昇腾NPU使用API推理报错

Reminder

[X] I have read the README and searched the existing issues.

Reproduction
华为昇腾NPU910b使用API推理时，能够正常启动。但通过API调用报错，错误信息：
05/17/2024 18:45:13 - INFO - llmtuner.model.utils.attention - Using torch SDPA for faster training and inference.
05/17/2024 18:45:13 - INFO - llmtuner.model.adapter - Adapter is not found at evaluation, load the base model.
05/17/2024 18:45:13 - INFO - llmtuner.model.loader - all params:<PHONE_NUMBER>
Visit http://localhost:7703/docs for API document.
INFO:     Started server process [40093]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://<IP_ADDRESS>:7703 (Press CTRL+C to quit)
INFO:      - "GET /docs HTTP/1.1" 200 OK
INFO:      - "GET /openapi.json HTTP/1.1" 200 OK
05/17/2024 18:46:14 - INFO - llmtuner.api.chat - ==== request ====
{
  "model": "",
  "messages": [
    {
      "role": "user",
      "content": "hello"
    }
  ]
}
[E OpParamMaker.cpp:273] call aclnnEqScalar failed, detail:EZ9999: Inner Error!
EZ9999: 2024-05-17-18:46:14.721.067  Op Equal does not has any binary.
        TraceBack (most recent call last):
        Kernel Run failed. opType: 88, Equal
        launch failed for Equal, errno:561000.

[ERROR] 2024-05-17-18:46:14 (PID:40093, Device:0, RankID:-1) ERR01005 OPS internal error
Exception raised from operator() at third_party/op-plugin/op_plugin/ops/base_ops/opapi/EqKernelNpuOpApi.cpp:60 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x68 (0xffff8e44a538 in miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x6c (0xffff8e3f78a0 in miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #2: <unknown function> + 0x952c90 (0xfffda8b83c90 in miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #3: <unknown function> + 0xe27f0c (0xfffda9058f0c in miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #4: <unknown function> + 0x56b960 (0xfffda879c960 in miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #5: <unknown function> + 0x56bd88 (0xfffda879cd88 in miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #6: <unknown function> + 0x569d90 (0xfffda879ad90 in miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #7: <unknown function> + 0xafe0c (0xffff8e47ce0c in miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #8: <unknown function> + 0x7a80 (0xffffa59f7a80 in /lib64/libpthread.so.0)
frame #9: <unknown function> + 0xe4d0c (0xffffa582bd0c in /lib64/libc.so.6)

INFO:     <IP_ADDRESS>:12592 - "POST /v1/chat/completions HTTP/1.1" 500 Internal Server Error
ERROR:    Exception in ASGI application
Traceback (most recent call last):
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/uvicorn/protocols/http/httptools_impl.py", line 411, in run_asgi
    result = await app(  # type: ignore[func-returns-value]
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 69, in __call__
    return await self.app(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/applications.py", line 123, in __call__
    await self.middleware_stack(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/errors.py", line 186, in __call__
    raise exc
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/errors.py", line 164, in __call__
    await self.app(scope, receive, _send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/cors.py", line 93, in __call__
    await self.simple_response(scope, receive, send, request_headers=headers)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/cors.py", line 148, in simple_response
    await self.app(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/exceptions.py", line 65, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 756, in __call__
    await self.middleware_stack(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 776, in app
    await route.handle(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 297, in handle
    await self.app(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 77, in app
    await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 72, in app
    response = await func(request)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/fastapi/routing.py", line 278, in app
    raw_response = await run_endpoint_function(
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/fastapi/routing.py", line 191, in run_endpoint_function
    return await dependant.call(**values)
  File "codex/llama-factory/src/llmtuner/api/app.py", line 85, in create_chat_completion
    return await create_chat_completion_response(request, chat_model)
  File "codex/llama-factory/src/llmtuner/api/chat.py", line 101, in create_chat_completion_response
    responses = await chat_model.achat(
  File "codex/llama-factory/src/llmtuner/chat/chat_model.py", line 56, in achat
    return await self.engine.chat(messages, system, tools, image, **input_kwargs)
  File "codex/llama-factory/src/llmtuner/chat/hf_engine.py", line 252, in chat
    return await loop.run_in_executor(pool, self._chat, *input_args)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "codex/llama-factory/src/llmtuner/chat/hf_engine.py", line 142, in _chat
    generate_output = model.generate(**gen_kwargs)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/transformers/generation/utils.py", line 1431, in generate
    model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/transformers/generation/utils.py", line 463, in _prepare_attention_mask_for_generation
    is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs)
  File "miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/_tensor.py", line 1091, in __contains__
    return (element == self).any().item()  # type: ignore[union-attr]
RuntimeError: The Inner error is reported as above.
 Since the operator is called asynchronously, the stacktrace may be inaccurate. If you want to get the accurate stacktrace, pleace set the environment variable ASCEND_LAUNCH_BLOCKING=1.
[ERROR] 2024-05-17-18:46:14 (PID:40093, Device:0, RankID:-1) ERR00100 PTA call acl api failed

运行命令：

ASCEND_RT_VISIBLE_DEVICES=6 python src/api.py --model_name_or_path Qwen/Qwen1.5-7B-Chat --template qwen

Expected behavior
No response
System Info
torch-npu=2.2.0
torch=2.2.0
Ascend-cann-toolkit_8.0.RC1_linux-aarch64
Ascend-cann-kernels-910b_8.0.RC1_linux
尝试更换cann的版本还是不行
Others
No response

设置 ASCEND_LAUNCH_BLOCKING=1 重新发一下报错

报错如下，感谢支持~
05/17/2024 21:03:59 - INFO - llmtuner.api.chat - ==== request ====
{
  "model": "",
  "messages": [
    {
      "role": "user",
      "content": "hello"
    }
  ]
}
INFO:      - "POST /v1/chat/completions HTTP/1.1" 500 Internal Server Error
ERROR:    Exception in ASGI application
Traceback (most recent call last):
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/uvicorn/protocols/http/httptools_impl.py", line 411, in run_asgi
    result = await app(  # type: ignore[func-returns-value]
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 69, in __call__
    return await self.app(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/applications.py", line 123, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/errors.py", line 186, in __call__
    raise exc
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/errors.py", line 164, in __call__
    await self.app(scope, receive, _send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/cors.py", line 93, in __call__
    await self.simple_response(scope, receive, send, request_headers=headers)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/cors.py", line 148, in simple_response
    await self.app(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/middleware/exceptions.py", line 65, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 756, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 776, in app
    await route.handle(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 297, in handle
    await self.app(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 77, in app
    await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/starlette/routing.py", line 72, in app
    response = await func(request)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/fastapi/routing.py", line 278, in app
    raw_response = await run_endpoint_function(
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/fastapi/routing.py", line 191, in run_endpoint_function
    return await dependant.call(**values)
  File "/codex/llama-factory/src/llmtuner/api/app.py", line 85, in create_chat_completion
    return await create_chat_completion_response(request, chat_model)
  File "/codex/llama-factory/src/llmtuner/api/chat.py", line 101, in create_chat_completion_response
    responses = await chat_model.achat(
  File "/codex/llama-factory/src/llmtuner/chat/chat_model.py", line 56, in achat
    return await self.engine.chat(messages, system, tools, image, **input_kwargs)
  File "/codex/llama-factory/src/llmtuner/chat/hf_engine.py", line 252, in chat
    return await loop.run_in_executor(pool, self._chat, *input_args)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/codex/llama-factory/src/llmtuner/chat/hf_engine.py", line 142, in _chat
    generate_output = model.generate(**gen_kwargs)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/transformers/generation/utils.py", line 1431, in generate
    model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/transformers/generation/utils.py", line 463, in _prepare_attention_mask_for_generation
    is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs)
  File "/miniconda3/envs/llama_factory_py3.9/lib/python3.9/site-packages/torch/_tensor.py", line 1091, in __contains__
    return (element == self).any().item()  # type: ignore[union-attr]
RuntimeError: call aclnnEqScalar failed, detail:EZ9999: Inner Error!
EZ9999: 2024-05-17-21:03:59.298.450  Cannot parse json for config file [/Ascend/ascend-toolkit/latest/opp/built-in/op_impl/ai_core/tbe//kernel/config/ascend910/equal.json].
        TraceBack (most recent call last):
        Failed to parse kernel in equal.json.
        AclOpKernelInit failed opType
        Op Equal does not has any binary.
        Kernel Run failed. opType: 88, Equal
        launch failed for Equal, errno:561000.

[ERROR] 2024-05-17-21:03:59 (PID:183739, Device:0, RankID:-1) ERR01005 OPS internal error

安装Ascend-cann-kernels-910_8.0.RC1_linux后解决~

训练出现类似错误，请帮忙看看，感谢支持！
训练命令：
ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 llamafactory-cli train examples/full_multi_gpu/interlm2_full_sft.yaml
Ascend-cann-kernels版本：
Ascend-cann-kernels-910b_8.0.RC1.alpha003_linux.run
信息如下：
RuntimeError: call aclnnCast failed, detail:EZ9999: Inner Error!
EZ9999  Cannot parse json for config file [/usr/local/Ascend/ascend-toolkit/latest/opp/built-in/op_impl/ai_core/tbe//kernel/config/ascend910/cast.json].
TraceBack (most recent call last):
Failed to parse kernel in cast.json.
AclOpKernelInit failed opType
Op Cast does not has any binary.
Kernel Run failed. opType: 53, Cast
launch failed for Cast, errno:561000.

