[Bug]: AssertionError: libcuda.so cannot found with vllm/vllm-openai:v0.4.0

Your current environment
Running in Kubernetes on H100 in vllm/vllm-openai:v0.4.0
🐛 Describe the bug
Seems like there have been some weird dependency issues since v0.2.7. We would love to use flash attention when you are able to fix this! Thank you! 🙇
INFO 04-02 23:55:08 api_server.py:148] vLLM API server version 0.4.0
INFO 04-02 23:55:08 api_server.py:149] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=True, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, served_model_name=None, lora_modules=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='mistralai/Mixtral-8x7B-v0.1', tokenizer=None, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', max_model_len=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=2, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, use_v2_block_manager=False, seed=0, swap_space=4, gpu_memory_utilization=0.9, forced_num_gpu_blocks=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=5, disable_log_stats=False, quantization=None, enforce_eager=False, max_context_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', max_cpu_loras=None, device='auto', image_input_type=None, image_token_id=None, image_input_shape=None, image_feature_size=None, scheduler_delay_factor=0.0, enable_chunked_prefill=False, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
2024-04-02 23:55:10,082	INFO worker.py:1752 -- Started a local Ray instance.
INFO 04-02 23:55:10 llm_engine.py:75] Initializing an LLM engine (v0.4.0) with config: model='mistralai/Mixtral-8x7B-v0.1', tokenizer='mistralai/Mixtral-8x7B-v0.1', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, seed=0)
INFO 04-02 23:55:16 selector.py:16] Using FlashAttention backend.
(RayWorkerVllm pid=2375) INFO 04-02 23:55:16 selector.py:16] Using FlashAttention backend.
INFO 04-02 23:55:18 pynccl_utils.py:45] vLLM is using nccl==2.18.1
(RayWorkerVllm pid=2375) INFO 04-02 23:55:18 pynccl_utils.py:45] vLLM is using nccl==2.18.1
INFO 04-02 23:55:28 weight_utils.py:177] Using model weights format ['*.safetensors']
(RayWorkerVllm pid=2375) INFO 04-02 23:55:29 weight_utils.py:177] Using model weights format ['*.safetensors']
INFO 04-02 23:55:39 model_runner.py:104] Loading model weights took 43.5064 GB
(RayWorkerVllm pid=2375) INFO 04-02 23:55:45 model_runner.py:104] Loading model weights took 43.5064 GB
INFO 04-02 23:55:50 fused_moe.py:272] Using configuration from /workspace/vllm/model_executor/layers/fused_moe/configs/E=8,N=7168,device_name=NVIDIA_H100_80GB_HBM3.json for MoE layer.
Traceback (most recent call last):
  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/workspace/vllm/entrypoints/openai/api_server.py", line 156, in <module>
    engine = AsyncLLMEngine.from_engine_args(
  File "/workspace/vllm/engine/async_llm_engine.py", line 348, in from_engine_args
    engine = cls(
  File "/workspace/vllm/engine/async_llm_engine.py", line 311, in __init__
    self.engine = self._init_engine(*args, **kwargs)
  File "/workspace/vllm/engine/async_llm_engine.py", line 422, in _init_engine
    return engine_class(*args, **kwargs)
  File "/workspace/vllm/engine/llm_engine.py", line 111, in __init__
    self.model_executor = executor_class(model_config, cache_config,
  File "/workspace/vllm/executor/ray_gpu_executor.py", line 65, in __init__
    self._init_cache()
  File "/workspace/vllm/executor/ray_gpu_executor.py", line 220, in _init_cache
    num_blocks = self._run_workers(
  File "/workspace/vllm/executor/ray_gpu_executor.py", line 324, in _run_workers
    driver_worker_output = getattr(self.driver_worker,
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/workspace/vllm/worker/worker.py", line 131, in profile_num_available_blocks
    self.model_runner.profile_run()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/workspace/vllm/worker/model_runner.py", line 742, in profile_run
    self.execute_model(seqs, kv_caches)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/workspace/vllm/worker/model_runner.py", line 663, in execute_model
    hidden_states = model_executable(**execute_model_kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/vllm/model_executor/models/mixtral.py", line 379, in forward
    hidden_states = self.model(input_ids, positions, kv_caches,
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/vllm/model_executor/models/mixtral.py", line 315, in forward
    hidden_states, residual = layer(positions, hidden_states,
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/vllm/model_executor/models/mixtral.py", line 274, in forward
    hidden_states = self.block_sparse_moe(hidden_states)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/vllm/model_executor/models/mixtral.py", line 128, in forward
    final_hidden_states = fused_moe(hidden_states,
  File "/workspace/vllm/model_executor/layers/fused_moe/fused_moe.py", line 397, in fused_moe
    invoke_fused_moe_kernel(hidden_states, w1, intermediate_cache1,
  File "/workspace/vllm/model_executor/layers/fused_moe/fused_moe.py", line 222, in invoke_fused_moe_kernel
    fused_moe_kernel[grid](
  File "<string>", line 63, in fused_moe_kernel
  File "/usr/local/lib/python3.10/dist-packages/triton/compiler/compiler.py", line 425, in compile
    so_path = make_stub(name, signature, constants)
  File "/usr/local/lib/python3.10/dist-packages/triton/compiler/make_launcher.py", line 39, in make_stub
    so = _build(name, src_path, tmpdir)
  File "/usr/local/lib/python3.10/dist-packages/triton/common/build.py", line 61, in _build
    cuda_lib_dirs = libcuda_dirs()
  File "/usr/local/lib/python3.10/dist-packages/triton/common/build.py", line 30, in libcuda_dirs
    assert any(os.path.exists(os.path.join(path, 'libcuda.so')) for path in dirs), msg
AssertionError: libcuda.so cannot found!

