legal-entity-identifier

@HadleyKing  - I want to add https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei to the spec. This suggestion came from Frank at FDA. This is optional. But will help with the identifiers.

For now this can be added as an Xref and referenced in the Usability Domain.
Need to find an example to demonstrate this.


GD0102 is incorrectly reported for generic types with Variant-compatible constraints

Godot version
4.1.1.stable.mono
System information
Godot v4.1.1.stable.mono - Windows 10.0.19045 - Vulkan (Forward+) - dedicated AMD Radeon RX 5700 XT (Advanced Micro Devices, Inc.; 31.0.21001.45002) - AMD Ryzen 7 3800X 8-Core Processor (16 Threads)
Issue description
The C# ExportAttribute only works on Variant-compatible types. For user-defined types, this pretty much means it needs to ultimately derive from GodotObject or be an Enum. However, this does work with generic that have these constraints on the type:
using Godot;

internal partial class Foo<T> : GodotObject
        where T : GodotObject
{
    [Export]
    private T _Bar;
}

internal partial class Baz<T> : GodotObject
        where T : Enum
{
    [Export]
    private T _Bar;
}

In the above cases, the source generator reports GD0102 from here. This is because the method used to find a Marshal-able type does not find one. The expected behavior is that the first case should resolve to MarshalType.GodotObjectOrDerived and the second case should resolve to MarshalType.Enum. A third case for T[] also fails and should resolve to MarsharlType.GodotObjectOrDerivedArray.
Steps to reproduce
Create the following C# script in a Godot project.
using Godot;

internal partial class Foo<T> : GodotObject
        where T : GodotObject
{
    [Export]
    private T _Bar;
}

internal partial class Baz<T> : GodotObject
        where T : Enum
{
    [Export]
    private T _Bar;
}

Minimal reproduction project
N/A

I'm actually not sure why the case of a simple GodotObject constraint fails. The check here for general object types should succeed since SimpleDeriveFrom iterates through ITypeSymbol.BaseType until it finds GodotObject or runs out of base types. ITypeSymol's documentation indicates that "The base type of a type parameter is its effective base class" which I would expect to be GodotObject.


fix: typos, repay with wrapped sol, withdraw overpay

Changes

Fix typos
Fix repays with wrapped sol (if the richest SOL account was a native SOL account, the repay flow did not wrap properly)
Fix withdraws (collateral calculation was too high, clamped it to current balance)

@jstarry  thanks a lot!

@jstarry  thanks a lot!


prl-3100 special guardianship order

JIRA link (if applicable)
https://tools.hmcts.net/jira/browse/PRL-3100
Change description
Changes required for ticket 3100
Does this PR introduce a breaking change? (check one with "x")
[ ] Yes
[x] No

Done as part of 1055


Recursively return new Arrayy objects

Just an idea...
When you retrieve a nested array from the parent Arrayy object I kind of expected that the child array would also be an instance of Arrayy.
For example:
$foo = Arrayy::create(['bar' => [1,2,3]]);
assert($foo->get('bar') instanceof Arrayy);

Should be reasonably simple to implement I think, updating the return from the get method like:
return is_array($usedArray) ? static::create($usedArray) : $usedArray;

FYI, I also ended up overriding the Iterator,  the following is what I ended up with to meet my current needs.
class Arrayy extends \Arrayy\Arrayy
{
    public function get($key, $default = null, &$array = null)
    {
        $value = parent::get($key, $default, $array);

        if (is_array($value)) $value = static::create($value);

        return $value;
    }

    public function getIterator()
    {
        return new class($this->array) extends \ArrayIterator
        {
            public function current()
            {
                $value = parent::current();

                if (is_array($value)) $value = Arrayy::create($value);

                return $value;
            }

            public function offsetGet($offset)
            {
                $value = parent::offsetGet($offset);

                if (is_array($value)) $value = Arrayy::create($value);

                return $value;
            }
        };
    }
}

👍 Thx for the new feature! Because it's maybe a breaking change for some user, I have increased the mayor-version. -> 3.0.0


Unable to build Llama-2-13B-Chat on RTX 4070Ti

System Info

GPU (Nvidia GeForce RTX 4070 Ti)
CPU 13th Gen Intel(R) Core(TM) i5-13600KF
32 GB RAM
1TB SSD
OS Windows 11

Package versions:

TensorRT version 9.2.0.post12.dev5
CUDA 12.2
cuDNN 8.9.7
Python 3.10.11

Who can help?
No response
Information

[X] The official example scripts
[ ] My own modified scripts

Tasks

[X] An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
[ ] My own task or dataset (give details below)

Reproduction

Download Llama-2-13b-chat-hf model from https://huggingface.co/meta-llama/Llama-2-13b-chat-hf

Download AWQ weights for building the TensorRT engine model.pt from https://catalog.ngc.nvidia.com/orgs/nvidia/models/llama2-13b/files?version=1.2

Initiate build of the model using a single GPU:
python build.py --model_dir .\tmp --quant_ckpt_path .\tmp\model.pt --dtype float16 --use_gpt_attention_plugin float16 --use_gemm_plugin float16 --use_weight_only --weight_only_precision int4_awq --per_group --enable_context_fmha --max_batch_size 1 --max_input_len 3000 --max_output_len 1024 --output_dir .\tmp\out

Expected behavior
Build a trt engine for GTX 4070 Ti
actual behavior
Build terminates after reporting memory allocation issue:
Requested amount of GPU memory (1024 bytes) could not be allocated. There may not be enough free memory for allocation to succeed.
Output:
[02/04/2024-20:42:02] [TRT] [W] Missing scale and zero-point for tensor LLaMAForCausalLM/lm_head/CONSTANT_2_output_0, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[02/04/2024-20:42:02] [TRT] [W] Missing scale and zero-point for tensor logits, expect fall back to non-int8 implementation for any layer consuming or producing given tensor
[02/04/2024-20:42:02] [TRT] [W] Detected layernorm nodes in FP16.
[02/04/2024-20:42:02] [TRT] [W] Running layernorm after self-attention in FP16 may cause overflow. Exporting the model to the latest available ONNX opset (later than opset 17) to use the INormalizationLayer, or forcing layernorm layers to run in FP32 precision can help with preserving accuracy.
[02/04/2024-20:42:02] [TRT] [W] [RemoveDeadLayers] Input Tensor position_ids is unused or used only at compile-time, but is not being removed.
[02/04/2024-20:42:02] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +1, GPU +0, now: CPU 29820, GPU 12281 (MiB)
[02/04/2024-20:42:02] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +14, GPU +0, now: CPU 29839, GPU 12281 (MiB)
[02/04/2024-20:42:02] [TRT] [W] TensorRT was linked against cuDNN 8.9.6 but loaded cuDNN 8.8.1
[02/04/2024-20:42:02] [TRT] [I] Global timing cache in use. Profiling results in this builder pass will be stored.
[02/04/2024-20:42:02] [TRT] [E] 1: [virtualMemoryBuffer.cpp::nvinfer1::StdVirtualMemoryBufferImpl::resizePhysical::127] Error Code 1: Cuda Driver (invalid argument)
[02/04/2024-20:42:02] [TRT] [W] Requested amount of GPU memory (1024 bytes) could not be allocated. There may not be enough free memory for allocation to succeed.

additional notes
I have a somewhat limited understanding of what I'm doing here.  I'm trying to run the developer reference project for creating Retrieval Augmented Generation (RAG) chatbots on Windows using TensorRT-LLM. Following these instructions:
https://github.com/NVIDIA/trt-llm-rag-windows/tree/release/1.0?tab=readme-ov-file#building-trt-engine
I'm not sure whether I'm pushing the limits of my hardware here or do I have any options to tweak parameters and get this processed in smaller batches to avoid a memory issue. I tried playing with different --max_input_len and --max_output_len values, reducing down to 512 but that doesn't seem to make any difference.

I have the same issue, with a 4090, 13900k, Win11, 32GB RAM.
I don't think the RAG application is workable at the moment.

Installing by following the directions in the RAG repo and the TensorRT-LLM repo installs 0.7.1, which requires a custom TensorRT engine, the build of which fails due to memory issues.
The RAG application specifically calls for using TensorRT <IP_ADDRESS> and TensorRT-LLM release 0.5.0 in order to use the pre-built engine, however, trying to force pip to install tensorrt-llm 0.5.0 fails due to missing requirements (See Pip Error below).
Trying to build the TensorRT engine results in a memory failure.
Trying to build release/0.5.0 from source, the output has 20 or so "CMake Error at CMakeLists.txt:" entries, then fails following a set_target_properties error. (See CMake Error below)

Pip Error

PS C:\Users\rw\inference\TensorRT> pip install "tensorrt_llm==0.5.0" --extra-index-url https://pypi.nvidia.com --extra-index-url https://download.pytorch.org/whl/cu121
Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com, https://download.pytorch.org/whl/cu121
Collecting tensorrt_llm==0.5.0
Using cached https://pypi.nvidia.com/tensorrt-llm/tensorrt_llm-0.5.0-0-cp310-cp310-win_amd64.whl (431.5 MB)
Collecting build (from tensorrt_llm==0.5.0)
Using cached build-1.0.3-py3-none-any.whl.metadata (4.2 kB)
INFO: pip is looking at multiple versions of tensorrt-llm to determine which version is compatible with other requirements. This could take a while.
ERROR: Could not find a version that satisfies the requirement torch==2.1.0.dev20230828+cu121 (from tensorrt-llm) (from versions: 1.11.0, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 2.0.0, 2.0.1, 2.1.0, 2.1.0+cu121, 2.1.1, 2.1.1+cu121, 2.1.2, 2.1.2+cu121, 2.2.0, 2.2.0+cu121)
ERROR: No matching distribution found for torch==2.1.0.dev20230828+cu121

CMake Error

CMake Error at tensorrt_llm/plugins/CMakeLists.txt:106 (set_target_properties):
set_target_properties called with incorrect number of arguments.