推理时报错
(MindSpore) [ma-user work]$python code/imdeploy.py
/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/utils/path_manager.py:82: UserWarning: Warning: The /usr/local/Ascend/ascend-toolkit/latest owner does not match the current user.
warnings.warn(f"Warning: The {path} owner does not match the current user.")
/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/utils/path_manager.py:82: UserWarning: Warning: The /usr/local/Ascend/ascend-toolkit/8.0.RC3.alpha001/aarch64-linux/ascend_toolkit_install.info owner does not match the current user.
warnings.warn(f"Warning: The {path} owner does not match the current user.")
[W compiler_depend.ts:615] Warning: expandable_segments currently defaults to false. You can enable this feature by export PYTORCH_NPU_ALLOC_CONF = expandable_segments:True. (function operator())
[W compiler_depend.ts:623] Warning: expandable_segments feature is not supportted                     and the possible cause is that driver and firmware packages do not match. (function operator())
/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/contrib/transfer_to_npu.py:301: ImportWarning:
*************************************************************************************************************
The torch.Tensor.cuda and torch.nn.Module.cuda are replaced with torch.Tensor.npu and torch.nn.Module.npu now..
The torch.cuda.DoubleTensor is replaced with torch.npu.FloatTensor cause the double type is not supported now..
The backend in torch.distributed.init_process_group set to hccl now..
The torch.cuda.* and torch.cuda.amp.* are replaced with torch.npu.* and torch.npu.amp.* now..
The device parameters have been replaced with npu in the function below:
torch.logspace, torch.randint, torch.hann_window, torch.rand, torch.full_like, torch.ones_like, torch.rand_like, torch.randperm, torch.arange, torch.frombuffer, torch.normal, torch._empty_per_channel_affine_quantized, torch.empty_strided, torch.empty_like, torch.scalar_tensor, torch.tril_indices, torch.bartlett_window, torch.ones, torch.sparse_coo_tensor, torch.randn, torch.kaiser_window, torch.tensor, torch.triu_indices, torch.as_tensor, torch.zeros, torch.randint_like, torch.full, torch.eye, torch._sparse_csr_tensor_unsafe, torch.empty, torch._sparse_coo_tensor_unsafe, torch.blackman_window, torch.zeros_like, torch.range, torch.sparse_csr_tensor, torch.randn_like, torch.from_file, torch._cudnn_init_dropout_state, torch._empty_affine_quantized, torch.linspace, torch.hamming_window, torch.empty_quantized, torch._pin_memory, torch.autocast, torch.load, torch.Generator, torch.Tensor.new_empty, torch.Tensor.new_empty_strided, torch.Tensor.new_full, torch.Tensor.new_ones, torch.Tensor.new_tensor, torch.Tensor.new_zeros, torch.Tensor.to, torch.nn.Module.to, torch.nn.Module.to_empty
*************************************************************************************************************
warnings.warn(msg, ImportWarning)
/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/contrib/transfer_to_npu.py:260: RuntimeWarning: torch.jit.script and torch.jit.script_method will be disabled by transfer_to_npu, which currently does not support them, if you need to enable them, please do not use transfer_to_npu.
warnings.warn(msg, RuntimeWarning)
[E compiler_depend.ts:270] call aclnnAdd failed, detail:EZ9999: Inner Error!
EZ9999: 2024-12-19-16:16:03.369.486  Op Add does not has any binary.
TraceBack (most recent call last):
Kernel Run failed. opType: 3, Add
launch failed for Add, errno:561000.
[ERROR] 2024-12-19-16:16:03 (PID:48795, Device:0, RankID:-1) ERR01005 OPS internal error
Exception raised from operator() at build/CMakeFiles/torch_npu.dir/compiler_depend.ts:43 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x68 (0xfffeebf77ee8 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x6c (0xfffeebf24d10 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #2:  + 0x8dff18 (0xfffde3605f18 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #3:  + 0xe9c320 (0xfffde3bc2320 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #4:  + 0x619a14 (0xfffde333fa14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #5:  + 0x619ec8 (0xfffde333fec8 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #6:  + 0x6174a0 (0xfffde333d4a0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch_npu/lib/libtorch_npu.so)
frame #7:  + 0xb967c (0xfffeebfae67c in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #8:  + 0x7a60 (0xffffa3e71a60 in /lib64/libpthread.so.0)
frame #9:  + 0xde13c (0xffffa3c9f13c in /lib64/libc.so.6)
2024-12-19 16:16:03,374 - lmdeploy - ERROR - base.py:56 - RuntimeError: The Inner error is reported as above. The process exits for this inner error, and the current working operator name is aclnnAdd.
Since the operator is called asynchronously, the stacktrace may be inaccurate. If you want to get the accurate stacktrace, pleace set the environment variable ASCEND_LAUNCH_BLOCKING=1.
[ERROR] 2024-12-19-16:16:03 (PID:48795, Device:0, RankID:-1) ERR00100 PTA call acl api failed
2024-12-19 16:16:03,374 - lmdeploy - ERROR - base.py:57 -  check failed!
PyTorch is not available.
/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/tempfile.py:821: ResourceWarning: Implicitly cleaning up <TemporaryDirectory '/tmp/tmpyrykyn3p'>
_warnings.warn(warn_message, ResourceWarning)
(MindSpore) [ma-user work]$ASCEND_RT_VISIBLE_DEVICES=6 python src/api.py --model_name_or_path pretrainmodel/Qwen2-7B-Instruct --template qwen
python: can't open file '/home/ma-user/work/src/api.py': [Errno 2] No such file or directory
(MindSpore) [ma-user work]$ASCEND_RT_VISIBLE_DEVICES=6 python src/api.py --model_name_or_path pretrainmodel/Qwen2-7B-Instruct --template qwen
python: can't open file '/home/ma-user/work/src/api.py': [Errno 2] No such file or directory
(MindSpore) [ma-user work]$ASCEND_RT_VISIBLE_DEVICES=6 python src/api.py --model_name_or_path ../pretrainmodel/Qwen2-7B-Instruct --template qwen
python: can't open file '/home/ma-user/work/src/api.py': [Errno 2] No such file or directory
(MindSpore) [ma-user work]$ASCEND_RT_VISIBLE_DEVICES=6 python src/api.py --model_name_or_path ../pretrainmodel/Qwen2-7B-Instruct --template qwen
python: can't open file '/home/ma-user/work/src/api.py': [Errno 2] No such file or directory


Add badges for Pub Points from pub.dev

Description
To quickly visualize the pub points of each package, adding badges next to the current ones.
This will help identifying what packages need attention to improve pub points and generally give confidence to users of the package quality.
Adding only 2 to get feedback (hence the Draft PR). If the decision is to go on with adding these badges, I'd complete the list.
Note that it's possible to get badges for likes and popularity too, for example:
The sentry package:

/cc @Salakar @helenaford
Checklist
Before you create this PR confirm that it meets all requirements listed below by checking the relevant checkboxes ([x]). This will ensure a smooth and quick review process.

[x] I read the Contributor Guide and followed the process outlined there for submitting PRs.
[x] If the pull request affects only one plugin, the PR title starts with the name of the plugin in brackets (e.g. [cloud_firestore])
[ ] My PR includes unit or integration tests for all changed/updated/fixed behaviors (See Contributor Guide).
[ ] All existing and new tests are passing.
[ ] I updated/added relevant documentation (doc comments with ///).
[ ] The analyzer (flutter analyze) does not report any problems on my PR.
[ ] I read and followed the Flutter Style Guide.
[ ] I updated pubspec.yaml with an appropriate new version according to the pub versioning philosophy.
[ ] I updated CHANGELOG.md to add a description of the change.
[x] I signed the CLA.
[x] I am willing to follow-up on review comments in a timely manner.

Yes please :) feel free to add the badges to the rest of the plugins in the list

@Salakar Added the 16

Thanks, this looks good!


How to get the count of subscribers to  a channel

Hi there,
I searched in the API and found a way to get the connected client count; but I need to get subscribers count of specific channel.
how I can do that?!

On each worker you should loop through the subscribers and count, and store that in e.g. redis so you can count the total number.

thank you,
how I can get the subscribers count in the worker, I can get the socket connected count but the subscribers I couldn't!?

That would be the same. You can look at each socket and see what channels it's subscribe to

do  you mean to get the sockets scServer.clients then loop on them socket.subscriptions() so  I can know how many subscribers to the channel in this worker and store in redis to sum them up ?!

@fkhateeb Sure that would be one way to do it.

@fkhateeb
I spent so much time finding a way to count subscribe channel users but I didn't found any. solution. Would you please provide a solution or share code with me to count subscribe channel users?
Thanks


haskell.packages.*.haskell-language-server: fix eval

Mark some packages as unbroken on specific GHC versions to fix eval errors.

I am so sorry, I duplicated your work again. Just not used to other people fixing my packages. I hope I will learn from this and start to always checking my mails before getting to work.


CRW Canon file support

Hello, thank you for creating this package, it's really useful for my small image renamer.
I found that it's not finding EXIF data in CRW image. Not sure how to read first bytes of files so I'm attaching this image.
CRW_1446.zip
I also found that Darktable have list of headers, maybe it will be useful for you:
https://github.com/darktable-org/darktable/blob/2e18924ffa35128da374e1608521c909260fe26d/src/common/imageio.c#L395-L451

Thanks for opening up this issue and your sample image. CRW images are not based on Tiff Directories, therefore they have a different directory structure.
See the link below for more information on the CRW file format. https://exiftool.org/canon_raw.html
It doesn't seem too complex of a structure to implement, unfortunately I can't dedicate time to implement this now.
I would welcome a PR if you would be willing to implement this.

