[Bug]: stuck at  "generating GPU P2P access cache in /home/luban/.cache/vllm/gpu_p2p_access_cache_for_0,1.json"

Your current environment

The output of `python collect_env.py`
python collect_env.py
Collecting environment information...
2024-09-23 17:57:46.577274: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-09-23 17:57:46.594737: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-09-23 17:57:46.616458: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-09-23 17:57:46.622847: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-09-23 17:57:46.638311: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-09-23 17:57:47.734082: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.11.10 | packaged by conda-forge | (main, Sep 10 2024, 11:01:28) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-4.18.0-193.6.3.el8_2.v1.4.x86_64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA RTX A6000
GPU 1: NVIDIA RTX A6000

Nvidia driver version: 535.129.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 57 bits virtual
CPU(s):                          128
On-line CPU(s) list:             0-127
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           106
Model name:                      Intel(R) Xeon(R) Platinum 8352Y CPU @ 2.20GHz
Stepping:                        6
CPU MHz:                         2200.000
CPU max MHz:                     3400.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4400.00
Virtualization:                  VT-x
L1d cache:                       3 MiB
L1i cache:                       2 MiB
L2 cache:                        80 MiB
L3 cache:                        96 MiB
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] galore-torch==1.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==<IP_ADDRESS>
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==<IP_ADDRESS>
[pip3] nvidia-cufft-cu12==<IP_ADDRESS>
[pip3] nvidia-curand-cu12==<IP_ADDRESS>
[pip3] nvidia-cusolver-cu12==<IP_ADDRESS>
[pip3] nvidia-cusparse-cu12==<IP_ADDRESS>
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.68
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] optree==0.12.1
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchao==0.5.0
[pip3] torchaudio==2.4.0
[pip3] torchtext==0.18.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.0.dev0
[pip3] triton==3.0.0
[pip3] zmq==0.0.0
[conda] galore-torch              1.0                      pypi_0    pypi
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu12        <IP_ADDRESS>                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.1.105                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         <IP_ADDRESS>                 pypi_0    pypi
[conda] nvidia-cufft-cu12         <IP_ADDRESS>                pypi_0    pypi
[conda] nvidia-curand-cu12        <IP_ADDRESS>               pypi_0    pypi
[conda] nvidia-cusolver-cu12      <IP_ADDRESS>               pypi_0    pypi
[conda] nvidia-cusparse-cu12      <IP_ADDRESS>               pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.6.68                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.1.105                 pypi_0    pypi
[conda] optree                    0.12.1                   pypi_0    pypi
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torchao                   0.5.0                    pypi_0    pypi
[conda] torchaudio                2.4.0                    pypi_0    pypi
[conda] torchtext                 0.18.0                   pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] transformers              4.45.0.dev0              pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
[conda] zmq                       0.0.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.5@09c7792610ada9f88bbf87d32b472dd44bf23cc2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    NIC0    NIC1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      SYS     SYS     SYS     0-31,64-95      0               N/A
GPU1    SYS      X      SYS     SYS     32-63,96-127    1               N/A
NIC0    SYS     SYS      X      PIX
NIC1    SYS     SYS     PIX      X

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1

Model Input Dumps
python vllm_test.py
2024-09-23 17:49:44.893334: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-09-23 17:49:44.910873: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-09-23 17:49:44.932328: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-09-23 17:49:44.938677: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-09-23 17:49:44.954346: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-09-23 17:49:46.068503: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
WARNING 09-23 17:49:48 utils.py:721] Gemma 2 uses sliding window attention for every odd layer, which is currently not supported by vLLM. Disabling sliding window and capping the max length to the sliding window size (4096).
INFO 09-23 17:49:48 config.py:813] Defaulting to use mp for distributed inference
INFO 09-23 17:49:48 llm_engine.py:184] Initializing an LLM engine (v0.5.5) with config: model='/ofs/llm-for-dptd/modelscope/LLM-Research/gemma-2-27b-it', speculative_config=None, tokenizer='/ofs/llm-for-dptd/modelscope/LLM-Research/gemma-2-27b-it', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=/ofs/llm-for-dptd/modelscope/LLM-Research/gemma-2-27b-it, use_v2_block_manager=False, enable_prefix_caching=False)
WARNING 09-23 17:49:50 multiproc_gpu_executor.py:59] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
INFO 09-23 17:49:50 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
(VllmWorkerProcess pid=2348) INFO 09-23 17:49:50 selector.py:142] Using Flashinfer backend.
INFO 09-23 17:49:50 selector.py:142] Using Flashinfer backend.
(VllmWorkerProcess pid=2348) INFO 09-23 17:49:50 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
[W923 17:49:50.014460463 socket.cpp:697] [c10d] The client socket cannot be initialized to connect to [localhost]:37597 (errno: 97 - Address family not supported by protocol).
[W923 17:49:51.076798643 socket.cpp:697] [c10d] The client socket cannot be initialized to connect to [localhost]:37597 (errno: 97 - Address family not supported by protocol).
INFO 09-23 17:49:51 utils.py:975] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=2348) INFO 09-23 17:49:51 utils.py:975] Found nccl from library libnccl.so.2
INFO 09-23 17:49:51 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=2348) INFO 09-23 17:49:51 pynccl.py:63] vLLM is using nccl==2.20.5
INFO 09-23 17:49:51 custom_all_reduce_utils.py:203] generating GPU P2P access cache in /home/luban/.cache/vllm/gpu_p2p_access_cache_for_0,1.json
^CINFO 09-23 17:54:16 multiproc_worker_utils.py:136] Terminating local vLLM worker processes
^C[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 220, in gpu_p2p_access_check
[rank0]:     returned.check_returncode()
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/subprocess.py", line 502, in check_returncode
[rank0]:     raise CalledProcessError(self.returncode, self.args, self.stdout,
[rank0]: subprocess.CalledProcessError: Command '['/home/data/miniconda/envs/llm/bin/python', '/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py']' died with <Signals.SIGINT: 2>.
[rank0]: The above exception was the direct cause of the following exception:
[rank0]: Traceback (most recent call last):
[rank0]:   File "/ofs/llm-for-dptd/modelscope/vllm_test.py", line 10, in
[rank0]:     model = LLM(model="/ofs/llm-for-dptd/modelscope/LLM-Research/gemma-2-27b-it",
[rank0]:             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 175, in init
[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
[rank0]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/engine/llm_engine.py", line 473, in from_engine_args
[rank0]:     engine = cls(
[rank0]:              ^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/engine/llm_engine.py", line 270, in init
[rank0]:     self.model_executor = executor_class(
[rank0]:                           ^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/executor/distributed_gpu_executor.py", line 25, in init
[rank0]:     super().init(*args, **kwargs)
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/executor/executor_base.py", line 46, in init
[rank0]:     self._init_executor()
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/executor/multiproc_gpu_executor.py", line 137, in _init_executor
[rank0]:     self._run_workers("init_device")
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/executor/multiproc_gpu_executor.py", line 192, in _run_workers
[rank0]:     driver_worker_output = driver_worker_method(*args, **kwargs)
[rank0]:                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/worker/worker.py", line 175, in init_device
[rank0]:     init_worker_distributed_environment(self.parallel_config, self.rank,
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/worker/worker.py", line 450, in init_worker_distributed_environment
[rank0]:     ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/parallel_state.py", line 965, in ensure_model_parallel_initialized
[rank0]:     initialize_model_parallel(tensor_model_parallel_size,
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/parallel_state.py", line 931, in initialize_model_parallel
[rank0]:     _TP = init_model_parallel_group(group_ranks,
[rank0]:           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/parallel_state.py", line 773, in init_model_parallel_group
[rank0]:     return GroupCoordinator(
[rank0]:            ^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/parallel_state.py", line 164, in init
[rank0]:     self.ca_comm = CustomAllreduce(
[rank0]:                    ^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce.py", line 130, in init
[rank0]:     if not _can_p2p(rank, world_size):
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce.py", line 31, in _can_p2p
[rank0]:     if not gpu_p2p_access_check(rank, i):
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 223, in gpu_p2p_access_check
[rank0]:     raise RuntimeError(
[rank0]: RuntimeError: Error happened when batch testing peer-to-peer access from (0, 0, 1, 1) to (0, 1, 0, 1):
[rank0]: 2024-09-23 17:49:54.998211: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
[rank0]: 2024-09-23 17:49:55.013891: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
[rank0]: 2024-09-23 17:49:55.032927: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
[rank0]: 2024-09-23 17:49:55.038641: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[rank0]: 2024-09-23 17:49:55.054505: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
[rank0]: To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
[rank0]: 2024-09-23 17:49:56.155623: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
[rank0]: 2024-09-23 17:50:02.535955: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
[rank0]: 2024-09-23 17:50:02.536583: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
[rank0]: 2024-09-23 17:50:02.551631: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
[rank0]: 2024-09-23 17:50:02.551680: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
[rank0]: 2024-09-23 17:50:02.571366: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
[rank0]: 2024-09-23 17:50:02.571366: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
[rank0]: 2024-09-23 17:50:02.577084: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[rank0]: 2024-09-23 17:50:02.577130: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[rank0]: 2024-09-23 17:50:02.591725: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
[rank0]: To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
[rank0]: 2024-09-23 17:50:02.591727: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
[rank0]: To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
[rank0]: 2024-09-23 17:50:03.665481: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
[rank0]: 2024-09-23 17:50:03.791268: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
[rank0]: Process SpawnProcess-1:
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
[rank0]:     self.run()
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/process.py", line 108, in run
[rank0]:     self._target(*self._args, **self._kwargs)
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 37, in producer
[rank0]:     handle = lib.cudaIpcGetMemHandle(pointer)
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/cuda_wrapper.py", line 162, in cudaIpcGetMemHandle
[rank0]:     self.CUDART_CHECK(self.funcs["cudaIpcGetMemHandle"](
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/cuda_wrapper.py", line 127, in CUDART_CHECK
[rank0]:     raise RuntimeError(f"CUDART error: {error_str}")
[rank0]: RuntimeError: CUDART error: invalid argument
[rank0]: Process SpawnProcess-2:
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 245, in
[rank0]:     result = can_actually_p2p(batch_src, batch_tgt)
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 147, in can_actually_p2p
[rank0]:     p_tgt.join()
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/process.py", line 149, in join
[rank0]:     res = self._popen.wait(timeout)
[rank0]:           ^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/popen_fork.py", line 43, in wait
[rank0]:     return self.poll(os.WNOHANG if timeout == 0.0 else 0)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/popen_fork.py", line 27, in poll
[rank0]:     pid, sts = os.waitpid(self.pid, flag)
[rank0]:                ^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: KeyboardInterrupt
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
[rank0]:     self.run()
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/process.py", line 108, in run
[rank0]:     self._target(*self._args, **self._kwargs)
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/site-packages/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 67, in consumer
[rank0]:     handle = producer_queue.get()
[rank0]:              ^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/queues.py", line 103, in get
[rank0]:     res = self._recv_bytes()
[rank0]:           ^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/connection.py", line 216, in recv_bytes
[rank0]:     buf = self._recv_bytes(maxlength)
[rank0]:           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/connection.py", line 430, in _recv_bytes
[rank0]:     buf = self._recv(4)
[rank0]:           ^^^^^^^^^^^^^
[rank0]:   File "/home/data/miniconda/envs/llm/lib/python3.11/multiprocessing/connection.py", line 395, in _recv
[rank0]:     chunk = read(handle, remaining)
[rank0]:             ^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: KeyboardInterrupt
^C
the code i'm using
'''
from vllm import LLM
import os
os.environ['VLLM_ATTENTION_BACKEND'] = 'FLASHINFER'
model = LLM(model="/ofs/llm-for-dptd/modelscope/LLM-Research/gemma-2-27b-it",
dtype="auto",
trust_remote_code=True,
tokenizer_mode="auto",
tensor_parallel_size=2)
'''
🐛 Describe the bug
it just stuck here for a few hours...
Before submitting a new issue...