So far, none of the examples in the Get Started blog post or next steps listed in the windows/README.md are usable. The inclusion of examples/llama as a showcase seems fairly short-sighted, as in order to generate the quantized weights file required one has to have Triton, which is apparent due to the errors when running the recommended GPTQ Weight Quantization.
I'd like to have a fix for this just because of general principles, but the amount of time it takes to discover that even the documented workflows don't work really makes me question the value in this context.

Good to know I'm not the only one running into this exact same issue.

@bormanst I tried your solution but I get the following errors. What version of TensorRT/TensorRT-LLM did you build was it 0.5 or higher? Did you manage to build the engine?
[03/06/2024-11:45:50] [TRT-LLM] [I] Loading weights from groupwise AWQ LLaMA safetensors... Traceback (most recent call last):   File "C:\Development\llm-models\trt\TensorRT-LLM\examples\llama\build.py", line 718, in <module>     build(0, args)   File "C:\Development\llm-models\trt\TensorRT-LLM\examples\llama\build.py", line 689, in build     engine = build_rank_engine(builder, builder_config, engine_name,   File "C:\Development\llm-models\trt\TensorRT-LLM\examples\llama\build.py", line 543, in build_rank_engine     load_func(tensorrt_llm_llama=tensorrt_llm_llama,   File "C:\Development\llm-models\trt\TensorRT-LLM\examples\llama\weight.py", line 1063, in load_from_awq_llama     assert False, "Quantized checkpoint format not supported!" AssertionError: Quantized checkpoint format not supported!

I'm pretty sure it was TensorRT-LLM 0.7.0; 0.7.1 was causing issues.
I'm using a RTX4070, but ran across a matrix on nVidia's website (can't find it now for some reason) that showed whether the gpu core supported int4_awq (all Ada's did). So hopefully that is not the issue, I'll assume not.
It's pretty much a mad rush right now and packages are not stable which causes plenty of confusion. If you do a default git clone it pulls the 'main' branch which is the, up to date, experimental branch. I used the 'clone -b rel' to get the release version which was 0.7.0 at the time; I just noticed that 'rel' branch just changed to 0.8.0 so...
I was just messing around trying to build on win10 so I used the provided https://github.com/NVIDIA/TensorRT-LLM/tree/v0.7.0/windows#quick-start to build my TensorRT-LLM.
Then proceeded to follow https://github.com/NVIDIA/trt-llm-as-openai-windows 'very closely' and it worked for CodeLlama-13b-Instruct-hf and Llama-2-13b-chat-hf.
If I remember correctly, I kept getting that error until I explicitly set -quant_ckpt_path to point to the the actual file llama_tp1_rank0.npz and not it's parent directory or the base dir of the model.pt.
Sorry, I can't be more helpful, but it was a blur of 'pip package version hell' that I wasn't expecting; should have conda'd or docker'd it to start.
Been using win10 because it's easier to develop UE5, but just activated Ubuntu on WSL2 and built a local tritonserver docker as well as tensorrt and tensorrt-main docker images; plan on building one of the starcoder2 llms using the new Ubuntu setup today. The nVidia 'gpu passthrough' tech works as advertised on win10, so far. Can't wait to build another Linux box though, as AI appears to be a Linux based frontier.

Just an FYI that should be obvious but wasn't:
I'm sure it's in the docs somehwere but there is ZERO chance of converting these large 13B model checkpoints on a 4070; have to wait for nVidia to post them to use for lower vram builds; the checkpoints posted by nVidia are about 25GB so...
The convert_checkpoint.py script always runs out of memory on my 4070: nvidia-smi. Maybe the new SuperTi 16GB cards will have better luck but I wouldn't hold my breath.

Hey @bormanst. Thanks a lot for the detailed instructions. I'm still getting issues but its with hugginface so a lot easier to resolve :-)

Scratch that. I had the the wrong path for the model directory and I am back to getting
assert False, "Quantized checkpoint format not supported!"

I think I will have to try to get 0.7.0 to compile again. Thanks for your help

@jonny2027
It looks like the headers are not synced correctly with 0.7.1; there's some serious refactoring happening with TensorRT at the moment so this should be expected outside the 'rel' branches.
I couldn't get 0.7.1 to work, just 0.7.0.
I did successfully build blip-2, whisper, CodeLlama-13b-Instruct-hf, and Llama-2-13b-chat-hf with v0.7.0 using the win10 build process.  I built Santacoder, CodeLlama-13b-Instruct-hf and Llama-2-13b-chat-hf using the WSL2/Ubuntu process yesterday using v0.7.0 after trying all the differnt combinations.
It just seemed futile to keep having to compile custom Win10 pip packages to comabat the frenetic pace of development going on. The Win10>>>WSL2>>>Ubuntu>>>Docker>>>TensorRT-LLM process sounds difficult but it's not; the only real downside is that the docker images take more space than a monolithic solution. The upside is that the newest shiniest smallest models can be swapped in/out of the stack w/o much effort; as you can see, all the models are improving monthly so I figured it would be best to be more flexible at the expense of storage space.
This guy had the goods for setting up Win10>>>WSL2>>>Ubuntu>>>Docker>>>Tensorflow  and the tensorflow build worked: . The only tricky stuff is making sure the mappings are correct for proper piping between the environments. i.e. There are plenty of examples how to map a Win10 folder to a directory in WSL2/Ubuntu as well as how to map WSL2/Ubuntu dirs to Docker dirs. Most of the commands, like the running docker images, allow for mappings to be sent on the command line. Any other mappings are usually 'one-and-done' types like repo locations, etc., that you only have to do as needed so not that big of a deal. I only had to use the command line mappings and didn't need to map anything special to get the building process working.
The TensorRT build process looks like it is currently being refactored (v0.9.0-) into a single build method instead of all the fractured model methods. At their current pace, that should be finished in a month and will make it much easier to build.
I did manage to get Starcoder2-3B almost built (WSL2/Ubuntu) using the latest 'main' branch but ran out of vram just when it was copying the model out; there were errors in downstream pip packages that had to be resolved and most of the model builds have not been ported to the new process yet.

@jonny2027
I haven't done anything special regarding the Batch Manager so I'm not sure if you included that separately or if it's just in the base build like I would assume. Anyway, I don't recall seeing any mention of 'Batch Manager' errors scrolling by. If you're building everything from source and 'choosing' the 'Batch Manager' as an option, then I would try w/o it first. Cpp has a couple of models for implemenation that can cause undesirable side effects for developers, especially when it comes to headers and inheritance - that is what your errors look like. There's many cooks in the kitchen and no standards yet so I would expect this stuff to continue for some time.
From https://github.com/NVIDIA/TensorRT-LLM/tree/v0.7.0/windows#quick-start :
[If you are using the pre-built TensorRT-LLM release wheel (recommended unless you need to directly invoke the C++ runtime), skip to Installation. If you are building your own wheel from source, proceed to Building from Source.](https://github.com/NVIDIA/TensorRT-LLM/tree/v0.7.0/windows#quick-start)
I used the prebuilt wheel as recommended (just be wary of wheel version being 'synced' with the TensorRT-LLM build as mentioned earlier):
pip install tensorrt_llm==v0.7.0 --extra-index-url https://pypi.nvidia.com/ --extra-index-url https://download.pytorch.org/whl/cu121
I already had the visual studio cpp dev boat installed on my system for UE5 so I skipped that part of the process; it's possible that they are missing a step to install a particular cpp component but I would rate that as a small possibilty. I also used the prebuilt wheel for the WSL2/Ubuntu builds w/o issue.

Ah ok. That makes sense that you are using the prebuilt wheel. I was trying to compile the wheel from source. I will give it a go tomorrow with the prebuilt wheel.
Thanks

The pain of ignorance; The 'sync' issue I was having between the tensorrt_llm wheel and TensorRT-LLM was a direct result of 'manually copying' the libs into the TensorRT-LLM/tensorrt_llm directory, and forgetting to do it again after trying a different wheel version.
They are refactoring the build process now which will render all this moot, so it's just an FYI.
I know v0.7.0 works, but I'm not sure if it was just me messing up the others.

Using Win10/WSL2/Ubuntu, I managed to get v0.8.0 to build Llama-2-13b-chat-hf using the new trtllm-build process and new nVidia checkpoint format v1.5.
There's some obvious missing steps in the install docs, like needing to 'apt-get install git-lfs', but it is the easiest process so far and produced a viable build on the first run. The new checkpoints are already in safetensor format and much, much smaller.
Note: Not all models have been incorporated into the new trtllm-build process, nor does nVidia have the new checkpoint formats for all models. Don't expect to convert the checkpoints of larger models on 12GB of vram.

@kaalen Do you still have the problem？If not, we will close it soon.

No issues for a while, running tensorrt-llm 0.14.0 just fine with the newer Llama3.2 and Llama3.1 models. Thanks for starting issue, though.

 I tried to follow the instructions to install on v014.0 but I get the following error when verifying:
PS C:\src\TensorRT-LLM> python -c "import tensorrt_llm; print(tensorrt_llm.utils.trt_version())"
Traceback (most recent call last):
File "", line 1, in
File "C:\src\TensorRT-LLM\tensorrt_llm_init.py", line 32, in
import tensorrt_llm.functional as functional
File "C:\src\TensorRT-LLM\tensorrt_llm\functional.py", line 28, in
from . import graph_rewriting as gw
File "C:\src\TensorRT-LLM\tensorrt_llm\graph_rewriting.py", line 12, in
from .network import Network
File "C:\src\TensorRT-LLM\tensorrt_llm\network.py", line 27, in
from tensorrt_llm.module import Module
File "C:\src\TensorRT-LLM\tensorrt_llm\module.py", line 17, in
from ._common import default_net
File "C:\src\TensorRT-LLM\tensorrt_llm_common.py", line 37, in
from ._utils import str_dtype_to_trt
File "C:\src\TensorRT-LLM\tensorrt_llm_utils.py", line 31, in
from tensorrt_llm.bindings import GptJsonConfig
ModuleNotFoundError: No module named 'tensorrt_llm.bindings'
@bormanst are you running this on Win 11 or Linux? I might try on WSL since the installation page says Windows release is still in beta.

