Watchdog caught collective operation timeout for llama2-70b-chat on tp=8

See full log below. It can handle the first few requests and then getting stuck
2024-02-03 09:54:56,181	INFO worker.py:1724 -- Started a local Ray instance.
INFO 02-03 09:54:57 llm_engine.py:70] Initializing an LLM engine with config: model='/models/llama2-70b-chat_tp=8_20240131200023421_3c3f075a-d95c-44f1-93e4-8dd63d09832c', tokenizer='/models/llama2-70b-chat_tp=8_20240131200023421_3c3f075a-d95c-44f1-93e4-8dd63d09832c', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=4096, download_dir=None, load_format=auto, tensor_parallel_size=8, quantization=None, enforce_eager=False, seed=0)
[pod-name]:1:1 [0] NCCL INFO Bootstrap : Using eth0:<IP_ADDRESS><0>
[pod-name]:1:1 [0] NCCL INFO cudaDriverVersion 12010
NCCL version 2.18.1+cuda12.1
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:4773 [1] NCCL INFO cudaDriverVersion 12010
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:4773 [1] NCCL INFO Bootstrap : Using eth0:<IP_ADDRESS><0>
[pod-name]:1:5421 [0] NCCL INFO Plugin Path : /opt/hpcx/nccl_rdma_sharp_plugin/lib/libnccl-net.so
[pod-name]:1:5421 [0] NCCL INFO P2P plugin IBext
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO Plugin Path : /opt/hpcx/nccl_rdma_sharp_plugin/lib/libnccl-net.so
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO P2P plugin IBext
[pod-name]:1:5421 [0] NCCL INFO NET/IB : No device found.
[pod-name]:1:5421 [0] NCCL INFO NET/IB : No device found.
[pod-name]:1:5421 [0] NCCL INFO NET/Socket : Using [0]eth0:<IP_ADDRESS><0>
[pod-name]:1:5421 [0] NCCL INFO Using network Socket
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO NET/IB : No device found.
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO NET/IB : No device found.
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO NET/Socket : Using [0]eth0:<IP_ADDRESS><0>
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO Using network Socket
[pod-name]:1:5421 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff
[pod-name]:1:5421 [0] NCCL INFO NVLS multicast support is not available on dev 0
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO Setting affinity for GPU 1 to ff,ffff0000,00ffffff
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO NVLS multicast support is not available on dev 1
[pod-name]:1:5421 [0] NCCL INFO Channel 00/24 :    0   1   2   3   4   5   6   7
[pod-name]:1:5421 [0] NCCL INFO Channel 01/24 :    0   1   2   3   4   5   6   7
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[pod-name]:1:5421 [0] NCCL INFO Channel 23/24 :    0   1   2   3   4   5   6   7
[pod-name]:1:5421 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1
[pod-name]:1:5421 [0] NCCL INFO P2P Chunksize set to 524288
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO P2P Chunksize set to 524288
 [36m(RayWorkerVllm pid=5085) [0m [pod-name]:5085:5435 [4] NCCL INFO Channel 00/0 : 4[901c0] -> 5[901d0] via P2P/IPC/read
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5310 [7] NCCL INFO cudaDriverVersion 12010 [32m [repeated 6x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.) [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5310 [7] NCCL INFO Bootstrap : Using eth0:<IP_ADDRESS><0> [32m [repeated 6x across cluster] [0m
[pod-name]:1:5421 [0] NCCL INFO Channel 00/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 01/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO Plugin Path : /opt/hpcx/nccl_rdma_sharp_plugin/lib/libnccl-net.so [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO P2P plugin IBext [32m [repeated 6x across cluster] [0m
[pod-name]:1:5421 [0] NCCL INFO Channel 02/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 03/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 04/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO NET/IB : No device found. [32m [repeated 12x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO NET/Socket : Using [0]eth0:<IP_ADDRESS><0> [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO Using network Socket [32m [repeated 6x across cluster] [0m
[pod-name]:1:5421 [0] NCCL INFO Channel 05/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 06/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 07/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 08/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 09/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 10/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 11/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 12/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 13/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
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[pod-name]:1:5421 [0] NCCL INFO Channel 15/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 16/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 17/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 18/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 19/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 20/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 21/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 22/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:5421 [0] NCCL INFO Channel 23/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
 [36m(RayWorkerVllm pid=5085) [0m [pod-name]:5085:5435 [4] NCCL INFO Connected all rings
[pod-name]:1:5421 [0] NCCL INFO Connected all rings
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO Setting affinity for GPU 7 to ffffff00,0000ffff,ff000000 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO NVLS multicast support is not available on dev 7 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO P2P Chunksize set to 524288 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO Channel 06/0 : 1[101d0] -> 0[101c0] via P2P/IPC/read [32m [repeated 229x across cluster] [0m
[pod-name]:1:5421 [0] NCCL INFO Connected all trees
[pod-name]:1:5421 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
[pod-name]:1:5421 [0] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO Connected all trees
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer
[pod-name]:1:5421 [0] NCCL INFO comm 0x558ecef9b620 rank 0 nranks 8 cudaDev 0 busId 101c0 commId 0x553d0a1a3119e239 - Init COMPLETE
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:5434 [1] NCCL INFO comm 0x5588a64707b0 rank 1 nranks 8 cudaDev 1 busId 101d0 commId 0x553d0a1a3119e239 - Init COMPLETE
[pod-name]:1:7510 [0] NCCL INFO Using network Socket
 [36m(RayWorkerVllm pid=4851) [0m [pod-name]:4851:7512 [2] NCCL INFO Using network Socket
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO Connected all rings [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5235) [0m [pod-name]:5235:5432 [6] NCCL INFO Channel 23/0 : 6[a01c0] -> 5[901d0] via P2P/IPC/read [32m [repeated 106x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO Connected all trees [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:5433 [7] NCCL INFO comm 0x5562ccc7ef40 rank 7 nranks 8 cudaDev 7 busId a01d0 commId 0x553d0a1a3119e239 - Init COMPLETE [32m [repeated 6x across cluster] [0m
[pod-name]:1:7510 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff
[pod-name]:1:7510 [0] NCCL INFO NVLS multicast support is not available on dev 0
 [36m(RayWorkerVllm pid=5085) [0m [pod-name]:5085:7514 [4] NCCL INFO Setting affinity for GPU 4 to ffffff00,0000ffff,ff000000
 [36m(RayWorkerVllm pid=5085) [0m [pod-name]:5085:7514 [4] NCCL INFO NVLS multicast support is not available on dev 4
[pod-name]:1:7510 [0] NCCL INFO Channel 00/24 :    0   1   2   3   4   5   6   7
[pod-name]:1:7510 [0] NCCL INFO Channel 01/24 :    0   1   2   3   4   5   6   7
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[pod-name]:1:7510 [0] NCCL INFO Channel 23/24 :    0   1   2   3   4   5   6   7
[pod-name]:1:7510 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1
[pod-name]:1:7510 [0] NCCL INFO P2P Chunksize set to 524288
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO P2P Chunksize set to 524288
[pod-name]:1:7510 [0] NCCL INFO Channel 00/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 01/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 02/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 03/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 04/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7517 [7] NCCL INFO Using network Socket [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO Channel 04/0 : 1[101d0] -> 2[201c0] via P2P/IPC/read [32m [repeated 30x across cluster] [0m
[pod-name]:1:7510 [0] NCCL INFO Channel 05/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 06/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 07/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 08/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 09/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 10/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 11/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 12/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 13/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 14/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 15/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 16/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 17/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 18/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 19/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 20/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 21/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 22/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Channel 23/0 : 0[101c0] -> 1[101d0] via P2P/IPC/read
[pod-name]:1:7510 [0] NCCL INFO Connected all rings
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO Connected all rings
 [36m(RayWorkerVllm pid=5010) [0m [pod-name]:5010:7515 [3] NCCL INFO Setting affinity for GPU 3 to ff,ffff0000,00ffffff [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5010) [0m [pod-name]:5010:7515 [3] NCCL INFO NVLS multicast support is not available on dev 3 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7517 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7517 [7] NCCL INFO P2P Chunksize set to 524288 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO Channel 11/0 : 1[101d0] -> 0[101c0] via P2P/IPC/read [32m [repeated 225x across cluster] [0m
[pod-name]:1:7510 [0] NCCL INFO Connected all trees
[pod-name]:1:7510 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
[pod-name]:1:7510 [0] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO Connected all trees
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer
[pod-name]:1:7510 [0] NCCL INFO comm 0x558f1b71df40 rank 0 nranks 8 cudaDev 0 busId 101c0 commId 0xee91d00f9a25e09b - Init COMPLETE
 [36m(RayWorkerVllm pid=4773) [0m [pod-name]:4773:7516 [1] NCCL INFO comm 0x5588f4138b50 rank 1 nranks 8 cudaDev 1 busId 101d0 commId 0xee91d00f9a25e09b - Init COMPLETE
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7517 [7] NCCL INFO Connected all rings [32m [repeated 6x across cluster] [0m
INFO 02-03 10:08:05 llm_engine.py:275] # GPU blocks: 22682, # CPU blocks: 6553
INFO 02-03 10:08:08 model_runner.py:501] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 02-03 10:08:08 model_runner.py:505] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode.
 [36m(RayWorkerVllm pid=4773) [0m INFO 02-03 10:08:08 model_runner.py:501] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
 [36m(RayWorkerVllm pid=4773) [0m INFO 02-03 10:08:08 model_runner.py:505] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode.
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7541 [7] NCCL INFO Channel 31/1 : 7[a01d0] -> 0[101c0] via P2P/IPC/read [32m [repeated 305x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7517 [7] NCCL INFO Connected all trees [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7517 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7517 [7] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m [pod-name]:5310:7517 [7] NCCL INFO comm 0x5562d7923cb0 rank 7 nranks 8 cudaDev 7 busId a01d0 commId 0xee91d00f9a25e09b - Init COMPLETE [32m [repeated 6x across cluster] [0m
[W CUDAGraph.cpp:145] Warning: Waiting for pending NCCL work to finish before starting graph capture. (function operator())
 [36m(RayWorkerVllm pid=4773) [0m [W CUDAGraph.cpp:145] Warning: Waiting for pending NCCL work to finish before starting graph capture. (function operator())
 [36m(RayWorkerVllm pid=5160) [0m INFO 02-03 10:08:43 model_runner.py:547] Graph capturing finished in 35 secs.
 [36m(RayWorkerVllm pid=5310) [0m INFO 02-03 10:08:08 model_runner.py:501] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. [32m [repeated 6x across cluster] [0m
 [36m(RayWorkerVllm pid=5310) [0m INFO 02-03 10:08:08 model_runner.py:505] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. [32m [repeated 6x across cluster] [0m
INFO 02-03 10:08:43 model_runner.py:547] Graph capturing finished in 35 secs.
INFO:root:took 829.11 seconds to start vllm engine for model llama2-70b-chat
INFO 02-03 10:09:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:11:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:14:10 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:15:30 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:18:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:21:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:23:11 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:27:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:34:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:37:19 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:39:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:43:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:44:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:47:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:51:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:54:33 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 10:57:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:00:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:02:14 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:05:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:15:10 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:16:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:18:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:20:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:29:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:31:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:36:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:39:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:49:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:50:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 11:53:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:01:18 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:02:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:08:31 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:10:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:16:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:17:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:22:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:24:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:29:12 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:37:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:42:08 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:54:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:56:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 12:59:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 13:01:15 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 13:07:19 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 13:09:11 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 13:12:03 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 13:14:04 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
INFO 02-03 13:18:39 llm_engine.py:706] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%
 [36m(RayWorkerVllm pid=5085) [0m [E ProcessGroupNCCL.cpp:475] [Rank 4] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=20518, OpType=ALLREDUCE, NumelIn=106496, NumelOut=106496, Timeout(ms)=1800000) ran for 1800270 milliseconds before timing out.
 [36m(RayWorkerVllm pid=5310) [0m [W CUDAGraph.cpp:145] Warning: Waiting for pending NCCL work to finish before starting graph capture. (function operator()) [32m [repeated 6x across cluster] [0m