[X] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

please update to the new vllm version.
and you can pass disable_custom_all_reduce=True to bypass the check. your gpus probably don't have p2p capability.

same problem when i use 2 nodes / workers, but works well at single node

i tried to pass disable_custom_all_reduce=True, and it just exits when loading the model

you need to debug this at first. it might not be an issue from vllm.

I have a node with 8xh100 and am seeing the same issue

@berserkr  please run the test script at https://docs.vllm.ai/en/latest/getting_started/debugging.html#incorrect-hardware-driver first


feat: customize gradio components with lightning colors

What does this PR do?
This PR is customizing the gradio components with lightning colors.

If you want to check out the newly styled-components you can run lightning run app lit_gradio_interface_demo.py in the tests dir.
These changes were discussed with @aniketmaurya here:

Was this discussed/agreed via a GitHub issue? (not for typos and docs)
[x] Did you read the contributor guideline, Pull Request section?
[x] Did you make sure your PR does only one thing, instead of bundling different changes together?
Did you make sure to update the documentation with your changes? (if necessary)
Did you write any new necessary tests? (not for typos and docs)
[x] Did you verify new and existing tests pass locally with your changes?
Did you list all the breaking changes introduced by this pull request?
Did you update the CHANGELOG? (not for typos, docs, test updates, or minor internal changes/refactors)

PR review
Anyone in the community is welcome to review the PR.
Before you start reviewing, make sure you have read the review guidelines. In short, see the following bullet-list:

Reviewer checklist

[x] Is this pull request ready for review? (if not, please submit in draft mode)
[x] Check that all items from Before submitting are resolved
[x] Make sure the title is self-explanatory and the description concisely explains the PR
[x] Add labels and milestones (and optionally projects) to the PR so it can be classified

@Borda can you please help in the PR review?

Hi, @awaelchli!
Can you help me with the review when you have time for it :slightly_smiling_face:

Hi @Borda :wave:
Can you help me with this, please? :3

Hi @aniketmaurya @awaelchli @Borda , can you look at this PR again?


Implement October/November Seasonal, Super Squad Showdown

I'm dead
I'll create a few more custom sets later (maybe after the work week is over)

Change the seasonal alias too

The what?
Also I forgot the ruleset. Silly me!

ok I saw it

Scarecrow now has a custom set with Gunk Shot in it.

Moving this seasonal to november

Last changes before vacation.
Some notes because I think balancing might be needed in the future:

Wonder Woman is a tad strong usually. Scrappy HJK 2fast2furious
Big Hero 6 has 4 Pokemon weak to Fighting, which makes them very weak to the aforementioned Wonder Woman. Maybe some type changes upon switch-in could help.

The way I set up the sets in the team generator should make it pretty easy to make changes if needed, I think.

No worries, I can check balance and update this for it.

@Joimer merge when

I think @Joimer still has the mindset that the 5th is when we release but we've been aiming for the 1st of the month when we can recently.
Merge whenever really but tomorrow is preferred haha. I might merge this myself before I get on the plane tomorrow, or I might not because whateverrrrrrrrrr

Monday is a good day to merge and add this.


cmd/go: 'go tool cover' fails when import path has a '.go' suffix

What version of Go are you using (go version)?
Go 1.11rc1 and bf80e3b5640447d213f68e0423ad37f34bf7a222
Does this issue reproduce with the latest release?
If by latest release means stable release, then no.
What operating system and processor architecture are you using (go env)?
GOARCH="amd64"
GOBIN=""
GOCACHE="/home/ms/.cache/go-build"
GOEXE=""
GOFLAGS=""
GOHOSTARCH="amd64"
GOHOSTOS="linux"
GOOS="linux"
GOPATH="/home/ms/go"
GOPROXY=""
GORACE=""
GOROOT="/home/ms/Programs/go"
GOTMPDIR=""
GOTOOLDIR="/home/ms/Programs/go/pkg/tool/linux_amd64"
GCCGO="gccgo"
CC="gcc"
CXX="g++"
CGO_ENABLED="1"
GOMOD=""
CGO_CFLAGS="-g -O2"
CGO_CPPFLAGS=""
CGO_CXXFLAGS="-g -O2"
CGO_FFLAGS="-g -O2"
CGO_LDFLAGS="-g -O2"
PKG_CONFIG="pkg-config"
GOGCCFLAGS="-fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=/tmp/go-build787046816=/tmp/go-build -gno-record-gcc-switches"

What did you do?