@kaalen
Currently have v0.14.0 running on Win10 with both ChatRTX and oobabooga.
Just finishing v0.14.0 with oobaboga in WSL (resolving the usual Torch version conflict issues).
Things have gotten markedly better in the last year for Windows; I have been able to successfully run v0.9.0 - v0.14.0 on Win10 and WSL. There's been new issues to hack with each version, but it is stabilizing. The error messages usually identify the cuprit, which are almost all now dealing with local .py file issues as opposed to the whole underbelly tech. Knowing that everthying actually works, as stated, is real light at the end of the tunnel.
ModuleNotFoundError: No module named 'tensorrt_llm.bindings'
Do a pip list and ensure the following are present:
tensorrt-cu12_bindings                   10.4.0
tensorrt-cu12_libs                       10.4.0
If not:
pip install --extra-index-url https://pypi.nvidia.com --extra-index-url https://download.pytorch.org/whl/cu124 tensorrt-cu12_bindings tensorrt-cu12_libs
Be sure that you use the cu124 wheel repo if building v0.14.0. It's usually worth adding the -extra-index-url https://pypi.nvidia.com --extra-index-url https://download.pytorch.org/whl/cu124 when you run pip installs including requirement.txt file.
It appears your not using a conda env; I would highly recommend it, if you're going down this rabbit hole. It is priceless to able to completely "whack" an env without affecting others with:
pip freeze > requirements-junk.txt
pip uninstall -r requirements-junk.txt -y
modify requirements-junk.txt and delete any packages causing removal errors and repeat:
pip uninstall -r requirements-junk.txt -y
until mostly clean (won't be able to wipe all)
If using conda BE SURE it's a python 3.10 environment - TensorRT-LLM is not tollerent.
The ChatRTX v0.4 can be hacked to run any converted TensorRTLLM engine and uses the conda tech.
oobabooga can be hacked to run (win10 build can share with ChatRTX) any converted TensorRTLLM engine and uses the conda tech.
NOTE: oobabooga wants to use python 3.11 env requiring modifying the initial  start_linux or start_windows files to use v 3.10 instead.
Just an FYI:
ChatRTX has RAG tech which is great if you have lots of your own content to inject, but it is "zero shot" only so your next query has no knowledge of the past.
oobabooga has chat logs, that you can switch on demand, that feed in the past conversations so it's more like an expected chat.
oobabooga doesn't do RAG like ChatRTX but it does have some extensions available that allow you to inject content.
oobabooga also has alltalk_tts for audio responses.
I recently discovered that the win10 implentation of oobabooga "must" wait until the response audio "wav" file is completely recorded which is annoying when there's a long response. This is because Win10 does not support async io and you get the missing "aio.lib" error. The weird part is that WSL does.
So if you're looking for single shot answers and/or RAG capabilities, go with the Win/ChatRTX hack route.
If you're looking for a responsive chatbot with low latency audio responses, then go WSL/oobabooga.
If you don't care about the audio responses then Win/oobabooga works well too.
If you go the WSL/oobabooga alltalk_tts route for the audio responses then you will need Pulseaudio working in BOTH Win and WSL:
https://pgaskin.net/pulseaudio-win32/
https://www.linuxuprising.com/2021/03/how-to-get-sound-pulseaudio-to-work-on.html >>> 3. Configure PulseAudio in WSL2.
https://answers.microsoft.com/en-us/windows/forum/all/is-it-possible-to-force-windows-to-pcm-output-only/b99b478b-adc9-4e0f-b70d-601d00fee77a
The first link handles the Win10 side of things and gets installed as "service" just like the guide.
The WSL side has to be done manually by 3. Configure PulseAudio in WSL2.
Force PCM audio mode in Win10: Win10 Alt-click "speaker" icon in tray >>> Open sound settings >>> Output Section: device properties >>> Additional device properties (rt nav area) >>> Advanced tab >>> uncheck "allow applications to take exclusive control of this device".
I keep explicit notes when resolving errors as many keep repeating themselves with newer versions. The underbellys of TensorRTLLM and oobabooga are constantly changing and most new issues are related to Torch versioning.
Hope this helps some.

Okay, hmm that's odd.
I followed installation instructions  on the same link you provided. That page has a note at the top about v0.14.0 being in beta for Windows, so I'll assume the instructions are up to date for v0.14.
I installed dependencies, namely the newer CUDA version. I already had Python 3.10. I create a conda environment, and I 100% activated the conda environment. I think it's just a quirk of running commands in powershell that it sometimes doesn't show active conda environment as a prefix, so that may give the false impression that I'm not using conda.
Python version should be fine.
python --version
Python 3.10.15

CUDA also:
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Thu_Jun__6_03:03:05_Pacific_Daylight_Time_2024
Cuda compilation tools, release 12.5, V12.5.82
Build cuda_12.5.r12.5/compiler.34385749_0

As per the instructions, I ran the uninstall commands first before installing tensorrt_llm==0.14.0. Everything appears to install fine, and if I rerun the pip install command, I get a bunch of " requirement already satisfied" messages, so it seems like all dependencies are present.
Not sure what else I can try to at least get it installed.

You are correct about conda and powershell, I forgot that I hacked mine to display the conda env prefix last year - don't remember what I did though and didn't take notes on that.
Also of note, is that I was partially wrong above:
"--extra-index-url https://download.pytorch.org/whl/cu124", which still works for now, has been replaced with "--extra-index-url https://download.pytorch.org/whl/ "
Looks like they're moving to standardize things so there's not so many "one-off gotchyas".
No hard rule but the big guys usually stick with anything prior to v1.0.0 being a "beta" offering.
pip has --force-reinstall and --no-cache-dir
First one is obvious.
The second disables the cache and should be used if you still encounter issues and notice the install is building intermediary wheels to install the final package. Sometimes a corruption gets cached; doesn't happen often but...
I just performed this on a clean "test" folder with PowerShell (direct copy/paste from terminal with "user name" changed to "you"):
Windows PowerShell
Copyright (C) Microsoft Corporation. All rights reserved.
Try the new cross-platform PowerShell https://aka.ms/pscore6
PS C:\Users\you> cd e:\ai
PS E:\ai> mkdir test
Directory: E:\ai

Mode                 LastWriteTime         Length Name

d-----        11/22/2024  10:21 AM                test
PS E:\ai> conda create -p E:\ai\test\env_trt python=3.10 -y
Channels:

defaults
Platform: win-64
Collecting package metadata (repodata.json): done
Solving environment: done

Package Plan
environment location: E:\ai\test\env_trt
added / updated specs:
- python=3.10
The following NEW packages will be INSTALLED:
bzip2              pkgs/main/win-64::bzip2-1.0.8-h2bbff1b_6
ca-certificates    pkgs/main/win-64::ca-certificates-2024.9.24-haa95532_0
libffi             pkgs/main/win-64::libffi-3.4.4-hd77b12b_1
openssl            pkgs/main/win-64::openssl-3.0.15-h827c3e9_0
pip                pkgs/main/win-64::pip-24.2-py310haa95532_0
python             pkgs/main/win-64::python-3.10.15-h4607a30_1
setuptools         pkgs/main/win-64::setuptools-75.1.0-py310haa95532_0
sqlite             pkgs/main/win-64::sqlite-3.45.3-h2bbff1b_0
tk                 pkgs/main/win-64::tk-8.6.14-h0416ee5_0
tzdata             pkgs/main/noarch::tzdata-2024b-h04d1e81_0
vc                 pkgs/main/win-64::vc-14.40-h2eaa2aa_1
vs2015_runtime     pkgs/main/win-64::vs2015_runtime-14.40.33807-h98bb1dd_1
wheel              pkgs/main/win-64::wheel-0.44.0-py310haa95532_0
xz                 pkgs/main/win-64::xz-5.4.6-h8cc25b3_1
zlib               pkgs/main/win-64::zlib-1.2.13-h8cc25b3_1
Downloading and Extracting Packages:
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

