[Bug]: 8xV100 gpus: Failed to infer device type

Your current environment
I'm trying to run inference in docker-compose, host: ubuntu 22.04
uname -r
5.19.0-1010-nvidia-lowlatency

version: '3.8'

services:
  mixtral7x8b:
    image: vllm/vllm-openai:v0.5.5
    restart: unless-stopped
    ports:
      - "<IP_ADDRESS>:8000:8000"
    volumes:
      - /opt/ai_models:/models
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]
    environment:
      - VLLM_API_KEY=<token here>
      # parameters for uvicorn (seconds)
      - TIMEOUT_KEEP_ALIVE=120
      - TIMEOUT_GRACEFUL_SHUTDOWN=30
    runtime: nvidia
    shm_size: 100gb
    command: >
      --host <IP_ADDRESS>
      --dtype=half
      --served-model-name Mixtral-8x7b
      --model /models/mistralai/Mixtral-8x7B-Instruct-v0.1

nvidia-smi
Fri Sep 27 04:43:53 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.90.12              Driver Version: 550.90.12      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| 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  Tesla V100-SXM3-32GB           Off |   00000000:1A:00.0 Off |                    0 |
| N/A   35C    P0             53W /  350W |       1MiB /  32768MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   1  Tesla V100-SXM3-32GB           Off |   00000000:1C:00.0 Off |                    0 |
| N/A   34C    P0             49W /  350W |       1MiB /  32768MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   2  Tesla V100-SXM3-32GB           Off |   00000000:1D:00.0 Off |                    0 |
| N/A   36C    P0             56W /  350W |       1MiB /  32768MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   3  Tesla V100-SXM3-32GB           Off |   00000000:1E:00.0 Off |                    0 |
| N/A   37C    P0             53W /  350W |       1MiB /  32768MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   4  Tesla V100-SXM3-32GB           Off |   00000000:3E:00.0 Off |                    0 |
| N/A   38C    P0             57W /  350W |       1MiB /  32768MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   5  Tesla V100-SXM3-32GB           Off |   00000000:3F:00.0 Off |                    0 |
| N/A   35C    P0             57W /  350W |       1MiB /  32768MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   6  Tesla V100-SXM3-32GB           Off |   00000000:40:00.0 Off |                    0 |
| N/A   37C    P0             53W /  350W |       1MiB /  32768MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   7  Tesla V100-SXM3-32GB           Off |   00000000:41:00.0 Off |                    0 |
| N/A   35C    P0             59W /  350W |       1MiB /  32768MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

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

Model Input Dumps
No response
🐛 Describe the bug
mixtral7x8b-1  | INFO 09-26 21:36:47 api_server.py:144] Multiprocessing frontend to use ipc:///tmp/32318298-afda-4100-99be-34a8f1cfb8ea for RPC Path.
mixtral7x8b-1  | INFO 09-26 21:36:47 api_server.py:161] Started engine process with PID 79
mixtral7x8b-1  | WARNING 09-26 21:36:56 config.py:1563] Casting torch.bfloat16 to torch.float16.
mixtral7x8b-1  | INFO 09-26 21:36:56 llm_engine.py:184] Initializing an LLM engine (v0.5.5) with config: model='/models/mistralai/Mixtral-8x7B-Instruct-v0.1', speculative_config=None, tokenizer='/models/mistralai/Mixtral-8x7B-Instruct-v0.1', 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=32768, 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, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=Mixtral-8x7b, use_v2_block_manager=False, enable_prefix_caching=False)
mixtral7x8b-1  | Process SpawnProcess-1:
mixtral7x8b-1  | Traceback (most recent call last):
mixtral7x8b-1  |   File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
mixtral7x8b-1  |     self.run()
mixtral7x8b-1  |   File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
mixtral7x8b-1  |     self._target(*self._args, **self._kwargs)
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 230, in run_rpc_server
mixtral7x8b-1  |     server = AsyncEngineRPCServer(async_engine_args, usage_context, rpc_path)
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 31, in __init__
mixtral7x8b-1  |     self.engine = AsyncLLMEngine.from_engine_args(
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 740, in from_engine_args
mixtral7x8b-1  |     engine = cls(
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 636, in __init__
mixtral7x8b-1  |     self.engine = self._init_engine(*args, **kwargs)
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 840, in _init_engine
mixtral7x8b-1  |     return engine_class(*args, **kwargs)
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 272, in __init__
mixtral7x8b-1  |     super().__init__(*args, **kwargs)
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 270, in __init__
mixtral7x8b-1  |     self.model_executor = executor_class(
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 46, in __init__
mixtral7x8b-1  |     self._init_executor()
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 37, in _init_executor
mixtral7x8b-1  |     self.driver_worker = self._create_worker()
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 104, in _create_worker
mixtral7x8b-1  |     return create_worker(**self._get_create_worker_kwargs(
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 23, in create_worker
mixtral7x8b-1  |     wrapper.init_worker(**kwargs)
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 444, in init_worker
mixtral7x8b-1  |     self.worker = worker_class(*args, **kwargs)
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 99, in __init__
mixtral7x8b-1  |     self.model_runner: GPUModelRunnerBase = ModelRunnerClass(
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 842, in __init__
mixtral7x8b-1  |     self.attn_backend = get_attn_backend(
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/attention/selector.py", line 108, in get_attn_backend
mixtral7x8b-1  |     backend = which_attn_to_use(num_heads, head_size, num_kv_heads,
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/attention/selector.py", line 215, in which_attn_to_use
mixtral7x8b-1  |     if current_platform.get_device_capability()[0] < 8:
mixtral7x8b-1  |   File "/usr/local/lib/python3.10/dist-packages/vllm/platforms/interface.py", line 28, in get_device_capability
mixtral7x8b-1  |     raise NotImplementedError
mixtral7x8b-1  | NotImplementedError
mixtral7x8b-1  | ERROR 09-26 21:36:57 api_server.py:171] RPCServer process died before responding to readiness probe

It used to work just few hours ago, we simply restarted docker compose, same image, same tag, same mode.
Not sure what might have happened
Why did it start checking compute capability <8 if it used to run on 7 just a day ago?
I appreciate your help, I almost broke my head already.
on v0.6.2 getting a different error:
llama-3.1-8b-1  | Process SpawnProcess-1:
llama-3.1-8b-1  | Traceback (most recent call last):
llama-3.1-8b-1  |   File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
llama-3.1-8b-1  |     self.run()
llama-3.1-8b-1  |   File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
llama-3.1-8b-1  |     self._target(*self._args, **self._kwargs)
llama-3.1-8b-1  |   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 388, in run_mp_engine
llama-3.1-8b-1  |     engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
llama-3.1-8b-1  |              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
llama-3.1-8b-1  |   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 134, in from_engine_args
llama-3.1-8b-1  |     engine_config = engine_args.create_engine_config()
llama-3.1-8b-1  |                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
llama-3.1-8b-1  |   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/arg_utils.py", line 873, in create_engine_config
llama-3.1-8b-1  |     device_config = DeviceConfig(device=self.device)
llama-3.1-8b-1  |                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
llama-3.1-8b-1  |   File "/usr/local/lib/python3.12/dist-packages/vllm/config.py", line 1081, in __init__
llama-3.1-8b-1  |     raise RuntimeError("Failed to infer device type")
llama-3.1-8b-1  | RuntimeError: Failed to infer device type

Before submitting a new issue...

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

Tried with v0.6.2
getting a different error:
llama-3.1-8b-1  | Process SpawnProcess-1:
llama-3.1-8b-1  | Traceback (most recent call last):
llama-3.1-8b-1  |   File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
llama-3.1-8b-1  |     self.run()
llama-3.1-8b-1  |   File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
llama-3.1-8b-1  |     self._target(*self._args, **self._kwargs)
llama-3.1-8b-1  |   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 388, in run_mp_engine
llama-3.1-8b-1  |     engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
llama-3.1-8b-1  |              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
llama-3.1-8b-1  |   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 134, in from_engine_args
llama-3.1-8b-1  |     engine_config = engine_args.create_engine_config()
llama-3.1-8b-1  |                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
llama-3.1-8b-1  |   File "/usr/local/lib/python3.12/dist-packages/vllm/engine/arg_utils.py", line 873, in create_engine_config
llama-3.1-8b-1  |     device_config = DeviceConfig(device=self.device)
llama-3.1-8b-1  |                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
llama-3.1-8b-1  |   File "/usr/local/lib/python3.12/dist-packages/vllm/config.py", line 1081, in __init__
llama-3.1-8b-1  |     raise RuntimeError("Failed to infer device type")
llama-3.1-8b-1  | RuntimeError: Failed to infer device type

As a sanity check, can you import pytorch and verify that it can access your GPUs?

Absolutely.  I'm not sure if this is the good way to test it, but this is what poped up instantly:
import torch

def check_pytorch_gpu():
    try:
        if torch.cuda.is_available():
            print(f"PyTorch can access {torch.cuda.device_count()} GPU(s).")
            for i in range(torch.cuda.device_count()):
                print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
        else:
            print("PyTorch cannot access any GPUs.")
    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    check_pytorch_gpu()

pyproject.toml
[tool.poetry]
name = "try-gpus"
version = "0.1.0"
description = ""
authors = ["Gleb Erokhin"]
readme = "README.md"

[tool.poetry.dependencies]
python = "^3.10"
torch = "^2.4.1"

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

Execution output:
PyTorch can access 8 GPU(s).
 It's possible that this issue has been fixed since v0.5.5.

Otherwise I'm not sure. @youkaichao any ideas?

please run https://github.com/vllm-project/vllm/blob/main/collect_env.py inside the docker image to collect the environment.

Perhaps you can try upgrading the vLLM version. It's possible that this issue has been fixed since v0.5.5.