Check / set your CUDA_PATH environment variable

Your current environment
Running in Kubernetes on H100 in vllm/vllm-openai:v0.4.0
🐛 Describe the bug
Seems like there have been some weird dependency issues since v0.2.7. We would love to use flash attention when you are able to fix this! Thank you! 🙇
INFO 04-02 23:55:08 api_server.py:148] vLLM API server version 0.4.0
INFO 04-02 23:55:08 api_server.py:149] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=True, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, served_model_name=None, lora_modules=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='mistralai/Mixtral-8x7B-v0.1', tokenizer=None, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', max_model_len=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=2, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, use_v2_block_manager=False, seed=0, swap_space=4, gpu_memory_utilization=0.9, forced_num_gpu_blocks=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=5, disable_log_stats=False, quantization=None, enforce_eager=False, max_context_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', max_cpu_loras=None, device='auto', image_input_type=None, image_token_id=None, image_input_shape=None, image_feature_size=None, scheduler_delay_factor=0.0, enable_chunked_prefill=False, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
2024-04-02 23:55:10,082	INFO worker.py:1752 -- Started a local Ray instance.
INFO 04-02 23:55:10 llm_engine.py:75] Initializing an LLM engine (v0.4.0) with config: model='mistralai/Mixtral-8x7B-v0.1', tokenizer='mistralai/Mixtral-8x7B-v0.1', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, seed=0)
INFO 04-02 23:55:16 selector.py:16] Using FlashAttention backend.
(RayWorkerVllm pid=2375) INFO 04-02 23:55:16 selector.py:16] Using FlashAttention backend.
INFO 04-02 23:55:18 pynccl_utils.py:45] vLLM is using nccl==2.18.1
(RayWorkerVllm pid=2375) INFO 04-02 23:55:18 pynccl_utils.py:45] vLLM is using nccl==2.18.1
INFO 04-02 23:55:28 weight_utils.py:177] Using model weights format ['*.safetensors']
(RayWorkerVllm pid=2375) INFO 04-02 23:55:29 weight_utils.py:177] Using model weights format ['*.safetensors']
INFO 04-02 23:55:39 model_runner.py:104] Loading model weights took 43.5064 GB
(RayWorkerVllm pid=2375) INFO 04-02 23:55:45 model_runner.py:104] Loading model weights took 43.5064 GB
INFO 04-02 23:55:50 fused_moe.py:272] Using configuration from /workspace/vllm/model_executor/layers/fused_moe/configs/E=8,N=7168,device_name=NVIDIA_H100_80GB_HBM3.json for MoE layer.
Traceback (most recent call last):
  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/workspace/vllm/entrypoints/openai/api_server.py", line 156, in <module>
    engine = AsyncLLMEngine.from_engine_args(
  File "/workspace/vllm/engine/async_llm_engine.py", line 348, in from_engine_args
    engine = cls(
  File "/workspace/vllm/engine/async_llm_engine.py", line 311, in __init__
    self.engine = self._init_engine(*args, **kwargs)
  File "/workspace/vllm/engine/async_llm_engine.py", line 422, in _init_engine
    return engine_class(*args, **kwargs)
  File "/workspace/vllm/engine/llm_engine.py", line 111, in __init__
    self.model_executor = executor_class(model_config, cache_config,
  File "/workspace/vllm/executor/ray_gpu_executor.py", line 65, in __init__
    self._init_cache()
  File "/workspace/vllm/executor/ray_gpu_executor.py", line 220, in _init_cache
    num_blocks = self._run_workers(
  File "/workspace/vllm/executor/ray_gpu_executor.py", line 324, in _run_workers
    driver_worker_output = getattr(self.driver_worker,
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/workspace/vllm/worker/worker.py", line 131, in profile_num_available_blocks
    self.model_runner.profile_run()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/workspace/vllm/worker/model_runner.py", line 742, in profile_run
    self.execute_model(seqs, kv_caches)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/workspace/vllm/worker/model_runner.py", line 663, in execute_model
    hidden_states = model_executable(**execute_model_kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/vllm/model_executor/models/mixtral.py", line 379, in forward
    hidden_states = self.model(input_ids, positions, kv_caches,
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/vllm/model_executor/models/mixtral.py", line 315, in forward
    hidden_states, residual = layer(positions, hidden_states,
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/vllm/model_executor/models/mixtral.py", line 274, in forward
    hidden_states = self.block_sparse_moe(hidden_states)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/workspace/vllm/model_executor/models/mixtral.py", line 128, in forward
    final_hidden_states = fused_moe(hidden_states,
  File "/workspace/vllm/model_executor/layers/fused_moe/fused_moe.py", line 397, in fused_moe
    invoke_fused_moe_kernel(hidden_states, w1, intermediate_cache1,
  File "/workspace/vllm/model_executor/layers/fused_moe/fused_moe.py", line 222, in invoke_fused_moe_kernel
    fused_moe_kernel[grid](
  File "<string>", line 63, in fused_moe_kernel
  File "/usr/local/lib/python3.10/dist-packages/triton/compiler/compiler.py", line 425, in compile
    so_path = make_stub(name, signature, constants)
  File "/usr/local/lib/python3.10/dist-packages/triton/compiler/make_launcher.py", line 39, in make_stub
    so = _build(name, src_path, tmpdir)
  File "/usr/local/lib/python3.10/dist-packages/triton/common/build.py", line 61, in _build
    cuda_lib_dirs = libcuda_dirs()
  File "/usr/local/lib/python3.10/dist-packages/triton/common/build.py", line 30, in libcuda_dirs
    assert any(os.path.exists(os.path.join(path, 'libcuda.so')) for path in dirs), msg
AssertionError: libcuda.so cannot found!

I have same issue, how you to fix it?

You may want to run your job on docker with specified cuda 11.8 or 12.1

I've tested multiple versions.
This issue didn't occur until version 0.2.7, but it started happening from version 0.3.0 onwards.
From examining the error logs, it seems there's an issue with the changed Fused MOE for Mixtral part in version 0.3.0.

This is not a problem of vllm, but a problem that triton cannot find libcuda.so .
In general, libcuda.so comes with nvidia drivers. And driver is usually configed by physical machine rather than containers. I would suggest reaching out to your k8s admin, to ask them where do they put/mount the nvidia driver inside containers.


Error parsing C++ cast operator

When I try to use dynamic_cast on a pointer type, I get a compiler error:
Error compiling Cython file:
------------------------------------------------------------
...
    property bit_width:

        def __get__(self):
            cdef CFixedWidthType* ty
            try:
                ty = dynamic_cast<CFixedWidthType*>(self.type)
                                                 ^
------------------------------------------------------------

/home/antoine/arrow/python/pyarrow/types.pxi:98:50: Expected an identifier or literal

If I workaround it with a ctypedef CFixedWidthType* _CFixedWidthTypePtr and then dynamic_cast[_CFixedWidthTypePtr], it runs fine.

dynamic_cast is defined as a templated function in libcpp.cast. To fit into Python's syntax, C++ templates are spelled with brackets in Cython, i.e. write dynamic_cast[type](arg). The normal C++ syntax would be very difficult to distinguish from a comparison.

Please, can you reopen? It doesn't work either:
Error compiling Cython file:
------------------------------------------------------------
...

    property bit_width:

        def __get__(self):
            cdef CFixedWidthType* ty
            ty = dynamic_cast[CFixedWidthType*](self.type)
                                             ^
------------------------------------------------------------

/home/antoine/arrow/python/pyarrow/types.pxi:102:46: Expected an identifier or literal

The problem is that at parse time, the content of dynamic_cast[...] is not special in any way, and it's currently parsed as subscript and not as type declaration, and subscript indices don't allow for a pointer-star at the end.
Thus, a typedef is currently the best way to deal with this syntactic specialty.
But I agree that it's a bug from the point of view of the language and should eventually get fixed.

Thus, a typedef is currently the best way to deal with this syntactic specialty.
But I agree that it's a bug from the point of view of the language and should eventually get fixed.

There is separate bug about pointers in template arguments already: https://github.com/cython/cython/issues/534.


[Pattern 2] Expose HTTP ports in Analytics nodes

Description:
HTTP port 9091, HTTPS 9444 need to be exposed in the current deployment as a new route.
Affected Product Version:
APIM analytics 2.6.0

Fixed in https://github.com/wso2/pivotal-cf-apim/pull/101.