Create a directory named "gotoolcover.go"
Create source and test file, for example, main.go

package main

func printHello() {
        println("Hello")
}

and  main_test.go
package main

import "testing"

func TestPrintHello(t *testing.T) {
        printHello()
}

Run test with profile coverage,

$ go test -count=1 -coverprofile=cover.out ./...

Convert test profile to HTML,

$ go tool cover -html=cover.out -o cover.html

What did you expect to see?
No error.
What did you see instead?
cover: cannot run go list: exit status 1
stat bug/gotoolcover.go: no such file or directory

Notes

If I change the directory name to any text, i.e. ".any", it run successfully.

16:03 ~/tmp/sandbox/go/src/bug/gotoolcover.go
master ms 1 % cd ..

16:04 ~/tmp/sandbox/go/src/bug
master ms 0 % mv gotoolcover.go gotoolcover.any

16:04 ~/tmp/sandbox/go/src/bug
master ms 2 % cd gotoolcover.any

16:04 ~/tmp/sandbox/go/src/bug/gotoolcover.any
master ms 0 % go test -count=1 -coverprofile=cover.out ./...
ok      bug/gotoolcover.any     0.001s  coverage: 100.0% of statements

16:04 ~/tmp/sandbox/go/src/bug/gotoolcover.any
master ms 0 % go tool cover -html=cover.out -o cover.html

This is not an issue in Go 1.10.3,

16:06 ~/tmp/sandbox/go/src/bug/gotoolcover.any
master ms 0 % go version
go version go1.10.3 linux/amd64

16:06 ~/tmp/sandbox/go/src/bug/gotoolcover.any
master ms 0 % go test -count=1 -coverprofile=cover.out ./...
ok      bug/gotoolcover.any     0.001s  coverage: 100.0% of statements

16:06 ~/tmp/sandbox/go/src/bug/gotoolcover.any
master ms 0 % go tool cover -html=cover.out -o cover.html

16:06 ~/tmp/sandbox/go/src/bug/gotoolcover.any
master ms 0 %

I ran into this as well. This has pushed me back to 1.10 for code coverage analysis.
@shuLhan FYI in your 1.10.3 example the directory suffix should be .go instead of .any to show it does work in 1.10.3. :smile:

@rsc @bcmills

Any update in this for a fix? We renamed our NATS.io client to nats.go and are getting support issues ourselves.

@derekcollison, work on this issue is unplanned (per its milestone).

Hmm well that's not good ;)
Seems like should be a one line fix in the Go tooling no?

@derekcollison, if you have a one-line fix then please send it. 🙂
(https://golang.org/doc/contribute.html)
(CC @jayconrod)

I have used that line before to for https://nats.io 🙂

This is fixed now:
11:29:22-agniva-/tmp/gotoolcover.go$ll
total 72
drwxr-xr-x   2 agniva agniva  4096 Oct 22 11:29 ./
drwxrwxrwt 107 root   root   57344 Oct 22 11:29 ../
-rw-r--r--   1 agniva agniva    63 Oct 22 11:21 main.go
-rw-r--r--   1 agniva agniva    92 Oct 22 11:21 main_test.go
11:29:23-agniva-/tmp/gotoolcover.go$gotip test -count=1 -coverprofile=cover.out ./...
ok  	_/tmp/gotoolcover.go	0.001s	coverage: 100.0% of statements
11:29:26-agniva-/tmp/gotoolcover.go$


ocamlPackages.gnuplot: 0.5.3 → 0.7

Motivation for this change
Drop dependency of ocaml-gnuplot on the core library.
The phylogenetics package must be updated for compatibility with ocaml-gnuplot (cc @bcdarwin).
Updated phylogenetics depends on ocaml-r, which is thus packaged in this PR too.
Things done

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

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

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

@GrahamcOfBorg build ocamlPackages.ocaml-r ocamlPackages.gnuplot ocamlPackages.phylogenetics


Bluetooth: Shell: BR: Set user data size for rfcomm tx pool

The user data size of the RFCOMM tx pool is zero.
There is not enough space to put the tx_mate.
Use CONFIG_BT_CONN_TX_USER_DATA_SIZE to set
the data size for the RFCOMM tx pool.

Do you mean tx_meta instead of tx_mate in the commit message?


upload spin linux amd64 artifacts to S3

@rajatjindal could you say more about the motivation for this?

Hi Ivan, currently we download spin artifacts for internal testing purposes and those skews the GitHub statistics for the releases.
so we wanted to have a different source for downloading these artifacts for internal purposes.

Hi @itowlson, do you see any concerns with this or we can merge this?

No, sorry, I just wanted to understand why, and now I do.  Sorry for not closing the loop.


Cannot handle sway toggle display

On output DP-3 toggle:
ERROR: output configuration has been cancelled src/listener_output_configuration.c:38, exiting

Reproduce:

start sway
disable output and wait for powerdown
start way-displays

Display will arrive/depart/arrive.

I [14:34:41.056] way-displays version 1.2.1-SNAPSHOT
I [14:34:41.056]
I [14:34:41.056] Found configuration file: /home/alex/.dotfiles/config/way-displays/cfg.yaml
I [14:34:41.056]   Arrange in a ROW aligned at the TOP
I [14:34:41.056]   Auto scale: ON
I [14:34:41.056]
I [14:34:41.056] HDMI-A-1 Arrived:
I [14:34:41.057]   info:
I [14:34:41.057]     name:     'HDMI-A-1'
I [14:34:41.057]     desc:     'Unknown ASUS XG32V 0x0000CF52 (HDMI-A-1)'
I [14:34:41.057]     width:    700mm
I [14:34:41.057]     height:   390mm
I [14:34:41.057]   current:
I [14:34:41.057]     (disabled)
I [14:34:41.057]
I [14:34:41.057] eDP-1 Arrived:
I [14:34:41.057]   info:
I [14:34:41.057]     name:     'eDP-1'
I [14:34:41.057]     desc:     'Unknown 0x05EF 0x00000000 (eDP-1)'
I [14:34:41.057]     width:    310mm
I [14:34:41.057]     height:   170mm
I [14:34:41.057]     dpi:      212.45 @ 2560x1440
I [14:34:41.057]   current:
I [14:34:41.057]     scale:    2.000
I [14:34:41.057]     position: 0,0
I [14:34:41.057]     mode:     2560x1440@60Hz (preferred)
I [14:34:41.057]
I [14:34:41.057] HDMI-A-1 Changing:
I [14:34:41.057]   from:
I [14:34:41.057]     (disabled)
I [14:34:41.057]   to:
I [14:34:41.057]     scale:    1.000
I [14:34:41.057]     position: 0,0
I [14:34:41.057]     mode:     2560x1440@60Hz
I [14:34:41.057]     (enabled)
I [14:34:41.057]
I [14:34:41.057] eDP-1 Changing:
I [14:34:41.057]   from:
I [14:34:41.057]     scale:    2.000
I [14:34:41.057]     position: 0,0
I [14:34:41.057]     mode:     2560x1440@60Hz (preferred)
I [14:34:41.057]   to:
I [14:34:41.057]     scale:    2.250
I [14:34:41.057]     position: 2560,0
I [14:34:41.501]
I [14:34:41.501] Changes successful
I [14:34:42.207]
I [14:34:42.207] Monitoring lid device: /dev/input/event1
I [14:34:44.181]
I [14:34:44.181] HDMI-A-1 Departed:
I [14:34:44.181]     name:     'HDMI-A-1'
I [14:34:44.181]     desc:     'Unknown ASUS XG32V 0x0000CF52 (HDMI-A-1)'
I [14:34:44.181]
I [14:34:44.181] eDP-1 Changing:
I [14:34:44.181]   from:
I [14:34:44.181]     scale:    2.250
I [14:34:44.181]     position: 2560,0
I [14:34:44.181]     mode:     2560x1440@60Hz (preferred)
I [14:34:44.181]   to:
I [14:34:44.181]     position: 0,0
I [14:34:44.720]
I [14:34:44.720] HDMI-A-1 Arrived:
I [14:34:44.720]   info:
I [14:34:44.720]     name:     'HDMI-A-1'
I [14:34:44.720]     desc:     'Unknown ASUS XG32V 0x0000CF52 (HDMI-A-1)'
I [14:34:44.720]     width:    700mm
I [14:34:44.720]     height:   390mm
I [14:34:44.720]   current:
I [14:34:44.720]     scale:    1.000
I [14:34:44.720]     position: 0,0
I [14:34:44.720]     mode:     2560x1440@60Hz
E [14:34:44.724]
E [14:34:44.724] ERROR: output configuration has been cancelled src/listener_output_configuration.c:38, exiting


Issue #104 - "mkdir -p" fail on Windows OS was fixed

Issue #104 - "mkdir -p" fail on Windows OS was fixed
Description
When I do npm run bundle:svg I see error, because flag "-p" (mkdir -p dist/svg) unsupported on Windows systems.
I fixed it whith Node.js mkdirp library.