I add support for identifying the CRW image type: da0d587ed51c53fa25001343653309fa9cb71950

I don't have time now for adding support for new formats but I found repository with RAW samples from many cameras on https://raw.pixls.us/
All are licensed with CC0 so could be used as test samples.


Ansible Modal Stylish and polish

https://github.com/open-cluster-management/backlog/issues/12838
The only un-fixed stylish issue is 7th - Extra variables styling is different. Use Clipboard copy - Expanded PF component
There are some issues to use MonacoEditor inside Clipboard, which will be fixed in a separate pr later.
https://github.com/open-cluster-management/backlog/issues/13079
Also fixed Ansible Modal console log error

A couple of nitpicks:
For item 3 in the issue, it says "Red Hat Text Medium 14px". In my browser I see it resolved to 16px font-size. The spacing is good, and it looks good, so I don't know if it needs to change, I just wanted to mention it.
For item 5, the tabs aren't quite the full width. Actually, when mousing over History, it's tab doesn't even go as far as some of the content below it:

Again, I don't know how strict we need to be with the change, so this might not need to be adjusted.

/lgtm


Schedule worker DRY 1

This is the first of low hanging fruit from #4071
Just want to chip away at this and get rid of the changes that we all agree upon.
/cc @imtayadeway I prefer enq but you had enqueue - I just deferred to your commit. Preference?
/cc @Fryguy thanks for linking me across
/cc @matthewd you were on original thread.

@kbrock LGTM. Even though it just delegates to Ruby's enq method, I kinda do prefer to the more verbose version, but no strong feelings about it

further simplified to 1 commit

ping @imtayadeway this seems pretty straight forward - you ok to merge?


CB-16926 Redbeams provisioning flow restart causes duplicate deployme…

…nt submission on Azure
In redbeams, when the provisioning flow restarts, the database template deployment is sometimes posted twice, causing an Azure error.
This commit has a generalized solution for the problem. If a template needs to be submitted, then the code

checks if there is a template already in the given resource group (RG) with the given name. If no, then a new teplate is submitted
if the template exists but is terminal (succeeded, failed, recently deleted etc), then a new template is submitted. If the previous and current templates had to provisioned the same resources, no change is done, if the resources still exist.
if the template is in progress, then a poller is started.

The poller in this case is a new databasePoller.It does not check the existence of the database as after the database provisioning either service or private endpoints are also created.
The new solution was added only for database provisioning. If needed it can be added to other places in the code.
See detailed description in the commit message.

do we still need this @gergopapi2 ?


Support for SQL based SDL in Gen 2.

Environment information
System:
  OS: macOS 14.5
  CPU: (10) arm64 Apple M1 Max
  Memory: 176.88 MB / 32.00 GB
  Shell: /bin/zsh
Binaries:
  Node: 20.14.0 - ~/.nvm/versions/node/v20.14.0/bin/node
  Yarn: 1.22.17 - /opt/homebrew/bin/yarn
  npm: 10.7.0 - ~/.nvm/versions/node/v20.14.0/bin/npm
  pnpm: 8.15.6 - /opt/homebrew/bin/pnpm
NPM Packages:
  @aws-amplify/backend: 1.0.4
  @aws-amplify/backend-cli: 1.1.0
  aws-amplify: 6.3.8
  aws-cdk: 2.147.2
  aws-cdk-lib: 2.147.2
  typescript: 5.5.2
AWS environment variables:
  AWS_SDK_LOAD_CONFIG = 1
  AWS_STS_REGIONAL_ENDPOINTS = regional
  AWS_NODEJS_CONNECTION_REUSE_ENABLED = 1
No CDK environment variables

Description
I appreciate the direction and development experience that Amplify Gen 2 offers. Features like personal sandboxes, TypeScript support, and CDK extensibility are excellent. However, the current Data Schema Builder in Amplify Gen 2 limits the options available to developers when defining their GraphQL APIs.
By introducing support for GraphQL SDL for SQL based schemas, Amplify can offer a familiar, more flexible and developer-friendly approach to schema design. I've detailed the challenges I've encountered while working with data schemas coupled to a relational database in this Discord discussion.
Key Issues with the Current Data Schema Builder:

Verbose Configuration: Configuring authentication, relationships, and renaming fields is excessively verbose compared to using AppSync directives. The configurations are split into different callbacks, making it difficult to get a comprehensive overview of a model's setup.

Limited API Surface Control: The Model Schema automatically exposes all database tables and columns without offering control over the API surface. This results in an unnecessary 1-to-1 mapping of DB rows to the GraphQL API. I prefer a tailored API that meets the specific needs of the client, allowing for custom naming conventions (e.g., snake_case to camelCase). The lack of flexibility in controlling the exposed API surface is a significant limitation.

Lack of Custom Field Resolvers: One of GraphQL's strengths is its ability to map each field to a resolver, enabling the client to request specific data. The Model Schema's approach of merely returning DB rows turns GraphQL into a glorified REST API. Custom field resolvers, such as the example below, are not possible:
type Post {
  id: String!
  title: String!
  localizedTitle(locale: String): String
  content: String!
}

Limitations of Custom Types: Custom types cannot be used in arguments, preventing the creation of queries with the same structure as generated ones. Additionally, custom types that reference models are not supported, hindering the creation of custom list queries. For example, defining ModelPostConnection or ModelPostFilterInput is not possible:
type ModelPostConnection {
  items: [Post]!
  nextToken: String
}

type Query {
  listPosts(
    id: ID,
    filter: ModelPostFilterInput,
    limit: Int,
    nextToken: String,
    sortDirection: ModelSortDirection
  ): ModelPostConnection
}

Proposed Solution
Currently, defineData supports SDL for DynamoDB-backed schemas, as outlined in this GitHub pull request. However, attempting to pass in a SQL-based schema results in errors because defineData defaults to a DYNAMO_DATA_SOURCE_STRATEGY. The DataProps interface lacks the necessary API surface to override this default behavior.
Introducing support for SQL-based schemas and allowing developers to specify the data source strategy would greatly enhance the flexibility and usability of Amplify Gen 2 for a broader range of applications.
I'm aware that this will result in the loss of end-to-end typing. However, this is not much of an issue for customers who prefers to not use the data client, but instead use GraphQL clients (TanStack, Apollo, URQ etc) on the frontend, and a bespoke SQL Query Builder on the backend.

Hey,👋 thanks for raising this! I'm going to transfer this over to our API repository for better assistance 🙂


Update test script to allow running only specifically named Django tests

Overview
The Django test suite has a long running time, which makes the process of writing new tests or updating old tests a little burdensome because the test --django option still goes through the whole suite by default.
We should allow passing an optional argument after --django that would name a specific test case to run without running all the others, like
./scripts/test --django OarIdTests.test_invalid_county

Describe the solution you'd like
Allow passing an optional argument after --django that would name a specific test case to run without running all the others, like
./scripts/test --django OarIdTests.test_invalid_county

Running ./scripts/test or ./scripts/test --django without the optional argument should continue to run the full test suite.

It appears that this will already work as a manage command:
./scripts/manage test api.tests.ContributorsListAPIEndpointTests

You can go down to a single test function, too
./scripts/manage test api.tests.DedupeMatchingTests.test_matches

Cool. I'm going to close this, then, since we already have an easy way to do this.


Support non-linguistic symbol in uio

resolves https://github.com/wenet-e2e/wenet/issues/796

Hi, thanks for your great contribution, however, i do think we can make the impl much simpler. For example, If we force that non-linguistic symbols must appear in a fixed format (i.e. {XXXXXX}), then function __tokenize_non_lang_syms can be simply replaced with three lines of code:
non_lang_syms_pattern = re.compile(r'(\{[A-Z]+\})')
pieces = non_lang_syms_pattern.split(txt.upper())
pieces = [w for w in pieces if len(w.strip()) > 0]

below is the verification:
import re

txt = "我是{NOISE} do you known im{AAA} haha {nnb}s {c}"

# Solution-a
non_lang_syms = ["{NOISE}", "{AAA}", "{nnb}", "{c}"]
rs = [re.compile(re.escape(x)) for x in non_lang_syms]

def exist_or_not(i, match_pos):
    start_pos = None
    end_pos = None
    for pos in match_pos:
        if pos[0] <= i < pos[1]:
            start_pos = pos[0]
            end_pos = pos[1]
            break

    return start_pos, end_pos

def __tokenize_non_lang_syms(txt, non_lang_sym_patterns):
    """split txt by non_lang_sym_patterns
    Args:
        txt: original transcript.
        non_lang_sym_patterns: non lang sym regex pattern.
    Returns:
        [[piece, is_non_lang_sym], [piece, True], ..., [piece, False]]
    """
    match_pos = []
    for r in non_lang_sym_patterns:
        i = 0
        while i >= 0:
            m = r.search(txt, i)
            if m:
                match_pos.append([m.start(), m.end()])
                i = m.end()
            else:
                break

    if len(match_pos) > 0:
        pieces = []
        i = 0
        while i < len(txt):
            start_pos, end_pos = exist_or_not(i, match_pos)
            if start_pos is not None:
                pieces.append([txt[start_pos: end_pos], True])
                i = end_pos
            else:
                if len(pieces) == 0 or (len(pieces) > 0 and pieces[-1][1]):
                    pieces.append([txt[i], False])
                else:
                    pieces[-1][0] += txt[i]
                i += 1

        return pieces
    else:
        return [[txt, False]]

parts_a = __tokenize_non_lang_syms(txt, rs)