11 boss chicken gets no damage

This pull request includes several enhancements and bug fixes across various files to improve the game's functionality and user experience. The most important changes include adding a restart button for mobile, refining character and boss behaviors, and fixing collision detection.
User Interface Enhancements:

Added a restart button for mobile users in index.html and styled it in style.css and responsive.css. [1] [2] [3]

Character and Boss Behavior:

Modified the Character class to only play the snooze sound if audio is enabled in models/character.class.js.
Updated the Endboss class to include an additional random action for movement in models/endboss.class.js.

Collision Detection and Game Logic:

Improved bottle collision detection by adding checkBottleHit method in models/throwable-object.class.js and updated related logic in models/world.class.js. [1] [2] [3] [4] [5] [6] [7]

a few small errors were found is entered in projects


Optimize getting last logon

Doing it all in one pass through the parsed log. Since the log
can be huge, we can't go thorugh the array for each user.
In my testing 80MB log had 210k of parsed fields.
WIP weird thing is when I test this, the parser doesn't see user
names in the log, so it actually doesn't work.
GH Issue:
https://github.com/ManageIQ/manageiq/issues/4763

@gmcculloug @roliveri Please review (even though it's WIP)

@gmcculloug @roliveri my main problem now is, that the code is not working for me (nor the original nor the fixed)
The parsed list doesn't have any recognized users in it. But when I do last -f /var/log/wtmp on the server, the info about logged users is there
So I guess there is a problem with parsing of the file? Though I remember seeing the last login in drift few months ago. :-)

This code is used during VM smart state analysis where the file-system handle passed in is MiqFS instead of a live system so last would not be available.  For example here in MIQExtract.rb.

@miq-bot remove_label wip

@gmcculloug it should be ready, can you tests this? The QE also hit this, so I guess we should backport it. Now sure why anybody hasn't hit this. What takes parsing one minute with this patch takes over hour without.
Also I am afraid it just timeouts somewhere, does SSA have timeout?
Also I am creating another issue, cause parsing of the wtmp log file is wrong, it doesn't parse all the records. I think it's because we parse by 384 bytes by hard, but e.g. from perl scripts i found, they do foreach ( split( /(.{384})/s, $recs ) ) {

@roliveri can you or your team review/test?

@roliveri check if you notice also this please https://github.com/ManageIQ/manageiq/issues/5197


Update release.yml

Update the release.yml file with the latest label changes

@thomasvl This is something we have been using in all of our server related repos. You can add a label to a PR and when drafting release notes GitHub can automatically fill them into the right groups.

@FranzBusch do you want to add a note to https://github.com/apple/swift-protobuf/blob/main/Documentation/STYLE_GUIDELINES.md about adding them?


Update readme to reflect WatchOS support for Realtime Database

This PR just updates the SwiftPackageManager readme to reflect WatchOS support for RealtimeDB

@googlebot I signed it!


Exposed to Log4J vulnerability ?

Hi,
What's the exposition of this driver to the following vulnerability?
https://github.com/metabase/metabase/releases/tag/v0.41.4
Thanks for your insight. :)
Best regards,
Memento.

Hi @memento , the driver should work fine with the 0.41.4 release of Metabase, which includes a fix for the log4j exploit. The driver itself does not bundle any logging framework.


Additionally publish to Open VSX

Does what it says on the tin. Not sure if we want to also bump the patch version and publish a release for these changes or not? But otherwise, hopefully the updates are fairly self explanatory. Open VSX publishing implemented per their documentation.

Reminder. Repo now contains graph analyzer updater so a new version that gets published will include this feature.


bin/warp transpile solc_file -o .  is not working anymore

bin/warp transpile solc_file -o .  is not working anymore, it used to work before. Possibly because of the dir name clash with the solidity file name itself.
Error: ENOTDIR: not a directory, open 'order_of_eval.sol/A_sol_abi.json'
    at Object.openSync (node:fs:599:3)
    at Object.writeFileSync (node:fs:2221:35)
    at Object.outputFileSync (/Users/rohit/nmd/warp/node_modules/fs-extra/lib/output-file/index.js:31:15)
    at outputResult (/Users/rohit/nmd/warp/build/io.js:104:12)
    at /Users/rohit/nmd/warp/build/index.js:73:35
    at Array.map (<anonymous>)
    at Command.<anonymous> (/Users/rohit/nmd/warp/build/index.js:72:14)
    at Command.listener [as _actionHa

...

#852


Is this a good design?

I am a developer who use threejs somewhere.
I've discovered  playcanvas and felt its sample  pretty good and BIG options instead of threejs
Howerver, when I check sample code, I feel something about this:
application.context.systems.model.addComponent
just question, is this a good design which call DEEPLY into child?
Thank you

We've streamlined the Entity/Component API in the latest release.
Now the code looks like this:
    var entity = new pc.Entity();
    entity.addComponent("camera");
    entity.addComponent("light", {
        type: "point"
    });

Much better!


Proxifying Facebook Bridge (or any bridge)

FACEBOOK's FIREWALL blocks repeated access to pages based on IP with the CAPTCHA challenge.  The blocking rules also seem to be tripped more frequently by requests using nonstandard parameters such as "_fb_noscript=1".
I have appended a randomized HTTPS proxy load leveler to the pertinent requests to overcome the issue.
NOTE: the seed proxy list will probably be short lived, as Facebook monitors this application and will probably add these proxies to their blacklist pretty quickly.  You can scour the web for free proxies and check them (i am using http://proxychecker.net/ downloaded app to check them), or buy a private list of proxies.  You will want the HTTPS, Anonymous (or Elite) level proxies for this...
This first part sets up the supporting functions.  It creates 2 files for you in the bridge root folder:
proxies.txt - a serialized array variable containing all the proxies you are using.
proxylog.txt - a log of proxy failures you can use to weed out your proxy list.
If you wish to change your proxy list, edit the "MANUALLY SEED HTTPS PROXIES HERE" list and delete your proxies.txt file.  The reason these are in external files is so they can be shared and managed by multiple bridge scripts.
Before you ask, I am using PHP5.4 compatible class variable syntax instead of the 5.6 faux constant syntax for compatibility reasons (see my post concerning 5.4 downgrade script).
On or About Line 17
Change:
	private $authorName='';