Yeah, I tried that with v9.6.2 (posted the results above). Getting “Failed to infer device type” exception.
I will run the python script in the container and post here, as requested.

please run https://github.com/vllm-project/vllm/blob/main/collect_env.py inside the docker image to collect the environment.
@youkaichao
Here you are. It looks like CUDA is not visible from docker, hovewer nvidia-docker-toolkit seems to be working fine and I can run nvidia-smi from cuda-12-6 container for test.

root@f6ca99dec4b4:/vllm-workspace/ttt# python3 collect_env.py
Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

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

Python version: 3.10.14 (main, Apr  6 2024, 18:45:05) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.19.0-1010-nvidia-lowlatency-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          80
On-line CPU(s) list:             0-79
Thread(s) per core:              2
Core(s) per socket:              20
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Xeon(R) Gold 6138 CPU @ 2.00GHz
Stepping:                        4
CPU MHz:                         999.871
CPU max MHz:                     3700.0000
CPU min MHz:                     1000.0000
BogoMIPS:                        4000.00
Virtualization:                  VT-x
L1d cache:                       1.3 MiB
L1i cache:                       1.3 MiB
L2 cache:                        40 MiB
L3 cache:                        55 MiB
NUMA node0 CPU(s):               0-19,40-59
NUMA node1 CPU(s):               20-39,60-79
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Mitigation; IBRS
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Mitigation; Clear CPU buffers; SMT vulnerable
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 pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm 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 md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] flashinfer==0.1.4+cu121torch2.4
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==<IP_ADDRESS>
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==<IP_ADDRESS>
[pip3] nvidia-cufft-cu12==<IP_ADDRESS>
[pip3] nvidia-curand-cu12==<IP_ADDRESS>
[pip3] nvidia-cusolver-cu12==<IP_ADDRESS>
[pip3] nvidia-cusparse-cu12==<IP_ADDRESS>
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.20
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.44.2
[pip3] triton==3.0.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.5
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect

Well, I managed to solve the problem in a weird way.
I commented the following section in docker compose file
    #deploy:
    # resources:
    #    reservations:
    #      devices:
    #       - driver: nvidia
    #          capabilities: [GPU]

I have no idea why it fixes the issue. Earlier, it was a requirement to explicitly add Nvidia device and GPU capabilities.
Maybe it's going to be userful for someone. The problem seems to have nothing to do with VLLM but the container toolkit.

thanks for reporting, you need to make cuda work before you can use vllm.

@youkaichao 请问这个问题可以怎么解决呢? 谢谢
尝试在k3s里跑,本机有nvidia GPU支持:
nvidia-smi
Sat Oct 19 20:28:30 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.107.02             Driver Version: 550.107.02     CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| 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 GeForce RTX 4070 ...    Off |   00000000

使用了gpu-operator,让k3s支持使用gpu:
NAME                                                              READY   STATUS      RESTARTS      AGE
pod/gpu-feature-discovery-kgfkv                                   1/1     Running     0             2d5h
pod/gpu-operator-57748cc9d8-frq59                                 1/1     Running     2 (90m ago)   2d5h
pod/gpu-operator-node-feature-discovery-gc-5fcdc8894b-8l782       1/1     Running     0             2d5h
pod/gpu-operator-node-feature-discovery-master-7d84b856d7-7xrjv   1/1     Running     0             2d5h
pod/gpu-operator-node-feature-discovery-worker-wk2v4              1/1     Running     0             2d5h
pod/nvidia-container-toolkit-daemonset-ldlb2                      1/1     Running     0             2d5h
pod/nvidia-cuda-validator-kkrvc                                   0/1     Completed   0             2d5h
pod/nvidia-dcgm-exporter-tq2h5                                    1/1     Running     0             2d5h
pod/nvidia-device-plugin-daemonset-f7tf5                          1/1     Running     0             2d5h
pod/nvidia-operator-validator-wpd4t                               1/1     Running     0             2d5h

NAME                                                 TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
service/gpu-operator                                 ClusterIP   <IP_ADDRESS>     <none>        8080/TCP   2d5h
service/gpu-operator-node-feature-discovery-master   ClusterIP   <IP_ADDRESS>   <none>        8080/TCP   2d5h
service/nvidia-dcgm-exporter                         ClusterIP   <IP_ADDRESS>   <none>        9400/TCP   2d5h

NAME                                                        DESIRED   CURRENT   READY   UP-TO-DATE   AVAILABLE   NODE SELECTOR                                      AGE
daemonset.apps/gpu-feature-discovery                        1         1         1       1            1           nvidia.com/gpu.deploy.gpu-feature-discovery=true   2d5h
daemonset.apps/gpu-operator-node-feature-discovery-worker   1         1         1       1            1           <none>                                             2d5h
daemonset.apps/nvidia-container-toolkit-daemonset           1         1         1       1            1           nvidia.com/gpu.deploy.container-toolkit=true       2d5h
daemonset.apps/nvidia-dcgm-exporter                         1         1         1       1            1           nvidia.com/gpu.deploy.dcgm-exporter=true           2d5h
daemonset.apps/nvidia-device-plugin-daemonset               1         1         1       1            1           nvidia.com/gpu.deploy.device-plugin=true           2d5h
daemonset.apps/nvidia-driver-daemonset                      0         0         0       0            0           nvidia.com/gpu.deploy.driver=true                  2d5h
daemonset.apps/nvidia-mig-manager                           0         0         0       0            0           nvidia.com/gpu.deploy.mig-manager=true             2d5h
daemonset.apps/nvidia-operator-validator                    1         1         1       1            1           nvidia.com/gpu.deploy.operator-validator=true      2d5h

NAME                                                         READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/gpu-operator                                 1/1     1            1           2d5h
deployment.apps/gpu-operator-node-feature-discovery-gc       1/1     1            1           2d5h
deployment.apps/gpu-operator-node-feature-discovery-master   1/1     1            1           2d5h

NAME                                                                    DESIRED   CURRENT   READY   AGE
replicaset.apps/gpu-operator-57748cc9d8                                 1         1         1       2d5h
replicaset.apps/gpu-operator-node-feature-discovery-gc-5fcdc8894b       1         1         1       2d5h
replicaset.apps/gpu-operator-node-feature-discovery-master-7d84b856d7   1         1         1       2d5h

构建了一个docker镜像:
FROM nvidia/cuda:12.6.2-cudnn-runtime-ubuntu24.04

RUN apt-get update \
    && apt-get install -y python3-pip pipx nvidia-container-toolkit \
    && apt-get clean
RUN pipx install vllm

# Ensure pipx binary directory is in the PATH
ENV PATH="/root/.local/bin:$PATH"
# /usr/local/cuda/compat/libcuda.so.1
ENV LD_LIBRARY_PATH="/usr/local/cuda/compat:$LD_LIBRARY_PATH"

在k3s集群里跑了deployment:
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-vllm
  namespace: ai-vllm
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ai-vllm
  template:
    metadata:
      annotations:
      labels:
        app: ai-vllm
    spec:
      containers:
        - name: main
          #image: vllm/vllm-openai:v0.6.3
          image: minixxie/vllm:0.0.1
          imagePullPolicy: IfNotPresent
          command: ["vllm", "serve", "--port", "80", "Qwen/Qwen2.5-Coder-7B-Instruct"]
          resources:
            limits:
              nvidia.com/gpu: 1 # requesting 1 GPU
          ports:
            - name: http
              containerPort: 80  # HTTP port
          readinessProbe:
            httpGet:
              path: /
              port: 80
            initialDelaySeconds: 10
            periodSeconds: 30
          env:
            - name: NVIDIA_VISIBLE_DEVICES
              value: "all"
            - name: NVIDIA_DRIVER_CAPABILITIES
              value: "compute,utility"

但是跑起来有错误(pod的日志):
Process SpawnProcess-1:
Traceback (most recent call last):
  File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/root/.local/share/pipx/venvs/vllm/lib/python3.12/site-packages/vllm/engine/multiprocessing/engine.py", line 390, in run_mp_engine
    engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.local/share/pipx/venvs/vllm/lib/python3.12/site-packages/vllm/engine/multiprocessing/engine.py", line 135, in from_engine_args
    engine_config = engine_args.create_engine_config()
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.local/share/pipx/venvs/vllm/lib/python3.12/site-packages/vllm/engine/arg_utils.py", line 902, in create_engine_config
    device_config = DeviceConfig(device=self.device)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.local/share/pipx/venvs/vllm/lib/python3.12/site-packages/vllm/config.py", line 1091, in __init__
    raise RuntimeError("Failed to infer device type")
RuntimeError: Failed to infer device type

sorry I think I've fixed it by adding this missing line of "runtimeClassName":
spec:
 template:
  spec:
   runtimeClassName: nvidia

and eventually switching back to the image vllm/vllm-openai:v0.6.3, it works (with other problems coming out), thanks.

please run https://github.com/vllm-project/vllm/blob/main/collect_env.py inside the docker image to collect the environment.