# Solution-b
non_lang_syms_pattern = re.compile(r'(\{[A-Z]+\})')
parts_b = non_lang_syms_pattern.split(txt.upper())
parts_b = [w for w in parts_b if len(w.strip()) > 0]

# Check
assert len(parts_a) == len(parts_b)
for i, j in zip(parts_a, parts_b):
    assert i[0].strip().upper() == j.strip().upper()

Besides, we can also simplify the control-flow of function tokenize() by reusing the BPE/non-BPE branch without any modification:
non_lang_syms_pattern = re.compile(r'(\{[A-Z]+\})')  # fixed format
for sample in data:
    assert 'txt' in sample
    txt = sample['txt'].strip()
    parts = non_lang_syms_pattern.split(txt.upper())
    parts = [w for w in parts_b if len(w.strip()) > 0]
    for part in parts:
        if part in non_lang_syms:
            tokens.append(part)
        else:
            assert part[0] != '{'  # do some checks
            if bpe_model is not None:
                 reuse BPE logic
            else:
                reuse non-BPE logic

In my own data non-lang symbols are all surrounded by "{".
So it's OK for me to use the regex split.
In some data, the format of the non-lang symbols is mixed. So we need to construct a complicated pattern. (eg. , {BRK}, #, ## and so on)
And most parts of this commit are copied from text2token.py. I am not sure if there is a potential purpose...

We can just add some format check after reading the non-ling symbol table
https://github.com/wenet-e2e/wenet/pull/819/files#diff-a5e6ec48421b12c254127394ff8947e243de71257d2d7b4868dc9fdcb06adb4eR42
if it contains # or something else， raise errors and give some suggestions within error messages: "non-ling symbol should be formatted in {XXXX}, consider change # to {SHARP}".
the reasons for choosing fixed format lies in:

code is easier to read and understand
the number of non-ling symbols is small, it is also easy for users to define and diy their own non-ling symbol table

@yzmyyff Seems xingchen's solution is more simple, could you refine it according to his suggestion?

In test code, If we change non_lang_syms to ["{NOISE}", "{AAA}", "{nnb}"]. These two method has different result.
We must construct a more complicated regex pattern to cover this. I think we just move the complexity to the construction stage.

In test code, If we change non_lang_syms to ["{NOISE}", "{AAA}", "{nnb}"]. These two method has different result.
We must construct a more complicated regex pattern to cover this. I think we just move the complexity to the construction stage.

leaving {c} as a normal linguistic symbol is kind of weird
we can double check non_lang_syms in tokenize(), see:

Got it. Thanks for your suggestion!

We have to take non-liguistic symbol pattern into account. for example, the pattern is:
[xxx] for swithboard and fisher
 for wsj and chime4

I mean we should take {xxx} <xxx> [xxx]  into account.

As I mentioned before, If we do not consider forward compatibility and add some documentation about the special symbols. The new method would be a good starting.
Otherwise, the previous version may be simpler.

Yes, we see. xingchen just want to keep it simple and stupid, and the pattern is just for some english dataset which has special symbols, and the above three patterns are enough to cover.

In fact, I have seen some corpus use "#" to indicate the length of speech pause. # means stop level 1, ## means stop level 2...
I think leaving it to the user is a good choice.

#1/#2/#3 is for TTS corpus. here we only consider the public speech recognition dataset so it's convenient for researchers.
This symbols are meaningless in production speech dataset.

Alright


Rethinking_2 Chp_2: Different pymc3 output between notebook and local run

Hi there. I ran the following code sections in my IDE and obtained different output to the output in the Jupyter notebook.
To Reproduce
For example, I ran:
d = pd.read_csv("Data/Howell1.csv", sep=";", header=0)
d2 = d[d.age >= 18]

# Code 4.27
with pm.Model() as m4_1:
    mu = pm.Normal("mu", mu=178, sd=20)
    sigma = pm.Uniform("sigma", lower=0, upper=50)
    height = pm.Normal("height", mu=mu, sd=sigma, observed=d2.height)
with m4_1:
    trace_4_1 = pm.sample(1000, tune=1000)

# Code 4.29
az.summary(trace_4_1, round_to=2, kind="stats")

And obtained the following output, compared to the output in the notebook. The decimal place values differ between them:
# Output: notebook
	mean 	sd 	hpd_5.5% 	hpd_94.5%
mu 	154.62 	0.41 	153.96 	155.25
sigma 	7.77 	0.29 	7.30 	8.23

# Output: mine
         mean    sd  hdi_3%  hdi_97%
mu     154.60  0.41  153.84   155.38
sigma    7.76  0.31    7.18     8.32

There were also numeric differences in the decimal place values for the variance-covariance matrix, variances, and the correlation matrix:
# Code 4.32 output
# variance-covariance: notebook
               mu 	    sigma
mu     	 0.178473 	-0.007866
sigma 	-0.007866 	0.087795
# variance-covariance: mine
             mu     sigma
mu     0.171058 -0.002112
sigma -0.002112  0.093520

# Code 4.33 output
# variances: notebook
array([0.17847295, 0.08779492])
# variances: mine
array([0.17105813, 0.09351962])

# Code 4.33 output
# correlations: notebook
	          mu    	sigma
mu   	1.000000 	-0.062842
sigma 	-0.062842 	1.000000
# correlations: mine
             mu     sigma
mu     1.000000 -0.016702
sigma -0.016702  1.000000

The differences in numeric output don't seem to be due to rounding errors, so I'm not sure what might explain those differences. Would appreciate if you had any pointers.
I'm a Python user but new to Bayesian analysis and the pymc3 package. Thanks very much for porting all the Rethinking R code to Python. The explanations and examples are great!
Python:
Python 3.8.5 (default, Sep  4 2020, 07:30:14)
[GCC 7.3.0] on linux
pymc3 3.9.3, arviz 0.10.0

Hi @joannadiong thanks for getting in touch. I am very happy that these resources are useful to you.
Regarding your question, I think that differences are within the expected range. The method used by PyMC3 to compute the posterior (NUTS) is an stochastic method, thus each time you run it you will get slightly different results. You can fix the random seed used by pm.sample with the argument "random_seed". Additionally az.summary (without the kind="stats" argument) provides an stimate of the mc error, that is the error intruduced by the stochastic method. If you need to reduce the error you can increase the number of draws (1000 in your example) to a higher number. As rule of thumb I would say that setting draws=1000 or 2000 should be ok (specially if you are running at least two chains). I do not remember the chapter number, but in the book you will find one chapter with a discussion on how to diagnose the mcmc chains you be sure you are getting trustworthy results. Nevertheless if you need more info or have some more doubts doubts, please do not hesitate to ask questions here (or in pymc3 discourse).

Hi @aloctavodia, thanks for the detailed and helpful explanation. I can manage those fixes and will keep working through the book.
For where I'm at in the book now, there are minor fixes needed for the notebook:
# Code 4.18
ax.imshow(zi, origin="bottom") # "bottom" would need to be "lower"

And trace_to_dataframe will soon be removed, which will break here:
# Code 4.32
trace_df = pm.trace_to_dataframe(trace_4_1)

but I think there is an open issue for this.
I'm not familiar enough just yet with the packages and compiling the notebooks for pymc3 standards, but hopefully in time I'll learn enough to contribute more. Happy for you to close this issue if you feel the main things are covered. Many thanks again!

Thanks for the report, this is all useful feedback. We should update the notebooks to directly work with ArviZ's InferenceData object.
Looking forward to more contributions from you. Let us know if you need help of have more doubts.


Minor fix for generating file URLs for Merritt Express for the cases …

…where we have a slash in the filename (like 1 annoying dataset).
I'm replacing the double-encoded slash with a single-encoded slash.  This is pretty much for one dataset.  :-/

Yes, this is sad, but seems like a reasonable fix.


Website Display Issue

For last day or so seeing odd issue when first going to https://forsetisecurity.org on Chrome
After navigating to links on the page, the issue resolves.

I am no longer able to reproduce this issue after making updates to my local PC including Chrome update.
I did clear cache and reproduce the issue before reporting it; however it may just have been an issue specific to my local setup.

Unable to reproduce issue. Closing.


[docker] refactor event subscription in DockerUtil

Refactor the event listening logic from AD's DockerListener and tagger's DockerCollector inside the DockerUtil class and implement a common fetching/filtering pipeline.
I also removed all usage of github.com/docker/docker/client in these classes to simplify the logic and mutualize error-handling and quirk-handling logics.
Common event retrieval
Defined in pkg/util/docker/event_stream.go, it handles re-connect on error (so we'll survive a docker daemon reboot) and
I keeps a single stream connection to the docker daemon, whatever the listener count is.
Generic fan-out logic
Defined in pkg/util/fanout/template/fanout.go and tested (91% coverage) in pkg/util/fanout/template/fanout_test.go.
It handles listeners suscribing/unsuscribing and queues data in one channel per listener. If a listener falls behind and its channel is full, we recover after a timeout and forcefully unsuscribe that listener, sending them a custom fanout.ErrTimeout error so they can detect it, restart from a clean state and re-suscribe.
The FanOut class handles all channel creation/closing to avoid any issues on these.
Code generation
As golang has no generics supports and passing around interface {}s will have impacts on reliability and performance, I went the route of code generation, see https://appliedgo.net/generics/
pkg/util/fanout/template/fanout.go is used as a template for writing  pkg/util/docker/autogen_fanout.go by replacing the types. See pkg/util/fanout/README.md for the process.
As we know we will need this logic in front of the kubelet.PodWatcher, and might need it again for other orchestrators, copy-pasting the logic and changing the types will lead to hard to maintain duplicates.
I chose to commit the generated code in the repo to be more reproducible (if gorewrite were to break, we'd see it in a branch when updating the code) and don't add one more build dep.
I introduced the inv gorewrite task to trigger the rewrite of all classes, to avoid derived classes falling out of sync too.

