[Bug]: Successfully deployed embedding model 'gte-Qwen2-7B-instruct', but got "TypeError: 'async for' requires an object with aiter method, got coroutine" when calling it

Your current environment

The output of `python collect_env.py`
PyTorch version: 2.4.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: openEuler 22.03 LTS (x86_64)
GCC version: (GCC) 10.3.1
Clang version: 12.0.1 (openEuler 12.0.1-1.oe2203 4fd5fb384b180c854df9bde29afbda6d40e8836f)
CMake version: version 3.22.1
Libc version: glibc-2.34

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.10.0-<IP_ADDRESS>.oe2203.x86_64-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: Could not collect
Nvidia driver version: 520.61.05
cuDNN version: Probably one of the following:
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.9.0.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv.so.9.0.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn.so.9.0.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_engines_precompiled.so.9.0.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_engines_runtime_compiled.so.9.0.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_graph.so.9.0.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_heuristic.so.9.0.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops.so.9.0.0
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
Address sizes:                   46 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          72
On-line CPU(s) list:             0-71
Vendor ID:                       GenuineIntel
BIOS Vendor ID:                  Intel(R) Corporation
Model name:                      Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz
BIOS Model name:                 Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz
CPU family:                      6
Model:                           85
Thread(s) per core:              2
Core(s) per socket:              18
Socket(s):                       2
Stepping:                        7
CPU max MHz:                     3900.0000
CPU min MHz:                     1000.0000
BogoMIPS:                        5200.00
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 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       1.1 MiB (36 instances)
L1i cache:                       1.1 MiB (36 instances)
L2 cache:                        36 MiB (36 instances)
L3 cache:                        49.5 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-17,36-53
NUMA node1 CPU(s):               18-35,54-71
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
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:   Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] mypy-protobuf==3.6.0
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu11==2.20.5
[pip3] pyzmq==26.1.0
[pip3] sentence-transformers==3.0.1
[pip3] torch==2.4.0+cu118
[pip3] torchaudio==2.4.0+cu118
[pip3] torchvision==0.19.0+cu118
[pip3] transformers==4.44.0
[pip3] transformers-stream-generator==0.0.4
[pip3] triton==3.0.0
[conda] blas                      1.0                         mkl    main
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344    main
[conda] mkl-service               2.4.0           py310h5eee18b_1    main
[conda] mkl_fft                   1.3.8           py310h5eee18b_0    main
[conda] mkl_random                1.2.4           py310hdb19cb5_0    main
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu11          2.20.5                   pypi_0    pypi
[conda] pytorch-cuda              11.8                 h7e8668a_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] pyzmq                     26.1.0                   pypi_0    pypi
[conda] sentence-transformers     3.0.1                    pypi_0    pypi
[conda] torch                     2.4.0+cu118              pypi_0    pypi
[conda] torchaudio                2.4.0+cu118              pypi_0    pypi
[conda] torchvision               0.19.0+cu118             pypi_0    pypi
[conda] transformers              4.44.0                   pypi_0    pypi
[conda] transformers-stream-generator 0.0.4                    pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.4
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect

🐛 Describe the bug
Deployed Embedding model named 'gte-Qwen2-7B-instruct' successfully via command:
 python -m vllm.entrypoints.openai.api_server --served-model-name gte-Qwen2-7B-instruct  --model /data1/iic/gte_Qwen2-7B-instruct  --port 9990 --gpu-memory-utilization 0.3
And it ran good, got following logs:
`
INFO 08-10 16:06:52 config.py:820] Chunked prefill is enabled with max_num_batched_tokens=512.
INFO 08-10 16:06:52 llm_engine.py:174] Initializing an LLM engine (v0.5.4) with config: model='/data1/iic/gte_Qwen2-7B-instruct', speculative_config=None, tokenizer='/data1/iic/gte_Qwen2-7B-instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, 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), seed=0, served_model_name=gte-Qwen2-7B-instruct, use_v2_block_manager=False, enable_prefix_caching=False)
[rank0]:[W810 16:07:03.460779226 ProcessGroupGloo.cpp:712] Warning: Unable to resolve hostname to a (local) address. Using the loopback address as fallback. Manually set the network interface to bind to with GLOO_SOCKET_IFNAME. (function operator())
INFO 08-10 16:07:03 model_runner.py:720] Starting to load model /data/iic/gte_Qwen2-7B-instruct...
Loading safetensors checkpoint shards:   0% Completed | 0/7 [00:00<?, ?it/s]
Loading safetensors checkpoint shards:  14% Completed | 1/7 [00:00<00:04,  1.44it/s]
Loading safetensors checkpoint shards:  29% Completed | 2/7 [00:01<00:03,  1.33it/s]
Loading safetensors checkpoint shards:  43% Completed | 3/7 [00:02<00:03,  1.26it/s]
Loading safetensors checkpoint shards:  57% Completed | 4/7 [00:03<00:02,  1.27it/s]
Loading safetensors checkpoint shards:  71% Completed | 5/7 [00:03<00:01,  1.54it/s]
Loading safetensors checkpoint shards:  86% Completed | 6/7 [00:04<00:00,  1.39it/s]
Loading safetensors checkpoint shards: 100% Completed | 7/7 [00:05<00:00,  1.25it/s]
Loading safetensors checkpoint shards: 100% Completed | 7/7 [00:05<00:00,  1.31it/s]
INFO 08-10 16:07:09 model_runner.py:732] Loading model weights took 14.2655 GB
INFO 08-10 16:07:09 gpu_executor.py:102] # GPU blocks: 9476, # CPU blocks: 4681
INFO 08-10 16:07:12 model_runner.py:1024] 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 08-10 16:07:12 model_runner.py:1028] 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. You can also reduce the max_num_seqs as needed to decrease memory usage.
INFO 08-10 16:07:26 model_runner.py:1225] Graph capturing finished in 14 secs.
WARNING 08-10 16:07:27 serving_embedding.py:171] embedding_mode is False. Embedding API will not work.
INFO 08-10 16:07:27 launcher.py:14] Available routes are:
INFO 08-10 16:07:27 launcher.py:22] Route: /openapi.json, Methods: HEAD, GET
INFO 08-10 16:07:27 launcher.py:22] Route: /docs, Methods: HEAD, GET
INFO 08-10 16:07:27 launcher.py:22] Route: /docs/oauth2-redirect, Methods: HEAD, GET
INFO 08-10 16:07:27 launcher.py:22] Route: /redoc, Methods: HEAD, GET
INFO 08-10 16:07:27 launcher.py:22] Route: /health, Methods: GET
INFO 08-10 16:07:27 launcher.py:22] Route: /tokenize, Methods: POST
INFO 08-10 16:07:27 launcher.py:22] Route: /detokenize, Methods: POST
INFO 08-10 16:07:27 launcher.py:22] Route: /v1/models, Methods: GET
INFO 08-10 16:07:27 launcher.py:22] Route: /version, Methods: GET
INFO 08-10 16:07:27 launcher.py:22] Route: /v1/chat/completions, Methods: POST
INFO 08-10 16:07:27 launcher.py:22] Route: /v1/completions, Methods: POST
INFO 08-10 16:07:27 launcher.py:22] Route: /v1/embeddings, Methods: POST
INFO:     Started server process [319217]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://<IP_ADDRESS>:9990 (Press CTRL+C to quit)
`
While I called it with following request body:
{ 	"input": "Your text string goes here", 	"model": "gte_Qwen2-7B-instruct" }
An Error occurred, error info were:
NFO:     <IP_ADDRESS>:62632 - "POST /v1/embeddings HTTP/1.1" 500 Internal Server Error ERROR:    Exception in ASGI application Traceback (most recent call last):   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/uvicorn/protocols/http/httptools_impl.py", line 419, in run_asgi     result = await app(  # type: ignore[func-returns-value]   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/uvicorn/middleware/proxy_headers.py", line 84, in __call__     return await self.app(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/fastapi/applications.py", line 1054, in __call__     await super().__call__(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/applications.py", line 123, in __call__     await self.middleware_stack(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/middleware/errors.py", line 186, in __call__     raise exc   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/middleware/errors.py", line 164, in __call__     await self.app(scope, receive, _send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/middleware/cors.py", line 85, in __call__     await self.app(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/middleware/exceptions.py", line 65, in __call__     await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app     raise exc   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app     await app(scope, receive, sender)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/routing.py", line 756, in __call__     await self.middleware_stack(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/routing.py", line 776, in app     await route.handle(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/routing.py", line 297, in handle     await self.app(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/routing.py", line 77, in app     await wrap_app_handling_exceptions(app, request)(scope, receive, send)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app     raise exc   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app     await app(scope, receive, sender)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/starlette/routing.py", line 72, in app     response = await func(request)   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/fastapi/routing.py", line 278, in app     raw_response = await run_endpoint_function(   File "/home/yangjie/.conda/qwen4jay/lib/python3.10/site-packages/fastapi/routing.py", line 191, in run_endpoint_function     return await dependant.call(**values)   File "/data1/yangjie/vllm/vllm/entrypoints/openai/api_server.py", line 218, in create_embedding     generator = await openai_serving_embedding.create_embedding(   File "/data1/yangjie/vllm/vllm/entrypoints/openai/serving_embedding.py", line 147, in create_embedding     async for i, res in result_generator:   File "/data1/yangjie/vllm/vllm/utils.py", line 346, in consumer     raise e   File "/data1/yangjie/vllm/vllm/utils.py", line 337, in consumer     raise item   File "/data1/yangjie/vllm/vllm/utils.py", line 312, in producer     async for item in iterator: TypeError: 'async for' requires an object with __aiter__ method, got coroutine
In contrast, when it comes to model Qwen2-7B, everything is good, as I can deploye Qwen2-7B model successfully with the same command, and it can return desired result when calling it.
I don't why I got an error when calling my Embedding model deployed by vllm
Thanks for helping!