To:
	private $authorName='';

	public static $PROXIES = array();
	public static $PROXYFILE = '';
	public static $PROXYLOG = '';

	public function logproxy($proxy){
		self::$PROXYLOG = dirname(__DIR__).'/proxylog.txt';
		file_put_contents(self::$PROXYLOG, 'Proxy: '.$proxy.' failed '.date('r').".\n", FILE_APPEND | LOCK_EX);
	}

	public function writeproxy(){
		if ((self::$PROXIES)&&(count(self::$PROXIES))) {
			self::$PROXYFILE = dirname(__DIR__).'/proxies.txt';
			file_put_contents(self::$PROXYFILE, serialize(self::$PROXIES), FILE_APPEND | LOCK_EX);
		}
	}

	public function getproxy(){
		self::$PROXYFILE = dirname(__DIR__).'/proxies.txt';
		srand((double)microtime()*1000000);
		srand((double)microtime()*rand(1000000,9999999));
		if ((!self::$PROXIES)||(count(self::$PROXIES) < 1)) {
			if (!file_exists(self::$PROXYFILE)) {
				// 	MANUALLY SEED HTTPS PROXIES HERE
				/* "{IP ADDRESS}:{PORT}", */
				self::$PROXIES = array(
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:8118",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:8137",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:8080",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:80",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:8088",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:80",
					"<IP_ADDRESS>:80",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
					"<IP_ADDRESS>:3128",
				);
				$this->writeproxy();
			}
			self::$PROXIES = unserialize(file_get_contents(self::$PROXYFILE));
		}
		return (self::$PROXIES)?self::$PROXIES[rand(0,count(self::$PROXIES)-1)]:NULL;
	}

This section implements content retrieval using HTTPS proxies.  Calls are load leveled by randomly selecting a proxy from the list to use.  This prevents blacklisting of the proxy allowing you automated access without tripping the CAPTCHA challenge via Facebook firewall.
Note that the request URI has been modified to remove the telltale _fb_noscript parameter.  This seems to work fine.   Also notice that the original request remains after the proxified request section to act as a fall-through algorithm.
On or about line 170
Change:
		//Retrieve page contents

To:

		//Retrieve page contents via proxy
		if (is_null($html)) {
			// set up URI
			if (!strpos($this->getInput('u'), "/")) {
				//$_u = 'https://m.facebook.com/'.urlencode($this->getInput('u'));
				$_u = self::URI.urlencode($this->getInput('u'));
			} else {
				//$_u = 'https://m.facebook.com/'.'pages/'.$this->getInput('u');
				$_u = self::URI.'pages/'.$this->getInput('u');
			}
			if ($_proxy = $this->getproxy()) {
				// set up proxy
				$_context = array('http' => array('proxy' => 'tcp://'.$_proxy,'request_fulluri' => true,),);
				$_stream = stream_context_create($_context);
				// GET page
				if (!($html = file_get_html($_u, false, $_stream))) {
					$this->logproxy($_proxy);
					returnServerError("Proxy: $_proxy \nURL: $_u \nNo results for this query.");
				}
			}
		}

		//Retrieve page contents

Finally, in this section, we remove telltale signatures from the request URI which can be used to trip the Facebook firewall...
On or about line 282 find:
					//Build and add final item
					$item['uri'] = self::URI.$post->find('abbr')[0]->parent()->getAttribute('href');

Replace this with:
					//Build and add final item
					//$item['uri'] = self::URI.$post->find('abbr')[0]->parent()->getAttribute('href');
					$item['uri'] = preg_replace(array(
							"/\&amp\;/i",
							"/.com\/\//"
						), array(
							"&",
							 ".com/"
						), self::URI.$post->find('abbr')[0]->parent()->getAttribute('href'));

You can add the first section to any bridge, then use the methods in sections commented by "// set up proxy" and "// GET page" above to proxify your request.
DEVELOPER NOTE: use m.facebook.com!
m.facebook.com may be the better option when scraping content, as this allows for sparse browser header information without tripping the firewall.  It also is more consistently formatted for better content based DOM scanning.  I highly recommend rewriting this to use the mobile site (** and convert the output URL's **).
Comments or questions: get at me.
Cheers,
ghostofzigz

You can add the first section to any bridge...

This is actually a very important fact about your solution :grin:
I don't think this is something we should put directly into the Facebook bridge to be honest. Instead a more general approach can be done:
Right now RSS-Bridge supports a single proxy via https://github.com/RSS-Bridge/rss-bridge/blob/master/index.php#L16-L20. By incorporating your approach this can be made a list of proxies from which RSS-Bridge can choose automatically.

indeed.  that is the proper approach to cover all bridges generically.
There is another important/related factor I am working on now... https://github.com/RSS-Bridge/rss-bridge/issues/474


Blog theme integration

I added some Blog views to allow customizing layout, removing widgets from the right side and setting container class for Bootstrap rendering. Is this necessary?

in general yes, the approach to customize any view is to copy it locally.
since the only change you made in Index.cshtml and Post.cshtml is adding the container class, I thought maybe I could add that class into the included view if it didn't cause any issue with the bootswatch themes I'm shipping but, it did throw off the layout.
I don't expect those views to change much or often so maintaining local copies shouldn't be a problem. If there are any needed changes for those in future versions I could keep you posted and help update them. Eventually will ship all new views for bootstrap4 as a separate nuget anyway.
Actually I think we should also include local copies of BlogScriptsPartial.cshtml and BlogStylePartial.cshtml in order to add syntax highlighter for blog posts, I do that on my own sites as well and can set that up if you like.
Actually by having the scripts view locally, an alternative would be to use js to add the extra container class without having to keep Index and Post views locally.  Just depends on which approach appeals to you more.


sensitive to 3-BrB-PP1. PMID:23986474

I think at present

3-BrB-PP1
is an exact synonym of terms for
sensitive to 1-NA-PP1" (FYPO:0000789)

but these are different inhibitors (with different phenotypic effects)
I requested a CHEBI term for 3-BrB-PP1
https://github.com/ebi-chebi/ChEBI/issues/4311

(CHEBI:194136).


cannot download v1.17.15

What happened:
When I tried to download 1.17.15, 1.17.16-rc0 was downloaded.
What you expected to happen:
I expect 1.17.15 to be downloaded
How to reproduce it (as minimally and precisely as possible):
$ curl -O https://storage.googleapis.com/kubernetes-release/release/v1.17.15/bin/linux/amd64/kubectl
$ chmod +x kubectl
$ ./kubectl version --short
Client Version: v1.17.16-rc.0
The connection to the server localhost:8080 was refused - did you specify the right host or port?

Anything else we need to know?:
Environment:

Kubernetes client and server versions (use kubectl version):
Cloud provider or hardware configuration:
OS (e.g: cat /etc/os-release): Alpine Linux v3.12

Thanks for reporting. Looks like folks are working on addressing it.
https://github.com/kubernetes/kubernetes/issues/97178
Let's track there.
/close


WFLY-5390 Fix ClusteredMessagingTestCase: ensure cluster topology is established before running the tests

Resolves
https://issues.redhat.com/browse/WFLY-5390

@pferraro if you have a minute... thanks!

I'm going to wait for a number of CI runs of https://github.com/wildfly/wildfly/pull/14492 - which may make these changes unnecessary.

@pferraro Lets stick a hold label on this until then.

The https://github.com/wildfly/wildfly/pull/14492 should resolve this - we can reopen this if really needed. Closing now.


Increasing timeout to elasticsearch to be up

Sometimes, starting azk fails due to elasticsearch timeout. Giving it a little more time to be up should do the trick and avoid this issue.

Already implemented.