Move to https://github.com/DataDog/datadog-agent/pull/974


Investigate persistence options for annotations

Any ideas around how to persist annotations across sessions and when sharing would be great.
Current ideas

Embed them in the URL - If they aren't too complex, they can theoretically be encoded into the URL itself
Save them clientside - e.g. using localStorage or IndexedDB - Client-side storage - Learn web development | MDN (mozilla.org) - Only relevant for users storing their own information, not suitable for sharing
Save them in our database - we could then retain a unique ID to the annotation(s) and load them on demand. Quite complex. Could be done anonymously (need to know the unique ID) or via a user account.

Investigation only at this point!

IndexedDB makes the most sense for persisting the annotations between user sessions. We can prompt users if they want to restore their previous annotations, and dumb the DB if they don't.
IndexedDB could potentially be used for persisting 'Added layers' as well between sessions, but that's out of scope for this ticket.
For sharing, this would probably need to be done via the sharing panel, with a checkbox for 'share my annotations' which would then persist it to our own database and attach a unique ID to the share link to get those annotations


Add basic support for YugabyteDB in Flyway

Adding support for YugabyteDB by extending the Flyway API implementation for PostgreSQL.
APIs overridden in YugabyteDB:

Database.ensureSupported()
Database.supportsDdlTransactions()
BaseDatabaseType.handlesJDBCUrl()
BaseDatabaseType.handlesDatabaseProductNameAndVersion()
BaseDatabaseType.getPriority()
BaseDatabaseType.getName()

Since YugabyteDB also works with PostgreSQL JDBC driver which is already shipped with the Flyway distribution, changes in pom.xml file to include maven coordinates of the driver are not needed.
Tested the changes with commands: info, migrate and clean - as outlined in this section.
Screenshot of the test run via IDE is available here.
Relevant GitHub Issue: https://github.com/flyway/flyway/issues/3186

Fantastic, thankyou! I'm just checking out the Docker image now to see how easy it is to hook up to our testing infrastructure - just done a migrate/clean/info locally and it's all looking very good.

Closing as this is now merged in flyway-community-db-support


static_compute_shape: reshape_lazy on axis that is not packed

MIGraphX commit: 431e714
GPU: MI210 (gfx90a)
ROCm version: 6.0.1

Model(s) to reproduce:

https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_128_Opset16_timm/levit_128_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_128_Opset17_timm/levit_128_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_128s_Opset16_timm/levit_128s_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_128s_Opset17_timm/levit_128s_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_192_Opset16_timm/levit_192_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_192_Opset17_timm/levit_192_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_256_Opset16_timm/levit_256_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_256_Opset17_timm/levit_256_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_384_Opset16_timm/levit_384_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/levit_384_Opset17_timm/levit_384_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_b_Opset16_torch_hub/swin_b_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_b_Opset17_torch_hub/swin_b_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_b_Opset18_torch_hub/swin_b_Opset18.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_base_patch4_window12_384_Opset16_timm/swin_base_patch4_window12_384_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_base_patch4_window12_384_Opset17_timm/swin_base_patch4_window12_384_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_base_patch4_window12_384_in22k_Opset16_timm/swin_base_patch4_window12_384_in22k_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_base_patch4_window12_384_in22k_Opset17_timm/swin_base_patch4_window12_384_in22k_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_base_patch4_window7_224_Opset16_timm/swin_base_patch4_window7_224_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_base_patch4_window7_224_Opset17_timm/swin_base_patch4_window7_224_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_base_patch4_window7_224_in22k_Opset16_timm/swin_base_patch4_window7_224_in22k_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_base_patch4_window7_224_in22k_Opset17_timm/swin_base_patch4_window7_224_in22k_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_large_patch4_window12_384_Opset16_timm/swin_large_patch4_window12_384_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_large_patch4_window12_384_Opset17_timm/swin_large_patch4_window12_384_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_large_patch4_window12_384_in22k_Opset16_timm/swin_large_patch4_window12_384_in22k_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_large_patch4_window12_384_in22k_Opset17_timm/swin_large_patch4_window12_384_in22k_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_large_patch4_window7_224_Opset16_timm/swin_large_patch4_window7_224_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_large_patch4_window7_224_Opset17_timm/swin_large_patch4_window7_224_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_large_patch4_window7_224_in22k_Opset16_timm/swin_large_patch4_window7_224_in22k_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_large_patch4_window7_224_in22k_Opset17_timm/swin_large_patch4_window7_224_in22k_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s3_base_224_Opset16_timm/swin_s3_base_224_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s3_base_224_Opset17_timm/swin_s3_base_224_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s3_small_224_Opset16_timm/swin_s3_small_224_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s3_small_224_Opset17_timm/swin_s3_small_224_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s3_tiny_224_Opset16_timm/swin_s3_tiny_224_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s3_tiny_224_Opset17_timm/swin_s3_tiny_224_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s_Opset16_torch_hub/swin_s_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s_Opset17_torch_hub/swin_s_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_s_Opset18_torch_hub/swin_s_Opset18.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_small_patch4_window7_224_Opset16_timm/swin_small_patch4_window7_224_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_small_patch4_window7_224_Opset17_timm/swin_small_patch4_window7_224_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_t_Opset16_torch_hub/swin_t_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_t_Opset17_torch_hub/swin_t_Opset17.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_t_Opset18_torch_hub/swin_t_Opset18.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_tiny_patch4_window7_224_Opset16_timm/swin_tiny_patch4_window7_224_Opset16.onnx
https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/Computer_Vision/swin_tiny_patch4_window7_224_Opset17_timm/swin_tiny_patch4_window7_224_Opset17.onnx

Fails at: /code/AMDMIGraphX/src/include/migraphx/op/reshape_lazy.hpp:285: static_compute_shape: reshape_lazy on axis that is not packed.

https://github.com/ROCm/AMDMIGraphX/pull/2721 most probably fixes these as it did for other ONXX Zoo models. We have to run a check on these models with that change included.

The above mentioned PR fixed it.


[Security] Strictly pin dependency versions

Once we have settled on the versions we want for various dependencies we need to pin the versions.

[x] Pin Substrate version to whatever it is using #358
[x] Pin versions of this and other dependencies in Cargo.toml
[x] Ensure that all Crates.io dependencies are using a specific version number
[x] Examine dependabot

@wilwade  learning from other projects its ideal we keep main updated with latest version, but branch out before doing so and tag the parity version that branch is supposed to hold, say 0.9.25 which is the current one
What you think ?
Cumulus for example does that

@saraswatpuneet Hmm... Good question. We should do branches for versions. (#282 should document that, I'll add a note to it)
I don't know that we want pinning to be a required part of the release process however.
What about this setup?

pin on main
branch for releases
commits to main to update pins

The goal being that we have explicit points where our dependencies change for dependency auditing purposes.

I examined the pros / cons of pinning Git hosted dependencies with release branches vs commit hashes.
Cargo will allow either a branch pin:
sp-std = { git = "https://github.com/paritytech/substrate", default-features = false, branch = "polkadot-v0.9.27" }
OR a rev pin
sp-std = { git = "https://github.com/paritytech/substrate", default-features = false, rev = "8eff668a42325aeb4433eace1604f4d286a6ec05" }
But NOT both.
We can either keep branch pinning to v0.9.27 and trust parity with updates / bug fixes / etc not breaking builds OR we can specify a commit hash and just comment which substrate / polkadot versions we are using.
After spinning my wheels on this some I've realized a caveat with the commit hash approach. We would have to lock each intra dependency that lives in the same GH repository to the same hash as the dependency that depends on it. Alcala patches each one in the rool Cargo.toml. For example,
frame_support would be locked to https://github.com/paritytech/substrate?branch=polkadot-v0.9.27#8eff668a42325aeb4433eace1604f4d286a6ec05" but it depends on sp-io which parity defines as `https://github.com/paritytech/substrate?branch=polkadot-v0.9.27
They both live in the same repository but the intra dependency (sp-io) will be set to the latest commit hash in the based on branch polkadot-v0.9.27, while the frame_support lib will be locked to an earlier state, at commit hash 8eff668a42325aeb4433eace1604f4d286a6ec05
This feels counter intuitive to how a package manager should work.
This approach would involve the up front work of locking each intra dependency to the same hash. Any new dependencies that live in substrate would have to be patched when we do version updates.
(This may be a reason moonbeam chose the maintain a fork route)
Perhaps we stay with the branch specification and let Cargo.lock do its job? The lock file locks each dependency and its intra dependencies to a hash revision. The only way we'd lose that hash lock is if we run a Cargo update or delete the lock file.