hi, have you fixed it?


remove dependency on fcrepo-mint module

See: https://jira.duraspace.org/browse/FCREPO-1696

@awoods @escowles the webapp-plus module has been updated to include the fcrepo-mint module. Basic configuration can be set with environment variables, or it is possible to completely override the spring file (e.g. to use an external http-based minter)

Resolved with: https://github.com/fcrepo4-exts/fcrepo-webapp-plus/commit/e064b44fb3eb38133372674ca39ff514db556df1


policy/v1beta1 PodDisruptionBudget is deprecated in v1.21+, unavailable in v1.25+; use policy/v1 PodDisruptionBudget

Couldn't find this policy in the current list.

https://kubernetes.io/docs/reference/using-api/deprecation-guide/#poddisruptionbudget-v125

This @garethahealy for this, will work on adding this over the weekend.


Memory Leak in getAuth(...).listUsers(...)

Environment

Operating System version: macOS 13.4.1 (but also in within a Google Cloud Run NodeJS environment)
Firebase SDK version:

firebase: 9.23.0 (also 9.21.0)
firebase-admin: 11.9.0 (also 11.7.0)
firebase-functions: 4.4.1 (also 4.3.1)

Firebase Product: auth
Node.js version: 16, 18, 20
NPM version: 9.5.1

Problem
Steps to reproduce:
We discovered a Memory Leak when running the list_all_users example code from https://firebase.google.com/docs/auth/admin/manage-users#list_all_users.
The data loaded from getAuth(app).listUsers(1000, nextPageToken) is not garbage collected and causes the memory footprint of our authentication backup to grow and eventually run out of memory.

Every blue bar is memory that is still used with is ~1MB/1000 Users making it almost impossible to use this function to backup multiple GB of user data.
The problematic path seems to be within: node_modules/firebase-admin/lib/utils/api-request.js.
Side-note: We only started noticing this issue with node v20, with node v18 and below we did not have the issue when using our production bundle that is generated with typescript and webpack. We could go back to previous versions and can see that the memory is correctly cleaned up in older node versions. Unfortunately we couldn't figure out what exactly the difference to the simple issue demo script I've added below is, which has the issue even on older versions.
It just shows us, that there is now an issue that didn't exist before.
We could also see the same when not using a loop and just calling auth.listUsers(1000) once, so the issue is not in the boilerplate around. Also the issue goes away when we mock this function call.
Here you can see a Memory Profile of the issue with our webpack output. You can clearly see, that in node20 the blue bars that are kept are way larger. In node18 and before the data was immediately cleaned up.

node 20
node 18 (16 looks similar)

Relevant Code:
const { initializeApp } = require("firebase-admin/app");
const { getAuth } = require("firebase-admin/auth");

const app = initializeApp({
  projectId: /* TODO */,
});

const auth = getAuth(app);

const listAllUsers = (nextPageToken) => {
  auth
    .listUsers(1000, nextPageToken)
    .then((listUsersResult) => {
      listUsersResult.users.forEach((userRecord) => {
        console.log("user", userRecord.toJSON());
      });
      if (listUsersResult.pageToken) {
        listAllUsers(listUsersResult.pageToken);
      }
    })
    .catch((error) => {
      console.log("Error listing users:", error);
    });
};

listAllUsers();

We have narrowed down the issue to https://github.com/firebase/firebase-admin-node/blob/master/src/utils/api-request.ts#L507-514
which was introduced to prevent a bug in Node12 but seems to be not necessary anymore.
When we just remove the req.on('socket', (socket) => { code entirely the memory leak is gone.
We figured out, that in newer node versions, socket.setTimeout(timeout, timeoutCallback); restarts the timeout after it added a new event listener causing socket.on('end', () => { to never be triggered. This results in the timeoutCallback never be released from memory and that one holds a reference to the entire req object.

BTW here is an even simpler example with the memory leak:
const { initializeApp } = require("firebase-admin/app");
const { getAuth } = require("firebase-admin/auth");

const app = initializeApp({
  projectId: "TODO",
});

const waitSeconds = (seconds) =>
  new Promise((resolve) => setTimeout(resolve, seconds * 1000));

async function main() {
  while (true) {
    await getAuth(app).listUsers(1000);
    waitSeconds(2);
  }
}
main();

Hi @adrianjost Thank you for the highly detailed issue report!
We really appreciate the time you spent on investigating this issue!
The fix you proposed in #2236 looks good to me. We can merge the fix once we complete the review. Thanks!


[Bitcoin] Remove the rt flag when using regtest address on Regtest network

At the moment, the rt flag is being added to the bitcoin ord address for inscription:
bcrt1pefsa2ttc38605jqz6hgyyepzx4wpt9mx6shtjak52ysf3mydufaq7d7p9n
The address isn't valid to use on any currently verified network (excluding usage on Regtest network). Removing rt the address will be:
bc1pefsa2ttc38605jqz6hgyyepzx4wpt9mx6shtjak52ysf3mydufaq7d7p9n
Which is a valid address on other networks.
I suggest if we could remove the rt strings in the address format for using on other networks.

Not with the mainnet of bitcoin use case for the regtest private network. Close this issue.


Setting viewsFolderPath has no effect

I want to set the Razor views path to something other than Views, but it seems that regardless of the path I am providing it always searches the default folders:
    let sp = services.BuildServiceProvider()
    let env = sp.GetService<IWebHostEnvironment>()
    let viewsFolderPath = env.WebRootPath
    services.AddRazorEngine(viewsFolderPath) |> ignore

The error message indicates that the newly registered views folder path wasn't registered:

Can you create a small repro?
Have you checked what the value is for env.WebRootPath? and in your file explorer if there is an index.cshtml file?