To activate this environment, use

$ conda activate E:\ai\test\env_trt

To deactivate an active environment, use

$ conda deactivate
PS E:\ai> conda activate E:\ai\test\env_trt
(env_trt) PS E:\ai> pip install tensorrt_llm==0.14.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://download.pytorch.org/whl/
Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com, https://download.pytorch.org/whl/
Collecting tensorrt_llm==0.14.0
Using cached https://pypi.nvidia.com/tensorrt-llm/tensorrt_llm-0.14.0-cp310-cp310-win_amd64.whl (1114.4 MB)
Collecting accelerate>=0.25.0 (from tensorrt_llm==0.14.0)
Using cached accelerate-1.1.1-py3-none-any.whl.metadata (19 kB)
Collecting build (from tensorrt_llm==0.14.0)
Using cached build-1.2.2.post1-py3-none-any.whl.metadata (6.5 kB)
Collecting colored (from tensorrt_llm==0.14.0)
Using cached colored-2.2.4-py3-none-any.whl.metadata (3.6 kB)
Collecting cuda-python==12.5.0 (from tensorrt_llm==0.14.0)
Using cached cuda_python-12.5.0-cp310-cp310-win_amd64.whl.metadata (12 kB)
Collecting diffusers>=0.27.0 (from tensorrt_llm==0.14.0)
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Collecting numpy<2 (from tensorrt_llm==0.14.0)
Using cached numpy-1.26.4-cp310-cp310-win_amd64.whl.metadata (61 kB)
Collecting onnx>=1.12.0 (from tensorrt_llm==0.14.0)
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Collecting polygraphy==0.49.9 (from tensorrt_llm==0.14.0)
Using cached https://pypi.nvidia.com/polygraphy/polygraphy-0.49.9-py2.py3-none-any.whl (346 kB)
Collecting psutil (from tensorrt_llm==0.14.0)
Using cached psutil-6.1.0-cp37-abi3-win_amd64.whl.metadata (23 kB)
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Collecting pulp (from tensorrt_llm==0.14.0)
Using cached PuLP-2.9.0-py3-none-any.whl.metadata (5.4 kB)
Collecting pandas (from tensorrt_llm==0.14.0)
Using cached pandas-2.2.3-cp310-cp310-win_amd64.whl.metadata (19 kB)
Collecting h5py==3.10.0 (from tensorrt_llm==0.14.0)
Using cached h5py-3.10.0-cp310-cp310-win_amd64.whl.metadata (2.5 kB)
Collecting pywin32 (from tensorrt_llm==0.14.0)
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Collecting StrEnum (from tensorrt_llm==0.14.0)
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Collecting tensorrt~=10.4.0 (from tensorrt_llm==0.14.0)
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Collecting tokenizers>=0.14 (from tensorrt_llm==0.14.0)
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Collecting torch==2.4.1+cu124 (from tensorrt_llm==0.14.0)
Using cached https://download.pytorch.org/whl/cu124/torch-2.4.1%2Bcu124-cp310-cp310-win_amd64.whl (2506.2 MB)
Collecting torchvision==0.19.1+cu124 (from tensorrt_llm==0.14.0)
Using cached https://download.pytorch.org/whl/cu124/torchvision-0.19.1%2Bcu124-cp310-cp310-win_amd64.whl (5.9 MB)
Collecting nvidia-modelopt~=0.17.0 (from nvidia-modelopt[torch]~=0.17.0->tensorrt_llm==0.14.0)
Using cached https://pypi.nvidia.com/nvidia-modelopt/nvidia_modelopt-0.17.0-cp310-cp310-win_amd64.whl (1.1 MB)
Collecting transformers<=4.45.1,>=4.38.2 (from tensorrt_llm==0.14.0)
Using cached transformers-4.45.1-py3-none-any.whl.metadata (44 kB)
Requirement already satisfied: wheel in e:\ai\test\env_trt\lib\site-packages (from tensorrt_llm==0.14.0) (0.44.0)
Collecting optimum (from tensorrt_llm==0.14.0)
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Collecting typing-extensions>=4.8.0 (from torch==2.4.1+cu124->tensorrt_llm==0.14.0)
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Collecting sympy (from torch==2.4.1+cu124->tensorrt_llm==0.14.0)
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Collecting networkx (from torch==2.4.1+cu124->tensorrt_llm==0.14.0)
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Collecting fsspec (from torch==2.4.1+cu124->tensorrt_llm==0.14.0)
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Collecting pillow!=8.3.*,>=5.3.0 (from torchvision==0.19.1+cu124->tensorrt_llm==0.14.0)
Using cached pillow-11.0.0-cp310-cp310-win_amd64.whl.metadata (9.3 kB)
Collecting huggingface-hub>=0.21.0 (from accelerate>=0.25.0->tensorrt_llm==0.14.0)
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Collecting packaging>=20.0 (from accelerate>=0.25.0->tensorrt_llm==0.14.0)
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Collecting pyyaml (from accelerate>=0.25.0->tensorrt_llm==0.14.0)
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Collecting safetensors>=0.4.3 (from accelerate>=0.25.0->tensorrt_llm==0.14.0)
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Collecting importlib-metadata (from diffusers>=0.27.0->tensorrt_llm==0.14.0)
Using cached importlib_metadata-8.5.0-py3-none-any.whl.metadata (4.8 kB)
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Collecting requests (from diffusers>=0.27.0->tensorrt_llm==0.14.0)
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Collecting cloudpickle>=1.6.0 (from nvidia-modelopt~=0.17.0->nvidia-modelopt[torch]~=0.17.0->tensorrt_llm==0.14.0)
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Collecting rich (from nvidia-modelopt~=0.17.0->nvidia-modelopt[torch]~=0.17.0->tensorrt_llm==0.14.0)
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Collecting torchprofile>=0.0.4 (from nvidia-modelopt[torch]~=0.17.0->tensorrt_llm==0.14.0)
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Collecting protobuf>=3.20.2 (from onnx>=1.12.0->tensorrt_llm==0.14.0)
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Collecting tensorrt-cu12==10.4.0 (from tensorrt~=10.4.0->tensorrt_llm==0.14.0)
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Using cached requests-2.32.3-py3-none-any.whl (64 kB)
Using cached safetensors-0.4.5-cp310-none-win_amd64.whl (285 kB)
Using cached tomli-2.1.0-py3-none-any.whl (13 kB)
Using cached tqdm-4.67.0-py3-none-any.whl (78 kB)
Using cached typing_extensions-4.12.2-py3-none-any.whl (37 kB)
Using cached tzdata-2024.2-py2.py3-none-any.whl (346 kB)
Using cached coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)
Using cached filelock-3.16.1-py3-none-any.whl (16 kB)
Using cached importlib_metadata-8.5.0-py3-none-any.whl (26 kB)
Using cached jinja2-3.1.4-py3-none-any.whl (133 kB)
Using cached networkx-3.4.2-py3-none-any.whl (1.7 MB)
Using cached ninja-<IP_ADDRESS>-py2.py3-none-win_amd64.whl (312 kB)
Using cached pyproject_hooks-1.2.0-py3-none-any.whl (10 kB)
Using cached rich-13.9.4-py3-none-any.whl (242 kB)
Using cached scipy-1.14.1-cp310-cp310-win_amd64.whl (44.8 MB)
Using cached sympy-1.13.3-py3-none-any.whl (6.2 MB)
Using cached xxhash-3.5.0-cp310-cp310-win_amd64.whl (30 kB)
Using cached aiohttp-3.11.7-cp310-cp310-win_amd64.whl (440 kB)
Using cached annotated_types-0.7.0-py3-none-any.whl (13 kB)
Using cached certifi-2024.8.30-py3-none-any.whl (167 kB)
Using cached charset_normalizer-3.4.0-cp310-cp310-win_amd64.whl (102 kB)
Using cached humanfriendly-10.0-py2.py3-none-any.whl (86 kB)
Using cached idna-3.10-py3-none-any.whl (70 kB)
Using cached markdown_it_py-3.0.0-py3-none-any.whl (87 kB)
Using cached MarkupSafe-3.0.2-cp310-cp310-win_amd64.whl (15 kB)
Using cached pyarrow-18.0.0-cp310-cp310-win_amd64.whl (25.1 MB)
Using cached pygments-2.18.0-py3-none-any.whl (1.2 MB)
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Using cached urllib3-2.2.3-py3-none-any.whl (126 kB)
Using cached zipp-3.21.0-py3-none-any.whl (9.6 kB)
Using cached aiohappyeyeballs-2.4.3-py3-none-any.whl (14 kB)
Using cached aiosignal-1.3.1-py3-none-any.whl (7.6 kB)
Using cached async_timeout-5.0.1-py3-none-any.whl (6.2 kB)
Using cached attrs-24.2.0-py3-none-any.whl (63 kB)
Using cached frozenlist-1.5.0-cp310-cp310-win_amd64.whl (51 kB)
Using cached mdurl-0.1.2-py3-none-any.whl (10.0 kB)
Using cached multidict-6.1.0-cp310-cp310-win_amd64.whl (28 kB)
Using cached propcache-0.2.0-cp310-cp310-win_amd64.whl (44 kB)
Using cached yarl-1.18.0-cp310-cp310-win_amd64.whl (90 kB)
Using cached pyreadline3-3.5.4-py3-none-any.whl (83 kB)
Installing collected packages: tensorrt-cu12-bindings, StrEnum, sentencepiece, pywin32, pytz, ninja, mpmath, cuda-python, zipp, xxhash, urllib3, tzdata, typing-extensions, tomli, sympy, six, safetensors, regex, pyyaml, pyreadline3, pyproject_hooks, pynvml, pygments, pyarrow, pulp, psutil, protobuf, propcache, polygraphy, pillow, packaging, nvidia-cuda-runtime-cu12, numpy, networkx, mdurl, MarkupSafe, janus, idna, fsspec, frozenlist, filelock, dill, colored, colorama, cloudpickle, charset-normalizer, certifi, attrs, async-timeout, annotated-types, aiohappyeyeballs, tqdm, tensorrt-cu12-libs, scipy, requests, python-dateutil, pydantic-core, onnx, multiprocess, multidict, markdown-it-py, jinja2, importlib-metadata, humanfriendly, h5py, build, aiosignal, yarl, torch, tensorrt-cu12, rich, pydantic, pandas, huggingface-hub, coloredlogs, torchvision, tokenizers, tensorrt, nvidia-modelopt, diffusers, aiohttp, accelerate, transformers, torchprofile, datasets, optimum, evaluate, tensorrt_llm
Successfully installed MarkupSafe-3.0.2 StrEnum-0.4.15 accelerate-1.1.1 aiohappyeyeballs-2.4.3 aiohttp-3.11.7 aiosignal-1.3.1 annotated-types-0.7.0 async-timeout-5.0.1 attrs-24.2.0 build-1.2.2.post1 certifi-2024.8.30 charset-normalizer-3.4.0 cloudpickle-3.1.0 colorama-0.4.6 colored-2.2.4 coloredlogs-15.0.1 cuda-python-12.5.0 datasets-3.1.0 diffusers-0.31.0 dill-0.3.8 evaluate-0.4.3 filelock-3.16.1 frozenlist-1.5.0 fsspec-2024.9.0 h5py-3.10.0 huggingface-hub-0.26.2 humanfriendly-10.0 idna-3.10 importlib-metadata-8.5.0 janus-1.1.0 jinja2-3.1.4 markdown-it-py-3.0.0 mdurl-0.1.2 mpmath-1.3.0 multidict-6.1.0 multiprocess-0.70.16 networkx-3.4.2 ninja-<IP_ADDRESS> numpy-1.26.4 nvidia-cuda-runtime-cu12-12.6.77 nvidia-modelopt-0.17.0 onnx-1.17.0 optimum-1.23.3 packaging-24.2 pandas-2.2.3 pillow-11.0.0 polygraphy-0.49.9 propcache-0.2.0 protobuf-5.28.3 psutil-6.1.0 pulp-2.9.0 pyarrow-18.0.0 pydantic-2.10.1 pydantic-core-2.27.1 pygments-2.18.0 pynvml-11.5.3 pyproject_hooks-1.2.0 pyreadline3-3.5.4 python-dateutil-2.9.0.post0 pytz-2024.2 pywin32-308 pyyaml-6.0.2 regex-2024.11.6 requests-2.32.3 rich-13.9.4 safetensors-0.4.5 scipy-1.14.1 sentencepiece-0.2.0 six-1.16.0 sympy-1.13.3 tensorrt-10.4.0 tensorrt-cu12-10.4.0 tensorrt-cu12-bindings-10.4.0 tensorrt-cu12-libs-10.4.0 tensorrt_llm-0.14.0 tokenizers-0.20.3 tomli-2.1.0 torch-2.4.1+cu124 torchprofile-0.0.4 torchvision-0.19.1+cu124 tqdm-4.67.0 transformers-4.45.1 typing-extensions-4.12.2 tzdata-2024.2 urllib3-2.2.3 xxhash-3.5.0 yarl-1.18.0 zipp-3.21.0
(env_trt) PS E:\ai> pip list
Package                  Version