It appears that all of our Crates.io deps are pinned to exact version numbers. Unless we wish to update any of those (and any related code depending on them), it seems there is nothing left to do for this issue.


[Storage] Migrate test_blob_api_version and test_blob_client to test proxy

API change check
API changes are not detected in this pull request.


topshelf seem not work in vnext

Can you describe the context a little more? What are you trying to do?

I get these errors when trying to start a vnext console app:
Topshelf.HostFactory Error: 0 : An exception occurred creating the host, Topshelf.HostConfigurationException: The service was not properly configured:
[Failure] Command Line An unknown command-line option was found: SWITCH: appbase (True)
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "C:\Users...\EventListner"
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "Microsoft.Framework.ApplicationHost"
[Failure] Command Line An unknown command-line option was found: SWITCH: configuration (True)
[Failure] Command Line An unknown command-line option was found: ARGUMENT: Debug
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "EventListner"
[Failure] Command Line An unknown command-line option was found: SWITCH: appbase (True)
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "C:\Users....\src\EventListner"
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "Microsoft.Framework.ApplicationHost"
[Failure] Command Line An unknown command-line option was found: SWITCH: configuration (True)
[Failure] Command Line An unknown command-line option was found: ARGUMENT: Debug
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "EventListner"
[Success] Name EventListner
[Success] ServiceName EventListner
at Topshelf.Configurators.ValidateConfigurationResult.CompileResults(IEnumerable1 results)    at Topshelf.HostFactory.New(Action1 configureCallback)
Topshelf.HostFactory Error: 0 : The service terminated abnormally, Topshelf.HostConfigurationException: The service was not properly configured:
[Failure] Command Line An unknown command-line option was found: SWITCH: appbase (True)
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "C:\Users\Fredrik\Source\Repos\PlannigHub\src\EventListner"
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "Microsoft.Framework.ApplicationHost"
[Failure] Command Line An unknown command-line option was found: SWITCH: configuration (True)
[Failure] Command Line An unknown command-line option was found: ARGUMENT: Debug
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "EventListner"
[Failure] Command Line An unknown command-line option was found: SWITCH: appbase (True)
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "C:\Users...\src\EventListner"
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "Microsoft.Framework.ApplicationHost"
[Failure] Command Line An unknown command-line option was found: SWITCH: configuration (True)
[Failure] Command Line An unknown command-line option was found: ARGUMENT: Debug
[Failure] Command Line An unknown command-line option was found: ARGUMENT: "EventListner"
[Success] Name EventListner
[Success] ServiceName EventListner
at Topshelf.Configurators.ValidateConfigurationResult.CompileResults(IEnumera
ble1 results)    at Topshelf.HostFactory.New(Action1 configureCallback)
at Topshelf.HostFactory.Run(Action`1 configureCallback)

Do you have a sample project that exhibits this behaviour? What version of Visual Studio/.NET is this running on?

This gist implements the Topshelf sample in an ASP.NET vNext Console Application. It displays the same output as above.

@alastairs thx. You saved me some time =)

@Lejdholt no worries :smile: I might have some time this afternoon to look into it myself; I'll post back here if I make any progress.

@TravisTheTechie now that Visual Studio 2015 is released, any idea when ASP.NET 5 will be supported?

@Lejdholt, this is on my radar but through next week I'm across the world. When I'm back, I'm hoping to carve out some time to take a swing at this.

Alright, I've spent some time with this, and I can confirm it's an issue. I do not yet have a fix for it.
It is possible that Topshelf, as it stands, won't run as a dnx executable. The execution environment is different enough, we might require some new magic to glue it together. Or a new solution comes to me while showering or is suggested to me.

It smells like there are arguments on the command-line with DNX that we have to accept/ignore.

That will get us running it in console mode, but getting it to install as a service will be more complicated.

Have You planned to fix problems with vnext?

We do not have a planned fix for vNext.

very bad. I seen articles about running dnx as win service
http://taskmatics.com/blog/run-dnx-applications-windows-service/
http://taskmatics.com/blog/host-asp-net-in-a-windows-service/

Well, we do accept pull requests...
That being said, I'm sure it's something we'll look at in the next beta or two.

bump - Any news on this front? It would be great if TopShelf worked for DNX apps, even if it was restricted to frameworks dnx451-dnx46 and not running on the core clr.

Turns out the dnx console apps are essentially being scrapped and will be replaced with CLI console apps that have a real entry point. My understanding is that Topshelf should "just work" as it does now. The CLI console apps will be much more stable from aspnet 5 RC2

That would be awesome. The early betas of ASP.NET make this a significant undertaking. We'll wait and see what it looks like when everything final.

If this is still an issue here's how I got it working for dnx451:
args = args.Where(a => a != "<service-name>").ToArray();
return (int)HostFactory.Run(c =>
            {
                c.ApplyCommandLine(String.Join(" ", args));
                ...
             }

To explain, inject actual args into the service, while removing the image name that's usually the first parameter in the array. It seems that when running it via DNX, some other parameters are injected by DNX itself to figure out what and how to execute.

@marono dnx console apps are due to change significantly in aspnet5 RC2 which is due in February. For instance, right now dnx console applications are just dlls. You then run that dll via dnx: dnx run blah
In RC2 the actual dotnet team have taken the reigns from the aspnet team and are formalizing the console applications so they are a true .exe with a real entry point. This should render any work arounds required for dnx obsolete.

Hi @LeeCheneler,
Sorry I haven't specified earlier, my work is around the dotnet cli, dnx's replacement.
Regards,

@marono ah OK cool! I'll be sure to check back here if I'm having trouble when RC2 is released. We're planning on migrating a few of our service apps over to the new stuff once it's out :P

Moved to #277 in favor of dotnet cli.


"Do not verify" does not work

Describe the bug
First off, thanks for the great adapter.
I'm facing an issue where I set the "Do not verify flag" for on and off state, however at the turn it is still is logging "target is already turned off, so not turning off again." not actually firing the off action (in this case a button).
To Reproduce
Steps to reproduce the behavior:

Create a target as shown on the screenshot:

Create a zone and fire an action (like motion)
Observe the log

Expected behavior
Since it is not supposed to verify the on and off status, I'd expect it to stop the music by executing the "deviceStop" Command (which is a button on the Alexa Adapter), but it still acts like it needs to verify the status.
In my eyes I've configured it like described here (https://forum.iobroker.net/topic/36237/smart-control-adapter-faq-use-cases -> Frage: Ich habe einen Button als Zielgeräte-Datenpunkt, geht das auch?)
Screenshots & Log files
2021-12-21 23:31:30.434  - info: smartcontrol.0 (2316507) Trigger [Motion.Test] activated Zone 'Badezimmer Musik'. Turned on: Bad.Echo.RadioFFN.
2021-12-21 23:31:30.435  - info: smartcontrol.0 (2316507) Trigger [Motion.Test] Zone 'Badezimmer Musik': Timer of 5s will be set to turn off once no more motion recognized.
2021-12-21 23:31:30.438  - info: smartcontrol.0 (2316507) Trigger [Motion.Test] 'zoneOff' 10s timer for zone 'Badezimmer Musik' initially started.
2021-12-21 23:31:40.441  - info: smartcontrol.0 (2316507) Trigger [Motion.Test] Timer 'timersZoneOff', target Bad.Echo.RadioFFN: - Timeout of '10' seconds reached, target is already turned off, so not turning off again.
2021-12-21 23:32:02.061  - info: smartcontrol.0 (2316507) Trigger [Motion.Test] Motion sensor 'Motion.Test', zone 'Badezimmer Musik': No more motion, so we set 5s timer.
2021-12-21 23:32:07.064  - info: smartcontrol.0 (2316507) Trigger [Motion.Test] Timer 'Motion', target Bad.Echo.RadioFFN: - Timeout of '5' seconds reached, target is already turned off, so not turning off again.
Versions:

Adapter version: 1.2.1
JS-Controller version: 3.3.18
Node version: 14.18.2
Operating system: linux (iobroker docker container)

The issue has been stall and I'm not using iobroker or the adapter anymore, I'll therefor close it.


