restart app
sudo stop aoprocessor || :
sudo start aoprocessor || :

echo "Waiting for 15 seconds while app is booting up"
sleep 15

echo "Checking is app booted or not?"
curl localhost:${PORT} || revert_app

# chown to support dumping heapdump and etc
sudo chown -R meteoruser app
 +6ms
  nodemiral:sess:ip spawning command- sshpass -p pw ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null root@ip "bash -s" < /tmp/5FvT4TNqcDPJkdgIf +1ms
  nodemiral:sess:ip spawn completed - code: 0 -
	stdout:
><EMAIL_ADDRESS>install /opt/aoprocessor/tmp/bundle/programs/server/node_modules/fibers
> node ./build.js

`linux-x64-v8-3.14` exists; testing
Binary is fine; exiting
<EMAIL_ADDRESS>node_modules/underscore

<EMAIL_ADDRESS>node_modules/eachline
└──<EMAIL_ADDRESS>
<EMAIL_ADDRESS>node_modules/semver

<EMAIL_ADDRESS>node_modules/chalk
├──<EMAIL_ADDRESS>├──<EMAIL_ADDRESS>├──<EMAIL_ADDRESS>├──<EMAIL_ADDRESS><EMAIL_ADDRESS>└──<EMAIL_ADDRESS><EMAIL_ADDRESS>
<EMAIL_ADDRESS>node_modules/source-map-support
└──<EMAIL_ADDRESS><EMAIL_ADDRESS>
<EMAIL_ADDRESS>node_modules/fibers
Waiting for MongoDB to initialize. (5 minutes)
connected
aoprocessor stop/waiting
aoprocessor start/running, process 1400
Waiting for 15 seconds while app is booting up
Checking is app booted or not?
<!DOCTYPE html>
<html>
<head>

<script type="text/javascript">__meteor_runtime_config__ = JSON.parse(decodeURIComponent("%7B%22meteorRelease%22%3A%22METEOR%4<IP_ADDRESS>%22%2C%22PUBLIC_SETTINGS%22%3A%7B%7D%2C%22ROOT_URL%22%3A%22http%3A%2F%2Fip%22%2C%22ROOT_URL_PATH_PREFIX%22%3A%22%22%2C%22autoupdateVersion%22%3A%22baa1dd1c6ebdba8937b55153407e0928eb4622df%22%2C%22autoupdateVersionRefreshable%22%3A%225a561270f831ad5b029b48f1f1659e7682941b28%22%2C%22autoupdateVersionCordova%22%3A%22none%22%2C%22kadira%22%3A%7B%22appId%22%3A%22TSr2EPyJLEcdNNdjc%22%2C%22endpoint%22%3A%22https%3A%2F%2Fengine.kadira.io%22%2C%22clientEngineSyncDelay%22%3A10000%2C%22enableErrorTracking%22%3Atrue%7D%7D"));</script>

  <script type="text/javascript" src="/89e4f228491ea5d4075f531def8b13e24e1b31f1.js"></script>

<title>Hiiiiiiiii</title>
</head>
<body>

</body>
</html>
 		stderr +44s Warning: Permanently added 'ip' (RSA) to the list of known hosts.
npm WARN package.json<EMAIL_ADDRESS>No description
npm WARN package.json<EMAIL_ADDRESS>No repository field.
npm WARN package.json<EMAIL_ADDRESS>No README data
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   864    0   864    0     0  14821      0 --:--:-- --:--:-- --:--:-- 14896

[ip] ✔ Invoking deployment process: SUCCESS
Completed TaskList: Deploy app 'aoprocessor' (linux)

I'm not seeing anything out of the ordinary, but yeah this results in the issue regardless.

I resolved this by deleting the app on the server, running a mup setup and then deploy. Unfortunately I won't be able to help troubleshoot any further as the issue is resolved on my end, so I'll close this.