Same error.

same error with model: intfloat/e5-mistral-7b-instruct, awq quantization

I've got the same issue with the dunzhang/stella_en_1.5B_v5 model, which is based on Qwen2. I use poetry with python3.10 inside this docker image: nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04. I run the model as an OpenAI compatible API.
My cuda setup (commands ran from within docker image):
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Mon_Apr__3_17:16:06_PDT_2023
Cuda compilation tools, release 12.1, V12.1.105
Build cuda_12.1.r12.1/compiler.32688072_0

+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.01             Driver Version: 535.183.01   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA L40S                    On  | 00000000:00:06.0 Off |                    0 |
| N/A   32C    P8              32W / 350W |      0MiB / 46068MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

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

My pyproject.toml file:
[tool.poetry]
name = "VLLM_SERVER"
version = "0.1.0"
description = ""
authors = ["Your Name<EMAIL_ADDRESS>readme = "README.md"

[tool.poetry.dependencies]
python = ">=3.10,<3.12"
vllm = "0.5.4"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

I ran it using this script
poetry run python -m vllm.entrypoints.openai.api_server --model /home/models/$MODEL_FOLDER_NAME \
    --served-model-name $MODEL_FOLDER_NAME \
    --host $VLLM_API_HOST \
    --port $VLLM_API_PORT \
    --api_key $VLLM_API_KEY \
    --dtype bfloat16 \
    --gpu_memory_utilization 0.275 \
    --trust-remote-code \
    --enforce-eager

Running this works fine, and gives me this output:
INFO 08-20 13:49:02 api_server.py:339] vLLM API server version 0.5.4
INFO 08-20 13:49:02 api_server.py:340] args: Namespace(host='<IP_ADDRESS>', port=8001, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key='<secret_key>', lora_modules=None, prompt_adapters=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, model='/home/models/stella_en_1.5B_v5', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', dtype='bfloat16', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.275, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['stella_en_1.5B_v5'], qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
INFO 08-20 13:49:02 config.py:1450] Downcasting torch.float32 to torch.float16.
INFO 08-20 13:49:02 config.py:1450] Downcasting torch.float32 to torch.bfloat16.
WARNING 08-20 13:49:02 arg_utils.py:766] Chunked prefill is enabled by default for models with max_model_len > 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False.
INFO 08-20 13:49:02 config.py:820] Chunked prefill is enabled with max_num_batched_tokens=512.
INFO 08-20 13:49:02 llm_engine.py:174] Initializing an LLM engine (v0.5.4) with config: model='/home/models/stella_en_1.5B_v5', speculative_config=None, tokenizer='/home/models/stella_en_1.5B_v5', 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=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, 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), seed=0, served_model_name=stella_en_1.5B_v5, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 08-20 13:49:02 model_runner.py:720] Starting to load model /home/models/stella_en_1.5B_v5...
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.92it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.92it/s]

INFO 08-20 13:49:03 model_runner.py:732] Loading model weights took 3.3428 GB
INFO 08-20 13:49:04 gpu_executor.py:102] # GPU blocks: 17510, # CPU blocks: 9362
WARNING 08-20 13:49:08 serving_embedding.py:171] embedding_mode is False. Embedding API will not work.
INFO 08-20 13:49:08 launcher.py:14] Available routes are:
INFO 08-20 13:49:08 launcher.py:22] Route: /openapi.json, Methods: HEAD, GET
INFO 08-20 13:49:08 launcher.py:22] Route: /docs, Methods: HEAD, GET
INFO 08-20 13:49:08 launcher.py:22] Route: /docs/oauth2-redirect, Methods: HEAD, GET
INFO 08-20 13:49:08 launcher.py:22] Route: /redoc, Methods: HEAD, GET
INFO 08-20 13:49:08 launcher.py:22] Route: /health, Methods: GET
INFO 08-20 13:49:08 launcher.py:22] Route: /tokenize, Methods: POST
INFO 08-20 13:49:08 launcher.py:22] Route: /detokenize, Methods: POST
INFO 08-20 13:49:08 launcher.py:22] Route: /v1/models, Methods: GET
INFO 08-20 13:49:08 launcher.py:22] Route: /version, Methods: GET
INFO 08-20 13:49:08 launcher.py:22] Route: /v1/chat/completions, Methods: POST
INFO 08-20 13:49:08 launcher.py:22] Route: /v1/completions, Methods: POST
INFO 08-20 13:49:08 launcher.py:22] Route: /v1/embeddings, Methods: POST
INFO:     Started server process [3049]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://<IP_ADDRESS>:8001 (Press CTRL+C to quit)

Taking a look at the warnings and trying to run it with --enable_chunked_prefill false still gives me the same result.
When I call the /v1/embeddings endpoint, I get the following stacktrace:
INFO:     <IP_ADDRESS>:59215 - "POST /v1/embeddings HTTP/1.1" 500 Internal Server Error
ERROR:    Exception in ASGI application
Traceback (most recent call last):
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/_utils.py", line 83, in collapse_excgroups
    yield
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/base.py", line 190, in __call__
    async with anyio.create_task_group() as task_group:
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 680, in __aexit__
    raise BaseExceptionGroup(
exceptiongroup.ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/uvicorn/protocols/http/httptools_impl.py", line 401, in run_asgi
    result = await app(  # type: ignore[func-returns-value]
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/uvicorn/middleware/proxy_headers.py", line 70, in __call__
    return await self.app(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/applications.py", line 123, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/errors.py", line 186, in __call__
    raise exc
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/errors.py", line 164, in __call__
    await self.app(scope, receive, _send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/base.py", line 189, in __call__
    with collapse_excgroups():
  File "/usr/lib/python3.10/contextlib.py", line 153, in __exit__
    self.gen.throw(typ, value, traceback)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/_utils.py", line 89, in collapse_excgroups
    raise exc
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/base.py", line 191, in __call__
    response = await self.dispatch_func(request, call_next)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 260, in authentication
    return await call_next(request)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/base.py", line 165, in call_next
    raise app_exc
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/base.py", line 151, in coro
    await self.app(scope, receive_or_disconnect, send_no_error)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/cors.py", line 85, in __call__
    await self.app(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/middleware/exceptions.py", line 65, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/routing.py", line 754, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/routing.py", line 774, in app
    await route.handle(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/routing.py", line 295, in handle
    await self.app(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/routing.py", line 77, in app
    await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/starlette/routing.py", line 74, in app
    response = await f(request)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/fastapi/routing.py", line 278, in app
    raw_response = await run_endpoint_function(
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/fastapi/routing.py", line 191, in run_endpoint_function
    return await dependant.call(**values)
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 218, in create_embedding
    generator = await openai_serving_embedding.create_embedding(
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/vllm/entrypoints/openai/serving_embedding.py", line 147, in create_embedding
    async for i, res in result_generator:
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/vllm/utils.py", line 346, in consumer
    raise e
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/vllm/utils.py", line 337, in consumer
    raise item
  File "/embedding_model_api/.venv/lib/python3.10/site-packages/vllm/utils.py", line 312, in producer
    async for item in iterator:
TypeError: 'async for' requires an object with __aiter__ method, got coroutine

I've tried to run different Qwen2 architecture models and get the same result when calling the /v1/embeddings endpoint. Also note that running meta-llama/Meta-Llama-3.1-8B-Instruct on the same setup, calling the /v1/chat/completions entpoint does work without issue.

Maybe this bug is caused by line 128 in vllm/entrypoints/openai/serving_embedding.py in function reate_embedding of class OpenAIServingEmbedding.
generator = self.async_engine_client.encode(
    {"prompt_token_ids": prompt_inputs["prompt_token_ids"]},
    pooling_params,
    request_id_item,
    lora_request=lora_request,
)

I add an await before self.async_engine_client.encode, and this bug disappers, i.e.,
generator = await self.async_engine_client.encode(
    {"prompt_token_ids": prompt_inputs["prompt_token_ids"]},
    pooling_params,
    request_id_item,
    lora_request=lora_request,
)
```.

However, I met another problem after solve this bug. I encountered `NotImplementedError("Embeddings not supported with multiprocessing backend")` in file `vllm/entrypoints/openai/rpc/client.py` in function `encode` of class `AsyncEngineRPCClient`.
```python
async def encode(self, *args,
                 **kwargs) -> AsyncIterator[EmbeddingRequestOutput]:
    raise NotImplementedError(
        "Embeddings not supported with multiprocessing backend")

Therefore, I noticed there are some feature request issues for embedding results, e.g., #5600, #5950, #6947, and the conclusion was that embedding feature has been supported for no model except e5-mistral-7b-instruct (#3734). I tried to implement the aforementioned encode function by myself, but I failed. I am a beginner using vllm.
I have an urgent need to use the embedding feature, which is also a crucial function as mentioned in #5950. If the vllm team could add this feature, I would be extremely grateful. Additionally, my usecase does not have the capacity to run a model as large as e5-mistral-7b-instruct, which is 7B in size.

Same error running 'TheBloke/Mistral-7B-Instruct-v0.2-AWQ' with OpenAI compatible server Docker image.
services:   vllm-service:       container_name: vllm_mistral       image: vllm/vllm-openai:latest       ports:         - "8000:8000"       deploy: # Enable GPU resources            resources:             reservations:               devices:                 - capabilities: ["gpu"]       volumes:          - vllm-volume:/root/.cache/huggingface        command:          --model TheBloke/openinstruct-mistral-7B-AWQ         --quantization awq         --max-model-len 2048

Qwen2 is not supported for Embeddings at the current moment. We need to improve the error message here


Not clear on how it is protecting the file from blob storage

Not clear on how it is protecting the file from blob storage. Can you please explain.
I have few questions on this.