accelerate               1.1.1
aiohappyeyeballs         2.4.3
aiohttp                  3.11.7
aiosignal                1.3.1
annotated-types          0.7.0
async-timeout            5.0.1
attrs                    24.2.0
build                    1.2.2.post1
certifi                  2024.8.30
charset-normalizer       3.4.0
cloudpickle              3.1.0
colorama                 0.4.6
colored                  2.2.4
coloredlogs              15.0.1
cuda-python              12.5.0
datasets                 3.1.0
diffusers                0.31.0
dill                     0.3.8
evaluate                 0.4.3
filelock                 3.16.1
frozenlist               1.5.0
fsspec                   2024.9.0
h5py                     3.10.0
huggingface-hub          0.26.2
humanfriendly            10.0
idna                     3.10
importlib_metadata       8.5.0
janus                    1.1.0
Jinja2                   3.1.4
markdown-it-py           3.0.0
MarkupSafe               3.0.2
mdurl                    0.1.2
mpmath                   1.3.0
multidict                6.1.0
multiprocess             0.70.16
networkx                 3.4.2
ninja                    <IP_ADDRESS>
numpy                    1.26.4
nvidia-cuda-runtime-cu12 12.6.77
nvidia-modelopt          0.17.0
optimum                  1.23.3
packaging                24.2
pandas                   2.2.3
pillow                   11.0.0
pip                      24.2
polygraphy               0.49.9
propcache                0.2.0
protobuf                 5.28.3
psutil                   6.1.0
PuLP                     2.9.0
pyarrow                  18.0.0
pydantic                 2.10.1
pydantic_core            2.27.1
Pygments                 2.18.0
pynvml                   11.5.3
pyproject_hooks          1.2.0
pyreadline3              3.5.4
python-dateutil          2.9.0.post0
pytz                     2024.2
pywin32                  308
PyYAML                   6.0.2
regex                    2024.11.6
requests                 2.32.3
rich                     13.9.4
safetensors              0.4.5
scipy                    1.14.1
sentencepiece            0.2.0
setuptools               75.1.0
six                      1.16.0
StrEnum                  0.4.15
sympy                    1.13.3
tensorrt                 10.4.0
tensorrt-cu12            10.4.0
tensorrt-cu12_bindings   10.4.0
tensorrt-cu12_libs       10.4.0
tensorrt-llm             0.14.0
tokenizers               0.20.3
tomli                    2.1.0
torch                    2.4.1+cu124
torchprofile             0.0.4
torchvision              0.19.1+cu124
tqdm                     4.67.0
transformers             4.45.1
typing_extensions        4.12.2
tzdata                   2024.2
urllib3                  2.2.3
wheel                    0.44.0
xxhash                   3.5.0
yarl                     1.18.0
zipp                     3.21.0
(env_trt) PS E:\ai> python -c "import tensorrt_llm; print(tensorrt_llm._utils.trt_version())"
[TensorRT-LLM] TensorRT-LLM version: 0.14.0
10.4.0
(env_trt) PS E:\ai> conda deactivate
PS E:\ai> pip list
Package    Version

pip        24.3.1
setuptools 69.2.0
wheel      0.43.0
PS E:\ai>
Last part is just a check that nothing accidentally installed to my "base" environment due to a incorrect "env" install.

...Also of note, is that I was partially wrong above...
Stick with the cu124 --extra-index-url https://download.pytorch.org/whl/cu124 link.
Been randomly getting a non-cudu versions with some scripts using the new link --extra-index-url https://download.pytorch.org/whl/
Best to force cu124 if doing nvidia gpu.
FWIW


genome-wize_normalize_reads_count

@TammushB - i am waiting until the checks pass, right?

@ilyasoifer I think we are done here. we added a normalization script that includes the option of gender estimation. also we updated cn.mops pull version to include latest fixes.


xxdiff-tip: <IP_ADDRESS>70623 -> 401

Semi-automatic update generated by https://github.com/ryantm/nix-update tools. These checks were done:

built on NixOS
Warning: no binary found that responded to help or version flags. (This warning appears even if the package isn't expected to have binaries.)
directory tree listing: https://gist.github.com/cb116be3c9ecff08d628f2c946273aa2

cc @pSub @7c6f434c for review

No attempt on x86_64-darwin
The following builds were skipped because they don't evaluate on x86_64-darwin: xxdiff-tip
No partial log is available.

Failure on aarch64-linux (full log)
Attempted: xxdiff-tip
Partial log (click to expand)
building '/nix/store/dj1ilff5mj1symx4rshj548qqqah4nni-source.drv'...

trying https://bitbucket.org/blais/xxdiff/get/5e5f885dfc43559549a81c59e9e8c9525306356a.tar.gz
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 1996k  100 1996k    0     0  9373k      0 --:--:-- --:--:-- --:--:-- 9329k
unpacking source archive /build/5e5f885dfc43559549a81c59e9e8c9525306356a.tar.gz
fixed-output derivation produced path '/nix/store/cfbk70h07069hp50hw5agc93k64hw64z-source' with sha256 hash '0gbvxrkwkbvag3298j89smszghpr8ilxxfb0cvsknfqdf15b296w' instead of the expected hash '0x954xinn91clf0v7bg4p5nx72lzy2jf6hzhx31zi2ff2v01p4qx'
cannot build derivation '/nix/store/xp8sj7in8r06qjh1p2isgzpgdwpkyg8j-xxdiff-401.drv': 1 dependencies couldn't be built
error: build of '/nix/store/xp8sj7in8r06qjh1p2isgzpgdwpkyg8j-xxdiff-401.drv' failed

Failure on x86_64-linux (full log)
Attempted: xxdiff-tip
Partial log (click to expand)
--     0

 11 1996k   11  236k    0
   0   307k      0  0:00:06 --:--:--  0:00:
06  307k

100 1996k  100 1996k    0     0  1967k      0  0:00:01
0:00:01 --:--:-- 1969k
unpacking source archive /tmp/nix-build-source.drv-0/5e5f885dfc43559549a81c59e9e8c9525306356a.tar.gz
output path ‘/nix/store/7qjyvciknhwyfb8w7jcci4nvmb3bhrx2-source’ has r:sha256 hash ‘0gbvxrkwkbvag3298j89smszghpr8ilxxfb0cvsknfqdf15b296w’ when ‘0x954xinn91clf0v7bg4p5nx72lzy2jf6hzhx31zi2ff2v01p4qx’ was expected
cannot build derivation ‘/nix/store/sq5xn6khxfah611bfx5vgzaiyh5f3v2f-xxdiff-401.drv’: 1 dependencies couldn't be built
error: build of ‘/nix/store/sq5xn6khxfah611bfx5vgzaiyh5f3v2f-xxdiff-401.drv’ failed


 Creating an equivalent image with GIMP, Inkscape or whatever is more or less the same in effort. ASCII art is nice, but we never really used it.
The PRs https://github.com/arangodb/arangodb/pull/8354 and https://github.com/arangodb/arangodb/pull/8386 were not merged because of issues with Oskar.

Already resolved in https://github.com/arangodb/docs/commit/b52391a51765ea7fb041246e3aeedeca0c305bd1


X-Upcache-Upstream: map a domain to an upstream name

This would allow a domain to auto-configure the upstream application it needs to be served with.
The upstream applications are responsible for handling "wrong first upstream request" case - when the domain is not yet mapped to an upstream name and the first request was on a wrong one, it's up to the application to make the client ask again. This happens only once per nginx restart though.
X-Upcache-Upstream values must match upstream names returned by lua-upstream-nginx-module's get_upstreams. However these upstreams are not really used.
Using balancer_by_lua, the peers are "imported" into the upstream that is specified in proxy_pass.

The default upstream instance can return X-Upstream header on the first /.well-known/pageboard request for a given domain. All future calls will then be done to that host:port for that domain.