Home Assistant is annoying with these error messages

Logger: homeassistant.const
Source: helpers/deprecation.py:205
First occurred: 12:41:41 (3128 occurrences)
Last logged: 12:41:53
DATA_RATE_KIBIBYTES_PER_SECOND was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use UnitOfDataRate.KIBIBYTES_PER_SECOND instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
DATA_RATE_MEBIBYTES_PER_SECOND was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use UnitOfDataRate.MEBIBYTES_PER_SECOND instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
DATA_RATE_GIBIBYTES_PER_SECOND was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use UnitOfDataRate.GIBIBYTES_PER_SECOND instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
ENTITY_CATEGORY_CONFIG was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use EntityCategory.CONFIG instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
ENTITY_CATEGORY_DIAGNOSTIC was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use EntityCategory.DIAGNOSTIC instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues

Same to me too.
I've updated to Home Assistant Core 2024.2.0 and the things goes mad. Even the integration won't load for this errors. Reverted back to last version with restore from backup and integration is working but filling log with this errors:
Logger: homeassistant.const
Source: helpers/deprecation.py:205
First occurred: 22:38:25 (3542 occurrences)
Last logged: 22:42:13
DATA_RATE_KIBIBYTES_PER_SECOND was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use UnitOfDataRate.KIBIBYTES_PER_SECOND instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
DATA_RATE_MEBIBYTES_PER_SECOND was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use UnitOfDataRate.MEBIBYTES_PER_SECOND instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
DATA_RATE_GIBIBYTES_PER_SECOND was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use UnitOfDataRate.GIBIBYTES_PER_SECOND instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
ENTITY_CATEGORY_CONFIG was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use EntityCategory.CONFIG instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
ENTITY_CATEGORY_DIAGNOSTIC was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use EntityCategory.DIAGNOSTIC instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
Logger: homeassistant.components.alarm_control_panel.const
Source: helpers/deprecation.py:205
Integration: Alarm control panel (documentation, issues)
First occurred: 22:41:29 (8 occurrences)
Last logged: 22:41:29
SUPPORT_ALARM_ARM_AWAY was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use AlarmControlPanelEntityFeature.ARM_AWAY instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_ALARM_ARM_NIGHT was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use AlarmControlPanelEntityFeature.ARM_NIGHT instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_ALARM_TRIGGER was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use AlarmControlPanelEntityFeature.TRIGGER instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_ALARM_ARM_CUSTOM_BYPASS was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use AlarmControlPanelEntityFeature.ARM_CUSTOM_BYPASS instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_ALARM_ARM_VACATION was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use AlarmControlPanelEntityFeature.ARM_VACATION instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
Logger: homeassistant.components.humidifier.const
Source: helpers/deprecation.py:205
Integration: Humidifier (documentation, issues)
First occurred: 22:41:06 (3 occurrences)
Last logged: 22:41:06
DEVICE_CLASS_HUMIDIFIER was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use HumidifierDeviceClass.HUMIDIFIER instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
DEVICE_CLASS_DEHUMIDIFIER was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use HumidifierDeviceClass.DEHUMIDIFIER instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_MODES was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use HumidifierEntityFeature.MODES instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
Logger: homeassistant.components.climate.const
Source: helpers/deprecation.py:205
Integration: Climate (documentation, issues)
First occurred: 22:38:25 (42 occurrences)
Last logged: 22:41:00
SUPPORT_TARGET_HUMIDITY was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use ClimateEntityFeature.TARGET_HUMIDITY instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_FAN_MODE was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use ClimateEntityFeature.FAN_MODE instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_PRESET_MODE was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use ClimateEntityFeature.PRESET_MODE instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_SWING_MODE was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use ClimateEntityFeature.SWING_MODE instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
SUPPORT_AUX_HEAT was used from xiaomi_miot, this is a deprecated constant which will be removed in HA Core 2025.1. Use ClimateEntityFeature.AUX_HEAT instead, please create a bug report at https://github.com/al-one/hass-xiaomi-miot/issues
and much more

I have same issue

+1

+1


Adding support for Motion Adaptive Deinterlace

I would like to know if it's possible to add support for Motion Adaptive Deinterlace, I know this adds one frame of lag, but the result looks much cleaner than bob deinterlace as it has no flickering of any kind and no combing artifacts, making 480i upscale to what looks like a slightly softer 480p.
If you want to know what Motion Adaptive Deinterlace looks like, take a closer look at GBS Control which implements it.

Motion Adaptive Deinterlacing isn't possible with the current hardware. It does not have the required RAM resources for the frame buffer needed.


I have some issue changing the batch

I train my own pascal_voc dataset on mmdetection but the result is way much worse than the old FPN code I used before. I was wondering it may be the batch problems, but I found difficulties changing the parameters of batch, couldn't find the parameters. May someone help? I would be really appreciate that!

total_batch_size = imgs_per_gpu * num_gpus


Sending signals to docker container with lxc-stop is useless for LXC 1.0.0 and above

Hello guys!
First of all, thanks for Docker! Unfortunately, LXC version 1.0.0 and above brings some problems that may be worth mentioning in documentation.
Basically, it doesn't allow docker to forward signals to running containers.
Docker's function stop and kill use following line of code:
https://github.com/dotcloud/docker/blob/master/daemon/execdriver/lxc/driver.go#L244
As per LXC documentation, lxc-stop doesn't provide any parameter that passes signal to container. Instead, lxc-stop -k just kills the process with default stop signal (I assume it's SIGKILL):
https://github.com/lxc/lxc/blob/master/doc/lxc-stop.sgml.in#L103
https://github.com/lxc/lxc/blob/6168ff15aac16bcccbcf22b4ba915870844e96a4/src/lxc/commands.c#L600
Thus, despite documentation for docker stop says that it sends SIGTERM signal, in fact, container receives SIGKILL signal generated by underlying lxc-stop -k call.
Also, it seems --signal argument of docker kill becomes useless - you can set any signal, but container will receive SIGKILL.
Could you please advise, is there any workaround for this situation?
At the moment I have to stick to LXC v.0.9.0 just because I need to stop my container with SIGTERM signal. With 0.9.0 docker stop works just fine, with 1.0.3 it sends SIGKILL instead of SIGTERM.

can you please try with the latest version of docker and lxc 1.0.7

closing as stale please ping me with exact steps to reproduce on the latest version and I will reopen

https://github.com/docker/docker/blob/master/daemon/execdriver/lxc/driver.go#L524 problem is that last argument to lxc-stop which is strconv.Itoa(sig) is simply ignored by lxc-stop. With -k option it always sends SIGKILL. It seems there is no way to specify what signal should be sent. To best support behaviour promised by docker stop, it should call lxc-stop -n <id> -t <timeout> which first sends SIGTERM and after timeout - SIGKILL.


blue pill to right middle

Let's leave the top right third free for pasting in the video in post production.
Hence, blue pill goes to middle instead of all the way at the top as previously discussed.

📌  Looking good?

let' move it down.
How about we divide the right third of screen, (where the pill is now) up into 3 vertically stacked thirds, and center the pill in the resulting box. Each Blue pill in the box above that. All blue pill link to the same page: http://www.theverylastpageontheinternet.com/, but they have no link icon to entice such needless wanderings.
Thank you Isaac!

No, never mind about linking the bluepills now. don't sweat that detail. just blank blue pill, let me get you a color code for that.

Doh! We have a blue color. Please just use our blue  from title page : )

👓

Bluepills work just like redpills as far as calling in from org-mode.
QR's still optional.

Perfect.
On Feb 9, 2017 6:22 AM, "ia"<EMAIL_ADDRESS>wrote:

👓
[image: screen shot 2017-02-09 at 12 20 15]
https://cloud.githubusercontent.com/assets/10228550/22781289/254e5462-eec2-11e6-9593-a062c8ef4ccd.png
Bluepills work just like redpills as far as calling in from org-mode.
QR's still optional.
—
You are receiving this because you authored the thread.
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Let's skip the QR code on blue. Just pill.
On Feb 9, 2017 6:23 AM, "Jay Martin"<EMAIL_ADDRESS>wrote:

Perfect.
On Feb 9, 2017 6:22 AM, "ia"<EMAIL_ADDRESS>wrote:

👓
[image: screen shot 2017-02-09 at 12 20 15]
https://cloud.githubusercontent.com/assets/10228550/22781289/254e5462-eec2-11e6-9593-a062c8ef4ccd.png
Bluepills work just like redpills as far as calling in from org-mode.
QR's still optional.
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or mute the thread
https://github.com/notifications/unsubscribe-auth/ABlYhLXHu2o1g14QXZrt0s0hhIErVOtNks5ravbjgaJpZM4L7jyG
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Do you want blue pills on every slide?

[...] Each Blue pill in the box above that. All blue pill link to the ...

Every slide where a red pill is found. This implies choice, which I'm a fan of, even though Isaac Einstein himself didn't believe in free will. We should (IMHO), for the sake of humanity, carry on as though we do have, regardless of reality.

Gotcha -- but no QR links on any of the bluepills? (--Limits the choice a little, eh?)

The choice, as morpheus states is to remain as you are, and believe whatever you want to believe.
The blue pill is a no-op.
Okay, you've changed my mind. Put the same QR code on each blue pill, which points to http://www.theverylastpageontheinternet.com/
Nice one messy brain!


[F3] Forest Integration

[ ] First step - integrate cert and certexchange when those are implemented for follow mode

Pausing Rust-F3 development, will re-create issues as needed.


highlighting active div?

For the love of god I can't figure out how to integrate a simple $(this).addClass('active') function to a highlighted div. Anyone mind throwing me a bone here?

Using isInViewport.js plugin inside a callback: function(nav){}


Empty Migration

I tried your plugins, then migration is created, but that's files are empty
what's problem your plugin ?

Hi @hhakangull, seems like it wasn't 🙂
The plugin relates on dotnet ef tool, so if your new migration doesn't differ from previous one, it will be empty


summary_metadata - methods

html tags present in methods section

I also noticed there can be markup tags present in the abstract, like <para> or <section>. We could remove them but with multiple section headings it might be confusing to read without the tags. Perhaps we could separate section headings from text with a dash " - " or something like that. I think in most cases (<para> tags are more common in my experience) it would be fine to remove.

I think it would be fine to remove them. Originally we thought we could try to leverage them to keep the door open for some rendering. Maybe add a parameter that let the user the option to overwrite the default behavior of removing the tags.