How the file is protected ?
Where can i set permissions?
How to open the file, once it is uploaded to blob storage?

Hi @prashantbhojani ,
Sorry about delayed response.  Please look at the WorkerRole.cs  code and within that section please review the following piece of code. This is running on the worker VM and looking at the files on associated blob storage that worker VM is monitoring
{
RMS.RmsContent rmsContent = new RMS.RmsContent(sourceStream, sinkStream);
rmsContent.RmsTemplateId = publishJob.TemplateId;
rmsContent.OriginalFileNameWithExtension = publishJob.OriginalFileName;
RMS.RmsContentPublisher rmsContentPublisher = RMS.RmsContentPublisher.Create(sp.TenantId, sp.AppId, sp.Key);
rmsContentPublisher.PublishContent(rmsContent);
                                publishJob.PublishedFileName = rmsContent.PublishedFileNameWithExtension;
                                sinkStream.Flush();
                                sinkStream.Seek(0, SeekOrigin.Begin);

                                //published file is uploaded to blob storage.
                                //Note: This sample code doesn't manage lifetime of this original and published file blob
                                //Actual code must manage the lifetime as appropriate
                                CloudBlockBlob destFileBlob = DataModel.StorageFactory.Instance.IpcAzureAppFileBlobContainer.GetBlockBlobReference(publishJob.PublishedFileBlobRef);
                                using (CloudBlobStream blobStream = destFileBlob.OpenWrite())


Query Data

Summary
The user wants information about some data
Basic Path

The use case starts when a user indicates that he/she wants information about some data by entering a search term.
The system retrieves the data.
The system presents the data including a data plot

Alternative Paths
Step 1: The system presents an advanced dialog for the user to enter more specific details about their query (on user indication), e.g., by clicking a button for ‘advanced search’.
Step 3: If the data does not exist, the system displays a message and repeats step 1.
Exception Paths
The user can cancel the operation at any time
Preconditions
The data exists in the repository.
Postconditions
The data is displayed to the user

Excalidraw link for UX draft:
https://excalidraw.com/#json=G9siQUPi99ukeor4fsHl6,71tn7T4HWbzKCHZGxkO_FQ

Currently facing a design decision on the components fir the view.
I currently have the following:

searchBar
AdvancedSearch
The search components emit the results back to the view. The question comes from the select columns and the results grid. With how I want to communicate with them. Either I put it into two separate components which emit to the view and have the view call an exposed method to the other component e.g. select column -> view -> results grid. Or, I make one component that has both of them which may simplify the communication logic. search bar -> view -> select column results. I am not entirely sure what is best practice since it feels messy to have a select column component communicate directly to the results grid if I have search components emit to the view parent. Keeping the communication logic behave the same for all components in a view seems like it is better.

Progress update. I have a data grid component which will dynamically render the columns based on the columns that are selected from a list.
Still need to think about pagination.

Search and pagination implemented

Noticed that the advanced search doesn't have a selector for the columns.
Currently, we have this spec'd out on excalidraw:

Should add a 'reset' button to reset the state after a search is done because it is not really clear that you need to search on an empty input box to get back all the data.

Instead of input 1, perhaps, I should just have a column drop down instead. The downside to this approach is that just having the comparators leaves the interpretation to JavaScript. What if one of the columns contains strings for example? what should it mean if I have a field value of "text" < 39s?


'App construction failed' when certificates set

Hello and thank you for HyperExpress.
I have a strange issue :
Server creation is ok when I do not pass certificates paths, but crashes with this message App construction failed when I fill key_file_name and cert_file_name options.
I checked: the file paths are valid and point to certificate and key generated with mkcert
Thank you!
Version: 5.9.2
Node: 16.13.0

I'm very sorry: this was just a typo in one of my path.
But, I admit that I would have loved to have a more explanatory error :-)
Thank you very much for the work!

Hey @benoitlahoz Good idea, will try and throw a more informative error for this exception. Thanks!


Replace deprecated gatsby-image dependency

The gatsby-image package is now deprecated. The new Gatsby image plugin has better performance, cool new features and a simpler API. See the migration guide to learn how to upgrade.
https://www.gatsbyjs.com/plugins/gatsby-image/

This was solved by the following PRs.

#268
#269
#270
#271


RuntimeError: cuDNN error: CUDNN_STATUS_NOT_SUPPORTED. This error may appear if you passed in a non-contiguous input.

I run the command like:  CUDA_VISIBLE_DEVICES=2,3 python tools/train_net.py --num-gpus 2 --config-file configs/fcos/fcos_imprv_R_50_FPN.yaml 2>&1 | tee log/train_log.tx
After the Evaluation, we got a problem about cuDNN.
**[05/31 13:10:41 d2.evaluation.coco_evaluation]: Evaluation results for segm:

AP
AP50
AP75
APs
APm
APl

0.000
0.000
0.000
0.000
0.000
0.000

[05/31 13:10:41 d2.evaluation.coco_evaluation]: Per-category segm AP:

category
AP
category
AP
category
AP

:--------------
:------
:-------------
:------
:---------------
:------

person
0.000
bicycle
0.000
car
0.000

motorcycle
0.000
airplane
0.000
bus
0.000

train
0.000
truck
0.000
boat
0.000

traffic light
0.000
fire hydrant
0.000
stop sign
0.000

parking meter
0.000
bench
0.000
bird
0.000

cat
0.000
dog
0.000
horse
0.000

sheep
0.000
cow
0.000
elephant
0.000

bear
0.000
zebra
0.000
giraffe
0.000

backpack
0.000
umbrella
0.000
handbag
0.000

tie
0.000
suitcase
0.000
frisbee
0.000

skis
0.000
snowboard
0.000
sports ball
0.000

kite
0.000
baseball bat
0.000
baseball glove
0.000

skateboard
0.000
surfboard
0.000
tennis racket
0.000

bottle
0.000
wine glass
0.000
cup
0.000

fork
0.000
knife
0.000
spoon
0.000

bowl
0.000
banana
0.000
apple
0.000

sandwich
0.000
orange
0.000
broccoli
0.000

carrot
0.000
hot dog
0.000
pizza
0.000

donut
0.000
cake
0.000
chair
0.000

couch
0.000
potted plant
0.000
bed
0.000

dining table
0.000
toilet
0.000
tv
0.000

laptop
0.000
mouse
0.000
remote
0.000

keyboard
0.000
cell phone
0.000
microwave
0.000

oven
0.000
toaster
0.000
sink
0.000

refrigerator
0.000
book
0.000
clock
0.000

vase
0.000
scissors
0.000
teddy bear
0.000

hair drier
0.000
toothbrush
0.000

[05/31 13:10:42 d2.engine.defaults]: Evaluation results for coco_2017_val in csv format:

[05/31 13:10:42 d2.evaluation.testing]: copypaste: Task: bbox

[05/31 13:10:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl

[05/31 13:10:42 d2.evaluation.testing]: copypaste: 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

[05/31 13:10:42 d2.evaluation.testing]: copypaste: Task: segm

[05/31 13:10:42 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl

[05/31 13:10:42 d2.evaluation.testing]: copypaste: 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

Traceback (most recent call last):

File "tools/train_net.py", line 161, in

args=(args,),

File "/data/zlq/code/BCNet-main/detectron2/engine/launch.py", line 48, in launch
daemon=False,
File "/home/zlq/anaconda3/envs/bcnet/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 171, in spawn
while not spawn_context.join():
File "/home/zlq/anaconda3/envs/bcnet/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 118, in join
raise Exception(msg)
Exception:
-- Process 1 terminated with the following error:
Traceback (most recent call last):
File "/home/zlq/anaconda3/envs/bcnet/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, args)
File "/data/zlq/code/BCNet-main/detectron2/engine/launch.py", line 83, in _distributed_worker
main_func(args)
File "/data/zlq/code/BCNet-main/tools/train_net.py", line 149, in main
return trainer.train()
File "/data/zlq/code/BCNet-main/detectron2/engine/defaults.py", line 373, in train
super().train(self.start_iter, self.max_iter)
File "/data/zlq/code/BCNet-main/detectron2/engine/train_loop.py", line 131, in train
self.run_step()
File "/data/zlq/code/BCNet-main/detectron2/engine/train_loop.py", line 224, in run_step
losses.backward()
File "/home/zlq/anaconda3/envs/bcnet/lib/python3.7/site-packages/torch/tensor.py", line 195, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/zlq/anaconda3/envs/bcnet/lib/python3.7/site-packages/torch/autograd/init.py", line 99, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: cuDNN error: CUDNN_STATUS_NOT_SUPPORTED. This error may appear if you passed in a non-contiguous input.
I followed the Step-by-step Installation.
Pytorch 1.4 .0
torchvision 0.5.0
cudatoolkit 10.1
Did anyone meet this problem before?
Is this a version problem? https://github.com/pytorch/pytorch/issues/32395

Maybe the answer under this (issue)[https://github.com/pytorch/pytorch/issues/32564#issuecomment-635062872] will help

Maybe the answer under this issue will help

Nope. i've tried reduced the batch size to 1. using Quadro RTX 5000 (16GB) still get to same error.
I've upgraded the pytorch to 1.7. But you're using a acient Version of detectron 2. it didn't support that.
I don't know why someone else can train with their own dataset....

Have you finally solved this problem? I am suffering it and reducing batch-size does not work.


Replace backslashes with forwardslashes

Most ppl use forwardslashes.

Hey @lightclient @djrtwo
Sorry for the very late reply!
This section is under "For Windows users". Do Windows people use backslashes?

@hwwhww honestly when I made this PR, I totally missed that there was a windows section. Windows accepts forward slashes, but I doubt it is the most common.

Reverting is probably to best line of action, but it might be good to note that the distinction between the two sections isn't super clear!


official_taps: deprecate homebrew/php.