I also encountered this problem, can you help me check the issue? Below is the information output by running collect_env.py.
`(xinf) root@DESKTOP-ESRGKIB:~/vllm# python3 collect_env.py
Collecting environment information...
PyTorch version: 2.5.1+rocm6.2
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.2.41133-dd7f95766
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (conda-forge gcc 12.1.0-17) 12.1.0
Clang version: Could not collect
CMake version: version 3.31.0
Libc version: glibc-2.35
Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-<IP_ADDRESS>-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: AMD Radeon RX 7900 XT (gfx1100)
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 6.2.41133
MIOpen runtime version: 3.2.0
Is XNNPACK available: True
CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        48 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               8
On-line CPU(s) list:                  0-7
Vendor ID:                            AuthenticAMD
Model name:                           AMD Ryzen 7 7700 8-Core Processor
CPU family:                           25
Model:                                97
Thread(s) per core:                   2
Core(s) per socket:                   4
Socket(s):                            1
Stepping:                             2
BogoMIPS:                             7599.84
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Virtualization:                       AMD-V
Hypervisor vendor:                    Microsoft
Virtualization type:                  full
L1d cache:                            128 KiB (4 instances)
L1i cache:                            128 KiB (4 instances)
L2 cache:                             4 MiB (4 instances)
L3 cache:                             32 MiB (1 instance)
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Mitigation; safe RET
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; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
Versions of relevant libraries:
[pip3] jj-pytorchvideo==0.1.5
[pip3] lion-pytorch==0.2.2
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-ml-py==12.560.30
[pip3] onnxruntime==1.20.0
[pip3] pytorch-lightning==2.4.0
[pip3] pytorch-triton-rocm==3.1.0
[pip3] pyzmq==26.2.0
[pip3] torch==2.5.1+rocm6.2
[pip3] torchaudio==2.5.1+rocm6.2
[pip3] torchmetrics==1.6.0
[pip3] torchvision==0.20.1+rocm6.2
[pip3] transformers==4.46.2
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.0.0
[pip3] vector-quantize-pytorch==1.20.9
[conda] jj-pytorchvideo           0.1.5                    pypi_0    pypi
[conda] lion-pytorch              0.2.2                    pypi_0    pypi
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] pytorch-lightning         2.4.0                    pypi_0    pypi
[conda] pytorch-triton-rocm       3.1.0                    pypi_0    pypi
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] torch                     2.5.1+rocm6.2            pypi_0    pypi
[conda] torchaudio                2.5.1+rocm6.2            pypi_0    pypi
[conda] torchmetrics              1.6.0                    pypi_0    pypi
[conda] torchvision               0.20.1+rocm6.2           pypi_0    pypi
[conda] transformers              4.46.2                   pypi_0    pypi
[conda] transformers-stream-generator 0.0.5                    pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
[conda] vector-quantize-pytorch   1.20.9                   pypi_0    pypi
ROCM Version: 6.2.41134-65d174c3e
Neuron SDK Version: N/A
vLLM Version: 0.6.4
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
PYTORCH_ROCM_ARCH=gfx1100
LD_LIBRARY_PATH=/root/miniconda3/envs/xinf/lib/python3.10/site-packages/cv2/../../lib64:/usr/lib/x86_64-linux-gnu:/usr/local/aotriton/lib:
CUDA_MODULE_LOADING=LAZY
`

please run https://github.com/vllm-project/vllm/blob/main/collect_env.py inside the docker image to collect the environment.

I also encountered this problem, can you help me check the issue? Below is the information output by running collect_env.py.
(xinf) root@DESKTOP-ESRGKIB:~/vllm# python3 collect_env.py Collecting environment information... PyTorch version: 2.5.1+rocm6.2 Is debug build: False CUDA used to build PyTorch: N/A ROCM used to build PyTorch: 6.2.41133-dd7f95766 OS: Ubuntu 22.04.5 LTS (x86_64) GCC version: (conda-forge gcc 12.1.0-17) 12.1.0 Clang version: Could not collect CMake version: version 3.31.0 Libc version: glibc-2.35 Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-<IP_ADDRESS>-microsoft-standard-WSL2-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: AMD Radeon RX 7900 XT (gfx1100) Nvidia driver version: Could not collect cuDNN version: Could not collect HIP runtime version: 6.2.41133 MIOpen runtime version: 3.2.0 Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 8 On-line CPU(s) list: 0-7 Vendor ID: AuthenticAMD Model name: AMD Ryzen 7 7700 8-Core Processor CPU family: 25 Model: 97 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 Stepping: 2 BogoMIPS: 7599.84 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm Virtualization: AMD-V Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 128 KiB (4 instances) L1i cache: 128 KiB (4 instances) L2 cache: 4 MiB (4 instances) L3 cache: 32 MiB (1 instance) Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; safe RET 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; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] jj-pytorchvideo==0.1.5 [pip3] lion-pytorch==0.2.2 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.4 [pip3] nvidia-ml-py==12.560.30 [pip3] onnxruntime==1.20.0 [pip3] pytorch-lightning==2.4.0 [pip3] pytorch-triton-rocm==3.1.0 [pip3] pyzmq==26.2.0 [pip3] torch==2.5.1+rocm6.2 [pip3] torchaudio==2.5.1+rocm6.2 [pip3] torchmetrics==1.6.0 [pip3] torchvision==0.20.1+rocm6.2 [pip3] transformers==4.46.2 [pip3] transformers-stream-generator==0.0.5 [pip3] triton==3.0.0 [pip3] vector-quantize-pytorch==1.20.9 [conda] jj-pytorchvideo 0.1.5 pypi_0 pypi [conda] lion-pytorch 0.2.2 pypi_0 pypi [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-ml-py 12.560.30 pypi_0 pypi [conda] pytorch-lightning 2.4.0 pypi_0 pypi [conda] pytorch-triton-rocm 3.1.0 pypi_0 pypi [conda] pyzmq 26.2.0 pypi_0 pypi [conda] torch 2.5.1+rocm6.2 pypi_0 pypi [conda] torchaudio 2.5.1+rocm6.2 pypi_0 pypi [conda] torchmetrics 1.6.0 pypi_0 pypi [conda] torchvision 0.20.1+rocm6.2 pypi_0 pypi [conda] transformers 4.46.2 pypi_0 pypi [conda] transformers-stream-generator 0.0.5 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi [conda] vector-quantize-pytorch 1.20.9 pypi_0 pypi ROCM Version: 6.2.41134-65d174c3e Neuron SDK Version: N/A vLLM Version: 0.6.4 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: PYTORCH_ROCM_ARCH=gfx1100 LD_LIBRARY_PATH=/root/miniconda3/envs/xinf/lib/python3.10/site-packages/cv2/../../lib64:/usr/lib/x86_64-linux-gnu:/usr/local/aotriton/lib: CUDA_MODULE_LOADING=LAZY

It seems that you are using AMD GPU on WSL. Are you able to use the GPU in PyTorch?

Yes, PyTorch can.

Can you open a new issue for this? The current issue is for nvidia gpu.

Can you open a new issue for this? The current issue is for nvidia gpu.

ok

Can you open a new issue for this? The current issue is for nvidia gpu.

A new issue has been filed, please see this https://github.com/vllm-project/vllm/issues/10653

I solved it through export LD_LIBRARY_PATH=/usr/lib64:$LD_LIBRARY_PATH


Tree shaking dynamic import from VIte bundled React component library

Describe the bug
I am building a private React component library using Vite, I am using a barrel file for all my exports.
src/core/index.ts

export * from './components';
export * from './hooks';
export * from './icons';
export type * from './types';
export * from './utils';

package.json

 "files": [
    "dist"
  ],
  "main": "./dist/index.js",
  "module": "./dist/index.js",
  "name": "ui-kit",
  "sideEffects": false,
  "type": "module",
  "types": "dist/index.d.ts",

The library is private and I am using it inside my company's internal repo using git+http
Directly importing my components works fine, every module is tree-shaked, but when using Nextjs dynamic import, it pulls all the library
import { Button } from "@ui-kit" => 2kb

const Button = dynamic(() => import('@ui-kit').then(mod => mod.Button), {
  ssr: false,
}); => 148kb

This is my vite config
/* eslint-disable import/no-extraneous-dependencies */
import react from '@vitejs/plugin-react';
import path from 'path';
import { defineConfig } from 'vite';
import dts from 'vite-plugin-dts';

// https://vitejs.dev/config/
export default defineConfig({
  plugins: [
    react(),
    dts({
      include: ['src/core'],
    }),
  ],
  build: {
    target: 'esnext',
    minify: false,
    copyPublicDir: false,
    sourcemap: true,
    lib: {
      entry: './src/core/index.ts',
      formats: ['es'],
      fileName: 'index',
    },
    rollupOptions: {
      output: {
        preserveModules: true,
      },
      external: ['react', 'react-dom', 'date-fns', '@emotion/react', '@emotion/styled'],
    },
  },
  resolve: {
    alias: {
      src: path.resolve(__dirname, './src'),
    },
  },
});

Reproduction
N/A
Steps to reproduce
No response
System Info
System:
    OS: Windows 11 10.0.22631
    CPU: (16) x64 AMD Ryzen 9 5980HX with Radeon Graphics
    Memory: 16.40 GB / 31.41 GB
  Binaries:
    Node: 20.9.0 - C:\Program Files\nodejs\node.EXE
    Yarn: 1.22.21 - C:\Program Files\nodejs\yarn.CMD
    npm: 10.7.0 - C:\Program Files\nodejs\npm.CMD
  Browsers:
    Edge: Chromium (125.0.2535.92)
    Internet Explorer: 11.0.22621.3527
  npmPackages:
    @vitejs/plugin-react: 4.2.1 => 4.2.1
    vite: 5.2.12 => 5.2.12

Used Package Manager
npm
Logs
No response
Validations

[X] Follow our Code of Conduct
[X] Read the Contributing Guidelines.
[X] Read the docs.
[X] Check that there isn't already an issue that reports the same bug to avoid creating a duplicate.
[X] Make sure this is a Vite issue and not a framework-specific issue. For example, if it's a Vue SFC related bug, it should likely be reported to vuejs/core instead.
[X] Check that this is a concrete bug. For Q&A open a GitHub Discussion or join our Discord Chat Server.
[X] The provided reproduction is a minimal reproducible example of the bug.