I've created a minimal repro based on the giraffe 1.0.0 template: https://github.com/toburger/giraffe-razor-custom-view-folder


filelocationkey

Object "filelocationkey" in function g.part1 and the csv-file storage that follows from it seems redundant and also not convenient when doing parallel processing. To do: remove this part of the code and check that rest of the code performs consistently.

I have now (as of version 1.8-2) removed the filelocationkey.csv output and instead fixed the storefilestructure functionality in part 2, 4 and 5. This means that in the day/night specific summary of each of these parts the full filepath and most distal folder are stored in the csv report if the user specifcies "storefilestructure=TRUE".


Can you add a licence

Hi,
Is it possible for you to add a licence to the code?
Thanks,
Varun

Dear Varun,
Sorry to reply you so late, now I have added the MIT license.
Best,
Ke


Main

This pull request fixes the build.gradle script

Hey! Sorry for the long radio silence, but I think this has now been fixed in V3


Markers ( rendered view) does not stay steady when mobile location changed

Markers ( rendered view) does not stay steady when mobile location changed.The marker move automatically. how can make it steady ?

I met the same issue.

The markers are re-calculated every few seconds, and will move a little bit as the calculated direction of north changes.
If this behaviour doesn't suit what you are trying to do, you can change the interval to something greater (such as 2 minutes) with:
locationScene.setAnchorRefreshInterval(120);

I have increased this interval to 960 from 60. But it changes frequently.

locationScene.setAnchorRefreshInterval(120); still it changes frequently. Any updates on this issue ?
@Ronakgadhia @johnwedgbury

No updates from them. @shahparth99999

The Markers are flickering and move parallel  to camera.

Please let us know if anyone found a solution for this

Same Problem

Consider that the refresh intervall is in milliseconds!


Flaky test: when passed unresolved inputs, may fail - Passed only 99 tests

Please ensure:

[X] This is actually a flaky test already present in the code and not caused by your PR.

Context
Combination of
        not (null dropped) ==> do

        shouldKeep <- vectorOf (length inputs) $ frequency
            [ (8, pure False)
            , (2, pure True)
            ]

Job name
cardano-wallet-core:unit
Test case name(s)
when passed unresolved inputs, may fail
Error message
test/unit/Cardano/Wallet/Shelley/TransactionSpec.hs:2036:13:
  1) Cardano.Wallet.Shelley.Transaction.balanceTransaction, when passed unresolved inputs, may fail
       *** Gave up! Passed only 99 tests; 5 discarded tests (10% unknown txins).

### Build link

https://hydra.iohk.io/build/13314576

Should be fixed.


No package metadata was found for envycontrol init.py line 563, in from_name

Describe the bug
Any command with envycontrol produces error
To Reproduce
Steps to reproduce the behavior:

Run envycontrol --query
See error:

> envycontrol --query
Traceback (most recent call last):
  File "/usr/lib/python3.11/importlib/metadata/__init__.py", line 563, in from_name
    return next(cls.discover(name=name))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
StopIteration

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/bin/envycontrol", line 33, in <module>
    sys.exit(load_entry_point('envycontrol==3.2.0', 'console_scripts', 'envycontrol')())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/bin/envycontrol", line 22, in importlib_load_entry_point
    for entry_point in distribution(dist_name).entry_points
                       ^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/importlib/metadata/__init__.py", line 981, in distribution
    return Distribution.from_name(distribution_name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/importlib/metadata/__init__.py", line 565, in from_name
    raise PackageNotFoundError(name)
importlib.metadata.PackageNotFoundError: No package metadata was found for envycontrol

Expected behavior
A clear and concise description of what you expected to happen.
System Information:

Distro: Arch
Kernel: Linux 6.3.1
DE/WM and Display Manager (if applicable): Gnome 42 with GDM]
EnvyControl version: 3.2.0-2
Nvidia driver version: 530.41.03
lspci output:

00:00.0 Host bridge: Intel Corporation 12th Gen Core Processor Host Bridge/DRAM Registers (rev 02)
00:01.0 PCI bridge: Intel Corporation 12th Gen Core Processor PCI Express x16 Controller #1 (rev 02)
00:02.0 VGA compatible controller: Intel Corporation Alder Lake-P Integrated Graphics Controller (rev 0c)
00:04.0 Signal processing controller: Intel Corporation Alder Lake Innovation Platform Framework Processor Participant (rev 02)
00:06.0 PCI bridge: Intel Corporation 12th Gen Core Processor PCI Express x4 Controller #0 (rev 02)
00:07.0 PCI bridge: Intel Corporation Alder Lake-P Thunderbolt 4 PCI Express Root Port #0 (rev 02)
00:07.1 PCI bridge: Intel Corporation Alder Lake-P Thunderbolt 4 PCI Express Root Port #1 (rev 02)
00:08.0 System peripheral: Intel Corporation 12th Gen Core Processor Gaussian & Neural Accelerator (rev 02)
00:0d.0 USB controller: Intel Corporation Alder Lake-P Thunderbolt 4 USB Controller (rev 02)
00:0d.2 USB controller: Intel Corporation Alder Lake-P Thunderbolt 4 NHI #0 (rev 02)
00:12.0 Serial controller: Intel Corporation Alder Lake-P Integrated Sensor Hub (rev 01)
00:14.0 USB controller: Intel Corporation Alder Lake PCH USB 3.2 xHCI Host Controller (rev 01)
00:14.2 RAM memory: Intel Corporation Alder Lake PCH Shared SRAM (rev 01)
00:14.3 Network controller: Intel Corporation Alder Lake-P PCH CNVi WiFi (rev 01)
00:15.0 Serial bus controller: Intel Corporation Alder Lake PCH Serial IO I2C Controller #0 (rev 01)
00:15.1 Serial bus controller: Intel Corporation Alder Lake PCH Serial IO I2C Controller #1 (rev 01)
00:16.0 Communication controller: Intel Corporation Alder Lake PCH HECI Controller (rev 01)
00:16.3 Serial controller: Intel Corporation Alder Lake AMT SOL Redirection (rev 01)
00:1c.0 PCI bridge: Intel Corporation Device 51bb (rev 01)
00:1f.0 ISA bridge: Intel Corporation Alder Lake PCH eSPI Controller (rev 01)
00:1f.3 Audio device: Intel Corporation Alder Lake PCH-P High Definition Audio Controller (rev 01)
00:1f.4 SMBus: Intel Corporation Alder Lake PCH-P SMBus Host Controller (rev 01)
00:1f.5 Serial bus controller: Intel Corporation Alder Lake-P PCH SPI Controller (rev 01)
01:00.0 3D controller: NVIDIA Corporation GA107GLM [RTX A2000 8GB Laptop GPU] (rev a1)
02:00.0 Non-Volatile memory controller: SK hynix Platinum P41 NVMe Solid State Drive 2TB
a5:00.0 Unassigned class [ff00]: Realtek Semiconductor Co., Ltd. RTS5260 PCI Express Card Reader (rev 01)

You need to reinstall/rebuild all your AUR Python package after a major
version change, not a problem with EnvyControl
On Sat, May 20, 2023, 05:52 gavr123456789 @.***> wrote:

Describe the bug
Any command with envycontrol produces error
To Reproduce
Steps to reproduce the behavior:

Run envycontrol --query
See error:

envycontrol --query
Traceback (most recent call last):
File "/usr/lib/python3.11/importlib/metadata/init.py", line 563, in from_name
return next(cls.discover(name=name))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
StopIteration

During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/bin/envycontrol", line 33, in
sys.exit(load_entry_point('envycontrol==3.2.0', 'console_scripts', 'envycontrol')())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/bin/envycontrol", line 22, in importlib_load_entry_point
for entry_point in distribution(dist_name).entry_points
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/importlib/metadata/init.py", line 981, in distribution
return Distribution.from_name(distribution_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/importlib/metadata/init.py", line 565, in from_name
raise PackageNotFoundError(name)
importlib.metadata.PackageNotFoundError: No package metadata was found for envycontrol
Expected behavior
A clear and concise description of what you expected to happen.
System Information:

Distro: Arch
Kernel: Linux 6.3.1
DE/WM and Display Manager (if applicable): Gnome 42 with GDM]
EnvyControl version: 3.2.0-2
Nvidia driver version: 530.41.03
lspci output:

00:00.0 Host bridge: Intel Corporation 12th Gen Core Processor Host Bridge/DRAM Registers (rev 02)
00:01.0 PCI bridge: Intel Corporation 12th Gen Core Processor PCI Express x16 Controller #1 (rev 02)
00:02.0 VGA compatible controller: Intel Corporation Alder Lake-P Integrated Graphics Controller (rev 0c)
00:04.0 Signal processing controller: Intel Corporation Alder Lake Innovation Platform Framework Processor Participant (rev 02)
00:06.0 PCI bridge: Intel Corporation 12th Gen Core Processor PCI Express x4 Controller #0 (rev 02)
00:07.0 PCI bridge: Intel Corporation Alder Lake-P Thunderbolt 4 PCI Express Root Port #0 (rev 02)
00:07.1 PCI bridge: Intel Corporation Alder Lake-P Thunderbolt 4 PCI Express Root Port #1 (rev 02)
00:08.0 System peripheral: Intel Corporation 12th Gen Core Processor Gaussian & Neural Accelerator (rev 02)
00:0d.0 USB controller: Intel Corporation Alder Lake-P Thunderbolt 4 USB Controller (rev 02)
00:0d.2 USB controller: Intel Corporation Alder Lake-P Thunderbolt 4 NHI #0 (rev 02)
00:12.0 Serial controller: Intel Corporation Alder Lake-P Integrated Sensor Hub (rev 01)
00:14.0 USB controller: Intel Corporation Alder Lake PCH USB 3.2 xHCI Host Controller (rev 01)
00:14.2 RAM memory: Intel Corporation Alder Lake PCH Shared SRAM (rev 01)
00:14.3 Network controller: Intel Corporation Alder Lake-P PCH CNVi WiFi (rev 01)
00:15.0 Serial bus controller: Intel Corporation Alder Lake PCH Serial IO I2C Controller #0 (rev 01)
00:15.1 Serial bus controller: Intel Corporation Alder Lake PCH Serial IO I2C Controller #1 (rev 01)
00:16.0 Communication controller: Intel Corporation Alder Lake PCH HECI Controller (rev 01)
00:16.3 Serial controller: Intel Corporation Alder Lake AMT SOL Redirection (rev 01)
00:1c.0 PCI bridge: Intel Corporation Device 51bb (rev 01)
00:1f.0 ISA bridge: Intel Corporation Alder Lake PCH eSPI Controller (rev 01)
00:1f.3 Audio device: Intel Corporation Alder Lake PCH-P High Definition Audio Controller (rev 01)
00:1f.4 SMBus: Intel Corporation Alder Lake PCH-P SMBus Host Controller (rev 01)
00:1f.5 Serial bus controller: Intel Corporation Alder Lake-P PCH SPI Controller (rev 01)
01:00.0 3D controller: NVIDIA Corporation GA107GLM [RTX A2000 8GB Laptop GPU] (rev a1)
02:00.0 Non-Volatile memory controller: SK hynix Platinum P41 NVMe Solid State Drive 2TB
a5:00.0 Unassigned class [ff00]: Realtek Semiconductor Co., Ltd. RTS5260 PCI Express Card Reader (rev 01)
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You are receiving this because you are subscribed to this thread.Message
ID: @.***>

Same problem here also tried to rebuild
pacman -Qoq /usr/lib/python3.11 |  yay -S - --rebuild -y
Hovewer I keep getting the same error

Remove and reinstall the envycontrol package
On Mon, May 22, 2023, 07:12 Zep @.***> wrote:

Same problem here also tried to rebuild
pacman -Qoq /usr/lib/python3.11 | yay -S - --rebuild -y
Hovewer I keep getting the same error
—
Reply to this email directly, view it on GitHub
https://github.com/bayasdev/envycontrol/issues/107#issuecomment-1557112061,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ALY7IYI43XHBJRQXJ3XRC6TXHNJ2PANCNFSM6AAAAAAYIVFWGQ
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You are receiving this because you commented.Message ID:
@.***>

Can confirm the problem.
Works when downgrading to 3.1.0, rebuild does not fix the issue.

found a solution. updating python packages works. i used this code:
yay -S $(pacman -Qoq /usr/lib/python3.10) --answerclean All

If you are not gonna switch the mode frequently, you may run the python script directly:

git clone https://github.com/bayasdev/envycontrol.git
python ./envycontrol.py --query or sudo python ./envycontrol.py -s <MODE>

For install it as a package, you may use pipx:

sudo pipx install .
sudo /root/.local/bin/envycontrol --query

Then you can put alias in your .bashrc or .zshrc:

alias envycontrol="sudo /root/.local/bin/envycontrol"
envycontrol --query

On Archlinux,
yay -Rs envycontrol
yay -Syyu envycontrol

worked for me.

On Archlinux,
yay -Rs envycontrol
yay -Syyu envycontrol

worked for me.

Yes, that works. The problem comes when you try to run it after the installation


shap values vs. data - length

Hi,
I am using shap kernel (but checked it also in your notebook 'Census income classification with scikit-learn') and I see that the length of shap_values is longer in one than the instance (X_train in your notebook).
Can you please explain this difference? I would like to use the results not with one of your plots and make sure which column names to use (first column in shap_values refer to the index in x_train and then I need to start from the second column in shap_values? or to the first column that is not the index?
Thanks alot

Perhaps you have an older version of shap installed? After v0.20 the shape of the feature importance matrix is the same shape as the input sample matrix. The last column used to be the expected value of the model over the background dataset, but now that is stored in explainer.expected_value. This change was made to avoid the confusion you are mentioning.

Sorry for the delayed reply.
Thank you! It solved my problem (:


[build] Proper MSVC Compiler support instead of mingw32

Can you properly support MSVC compiler for windows?
Maybe make a .bat file with the commands?
Or fix the errors with jar_xm.h when building in MSVS.
I tried all the things said in the wiki still not compiling.

Have you tried the VS2015 project provided? It works ok on my side.

Just in case, try using develop branch but master should work ok.

Yes,I'm using it.

But it doesn't work

It would be nice if it was a .bat file like how handmadehero does it.

ok, it seems related to Release mode, please, could you try Debug mode?

Same issue

The error is in jar_xm.h
The error states 'func': undeclared identifier

Could you also send me a .bat sample to see how it's done?
Command line parameters for compiler should be:
/GS /TC /analyze- /W3 /Zc:wchar_t /I"C:\GitHub\raylib\project\vs2015\..\..\src" /ZI /Gm /Od /Fd"Debug\vc140.pdb" /Zc:inline /fp:precise /D "WIN32" /D "_DEBUG" /D "_CONSOLE" /D "PLATFORM_DESKTOP" /D "_UNICODE" /D "UNICODE" /errorReport:prompt /WX- /Zc:forScope /RTC1 /Gd /Oy- /MDd /Fa"Debug\" /EHsc /nologo /Fo"Debug\" /Fp"Debug\core_basic_window.pch"

Command line parameters for linker should be:
/OUT:"C:\GitHub\raylib\project\vs2015\Debug\core_basic_window.exe" /MANIFEST /NXCOMPAT /PDB:"C:\GitHub\raylib\project\vs2015\Debug\core_basic_window.pdb" /DYNAMICBASE "kernel32.lib" "user32.lib" "gdi32.lib" "winspool.lib" "comdlg32.lib" "advapi32.lib" "shell32.lib" "ole32.lib" "oleaut32.lib" "uuid.lib" "odbc32.lib" "odbccp32.lib" "glfw3.lib" "C:\GitHub\raylib\project\vs2015\Debug\raylib.lib" /DEBUG /MACHINE:X86 /INCREMENTAL /PGD:"C:\GitHub\raylib\project\vs2015\Debug\core_basic_window.pgd" /SUBSYSTEM:CONSOLE /MANIFESTUAC:"level='asInvoker' uiAccess='false'" /ManifestFile:"Debug\core_basic_window.exe.intermediate.manifest" /ERRORREPORT:PROMPT /NOLOGO /LIBPATH:"C:\GitHub\raylib\project\vs2015\external\glfw3\lib\win32" /LIBPATH:"C:\GitHub\raylib\project\vs2015\external\openal_soft\lib\win32" /TLBID:1

Here's my compiler flags /GS /TC /analyze- /W3 /Zc:wchar_t /I"E:\shazan\raylib-master\project\vs2015\..\..\src\external\openal_soft\include" /I"E:\shazan\raylib-master\project\vs2015\..\..\src\external\glfw3\include" /I"E:\shazan\raylib-master\project\vs2015\..\..\src\external" /ZI /Gm /Od /Fd"Debug\raylib.pdb" /Zc:inline /fp:precise /D "WIN32" /D "_DEBUG" /D "_LIB" /D "_UNICODE" /D "UNICODE" /D "GRAPHICS_API_OPENGL_33" /D "PLATFORM_DESKTOP" /errorReport:prompt /WX- /Zc:forScope /RTC1 /Gd /Oy- /MDd /Fa"Debug\" /EHsc /nologo /Fo"Debug\" /Fp"Debug\raylib.pch"

@raysan5  I'm trying to compile the library not the example

@raysan5 I think you compiled the library already

Just trying to reproduce the error but it works on my side...
I'm trying to clean and recompile, actually, I solved the issue with Release mode.
Don't know where is the problem...

It's in jar_xm.h the Debug macro does it

Are you working on Windows 10? Visual Studio 2015?
Please, could you try raylib develop branch?

Yes , Windows 10 ,VS 15

@raysan5  I would prefer if raylib had prebuilt binaries like SDL2 does

Still getting same errors

Its the identical same errors.