After all these formulae have been deleted (not until April 7th, most likely) we want to warn people on tapping it.

This change will break MainImport from formulae.brew.sh and maybe other code out there.
We might better phase this out:
-OFFICIAL_TAPS = %w[
-  php
-].freeze
+OFFICIAL_TAPS = [].freeze

This change will break MainImport from formulae.brew.sh

@koraktor I've opened https://github.com/Homebrew/formulae.brew.sh/pull/112 to handle this. I would rather not phase this out as it's an undocumented, internal variable.

@MikeMcQuaid Ok, I’m fine with that if it’s internal.


[BUG] Hats being rendered in first person (First-person Model & Trinkets)

Describe the bug
When using the First-person Model with Clutter, when equipping a hat in the trinket slot, the hat is rendered in first person, obstructing the view
Screenshots

using the Trinket slot:

using the Vanilla Helmet slot (work as expected)

This is a common problem to happen, apparently it is the mod that adds the accessories (in this case the Clutter mod) that needs to change the code so that the accessory is not rendered when equipped in the Trinket slot while you are in first person, only in third person
check this Issue where the creator talks a bit about this:
https://github.com/tr7zw/FirstPersonModel/issues/385
Mod Version:
"Clutter 1.20.(0-1)-0.5.7"
"First-Person Model 2.3.4"
"Trinkets 3.7.2"
If it is possible to add compatibility I would love to have an update to 1.20.1

Hi, thanks for pointing this out, it was an easy fix.
This will be released with the 0.6.0 update, which will be for 1.20.1 (which is currently the main version of Clutter).


[Bug] Using --threshold E option hides an error

What happened?
Running robocop on a file containing one ERROR (file-too-long) gets hidden when using --threshold E argument.
Rule is configured as follows:

--configure file-too-long:severity_threshold:warning=600:error=700

On the output list ERROR is the last one in the list.
When additional errors are created (listed before file-too-long) they are showing fine but still the file-too-long is missing.
F.g if there are 3 long line erros and file-too-long error, other are shown but not file-too-long error.
If --threshold W is used then in all cases all errors are shown.
What command/code did you try to run?
poetry run robocop -t E -A config/robocop.cfg D:\dev\atc\resources\active_cables\ure.robot
What is the full error message?
No error message.
Expected output is not produced.
What did you expect to happen instead?
No response
Operating System
Windows
Robocop version
4.0.1

Thanks for the report, it seems it's connected to severity_threshold - it's not respected by threshold.


The amount of warnings should be reduced as well. Branch is in master.


could not convert BatchDescriptor to cudnn tensor descriptor: CUDNN_STATUS_BAD_PARAM

Hi! Recently I came across a problem when using your batchnorm layer, I used it like
from tensorpack.models.batch_norm import BatchNorm
BatchNorm('bn', x, data_format='NCHW')

but I got
2018-03-28 17:14:56.970757: F tensorflow/stream_executor/cuda/cuda_dnn.cc:444] could not convert BatchDescriptor {count: 5 feature_map_count: 0 spatial: 24 78  value_min: 0.000000 value_max: 0.000000 layout: BatchDepthYX} to cudnn tensor descriptor: CUDNN_STATUS_BAD_PARAM
Aborted

I saw you have a bug PR for tensorflow https://github.com/tensorflow/tensorflow/issues/14657 on this problem, but I checked that I dont have 0-batch size input, I'm using tensorflow 1.4.1, could you pls point out where possibly could be wrong?

You inputs have zero channel. How are you supposed to do batch norm.


Typescript generics seems broken even with explicit drafts

🐛 Bug Report
I am well aware there has been a couple of generics related issues, I tried to apply the solution from https://github.com/immerjs/immer/issues/356#issuecomment-487912575 but it doesn't seem to work.
The only way I was able to figure is typecasting to unknown, but it feels it kinda beats the purpose.
To Reproduce
Playground link
Expected behavior
I would be expecting something that simple would work, but I am either missing something obvious or it's not really possible to use generics with TypeScript without hassle.
It is curious why in the case of useImmer the error is much more head-scratching, but I can only assume it's coming down from produce itself having a problem.
Environment

Immer version: 5.0.0

The error generated by TypeScript is entirely correct, as TUser is a concrete type, but TDoc isn't. So as far as TypeScript is concerned, TDoccould just as well be{ foo: "string", bar: "string}, which is completely unrelated to TUser, and TUserwould not be assignable to that. Probably you want to either set a better constraint here (e.g.TDoc extends TUserinstead of some randomRecord`).
I think this issue is further not related to Immer at all. But what you are probably looking for, is that your hook accepts any kind of Record, rather than some very specific type of Record. So the following does I think what you want to achieve, and is valid, as TUser is a valid Record:

function useQueryDoc() {
  const [state, updateState] = useImmer<Record<string, any>>({
    data: null,
    loading: true
  })

  updateState((draft: Record<string, any>) => {
    draft.data = fakeData()
  })

  produce(state, (draft: Record<string, any>) => {
    draft.data = fakeData()
  })

  // this works, but it feels just wrong
  produce(state, draft => {
    draft.data = fakeData() as unknown as Draft<TDoc>
  })
}


Dynamic validator registration

Pull Request Test Coverage Report for Build<PHONE_NUMBER>4
Details

7 of 7 (100.0%)  changed or added relevant lines in 2 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.02%) to 97.854%

Totals

Change from base Build<PHONE_NUMBER>8:
0.02%

Covered Lines:
3556

Relevant Lines:
3634

💛  - Coveralls


[BUG] Query 'Properties' returns 0 results

Library name and version
Azure.Monitor.Query 1.1.0
Describe the bug
I'm attempting to use the LogsQueryClient to pull data from a Log Analytics workspace and having issues with the query.
Using the following queries I'm getting inconsistent results when the 'Properties' field is used in a Kusto query:
AppMetrics | where Name == 'PublicationView' and Properties has 'R2P9780986027956' => returns 0 results with LogsClientQuery.QueryWorkspaceAsync / Log query in Azure workspace returns 174 results with same query
AppMetrics | where Name == 'PublicationView' and Properties has 'R2P9780986027956' and TimeGenerated between (datetime('2022-07-01 00:00:00') .. datetime('2022-07-06 23:59:59')) => returns 0 results with LogsClientQuery.QueryWorkspaceAsync / Log query in Azure workspace returns 42 results with same query
AppMetrics | where Name == 'PublicationView' and TimeGenerated between (datetime('2022-07-01 00:00:00') .. datetime('2022-07-06 23:59:59')) => returns 716 results with LogsClientQuery.QueryWorkspaceAsync / Log query in Azure workspace returns 716 results with same query
Expected behavior
Using the following code, the LogsQueryClient should return 42 results.
var query = $"AppMetrics | where Name == '{metricType}' and Properties has '{metricNamespace}'";
var response = await _logsQueryClient.QueryWorkspaceAsync(_workspaceId, query, new QueryTimeRange(startDate, endDate));
metricType = PublicationView
metricNamespace = 'R2P9780986027956'
startDate = '2022-07-01 00:00:00'
endDate = '2022-07-06 23:59:59'
Actual behavior
0 results are returned.
Removing " and Properties has '{metricNamespace}'" does return results but any usage of Properties in the query returns 0 results

.
Reproduction Steps
` var credential = new ClientSecretCredential(tenantId, clientId, clientSecret);
_logsQueryClient = new LogsQueryClient(credential);
var startDate = DateTime.Parse("2022-07-01 00:00:00");
var endDate = DateTime.Parse("2022-07-06 23:59:59");
var metricType = "PublicationView";
var metricNamespace = "R2P9780986027956";
var query = $"AppMetrics | where Name == '{metricType}' and Properties has '{metricNamespace}'";
var response = await _logsQueryClient.QueryWorkspaceAsync(_workspaceId, query, new QueryTimeRange(startDate, endDate));
`
Environment
Visual Studio 2022 (17.2.5)
.NET SDK (reflecting any global.json):
Version:   6.0.301
Commit:    43f9b18481
Runtime Environment:
OS Name:     Windows
OS Version:  10.0.19044
OS Platform: Windows
RID:         win10-x64
Base Path:   C:\Program Files\dotnet\sdk\6.0.301\
Host (useful for support):
Version: 6.0.6
Commit:  7cca709db2
.NET SDKs installed:
3.1.420 [C:\Program Files\dotnet\sdk]
6.0.102 [C:\Program Files\dotnet\sdk]
6.0.106 [C:\Program Files\dotnet\sdk]
6.0.202 [C:\Program Files\dotnet\sdk]
6.0.203 [C:\Program Files\dotnet\sdk]
6.0.301 [C:\Program Files\dotnet\sdk]
.NET runtimes installed:
Microsoft.AspNetCore.App 3.1.22 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 3.1.23 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 3.1.25 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 3.1.26 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 5.0.15 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 5.0.17 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 6.0.2 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 6.0.3 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 6.0.5 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.AspNetCore.App 6.0.6 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
Microsoft.NETCore.App 3.1.22 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 3.1.23 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 3.1.25 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 3.1.26 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 5.0.15 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 5.0.17 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 6.0.2 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 6.0.3 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 6.0.4 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 6.0.5 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.NETCore.App 6.0.6 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
Microsoft.WindowsDesktop.App 3.1.22 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 3.1.23 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 3.1.25 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 3.1.26 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 5.0.15 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 5.0.17 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 6.0.2 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 6.0.3 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 6.0.4 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 6.0.5 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]
Microsoft.WindowsDesktop.App 6.0.6 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]

Thank you for your feedback.  Tagging and routing to the team member best able to assist.

Label prediction was below confidence level 0.6 for Model:ServiceLabels: 'Storage:0.38190386,App Services:0.08503543,Extensions:0.06467396'

I'm unable to reproduce this issue. @nisha-bhatia Can you please dig into this one?