Is this the same as https://github.com/vitejs/vite/issues/14145? It's fixed in the 5.3 beta

Member

I am not sure if this is related, correct me if I am wrong, as I understood the mentioned issue talks about building the final bundle, that uses the library, with Vite. Maybe my issue is a configuration issue?

when using Nextjs dynamic import, it pulls all the library

This sounds like a Next.js bundler concern and I'm not sure how this relates to Vite library mode.
Do you know if this issues shows up only for ui library built with Vite?
They seem to have this https://nextjs.org/docs/pages/building-your-application/optimizing/lazy-loading#with-named-exports, but either way, this seems more Next.js side inconsistency (static named import vs dynamic named import), so you should probably look for help there.


Hide tray icon

Hi! Thanks for Stretchly.
Please add possibility to hide tray icon for more control, because I can skip breaks sometimes, but I really don't want it.

Hi, I am not sure if I understand. How would hiding stretchly icon help? We would not be able to control/change/quit/see info anymore...
You probably want to enable "strict mode" in settings which wont let you skip breaks

Hi, I am not sure if I understand. How would hiding stretchly icon help? We would not be able to control/change/quit/see info anymore...
You probably want to enable "strict mode" in settings which wont let you skip breaks

It's enabled, but when I press Start button (or alt+tab) and then right mouse button on tray icon I can close the programm.  Like this:

Sorry but help here ;/ Even if I close remove the icon tray, one can always run some sort of process manager and quit stretchly... There is no way to prevent user from closing app
I was looking into preventing alt-tab some time ago but never found any solution for electron apps


JENKINS-69301 replace js-libs with parcel2 and update handlebars

[ ] Make sure you are opening from a topic/feature/bugfix branch (right side) and not your main branch!
[ ] Ensure that the pull request title represents the desired changelog entry
[ ] Please describe what you did
[ ] Link to relevant issues in GitHub or Jira
[ ] Link to relevant pull requests, esp. upstream and downstream changes
[ ] Ensure you have provided tests - that demonstrates feature works or fixes the issue

I am not sure why there is a copy of bootstrap3 in the folder :man_shrugging:

#240 includes this


py3++: test/mitmproxy/test_server

Not all tests have yet passed. So this is WIP!

Fixed & Merged! :smiley:


Usage with Mini Connected account?

Describe the issue
How is access of a Mini vehicle supposed to work? Is it possible to use the library with a Mini Connected account instead of a MyBMW account? Or is it possible to register a MyBMW account for a Mini vehicle that's usually accessed through the Mini Connected App?
For our Mini Cooper SE from 2022 (Germany), we get access through the Mini Connected App instead of the MyBMW App. The main page mentions compatibility with recent Mini models, als well as Mini Connected (in the repo description). However, my Mini Connected credentials are rejected when trying to sign in using the library. Does that mean that using the library with Mini vehicles is not possible? Was it possible in the past? Or am I missing something?
Expected behavior
Compatibility with Mini Connected
Which Home Assistant version are you using?
Home Assistant 2023.5.2
What was the last working version of Home Assistant Core?
No response
What is your region?
Rest of world
MyBMW website

[ ] I can still successfully login to the BMW MyBMW website and the car status is available there.
[ ] I have MyBMW enabled for my vehicle.

Number of cars

[ ] I have 2 or more cars linked to the MyBMW account.
[X] I have a Mini vehicle linked to my account.

Output of bimmer_connected fingerprint
No response
Anything in the logs that might be useful for us?
No response
Additional information
No response

Updating both bimmer_connected to 0.13.6 and HomeAssistant to 2023.06.01 fixed the issue. Thanks a lot for your awesome software!


Questions: is it possible to block access to direct route?

Hi, I am having an api under my kong. I added all routes using POST route (ex. under /myapi). How can I exclude someone of them ? Ex. I need to block acess to <kong_url>/myapi/somecontroller/somemethod. Is it possible ?

We appreciate you took the time to open an issue for this. However, kong github issues are for bug reports only. I'd like to ask you to post this on the Kong Nation forum
Either you can enable the endpoints to be exposed individually, an api for each. Or alternatively, create one api for all of them and then add the one to disallow separately and configure the request-termination plugin on that one.
I'll close this issue now. If you think this was closed in error, then please reopen.


Why is where() so slow?

Sorry, this is more a question than an issue.. probably.
This takes few seconds:
import pyspark_cassandra
rdd=sc.cassandraTable('eos','articles').select('title','content')
%time rdd.count()

While this takes 7 mins:
import pyspark_cassandra
rdd=sc.cassandraTable('eos','articles').select('title','content').where('sourcecode=? and capturedatetime>?','fbis',dt)
%time rdd.count()

A pointer to where to read about the internals of this cassandra interfacing would be much appreciated.
Thanks a lot,
Mohamed.

Hi,
No, worries. Maybe I'll open a forum somewhere someday. Until then an issue is fine.
pyspark_cassandra is thin layer over the Spark Cassandra Connector by DataStax, so their docs are a good starting point.
To give you an idea: the where clause is pushed down to Cassandra. I.e. without the where clause, simply the entire table is read and is fed through pyspark. In principle you could invoke RDD.filter(...) to filter the table in Spark. But if possible, filtering in Cassandra using CassandraRDD.where(...) is preferable because this can prevent a lot of IO.
Could you share some more details about your setup? How is this particular table structured? What are the primary, clustering and partition keys? How much rows does it hold? How many partitions?
Have you tested the difference in query time in e.g. CQLSH? I.e. something like:
select count(*) from eos.articles where sourcecode='fbis' and capturedatetime > '2015-03-18 04:19:00'

versus the same query without the where clause.
Cheers,
Frens Jan

Thanks Frens. I have some other issues with my cassandra cluster... still need to figure out. most of my cql queries, unless I use LIMIT to some small enough number, don't seem to execute at all, with no errors either. But anyways... here is the only table I have there, and it has some 180K rows.
How do I check how many partitions? you mean rdd partitions? The  cluster has 22 nodes.
cqlsh:eos> DESC TABLE articles ;

CREATE TABLE eos.articles (
    id text PRIMARY KEY,
    capturedatetime timestamp,
    content text,
    language text,
    publicationdatetime timestamp,
    sourcecode text,
    title text
) WITH bloom_filter_fp_chance = 0.01
    AND caching = '{"keys":"ALL", "rows_per_partition":"NONE"}'
    AND comment = ''
    AND compaction = {'min_threshold': '4', 'class': 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy', 'max_threshold': '32'}
    AND compression = {'sstable_compression': 'org.apache.cassandra.io.compress.LZ4Compressor'}
    AND dclocal_read_repair_chance = 0.1
    AND default_time_to_live = 0
    AND gc_grace_seconds = 864000
    AND max_index_interval = 2048
    AND memtable_flush_period_in_ms = 0
    AND min_index_interval = 128
    AND read_repair_chance = 0.0
    AND speculative_retry = '99.0PERCENTILE';
CREATE INDEX articles_capdate ON eos.articles (capturedatetime);
CREATE INDEX articles_language ON eos.articles (language);
CREATE INDEX articles_pubdate ON eos.articles (publicationdatetime);
CREATE INDEX articles_sourcecode ON eos.articles (sourcecode);

I guess this is more related to the layout of your database than that it is related to a performance issue / bug / something which can be improved in pyspark cassandra or the spark cassandra connector. You could always ask around in the<EMAIL_ADDRESS>mailing list.
You could test with by constraining only the id column in the where clause. I think this should be snappy with your schema.
With partitions I meant the number of Cassandra partitions, or the number of primary keys leaving the clustering keys out of the equation. Read more about it e.g. in the description of CQL.
As for your table structure: The select on sourcecode might just work with the index. The range predicate on publicationdatetime will not. Cassandra indices - afaik - only support point queries, which makes sense. If the dominant query pattern would be to scan over articles from a given point in time, or in an interval, etc. you could probably adopt a lot of Cassandra strategies for time series data.
E.g. using a layout like so:
CREATE TABLE articles (
    sourcecode text,
    publicationdatetime timestamp,
    id text,
    capturedatetime timestamp,
    content text,
    language text,
    title text,
    primary key ((sourcecode), publicationdatetime, id)
)

would likely make queries like
select * from articles where sourcecode='x' and publicationdatetime > '2014-01-01 00:00+0000';

a bunch faster because:

the articles are distributed by sourcecode`, so a client / the coordinator immediadely knows on which node to look (in contrast with an index in Cassandra)
the articles in a partition (i.e. with the same sourcecode) are sorted by publication date, allowing to read a bunch of data in an sequential read without reading a bunch of unnecessary data

Of course, if you also access this table by the original primary key ... which makes sense since you picked it as the primary key, then you might want to index that, or duplicate the table, or find another layout that works better. It all depends on the data that you have, how it arrives and most importantly how you access it.
Hope this helps you out a little. I'll close the ticket for know. If it turns out that this is related to pyspark cassandra, feel free to reopen it.


[Lists] Max. 26 entries per list are present

I´ve restricted the number of fetched entries to be 21 see: https://github.com/asciidisco/plugin.video.netflix/blob/master/resources/lib/NetflixSession.py#L1363
Of course that number isn't well thought through, I´ll bump it to 70 and see how much of an impact that has on response times. Maybe a pagination is needed, which in the end would brake some of Kodis functionality in sorting items. But first, lets give the bumped number of entries a try.

I´ve seen that anything beyond a max. value of 26 returns a 417 status code from Netflix.
Therefor we need to change the code to paginate or to do multiple requests depending on the size parameter of the response. A bit tricky & it needs toi be observed well, which is impact this has on response times.

I think the old flix2kodi or the older netflix plugin which used Chrome to play the videos had a way of paginating the output of lists. I think it worked by presenting it to Kodi as sub directories etc...

Yes, I used Flix2Kodi, and it displays all itens on lists, and have a pagination. It be used something like that?