@raysan5 The errors are same as in the image i uploaded.

@raysan5 where did you get jar_xm.h

Just uploaded a reviewed VS2015 project (https://github.com/raysan5/raylib/commit/65cf3080a5392ebef594c684ee75f9fa207dac77) with Debug and Release properly configured but it seems not related to this issue. :(
jar_xm.h was created by a user but it works ok on my side, it's just used to play XM module files on audio, you can just undefine SUPPORT_FILEFORMAT_XM in audio.c to avoid that file inclusion.
Just added VS2015 raylib compiled release files: https://github.com/raysan5/raylib/tree/develop/release/win32/msvc

@raysan 64bit binaries would be nice.

Can you tell me what does this mean __func__ undeclared identifier

The issue you get is related to DEBUG(...) macro defined at line 318, but it should be ok... maybe you have that macro defined somewhere else... Do you have any other IDE a part from VS2015 installed in your system? MonoDevelop? Code:Blocks? Other Visual Studio versions?

Just try to commenting DEBUG(...) lines in the code (there are only 7 lines) to see if it works.

@raysan5 no need to comment DEBUG change #ifdef JAR_XM_DEBUG to#if JAR_XM_DEBUG in line 311 in jar_xm.h

And in line 785 static inline void memcpy_pad(void* dst, size_t dst_len, const void* src, size_t src_len, size_t offset) to static void memcpy_pad(void* dst, size_t dst_len, const void* src, size_t src_len, size_t offset)

Does it work with those changes?

@raysan5  I made a .bat file script to build without opening visual studio just like makefiles.
Here it is,
@echo off
set Libraries=glfw3.lib raylib.lib user32.lib gdi32.lib shell32.lib
set CompilerFlags= /MD /Z7 /FC /nologo
set LinkerFlags=-subsystem:Console
set bits=x86
set LibraryLocation=..\deps\lib\%bits%\
for %%* in (.) do set CurrDirName=%%~nx*
mkdir build > NUL 2> NUL
pushd build
IF NOT DEFINED vcvars_called (
    call "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall.bat" %bits%
    set vcvars_called=1
)
REM xcopy %LibraryLocation%glfw3.dll > NUL 2> NUL
ctime -begin %CurrDirName%.ctm
cl %CompilerFlags% ..\code\main.cpp /I..\deps\include   /link -incremental:no /LIBPATH:%LibraryLocation% %Libraries%  %LinkerFlags% -out:main.exe
set LastError=%ERRORLEVEL%
ctime -end %CurrDirName%.ctm
popd

Implemented changes in commit https://github.com/raysan5/raylib/commit/53fbeeea718db5ab0a5ff16fc7971144588685cd
Added build.bat for reference in commit https://github.com/raysan5/raylib/commit/013f80f3a3b5875559f499694d1128ae36b7ba92


reformat, refactor basic ds: object challenges

Pre-Submission Checklist

[x] Your pull request targets the staging branch of freeCodeCamp.
[x] Branch starts with either fix/, feature/, or translate/ (e.g. fix/signin-issue)
[x] You have only one commit (if not, squash them into one commit).
[x] All new and existing tests pass the command npm test. Use git commit --amend to amend any fixes.

Type of Change

[x] Curriculum update - modifying basic DS section
[ ] Small bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds new functionality)
[ ] Breaking change (fix or feature that would change existing functionality)
[ ] Add new translation (feature adding new translations)

Checklist:

[x] Tested changes locally.

Description

This is a partial completion of issue #12693
This PR:

Title case array challenges
Reformat Object challenges to FCC format:

code tags for language keywords
dfn tags for key terms
blockquotes for examples, etc.

Fixed the way the test cases were being displayed by adding 'message: ...
Replaced all "Instructions" h4 headers with hr elements as discussed in #12688
Added console.log() around all function calls to log the result in the FCC challenge console
Other reformatting and refactoring to prevent challenges from being cheated, etc.

I've worked through this entire section locally and all tests pass and I can see no obvious bugs. This completes what the issue was originally opened for and I would say these are pretty well QAd at this point, but still leaving the original issue open for the time being in case there are a couple of other small things to be done. @bonham000, as discussed, if you want to go over the array section and see if there's anything you'd like to add...

Thanks @raisedadead!


v3 Breaking Changes: log files

Describe the bug
On v2, you're able to set the logging config with:
stdout: false
this would generate the logfiles which can be ingested by other systems such as fail2ban
https://github.com/postalserver/postal/blob/152633adfe861699fd7d762690d0c521c31d2dbf/lib/postal/config.rb#L100-L107
on v3, there was a large refactor to logging,
stdout was removed and a new config option was added instead: enabled: false
when set to false, logs are sent to /dev/null rather than a file
https://github.com/postalserver/postal/blob/4c27baee7ff554097c4a982c289dbbd4edc9db7a/lib/postal/config.rb#L76
this effectively breaks any bruteforce mitigation solutions that currently exist
To Reproduce

install v3
set logging > enabled: false in config.yml
check container /opt/postal/app/logs

Expected behaviour
expect to see a logfile named {servername}_server.log

Logging to files within containers is not a good practice and not supported in v3 - as you have found.
Docker itself will store these logs to a file on the host machine with appropriate rotation. You should look at ingesting these logs. You can find the path to a container's log file using something like this.
docker inspect --format='{{.LogPath}} name-of-container

Do feel free to start a discussion under Feature Requests to add support for brute force protection directly in the SMTP server.
This is functionality I would like to see without needing to use fail2ban.

We are using the syslog logging driver provided by docker directly,
https://docs.docker.com/config/containers/logging/configure/#supported-logging-drivers
Below example will produce log files in /var/log/docker/postal-smtp.log
docker-compose.yml
smtp:
    image: ghcr.io/postalserver/postal:3.1.1
    command: postal smtp-server
    network_mode: host
    cap_add:
      - NET_BIND_SERVICE
    volumes:
      - /opt/postal/config:/config
    restart: always
    logging:
      driver: syslog
      options:
        tag: "postal-smtp"

and fail2ban:
jail.local:
[POSTAL]
enabled  = true
port     = 25
filter   = postal
maxretry = 3
findtime = 3600
logpath  = /var/log/docker/postal-smtp.log

filter.d > postal.conf
[Definition]
failregex = (.*)WARN   Authentication failure for <HOST>

ignoreregex =

@erichk4 tried this, didnt output any logs
will try doing a fresh install

@McPizza0
try: postal stop and then postal start (postal restart does not work)

If you can avoid making changes to your docker-compose.yml it will make upgrades easier. These configuration options should be able to be made globally which may be sufficient to redirect the logs. I haven't tried though.

correct way would be a docker-compose.override.yml with the following contents:
version: "3.9"
services:
  smtp:
    logging:
      driver: syslog
      options:
        tag: "postal-smtp"

correct way would be a docker-compose.override.yml

This works well. I don't know why I haven't thought about this before. I will update the docs to include this for those who might find it useful.

@erichk4 checked many things
no logs are created in that location
seems that those log files only get created when logs are sent to STDOUT (now disabled in v3)
are you running on v3?
can you confirm that your log files are actually being updated?
container is set up to log correctly:

@adamcooke you want this in the guide (fail2ban) ?

We are using the syslog logging driver provided by docker directly, https://docs.docker.com/config/containers/logging/configure/#supported-logging-drivers
Below example will produce log files in /var/log/docker/postal-smtp.log
docker-compose.yml
smtp:
    image: ghcr.io/postalserver/postal:3.1.1
    command: postal smtp-server
    network_mode: host
    cap_add:
      - NET_BIND_SERVICE
    volumes:
      - /opt/postal/config:/config
    restart: always
    logging:
      driver: syslog
      options:
        tag: "postal-smtp"

and fail2ban:
jail.local:
[POSTAL]
enabled  = true
port     = 25
filter   = postal
maxretry = 3
findtime = 3600
logpath  = /var/log/docker/postal-smtp.log

filter.d > postal.conf
[Definition]
failregex = (.*)WARN   Authentication failure for <HOST>

ignoreregex =

Don't work in Debian 12.  Debian 12 don't have syslog.  It use journalctl to manage log.


bug: Two different keys that decode into the same field are not considered duplicate keys when decoding into struct

What version of fxamacker/cbor are you using?
Current HEAD of master, https://github.com/fxamacker/cbor/commit/2da2d676282a4ab781705794afc203e331b49eee.
Does this issue reproduce with the latest release?
Yes.
What OS and CPU architecture are you using (go env)?
go env Output
$ go env

What did you do?