@scottaddie Here is the requested screenshot. IsError does say false and Status does return 200.
Let me know if I can provide you any additional information.

@ArkeBcacy Can you please enable logging and then try running the app again? We'll need to see the HTTP request body to determine what's being sent to the service itself. You can enable console logging with these instructions.
/cc: @srnagar

@scottaddie Here is the output from console logger for the function call to get the data:
`[2022-07-18T22:04:54.848Z] Executing 'GetMetrics' (Reason='This function was programmatically called via the host APIs.', Id=985bebd3-1ae2-48b5-a48a-e9e664919faa)
[Informational] Azure-Identity: ClientSecretCredential.GetToken invoked. Scopes: [ https://api.loganalytics.io//.default ] ParentRequestId: 03375a35-f408-4089-a172-d9ab1f24e348
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:54.96 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] MSAL MSAL.NetCore with assembly version '<IP_ADDRESS>'. CorrelationId(1f042ac8-fa7f-4517-9854-06b92c84ae19)
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:54.97 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] === AcquireTokenForClientParameters ===
SendX5C: False
ForceRefresh: False
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:54.98 - 1f042ac8-fa7f-4517-9854-06b92c84ae19]
=== Request Data ===
Authority Provided? - True
Scopes - https://api.loganalytics.io//.default
Extra Query Params Keys (space separated) -
ApiId - AcquireTokenForClient
IsConfidentialClient - True
SendX5C - False
LoginHint ? False
IsBrokerConfigured - False
HomeAccountId - False
CorrelationId - 1f042ac8-fa7f-4517-9854-06b92c84ae19
UserAssertion set: False
LongRunningOboCacheKey set: False
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:54.99 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] === Token Acquisition (ClientCredentialRequest) started:
Scopes: https://api.loganalytics.io//.default
Authority Host: login.microsoftonline.com
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.01 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] [Region discovery] Not using a regional authority.
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.02 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Fetching instance discovery from the network from host login.microsoftonline.com.
[Informational] Azure-Core: Request [5a8026bb-91d9-45a5-bac1-59726e0357b5] GET https://login.microsoftonline.com/common/discovery/instance?api-version=1.1&authorization_endpoint=REDACTED
x-client-SKU:REDACTED
x-client-Ver:REDACTED
x-client-CPU:REDACTED
x-client-OS:REDACTED
client-request-id:REDACTED
return-client-request-id:REDACTED
x-app-name:REDACTED
x-app-ver:REDACTED
x-ms-client-request-id:5a8026bb-91d9-45a5-bac1-59726e0357b5
x-ms-return-client-request-id:true
User-Agent:azsdk-net-Identity/1.6.0,(.NET 6.0.6; Microsoft Windows 10.0.19044)
client assembly: Azure.Identity
[Informational] Azure-Core: Request [eed09e3c-97aa-471d-b6eb-f31844406f22] GET https://{REMOVED}.queue.core.windows.net/events-aggregation/messages?numofmessages=10&visibilitytimeout=600
x-ms-version:2018-11-09
Accept:application/xml
x-ms-client-request-id:eed09e3c-97aa-471d-b6eb-f31844406f22
x-ms-return-client-request-id:true
User-Agent:azsdk-net-Storage.Queues/12.8.0,(.NET 6.0.6; Microsoft Windows 10.0.19044)
x-ms-date:Mon, 18 Jul 2022 22:04:55 GMT
Authorization:REDACTED
client assembly: Azure.Storage.Queues
[Informational] Azure-Core: Response [5a8026bb-91d9-45a5-bac1-59726e0357b5] 200 OK (00.3s)
Cache-Control:max-age=86400, private
Strict-Transport-Security:REDACTED
X-Content-Type-Options:REDACTED
Access-Control-Allow-Origin:REDACTED
Access-Control-Allow-Methods:REDACTED
P3P:REDACTED
client-request-id:REDACTED
x-ms-request-id:0e0933f8-64a5-4a8e-aa26-ea7343ab9300
x-ms-ests-server:REDACTED
X-XSS-Protection:REDACTED
Set-Cookie:REDACTED
Date:Mon, 18 Jul 2022 22:05:34 GMT
Content-Type:application/json; charset=utf-8
Content-Length:980
[Informational] Azure-Core: Response [eed09e3c-97aa-471d-b6eb-f31844406f22] 200 OK (00.0s)
Cache-Control:no-cache
Transfer-Encoding:chunked
Server:Windows-Azure-Queue/1.0,Microsoft-HTTPAPI/2.0
x-ms-request-id:9d4df49f-a003-003a-55f2-9a4f85000000
x-ms-version:2018-11-09
Date:Mon, 18 Jul 2022 22:05:35 GMT
Content-Type:application/xml
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.44 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Authority validation enabled? True.
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.44 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Authority validation - is known env? True.
[Informational] Azure-Core: Request [1a842534-e3ab-47d5-b795-93951ccb34f5] POST https://login.microsoftonline.com/80dddcec-f312-4134-9db4-364b5c2421e0/oauth2/v2.0/token
x-client-SKU:REDACTED
x-client-Ver:REDACTED
x-client-CPU:REDACTED
x-client-OS:REDACTED
x-client-current-telemetry:REDACTED
x-client-last-telemetry:REDACTED
x-ms-lib-capability:REDACTED
client-request-id:REDACTED
return-client-request-id:REDACTED
x-app-name:REDACTED
x-app-ver:REDACTED
x-ms-client-request-id:1a842534-e3ab-47d5-b795-93951ccb34f5
x-ms-return-client-request-id:true
User-Agent:azsdk-net-Identity/1.6.0,(.NET 6.0.6; Microsoft Windows 10.0.19044)
Content-Type:application/x-www-form-urlencoded
client assembly: Azure.Identity
[Informational] Azure-Core: Request [ebd4b142-08c7-410c-8eee-37374f777dfe] GET https://{REMOVED}.queue.core.windows.net/metrics-aggregation/messages?numofmessages=10&visibilitytimeout=600
x-ms-version:2018-11-09
Accept:application/xml
x-ms-client-request-id:ebd4b142-08c7-410c-8eee-37374f777dfe
x-ms-return-client-request-id:true
User-Agent:azsdk-net-Storage.Queues/12.8.0,(.NET 6.0.6; Microsoft Windows 10.0.19044)
x-ms-date:Mon, 18 Jul 2022 22:04:55 GMT
Authorization:REDACTED
client assembly: Azure.Storage.Queues
[Informational] Azure-Core: Response [ebd4b142-08c7-410c-8eee-37374f777dfe] 200 OK (00.0s)
Cache-Control:no-cache
Transfer-Encoding:chunked
Server:Windows-Azure-Queue/1.0,Microsoft-HTTPAPI/2.0
x-ms-request-id:9d4df547-a003-003a-2ef2-9a4f85000000
x-ms-version:2018-11-09
Date:Mon, 18 Jul 2022 22:05:35 GMT
Content-Type:application/xml
[Informational] Azure-Core: Response [1a842534-e3ab-47d5-b795-93951ccb34f5] 200 OK (00.2s)
Cache-Control:no-store, no-cache
Pragma:no-cache
Strict-Transport-Security:REDACTED
X-Content-Type-Options:REDACTED
P3P:REDACTED
client-request-id:REDACTED
x-ms-request-id:2006c838-a19a-4efc-aa8a-7a4afba24700
x-ms-ests-server:REDACTED
x-ms-clitelem:REDACTED
X-XSS-Protection:REDACTED
Set-Cookie:REDACTED
Date:Mon, 18 Jul 2022 22:05:34 GMT
Content-Type:application/json; charset=utf-8
Expires:-1
Content-Length:1320
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.65 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] ScopeSet was missing from the token response, so using developer provided scopes in the result.
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.65 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Checking client info returned from the server..
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.65 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Saving token response to cache..
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.67 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] ID Token not present in response.
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.67 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Cannot determine home account id - or id token or no client info and no subject
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.67 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Saving AT in cache and removing overlapping ATs...
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.68 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Looking for scopes for the authority in the cache which intersect with https://api.loganalytics.io//.default
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.68 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Intersecting scope entries count - 0
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.69 - 1f042ac8-fa7f-4517-9854-06b92c84ae19] Fetched access token from host login.microsoftonline.com.
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.69 - 1f042ac8-fa7f-4517-9854-06b92c84ae19]
=== Token Acquisition finished successfully:
[Informational] Azure-Identity: False MSAL <IP_ADDRESS> MSAL.NetCore .NET 6.0.6 Microsoft Windows 10.0.19044 [07/18 22:04:55.70 - 1f042ac8-fa7f-4517-9854-06b92c84ae19]  AT expiration time: 7/18/2022 11:04:54 PM +00:00, scopes https://api.loganalytics.io//.default source IdentityProvider from login.microsoftonline.com appHashCode 27741726
[Informational] Azure-Identity: ClientSecretCredential.GetToken succeeded. Scopes: [ https://api.loganalytics.io//.default ] ParentRequestId: 03375a35-f408-4089-a172-d9ab1f24e348 ExpiresOn: 2022-07-18T23:04:54.6784061+00:00
[Informational] Azure-Core: Request [03375a35-f408-4089-a172-d9ab1f24e348] POST https://api.loganalytics.io/v1/workspaces/2f48cb40-8957-4146-8e57-d6ae731c5eab/query
Accept:application/json
x-ms-client-request-id:03375a35-f408-4089-a172-d9ab1f24e348
x-ms-return-client-request-id:true
User-Agent:azsdk-net-Monitor.Query/1.1.0,(.NET 6.0.6; Microsoft Windows 10.0.19044)
Authorization:REDACTED
Content-Type:application/json
client assembly: Azure.Monitor.Query
[Informational] Azure-Core: Response [03375a35-f408-4089-a172-d9ab1f24e348] 200 OK (00.2s)
Date:Mon, 18 Jul 2022 22:05:35 GMT
Connection:keep-alive
Vary:REDACTED
Strict-Transport-Security:REDACTED
Via:REDACTED
X-Content-Type-Options:REDACTED
Access-Control-Allow-Origin:REDACTED
Access-Control-Expose-Headers:REDACTED
Age:REDACTED
Content-Type:application/json; charset=utf-8
Content-Length:1315
[2022-07-18T22:04:55.963Z] Executed 'GetMetrics' (Succeeded, Id=985bebd3-1ae2-48b5-a48a-e9e664919faa, Duration=1170ms)`

@ArkeBcacy A couple more things:

I noticed in the log you provided that you're calling GetMetrics. We'll need to see the log for QueryWorkspaceAsync instead.