Bug with reading cookie set by other libraries

I have experienced strange behaviour on some browsers. The library suddenly stopped working with type error URIError: malformed URI sequence
Stack trace:
cookieFun@............./angular-cookie.js:77:1
The problem comes with wrong assumption that all cookies for that domain is created only by this library and are encoded with encodeURIComponent. In some cases there are some other cookies for the domain created by another scripts which is not encoded on the same way.
The problem could be fixed by replacing:
value = decodeURIComponent(cookie.substring(pos + 1));   /* 77 row */
with:
try {
value = decodeURIComponent(cookie.substring(pos + 1));
} catch (e) {
value = cookie.substring(pos+1);
}
The whole idea to encode cookies seems to be wrong because you can accidentally damage other third party cookies for instance: google analytics cookie.

https://github.com/ivpusic/angular-cookie/pull/48


First text added to main intro page

@marqh @bjlittle @dkillick
This is a first attempt at adding some text to the introduction on the homepage.  Please let me know if you can see some areas of improvement or some better wording, or whatever.

Coverage remained the same at 99.481% when pulling 599dc006f3bd0f0c415d42acb5f3c3530a3141c2 on corinnebosley:docs1 into ab5872b672fea84a516e90696d3de48d749b6cf2 on SciTools:master.

Yes.  They sound better.

Coverage remained the same at 99.481% when pulling eb9be4ea7db0dc743017af626630c320ac25c176 on corinnebosley:docs1 into ab5872b672fea84a516e90696d3de48d749b6cf2 on SciTools:master.

Coverage remained the same at 99.481% when pulling 8ddad424c2d77be06b548e13e6cae669471ea11e on corinnebosley:docs1 into ab5872b672fea84a516e90696d3de48d749b6cf2 on SciTools:master.

@marqh
I am now happy with this iteration of the main docs page.  Unless you have any more suggestions I am keen to get this merged and move forward.

Thank you

Coverage remained the same at 99.481% when pulling 2f4c786383f1f6cd7b4078689b6014f3da0a46f4 on corinnebosley:docs1 into ab5872b672fea84a516e90696d3de48d749b6cf2 on SciTools:master.


do not change the order of the remotes

Changelog: (Bugfix):
Fixes #613

[x] I've read the Contributing guide.
[x] I've followed the PEP8 style guides for Python code.

Do not change the order of the remotes by using the default value from the Conan API instead of -1 in add_remotes_to_conan -> _add_remote
Probably in Remotesmanager._add_remote the default argument of insert should be changed to None instead of False. The ConanAPI uses None as a the default value. False would be converted to index 0 which could lead to problems. None has it's own handling and adds the remote to the end of the remotes OrderedDict [see remote_registry.py : Remotes._remotes].

 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.


Problem in add_custom_model with data_path

Hi! I was using the new version, but I get an error when using add_custom_model. It doesn't recognize the data_path as a str (?)
I get this error:
TypeCheckError: value assigned to data_path (pathlib.PosixPath) is not an instance of str
I tried using

'/Users/cosmicbabs/libraries/raw/
"/Users/cosmicbabs/libraries/raw/"
from pathlib import Path -> data_path = str(Path('/Users/cosmicbabs/libraries/raw/'))

I downgraded to v0.8.3, and I got no errors.
Here is the full error (the problem is in _):

TypeCheckError                            Traceback (most recent call last)
Cell In[15], line 6
1 # ADD all models
2
3 # add btsett-agss
4 data_path_btsett_agss = str(Path('/Users/cosmicbabs/libraries/raw/btsett-agss/ascii_4Species_R250'))
----> 6 database.add_custom_model(model='btsett-agss',
7                           data_path=data_path_btsett_agss,
8                           parameters = ['teff', 'logg', 'feh'],
9                           wavel_range=(0.30, 5.49),
10                           teff_range=(2700,3500)
11                          )
12 # add btsett-cifist
13 database.add_custom_model(model='btsett-cifist',
14                           data_path='/Users/cosmicbabs/libraries/raw/btsett-cifist/ascii_4Species_R250/',
15                           parameters = ['teff', 'logg', 'feh'],
16                           wavel_range=(0.30, 5.49),
17                           teff_range=(2700,3500)
18                          )
File ~/anaconda3/envs/speciesenv/lib/python3.10/site-packages/species/data/database.py:821, in add_custom_model(self, model, data_path, parameters, wavel_range, wavel_sampling, teff_range)
818 from species.data.model_data.custom_model import add_custom_model_grid
820 with h5py.File(self.database, "a") as hdf5_file:
--> 821     add_custom_model_grid(
822         model,
823         data_path,
824         parameters,
825         hdf5_file,
826         wavel_range,
827         teff_range,
828         wavel_sampling,
829     )
File ~/anaconda3/envs/speciesenv/lib/python3.10/site-packages/species/data/model_data/custom_model.py:152, in add_custom_model_grid(model_name, data_path, parameters, database, wavel_range, teff_range, wavel_sampling)
149 print_message = ""
150 count = 0
--> 152 data_path = Path(data_path)
153 model_files = sorted(data_path.glob("*"))
155 for file_item in model_files:
File ~/anaconda3/envs/speciesenv/lib/python3.10/site-packages/typeguard/_functions.py:251, in check_variable_assignment(value, varname, annotation, memo)
248     return value
250 try:
--> 251     check_type_internal(value, annotation, memo)
252 except TypeCheckError as exc:
253     qualname = qualified_name(value, add_class_prefix=True)
File ~/anaconda3/envs/speciesenv/lib/python3.10/site-packages/typeguard/_checkers.py:866, in check_type_internal(value, annotation, memo)
864 if isclass(origin_type):
865     if not isinstance(value, origin_type):
--> 866         raise TypeCheckError(f"is not an instance of {qualified_name(origin_type)}")
867 elif type(origin_type) is str:  # noqa: E721
868     warnings.warn(
869         f"Skipping type check against {origin_type!r}; this looks like a "
870         f"string-form forward reference imported from another module",
871         TypeHintWarning,
872         stacklevel=get_stacklevel(),
873     )
TypeCheckError: value assigned to data_path (pathlib.PosixPath) is not an instance of str

Hi Bárbara, thanks for opening this issue and trying out add_custom_model!
There was indeed a problem introduced in this function when I recently changed the use of the os.path module to pathlib.
I have fixed this in the latest commit that I just pushed so if you install the Github version then it should work.
Let me know in case you run into any further problems!

Closing this issue since it has been resolved.


How to create/use the payload ?

I install the plugin and it work right. I want to send payload notification but i don't know how to create and how to use.
The  navigator.notification.alert(
data.message,         // message
null,                 // callback
data.title,           // title
'Ok'                  // buttonName
);
Doesn't display.
Thanks to help/give to me the step ?
Cordova CLI version and cordova platform version
cordova --version                                     6.1.1
cordova platform version android                      4.4.2

@tahoura many example on how to use node to send pushes are available at: https://github.com/phonegap/phonegap-plugin-push/blob/master/docs/PAYLOAD.md

@macdonst  thanks but i try an example but doesn't work. Is there any configuration to do ?


how to start MAUI project on mac system. need a documentation for visual studio for mac

[Enter feedback here]

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

ID: 3275e223-a9cc-1efb-07ec-01c8501d7a46
Version Independent ID: 538673ca-22c5-2a5c-97c0-1552ae9f7b79
Content: Migrate your app from Xamarin.Forms to .NET MAUI
Content Source: docs/get-started/migrate.md
Product: dotnet-mobile
Technology: dotnet-maui
GitHub Login: @davidbritch
Microsoft Alias: dabritch

Hi @Win14-dev
There is no VSMac support right now. To use it on a Mac you'd have to use .NET CLI, and an editor such as VSCode.


Event Request - Parameter by reference in table 5870 "BOM Buffer" event publisher OnAfterCalcUnitCost

Dear AL Development Team
In table 5870 "BOM Buffer" we need that in event publisher OnAfterCalcUnitCost the parameter BOMBuffer declared by reference.
OLD CODE:
[IntegrationEvent(false, false)]
local procedure OnAfterCalcUnitCost(BOMBuffer: Record "BOM Buffer")
begin
end;
NEW CODE:
[IntegrationEvent(false, false)]
local procedure OnAfterCalcUnitCost(var BOMBuffer: Record "BOM Buffer")
begin
end;
Thanks in advance!

This is added and will be available from BC23.

Thanks for reporting this. We agree, and we’ll publish a fix asap, either in an update for the current version or in the next major release. Please do not reply to this, as we do not monitor closed issues. If you have follow-up questions or requests, please create a new issue where you reference this one.
Build ID: .


Publisher Datastore model

#1518

I remember that we've talked about Publisher containing the member entries, but I don't think we should do that. Having a separate entity for the membership allows us to track invites and roles separately.
The invite also differs from the PackageInvite because it doesn't need a nonce, we can do it on the pub site, without a shared secret.
Added publisherId to both Package and PackageVersion, the later to track if a specific version was published under the publisher or earlier (or under a different publisher if it gets moved between them).

A structure concern to discuss: should we start a new directory or put it somewhere else? E.g. validating an uploader would tie this to the frontend's Package object, but otherwise it feels like it should be separate from it, and hasUploader check should call publisher/backend's check if needed.


[WIP] Add Swift Package Manager Support

Can't merge for now, waiting for Device to add SPM support (There's a PR)

Awesome @cipi1965 ! Thanks


Activate of a module such as camera failed

Hello!
I have set up the Apollo and carla bridge as per the guide in the README, but several modules are not working, including the camera.
As you can see in the image below, I could not enable some modules and nothing was taken from the camera in the Tasks bar.