Create a DecMode with DupMapKey set to DupMapKeyEnforcedAPF.
Unmarshal to struct a CBOR map containing two keys, both of which match the same struct field.

https://go.dev/play/p/jHzyzyfWCVD
What did you expect to see?
The caller of Unmarshal should have an indication that a map key in the input was effectively ignored. More specifically, if two map keys match the same struct field, they should be treated as equal for the purposes of detecting duplicate keys. This is not compatible with the current behavior of DupMapKeyEnforcedAPF, but since there is no way to tell that this has happened (other than doing a parallel decode into map), it seems reasonable to me to modify DupMapKeyEnforcedAPF rather than adding a new DupMapKeyMode.
I mentioned this in https://github.com/fxamacker/cbor/issues/432 in the context of case-insensitivity, but there may be other ways to reproduce it (possibly a separate issue, but I included in the playground link an example that relies on a keyasint field tag matching either an integer key or a string key containing the int encoded to base-ten).
What did you see instead?
No DupMapKeyError is returned from Unmarshal.
I understand that this raises interesting questions about consistency on the meaning of DupMapKeyError depending on whether the destination object has a struct type or a map type. I think some behavior differences are expected there. Today, for example, decoding into struct already forbids CBOR keys that are neither strings nor integers, even though the same keys may be permitted when decoding a map.
I'm especially interested in this because my use case is always configured to reject inputs with duplicate map keys. Populating the map used to detect duplicates (https://github.com/fxamacker/cbor/blob/2da2d676282a4ab781705794afc203e331b49eee/decode.go#L1902) makes many short-lived heap allocations. For the common case where all map keys uniquely match one struct field, all of those allocations can be avoided if we can rely on the existing foundFldIdx []bool (or similar) to recognize duplicates. I've explored that optimization, and my (application-specific) benchmarks suggest that allocations during decode can be reduced by more than 50% along with a more than 1.6x speedup.

@benluddy Thanks for reporting this bug and sharing your insights!

The caller of Unmarshal should have an indication that a map key in the input was effectively ignored. More specifically, if two map keys match the same struct field, they should be treated as equal for the purposes of detecting duplicate keys.

I agree.

I understand that this raises interesting questions about consistency on the meaning of DupMapKeyError depending on whether the destination object has a struct type or a map type.

Yeah, this is about key equivalency for specific data models in section 2.2 of RFC 8949.
User can:

tweak destination type when decoding to any, or
specify destination type to decode to

So destination type is specific data model, and currently duplicate map key detection evaluates key based on key's destination type.
For struct destination, if different map keys can be decoded to the same field, those keys should be considered duplicate.
Also, potential json/v2 handles duplicate names depending on destination object.

it seems reasonable to me to modify DupMapKeyEnforcedAPF rather than adding a new DupMapKeyMode.

I agree!  Please feel free to open PR to modify DupMapKeyEnforceAPF to resolve this issue.

I'm especially interested in this because my use case is always configured to reject inputs with duplicate map keys.
For the common case where all map keys uniquely match one struct field, all of those allocations can be avoided if we can rely on the existing foundFldIdx []bool (or similar) to recognize duplicates.

mapKeys is also used to detect duplicate map keys for fields not specified in struct destination.  So maybe it is still needed even if decoder rejects keys that can be decoded to the same field.
Do you also reject unknown struct fields ExtraDecErrorUnknownField?  If the use case is to reject unknown fields and no duplicate keys, the input data should be a subset for struct destination type, and I think mapKeys can be removed.  Maybe we can special case this combination and take advantage of the optimization.
Thoughts?

I agree! Please feel free to open PR to modify DupMapKeyEnforceAPF to resolve this issue.

Great! I'll get a PR open soon.

Do you also reject unknown struct fields ExtraDecErrorUnknownField? If the use case is to reject unknown fields and no duplicate keys, the input data should be a subset for struct destination type, and I think mapKeys can be removed. Maybe we can special case this combination and take advantage of the optimization.

We sometimes reject and sometimes allow with a warning, but either way unknown fields must be detected. I think the optimization would be safe in general if mapKeys is used only for keys that don't match a struct field. You'd get a duplicate key error either when decoding into a field for the second time, or when two unknown fields are equal. If most or all keys map to fields in common inputs, most or all of the allocations related to mapKeys can be avoided. The existing limitation on which CBOR key types may be mapped to a struct field helps to rule out surprising cases, like having two identical strings with one enclosed in a tag.

Closed by #492


Integration test of static policy plugin for resource manager

Integration test or manual test script that uses the /etc/geopm/environment* files and a configured plugin for the system.  If implemented in the integration test infrastructure, the test would need to have extra checks to be skipped if the plugin is not enabled or the environment is not set up on compute nodes.
Requirements for system:

/usr/lib system install of libgeopmpolicy
geopm_spank.so plugin enabled in plugstack.conf, if using slurm
/etc/geopm/environment* files installed on compute nodes with a policy file.

Depends on #786 and #708

Related to #738


[IE11] EventSource requests are loaded from cache

In IE11 all the EventSource requests are loaded from the cache. I can circumvent the problem if I open the IE developer tools and check the feature to disable the cache.
The server's respinse header is:
Content-Type: text/event-stream;charset=UTF-8
Transfer-Encoding: chunked

There is no hint for using a cache so I do not know why this happens.
My expectation is that EventSource requests are never cached...

The server did not send Cache-Control: no-cache. After fixing that it works as expected.


Add -f flag to packwiz list

packwiz list -f [filename] will write a list of mods to a file in the format of a Markdown table. The flag is compatible with all other flags for list.
Sample outputs
packwiz list -f list.md

Name

A Man With Plushies (AMWPlushies)

BetterF3

Discord Integration

packwiz list -v -f list.md

Name
Version

A Man With Plushies (AMWPlushies)
amwplushies-forge-1.19.2-2.0.1.jar

BetterF3
BetterF3-4.0.0-Forge-1.19.2.jar

Discord Integration
dcintegration-forge-3.0.5-1.19.2.jar

packwiz list -s client -f list.md

Name
Side

A Man With Plushies (AMWPlushies)
both

BetterF3
client

packwiz list -s server -f list.md

Name
Side

A Man With Plushies (AMWPlushies)
both

Discord Integration
server

packwiz list -v -s both -f list.md

Name
Version
Side

A Man With Plushies (AMWPlushies)
amwplushies-forge-1.19.2-2.0.1.jar
both

BetterF3
BetterF3-4.0.0-Forge-1.19.2.jar
client

Discord Integration
dcintegration-forge-3.0.5-1.19.2.jar
server

Notes
The Markdown table is written so that there is no unevenness in each column. Here's a screenshot from Sublime Text with Word Wrap disabled:

This is an awesome idea, and functionality that I was looking for!
I'd suggest decoupling the write list to file concept from the format though.  Perhaps -f <filename> to write out a file, and -o text|markdown for controlling the output format?  That way its easier for other formats to be added in the future as needed.
Also you could simplify the code writing the markdown with something like tablewriter


Import performance.memory() documentation

Request type

[x] New documentation
[x] Correction or update

Page to change
I would like to add https://developer.mozilla.org/en-US/docs/Web/API/Performance/memory (which is currently a dead link on the https://developer.mozilla.org/en-US/docs/Web/API/Performance page)
Details
Existing docs are here: https://webplatform.github.io/docs/apis/timing/properties/memory/
source: https://github.com/webplatform/webplatform.github.io/blob/8855897b894e17a653ee3de8745ea07aa382d214/docs/apis/timing/properties/memory/index.html
I want to update the note about memory quantization: In Chrome 68+, you can sample every 50ms if your script was served from the page origin (eTLD+1). Chrome patch: https://chromium.googlesource.com/chromium/src/+/7c7847f69de403f6c798dfccba10812039a60480
I haven't seen this documented anywhere online, and there's lots of places still falsely advertising the 20 minute minimum interval. I know this isn't standardized, but my users are mostly using Chrome and I need to monitor memory usage for a complex WebGL application.

@ccope Please email<EMAIL_ADDRESS>to request Page Creation permission so you can add the page.

And be sure to attribute the source, per the CC-BY license of WebPlatform.org.

@ccope did you get the permissions you need to do this?

Performance.memory() article now exists, though it does not have all the content that the WebPlatform page has. So, it could still be improved by adding that content (with CC-BY attribution).


Show how to trigger the shield rule in the Node.js + express getting started guide

The Node.js + Express getting started guide shows how to trigger both the tokenBucket and detectBot rules. It would be nice to show an example of how to trigger shield.

We might want to explain that's the case if we elaborate on the rules a little, like suggested in https://github.com/arcjet/arcjet-docs/issues/181.


Added Repo Size

Thanks for noting it may be benficial to display the repo size. This is something that will fluctuate as we iterate on the contents, and work to integrate new technologies so it will be difficult to have this information in an image. I will close this PR in favor of a future enhancement to dynamicaly display this information.


#26 user model mongo

fix  User schema

please see https://mongoosejs.com/docs/typescript.html
no need for UserDocument interface


Chain ids in Safe Apps

Problem
Atm we have to enter raw chain ids in Safe Apps entries.
This is very inconvienient and prone to error.

Request
It would be much better to use the Chain configs for this, like we do for Features.