Thank you for your contribution to TOAST UI product. 🎉 😘 ✨

Can one of the admins verify this patch?

I reopen this PR from my new account (contribution statistic)
https://github.com/nhnent/tui.image-editor/pull/106


Adding python badge displaying the versions tested in the CI

:books: Documentation preview :books:: https://scikit-matter--200.org.readthedocs.build/en/200/

I added a section talking about on what exact platforms we do CI tests. I think that is useful for a  user to know. The meta information is more about what we expect it supposed to work on. Also I just wrote a sentence because a table seemed a bit too much. I would like it more if this this is somehow integrated in our test badge, but I found no way to do it. I think it is good enough.
Also added badge for the doi of the paper.


Add support for autocomplete.

This is to add support for autocomplete for string/number fields.
The suggested structure is as follows, each option is given a name, for example for a to-do list, you may have a remove method which can be used to lookup the available tasks to remove.
To do this, we would want to support, general catch all's where the context can be given appropriately but you can control how you want to access each autocomplete, and then additionally lower level so you can say which option and which subcommand if applicable.
To do this, we shall add a new method decorator as: @On.Autocomplete the parameters would be:

optionName - An optional parameter that defaults to *, but if passed will only allow autocomplete for that specific option name.
subCommand - An optional parameter, again defaulting to * that will verify if a subcommand is there, then only allow that.

By default all autocompletions would be managed inside of a command class, and would be processed under that command, so if you had 3 different commands, you would need at the minimum, 3 autocomplete commands inside of each command class.
An example of the above may look like:
Assume the below method would be part of the TodoCommand class.
@On.Autocomplete()
public async onAutocomplete(context: Context): Promise<void> {
  const interaction = context.getInteraction<CommandInteraction>();
  const query = interaction.options.getFocused() as string;

  interaction.respond(
    this.tasks.filter(task =>

      // If no query, return all.
      if (!query || String(query).length === 0) return true;

      // Otherwise filter against name matches.
      return task.name.toLowerCase().includes(query.toLowerCase());
    })
    .map(task => {
      return {
        name: task.name,
        value: task.id,
      };
    });
  );
}

Added here:
https://github.com/Symbux/Turbo-Discord/commit/52a47c3922dc97d2e6860196dd8183119b0a4a0e


DOMA: dump when uninstalling

due to recent change,
https://github.com/larshp/abapGit/commit/82ece0ff3c0e452b5ad1c24e07ff4a138b7b6826

looks like sometime in a SP for 740
Just catch the exception and call the FM without the parameter?


Handle nullable object arrays

If a JSON payload contains a property that can be array|null the current functionality breaks when attempting to pass a null value to array_map in Json.php line 76.
A null check has been added so that if the value from the JSON payload is null it returns the null value, otherwise it processes the value as normal.
In the example below an error would have previously been thrown:
INPUT:
{
    "nullable_schedules": null
}

CLASS:
class Category
{
    use JsonSerialize;

    #[Json('nullable_schedules', type: Schedule::class)]
    protected ?array $nullableSchedules;
}

ERROR:
Fatal error: Uncaught TypeError: array_map(): Argument #2 ($array) must be of type array, null given

PR looks good, I need to figure out a way to enable our CLA bot on this repo. I'll get back to you in a bit.

As this is a Square hosted project, individual contributors are required to sign the CLA in order to contribute.
Once you have signed, we'll be able to move forward with the PR.

CLA signed and submitted!
Thanks...


The V2V and The I2V model has opened? when to update the finetune readme and the code for the v2v and i2v model

Feature request  / 功能建议
The V2V and The I2V model has opened? when to update the finetune readme and the code for the v2v and i2v model
Motivation / 动机
The V2V and The I2V model has opened? when to update the finetune readme and the code for the v2v and i2v model
Your contribution / 您的贡献
The V2V and The I2V model has opened? when to update the finetune readme and the code for the v2v and i2v model

V2V is the same as T2V and I2V is different please check our readme


Adds ability to supply template files to catalog request

Description
Adds ability to supply custom request templates from file.  If you have a bunch of
saved catalog templates you can now just create catalog requests from those templates
or even just merge in those templates with your current catalog request object.
Why is this important?  Test-kitchen automates this process for you.  But if you are not using this for some other project, you will eventually want to have this feature since trying to merge properties via set_parameter is very painful.  As noted in #54
This MR also supersedes #56 since it was required to make this feature.  Merging this request negates the need to merge #56
This MR also fixes some tests that were skipped in the catalog_request specs.
For an example catalog template, please see spec/fixtures/catalog_request_template.json
Issues Resolved
N|A
Check List

[x] All tests pass.
[x] All style checks pass.
[x] Functionality includes testing.
[x] Functionality has been documented in the README if applicable

Travis isn't responding correctly despite passing tests.

@jjasghar have you had a chance to look at this?

I just got my vRA instance up last week. I'll be revisiting this ASAP.

For reference real world usage is documented here: https://github.com/nwops/kitchen-vmpool#vmware-vra-pool-creation-scripts
Scripts
https://github.com/nwops/kitchen-vmpool/tree/master/exe
Although the code is very specific to my use case and contains a few monkey patches.

Hi all,
I've tested @logicminds changes successful in our PoC environment against vRA 7.3. Great work, hope it will be merged soon. :-)

@okleinschmidt can you verify the destroy method works with 7.3? I think I found a bug with it, downstream with https://github.com/chef-partners/knife-vrealize/issues/33 but it resonates in this gem.
As you can see from the second comment they moved the operations api out of ResourceData. :(

@jjasghar for me destroy works as well. I've verified with my own code which uses vmware-vra gem as well as with knife-vrealize (see below). The chef server error happens because there is no chef at the moment.
`knife vra server delete aaad0736-ad95-4aa9-a1ac-55722cf091d6
Server ID: aaad0736-ad95-4aa9-a1ac-55722cf091d6
Server Name: XXXX
Waiting For vRA to collect the IPWaiting For vRA to collect the IPIP Addresses: fe80::86de:6ec6:a8a8:77ae
Status: ACTIVE
Owner IDs: XXXX
Owner Names: Ole Kleinschmidt
Do you really want to delete this server? (Y/N) y
Destroy request 4d29a977-dd2b-4a09-8ab6-05325c7ccc30 submitted.
Waiting for request to complete.
Current request status: PENDING_PRE_APPROVAL.
Current request status: IN_PROGRESS..............................
Current request status: PENDING_POST_APPROVAL.
Destroy request complete.
Request Status: SUCCESSFUL
Completion State: SUCCESSFUL
WARNING: Corresponding node and client for the XXXX server were not deleted and remain registered with the Chef Server
`

Oh wow, it must be my environment then. Thanks! I'll work on it asap, and then get this merged and released.


Server crash when using Zephyr Bench

AoF4 Public Server
https://github.com/TeamAOF/All-of-Fabric-4/issues/42
https://paste.alloffabric.com/ujideyemem.yaml

Thanks for reporting, this is fixed as of 3.0.0 which will be available on CurseForge shortly.


Re-work Parse Workflow Step To be poll based

In testing the clinvar-ingest workflow, we realized that there is a response timeout configured at the cloud run service level. This means that each FastAPI call we make into the service (copy, parse, etc.) will timeout if not complete within the max timeout of 1 hour(3600 sec).
The parse step of the clinvar-ingest workflow will likely take more time than 1 hour to process > 2million VCVs.
I spoke with Kyle about rethinking the parse step to be poll based. It might work as follows:

Make a call to the parse step in the workflow, exactly as we do today (passing in proper clinvar payload) but have it write a file to a GCP bucket that contains info on the file being processed, and return back to the caller.
The workflow would then periodically poll the GCP bucket based on the clinvar file name and date, to see if there is either a "success file" or an "error file" that has been generated by the parsing step.