Actually I don't like the idea of paginating (as long as Kodi doesn't come with a proper API/View for that, as I don't like the idea of hacking around it using folder items) components.
The idea I´ve sketched to solve this problem is the following:
When the main menu (after the profiles) gets fetched, you get the size of the sublists, using that size (and the knowledge that we can grep max. 26 entries per list) we can determine how many requests we need to do (with which ranges) to determine the exact requests to be made upfront.
I´d like to also do those requests in separate thread, e.g. in parallel, so that we have everything in one list (without pagination) & only have a few hundred milliseconds on top.
Why isn't that implemented yet? Because I don't have time to do so, if anyone wants to give it a spin, I´m happy to take the contribution.

I came across this issue as well and found this bug, and thought I would +1 it. I would code it could if I could but would love to see this added in the future :)

While I have created some python code in the past, it will take a while to figure out the layout of your code and where everything is happening.  But if I do, I will happily try and make it work.  But no holding your breath ok :)

Pagination has been added in 0.11.24, not my favourite solution, but at least something that enables you to access all the content. We need to get in discussion with the Kodi team, as we might need other APIs to implement our dream solution.

I've got the latest code, but for some reason the --- NEXT PAGE --- is not showing up, and I only get a maximum of 40 entries no matter what I am looking at..?  I've tried on a number of different skins, different view types..  Same result.  Am I missing some config variable?

you get 40 entries because the addon is show categories from the netflix homepage. they have about 40 entries. check netflix homepage ;)

I never see a --- NEXT PAGE --- once I am inside a category/genre or any list etc..  It only shows the first 40 entries, I was assuming it would be 26 entries per page/folder ?

i have "My list" with over 120 entries and i see it,
In Genres, the addon load categories from the homepage that have about 40 entries.
The addon behaviour is the following: it requests next entries until it found nothing.
I know if you click on the category name from the homepage you get all the entries and now we are getting only the entries from the homepage. I don't know how to fix this

@bhaal2 @pinoelefante That´s nothing I want to be fixed right now as this is the behaviour I desired.
I´m not fond of the way pagination can be introduced in Kodi, as well as async operations on views are done. Once we have a fine API to do so, we´re going to "fix" the categories.
For now, I´m happy that everything from "My List" can be made accessible.

@asciidisco No worries, I can deal with that.  Is Kodi 18 feature frozen now that it's in Alpha?


Renamed the file to Blockchain.md and update resources

This PR contains a new section containing Blockchain for Enterprises. The links are pointing to the official documentation of the projects in Hyperledger Foundation and quite useful for someone starting in the Enterprise Blockchain domain.
An additional change is made respective to the filename which should be: Blockchain.md instead of Cryptocurrency.md.
Reason: Cryptocurrency is a subset of the Blockchain domain.

@intellectape could the enterprise section links not be put under one of the existing ones? Just the title mentioning it is for enterprise if you want it to, though I think that it is not necessary either

@intellectape could the enterprise section links not be put under one of the existing ones? Just the title mentioning it is for enterprise if you want it to, though I think that it is not necessary either

@saranshkataria  I have updated the README file. It was my general thought process to keep it separate but both ways works.


X Labels Overlap Again

X labels overlap again. This issue was fixed in 2.1.0 but moving on to a newer version of ChartJS breaks the x labels again. They will occasionally overlap, it seems like whatever skipping mechanism was fixed in commit #2017 is broken again.

Any progress on this?  Are also effected

I had some preliminary work on improving this in #3233 but I'm unsure what users expect from the auto-skip code

That's pretty cool but I'm not sure that solves this issue?  Here's a screenshot of what we're seeing.   The original fix skipped the Aug 31 label so the last one was clearly visible

Hi! any news on this? I was hit as well.

I see this in 2.4.0 as well:

Experienced this issue in the latest release (2.1.5). Downgrading to 2.1.0 fixed it for me.

Using 2.1.0 Does not actually fix the the problem. 2.1.0 has consistent spacing on the x axis so the labels don't overlap, but the increments are 30 min, then the same size increment is 9 min at the end. Additionally, I found having multiple lines/points doesn't work on mobile on this version. The best solution I could come up with was setting a minRotation on my xAxes labels
scales: {
  xAxes: [{
    ticks: {
      minRotation: 45,
     },
  }]
}

2.5 with manually setting a min rotation:

2.5 without setting a min rotation:

2.1.0 x axis scale and data points display bugs(fixed in 2.1.5):

Same here, this is a critical bug. I wish there is an option for not forcing the last label, so they will not overlap even without rotation.

Facing the same using 2.5.0

I love using chartjs! I am having the same issue using version 2.5.0. It would be nice if the x-axis labels could be displayed horizontally with the text wrapping.

Same issue here too

same issue

I have the same issue as well, along with the first label clipped by the edge. Would it be possible to skip the first and last labels in order to temporarily fix the issue?

@ivarkallejarv Your fix saved my ass, thanks!

The issue is also visible in the time series demo:
http://www.chartjs.org/samples/latest/scales/time/combo.html

Same issue (Chart.js version: 2.6.0)
A workaround for me is setting autoSkip to true.
xAxes: [{
  ticks: {
    autoSkip: true
  }
}]

Will this be for real fixed at some point instead of these option hacks? It's been about a year now this is broken.

+1

@IMM0rtalis It works when data is full spread. Here I set maxTicksLimit and autoSkip: true:

xAxes options:
      xAxes: [
        {
          type: 'time',
          display: true,
          bounds: 'data',
          ticks: {
            source: 'data',
            maxRotation: 0,
            maxTicksLimit: 4,
            autoSkip: true,
          },
          time: {
            parser: formatUnixTime,
            tooltipFormat: 'YYYY-MM-DD HH:mm:ss',
            min: start,
            max: end,
            round: true,
            minUnit: 'minute',
            displayFormats: {
              minute: 'HH:mm',
              hour: 'MM-DD HH:mm',
              day: 'MM-DD',
            },
          },
        },
      ],

Still getting this issue on 2.8
Every time this plugin got updated, new issues came

I think the best way is to use the callback from ticks to custom the tick values:
     ticks: {
          callback: (tickValue, index, ticks) =>
            console.info(tickValue, index, ticks)
        },

I decided to show only 4 ticks: One first, one last and two in the middle.
On the mobile, we can only show 3 ticks also
This way gives us more control over the X-axis

The following solution works for me:
callback: (tickValue, index, ticks) => {     if (index === 0) return;     if (index === (ticks.length - 1)) return;     return tickValue; }
I just skip first and  last ticks.

I can solve the issue with autoSkipPadding:
  ticks:      {
                        autoSkipPadding: 15,
                    },

For some reason solution with settings autoSkip to "true" doesn't help me.

This is a collection of a lot of different issues. Most of these, if not all are fixed in master (v3). I'm going to close this to leave room to more specific issues, if there are any left.


Error: Need more diverse seeds

Hello!
Your paper makes it a little unclear as to what the intended corpus of seeds must look like. I took a guess and used the ECMAScript conformance test suite but got the error:
[ERROR] Need more diverse seeds
Can you give some suggestions of what a valid suite of seed javascript files should look like?

If there is no code brick, which has an empty pre-condition, CodeAlchemist will exit with that error message. And CodeAlchemist only can use a part of seeds, which do not raise any errors.
So, you should add more seeds including code bricks, which has an empty pre-condition, and make sure that they do not raise errors.

are there any pre-existing Javascript code bases that would recommend or should I focus on making something entirely from scratch?

There are bunch of test JS code snippets in each JS engine repositories.
They will be helpful to construct JS seeds.

@DaramG Could you provide specific commands for construct usable js seeds?
I have tried stress and test262, both leading to this error Error: Need more diverse seeds.


STU3 validation doens't report invalid slices when slicing is closed

Consider the profile https://simplifier.net/FinnishPHR/fiphr-bodytemperature-stu3 which defines a closed slicing for Observation.code.coding.
Validator accepts the following Observation (shortened for clarity, full resource attached) even though
"Errord" is not in the allowed codes.
{
	"resourceType": "Observation",
	"meta": {
		"profile": [
			"http://phr.kanta.fi/StructureDefinition/fiphr-bodytemperature-stu3"
		],
		"security": [{
				"system": "http://hl7.org/fhir/v3/Confidentiality",
				"code": "U",
				"display": "unrestricted"
			}
		]
	},
	.
	.
	.
	"code": {
		"coding": [{
				"system": "http://loinc.org",
				"code": "8310-5",
				"display": "Body temperature"
			}, {
				"system": "http://loinc.org",
				"code": "Errord",
				"display": "Oral temperature"
			}
		]
	},
	.
	.
	.
}

Attached are used resource and profile.
Observation_slicing_defect.txt
fiphr-bodytemperature-stu3.txt

Issue seems to have been fixed in the latest releases.


Development server

Added database.sql file. Please reference this for variable names in the front and back end.

database.sql file complete. Use this for variable naming in the front and back ends.


Colorwaves! Like many others, Spheres. Only multiple.

Also, like many others, using simulator for Pull request #5, hope it works fine.
Did send it to Matt, though I did so last evening and noticed some mistakes today.. :)
Except for multiple spheres, each sphere is different from the others in slight ways, which I belive can easily be changed.
Not so used with Python, so seems like I wrote some functions myself that are probably better to use numpy or other libraries for, which I've seen afterwards many others have been using.

Hey @Tirine! Matt asked if I could help with maintaining this repository, so I've been thinking about how best to do that. I've decided to create an examples folder where we can add contributions like this one - you can find some more information in the README.
Would you be up for updating your PR, to move your effects in to the examples folder?

Hey @Tirine! Matt asked if I could help with maintaining this repository, so I've been thinking about how best to do that. I've decided to create an examples folder where we can add contributions like this one - you can find some more information in the README.
Would you be up for updating your PR, to move your effects in to the examples folder?