I agree. One main reason why it was done initially like this was because the Safe Config Service has two "apps" (in the Django sense).
Safe Apps App is independent of the Chains App so you can run the config service with Safe Apps only (if that's what you'd need).
However this is then reflected on the lack of structured data on the Safe Apps Admin pages when it comes to Chains. I see two alternatives to improve this:

Add a Chains base app – simple app that only has the chain id and name. Then the full fledged Chains app would depend on the base one. The same for the Safe Apps App.
Make the Safe Apps App depend on the Chains App.

Option 1 is more robust, more modular, bigger refactor.
Option 2 is less modular but simpler.
With the current state of the project I think it's important that we have some kind of structured data that can be shared (for Chains).

I would personally be OK even with just a duplicate Chains enum inside the Safe Apps. Just a chain id and name, so that we can easily enable/disable chains per app.


constructor injection vs resolver for DI

This is an important design choice I'm not sure about yet.
With dependency injection, there's a couple ways to get the dependencies. There's constructor injection:
public class Foo
{
    private readonly ILogger Logger;
    Foo(ILogger logger)
    {
        Logger = logger;
    }
}

And there's resolvers:
public class Foo
{
    private readonly ILogger Logger;
    Foo()
    {
        Logger = IoCManager.Resolve<ILogger>();
    }
}

Both of these approaches have their up and downsides, and I have a hard time deciding so I'm making this discussion.
Advantages of constructor injection (to resolvers):

Allows parallelized unit tests.
Makes dependency graphs easier to track (read: easier to see what goes where)
Infinitely easier to manage state properly. You can actually have separate containers.

Disadvantages:

Circular references are impossible, and require abstraction into even smaller components, which often just makes the code hard to follow so you have your idealistic DI pattern.
Confusing
Harder to manage. Constructors on MANY classes will now need this.
Memory inefficient because even uncommon services need to have references kept.

Personally I feel like resolvers are the better option because they're much easier to work with. Issue is that if we need unit tests to be parallel (and we probably do want that) or we need to be able to keep multiple containers we cannot go this route and we're forced to constructor injection.

With the amount of stuff we would need to pass in a constructor and the thought of that refactor I vote for resolvers right off the start.
Aren't unit tests currently running parallel? what is preventing them from doing so? is IoC only serving one at a time?
Does this also apply to the container issue you mentioned? What prevents multiple containers?

You can't have multiple containers when IoC is static, because I believe unit tests simply run on different threads, so the unit tests would modify the static variable at the same time and break everything.

Could modify IoC to use ThreadLocal<T> so that each thread has their own version.

That would ruin any chance of multithreading later down the line.

@volundr- I'd like some input from you too on this issue.

Is it possible to remove them from constructors and maybe us annotations to inject the deps?
Either either way, i'd like to remove the IoC calls but also don't want to see a bunch of stuff added to a constructor, that should be abstracted away from me.

Something i've seen is like this here - https://msdn.microsoft.com/en-us/library/ff660864(v=pandp.20).aspx

@psykzz sounds good.

Could probably do some code generation magic to speed that up too. I'm down.

Right now I'm leaning towards static resolvers for convenience and property injection as the preferred option.

I think we've decided.


build: select the correct DLL build type

The Swift project moved the default branch to main and deleted master branch, so GitHub automatically closed the PR. Please re-create the pull request with main branch.
More detail about the branch update - https://forums.swift.org/t/updating-branch-names/40412


Make ShareUsageBytes uint64

This fixes #28.

@adreed-msft please re-submit the PR from a branch on this repo (I've added you as a collaborator now)

ShareUsageBytes int32 xml:"ShareUsageBytes"

the directive doesn't seem to be working?

Refers to: azfile/zz_generated_models.go:2958 in eff9989. [](commit_id = eff9989e82d5edb2e8d4439a808f49399f9d9a43, deletion_comment = False)


typo on the wiki

https://github.com/rain1017/memdb/wiki/Guide#concurrencylockingtransaction
If any error accured, the entire transaction will be rolledback immediately.
should be
If any error occurred, the entire transaction will be rolled back immediately.

Fixed. thanks


Separate concerns in the ReadOperation

Currently the ReadOperation serves as a base class for FindOperation and CountOperation. However, the count operation does not implement any of the abstract methods:
  @Override
  public Uni<FindResponse> getDocuments(QueryExecutor queryExecutor, String pagingState) {
    return Uni.createFrom().failure(new JsonApiException(ErrorCode.UNSUPPORTED_OPERATION));
  }

  @Override
  public ReadDocument getNewDocument() {
    throw new JsonApiException(ErrorCode.UNSUPPORTED_OPERATION);
  }

In addition the count related method countDocuments is used only by the CountOperation, thus is not needed at all in the base interface. This all looks like a bad design.
Tasks:

Count operation does not need to extend the read operation

Move count related method to the count operation

Same can be done for Find operation, effectivelly leaving Read operation as empty interface
In order for this to work the FilterableResolver class should be transitioned to FilterableResolverService and resolver can directly use this service to resolve the filter clause

@amorton @amorton  Please review and approve issue.

Imo changing the FilterableResolver as FilterableResolverService is not required.
Easier refactoring for the 2 methods, would be to add default implementation to return Unsupported_Operation exception in ReadOperation interface and remove it from CountOperation class. Even if any new implementation for this interface will be taken care of.


[WIP] Relax graphql_object

WIP
Relax graphql_object procedural macro to include fn items provided by external declarative macros.
This will allow the inclusion of definitions declared outside of the main project for more reusable and self-contained parts.
struct Query;

macro_rules! macro_from_another_project_exporting_items {
    () => {
        pub fn a() -> i32 {
            1
        }

        pub fn b(&self) -> i32 {
            2
        }
    }
}

#[juniper::graphql_object]
impl Query {
    macro_from_another_project_exporting_items!();
}

Couldn't personally find another way to build compositions with Juniper. Please, tell me if this approach is too "hacky" to be included upstream.

Nah, NVM. Syn is a parsing library, not a engine that expands macros.


ability to specify queue on which the serializer will be dispatched

Added ability to specify queue for serializer, for me its highly usable if you have framework (like Realm) which don't allow to pass serialized objects between threads.

Hi @krezzoid,
Thanks for putting this PR together. Unfortunately we don't think this is a good set of changes to include in Alamofire itself. This deviates greatly from the delegate queue serial processing pattern leveraged heavily by the method chaining pattern. If you need this type of functionality, then I'd suggest you create your own extensions on the Request object to do this.
Cheers. 🍻

Hi @cnoon,
unfortunately I can't create my own Request extension, because in this way I will not be able to access
               self.delegate.data,                 self.delegate.error etc.,
from my extension (they are don't have public declaration and will not be visible outside Alamofire framework). Or you suggestion was about to create new extension for Request as a part of Alamofire framework?

Ah ic. In this case I think you'll want to create a custom response serializer that takes in the queue you want to run the serialization on and does it that way. For example, just execute something like responseData on the TaskDelegate queue, then process the data into your Realm model object on your provided queue. That should allow you to do what it is you're trying to accomplish.


Refactoring for Add Item.vi in Write/Read Waveform Case

[x] This contribution adheres to CONTRIBUTING.md.

What does this Pull Request accomplish?
Refactors the Write/Read Waveform case in Add Item.VI
Why should this Pull Request be merged?
In order to have a better modular approach.
What testing has been done?
NA - no dataflow change, only adding the current code in a subVI

Closed as all the forks were disconnected when the repo became public from internal, a new pull request will be opened that will implement also the feedback.


Possible gray view for “big” file (changes)

Was like +100 -100, got this when viewing the diff.
Could dismiss by swiping back as normal, but nothing other than that.

Bug Report Dump (Auto-generated)

Version 1.17.0<PHONE_NUMBER>)
Device: iPhone X (iOS 11.2)
TestFlight: true

Was the InspectorVC diff here
Sent with GitHawk


Regent: nested types not serialized correctly

In Regent, regentlib.string has special serialization magic to ensure that it can be passed around correctly. This does not apply to types like regentlib.string[N] (i.e., an array of strings), or other ones like structs that contain strings, that contain nested types that require special serialization.
Regent needs to auto-generate the serialization implementations for such types, and if we're going to do that, might as well support user-defined serialization while we're at it. This is therefore likely to build on #720, but I want to track it separately so I don't forget the specific issue with strings.

This is now working for (nested) array types as of bcd6fb597616810fd4ab803e6dff999b020b5611. I'm punting on structs because those are user-defined types and arguably should use the custom serialization framework anyway. I think this resolves the immediate problem tracked by this issue. Once CI passes I will close this.


Add support for multiple Mongo aggregation pipeline stages

The current implementation only allows you to specify a single Mongo pipeline stage, e.g.
in:
  type: mongodb
  aggregation: >
    {
      $match: { int32_field: { $gte: 5 }}
    }

But the power of Mongo's aggregation pipeline comes from supporting multiple stages, e.g.
in:
  type: mongodb
  aggregation: >
    [
      {
        $match: {
          int32_field: { $gte: 1 }
        }
      },
      {
        $sort: {
          int32_field: -1
        }
      }
    ]

This PR adds support for multiple stages, keeping backwards compatibility.