Validate special symbols

Is it possible to also validate special characters in fields?
Specifically, I'm trying to reject expressions using syntax for stepping like 1-59/3 * * * *, 2/9 * * * * *
Reason is, none of the cron matching libraries I've found support them properly, so I would like to should the user a warning if they input such expression.

So you basically want the option to disable stepping as a valid expression so it raises an error?
I can take a look and change the option / configuration to include such an option.

Yes, basically exactly that.


DNN library initialization failed.

Description
I tried run AlphaFold-Multimer on GPU, after the parameters and inputs are loaded, the running stopped by raising: jaxlib.xla_extension.XlaRuntimeError: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.
I can't find out the exact error information from the error message I have. Following is the errors:

I0425 13:55:15.099713<PHONE_NUMBER>89760 af_multimer_multiseed.py:552] Running model model_1_multimer_v2_pred_0
I0425 13:55:15.100178<PHONE_NUMBER>89760 model.py:165] Running predict with shape(feat) = {'aatype': (247,), 'residue_index': (247,), 'seq_length': (), 'msa': (4096, 247), 'num_alignments': (), 'template_all_atom_positions': (4, 247, 37, 3), 'template_all_atom_mask': (4, 247, 37), 'template_aatype': (4, 247), 'asym_id': (247,), 'sym_id': (247,), 'entity_id': (247,), 'deletion_matrix': (4096, 247), 'deletion_mean': (247,), 'all_atom_mask': (247, 37), 'all_atom_positions': (247, 37, 3), 'assembly_num_chains': (), 'entity_mask': (247,), 'num_templates': (), 'cluster_bias_mask': (4096,), 'bert_mask': (4096, 247), 'seq_mask': (247,), 'msa_mask': (4096, 247)}
2023-04-25 13:55:17.465601: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:417] Loaded runtime CuDNN library: 8.4.1 but source was compiled with: 8.6.0.  CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library.  If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration.
Traceback (most recent call last):
File "/home/imhaoyu/project/alphafold/multiseed/af_multimer_multiseed.py", line 676, in
app.run(main)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/absl/app.py", line 308, in run
_run_main(main, args)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/absl/app.py", line 254, in _run_main
sys.exit(main(argv))
File "/home/imhaoyu/project/alphafold/multiseed/af_multimer_multiseed.py", line 666, in main
predict_structure(FLAGS.output_dir, model_runners, random_seed, multimer_feature, FLAGS.num_prediction, FLAGS.model_preset, FLAGS.enable_single_seed, FLAGS.single_seed)
File "/home/imhaoyu/project/alphafold/multiseed/af_multimer_multiseed.py", line 564, in predict_structure
prediction_result = model_runner.predict(processed_feature_dict,
File "/home/imhaoyu/project/alphafold/alphafold/alphafold/model/model.py", line 167, in predict
result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/random.py", line 136, in PRNGKey
key = prng.seed_with_impl(impl, seed)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/prng.py", line 270, in seed_with_impl
return random_seed(seed, impl=impl)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/prng.py", line 561, in random_seed
return random_seed_p.bind(seeds_arr, impl=impl)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/core.py", line 360, in bind
return self.bind_with_trace(find_top_trace(args), args, params)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/core.py", line 363, in bind_with_trace
out = trace.process_primitive(self, map(trace.full_raise, args), params)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/core.py", line 817, in process_primitive
return primitive.impl(*tracers, **params)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/prng.py", line 573, in random_seed_impl
base_arr = random_seed_impl_base(seeds, impl=impl)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/prng.py", line 578, in random_seed_impl_base
return seed(seeds)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/prng.py", line 813, in threefry_seed
lax.shift_right_logical(seed, lax_internal._const(seed, 32)))
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/lax/lax.py", line 458, in shift_right_logical
return shift_right_logical_p.bind(x, y)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/core.py", line 360, in bind
return self.bind_with_trace(find_top_trace(args), args, params)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/core.py", line 363, in bind_with_trace
out = trace.process_primitive(self, map(trace.full_raise, args), params)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/core.py", line 817, in process_primitive
return primitive.impl(*tracers, **params)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/dispatch.py", line 117, in apply_primitive
compiled_fun = xla_primitive_callable(prim, *unsafe_map(arg_spec, args),
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/util.py", line 253, in wrapper
return cached(config._trace_context(), *args, **kwargs)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/util.py", line 246, in cached
return f(*args, **kwargs)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/dispatch.py", line 208, in xla_primitive_callable
compiled = _xla_callable_uncached(lu.wrap_init(prim_fun), prim.name,
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/dispatch.py", line 254, in _xla_callable_uncached
return computation.compile(_allow_propagation_to_outputs=allow_prop).unsafe_call
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/interpreters/pxla.py", line 2816, in compile
self._executable = UnloadedMeshExecutable.from_hlo(
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/interpreters/pxla.py", line 3028, in from_hlo
xla_executable = dispatch.compile_or_get_cached(
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/dispatch.py", line 526, in compile_or_get_cached
return backend_compile(backend, serialized_computation, compile_options,
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/profiler.py", line 314, in wrapper
return func(*args, **kwargs)
File "/home/imhaoyu/miniconda3/envs/alpha_mul_v3/lib/python3.8/site-packages/jax/_src/dispatch.py", line 471, in backend_compile
return backend.compile(built_c, compile_options=options)
jaxlib.xla_extension.XlaRuntimeError: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.

What jax/jaxlib version are you using?
jax 0.4.8
Which accelerator(s) are you using?
GPU
Additional system info
No response
NVIDIA GPU info
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01    Driver Version: 515.65.01    CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA RTX A6000    Off  | 00000000:81:00.0 Off |                  Off |
| 43%   70C    P2   111W / 300W |  10575MiB / 49140MiB |    100%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA RTX A6000    Off  | 00000000:82:00.0 Off |                  Off |
| 30%   52C    P8    34W / 300W |    123MiB / 49140MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   2  NVIDIA RTX A6000    Off  | 00000000:C1:00.0 Off |                  Off |
| 32%   61C    P8    38W / 300W |      8MiB / 49140MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   3  NVIDIA RTX A6000    Off  | 00000000:C2:00.0 Off |                  Off |
| 34%   62C    P8    41W / 300W |      8MiB / 49140MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      2282      G   /usr/lib/xorg/Xorg                  4MiB |
|    1   N/A  N/A      2282      G   /usr/lib/xorg/Xorg                 64MiB |
|    1   N/A  N/A      2592      G   /usr/bin/gnome-shell                7MiB |
|    2   N/A  N/A      2282      G   /usr/lib/xorg/Xorg                  4MiB |
|    3   N/A  N/A      2282      G   /usr/lib/xorg/Xorg                  4MiB |
+-----------------------------------------------------------------------------+

Version: 0.4.7+cuda11.cudnn86

The error message explains the problem:
 Loaded runtime CuDNN library: 8.4.1 but source was compiled with: 8.6.0. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library.

Upgrade your CuDNN installation?

Thanks! Problem solved. I upgraded the installation by running
# Installs the wheel compatible with Cuda 11.4+ and cudnn 8.2+ (deprecated). pip install "jax[cuda11_cudnn82]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
Following https://github.com/google/jax#installation


INS-1375: Update to work with modern Webpack

This update imposes using the npm Buffer package regardless of whether we're in Webpack or not.