Closing in favour of https://github.com/standupmaths/xmastree2020/pull/24 :)


Panic after adding a node to a custom cluster

Rancher versions:
rancher/rancher:2.0.0.-rc1
Steps to Reproduce:

Create a custom cluster using private registry
After the cluster comes up, add another custom node with all roles(control, etcd, worker)

Server crashes with the below panic:
2018/04/27 18:42:24 [INFO] Updating cluster [c-btrmx]
2018/04/27 18:42:25 [INFO] cluster [c-btrmx] provisioning: Building Kubernetes cluster
2018/04/27 18:42:25 [INFO] cluster [c-btrmx] provisioning: [dialer] Setup tunnel for host [<IP_ADDRESS>]
2018/04/27 18:42:25 [INFO] cluster [c-btrmx] provisioning: [dialer] Setup tunnel for host [<IP_ADDRESS>]
2018/04/27 18:42:25 [INFO] cluster [c-btrmx] provisioning: [state] Found local kube config file, trying to get state from cluster
2018/04/27 18:42:25 [INFO] cluster [c-btrmx] provisioning: [state] Fetching cluster state from Kubernetes
2018/04/27 18:42:25 [INFO] cluster [c-btrmx] provisioning: [state] Successfully Fetched cluster state to Kubernetes ConfigMap: cluster-state
2018/04/27 18:42:25 [INFO] cluster [c-btrmx] provisioning: [certificates] Getting Cluster certificates from Kubernetes
panic: runtime error: index out of range

goroutine 6967046 [running]:
github.com/rancher/rancher/vendor/github.com/rancher/rke/cluster.getClusterCerts(0xa8524e0, 0xc447601080, 0xc452c844e0, 0xc449c4d040, 0x2, 0x2, 0x0, 0xc4443d8600, 0x0)
	/go/src/github.com/rancher/rancher/vendor/github.com/rancher/rke/cluster/certificates.go:134 +0xab7
github.com/rancher/rancher/vendor/github.com/rancher/rke/cluster.(*Cluster).GetClusterState(0xc4443d8600, 0xa8524e0, 0xc447601080, 0x0, 0x0, 0x0)
	/go/src/github.com/rancher/rancher/vendor/github.com/rancher/rke/cluster/state.go:71 +0x42f
github.com/rancher/rancher/vendor/github.com/rancher/rke/cmd.ClusterUp(0xa8524e0, 0xc447601080, 0xc450815400, 0xc4274001c0, 0xc4274001d0, 0xc455d0c220, 0x0, 0xc450530270, 0x2e, 0x0, ...)
	/go/src/github.com/rancher/rancher/vendor/github.com/rancher/rke/cmd/up.go:71 +0x191
github.com/rancher/rancher/vendor/github.com/rancher/kontainer-engine/drivers/rke.(*Driver).Update(0xc420490000, 0xa8524e0, 0xc447601080, 0xc4414f13f0, 0xc455c97c60, 0x0, 0x0, 0x0)
	/go/src/github.com/rancher/rancher/vendor/github.com/rancher/kontainer-engine/drivers/rke/rke_driver.go:182 +0x268
github.com/rancher/rancher/vendor/github.com/rancher/kontainer-engine/types.(*GrpcServer).Update(0xc420152160, 0xa8524e0, 0xc443633bc0, 0xc441142f10, 0xc420152160, 0x0, 0x0)
	/go/src/github.com/rancher/rancher/vendor/github.com/rancher/kontainer-engine/types/rpc_server.go:55 +0x79
github.com/rancher/rancher/vendor/github.com/rancher/kontainer-engine/types._Driver_Update_Handler(0x4ad72c0, 0xc420152160, 0xa8524e0, 0xc443633bc0, 0xc4530e0230, 0x0, 0x0, 0x0, 0x433d64, 0xc446401200)
	/go/src/github.com/rancher/rancher/vendor/github.com/rancher/kontainer-engine/types/drivers.pb.go:598 +0x276
github.com/rancher/rancher/vendor/google.golang.org/grpc.(*Server).processUnaryRPC(0xc420157760, 0xa867460, 0xc44d1f4a80, 0xc455e5d540, 0xc420a1b410, 0xa7f67b8, 0x0, 0x0, 0x0)
	/go/src/github.com/rancher/rancher/vendor/google.golang.org/grpc/server.go:921 +0x96e
github.com/rancher/rancher/vendor/google.golang.org/grpc.(*Server).handleStream(0xc420157760, 0xa867460, 0xc44d1f4a80, 0xc455e5d540, 0x0)
	/go/src/github.com/rancher/rancher/vendor/google.golang.org/grpc/server.go:1143 +0x1528
github.com/rancher/rancher/vendor/google.golang.org/grpc.(*Server).serveStreams.func1.1(0xc44c2647e0, 0xc420157760, 0xa867460, 0xc44d1f4a80, 0xc455e5d540)
	/go/src/github.com/rancher/rancher/vendor/google.golang.org/grpc/server.go:638 +0x9f
created by github.com/rancher/rancher/vendor/google.golang.org/grpc.(*Server).serveStreams.func1
	/go/src/github.com/rancher/rancher/vendor/google.golang.org/grpc/server.go:636 +0xa1

Tested with v2.0.0-rc3. Created a single node cluster(all roles) using private registry. After the cluster was created successfully, added another node with all roles.
The node got added successfully without any panic. Workloads were also created fine.


♥ Function Calling-only model

I'm very impressed of the work made by web-llm team and really wanted to try to integrate it into an existing project. Unfortunately current models that capable to do something useful too heavy and my average-performant laptop doesn't feel well when I run the common LLMs in the browser such as LLama2. I'm a web developer and I can't promote the project to my clients since it consumes too much resources making computers to glitch. For common use-cases I'm still forced to use third-party APIs such as OpenAI. But what bothers me is the idea to have a small model that is unable to perform to do the common text completion actions but capable to generate Function-Calling-like objects from user's input.
I'm not a data scientist at all, and my work load (currently) doesn't allow me to find out the solution by myself, but as I fantasise I can imagine that web-llm could gain a lot of real users (not just geeks like me who likes to try things) by providing a way to turn user input into actions within the browser. My hypothesis is not based on real experience but on intuition that often is wrong in such situations. I guess that such model would be quite performant and compact because it doesn't need to know about "everything" and I also guess that this would be a real killer feature that would make everybody look at web-llm as on a real business solution, not just an experiment that is going to have more sense in decades when average computers are going to be much more performant.
Good idea? Bullshit? If it makes at least some sense I hope someone who's not that dumb as me could take care of that. Let me know what you think!

Really appreciate your input here @finom! Function calling is part of our roadmap as shown in O2 in here: https://github.com/mlc-ai/web-llm/issues/276. The goals are to support OpenAI-compatible APIs and features (function calling, multimodal inputs, embeddings, etc.)
Hopefully we can start from here and then let WebLLM make actual actions in the browser!

@CharlieFRuan that sounds fantastic! I imagine a lot of interesting use-cases that I would be able to integrate into internal tools that I usually build. Fingers crossed!

Hi! Please check out examples/function-calling. It is supported in 0.2.41, via PR https://github.com/mlc-ai/web-llm/pull/451. Currently only Hermes-2-Pro models are supported:

https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B
https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B


zeroes in a number

Zeroes in a number

Close #2788

Checklist:

[x] I have mentioned the issue number in my Pull Request.
[x] I have commented my code, particularly in hard-to-understand areas
[x] I have created a helpful and easy to understand README.md

@MohitGupta121 kindly review this PR


Have you tried RGB+Lidar semantic segmentation on the KITTI-360 dataset? How should I modify it?

Very great job, have you tried RGB+Lidar semantic segmentation on the KITTI-360 dataset? How should I modify it? Can you provide some suggestions? Looking forward to your reply, thank you!!!

Thank you for your interest in our work! Due to time and resource limitation, we did not try the  KITTI-360 semantic segmentation task. But it would not be hard to do that: [CMNeXt] is able to do the KITTI-360 semantic segmentation and we have already applied our GeminiFusion into it. I believe that with only minor tweaks, our code will work on the KITTI-360 dataset, but some of the hyper-parameters may need to be tweaked for optimal performance. You are more than welcome to experiment with our code.


Add tagging feature for Ansible Repositories

More in: https://bugzilla.redhat.com/show_bug.cgi?id=1526217
This PR is a part of making tagging support work in Ansible Repositories, and also with another PR in UI repo (coming soon), completes adding tagging support for Ansible Repos (in Automation > Ansible > Repositories). It needs to be merged first (before the UI, to remain everything work properly).

@miq-bot add_label blocker, enhancement, gaprindashvili/yes

@hstastna who can review this blocker PR?

@h-kataria @martinpovolny Could anyone of you review this PR? The PR is an important part of making tagging support work in Ansible Repositories (the UI part also already done). Thanks! :+1:

@hstastna can you please add link to UI PR here.

UI PR: https://github.com/ManageIQ/manageiq-ui-classic/pull/3513

Gaprindashvili backport details:
$ git log -1
commit 8dae52276496c0f250e4e2b821e9609e8c8837d0
Author: Harpreet Kataria<EMAIL_ADDRESS>Date:   Tue Mar 6 14:18:07 2018 -0500

    Merge pull request #17083 from hstastna/Add_RBAC_Tagging_Ansible_Repos

    Add tagging feature for Ansible Repositories
    (cherry picked from commit 2e5f2d6bf4845b38d9a03a6113936b475bb23b24)

    https://bugzilla.redhat.com/show_bug.cgi?id=1553396


http://local.drupal8-host.dev:8000/node/2 401 (Unauthorized)

I am getting this error http://local.drupal8-host.dev:8000/node/2 401 (Unauthorized) .Any idea what's is wrong with my setup? Basically I just followed the instructions on README.
I am not sure if I understand #3 Copy in the provided rest.settings.yml configuration. though.