To include the request and response bodies in the log, you'll need to pass EventLevel.Verbose to CreateConsoleLogger. You'll also need to set the Diagnostics.IsLoggingContentEnabled property to true. For example:
using AzureEventSourceListener listener = AzureEventSourceListener.CreateConsoleLogger(EventLevel.Verbose);

var client = new LogsQueryClient(
    new DefaultAzureCredential(),
    new LogsQueryClientOptions
    {
        Diagnostics = { IsLoggingContentEnabled = true }
    });

@ArkeBcacy Nothing that I'm aware of has changed in the service or client library. I'll close this issue for now, but please let me know if you see this issue again.


Update claude_api.py

Increase timeout to 5 minutes instead of default 30 seconds in curl_cffi

Hi, thank you for your amazing repo (I use it alot)!
Since switching to curl_cffi, my requests often timeout after 30 seconds, especially with attachments.
This seems to be from the default curl_cffi timeouts. My hot fix is adding , timeout=300 equal 5 minutes to line 122

Let the default timeout be as it is ,It helps for the debugging issues if any occured.

And Actually curl_cffi is more faster and efficient in making requests,so 30 secs is fine for default.
And soon will add timeout parameter in code too
And Thank you @Luvata for figuring this !


🐛 Bug Report: ui:field RepoUrlPicker ui:options requestUserCredentials | Github Auth

📜 Description
When using the ui:options of "requestUserCredential" and parameter of additionalScopes, the token that is generated does not contain the additionalScopes
properties:
      repoUrl:
        ui:field: RepoUrlPicker
          ui:options:
            allowedHosts:
              - github.com
            allowedOwners:
              - <org>
            requestUserCredentials:
              secretsKey: USER_OAUTH_TOKEN
              additionalScopes:
                github:
                  - repo:status
                  - repo_deployment
                  - public_repo
                  - repo:invite
                  - security_events

👍 Expected behavior
The token that is generated when the user is authenticated should contain the scopes that were in the additionalScope parameter.
👎 Actual Behavior with Screenshots
The token is generated, but no additional scopes are added to the x-oauth-scopes
👟 Reproduction steps
I use a custom action that takes a token input.
In the template that uses this custom action, I use the RepoUrlPicker to request the credentials.
For debugging purposes, I have the custom action log the token that is passed to it. Once I have grabbed the token from the logs, I then use a curl command to get the header info. The "x-oauth-scopes" are empty for the token that is generated.
If compared to a known working token, I see the scopes present.
curl command:
curl -H "Authorization: Bearer <OAUTH_TOKEN>" https://api.github.com/users/codertocat -I

Example Token Output:

📃 Provide the context for the Bug.
I'm using a custom action to add a GitHub repository to a GitHub App installation. The token used for the API call is required to have the "repo" scope.
🖥 Your Environment
No response
👀 Have you spent some time to check if this bug has been raised before?

[X] I checked and didn't find similar issue

🏢 Have you read the Code of Conduct?

[X] I have read the Code of Conduct

Are you willing to submit PR?
None

I also started a thread in Discord
https://discord.com/channels/687207715902193673/687207715902193679/1274780868782723093

@acierto could you take a look at this and see if you can reproduce this? :pray:

@benjdlambert Yeah, I can reproduce it. I have slightly different result. But the problem is the same.
In my case it's always  "x-oauth-scopes: read:user"

But when I debug the code, it shows that the session has all scopes

Initially I thought maybe it's due to that when I generated PAT I gave few permissions there, but it is the same if I grant for PAT all permissions

I came across this issue https://github.com/backstage/backstage/issues/9380 ...and now it all makes sense.
Our Backstage Instance  is currently using a Github App for auth instead of a Github OAuth App. I was able to test authentication using an OAuth App instead, and now the token that is generated does have the requested scope(s).
Below is an example screenshot of the token headers when only requesting the "repo" scope as an additional scope from the RepoUrlPicker ui:option: RequestUserCredentials.
The OAuth app by default also requests the gist, read:org, and read:user scopes.

I also went back and looked at the Backstage documentation here

So, it appears that we just need to use the Github OAuth App to resolve the problem.


Fix code coverage in IntelliJ

jvector-base/jvector-twenty must be compile time deps for the classes files to be included in the scope for code coverage generation.


⚠ Bofrost.be has degraded performance

In 0cf5548, Bofrost.be (https://www.bofrost.be) experienced degraded performance:

HTTP code: 200
Response time: 3134 ms

Resolved: Bofrost.be performance has improved in 92f1ef1.


Pip install fails due to issue with metadata generation

Attempting to install within a virtual environment running python 3.9.11 with pip install nis2pyr leads to the following error:
Collecting nis2pyr
  Using cached nis2pyr-0.6.2-py3-none-any.whl (11 kB)
Collecting nd2==0.1.4
  Using cached nd2-0.1.4-cp39-cp39-macosx_11_0_arm64.whl (132 kB)
Collecting opencv-python==<IP_ADDRESS>
  Using cached opencv_python-<IP_ADDRESS>-cp39-cp39-macosx_11_0_arm64.whl (10.7 MB)
Collecting tifffile==2021.8.30
  Using cached tifffile-2021.8.30-py3-none-any.whl (171 kB)
Collecting ome-types==0.2.9
  Using cached ome_types-0.2.9-py3-none-any.whl (137 kB)
Collecting imagecodecs==2021.8.26
  Using cached imagecodecs-2021.8.26.tar.gz (10.4 MB)
  Preparing metadata (setup.py) ... error
  error: subprocess-exited-with-error

  × python setup.py egg_info did not run successfully.
  │ exit code: 1
  ╰─> [40 lines of output]
      running egg_info
      creating /private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-pip-egg-info-u3dftrg8/imagecodecs.egg-info
      writing /private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-pip-egg-info-u3dftrg8/imagecodecs.egg-info/PKG-INFO
      writing dependency_links to /private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-pip-egg-info-u3dftrg8/imagecodecs.egg-info/dependency_links.txt
      writing entry points to /private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-pip-egg-info-u3dftrg8/imagecodecs.egg-info/entry_points.txt
      writing requirements to /private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-pip-egg-info-u3dftrg8/imagecodecs.egg-info/requires.txt
      writing top-level names to /private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-pip-egg-info-u3dftrg8/imagecodecs.egg-info/top_level.txt
      writing manifest file '/private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-pip-egg-info-u3dftrg8/imagecodecs.egg-info/SOURCES.txt'
      Traceback (most recent call last):
        File "<string>", line 2, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "/private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-install-h1tf5g4f/imagecodecs_20b8e6a946b64134bb551a3a87b00a95/setup.py", line 613, in <module>
          setup(
        File "/Users/rogangrant/Documents/GitHub/image_analysis/image_analysis_venv/lib/python3.9/site-packages/setuptools/__init__.py", line 153, in setup
          return distutils.core.setup(**attrs)
        File "/Users/rogangrant/.pyenv/versions/3.9.11/lib/python3.9/distutils/core.py", line 148, in setup
          dist.run_commands()
        File "/Users/rogangrant/.pyenv/versions/3.9.11/lib/python3.9/distutils/dist.py", line 966, in run_commands
          self.run_command(cmd)
        File "/Users/rogangrant/.pyenv/versions/3.9.11/lib/python3.9/distutils/dist.py", line 985, in run_command
          cmd_obj.run()
        File "/Users/rogangrant/Documents/GitHub/image_analysis/image_analysis_venv/lib/python3.9/site-packages/setuptools/command/egg_info.py", line 299, in run
          self.find_sources()
        File "/Users/rogangrant/Documents/GitHub/image_analysis/image_analysis_venv/lib/python3.9/site-packages/setuptools/command/egg_info.py", line 306, in find_sources
          mm.run()
        File "/Users/rogangrant/Documents/GitHub/image_analysis/image_analysis_venv/lib/python3.9/site-packages/setuptools/command/egg_info.py", line 541, in run
          self.add_defaults()
        File "/Users/rogangrant/Documents/GitHub/image_analysis/image_analysis_venv/lib/python3.9/site-packages/setuptools/command/egg_info.py", line 578, in add_defaults
          sdist.add_defaults(self)
        File "/Users/rogangrant/.pyenv/versions/3.9.11/lib/python3.9/distutils/command/sdist.py", line 228, in add_defaults
          self._add_defaults_ext()
        File "/Users/rogangrant/.pyenv/versions/3.9.11/lib/python3.9/distutils/command/sdist.py", line 311, in _add_defaults_ext
          build_ext = self.get_finalized_command('build_ext')
        File "/Users/rogangrant/.pyenv/versions/3.9.11/lib/python3.9/distutils/cmd.py", line 299, in get_finalized_command
          cmd_obj.ensure_finalized()
        File "/Users/rogangrant/.pyenv/versions/3.9.11/lib/python3.9/distutils/cmd.py", line 107, in ensure_finalized
          self.finalize_options()
        File "/private/var/folders/3m/ms22yqxx2dq38gxl025800nh0000gn/T/pip-install-h1tf5g4f/imagecodecs_20b8e6a946b64134bb551a3a87b00a95/setup.py", line 582, in finalize_options
          import numpy
      ModuleNotFoundError: No module named 'numpy'
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

This error persists if I specify commit fae5229910d97a23f360f323953541c83c80f4b6. If you have any insight, I would very much appreciate it. Thank you for your help!