Improved stability/performance for storage

readers: be more resilient to the loss of a single backend
storage: try to shuffle nodes
finder: avoid calling get_intervals() when listing metrics

Generally LGTM. What's the advantage to using random.shuffle()?
/cc @DanCech

The idea of the random.shuffle() is that it changes the first node that get contacted, this help spreading the load, on our setup we found out that one node would timeout way more than the others, and it was always the first node in the list.

code changes look reasonable to me, need to figure out the failing test though.

looks like it calls listdir in ceres less now because it doesn't try to get the intervals even when you don't need them, which is good :)

Current coverage is 65.68% (diff: 57.89%)

Merging #1774 into master will decrease coverage by 0.77%

@@             master      #1774   diff @@
==========================================
  Files            55         55
  Lines          6262       6275    +13
  Methods           0          0
  Messages          0          0
  Branches       1243       1245     +2
==========================================
- Hits           4162       4122    -40
- Misses         1877       1936    +59
+ Partials        223        217     -6

Powered by Codecov. Last update f7634f3...41cc96d

@obfuscurity test are fixed, do I get a final LGTM ? :)

👍


[fea-rs] Support inline 'by Null' in contextual sub

closes https://github.com/cmyr/fea-rs/issues/246

cc @simoncozens

Nice, thank you!


AUTH sent for every request

<EMAIL_ADDRESS>node v6.1.0, ubuntu 16.04
Currently it appears that the AUTH command is sent for every MULTI request.
I notice this when I issue the MONITOR command I can see AUTH being sent. The AUTH command only needs to be sent once when the connection is established afterwards the server will consider the client authenticated.
Because we have to choose very strong passwords this is unnecessary overhead.
If you would be willing to accept a PR on this I am happy to look into it further.

Hi tmpfs-
I tried to re-create the issue but couldn't get the results you were describing. Here is what I did:
var
  redis   = require('redis'),
  client  = redis.createClient({
    port      : 6371,
    password  : "1234"
  }),
  doAMulti = function() {
    client
      .multi()
      .set('test',1)
      .exec(function(err) {
        if (err) { throw err; }
      })
  };
setTimeout(doAMulti, 1000);
setTimeout(doAMulti, 1500);
setTimeout(function() {
  client.quit();
}, 2000);

and my monitor output was:
1483365486.196369 [0 <IP_ADDRESS>:55801] "auth" "1234"
1483365486.197126 [0 <IP_ADDRESS>:55801] "info"
1483365487.198091 [0 <IP_ADDRESS>:55801] "multi"
1483365487.198114 [0 <IP_ADDRESS>:55801] "set" "test" "1"
1483365487.198124 [0 <IP_ADDRESS>:55801] "exec"
1483365487.697004 [0 <IP_ADDRESS>:55801] "multi"
1483365487.697024 [0 <IP_ADDRESS>:55801] "set" "test" "1"
1483365487.697032 [0 <IP_ADDRESS>:55801] "exec"

Could you provide the code and the version of the module you are using?

@tmpfs please have a look at the code that executes the multi command. It is very likely, that each time you use a multi command a new Redis instance is created before firing the multi command.

@BridgeAR you are completely correct, my connection caching logic was bad; I do apologize, sorry!


Enhancement: add more options to todo task set

There are a few additional properties available to the todoTask lately so let's include them in our command todo task set Graph - todoTask properties
Options to add

Option
Description

--categories [categories]
Comma-separated list of categories associated with the task.

--completedDateTime [completedDateTime]
The date and time when the task was finished. This should be defined as a valid ISO 8601 string. 2021-12-16T18:28:48.6964197Z

--startDateTime [startDateTime]
The date and time when the task is scheduled to start. This should be defined as a valid ISO 8601 string. 2021-12-16T18:28:48.6964197Z

Additional examples
Update a task to Microsoft To Do with several categories
m365 todo task set --title "New task" --listName "My task list" --categories "Red category,Important"

Update a task to Microsoft To Do with a start date and set a reminder for the task
m365 todo task set --title "New task" --listName "My task list" --startDateTime "2023-12-16T18:28:48.6964197Z" --isReminderOn true

Remarks
When you specify the values for categories, consider the following. Each category can correspond to the displayName property of an outlookCategory that the user has defined. It is permissible to use distinct names. To remove all assigned categories you can provide an empty string as value.

Let's also specify that the user can provide an empty string to clear all categories.

Would like to work on this one!


No free volumes left

how can i fix this error?

Starts volume server with a bigger number for "-max"

@chrislusf
i started weed with this option
weed volume -dir=/home/vagrant/data -max=10
sudo weed master --volumeSizeLimitMB 20000 &
what does mean of '-max'?

Please use "weed volume -h" to see this and other options.
On Thu, Aug 20, 2015 at 11:06 PM, jaemoon,lee<EMAIL_ADDRESS>wrote:

@chrislusf https://github.com/chrislusf
i started weed with this option
weed volume -dir=/home/vagrant/data -max=10
sudo weed master --volumeSizeLimitMB 20000 &
what does mean of '-max'?
—
Reply to this email directly or view it on GitHub
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.

-max="7": maximum numbers of volumes, count[,count]...
yes. is it important volume numbers when i upload files or total limit size?
@chrislusf

this is the number of volumes you allocate to the volume server.
On Thu, Aug 20, 2015 at 11:08 PM, jaemoon,lee<EMAIL_ADDRESS>wrote:

-max="7": maximum numbers of volumes, count[,count]...
yes. is it important volume numbers when i upload files or total limit
size?
@chrislusf https://github.com/chrislusf
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Reply to this email directly or view it on GitHub
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I have two computers running weed  . <IP_ADDRESS> and <IP_ADDRESS>
<IP_ADDRESS> option:   ./weed master
<IP_ADDRESS> option: ./weed volume -dir="/tmp/v1" -max=10 -mserver="<IP_ADDRESS>" -port=8080
but then i run curl -X POST http://localhost:9333/dir/assign   in<PHONE_NUMBER>1
i have a error  " no free volume left!"
help help ..

-mserver="<IP_ADDRESS>:9333"


Fixed typo

There is a typo changit ge. Fixed it to the word change

Looks good. Thanks @bubbletroubles


In cluster provisioning with RBAC

What problem are you facing?
It is very possible to use provider-helm for in-cluster provisioning as a regular helm controller instead of deploying into external cluster. Today, this could be realized by putting kubeconfig of the local cluster into a secret and creating a ProviderConfig pointing to that secret. However, this approach is not the expected way to communicate/authenticate cluster api from inside the cluster.

How could Crossplane help solve your problem?
Is should be possible to do in cluster provisioning by relying on RBAC.
This could be achievable by introducing a new credentials.source, e.g. "InCluster" and configuring provider controller with proper service account during deployment.

This would be very useful indeed.


TypeError: from_pretrained() missing 1 required positional argument: 'model_id'

Hello @hkonsti @justusmattern27
I've successfully fine-tuned the Llama2-13b-chat-hf model using Llamatune. The fine-tuning process went well, and I was able to fine-tune my model. However, when attempting to run the fine-tuned model using PEFT, I encountered the following error:
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (2.0.4) or chardet (3.0.4) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
Traceback (most recent call last):
  File "gradio.py", line 7, in <module>
    peft_model = PeftModel.from_pretrained("my_finetunedmodel_path_on_huggingface", use_auth_token="xxxxx")
TypeError: from_pretrained() missing 1 required positional argument: 'model_id'

Here is my code that i used to run the model:
config = PeftConfig.from_pretrained("my_finetunedmodel_path_on_huggingface")
peft_model = PeftModel.from_pretrained("my_finetunedmodel_path_on_huggingface", use_auth_token="xxxxx">
lm_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf", use_auth_token="xxxxx")
# Define the function that combines the models
def generate_response(text):
    peft_output = peft_model.generate(text)
    peft_response = peft_output[0]['generated_text']

    lm_input = peft_response + " "
    lm_output = lm_model.generate(lm_input, max_length=50, num_return_sequences=1)
    lm_response = lm_output[0]['generated_text']

    return lm_response

It appears that I'm encountering an issue with the from_pretrained function in the PeftModel class, where it's expecting a 'model_id' argument. I'm uncertain about how to resolve this issue. Any insights or suggestions would be greatly appreciated.

When using PeftModel.from_pretrained, you need to specify two things: the base model on which you want to put the adapters, as well as the weights of the adapters. In this case (assuming that you fine-tuned the model with lora), you are only specifying the adapter weights, not the base model. See here for the documentation: https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel.from_pretrained


Installation failure

Running 64-bit Chrome, version number 78.0.3904.108. I attempted to install ARChon in hopes of running an app, and got two errors:

Line 5:   "minimum_chrome_version": "32", returned 'minimum_chrome_version' is only allowed for extensions, legacy packaged apps, hosted apps, and packaged apps, but this is a shared module.
Line 8:  "arc_build_tag": "arc-runtime-38.4410.120.43", returned Unrecognized manifest key 'arc_build_tag'.

I think the project has been abandoned.  Just as well, I hate having to run GoogleChrome for anything anyway.

Can you believe there's even sites still listing this as a "best" Android runtime, even for 2021?   Some bloggers can't be bothered to do some actual WORK before they spout off on the page.


ldap: Continue syncing other fields even if a field is missing.

Earlier the behavior was to raise an exception thereby stopping the
whole sync. Now we log an error message and skip the field. Also
fixes the query_ldap command to report missing fields without
error.
Fixes: #11780.

This looks great, merged, thanks @HarshitOnGitHub!  Though after reading it, I'm thinking we should migrate the main query_ldap function to zproject/backends.py and add full unit test coverage, since it's gotten complex enough that manual testing isn't really a satisfactory way to QA it.

Opened #11841 for addressing your comment. :)