Ambiguous use of 'map(JSONObject:)'

I am getting error "Ambiguous use of 'map(JSONObject:)'" and "Module 'Alamofire' has no member named 'request'" and "Generic type 'DataResponse' specialized with too few type parameters (got 1, but expected 2)".
I am using below in pod files
pod 'Alamofire', '~> 5.0.0-rc.3'
pod 'AlamofireObjectMapper', :git => 'https://github.com/RomanPodymov/AlamofireObjectMapper.git'
I am using xCode 11.2.1 and swift 5.
Can you please provide solution to this ? I am stuck on this issue since last 5 days and not able to  fix it.
I am attaching image too.

You need to update your code for the Alamofire 5 RC releases, where we made the various *Response types doubly generic. You may also need to fix AlamofireObjectMapper (which is not an Alamofire project).

Do I need to use pod 'Alamofire', '~> 5.0.0-rc.3' ? What is the compatible AlamofireObjectMapper version with this ?

Please share updated pod details.

AlamofireObjectMapper is not our project, I suggest you ask on that repo.


Change FB/TWTR share buttons to be more generic

This removes the 'Share on Twitter' and 'Share on Facebook' buttons and turns them into a single generic 'Share' button. The 'Share on Facebook' button has been broken for some time - it uses an older Facebook sharing approach which needs an app registered with FB, which probably needs some configuration changes but to which no one has any access to anymore, so at a minimum we need to change the FB button. I also think having a good share is important - see #324 - and also just see the Wordle craze going around.
We could update the share to FB button to use a more recent Facebook share dialog, and I think there's a way to do that without needing to load Facebook javascript, but I also think we've seen from Wordle that just doing a copy-paste is good enough.
This PR scraps the Twitter and FB buttons at the end of a game and replaces them with a 'Share' button, that just copies a share message to the clipboard. The copy to clipboard code comes from an MIT-licensed javascript library:
https://www.npmjs.com/package/clipboard
In keeping with the Wordle craze, I updated the sharing message a bit - I was even, um, "inspired" by Wordle and took the green box and put it into the message. (I actually think that's useful right now, in early 2022, to let people know that it's a challenge game that they should click on and try. In 2024 when no one remembers what Wordle was, we can take it out)
Anyway, it looks like this now:

and after clicking on 'Share'

and then pasting that into Facebook:

@fnogatz any thoughts on this? I'd like to get the broken FB share button gone soon, but I think we definitely want some kind of 'share' button

Hm, in your initial post, the "Copied results to clipboard" text had a dark shadow box as its background. In my tests it was not present :thinking:

Hrm - I see a black shadow on Mac in Safari, Firefox, and Chrome, so I'm not sure. Did you rebuild less?

(I mean rebuild the css with less)

Alright, apparently it was a little bit too late last night for my brain to work :D Just compiled and checked again, works as expected. Thank you very much!


[2.x] hmr bug: can't implement hmr for custom file type such as .mdx

Describe the bug
I can't make hmr works with custom file type such as .mdx. It alway do full reload.
Reproduction
https://github.com/csr632/test-vite/tree/hmr-bug
vite serve and edit Doc.mdx. It alway do full reload.
After some debugging, I found out that the module graph is incorrect:
For a single .mdx module, vite creates two duplicate ModuleNode:

The ?import query cause duplicate ModuleNode. The query is added here:
https://github.com/vitejs/vite/blob/b782af44cc968cc4f18ef0722302991406fa82de/packages%2Fvite%2Fsrc%2Fnode%2Fplugins%2FimportsAnalysis.ts#L260
This line doesn't add ?import query to .vue file, that's why @vitejs/plugin-vue doesn't suffer from this bug.