Note that installing numpy separately does partially fix, but the standard install does not work


Giscus support

Is this enhancement or feature is related to a problem?
Thanks for making this awesome theme and making it open source. All the themes I could find were very minimalist.
https://giscus.app/ or https://github.com/giscus/giscus is a recent alternative to discus. It is free, open source, privacy focused and uses GitHub discussions.
Add giscus support to this theme.
Describe the solution you'd like
I can add it very easily using the steps at https://giscus.app/ but I myself can't do fine changes like configure such that when the dark toggle is clicked the giscus comments also change the theme. But I guess you will be able to do this. So official support is better. by default you can keep disqus but give the option to change to giscus in the config file or somewhere.
Thanks.
Describe alternatives you've considered
No response
Additional context
No response

There is a small problem here though, giscus as of now doesn't have the ability to show the number of comments outside the post page. https://github.com/giscus/giscus/issues/145
But that feature is not that important in my opinion. You can set up if else functions such that in the config file if giscus is selected then the number of comments will be automatically removed.


U14: Each player gets a visual representation of the items in his possession

I added displayDrawnTiles() (and a mockfunction placeholder for getTilesInHand), which distributes the tiles dynamically into the rectangle called back.
What I am unsure about is how @Twhining can build on the code to be able to place the tiles into the game world. So if you have any suggestions how I can make it easier to build upon the code please let me know:)

displayDrawnTiles() function shows the "idle state" of items, i.e. when it is not the players turn or when he is not placing a tile. Call it together with the displayPlacedTiles() would make sense.
Might need to create another function displayDrawnTilesDuringPlacingTileAction() that shows every drawn tiles/ items but leaving the selected tile/ item blank in the current this.back (UI tile/ item box). @Twhining when the user drags an item, this function is called to update the "dragging item" state. Probably call displayDrawnTiles() again if user decides to cancel placing tile action.
As of now, if the user window size smaller than the width of 6 tiles, the drawn tiles are squeezed and stacked over each other. They cross over the border of this.back. Maybe aesthetically more pleasing to set the width of this.back to some fixed value according to nrTiles?

Something about our codes is laggy....Often I have to restart the server because the server doesn't start a session or I had to also restart both because when running the client i was put directly into the gamescreen....Something to lookout for
@paul891112 is it okey if we discuss the things you've written tomorrow? I think we should first merge the pr if everything is correct and then discuss it with leon...


Using a hierarchy over sample-ids in an Empire plot

We have a usecase where we have a tree structure that relates samples together, and it would be nice to use this within an Empire plot. The general usecase with Empire plots is a tree with tips that correspond to feature-ids, whereas in this instance, the tips of tree correspond to sample-ids.
Is it possible to use the empire plots right now for this situation?
(sorry if this is a replicate issue, I didn't see it when looking though)
cc @zoechallacombe

Good question! Now, I'm wondering if we could use something like this in the beta rarefaction results and display the "UPGMA clustering" and PCoA side by side ...

I don't think this will be possible with Empire plots (currently!) unless the ordination you're looking at is also "inverted", i.e. the points are samples. Even if that is the case I think this would require some confusing CLI stuff, e.g. passing in the sample metadata as "feature metadata" and vice versa (which IIRC is a paradigm that has caused a bunch of headaches for users of mmvec/songbird biplots in some cases).
Altering the Emperor integration code to treat the tips as samples would be feasible with some effort, but I think this would require rewriting most of the callback code—and since a lot of these callbacks are not formally covered by tests yet, things could get hairy.
This is a really interesting case though!

@antgonza that would be awesome!
@fedarko, ya I think it would be a terrible abuse at the moment. What about a boolean flag with plugin setup that is --p-tree-tips-as-samples or something? I can see why some of the callbacks may get weird but for much of this it is effectively the logical opposite axis to features (relative to the feature table at least) so it is possible the delta may be smallish

Not the same use-case, but in general I find it useful to organize samples hierarchically for paired displays, for example ordering heatmaps around the edge of the tree when looking phylogenetically organized host samples.

This is a great idea, like @fedarko notes this might require some finesse but it would ultimately be great to add. @kwcantrell and @gwarmstrong are working on formalizing the callback code as part of some related work they are currently doing.
@antgonza's point is great, this could potentially replace the two separate tabs in the beta-rarefaction visualizer over in qiime2/q2-diversity.


Change css for headings

Change css for heading in Tsv translate component
Arrange spacing for columns(for every columns)
Change source text color(disabled)

FYI:CSS changes is for row heading for eg: Book, Chapter, Verse, ID etc

Few suggestions,

you can make the source column disable(not editable) and add some bgcolor(#d4d4d4).
Remove "Bold" style from the sub-heading and  add font color(#6b1010)


pull request for e2e test at 12/03/2016 07:26:04

:white_check_mark: Validation status: passed
For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


(PDB-1934) remove two-database events query acceptance test

This is duplicated in the http facts test.

this should address @senior 's concern.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://jenkins.puppetlabs.com/job/platform_puppetdb_intn-sys_pr/1859/

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
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Lgtm @mullr you ok on this?


Add Lennox iComfort WiFi thermostat

Description:
Adds support for Lennox iComfort WiFi thermostats.
Related issue (if applicable): NA
Pull request in home-assistant.io with documentation (if applicable): home-assistant/home-assistant.io#8367
Example entry for configuration.yaml (if applicable):
climate:
  - platform: myicomfort
    name: Downstairs
    username: YOUR_MYICOMFORT_USERNAME
    password: YOUR_MYICOMFORY_PASSWORD

Checklist:

[x ] The code change is tested and works locally.
[~ ] Local tests pass with tox. Your PR cannot be merged unless tests pass
Tox fails on my system (OSX) prior to adding new code but individual tests (flake8, pylint, etc.) of my file work.
[x ] There is no commented out code in this PR.

If user exposed functionality or configuration variables are added/changed:

[x] Documentation added/updated in home-assistant.io

If the code communicates with devices, web services, or third-party tools:

[x] New dependencies have been added to the REQUIREMENTS variable (example).
[x] New dependencies are only imported inside functions that use them (example).
[x] New or updated dependencies have been added to requirements_all.txt by running script/gen_requirements_all.py.
[x] New files were added to .coveragerc.

If the code does not interact with devices:

[ ] Tests have been added to verify that the new code works.

New PR for documentation home-assistant/home-assistant.io#8367

This PR seems to have gone stale. Closing it.


Add brief descriptions about end-to-end ML Pipelines

This PR aims to add brief description about different end-to-end pipelines in the overview page.
Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

[ ] Choose reviewer(s) and mention them in a comment (R: @username).
[ ] Mention the appropriate issue in your description (for example: addresses #123), if applicable. This will automatically add a link to the pull request in the issue. If you would like the issue to automatically close on merging the pull request, comment fixes #<ISSUE NUMBER> instead.
[ ] Update CHANGES.md with noteworthy changes.
[ ] If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.
To check the build health, please visit https://github.com/apache/beam/blob/master/.test-infra/BUILD_STATUS.md
GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI.

@damccorm please have a look


gfe:entry-ttl and gfe:entry-ttielements are not allowed under gfe:replicated-region

Im using Apache Geode 1.14.4 and spring-data-geode version 2.7.2.
I have following region config which fails to bring up my app.
<beans xmlns="http://www.springframework.org/schema/beans"
       xmlns:gfe="http://www.springframework.org/schema/geode"
       xsi:schemaLocation="http://www.springframework.org/schema/geode http://www.springframework.org/schema/geode/spring-geode.xsd">
    <gfe:replicated-region id="regionId" cache-ref="myCacheRef">
        <!-- When region.get(k) is a cache-miss, this loader will be called to load the value (eg. from db) -->
        <gfe:cache-loader ref="myLoader"/>
        <gfe:cache-writer ref="myWriter"/>
        <!-- Time-to-live (TTL) is used for creation and put operations -->
        <gfe:entry-ttl timeout="300" action="DESTROY"/>
        <!-- Idle timeout (TTI) is used for get and netSearch operations -->
        <gfe:entry-tti timeout="300" action="DESTROY"/>
    </gfe:replicated-region>
</beans>

The exact error is:
org.springframework.beans.factory.xml.XmlBeanDefinitionStoreException: Line 29 in XML document from file [C:\Users\sjoshi\context\geode-context.xml] is invalid; nested exception is org.xml.sax.SAXParseException; lineNumber: 29; columnNumber: 56; cvc-complex-type.2.4.a: Invalid content was found starting with element '{"http://www.springframework.org/schema/geode":entry-ttl}'. One of '{"http://www.springframework.org/schema/geode":membership-attributes, "http://www.springframework.org/schema/geode":gateway-sender, "http://www.springframework.org/schema/geode":gateway-sender-ref, "http://www.springframework.org/schema/geode":async-event-queue, "http://www.springframework.org/schema/geode":async-event-queue-ref, "http://www.springframework.org/schema/geode":subscription, "http://www.springframework.org/schema/geode":eviction, "http://www.springframework.org/schema/geode":lookup-region, "http://www.springframework.org/schema/geode":replicated-region, "http://www.springframework.org/schema/geode":partitioned-region, "http://www.springframework.org/schema/geode":local-region, "http://www.springframework.org/schema/geode":client-region}' is expected.