Once either file exists, the workflow can proceed.

I will move response models like CopyResponse from the initial copy step call, and write it into a 'payload' key in the STEP-SUCCEEDED.json file. The initial call to an endpoint like POST /copy will return immediately with a 201. And when it completes, the the <STEP>-SUCCEEDED.json file will be a StatusValue dict, with a payload key added.
e.g. COPY-SUCCEEDED.json
{
  "status": "SUCCEEDED",
  "step": "COPY",
  "timestamp": "<timestamp>"
  "payload": {<…CopyResponse> }
}

This same structure will be returned by GET /step_status

I have been experimenting with FastAPI's BackgroundTasks functionality, and it seems to work perfectly for what we want.
https://fastapi.tiangolo.com/tutorial/background-tasks/
Another option would be to use Python's asyncio directly, but this would require more custom code and cleanup, etc. BackgroundTasks seems to work the same way but more friendly for simple stuff.

One extremely important thing to note about BackgroundTasks is related to python's threading issues, and how FastAPI handles sync vs async functions. If the api function is defined as async, FastAPI will make the task sent to BackgroundTasks run in the asyncio event loop, which will block the main server thread while it is busy working.
https://stackoverflow.com/a/67601373/2172133
So for example the /copy task endpoint, if async, will send its task to BackgroundTasks, and that will be executed in the asyncio loop after the /copy request returns. When the status check request is then received, it will be blocked, since the main thread of the server is now busy executing the BackgroundTask, and the status check request will not be serviced until that task completes. Which negates the whole purpose of having the long-running task be executed in the background.
As a workaround, I have defined the /copy endpoint to just be a regular def, not an async def. Which, counterintuitively, makes the BackgroundTasks execute the task asynchronously on a separate thread, not blocking the thread which created the task. Since from a def function, the BackgroundTask gets executed in a separate threadpool, not in the asyncio loop.
Another workaround if this does not work, is to just create our own threadpool as a global in the api module and add tasks to it manually. This is not that complicated and if there's any downsides found in using def (not async def) or the BackgroundTasks from here forward, I think we should move away from it and just use our own thread/process pool. The data passed to the task and returned from it is small, so we multiprocessing is also an option.

Does seem a little counterintuitive to have to define the task with def and not async def to have it not block. But it is worth trying this out and seeing if it gets us where we need to go.

A demo like, or something like it, is good for head-wrapping:
def sleep_1(request: Request):
  import time
  time.sleep(5)
  return {}

def sleep_2(request: Request):
   import asyncio
   asyncio.sleep(5)
   return {}

async def sleep_3(request: Request):
  import time
  time.sleep(5)
  return {}

async def sleep_4(request: Request):
   import asyncio
   asyncio.sleep(5)
   return {}

Maybe it's because all the functions down the call stack within the task are not also async, that makes that task in asyncio block the main thread from responding to other requests? I'm not sure, I can play around with a simple example using these functions.
This version of copy using def seems to work as intended. It adds the inner task to the BackgroundTasks, returns a response, and the task executes in a way that does not block the uvicorn process from responding to other requests (like status checks) while the task is executing.
https://github.com/clingen-data-model/clinvar-ingest/blob/fbe9a9eb21ec3a0ba6dc7547b0d90130aab48496/clinvar_ingest/api/main.py#L205-L274

If you change the def copy to async def copy, other requests are blocked while the task is executing.
This script runs a copy step on a small file that takes less than a minute to copy, and polls until it completes:
https://github.com/clingen-data-model/clinvar-ingest/blob/issue-88-wf-job-polling/misc/bin/poll-workflow-test.py

Yeah, I think your understanding (all the functions down the call stack within the task are not also async) is exactly correct.
Basically every standard function in python is blocking by default, so unless we're using async utilities, for example this, it's gonna be a bad time.
At my previous job we basically just set a rule to not use async in FastAPI because it's way to easy to accidentally put a sync function inside an async block and nuke your web server.

Added a script here: https://github.com/clingen-data-model/clinvar-ingest/blob/issue-88-wf-job-polling/misc/bin/async-request-handling.py
In both scenarios, the inner task function is a busy cpu-bound task, and the FastAPI request handler function is async. But one inner task function is inside a def and one is inside a async def. The async one blocks the async event loop, which prevents the second request from being responded to until the task finishes (~30 seconds). The sync version of the task does not block the event loop, so the second request returns immediately after it's sent.

One other potential approach here is to experiment with limited the concurrency of the cloud run job to 1.  So, any request made while a request is in progress would create a new container rather than entering the same container where you have to content with python concurrency nonsense.


RaspberryPi Pico(RP2040) with MCP2518FDClick

Hi Pierre,
I was successful in using acan2517 lib on Arduino nano with MCP2518FDClick board as sender and UNO with MCP2515 as receiver. I am unable to do the same with pico development board, The program compiles and uploads without any errors but not receiving any frames at UNO side. I have read your pdf document and made the changes to use custom SPI pins, still no success. Please point the necessary settings required to make pico work with MCP2518FDClick.
`SPI.setRX(16);
SPI.setTX(19);
SPI.setSCK(18);
SPI.setCS(17);
SPI.begin();
ACAN2517Settings settings (ACAN2517Settings::OSC_20MHz, 500UL * 1000UL) ;
settings.mRequestedMode = ACAN2517Settings::Normal20B; // Select loopback mode`

Hello @rn-ksheer
These pins correspond to SPI1, not SPI (aka SPI0 according to the documentation)
Best regards

hi @Koryphon ,
The pins I have used are default SPI (SPI0),
Ref- https://github.com/earlephilhower/arduino-pico/blob/master/variants/generic/pins_arduino.h#L18-L21  &
https://cdn-learn.adafruit.com/assets/assets/000/099/339/original/raspberry_pi_Pico-R3-Pinout-narrow.png?1612915004.
When i disconnect any one of the SPI pins from pico , i get Configuration error 0x1  & when all pins are connected the below info gets printed on serial monitor which confirms lib is communication with pico.
sizeof (ACAN2517Settings): 32 bytes
Configure ACAN2517
MCP2517FD RAM Usage: 1024 bytes
Bit Rate prescaler: 1
Phase segment 1: 31
Phase segment 2: 8
SJW:8
Actual bit rate: 500000 bit/s
Exact bit rate ? yes
Sample point: 80%
Sent: 1
Sent: 2

I must be missing some other setting.
In ACAN2517.cpp, line 265 has mSPISettings = SPISettings (800UL * 1000, MSBFIRST, SPI_MODE0) ;
Does pico need clock speed adjustment here?

Oups. Sorry. I checked the PIN number instead of the GPIO number.

Finally got it working !!
Since i used default SPI pins, defining pin numbers in void setup was not required, SPI.begin handels it.
Only if alternate pins are being used it has to be SET in void setup.
VIO SEL is set to 3v3 , STBY & STBY SEL are in their default position.
Tested with both 20MHz & 40MHz.
regards,
𝖱𝖾𝗇𝗎 𝖪𝗌𝗁𝗂𝗋𝗌𝖺𝗀𝖺𝗋

pico
mcp2518FD

GP16
SDO

GP17
CS

GP18
SCK

GP19
SDI

GP20
INT

3V3
3V3

VBUS
5V

GND
GND


How to speed up Wav2vec 2.0 model performance and deploy this model on production?

I fine-tuned the Wav2vec model on my database and we want to deploy it on the production, due to the high parameters (317M) of this model and the high speed of converting speech to text, so I can not deploy it on the production.
please help me!!!

Hi @miladfa7
I don't think there are any tools for speed up in fairseq
Although you can search for pruning/quantization etc


Move content to relevant pages

This page is already a couple years old and not all information is up te date. I think it's best to

remove this page and,
bring the content up-to-date with the latest features
move the section to the relevant related pages in the "Manage your financials" section

[Enter feedback here]

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 63bd04cd-0ceb-8d00-8dc0-49d49305d65c
Version Independent ID: 4ed86b9b-3f3f-0d62-ccfc-67677be18eec
Content: Improvements to statement posting functionality - Commerce | Dynamics 365
Content Source: articles/commerce/statement-posting-EOD.md
Service: dynamics-365-commerce
Product: ****
Technology: ****
GitHub Login: @josaw1
Microsoft Alias: anpurush

@ransoentjens , thank you for reporting this issue. I have passed it on for consideration.