To answer the original question a little bit. After enabling the rest modules, enable permissions for the Anon user or authenticated user depending on what access you want for the GET, POST, PATCH, DELETE operations.
Using the Rest UI module adds a GUI to set the Rest configuration instead of using the rest.settings.yml file.


Update to bevy 0.5

Updated Kurinji to use Bevy 0.5

Fixed Res and ResMut import declarations
Transitioned Event Reading to new syntax
Transitioned System Stages to System Sets

Aw, you beat me to it. Nice work 😄 . Our PRs are almost identical, but in mine I added the KurinjiPlugin systems to CoreStage::PreUpdate, since it seems like we want input to run before most other systems (see here).
One other thought I had was that it seems like Kurinji::action_event_producer should run after all of the other Kurinji systems. The current system order makes it so we don't get events until the next frame.

I like your changes! I think between the two of ours yours is definitely a better solution for the package so I will be closing this PR. Thanks!


camera: add set_mode (synchronous)

Add a synchronous set_mode function to the camera interface.
Connects to #334.

I will add integration tests in integration_tests/camera_mode.cpp.


Should uploads be surfaced as resources to users?

at the moment, we don't know the reason why Uploads (as a resource) exist. so far, we've assumed that there's a technical reason why it made sense to allow files to be grouped at upload time and to have that grouping persist as an independently named entity that links Files to Streams.
this is reflected in some inconsistencies in the UI, including the unstyled upload list page, upload show page, and "activity" page as noted in https://github.com/pod4lib/aggregator/issues/479.
organizations seem to be treating the concept of uploads differently: some of them always upload a single file per upload and name the upload after that file (e.g. duke), while some upload multiple files and may not specify a name. dartmouth seems to take advantage of the ability to point at a file already uploaded elsewhere on the web and doesn't "upload" files at all.
presuming that there's a compelling technical reason for Uploads to exist as a resource, this issue asks whether that design decision needs to be surfaced to users (and if so, how).
a potential user story from the "no" perspective:
As a user, I want the concept of uploads to be hidden from me, so that I don’t have to think about how files were grouped when they were uploaded.

Thanks for opening this issue!  I suspect my confusion about file and upload names in today's standup may also be present in others.  I'd actually assumed that mentions of "uploads" in the stats page referred to uploaded files, rather than to sets of files organized into an "upload" grouping.  And while our documentation talks about files and the streams that contain them, there isn't really anything discussing uploads as distinctive data structures.  Nor have I yet seen a compelling reason to maintain them as distinctive data structures.  (I have seen some hints that files grouped into an upload are processed together in ways that files that are not aren't, but it's unclear that that's actually happening, what specifically happens, and whether directing files to be processed together is necessary.)
In the absence of a compelling reason, I'm inclined to apply the "do not multiply entities unnecessarily" principle, and simplify the provider data model and just report on files and streams.  But I'm open to justifications for keeping "uploads" as entities as well.


All variables are sliced into batch size when training

In executor_manager.py line 219, all input variables are sliced into batch-sized tensors. This might cause an error when providing some supporting variables that are not batch-sized.
For example, If setting the batch_size to 20, and data has shape of (20, 256), while having a supporting variable with shape of (50, 300), the supporting variable will be sliced into (20, 300).
I don't know whether this is a bug or a feature, but it will be good to show that in documents.

what do you mean by support variable? If they are constants, they shouldn't be provided as input

For example, I have a matrix as a memory for each batch, the matrix participates in the computation and is not a constant.

you can duplicate it for n times


PEP8 issues in Python scripts fixed

see https://www.python.org/dev/peps/pep-0008/

r? @wycats
(rust_highfive has picked a reviewer for you, use r? to override)

It'd be cool to add a linter while you're here.

I'm not sure if it would really be worth it for the short four scripts...

@bors: r+ 5d86b735d50a973bc158598d53e5855630e6e231
Thanks!

:hourglass: Testing commit 5d86b73 with merge af1b359...

:sunny: Test successful - cargo-linux-32, cargo-linux-64, cargo-mac-32, cargo-mac-64, cargo-win-gnu-32, cargo-win-gnu-64, cargo-win-msvc-32, cargo-win-msvc-64


add input visualization

Add input visualization: input image, input image + BB, input image segmentation.
currently prediction visualization not included (although prepared code to plug in fast in the future)

added predictions visualization.
mask visualization is not very informative at the moment, because we have 81 available classes per proposal.
we can filter out proposals with low score in the future.


AMAROC-784 update setup-python in pytest workflow

Description:
Please add a brief summary of what this PR does.
Error(s) fixed:
Please tag them using #, e.g. Fixed #42.
Checklists:

[ ] New code includes dedicated tests.
[ ] New code has been linted.
[ ] New code follows the project's style.
[ ] New code is compatible with the 3-Clause BSD license.
[x] CHANGELOG has been updated.
[ ] AUTHORS has been updated.
[ ] Copyright years in module docstrings have been updated.

Hi @srethore and @fpavogt this includes @srethore 's fixes on the actions workflow for #121 and adds some more tweaks to make the actions workflow run and not issue any warnings anymore. As there was some trial and error involved and the scope of the changes is quite limited I would suggest to do a squash merge.


Separate "enum StringValue" to "struct StringValue"s

I'm glad I found apollo-rs.
Looking at the source code, it seems that it is not yet complete.
I tried to correct the part that was difficult to understand the intention.
The purpose of PR is simple.
Four StringValues grouped by enum are separated into each struct.
The reason for the separation is that each StringValue is used in a different context.

@Danue1: Thank you for submitting a pull request! Before we can merge it, you'll need to sign the Apollo Contributor License Agreement here: https://contribute.apollographql.com/

I signed the CLA.

hi @Danue1, thank you for taking the time to submit a PR! I must say, this change does not make a lot of sense to me. Since StringValue is a singular token in the GraphQL spec, it makes the most sense for it to be a single item in the encoder. It's written as an enum in this case to separate out the various ways that indentation is done when encoding different parts of the grammar. If this is what was unclear, it may be better to clarify the variants' names.

This change makes sense.
In this code, you could "accidentally" put StringValue::Field { value: String } where you should have StringValue::Top { value: String }.
Of course, there are tests to prevent this, but if you only look at the syntax and semantics, you can think like the above.
That's why I made this PR. I hope you understand.
If you do not like this method, there is a way to modify it as follows. (I don't like the way.)
mod string_value {
   pub struct Top {
     value: String,
   }

   // ... and 3 structs
}

@lrlna
If you understand the purpose of this PR and decide it's worth accepting, I'll track it further in this issue.
Because the changes are conflicting.

In this code, you could "accidentally" put StringValue::Top { value: String } where you should have StringValue::Input { value: String }.

By the same logic users can also accidentally use ObjectDef::new() instead of InputObjectDef::new(). What may be useful here is documentation to prevent this sort of thing from happening, changing it into separate structs won't eliminate the possibility that an incorrectly indented StringValue is used.
Something to be said about StringValue is at the moment it is only used by other Structs in apollo-encoder, so the initial example you provided cannot be implemented by users of the library, the library does that implementation. To remove this confusion, it may actually be better to make the StringValue enum pub(crate) as there is no real case of setting just a StringValue on its own - at the moment it must always be part of a Definition.
I am going to close this PR as at the moment this change is not suitable.


Export data to Linkspace

MTP-1873
depends on #665 and mtp-deploy#321
depends on unreleased mtp-common (where PyJWT dependency has been removed and therefore will not conflict)

No changes; just rebased


How do I multipart?

Hello everyone,
I saw the docs and I have managed to successfully receive every type of requests except for multipart..
I copied the given code:
``admin.route({
method: 'post'
, path: '/blog'
, validate: { type: 'multipart' }
, handler: function *(){
var parts = yield this.request.parts
var part
while (part = yield parts) {
  // do something with the incoming part stream
  part.pipe(someOtherStream)
}

console.log(parts.field.name) // form data

}
})``
But when I run my server I immediatly get the following error:
TypeError: You may only yield a function, promise, generator, array, or object, but the following object was passed: "undefined"
If I use co-busboy myself meaning:
    var parts = parse(this)     var part     while (part = yield parts) {       console.log (part);       if (part.length) {         // arrays are busboy fields         console.log('key: ' + part[0])         console.log('value: ' + part[1])       } else {         // otherwise, it's a stream         part.pipe(fs.createWriteStream('some file.txt'))       }     }
It works amazingly perfect
I know it has something to do with generators but don't get why..
Also - am I supposed to validate the fields myself? because I can't do:
validate: { 	type: 'multipart', 	body: { 	  username: Joi.string().required(), 	  email: Joi.string().optional() 	} }
Din.

the best way to help debug is to post a file we can run to reproduce the issue
"can you validate multipart fields?" - interesting idea. no, this is not currently supported. parts is simply an untouched co-busboy object

closing, no response


Sentry: Fix root cause of gateway_mfr exited with a non-zero status

Follow up to:

https://github.com/NebraLtd/hm-diag/issues/352

Related work:

https://github.com/NebraLtd/hm-diag/pull/359/files
https://github.com/NebraLtd/helium-miner-software/pull/448

Source code:

https://github.com/NebraLtd/hm-pyhelper/blob/77eb361e2fe1510d088beb5499c15e1d25c78add/hm_pyhelper/miner_param.py#L42

Thoughts:

There are many different possible errors, and we don't have a handle on what is causing them.

Related to #348


Print useful IP information during tests

Each commit is standalone. We now print DNS and IP info during the hostname lookup, we fail back to traceroute when mtr isn't installed and we ask ifconfig.co for a location information, along with the client IP.
r? @dougbarth-stripe since you asked :)

Ooops, I should have commented back. Indentation is sorted.


[Improvement] text change

? Select a provider [API] [Direct] GoGoAnime
? what novel do you want to look for? naruto

? what novel do you want to look for? naruto
? which novel do you want to select? Naruto Shippuden

as you can see that it should be anime instead of novel?