The source code of vite-plugin-mdx is here

How to fix
I suggest we remove the vue in this line:
https://github.com/vitejs/vite/blob/206b7b70943bebb4b6db12edbeb0d80b2efea6bf/packages%2Fvite%2Fsrc%2Fnode%2Futils.ts#L74
This is the only place where vite is still coupling with vue, giving "privilege" to vue.
After we change this line, @vitejs/plugin-vue should also be suffering from this bug.
Then, if the bug is fixed for @vitejs/plugin-vue, we can be sure that no other file types will suffer from this bug.
@yyx990803 Could you change this line and fix the bug for @vitejs/plugin-vue?
System Info

required vite version: 2.0.0-beta.3
required Operating System: Ubuntu
required Node version: 12.13.0

https://vitejs.dev/config/#transforminclude

https://vitejs.dev/config/#transforminclude

https://vitejs.dev/config/#transforminclude
For reference: https://github.com/vuejs/vitepress/blob/master/src/node/plugin.ts

The transforminclude doesn't help. I have updated the reproduction: https://github.com/csr632/test-vite/tree/hmr-bug

https://vitejs.dev/config/#transforminclude
For reference: https://github.com/vuejs/vitepress/blob/master/src/node/plugin.ts

The transforminclude doesn't help. I have updated the reproduction: https://github.com/csr632/test-vite/tree/hmr-bug


feat(server): allow non-Send closures to be passed into Http::bind()

If you need to access other HTTP-based APIs in order to service
requests, then the most ergonomic way to implement a server is to
create a global Core and Client, and reuse them within all
Service instances.
Unfortunately, Handle doesn't implement Send, so the shared-Core
approach is incompatible with the previous signature of Http::bind().
If we're planning to write an implementation that maintains a thread
pool for serving requests, then we will want to have a way to expose
thread-local resources to be shared between Service instances on
the same thread. The Http::bind() interface isn't capable of that,
so it's probably worth admitting that it's mainly suitable for
single-threaded servers, and then work on a Http::bind_threaded()
interface that will set up a threadpool for you properly.
Of course, concurrent Rust is a fairly new thing for me, so it's quite likely that I'm missing something, and Send is required. If so, feel free to reject this PR.

[ ] The commit messages match the guidelines in https://github.com/hyperium/hyper/blob/master/CONTRIBUTING.md#git-commit-guidelines
[] If you want me to write some examples of how to use Http::bind() with shared-Core approach, then I can.

Oh wait a second: bind() creates the Core itself rather than getting it passed in, so there's no way for the caller to close over it anyway :( .


How to replace 8 residual blocks of EageConnnect with RFR Moudle

EageConnect is ordinary convolution, not partial convolution，Please tell me how to replace it, thank you very much

大佬，你这边代码改成功了吗

大佬，你这边代码改成功了吗

没有，我已经放弃了


Custom Sets for Cubedrafting

Hey everyone,
sorry if this is the wrong place to contact you but I haven't found another option.
I want to make custom MTG sets for cubedrafting, so that i can load my cube as a set and have a custom landbase. Can you give me a rough Idea what I'd have to do, to add a custom set to the game definition? Specifically:
The GUIDs found in the set definitions, do they have to be registered somewhere? Is there any script to convert decks to sets or to generate sets from any other source?
Thank you for your great work!

there are some plans to implement a custom sets feature in OCTGN, I don't know if it's feasible or not though.  Currently the game feed system has made it very difficult to maintain custom sets, as OCTGN wipes out your entire set database every time the game updates.

there was an update a while ago that allows sets XMLs to include cards from other sets, to expand the sealed/limited booster generator.  We still don't have a proper way to add custom sets to a plugin (the current method is kinda hacky and will need to be re-applied each time a game def update is released), but technically "cube" sets can be done.  Cube in quotations cuz it's not technically an actual cube, you'd just be creating a compilation booster set which borrows card data and images from pre-existing sets.

the rest of the ticket isn't really something I can manage on the game plugin side, if there's not already a ticket for something similar on OCTGN's issue tracker, I'd suggest posting something there.


add dhclient to kali container

dhclient is not on the kali container by default. can be added with apt install isc-dhcp-client.

added in 8ed58bb8


Support for Form params request

Looks like the library doesn't seem to support a post using form parameters. If it does, can you perhaps provide an example ?