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


[IOPLT-139] Pressable module base component

Short description
This PR implements the PressbaleModuleBase component refactoring part of the code on ModuleIDP and ModulePaymentNotice
List of changes proposed in this pull request

Creates PressbaleModuleBase
Refactors ModuleIDP and ModulePaymentNotice

How to test
Check the example app on Modules Section

@dmnplb waiting your approval too for the UI/UX 😄


Validation errors for dbt > 1.4.1

FYI if you're noticing manifests not parsing for dbt > 1.4.1
https://github.com/dbt-labs/dbt-core/issues/7119

@dlawin @yu-iskw we are facing the same issue. Is there any update to currently fix this?

Thank you for opening the issue. I will look into it tomorrow.

@dlawin @OnkarVO7 Can you check if v0.2.5 behaves as you expect or not? I have re-generate the pydantic class of manivest v8 with the latest JSON schema in the dbt-core repository.

@yu-iskw it is working for me now with the v0.2.5

I will test later today, really appreciate it!

@dlawin @OnkarVO7 Can you check if v0.2.5 behaves as you expect or not? I have re-generated the pydantic class of manivest v8 with the latest JSON schema in the dbt-core repository.
https://github.com/yu-iskw/dbt-artifacts-parser/releases/tag/v0.2.5

works great ty!


Feature/new feature

The application allows users to select a source currency, a target currency, and enter an amount to convert. When the user clicks the convert button, the application fetches the latest exchange rates and performs the conversion, displaying the result in an alert message.

Creation of the currency API, merge to Dev


Fix rc.local

autorun_once.sh not being launched by rc.local

edits to permissions, rc.local, autorun.sh, and autorun_install.sh solved.


Recall at fixed precision does not scale well for large batch sizes

🐛 Bug
I would like to measure BinaryRecallAtFixedPrecision on a validation dataset with size on the order of hundreds of thousands. When I try to train my model, execution hangs at the validation step. My profiler tells me that the method torchmetrics.functional.classification.recall_fixed_precision._recall_at_precision is consuming endless amounts of CPU time. This happens because I use BinaryRecallAtFixedPrecision.thresholds=None, so BinaryRecallAtFixedPrecision.update() appends all of my validation batches together and therefore BinaryRecallAtFixedPrecision.compute() has to process the entire validation dataset at once.
To Reproduce
Set NUM_PROCESSES = 1 in tests/unittests/conftest.py for the sake of reproducibility.
Measure the run time of test_binary_recall_at_fixed_precision with the following command
time pytest tests/unittests/classification/test_recall_fixed_precision.py::TestBinaryRecallAtFixedPrecision::test_binary_recall_at_fixed_precision

It takes 8-9 seconds on my machine.
Now increase increase the testing batch size, by setting BATCH_SIZE in tests/unittests/conftest.py . This simulates the real world scenario of running BinaryRecallAtFixedPrecision.compute() with BinaryRecallAtFixedPrecision.thresholds=None and a large validation dataset.
Here are my crude measurements:

Batch Size
Test Time (s)

100
~9

1000
~20

2000
~30

5000
~70

10000
~124

100000
~1200

Expected behavior
Yes, the run time scales linearly with input size, however it is not fast enough. Other metrics derived from BinaryPrecisionRecallCurve run much faster, and I don't see why this one should be particularly slower than those.
I see one area where potential improvements could be made: This line of code in torchmetrics.functional.classification.recall_fixed_precision._recall_at_precision. My profiler measured that this is where the majority of CPU time is spent during computation of BinaryRecallAtFixedPrecision. I suspect that finding the maxima of tensors by iterating through them with a Python for loop is a major source of inefficiency. Using native PyTorch operations instead could result in great speedups.
I'm proposing the following changes to the method in question. Measurement of my code under the same conditions as before gives the following results:

Batch Size
Test Time (s)

100
~8

1000
~9

2000
~10

5000
~14

10000
~19

100000
~110

This alone may not be a conclusive benchmark, but the time savings during model validation are noticeable in my experience.
Environment
No GPU, no CUDA, CPU only.
torchmetrics==1.1.1
torch==2.0.1+cpu
python_version==3.11.5
# and others

Hi @GlavitsBalazs, thanks for raising this issue.
That is a huge finding. We in general do not necessarily test how well some of our implementations actually scale with the number of samples. We often try to do it while implementing them, but we may have missed it here.
Feel free to send a PR with the proposed fix (else I do it myself in one of the following days).
Thanks!


hash kwargs

@thieman

If you think this is good, should we do this in gclib too?

Arguably we should just grab a library for this, but yeah seems fine

Like what kind of library?


feate(decompress) middleware decompress body

If the content encoding is set at GZIP, the middleware decompresses the body.
fix #72

Thank you for the contribution !
Since this PR need to fix a few things and #71 needs to fix one more issue, I will fix this problem myself.


not found "GetLastKnownLocationAsync"

using Plugin.Geolocator;
...
var cached = await CrossGeolocator.Current.GetLastKnownLocationAsync();
Not found method ".GetLastKnownLocationAsync()"

This is in the beta pre-release nuget


Tool not starting: Convert image format

Traceback (most recent call last):
  File "/shed_tools/testtoolshed.g2.bx.psu.edu/repos/iuc/imagej2_convert_format/a3806b0a32ed/imagej2_convert_format/imagej2_convert_format.py", line 4, in <module>
    import imagej2_utils
  File "/export/shed_tools/testtoolshed.g2.bx.psu.edu/repos/iuc/imagej2_convert_format/a3806b0a32ed/imagej2_convert_format/imagej2_utils.py", line 1, in <module>
    import bioformats
ImportError: No module named bioformats

@ThomasWollmann @bgruening I eliminated the imagej2_convert_format tool from the git repo when I de-multiplexed the ImageJ2 tools.  This tool is really not necessary and was the first tool I developed for the ImageJ2 suite, so I was not aware at that time that ImageJ2 has its own built-in conversion capability.  So for now, this tool can be eliminated from the docker image.  If we feel that we want to keep a version of this tool, it should be re-written to use only ImageJ2 with no dependency on bioformats.  I can do this if you want to keep this tool - please just one an issue and ping me.  I'll get it done as soon as I can, but it may take a bit of time.  ;)

O.K. I'll remove it from the image.


fix(curriculum): update feedbacks and comprehension check questions for div element video

Checklist:

[x] I have read and followed the contribution guidelines.
[x] I have read and followed the how to open a pull request guide.
[x] My pull request targets the main branch of freeCodeCamp.
[x] I have tested these changes either locally on my machine, or GitPod.

Closes #57769

Fixed feedbacks of the first question.
Replaced the last question with a new question about section.

I'm closing this one, as problems with updating #57774 were solved.

I'm closing this one, as problems with updating #57774 were solved.

Sounds good. Thank you for your help @gikf.