I will probably change it to something more general since some are movies or novels among others.


How to automate getting login session for Apple ID?

My question is how to automate getting login session for Apple ID with fastlane in Jenkins's pipeline?
According to documentation, at first I should manually execute such command
fastlane spaceauth -u<EMAIL_ADDRESS>and then paste the session value to FASTLANE_SESSION environment variable in pipeline.
But I don't want to make it manually all the time. How to automate this process ?

Please check out the official docs for Jenkins integration: https://docs.fastlane.tools/best-practices/continuous-integration/jenkins/

Please check out the official docs for Jenkins integration: https://docs.fastlane.tools/best-practices/continuous-integration/jenkins/

Didn't find appropriate answer to me

https://docs.fastlane.tools/best-practices/continuous-integration/

https://docs.fastlane.tools/best-practices/continuous-integration/

max-ott, I saw your documentation, I've mentioned it in my question. But I didn't find an answer for my question in your doc. Could you please provide explicit answer for me? Of course if it's present in your doc ?

Fastlane is open source. Issues are primarily for issues and bugs. We cannot really provide support or consulting work for free. There are also ton of free tutorials and videos on how to set this up available on the web.


fix version 1.5.0 in knownissues

Docs Build status updates of commit e00d82a:
:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

prosepy/docs-ref-conceptual/knownissues.md
:white_check_mark:Succeeded

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.


Page component in docs shows skeleton description

I'm not sure if this is the appropriate channel but I noticed while browsing:-
https://ownego.github.io/polaris-vue/components/Page

@bespokely-digital Could you describe more? I don't understand the issue you're facing.

@bespokely-digital Could you describe more? I don't understand the issue you're facing.

If you go to the link above, it describes "Skeleton page" component and not "Page" component

Oh I see, this description is not correct.


Adding Folder Structure / Titlebar

Good Evening,
Is it possible to add support for folders within the rdp folder. Basicly i would like to have it look and work kinda like citrix store front.
Where apps can be grouped or seperated in folders.. e.g (MS Office wich contains Excel, Word, Powerpoint,...; Creative Cloud -> wich conatins Photoshop etc....
Kind Regards
Philipp

This would indeed be a cool feature.
@vanitysan out of interest, are you using the ASP or the ASPX version?

Thanks for the response 😀
Currently im using the .asp version on IIS10
I will also give the .aspx a try in the future

Well im stuck now, Folder and Application Display works now correctly, The Download in the Maindirectory also works, but as soon as i want to download something within an subfolder it somehow dont give me the correct file ( meaning the original .rdp has 5kb of size but i only receive a 1kb file) and i kinda cant resolve this problem.... ive got something wrong with the server mappath i think.... or something is wrong with http url request.

Subfolders now working in the latest push to the next branch. Need more testing. Some notes:

I believe subfolders are a feature of schema 2.1. Does this need to be changed from 1.1? If so, will it break older clients?
Subfolders will not be shown when adding webfeed via RemoteApp and Desktop Connections. All apps from subfolders will be there but in a flat structure
The web interface (Default.aspx) has been updated to work with the new XML (with subfolders), however, it still displays all apps in a flat structure. I intend to add folder browsing capability
Nested subfolders seem to work ok on the Android client (In this case the folder structure is: root > More > Even More):


clear execution count of all cells when user presses 'Clear All Output'

For #7853

[x] Pull request represents a single change (i.e. not fixing disparate/unrelated things in a single PR)
[x] Title summarizes what is changing
[x] Has a news entry file (remember to thank yourself!)
[ ] Appropriate comments and documentation strings in the code
[ ] Has sufficient logging.
[ ] Has telemetry for enhancements.
[ ] Unit tests & system/integration tests are added/updated
[ ] Test plan is updated as appropriate
[ ] package-lock.json has been regenerated by running npm install (if dependencies have changed)
[ ] The wiki is updated with any design decisions/details.

Codecov Report

Merging #8318 into master will increase coverage by 0.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #8318      +/-   ##
==========================================
+ Coverage   59.32%   59.33%   +0.01%
==========================================
  Files         504      504
  Lines       23096    23096
  Branches     3737     3737
==========================================
+ Hits        13701    13704       +3
+ Misses       8501     8500       -1
+ Partials      894      892       -2

Impacted Files
Coverage Δ

src/client/common/installer/productInstaller.ts
77.24% <0%> (+0.52%)
:arrow_up:

src/client/common/utils/platform.ts
88.23% <0%> (+11.76%)
:arrow_up:

Continue to review full report at Codecov.

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oops


App terminated due to memory issue

Hi, thanks for this nice effect-library! I use ConfettiView which works perfectly. Unfortunately, this is not the case with FireWorksView, which causes memory problems. When I run FireWorksView it doesn't release all the memory, and running it again makes the problem even more apparent. Then it consumes memory continuously and builds more and more memory until the app crashes. I am running SwiftUI and latest release of iOS and Xcode.

Looks like the issue is that fireworks explosions are continually added to the scene and never removed.  A quick fix is in FireworksScene.swift, at the end of func explosion(at point: CGPoint), to add:
DispatchQueue.main.asyncAfter(deadline: .now() + 3.0) { node.removeFromParent() }

(I'd put in a PR for this, but looks like this is an old issue and the repo is not really being maintained)


_.capitalize not recognizing

im getting undefined is not a function

---
Want to back this issue? **[Post a bounty on it!](https://www.bountysource.com/issues/11115792-_-capitalize-not-recognizing?utm_campaign=plugin&utm_content=tracker%2F522373&utm_medium=issues&utm_source=github)** We accept bounties via [Bountysource](https://www.bountysource.com/?utm_campaign=plugin&utm_content=tracker%2F522373&utm_medium=issues&utm_source=github).

Sorry, just made a quick change of library to the official. Didn't realize the api was different.

mmm nope , im getting s is not defined
  name = name + 'Active' + s.capitalize type

This is fixed in 1.0.4.
I'm so sorry for the inconvenience, I really need to implement tests as this could have been avoided.
I shouldn't push new releases after I've done quick fixes on the coffee break. :stuck_out_tongue_closed_eyes:

ha :) no worries! Thanks for the work! great job


widgets: Poll widget disappears on narrow to 'All messages'.

Steps to recreate:

Start the poll widget in a stream.
Narrow down to All messages. The poll widget disappears here. If it doesn't, narrow to the stream and then back to All messages.
Stay in All messages. Refresh your browser. The poll reappears.

@zulipbot label "bug"

@zulipbot claim

The problem here is that we have different rows for "All messages" and narrowed stream/topic, so trying to figure out a generic solution here.


when sorting is enabled, moving a column will also sort it.

The new feature for moving columns on touch devices works well. However, when sorting is enabled, a column will also be sorted when moving, on both touch and desktop.
FYI: There are also some console debug messages printed when moving a column.

re console logs, thanks, i have taken these out.
re sort getting called, i cannot reproduce, i've tried on iPad Pro and my Android phone. can you advise what you are on?
when i touch and drag, the column moves, but no sorting happens.


Scan private repositories

Is is possible to scan private repos?

Hi @mtorres10.
The depclean-web backend only supports scanning public repositories on GitHub.
If you have private repos in your organization that you want to analyze, you can use the DepClean tool to generate a JSON file with the analysis report. Then, you can use depclean-web to produce a nice visualization of your dependencies.


How does one add header CORS in Pistache ?

Title is question, cannot seem to find any documentation on it. Thanks

@oktal

Looking at the set of headers supported, it seems that Access-Control-Allow-Origin is not one of them, so you would have to add it following the documentation http://pistache.io/guide/#defining-your-own-header . I need it for a project too, so once tested, I'll probably send a PR with this header part of the official set of headers supported.

@GuacheSuede As you can see, this is fairly simple, then you just have to add a header as you would normally with Pistache. I think it has its place in the set of headers by default as it can be very useful for jQuery users that would like to use a REST API provided by Pistache (for example).

Great, Thank you for the pull request.

response.headers().add<Http::Header::AccessControlAllowOrigin>("*");
response.send(Pistache::Http::Code::Ok, "Hello World");

response.headers().add<Http::Header::AccessControlAllowOrigin>("*");
response.send(Pistache::Http::Code::Ok, "{\"message\":\"success\"}");

Hi xinthose
i add this code to my doLigin() method, but nothings happen.
where should I add this code?

At the beginning of the route function.  So like so:
void doLogin (const Pistache::Rest::Request& request, Pistache::Http::ResponseWriter response)
{
    response.headers().add<Pistache::Http::Header::AccessControlAllowOrigin>("*");

    // your code
}

response.headers().add<Http::Header::AccessControlAllowOrigin>("*");
response.send(Pistache::Http::Code::Ok, "{\"message\":\"success\"}");

Hi xinthose
i add this code to my doLigin() method, but nothings happen.
where should I add this code?

At the beginning of the route function. So like so:
void doLogin (const Pistache::Rest::Request& request, Pistache::Http::ResponseWriter response)
{
    response.headers().add<Pistache::Http::Header::AccessControlAllowOrigin>("*");

    // your code
}

many thanks. this is very helpful.


Fix published files and typedefs

don't publish config files to npm
don't publish internal typedefs with typedef dependencies

Coverage remained the same at 86.957% when pulling 6251c61aa001f6fa3c495028371726a4bc55325a on fix-publication into b2a4c42789a750b7ef3fdbf71c954923c87f23d1 on master.

Coverage remained the same at 86.957% when pulling 6251c61aa001f6fa3c495028371726a4bc55325a on fix-publication into b2a4c42789a750b7ef3fdbf71c954923c87f23d1 on master.


Create user signup page

Create a form so that a user can create a new account

Added a very basic registration page, "registration.ejs". Needs styling and some sort of UI design.