BUG 586413 created to track this issue.

Closed as won't fix.


armv7-musleabihf: rodio, alsa-sys, pkg-config issues

Hi, first of all thanks for this repo, it's really useful :-)
As a small disclaimer I'm completely out of my depth  when it comes to compiling Rust applications -- usually cargo build --release is the extent of my needs.
I've used the Dockerfile from this repo as the basis for a few apps I've built to run on ARMv7, but every time I have a dependency that uses pkg-config I end up having an issue. I'm currently trying to compile an app that plays an audio file a couple of times, using rodio for audio playback. rodio depends on alsa-sys, which in turns depends on the pkg-config crate as a build depenency.
When building the application I get the following error:
#6 64.36 error: failed to run custom build command for `alsa-sys v0.3.1`
#6 64.36
#6 64.36 Caused by:
#6 64.36   process didn't exit successfully: `/home/rust/src/target/release/build/alsa-sys-b3fdfece193bda1f/build-script-build` (exit status: 101)
#6 64.36   --- stdout
#6 64.36   cargo:rerun-if-env-changed=ALSA_NO_PKG_CONFIG
#6 64.36   cargo:rerun-if-env-changed=PKG_CONFIG_ALLOW_CROSS_armv7-unknown-linux-musleabihf
#6 64.36   cargo:rerun-if-env-changed=PKG_CONFIG_ALLOW_CROSS_armv7_unknown_linux_musleabihf

// ...

#6 64.36   --- stderr
#6 64.36   thread 'main' panicked at 'Could not run `"pkg-config" "--libs" "--cflags" "alsa"`
#6 64.36   The pkg-config command could not be found.
#6 64.36
#6 64.36   Most likely, you need to install a pkg-config package for your OS.
#6 64.36   Try `apt install pkg-config`, or `yum install pkg-config`,
#6 64.36   or `pkg install pkg-config` depending on your distribution.
#6 64.36
#6 64.36   If you've already installed it, ensure the pkg-config command is one of the
#6 64.36   directories in the PATH environment variable.

I can get around it by creating a Dockerfile and installing pkg-config, like so
FROM messense/rust-musl-cross:armv7-musleabihf
RUN apt update -y
RUN apt install -y pkg-config
COPY . .
RUN cargo build --release

Then I still get an error with
#9 67.19   --- stderr
#9 67.19   thread 'main' panicked at '`"pkg-config" "--libs" "--cflags" "alsa"` did not exit successfully: exit status: 1
#9 67.19   error: could not find system library 'alsa' required by the 'alsa-sys' crate
#9 67.19
#9 67.19   --- stderr
#9 67.19   Package alsa was not found in the pkg-config search path.
#9 67.19   Perhaps you should add the directory containing `alsa.pc'
#9 67.19   to the PKG_CONFIG_PATH environment variable

Well duh, I need to install Alsa, so I add librust-alsa-sys-dev to the apt install command on the Dockerfile.
But then I still have an issue because alsa.pc ends up being put at /usr/lib/x86_64-linux-gnu/pkgconfig, as opposed to any of the directories defined in the original Dockerfile ENVs (/usr/local/musl/lib/pkgconfig and /usr/local/musl/armv7-unknown-linux-musleabihf/lib/pkgconfig).
From here on I'm not sure how to proceed -- I've tried adding the directory as one of the pkg-config paths and I've also tried copying alsa.pc to the musleabihf pkgconfig folder, but this not only feels like the wrong thing, I still get errors with it. I would appreciate any pointers here as I'm sure my lack of knowledge on this is creating a lot more trouble than it should 🙏
Below is the application I'm trying to compile:
# Cargo.toml
[package]
name = "audio"
version = "0.1.0"
edition = "2021"

[dependencies]
rodio = "0.16.0"

// main.rs
use rodio::{Decoder, OutputStream, Sink, Source};
use std::{error::Error, fs::File, io::BufReader};

pub fn main() -> Result<(), Box<dyn Error>> {
    let (_stream, stream_handle) = OutputStream::try_default()?;
    let file = BufReader::new(File::open("coin.wav")?);
    let source = Decoder::new(file)?;
    let sink = Sink::try_new(&stream_handle).unwrap();
    let x = source.buffered();
    let y = x.clone();
    sink.append(x);
    sink.append(y);

    sink.play();

    Ok(())
}

You can't just apt install librust-alsa-sys-dev and expect it to work since it will install a x86_64 version.
Perhaps you can try
dpkg --add-architecture armv7
apt update
apt install -y librust-alsa-sys-dev:armv7

On second thought, I don't think install a system package for musl target will work because they are built for glibc. You need to compile it from source to target musl libc.

@messense Do you happen to have any pointers (documentation, examples, ...) of how one could accomplish that?


Support lower case drive letters on Windows

The combination of running a Git Bash terminal in Visual Studio Code has turned out to produce current directories that start with a lower case drive letter when RuboCop reads it. For example, c:/Users/me/project.
This is not good since it will mess up the conversion of Exclude paths to absolute paths. We need to recognize the above path as already being absolute.

Thanks!


Update bazel to 0.16.1

Needed by #583 cc @jjudd

Awesome. Thanks for upgrading this.

Sure thing. It even seems it will pass. Unfortunately we needed to disable
our own strict checks for bazel issues due to much noise
On Thu, 16 Aug 2018 at 6:54 James Judd<EMAIL_ADDRESS>wrote:

Awesome. Thanks for upgrading this.
—
You are receiving this because you authored the thread.
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https://github.com/bazelbuild/rules_scala/pull/586#issuecomment-413417411,
or mute the thread
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.

Can you update the shas in the README.md to help people see which shas work with which versions?

Not sure I understand which shas you’re referring to

https://github.com/bazelbuild/rules_scala#bazel-compatible-versions
I think we should set the 14.1 version to the current sha. If we aren’t testing it any longer we aren’t sure it works.

@johnynek PTAL

Oscar,
I’m planning of merging tomorrow morning if I don’t hear any objections now
that I’ve updated the readme.
On Thu, 16 Aug 2018 at 9:00 P. Oscar Boykin<EMAIL_ADDRESS>wrote:

https://github.com/bazelbuild/rules_scala#bazel-compatible-versions
I think we should set the 14.1 version to the current sha. If we aren’t
testing it any longer we aren’t sure it works.
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Gridview hide row on condition

Why there is no visible property for row like in columns.
Ex:

 <?= GridView::widget([
        'dataProvider' => $dataProvider,
        'filterModel' => $searchModel,
       'rowVisible'=>true,
        'columns' => $gridColumns
    ]); ?>

Why do you want to select a row and then not display it?

So why I want select column and hide it by visible property ? @samdark

I think the point is that you don't need to request that data row that you don't want to display.
This is request logic, it's not for GridView actually.
And there are difference between rows and columns. Column might represent calculated field, or might represent such field that needed inside AR (e.g. to make some calculations) but don't need to display for some user groups. It's pretty good applicate to column logic, and not so much for row.
For example: I have some goods, and they have some prime cost, but it's secret for customers, it's available only for managers and so on. But I also have some final cost, that depends on prime cost, that's why I need to require that cost too.
And if some customer mustn't see some goods (maybe, only for 18+? :) ), when it's better to request only suitable goods, because there is no sense to request other, they don't needed...

the main issue about this is pagination, if you hide some rows you would have different count of items on different pages, which is not good. Therefor visibility of rows needs to be decided on dataprovider or query level.

In some scenarios like in reporting its very difficult to query in Active Data Provider.In that kind of situation I made one function and called in grid view attribute.
So in this situation need to control rowVisible in gridview level.

http://stackoverflow.com/questions/39607305/yii2-gridview-hide-filter-row-conditionally

@pptyasar that would put access control logic into the view, I would recommend to filter the results on model or controller level and use ArrayDataProvider to provide the result of that to the gridview.