It doesn't have any built in support.  If you need this, you should start by adding form support to then-request.

The following worked for me
request('POST',
             'https://myserver.com/login',
            {
               headers:{'Content-Type':'application/x-www-form-urlencoded'},
               body:'username=admin&password=admin123'
             }
);

Any update on this one? solution by @gunalmel didn't help in my case :/
Thanks

My comment still stands, if you need this you should add support for it in then-request.


[Bug]: Unable to download file from Image widget when file uploaded from local device

Is there an existing issue for this?

[X] I have searched the existing issues

Current Behavior
The image uploaded via local device is not getting downloaded but redirecting the link to new tab and nothing happens there

Steps To Reproduce

Add Image widget to canvas
Add File picker widget to canvas and bind it with image widget {{FilePicker1.files[0].data}}
Upload any files from your device using File Picker widget
Enable download option from property pane of Image Widget
Try to download the image you have uploaded

Environment
Release
Version
Cloud

@somangshu , @Tooluloope
This issue is production specific issue, in particular, CSP(Content Security Policy) relevant one.
Our current CSP setting doesn't allow data url, so it is blocked and lead to the above issue.
That's why, this issue should be handled by server-side

@somangshu who should we alert on this,
And should I close this issue?

@sharat87 can you check this out please

This is because our CSP  doesn't allow connecting to image URLs, data: or otherwise. But this is release environment only, not production.
I can add this to the CSP to allow this, but I'm wondering why this was implemented in such a way. If the download button was a download link, something like this:
<a href="data:base64ImageURI" download>

Then it would work without the need for whitelisting more things in the CSP right? I think it won't even open a new tab, it'll just download, much cleaner even. No Javascript needed even. Any reason we aren't doing that instead?

Example of what I'm detailing in my previous comment:

@sharat87  Here we were facing a CROS issue with 3rd party URL which has Disable CROS. so users can’t download images.
related issue 5764
cc: @somangshu  @wmdev0808

@techbhavin Same problem. We are trying to load the image like an XHR, CORS will be denied, that's the default behavior. Since we actually don't do anything with the loaded data, a download link works best in my opinion. It'll work even if the image is an external URL, is a data: URL, is an Appsmith image URL, it'll work irrespective of CORS. Can we move to use that for the download link here?

agree with @sharat87,  we can continue with your suggestion
@wmdev0808  can you plz continue with @sharat87 suggestion

@techbhavin I suggest you take this up since you have worked on it before, I dont understand why this is in review right now, Moving it back to product backlog and assigning you

@techbhavin please confirm with @wmdev0808 if there is a PR that exists already


feat: ability to specify CA certificates for LDAP

LDAP servers are often hosted on an organization's internal network. And access to the LDAP server is open only via the LDAP protocol over SSL (LDAPS).
In this case, there may be a problem of the LDAP client not trusting the server. The X.509 certificate trust chain will not be built.
You need to specify trusted CA certificates manually.
This PR offers a solution to this problem.
This is the corrected version of https://github.com/casdoor/casdoor/pull/2069

@yamaritta don't just hide so many UI elements. If the option asks to input a value like algorithm, just add an option of your algorithm. Don't add so many diffs and make differences.

Similar to: https://github.com/casdoor/casdoor/pull/2096#issuecomment-1643665200
Think it in another way, you can also choose to fork it and have your complex features in the fork. If you find it's difficult to rebase on top of upstream, then you know this code change is big and it's also a burden to this repo's maintainers.
The code in this PR considered your own enterprise requirement well, but create burdens or even bugs for other use cases. If your code doesn't want to fit into the existing code format and "atmosphere", why don't maintain it in a fork or a private repo inside the company? I think it's a better way.


⚠ Bofrost.de has degraded performance

In 7d6e2ea, Bofrost.de (https://www.bofrost.de) experienced degraded performance:

HTTP code: 200
Response time: 1300 ms

Resolved: Bofrost.de performance has improved in 21f87ee.