Your SDG XML configuration metadata (above) is not correctly defined (formed) according to the SDG XSD (see here).
Specifically, the ordering of the CacheLoader/Writer configuration elements (i.e. <gfe:cache-loader> and <gfe:cache-writer>) inside the Replicated Region element is not correct.
It should be defined as:
<beans xmlns="http://www.springframework.org/schema/beans"
              xmlns:gfe="http://www.springframework.org/schema/geode"
              xsi:schemaLocation="http://www.springframework.org/schema/geode
                  http://www.springframework.org/schema/geode/spring-geode.xsd">

    <gfe:replicated-region id="regionId" cache-ref="myCacheRef">
        <gfe:entry-ttl timeout="300" action="DESTROY"/>
        <gfe:entry-tti timeout="300" action="DESTROY"/>
        <gfe:cache-loader ref="myLoader"/>
        <gfe:cache-writer ref="myWriter"/>
    </gfe:replicated-region>
</beans>

I had no clue entries need to be ordered 🤯

I was able to reproduce your outcome using the SDG RegionExpirationAttributesNamespaceIntegrationTests class (see here) with its associated XML configuration file (see here). You can see the changes that I made in this commit.
Specifically, I defined CacheListener, CacheLoader and CacheWriter configuration elements for the Replicated Region,
here. This works as expected and the test passes!

In general, the ordering of elements defined by the SDG XSD has existed for as long as SD Geode has been project. While some of the restrictions defined in the SDG XSD may be possible to lift, changing the XSD even a slight bit, could potentially and adversely affect other users's existing configuration metadata.
So, we don't generally make many changes to the XSD, such as re-ordering of the elements, changing type definitions, or otherwise, in any significant way.
Also, among many others reasons, this is also 1 reason why Spring overall has gravitated away from XML configuration metadata in favor of JavaConfig.
SDG has comprehensive support for Annotation-based (see here) as well as Java-based configuration metadata (see the API).
On top of that Spring Boot for Apache Geode (SBDG) makes this even easier with Spring Boot's auto-configuration, convention over configuration approach. See project page here, and documentation here (Ref Doc) and here (Javadoc).
A really good way to get started is by looking at the Samples, and specifically, the Getting Started sample.
Spring Boot is the way.
I recommend moving away from XML configuration.

Any reason why the ordering matters here? It is so unexpected.

That all makes sense. I honestly didnt even know ordering can be mandated by an xsd like that.
I would like to switch to Spring Boot. One day...


operations/convertToGrey

Is it ok to add a new method 'convertToGrey' that allows all the existing algorithms ?
I would also like to write as code:
let greyImage = colorImage.grey({algorithm: 'minimum');
https://github.com/image-js/image-js/blob/legacy/src/image/transform/grey.js
https://github.com/image-js/image-js/blob/legacy/src/image/transform/greyAlgorithms.js

That said, I think that for simplicity, image.grey() should be only applicable to RGB and RGBA images.
For generic color conversions in all directions, we keep image.convertColor(newColorModel)

Things left to do: make the grey test work and check they are no more bugs.


Issue regarding product detials are not showing on frontend

Hello,
I have installed this extension and it is working properly but i am facing problem that in product list page the product which is selected by default in configurable product so it is not showing in product list page.but it is showing default product for that configurable product so is there any solution so that product default values will also show in frontend product list page.??

@shubh1105 I'm sorry, but this extension is discontinued due to lack of time to take care of it.


Laggy Map since 1.6.1

Since v1.6.1 (I think) I've noticed that the map lags more than before in areas with lots of chargers.
I think EVMap now loads more chargers at once? This is a good thing as before sometimes we'd have to move around or zoom in to load the chargers in a specific area, but the downside is that the map sometimes freezes or lags a lot.
The following video is from the current beta on the Play Store (1.6.3-google), but the same thing happens on 1.6.2 stable. Default settings, clean install, open charge map used as source. Central London is a good position to test as there are lots of lamp post chargers and the map is packed.
https://github.com/ev-map/EVMap/assets/4492221/183c586c-8c0e-41aa-a175-9622eb23a233
I have a OnePlus 8 Pro. Not the fastest phone anymore, but it's powered by a Snapdragon 865 which was used by most flagships from 2020. I assume performance is worse on slower hardware.

seems like the new caching feature (#164) broke support for marker clustering at higher zoom levels for OpenChargeMap. The map can't handle that many markers at once, so we need clustering to make it work. I'll look into it.

sorry, I was wrong. It's not broken due to the caching feature, it's actually loading more chargers. OCM limits the results per page, and in the process of implementing the caching feature, I tried increasing the number of pages that will be loaded. So I guess I need to just allow for clustering at higher zoom levels to make it work.
In contrast to GoingElectric, OCM doesn't support server-side clustering, that's another reason why it may be slower in some cases.

should be improved in the upcoming update 1.6.4


Passive event listeners

adding {passive : true} if passiveEventListeners are supported else let the capture flag default to false.
https://github.com/WICG/EventListenerOptions/blob/gh-pages/explainer.md
https://github.com/GoogleChrome/ui-element-samples/pull/32

Current coverage is 97.92% (diff: 28.57%)

Merging #407 into master will decrease coverage by 0.23%

@@             master       #407   diff @@
==========================================
  Files            61         62     +1
  Lines          4220       4232    +12
  Methods         893        895     +2
  Messages          0          0
  Branches        316        319     +3
==========================================
+ Hits           4142       4144     +2
- Misses           78         88    +10
  Partials          0          0

Powered by Codecov. Last update ea90d30...03a4746

Hi @dxtr026. 😄
I have a branch locally where I've been testing passive event listeners with Grid but I haven't noticed any performance improvements with my own test harness and Chrome's Timeline view. I wonder if you've seen improvements with the WindowScroller component? And if so, how much of an improvement?
I'm open to doing something non-standard (like using a passive listener instead of the synthetic scroll event) but only if I'm confident that it offers non-trivial performance gains. 😁

@bvaughn same here, couldn't find any concrete results to actually see the performance improvement. I tried doing tests but I think Chrome auto handles passive listeners and that's the reason we are not able to capture any difference.

Ok. I plan to look into it more, perhaps over the weekend. As soon as I find time.

Closing this (for now) as it adds complication without measurably improving performance. If you find anything else related to this effort though please let me know. I'm happy to continue looking into passive listeners. I think there's potential there. I just don't want to make the move until there's concrete gains.
See my comments on issue #398 for more.

Example: https://rbyers.github.io/scroll-latency.html
So Chrome assumes passive by default. And if you preventDefault a scroll event for any further events, Chrome will run your function before paint.
Check it out in the example, initially if you scroll, you should get 60fps, after to tick the preventDefault button, scroll, untick and then scroll, you will get degraded performance. Then you need to set passive:true to get good performance.
So in Chrome you will get the best performance out of the box. if you don't preventDefault, you don't need to set passive:true
Plus timeline will actually not tell you what frame rate you are getting. You will need to use the FPS Meter. For example, I get 60fps while scrolling a long PR in github (https://github.com/openstreetmap/iD/pull/8/files) But timeline tells me that its actually slow.

I think the Timeline is pretty trustworthy (and more detailed than the Frame Rate viewer). Looking at the timeline view you've pasted- there's a ton of repainting going on. That would make for some long frames.
Scrolling the page at the PR you posted does feel pretty janky to me at first (and the Timeline view kind of validates this). Not sure why the Frame Rate view doesn't pick up on it. Things level out after I've scrolled for a bit.

Ok ditch the github example.
Take the first example: https://rbyers.github.io/scroll-latency.html
Apply 50ms Handler Jank, And then tick passive.
Now scroll in the page and record timeline and see the FPS meter.
Timeline will tell you that you are getting a slow frame rate while you are getting smooth scroll.

Closing this (for now) as it adds complication without measurably improving performance.

The point I'm trying to make is that because Passive Event Listeners are a new feature, they are not correctly recorded in the Timeline, but there is significant performance gain.

This is interesting. Point recognized.
I have a lot on my plate right now, preparing to start a new job on Monday and presenting at a conference next weekend- so I probably won't be able to pick this thread back up for a bit. But it's on my list. 😄

Thanks Brian,
No issues. Take your time. Best of Luck with your new job and conference. Rock it.
Sorry for my rant.


Add the code checks that was implemented in the pre-commit to the CI

Motivation
https://issues.jboss.org/browse/AEROGEAR-8547
What
Add the code checks that was implemented in the pre-commit to the CI
Why

It is the only way to ensure that the CI will fail if the errors are found.
The pre-commit hook can be ignored by --no-verify

How

centralized actions in the code-check.sh script
call the script in the CI

Verification Steps

Check if the CI is calling the code-check.sh script and finishes with success

Checklist:

[X] Code has been tested locally by PR requester
[ ] Changes have been successfully verified by another team member

Progress

[x] Finished task
[ ] TODO

Additional Notes

Coverage remained the same at 64.769% when pulling 81cafc81710c24cd506b93b57630360d2acdc7c9 on camilamacedo86:IMP_CI_CODE_CHECKS into aeebd9ce8fa0b3d218acbd504edc8ee2b0e08520 on aerogear:master.

@craicoverflow and @davidffrench,
The changes were made as you suggested folks. Really tks for the review 👍

HI @davidffrench,
Thank you. Regards " It would be nice to not have the checks duplicated though and have the githook call the same script created here."  -> it is exactly what was done here.


Tweak package.json

Coverage remained the same at 100.0% when pulling 9ab3d9d28e20469e0a5484334a3a9f3380caecdc on stevemao:travis into 4cb1bb5d4bf8404b0046a5ced37ff7bc7c5ea57d on cht8687:master.


Add badge to operator in release checklist template

add a version badge to last stage Talk to Vincent (Operator WG Lead) and have him cut (or help you cut) the knative/operator check list item

@pradnyavmw are you still working on this issue?

@pradnyavmw are you still working on this issue?

I think its done - we can see the badge here

great - thanks