Missing support for multiline commands with backslash at end of line

In this Python program, one statement is separated over multiple lines by using the backslash \ character at the end of the first line:
len("a") \
or len("aa")

Expected:
(module
  (expression_statement
    (boolean_operator
      left: (call
        function: (identifier)
        arguments: (argument_list
          (string)))
      right: (call
        function: (identifier)
        arguments: (argument_list
          (string))))))

Actual:
(module
  (expression_statement
    (call
      function: (identifier)
      arguments: (argument_list
        (string))))
  (expression_statement
    (call
      function: (identifier)
      (ERROR
        (identifier))
      arguments: (argument_list
        (string)))))
fail/distro.py	0 ms	(ERROR)

I can't reproduce this anymore, even though nothing seems to have changed since when I filed this bug.

I found out what happened. Multi-line statements don't work when using DOS-style line endings, CRLF.
$ tree-sitter parse <(printf 'len("a") \\\x0d\x0aor len("aa")')
(module [0, 0] - [1, 12]
  (expression_statement [0, 0] - [0, 8]
    (call [0, 0] - [0, 8]
      function: (identifier [0, 0] - [0, 3])
      arguments: (argument_list [0, 3] - [0, 8]
        (string [0, 4] - [0, 7]))))
  (expression_statement [1, 0] - [1, 12]
    (call [1, 0] - [1, 12]
      function: (identifier [1, 0] - [1, 2])
      (ERROR [1, 3] - [1, 6]
        (identifier [1, 3] - [1, 6]))
      arguments: (argument_list [1, 6] - [1, 12]
        (string [1, 7] - [1, 11])))))
/dev/fd/11	0 ms	(ERROR [1, 3] - [1, 6])

I guess the problem is in the scanner, here. \ followed by whitespace is skipped, but with CRLF we have \ followed by two whitespace.
  } else if (lexer->lookahead == '\\') {
    skip(lexer);
    if (iswspace(lexer->lookahead)) {
      skip(lexer);
    } else {
      return false;
    }
  }

Is there a particular reason to check with iswspace, and not check explicitly for \n?


tree-sitter-grammars: update smithy grammar

update to new version of tree-sitter-smithy that supports smithy 2.0
Description of changes

Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[x] x86_64-darwin
[x] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[x] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
22.11 Release Notes (or backporting 22.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module
[ ] (Release notes changes) Ran nixos/doc/manual/md-to-db.sh to update generated release notes

[ ] Fits CONTRIBUTING.md.

Result of nixpkgs-review pr 195446 run on x86_64-linux 1

1 package built:

tree-sitter-grammars.tree-sitter-smithy


Customize checkout screen

Is it possible to change the bg color and more on the screen that appears for a checkout ? i.e. after calling [SumupSDK checkoutWithRequest:request fromViewController:self ...] ?
Many thanks.

Hi.
That's not supported currently. What exactly would you like to tweak?
Regards,
Lukas

Change the bd color, add the logo of my client etc....

Hi @Alkashi .
Feel free to send an email to<EMAIL_ADDRESS>outlining the features you are missing.
Regards,
Lukas


CalculateIdentityCommitment has one redundant hash

In CalculateSecret, we have secret = hash(identityNullifier + identityTrapdoor)
but in CalculateIdentityCommitment, we compute idcommit = hash(secret) = hash(hash(identityNullifier + identityTrapdoor))
It looks like the second hash is redundant, we can remove it to gain some performance.
Thank @vivianjeng for discovering this issue.

Hey @ChihChengLiang , I think we're using that secret hash to make Semaphore identities compatible with RLN identities.

I see, thank you @cedoor. Closing now.


Update tests

We're updating the tests, following the same pattern used by the
other rocks.
The integration tests ensure that every supported version can be
deployed using helm charts, while the sanity tests verify the
image filesystem and executables.

Follow-up on https://github.com/canonical/multus-rock/pull/4


arash/ 1 request for auto carousel

Description
Summary of the change and/or issue that is fixed.
Changes:

fixes issue with item
...

Type of change

[ ] Bug fix
[ ] New feature
[ ] Breaking change
[ ] Dependency update
[ ] This change requires a documentation update

@arash-fs conflict


[Request] Possession on ship config option

I'd like to have possession on the ship

It was deactivated because, for some reason, it spawned a million of monster. Won't reactivate it


Do not make isRegistered() synchronized.

This one does not really need to be synchronized.
The field itself is volatile and that is enough.
Probably being too pedantic on this, but it feels this is the correct thing to do ;)
@yurishkuro @sjoerdtalsma

Coverage remained the same at 77.301% when pulling 5e23e0d1099083e1c37a92998b9ce3ef01d1bb71 on carlosalberto:global_tracer_registered_non_synch into 81762085f2e07ffe616c230ada8ad80a1532cc9c on opentracing:v0.32.0.


Sometimes logs are appended to a existing logs files of previous runs

If run 2 is running, logs file 2 is empty while logs file 1 contains its own logs and also 2 logs

not able to reproduce, the root cause of this issue may be bound to python's ContextVar logic used to get the current run ID. Probably it happened because there was an exception in the code and the context var got corrupted

Steps to reproduce:

In an empty folder, create a requirements.txt file with two lines: "plombery" and "uvicorn"
Run pipenv3 --python 3.11 install -r requirements.txt, then pipenv3 shell (reproduces with both python 3.11 and 3.9)
Copy the src/app.py file from the "your first pipeline" tutorial (https://lucafaggianelli.github.io/plombery/create-a-pipeline/)
Run python src/app.py
Open localhost:8000 and from the top-level page press "Run" button multiple times.

Expected results

Every run shall have its own logs

Actual result:

All the logs belong to the very first run

$ cat .data/runs/run_1/logs.jsonl
{"level": "INFO", "message": "Executing task fetch_raw_sales_data", "loggerName": "plombery.sales_pipeline", "timestamp": "2023-09-09T15:16:15.692Z", "pipeline": "sales_pipeline", "task": null}
{"level": "DEBUG", "message": "Fetching sales data...", "loggerName": "plombery.fetch_raw_sales_data", "timestamp": "2023-09-09T15:16:15.692Z", "pipeline": "sales_pipeline", "task": "fetch_raw_sales_data"}
{"level": "INFO", "message": "Fetched 50 sales data rows", "loggerName": "plombery.fetch_raw_sales_data", "timestamp": "2023-09-09T15:16:15.693Z", "pipeline": "sales_pipeline", "task": "fetch_raw_sales_data"}
{"level": "INFO", "message": "Executing task fetch_raw_sales_data", "loggerName": "plombery.sales_pipeline", "timestamp": "2023-09-09T15:16:46.473Z", "pipeline": "sales_pipeline", "task": null}
{"level": "DEBUG", "message": "Fetching sales data...", "loggerName": "plombery.fetch_raw_sales_data", "timestamp": "2023-09-09T15:16:46.473Z", "pipeline": "sales_pipeline", "task": "fetch_raw_sales_data"}
{"level": "INFO", "message": "Fetched 50 sales data rows", "loggerName": "plombery.fetch_raw_sales_data", "timestamp": "2023-09-09T15:16:46.473Z", "pipeline": "sales_pipeline", "task": "fetch_raw_sales_data"}
{"level": "INFO", "message": "Executing task fetch_raw_sales_data", "loggerName": "plombery.sales_pipeline", "timestamp": "2023-09-09T15:18:44.596Z", "pipeline": "sales_pipeline", "task": null}
{"level": "DEBUG", "message": "Fetching sales data...", "loggerName": "plombery.fetch_raw_sales_data", "timestamp": "2023-09-09T15:18:44.597Z", "pipeline": "sales_pipeline", "task": "fetch_raw_sales_data"}
{"level": "INFO", "message": "Fetched 50 sales data rows", "loggerName": "plombery.fetch_raw_sales_data", "timestamp": "2023-09-09T15:18:44.597Z", "pipeline": "sales_pipeline", "task": "fetch_raw_sales_data"}