@dmikurube 👋 I don't know how long it typically takes to review and approve PRs. If it can be approved and released quickly, it would avoid me having to set up a private Gem source.

@dmikurube 👋 I don't know how long it typically takes to review and approve PRs. If it can be approved and released quickly, it would avoid me having to set up a private Gem source.

@emersonf Thanks for your contribution.
We, as the Embulk team, took over maintenance of this plugin from another individual developer very recently, and we have had little time to learn it so far, unfortunately. Honestly speaking, please expect a certain amount of time to review, merge, and release a new version.

@emersonf Thanks for your contribution.
We, as the Embulk team, took over maintenance of this plugin from another individual developer very recently, and we have had little time to learn it so far, unfortunately. Honestly speaking, please expect a certain amount of time to review, merge, and release a new version.

Is it really safe, for example, against a kind of "JSON injection"? I'd choose a type of foolishly naive way rather than this type of quick string techniques.
The code uses Mongo libraries to parse JSON, and I couldn't find a generic method to parse an array or a document. This avoids the problem by turning the array into a subfield of a document, letting the parsing deal with documents.

A better solution is to use a JSON processing library like Jackson instead. But to your point, I don't see any new meaningful attack here, any attack would have been possible before. Plus the parse function will break on invalid JSON.

Is it really safe, for example, against a kind of "JSON injection"? I'd choose a type of foolishly naive way rather than this type of quick string techniques.
The code uses Mongo libraries to parse JSON, and I couldn't find a generic method to parse an array or a document. This avoids the problem by turning the array into a subfield of a document, letting the parsing deal with documents.

A better solution is to use a JSON processing library like Jackson instead. But to your point, I don't see any new meaningful attack here, any attack would have been possible before. Plus the parse function will break on invalid JSON.


any body test in aishell1?

when i test on aishell1 dataset,
the WER is always 1.0, no drop

do you train the model with CTC in aishell1?


[RESOLVED] Unavailable by ICMP ping

Problem started at 19:41:16 on 2023.06.24
Problem name: Unavailable by ICMP ping
Host: ST-FG
Severity: High
Operational data: Down (0)
Original problem ID: 29129
Event details in Zabbix: http://<IP_ADDRESS>/tr_events.php?triggerid=23133&eventid=29129

Problem has been resolved in 11h 50m 0s at 07:31:16 on 2023.06.25
Problem name: Unavailable by ICMP ping
Host: ST-FG
Severity: High
Original problem ID: 29129


BNN saved model weights

When I use model.save() and model.get_weights(), the saved weight values are not binary.
I'm not sure if it's my miss.
Really thanks for any reply:)

Hi , See same issue here.
Weights are not printed in binary -- did i miss anything here  ?
loss: 0.3951 - acc: 0.8783 - val_loss: 0.1369 - val_acc: 0.9410
[array([[ 3.41787934e-03,  1.03282835e-02, -1.42487716e-02, ...,
7.11111538e-03,  2.02730242e-02,  1.32185612e-02],
[-1.01926271e-02, -1.31833507e-02,  1.67875085e-02, ...,

Hi, I have the same issue. Did you solve it?

I have the same problem..

I have the same problem, did you solve it?


feat: add traverseiterator utility methods to Query.ts and NeoServerRpcClient.ts

Raul is the author, I only sent the PR with the correct commit name and correct branch location.

Task linked: CU-3ccgz10 Implement traverseIterator

feature "description"
https://docs.neo.org/docs/en-us/reference/rpc/latest-version/api/traverseiterator.html

Hi @snowypowers can you review this PR please? :)


MoM - 6th Gathering

MoM scribed by @lakruzz
Participants
Kamstrup, Volvo, MAN D&T
A small recap.
What choices have been made implicitly in the tool, what if we have long lived branches.
Support for different branching strategies - especially long-lived feature branches and release branches.
When we are at a brink of a release - and need to go on to the next. We need a temporary stabilization branch. as in the git-flow.
We explain how resuming work on a branch using workon implicitly makes a pull --rebase
This should be configurable.
Talking about declarative pipelines.
Windows support is required.
Dan is suggesting that workon should take a parameter too - we seem to agree that the current implementation is better.
What happens if you forget to wrapupand do workon+ deliver
We're talking about Atlassian integration.
Jira+Bitbucket
Jira+GitHub
Volvo hasn't decided yet if they go with one on the other.
Jira is already integration with Bitbucket - so maybe it's really just a Jira integration.
How do we handle pull requests? It's required
Multiple long-lived/integration branch support is required (Samuel)
Task creating form the command line? is it relevant? Needed?
The Command line focus is kinda cool, and might help people actually use the command line better.
We're discussing - park/share - it's needed.

We created some post-its with ideas during the workshop. We only half-finished prioritisation of these.
Based on that work and my judgement call on what I heard during the rest of the workshop, my best summary would be the following:
M (Must have):

JIRA support for issues (Kamstrup, volvo, MAN?)
Windows support (MAN - must have!)
Configurable target branch

S (should have):

Park/share feature
Command:  (status?) show what I have:

which branches in progress
what unfinished work
what is waiting in queue?

Investigate best support for good branching strategy/workflow during stabilisation phase
Investigate best support for multiple long lived branches

C (could have):

Create new issues from git phlow
Investigate if there is special needs when working with hierarchical issue types in e.g. JIRA

The statements of work have been completed and the gathering is over so I will close this issue


First release

For the moment, the documentation is dev.
@remydutto When you think a first version is ready, then we will make the first release. It consists in declaring a tag.
Then, the documentation associated to this tag will be in stable.
To make a release, it is very simple. I give you the process below.

Update the version in Project.toml

Click on create a new release

Enter data for the new release
Here we have to set two times the tag. Once for the tag and once for the title of the release.

Create the release


Broken for issuing new certificates

Describe the bug
DNS TXT record is deleted and recreated during verification to Let's Encrypt Servers but changes do not refresh in Azure immediately.
Fix is to wait before validating for Azure DNS zone to update
        Thread.Sleep(10 * 1000);
        var validatedChallege = await dnsChallenge.Validate();

To Reproduce
Steps to reproduce the behavior. For Example:
Try to issue new cert.
Expected behavior
A clear and concise description of what you expected to happen.
Screenshots
If applicable, add screenshots to help explain your problem.
Additional context
Add any other context about the problem here.

Also just suddenly stated seeing this:
2023-06-21T21:09:53.366 [Information] Validating DNS authorization challenge. Can take up to 90 seconds...
2023-06-21T21:09:54.892 [Error] Unable to complete the processing for *.dev1.shinydev.co.ukCertes.AcmeRequestException : Fail to load resource from 'https://acme-v02.api.letsencrypt.org/acme/chall-v3/...'.urn:ietf:params:acme:error:malformed: Unable to update challenge :: authorization must be pending
The TXT record is created and accessible shortly after.


Centralised secrets management for volunteers

In what tool should we use to give access to volunteers to different keys?

One option I am thinking of is using a tool like keypass. It holds secrets in an encrypted file, and requires a password to open the file. we can:

have a repo with read access to only those that have permission to read the secrets.
Upload the keypass file to that repo.
To give access to a person we will

give him read permission to the repo
send him the password to the file

To access the secret one will need to install keypass.

If this is too much, we can store the secrets in open text in the repository.
What do you think?


Update third party module version

문서의 3rd party version을 0.3.1로 업데이트 합니다.

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


Amplifier should reload its configuration when it traps a HUP signal

now that the configuration is managed with Docker secrets and configs, this issue is obsolete


Seperate connection to Metamask and Trust Wallet?

Hi, im using walletconnect_dart to link to metamask and trustwallet. But when i clicked button to run code, it asked me to choose between metamask or trust wallet, but i want to click MM button to open MM only and the same with trust wallet. How can i do that( on Android)?

Hi, im using walletconnect_dart to link to metamask and trustwallet. But when i clicked button to run code, it asked me to choose between metamask or trust wallet, but i want to click MM button to open MM only and the same with trust wallet. How can i do that( on Android)?

Contact the support center to chat with a live agent on a live chat to report this for further assistance via LIVE CHAT

Any helps guys?

@LongTien15 Please take a look at this package: https://pub.dev/packages/walletconnect_qrcode_modal_dart
It lets you choose which wallet to open.


Add content from glossario

https://github.com/carpentries/glosario
License: CC-BY-4.0

I need to check if this license is OK to integrate the data

Seems it would be "OK"
https://opensource.stackexchange.com/questions/6110/is-there-any-particular-reason-to-license-docs-with-cc-by-4-0-rather-than-mit
What are your thoughts @Jason3S ?

Moved to #31


Add Singapore vehicle license plate

Hey @vbrazo, how do we move forward with this PR?

@anonoz let me pull the code and take a look.