Environment
Apollo: 8.0.0
carla_apollo_bridge: commit: 3ac71a1762b1557736bd0fcb23579051bec345b5
Carla: 0.9.14
Logs

bridge log

2023-09-07 13:50:22.475 | INFO     | __main__:main:187 - Trying to connect to <IP_ADDRESS>:2000
2023-09-07 13:50:24.679 | INFO     | __main__:main:199 - Connect to <IP_ADDRESS>:2000 successfully.
2023-09-07 13:50:25.817 | DEBUG    | carla_bridge.core.carla_spawn_objects:setup_sensors:144 - sensor: {'type': 'sensor.pseudo.traffic_lights', 'id': 'traffic_lights'}, attach: None
2023-09-07 13:50:25.818 | INFO     | carla_bridge.core.carla_spawn_objects:setup_vehicles:118 - Spawn point selected at random
2023-09-07 13:50:26.139 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created EgoVehicle(id=377)
2023-09-07 13:50:26.139 | DEBUG    | carla_bridge.core.carla_spawn_objects:setup_sensors:144 - sensor: {'type': 'sensor.camera.rgb', 'id': 'front_6mm', 'spawn_point': {'x': 2.0, 'y': 0.0, 'z': 2.0, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0}, 'image_size_x': 800, 'image_size_y': 600, 'fov': 90.0, 'fstop': 8.0}, attach: 377
2023-09-07 13:50:26.140 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=376)
2023-09-07 13:50:26.140 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=375)
2023-09-07 13:50:26.141 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=374)
2023-09-07 13:50:26.141 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=373)
2023-09-07 13:50:26.141 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=372)
2023-09-07 13:50:26.141 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=371)
2023-09-07 13:50:26.142 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=370)
2023-09-07 13:50:26.142 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=369)
2023-09-07 13:50:26.142 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=368)
2023-09-07 13:50:26.142 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=367)
2023-09-07 13:50:26.143 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=366)
2023-09-07 13:50:26.143 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=365)
2023-09-07 13:50:26.143 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=364)
2023-09-07 13:50:26.143 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=363)
2023-09-07 13:50:26.144 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=362)
2023-09-07 13:50:26.144 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=361)
2023-09-07 13:50:26.144 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=360)
2023-09-07 13:50:26.144 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=359)
2023-09-07 13:50:26.145 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=358)
2023-09-07 13:50:26.145 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=357)
2023-09-07 13:50:26.145 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=356)
2023-09-07 13:50:26.146 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=355)
2023-09-07 13:50:26.146 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=354)
2023-09-07 13:50:26.146 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=353)
2023-09-07 13:50:26.146 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=352)
2023-09-07 13:50:26.147 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=351)
2023-09-07 13:50:26.147 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=350)
2023-09-07 13:50:26.147 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=349)
2023-09-07 13:50:26.147 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=348)
2023-09-07 13:50:26.147 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=347)
2023-09-07 13:50:26.148 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=346)
2023-09-07 13:50:26.148 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=345)
2023-09-07 13:50:26.148 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=344)
2023-09-07 13:50:26.148 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=343)
2023-09-07 13:50:26.149 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=342)
2023-09-07 13:50:26.149 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=341)
2023-09-07 13:50:26.149 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=340)
2023-09-07 13:50:26.149 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=339)
2023-09-07 13:50:26.149 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=338)
2023-09-07 13:50:26.150 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=337)
2023-09-07 13:50:26.150 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=336)
2023-09-07 13:50:26.150 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=335)
2023-09-07 13:50:26.150 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=334)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=333)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=332)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=331)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=330)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=329)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=328)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=327)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=326)
2023-09-07 13:50:26.151 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=325)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=324)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=323)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=322)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=321)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=320)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=319)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=318)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=317)
2023-09-07 13:50:26.152 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=316)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=315)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=314)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=313)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=312)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=311)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=310)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=309)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=308)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=307)
2023-09-07 13:50:26.153 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=306)
2023-09-07 13:50:26.154 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=305)
2023-09-07 13:50:26.154 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=304)
2023-09-07 13:50:26.154 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=303)
2023-09-07 13:50:26.154 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=302)
2023-09-07 13:50:26.155 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=301)
2023-09-07 13:50:26.155 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=300)
2023-09-07 13:50:26.155 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=299)
2023-09-07 13:50:26.155 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=298)
2023-09-07 13:50:26.155 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=297)
2023-09-07 13:50:26.156 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=296)
2023-09-07 13:50:26.156 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=295)
2023-09-07 13:50:26.156 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=294)
2023-09-07 13:50:26.156 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=293)
2023-09-07 13:50:26.156 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=292)
2023-09-07 13:50:26.157 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=291)
2023-09-07 13:50:26.157 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=290)
2023-09-07 13:50:26.157 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=289)
2023-09-07 13:50:26.157 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=288)
2023-09-07 13:50:26.158 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=287)
2023-09-07 13:50:26.158 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=286)
2023-09-07 13:50:26.158 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=285)
2023-09-07 13:50:26.158 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=284)
2023-09-07 13:50:26.158 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=283)
2023-09-07 13:50:26.159 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=282)
2023-09-07 13:50:26.159 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=281)
2023-09-07 13:50:26.159 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=280)
2023-09-07 13:50:26.159 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=279)
2023-09-07 13:50:26.159 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=278)
2023-09-07 13:50:26.160 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=277)
2023-09-07 13:50:26.160 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=276)
2023-09-07 13:50:26.160 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=275)
2023-09-07 13:50:26.160 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=274)
2023-09-07 13:50:26.160 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=273)
2023-09-07 13:50:26.161 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=272)
2023-09-07 13:50:26.161 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=271)
2023-09-07 13:50:26.161 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=270)
2023-09-07 13:50:26.161 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=269)
2023-09-07 13:50:26.161 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=268)
2023-09-07 13:50:26.162 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=267)
2023-09-07 13:50:26.162 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=266)
2023-09-07 13:50:26.162 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=265)
2023-09-07 13:50:26.162 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=264)
2023-09-07 13:50:26.162 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=263)
2023-09-07 13:50:26.163 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Static(id=262)
2023-09-07 13:50:26.163 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=261)
2023-09-07 13:50:26.163 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=260)
2023-09-07 13:50:26.163 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=259)
2023-09-07 13:50:26.164 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=258)
2023-09-07 13:50:26.164 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=257)
2023-09-07 13:50:26.164 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=256)
2023-09-07 13:50:26.164 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=255)
2023-09-07 13:50:26.164 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=254)
2023-09-07 13:50:26.165 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=253)
2023-09-07 13:50:26.165 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=252)
2023-09-07 13:50:26.165 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=251)
2023-09-07 13:50:26.165 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=250)
2023-09-07 13:50:26.165 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=249)
2023-09-07 13:50:26.166 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=248)
2023-09-07 13:50:26.166 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=247)
2023-09-07 13:50:26.166 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=246)
2023-09-07 13:50:26.166 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=245)
2023-09-07 13:50:26.166 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=244)
2023-09-07 13:50:26.167 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=243)
2023-09-07 13:50:26.167 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=242)
2023-09-07 13:50:26.167 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Traffic(id=241)
2023-09-07 13:50:26.167 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=240)
2023-09-07 13:50:26.167 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=239)
2023-09-07 13:50:26.168 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=238)
2023-09-07 13:50:26.168 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=237)
2023-09-07 13:50:26.168 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=236)
2023-09-07 13:50:26.168 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=235)
2023-09-07 13:50:26.168 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=234)
2023-09-07 13:50:26.169 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=233)
2023-09-07 13:50:26.169 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=232)
2023-09-07 13:50:26.169 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=231)
2023-09-07 13:50:26.169 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=230)
2023-09-07 13:50:26.169 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=229)
2023-09-07 13:50:26.169 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=228)
2023-09-07 13:50:26.170 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=227)
2023-09-07 13:50:26.170 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=226)
2023-09-07 13:50:26.170 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=225)
2023-09-07 13:50:26.170 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=224)
2023-09-07 13:50:26.170 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=223)
2023-09-07 13:50:26.170 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=222)
2023-09-07 13:50:26.170 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=221)
2023-09-07 13:50:26.171 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=220)
2023-09-07 13:50:26.171 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=219)
2023-09-07 13:50:26.171 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=218)
2023-09-07 13:50:26.171 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=217)
2023-09-07 13:50:26.171 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=216)
2023-09-07 13:50:26.171 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=215)
2023-09-07 13:50:26.171 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=214)
2023-09-07 13:50:26.171 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=213)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=212)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=211)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=210)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=209)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=208)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=207)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=206)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLight(id=205)
2023-09-07 13:50:26.172 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Spectator(id=204)
2023-09-07 13:50:26.303 | DEBUG    | carla_bridge.core.carla_spawn_objects:setup_sensors:144 - sensor: {'type': 'sensor.camera.rgb', 'id': 'front_12mm', 'spawn_point': {'x': 2.5, 'y': 0.0, 'z': 2.8, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0}, 'image_size_x': 800, 'image_size_y': 600, 'focal_distance': 2000, 'fov': 90.0, 'fstop': 8.0}, attach: 377
2023-09-07 13:50:26.305 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created TrafficLightsSensor(id=10000)
2023-09-07 13:50:26.466 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created RgbCamera(id=378)
2023-09-07 13:50:26.654 | DEBUG    | carla_bridge.core.carla_spawn_objects:setup_sensors:144 - sensor: {'type': 'sensor.lidar.ray_cast', 'id': 'lidar128', 'spawn_point': {'x': 0.0, 'y': 0.0, 'z': 2.4, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0}, 'range': 50, 'channels': 128, 'points_per_second': 320000, 'upper_fov': 2.0, 'lower_fov': -26.8, 'rotation_frequency': 20, 'noise_stddev': 0.0}, attach: 377
2023-09-07 13:50:26.804 | DEBUG    | carla_bridge.core.carla_spawn_objects:setup_sensors:144 - sensor: {'type': 'sensor.other.radar', 'id': 'radar/front', 'spawn_point': {'x': 2.0, 'y': 0.0, 'z': 2.0, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0}, 'horizontal_fov': 30.0, 'vertical_fov': 10.0, 'points_per_second': 1500, 'range': 100.0}, attach: 377
2023-09-07 13:50:26.989 | DEBUG    | carla_bridge.core.carla_spawn_objects:setup_sensors:144 - sensor: {'type': 'sensor.other.gnss', 'id': 'gnss', 'spawn_point': {'x': 1.0, 'y': 0.0, 'z': 2.0}, 'noise_alt_stddev': 0.0, 'noise_lat_stddev': 0.0, 'noise_lon_stddev': 0.0, 'noise_alt_bias': 0.0, 'noise_lat_bias': 0.0, 'noise_lon_bias': 0.0}, attach: 377
2023-09-07 13:50:27.584 | DEBUG    | carla_bridge.core.carla_spawn_objects:setup_sensors:144 - sensor: {'type': 'sensor.other.imu', 'id': 'gnss/imu', 'spawn_point': {'x': 2.0, 'y': 0.0, 'z': 2.0, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0}, 'noise_accel_stddev_x': 0.0, 'noise_accel_stddev_y': 0.0, 'noise_accel_stddev_z': 0.0, 'noise_gyro_stddev_x': 0.0, 'noise_gyro_stddev_y': 0.0, 'noise_gyro_stddev_z': 0.0, 'noise_gyro_bias_x': 0.0, 'noise_gyro_bias_y': 0.0, 'noise_gyro_bias_z': 0.0}, attach: 377
2023-09-07 13:50:27.584 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Radar(id=381)
2023-09-07 13:50:27.767 | DEBUG    | carla_bridge.core.carla_spawn_objects:setup_sensors:144 - sensor: {'type': 'sensor.pseudo.objects', 'id': 'obstacles'}, attach: 377
2023-09-07 13:50:27.767 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Lidar(id=380)
2023-09-07 13:50:27.768 | INFO     | carla_bridge.core.carla_spawn_objects:spawn_objects:88 - All objects spawned.
2023-09-07 13:50:27.928 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created RgbCamera(id=379)
2023-09-07 13:50:28.139 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created Gnss(id=382)
2023-09-07 13:50:28.290 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created ImuSensor(id=383)
2023-09-07 13:50:28.294 | INFO     | carla_bridge.core.actor_factory:_create_object:365 - Created ObjectSensor(id=10001)