Suspicious supervision behaviour during cancellation

Hey, I've been running into issues with fork cancellation and supervisors and decided to spend some time narrowing the issue down. Below are two snippets highlighting what I would consider strange behaviour. It's not obvious to me why this behaviour occurs and looks like a bug on the surface.
The following snippet throws off-stack, due to:
https://github.com/slamdata/purescript-aff/blob/3457df9993a78e408ed56f0bb619941911d375d0/src/Control/Monad/Aff.js#L500-L505
        outer <- forkAff do
          bracket
            (pure unit)
            (const $ pure unit)
            \_ -> supervise do
              fiber <- forkAff $ supervise $ supervise do -- notice the double 'supervise'
                delay $ 10.0 # Milliseconds
              joinFiber fiber
        killFiber (error "done") outer
        delay $ 100.0 # Milliseconds

and this blocks forever:
        outer <- forkAff do
          bracket
            (pure unit)
            (const $ pure unit)
            \_ -> supervise do
              fiber <- forkAff $ supervise do
                delay $ 10.0 # Milliseconds
              joinFiber fiber
        killFiber (error "doen") outer
        delay $ 100.0 # Milliseconds

You can play around with these, but from my observations this only happens when inside a bracket, but the problem is probably broader.

I think this is because Supervisor does not handle the case where there are no forks, so it never invokes the callback.


not working on alfred 4

I Think this cause by:
VPREFS="$HOME/Library/Caches/com.runningwithcrayons.Alfred-2/Workflow Data/"
NVPREFS="$HOME/Library/Application Support/Alfred 2/Workflow Data/"
by  change NVPREFS variable:
NVPREFS="$HOME/Library/Application Support/Alfred 2/Workflow Data/"
It works!

it should be
NVPREFS="$HOME/Library/Application Support/Alfred/Workflow Data/"

Alfred 2 => Alfred

it works


Layout Service

Create the honeycomb-layout for tailor

I've published my middleware, see https://github.com/danielbayerlein/honeycomb/tree/layout-service

TODO´s:

change template to use absolute-link for js/css-files or other solution - currently 404
maybe extract middleware into extra package
demo-setup (with reverse-proxy and co?)
unit-tests

i´m working on it :)

See #59


Simultaneous sending limit

GCM has a hard limit of 1000 device ids per request to their server. Just wondering if your library has taken care of this details? Or do we need to array_chunk ourselves?
Cheers
Ken

It does not currently handle splitting the array into chunks, but it would be a very useful feature to have. If you're interested in adding support for it, I would be very happy to review and merge the pull request.

After seeing your post (and realizing I already encountered the same requirement) I implemented a version that does just that. The only (major) difference to the current implementation is that it returns an AggregateResult class which encapsulates the separate MulticastResults from each chunk.
The AggregateResult class contains exactly the same methods as the MulticastResult class, so it should be a drop-in replacement.
Check here: https://github.com/chancezeus/php-gcm/commit/e0979ae7f98a4976adbcaa31f5b8fb864875d4af

👍🏻 awesome, i'll check it out
On Wed, 13 Apr 2016 at 9:47 AM, Mark van Beek<EMAIL_ADDRESS>wrote:

After seeing your post (and realizing I already encountered the same
requirement) I implemented a version that does just that. The only (major)
difference to the current implementation is that it returns an
AggregateResult class which encapsulates the separate MulticastResults from
each chunk.
The AggregateResult class contains exactly the same methods as the
MulticastResult class, so it should be a drop-in replacement.
Check here: chancezeus@e0979ae
https://github.com/chancezeus/php-gcm/commit/e0979ae7f98a4976adbcaa31f5b8fb864875d4af
—
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Reply to this email directly or view it on GitHub
https://github.com/lkorth/php-gcm/issues/20#issuecomment-209116714

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/ M +64 21 2800 450 /
79 ST GEORGES BAY, PARNELL, AUCKLAND, NEW ZEALAND

Fixed in 647138d1a34b3b57dc980a56f1544713d5c196fa


CHANGELOG.md not up to date

The CHANGELOG.md file is still at version 3.6.1;)

Fixed in latest Gladys release :)


Block scoped function isn't correctly transformed

Describe the bug
In ES6 and after, function declaration is strictly block scoped.
Input code
if (false) {
  function foo() {}
}

console.log(foo())

Config
No response
Playground link
https://play.swc.rs/?version=1.2.196&code=H4sIAAAAAAAAA8tMU9BIS8wpTtVUqOZSUEgrzUsuyczPU0jLz9cACtVy1XJxJefnFefnpOrl5KdrgMU1AXK46TA2AAAA&config=H4sIAAAAAAAAA0WMQQ7DIAwE%2F%2BIz1174Qx9hUSciAoxsRypC%2FL2gJsrJ1u7sdDg0gO9QUZRkfdqK4Rc8UMioQWI1cBObkclJw4Gh7GSL0NesErMS%2BA2TkoMcS9zaEgXOVUj1qbDs6SbHFGX%2BnCvoYK3OuyaZy6EwHtE1jvq%2B6P%2F8B7PHho66AAAA
Expected behavior
if (false) {
    var _$foo = function _$foo() {};
}
console.log(foo());

Actual behavior
if (false) {
    var _$foo = function _$foo() {};
}
console.log(_$foo());

Version
1.2.196
Additional context
No response

This is actually more complicated -- according to spec, browser could have extra behavior for scoped function. However babel and nodejs -- they're not browsers anyway -- don't implement that. And in both chrome and firefox, under strict mode, scoped function wouldn't be exposed to outer environment, which is also the behavior of terser. I don't know which one is the correct thing to do

Just FYI, the behavior in sloppy mode gets very complicated indeed. A function nested in a block creates two independent variable bindings, with an unintuitive order of initialization.
Please see in illustration here in conversation around a PR I initiated on Babel to fix this same issue. I'm afraid I've largely abandoned the attempt to fix in Babel due to these complications, and the fact that it's quite an uncommon case. So I never entirely pinned down the correct behaviour.

Thanks for the info! After find out there's cross browser incompliance in block scoped function, I decided to only implement what's written in spec.

or at least what's in spec according to my interpretation


Mapper().toJSON and Realm

Hello everyone!
I use Xcode 8 and Swift 3. When I tried to transform motel to JSON  I got the error libc++abi.dylib: terminating with uncaught exception of type NSException.
I read this issue  but it didn't help me. How can I fix it?
My model
class ChatRealmMessageModel: Object, Mappable {

    ///  for interlocator id
    dynamic var id = ""
    dynamic var date = ""
    /// This is text.
    dynamic var message = ""
    dynamic var imageData: Data? = nil
    dynamic var imageURL: String? = nil
    dynamic var senderID = ""
    dynamic var conversationID = ""
    dynamic var isIncoming = true
    dynamic var isNew = false
    // local paramers
    dynamic var messageStatus = MessageStatus.success.rawValue

    override static func primaryKey() -> String? {
        return "id"
    }

    func mapping(map: Map) {
        id <- map["id"]
        date <- map["date"]
        message <- map["message"]
        imageData <- map["imageData"]
        imageURL <- map["imageURL"]
        senderID <- map["senderID"]
        conversationID <- map["conversationID"]
        isIncoming <- map["isIncoming"]
        isNew <- map["isNew"]
        messageStatus <- map["messageStatus"]
    }

    required convenience init?(map: Map) {
        self.init()
    }

    enum MessageStatus: String {
        case sending, success, failed
    }
}

So I transform to JSON
let JSON = Mapper().toJSON(messageModel)

@alexsanderkhitev did you find one solution?

Hi @Sweefties ! Yes, I did. I show you my example
class RealmUserInfo: Object, Mappable {

    dynamic var id = ""
    dynamic var firstName = ""
    dynamic var lastName = ""
    dynamic var photoPath: String?

    override class func primaryKey() -> String? {
        return "id"
    }

    convenience required init?(map: Map) {
        self.init()
    }

    func mapping(map: Map) {
        if map.mappingType == .fromJSON {
            id <- map["id"]
            firstName <- map["firstName"]
            lastName <- map["lastName"]
            photoPath <- map["photoPath"]
        } else {
            id >>> map["id"]
            firstName >>> map["firstName"]
            lastName >>> map["lastName"]
            photoPath >>> map["photoPath"]
        }
    }

}

@alexsanderkhitev you saved me...
Thanks for your solution.

This is so inconvenient.. I have 15 models in my project and on each it looks like I have duplicate code for just one simple operation switch. I know that the ref/val type must be declared in compile time. However, can we do better that ?