Notes:
Restarting python src/app.py "forgets" about the run that stuck getting all the logs, and after a couple of restarts:
$ find . -name "logs.jsonl" -exec wc -l {} \; | sort
0 ./.data/runs/run_10/logs.jsonl
0 ./.data/runs/run_11/logs.jsonl
0 ./.data/runs/run_2/logs.jsonl
0 ./.data/runs/run_3/logs.jsonl
0 ./.data/runs/run_5/logs.jsonl
0 ./.data/runs/run_6/logs.jsonl
0 ./.data/runs/run_8/logs.jsonl
6 ./.data/runs/run_7/logs.jsonl
9 ./.data/runs/run_1/logs.jsonl
9 ./.data/runs/run_4/logs.jsonl
9 ./.data/runs/run_9/logs.jsonl

Thanks for the info, I'll try to reproduce it following your steps


Why not using docker-machine env instead of docker-machine ssh ?

The first step of this tutorial is to connect through ssh to the remote docker machine. What about using docker-machine env manager1 which will have the same result without connecting through ssh into the machine?
I have read many times that ssh'ing into container is a bad practice, what about ssh'ing into the docker machine?
Thanks for your time and the set of great tools you provide,
Regards.
File: engine/swarm/swarm-tutorial/create-swarm.md, CC @mistyhacks

Closing this ticket due to its age, and the impending refactor. If you think this is in error, feel free to reopen. Thanks!


feat: is_offline command & handle reload and offline

Fixes https://github.com/premAI-io/prem-app/issues/476

Shall we add also a Tauri event so we can attach a listener on JS side instead of polling is offline command?

That works as well but the question is when should we emit this event?

Should we just emit is_offline if the load of service manifest fails, if we are just emitting once I think is_offline is better.

This PR conflicts with Registry PR in setup hook

That works as well but the question is when should we emit this event?

didn't checked the code yet, but I assume we should continuously monitor the state of the internet no? so when state changes (from online to offline or viceversa) we should emit the event

closing will redo in a separate PR, rebasing is a lot more annoying than expected.


Problem reading certain GMV files.

Michael Jhn, an external VisIt user, reported a failure with reading data from GMV files. Files are attached. Email #2: =========I already found out that VisIt shows some strange cut cells in my example. You may have a look at the attached files.There, VisIt is at least able to open these files. But when I go to higher resolutions, I get an error message (c.f. link from my last mail).If I open these files in GMV, everything is looking 'normal'. Email #1: =========I have a very similar problem to this one: https://elist.ornl.gov/pipermail/visitusers/2012August/012792.htmlIt is about a file that contains topographical data (cartesian grid, cut cells) that can be opened with GMV but no with VisIt.I used the current VisIt version (2.6.2) on Mac OS Darwin 10.6.3 and on Ubuntu 12.04. Was this bug solved in any ways? I could also send you my file but is has a size of 24 MB.
-----------------------REDMINE MIGRATION-----------------------
This ticket was migrated from Redmine. As such, not all
information was able to be captured in the transition. Below is
a complete record of the original redmine ticket.
Ticket number: 1430
Status: Resolved
Project: VisIt
Tracker: Bug
Priority: Normal
Subject: Problem reading certain GMV files.
Assigned to: Brad Whitlock
Category: -
Target version: 2.7
Author: Brad Whitlock
Start: 04/22/2013
Due date:
% Done: 100%
Estimated time:
Created: 04/22/2013 01:37 pm
Updated: 04/26/2013 07:54 pm
Likelihood: 2 - Rare
Severity: 3 - Major Irritation
Found in version: 2.6.2
Impact:
Expected Use:
OS: All
Support Group: Any
Description:
Michael Jhn, an external VisIt user, reported a failure with reading data from GMV files. Files are attached. Email #2: =========I already found out that VisIt shows some strange cut cells in my example. You may have a look at the attached files.There, VisIt is at least able to open these files. But when I go to higher resolutions, I get an error message (c.f. link from my last mail).If I open these files in GMV, everything is looking 'normal'. Email #1: =========I have a very similar problem to this one: https://elist.ornl.gov/pipermail/visitusers/2012August/012792.htmlIt is about a file that contains topographical data (cartesian grid, cut cells) that can be opened with GMV but no with VisIt.I used the current VisIt version (2.6.2) on Mac OS Darwin 10.6.3 and on Ubuntu 12.04. Was this bug solved in any ways? I could also send you my file but is has a size of 24 MB.
Comments:
I'm attaching a new, smaller dataset from the user as well as screen shots of what happens in VisIt and in GMV. I made some fixes to support polygon cells encoded as general polyhedral cells.

1437_Barbados_R.Cut.out.gmvG.gz
1437_Barbados_R.Cut2.out.gmvG.zip
1437_Barbados_R.Oro.out.gmvG.zip
1437_Barbados_R.out.gmvG.gz


Check for IRODS_SSSD_AUTH not working in irods.pam template

Line gets added even if value is not true, probably the same for IRODS_SODAR_AUTH.

My bad, this was actually a bug in my docker-compose environment setup.


Confusion RE: how to include

I added this as:

first: in Brocfile... app.import('bower_components/active-model-adapter/active-model-adapter.js')
second: added it as a direct link to a file in my index.html

In both cases, using the usage provided, I get

Error while processing route: admin.index Could not find module active-model-adapter imported from doki/adapters/application Error: Could not find module active-model-adapter imported from doki/adapters/application

Using
import DS from 'ember-data';
export default DS.ActiveModelAdapter.extend()

instead of
import ActiveModelAdapter from 'active-model-adapter';
export default ActiveModelAdapter.extend()

The code works again and deprecation has gone away. Does this addon add ActiveModelAdapter back to the DS namespace? Is there a more thorough "usage" documentation?

 Thanks, @fivetanley, that did the trick!


Integration Test - Release - 2018/05/25 15:00:53

Auto create for integration test. AnotherAuthorUpdatedArticleFlow

@PRMergerTest3 : Thanks for your contribution! The author, @ meganbradley, has been notified to review your proposed change.

@PRMergerTest1 : Thanks for your contribution! The author, @meganbradley, has been notified to review your proposed change.