Thank you in advance.

Seems like the camera object is created correctly. How about the Camera View in Task window? Any image?
Another suggestion is to look into the log in the apollo/data/log folder of the apollo container. There should be error in mainboard or camera describing why the module start failed.

I0911 11:52:31.508060 11305 processor.cc:42] processor_tid: 11305
I0911 11:52:31.508106 11306 processor.cc:42] processor_tid: 11306
I0911 11:52:31.508121 11307 processor.cc:42] processor_tid: 11307
I0911 11:52:31.508188 11308 processor.cc:42] processor_tid: 11308
I0911 11:52:31.508210 11309 processor.cc:42] processor_tid: 11309
I0911 11:52:31.508224 11310 processor.cc:42] processor_tid: 11310
I0911 11:52:31.508306 11311 processor.cc:42] processor_tid: 11311
I0911 11:52:31.508311 11313 processor.cc:42] processor_tid: 11313
I0911 11:52:31.508314 11312 processor.cc:42] processor_tid: 11312
I0911 11:52:31.508359 11314 processor.cc:42] processor_tid: 11314
I0911 11:52:31.508369 11315 processor.cc:42] processor_tid: 11315
I0911 11:52:31.508392 11316 processor.cc:42] processor_tid: 11316
I0911 11:52:31.508482 11271 init.cc:113] Register exit handle succ.
I0911 11:52:31.508487 11271 py_cyber.h:58] cyber init succ.
I0911 11:52:31.508492 11320 processor.cc:42] processor_tid: 11320
I0911 11:52:31.508507 11317 processor.cc:42] processor_tid: 11317
I0911 11:52:31.508517 11319 processor.cc:42] processor_tid: 11319
I0911 11:52:31.508510 11318 processor.cc:42] processor_tid: 11318
I0911 11:52:37.609290 11363 scheduler.cc:55] create croutine: cyber_bridge_node_/apollo/control
I0911 11:52:37.609606 11363 py_cyber.cc:250] reader regist fun
I0911 11:53:09.464458 11363 segment.cc:51] msg_size: 93846 larger than current shm_buffer_size: 16384 , need recreate.

mainboard.INFO

Log file created at: 2023/09/11 11:53:10
Running on machine: in-dev-docker
Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg
W0911 11:53:10.338419 12132 scheduler_factory.cc:63] Scheduler conf named /apollo/cyber/conf/mainboard_default.conf not found, use default.
I0911 11:53:10.338524 12141 processor.cc:42] processor_tid: 12141
I0911 11:53:10.338567 12142 processor.cc:42] processor_tid: 12142
I0911 11:53:10.338590 12143 processor.cc:42] processor_tid: 12143
I0911 11:53:10.338608 12144 processor.cc:42] processor_tid: 12144
I0911 11:53:10.338625 12145 processor.cc:42] processor_tid: 12145
I0911 11:53:10.338654 12146 processor.cc:42] processor_tid: 12146
I0911 11:53:10.338675 12147 processor.cc:42] processor_tid: 12147
I0911 11:53:10.338697 12148 processor.cc:42] processor_tid: 12148
I0911 11:53:10.338716 12149 processor.cc:42] processor_tid: 12149
I0911 11:53:10.338747 12150 processor.cc:42] processor_tid: 12150
I0911 11:53:10.338760 12151 processor.cc:42] processor_tid: 12151
I0911 11:53:10.338776 12152 processor.cc:42] processor_tid: 12152
I0911 11:53:10.338794 12153 processor.cc:42] processor_tid: 12153
I0911 11:53:10.338816 12154 processor.cc:42] processor_tid: 12154
I0911 11:53:10.338835 12155 processor.cc:42] processor_tid: 12155
I0911 11:53:10.338855 12156 processor.cc:42] processor_tid: 12156
I0911 11:53:10.338860 12132 init.cc:113] Register exit handle succ.
E0911 11:53:10.338872 12132 file.cc:76] Failed to open file /apollo/modules/drivers/velodyne/dag/velodyne.dag in text mode.
E0911 11:53:10.338887 12132 file.cc:102] Failed to open file /apollo/modules/drivers/velodyne/dag/velodyne.dag in binary mode.
I0911 11:53:10.338893 12132 module_controller.cc:65] Start initialize dag: /apollo/modules/drivers/velodyne/dag/velodyne.dag
E0911 11:53:10.338896 12132 file.cc:76] Failed to open file /apollo/modules/drivers/velodyne/dag/velodyne.dag in text mode.
E0911 11:53:10.338898 12132 file.cc:102] Failed to open file /apollo/modules/drivers/velodyne/dag/velodyne.dag in binary mode.
E0911 11:53:10.338900 12132 module_controller.cc:119] Get proto failed, file: /apollo/modules/drivers/velodyne/dag/velodyne.dag
E0911 11:53:10.338901 12132 module_controller.cc:67] Failed to load module: /apollo/modules/drivers/velodyne/dag/velodyne.dag
E0911 11:53:10.338904 12132 mainboard.cc:39] module start error.

Are there any other logs needed?

I have the same problem. did the problem be solved now?

For me https://github.com/guardstrikelab/carla_apollo_bridge/issues/171#issuecomment-1868243510 fixed it.

For me #171 (comment) fixed it in the Tasks View. But still, I am not able to activate the camera module in the Module Controller View.

Camera module in the Module Controller actually means nothing. It was designed for Apollo to use for real car deployment to turn on the camera "Sensor" and since we get the image from Carla we don't need to turn on the Camera. If you want to use image-based perception, you should use "mainboard" command in the terminal to activate those related modules instead of using the Dreamview. The perception module started by turning on the "Perception" button is actually only pointcloud-based. You can check it in the dag_streaming_perception.dag file to find out which modules it activated.


Websocket Logs are backfilled

The websocket should send all the logs up until that point on a new connection. This includes the scenario where the build is complete, we should send all the logs.

Ha, yeah I forgot to implement this in the new JSON controller. The way we implemented it in the non-JSON controller is not by using a socket connection, but just the regular API
More info here https://github.com/fastlane/ci/blob/111cdc06add3ce501ff73f4a83bd5bd0caacae4b/app/features/build/build_controller.rb#L85-L100
When it comes to fetching the previous output, this is blocked by the distributed worker work, as we'll need to know how it's being stored

Yeah i think i'm gonna copy the current implementation for now.
I think for the worker it would make sense to always use the websocket and just backfill the logs that have been missed on a new connection. With the last line being an EOT we will close the websocket.

@snatchev do you think this fits as a part of the distributed builds?

Yup, it's mostly working at the moment on my branch
https://github.com/fastlane/ci/pull/951
On Tue, Jul 3, 2018 at 1:49 PM Nima Akhbari<EMAIL_ADDRESS>wrote:

@snatchev https://github.com/snatchev do you think this fits as a part
of the distributed builds?
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You are receiving this because you were mentioned.
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This feature is live in #1093


mismatch size of tensor

Hi! I got this error during test.
clear_feature_ch[:, :, ks:-ks, ks:-ks] 's shape is torch.Size([1, 1, 256, 256, 2, 2])
I'm using 2 scale pretrained model and Set5 lmdb.
The only thing I modified from the existing code is the fft part in utils.

how can I solve this?

Hi, if you are running our code on PyTorch 1.8+, you may want to try this code for torch.fft function: https://github.com/megvii-research/DCLS-SR/issues/12#issuecomment-1312684964