Unfortunately I do not have a better solution. Suggestions are welcomed


moveit/copyit - better file missing reporting

When a file is ina manifest but not in a directory, validate.py works well at reporting but moveit.py does a dreadful job, usually displaying a list of checksums for some random reason. Fix plzzzz.

closed via https://github.com/kieranjol/IFIscripts/commit/e69c49ac507c61d276cdf88bcb12b5fe6f861d3a


KOGITO-4002  Move process serialization to process-serialization-protobuf

https://issues.redhat.com/browse/KOGITO-4002
serialization depends on drools (v7) and is part of jbpm-flow.
refactor it to an optional module so that the v7 dependency is scoped to that module

Many thanks for submitting your Pull Request :heart:!
Please make sure that your PR meets the following requirements:

[x] You have read the contributors guide
[x] Pull Request title is properly formatted: KOGITO-XYZ Subject
[x] Pull Request title contains the target branch if not targeting master: [0.9.x] KOGITO-XYZ Subject
[x] Pull Request contains link to the JIRA issue
[x] Pull Request contains link to any dependent or related Pull Request
[x] Pull Request contains description of the issue
[x] Pull Request does not include fixes for issues other than the main ticket

jenkins please retest this

@evacchi should we also take the opportunity to drop jbpm from the new artifact name?

@cristianonicolai as you wish. process- ?

+1, process- sounds good.

jenkins please retest this

jenkins please retest this

jenkins please retest this

jenkins please retest this

jenkins please retest this

jenkins please retest this


errors: examples need improvement

There is a total of 4 examples in the package. Two are for New, one doesn't use the package at all, and one incorrectly uses As by dropping the error on the floor if it's not a os.PathError.
	_, err := os.Open("non-existing")
	if err != nil {
		var pathError *os.PathError
		if errors.As(err, &pathError) {
			fmt.Println("Failed at path:", pathError.Path)
		}
	}

@FiloSottile Can I take a look at this issue?


any intuition of tuning number of epochs and redundancy_factor?

Hi,
could you please provide some insights regarding the range of number of epochs and redundancy_factor for parameter tuning?
Thanks!

Regarding the number of epochs, we found in practice that the parameters converged after 3-4 epochs, so it's often unnecessary to run more iterations (but the code prints out the decrease in total loss after each iteration, so it's easy to verify how many epochs are necessary).
As for the redundancy factor, it depends on whether you have many labelling functions that are heavily correlated (i.e. when one labelling function builds upon the result of another). If you don't, you can simply ignore this redundancy factor. If you do have such correlated LFs, and their name express this dependence (see the method _get_correlated_sources), the easiest is to fit the HMM with several values for this factor, and see which one works best on some development data.

Yes, you can surely use 10 epochs, no problem.


test_simple_dictionary_input is failing in latest version

This test is failing after a453437 with the following error:
tests/unittest/test_convert_to_markdown.py:98: AssertionError
=========================== short test summary info ============================
FAILED tests/unittest/test_convert_to_markdown.py::TestConvertToMarkdown::test_simple_dictionary_input - assert "- 🎯 **Main t...de after 2'}}" == '- 🎯 **Main t...\n        ```'
    - 🎯 **Main theme:** Test
    - 📌 **Type of PR:** Test type
    - 🧪 **Relevant tests added:** no
    - ✨ **Focused PR:** Yes
    - 💡 **General PR suggestions:** general suggestion...
  + -  **Code suggestions:**

  +   - {'Code example': {'Before': 'Code before', 'After': 'Code after'}}
  +   - {'Code example': {'Before': 'Code before 2', 'After': 'Code after 2'}}
  - - 🤖 **Code suggestions:**
  -
  -   - **Code example:**
  -     - **Before:**
  -         ```
  -         Code before
  -         ```
  -     - **After:**
  -         ```
  -         Code after
  -         ```
  -
  -   - **Code example:**
  -     - **Before:**
  -         ```
  -         Code before 2
  -         ```
  -     - **After:**
  -         ```
  -         Code after 2
  -         ```
========================= 1 failed, 39 passed in 0.79s =========================

Thanks, fixed ('Code suggestions'-> 'Code feedback')


SPOI-6048: test-PR-build

Testing to automatically trigger PR build in Jenkins.

please close this PR


intro-to-deep-learning-using-python

This fixes issue #4168

It is done @WanjaMIKE

@WanjaMIKE
Can we get a status on this PR?

Plagiarism check done.
It would be wise to go over the PDF to see what came up @mbuguac
intro-to-deep-learning-using-python #5080.pdf

@mbuguac could you resolve the plagiarism report before we proceed. Thanks

It is all done. Am still waiting on your review.
On Sat, Dec 4, 2021, 01:47 WanjaMIKE @.***> wrote:

@mbuguac https://github.com/mbuguac could you resolve the plagiarism
report before we proceed. Thanks
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Am am requesting that you close this pull request. Have having some challenges. I will request for a reopening when everything is ready. @WanjaMIKE

What challenges are you facing? @mbuguac

It's about the machine I was using. It is no longer available.
On Sun, Dec 5, 2021, 20:03 WanjaMIKE @.***> wrote:

What challenges are you facing? @mbuguac https://github.com/mbuguac
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Okay


Define configuration store settings on proxy configmap

Motivation
This is required to active websockets and while it can be provided via
proxy.configData this is more ergonomic for users of the chart.
Modifications
Added zookeeperServers/configurationStoreServers to proxy config map
Verifying this change

[ ] Make sure that the change passes the CI checks.

@codelipenghui was this closed accidentally?

@josephglanville that was my screw-up - apologies. @codelipenghui can you help? Thanks!


std.container.Array - documentation cleanup

make formatting consistent
add missing details, e.g. about exception being thrown
fix wrong descriptions
fix wrong complexity specifications
delete redundant comments

Could you point me the undocumented methods? It seems insertBack is documented... or maybe you mean the struct Payload which is private?

Gah, the way github shows the diffs makes it easy to miss that fold that hid the "private struct Payload" declaration.  I should have looked at it locally from a git fetch instead. Sorry for these unfounded nitpicks. Your changes look good. Let's give it the go.

Great, thanks!


Rails 5 compatiblity

Hi, @nathanvda
I am a RoR developer and I am using this gem. Thanks for your effort.
I am wondering if it is possible to use in Rails 5.
or are you in progress of upgrading this gem for Rails 5?
Please let me know.

I am using this in Rails 5 and it's working well :) let me know if you need
a hand with anything!
Cheers,
Nick
On Thursday, 13 October 2016, seniordev555<EMAIL_ADDRESS>wrote:

Hi, @nathanvda https://github.com/nathanvda
I am a RoR developer and I am using this gem. Thanks for your effort.
I am wondering if it is possible to use in Rails 5.
or are you in progress of upgrading this gem for Rails 5?
Please let me know.
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Yep, I also use it in rails 5 :+1: are you encountering a specific issue?

No, just confirming.
Because Rails 5 provides many features and just checking it if it is able to use without any errors.
so do you have plan to upgrade it with Rails 5 features?

This is very vague or unclear ("rails 5 features"???). Could you be more specific?

Turbolinks, Independent views, CoffeeScript(if possible), some usable operations like & (.try function) etc.

I see no point in converting to coffeescript now (while I use it in my own projects exclusively).
Indeed, the turbolinks event names changed, right? I should look into that.
Completely unclear to me what you mean with the following terms:

independent views
"usable operations"

Could you elaborate on that?

I think I misunderstand the feature of Rails 5.
Sorry for that.
Anyway, can you update the README file? It shows that this gem seems to be able to integrate only Rails 4.
Please update accordingly so that developers can use this gem without confusion.
Thanks.

I can't get this gem working with rails <IP_ADDRESS>. I opened an issue with a link to a demo app containing the issue. https://github.com/nathanvda/cocoon/issues/390

I was still learning the gem in rails 4, but in rails 5 I have to remove turbo links or I get various errors - like two fields added for a single button click - but only after I update the page once to hold new values.

I'm using this with Rails 5 on my recipe box app. It creates multiple ingredients when clicking the button once. It will randomly create 3, 6, 9 of them instead of just one. I could be calling it wrong?
= link_to_add_association 'Add Ingredient', f, :ingredients, class: "btn btn-default add-button"

This sounds like a turbolinks issue. Where do you include your javascript? Please open another issue.

Or check #177 as mentioned before.

I have my application.js call in the header and // require turbolinks is near the bottom. I'm going to open a new issue. Thanks!