Keyboard user cannot access overflow menu items in Data Table v2 component

Environment
Chrome 70
macOS Mojave
Violation
The user can fire the overflow menu on a Data Table row, but cannot enter the menu or select the menu items inside.

Need to update the markup, putting data-flowing-menu-primary-focus to the <button> of first item.

@asudoh Is there any expected deadline to fix this issue?

@dakahn @aparnacyanam Is this issue about how the overflow menu doesn't bring the first item to focus and it should? Or just that the menu items aren't accessible? I'm looking at the latest vanilla version locally and I'm able to access the items by using my arrow keys, and selecting by hitting enter. Is seems like that's what this issue is about but I'm not sure if I'm missing something.

@jnm2377 With keyboard overflow menu is accessible(what you have shown in the above comment is completely working) but click on those functions is not working. In our product, we have features like redirecting to a new page or opening a modal or calling a click function, when you click on the overflow menu option which is not keyboard accessible. This particular issue has blocked our product from passing the accessibility test and hence needed a fix at the earliest possible. Let me know if you need an example, I can forward you our product working env link.

For reviewers reference: the issue is that overflow menu items are not triggering something (i.e. modal opens, or link takes you somewhere) on keydown, but they do on click. Seems to be related to the keydown event listener.
Here's an example: https://codepen.io/anon/pen/pGGbyR

Also, this may be an overall overflow menu issue not just data table, since data table is just using overflow menu markup/js.


Fix: pathspec 'gh-pages' did not match any file(s) known to git

As seen at https://travis-ci.org/openzipkin/zipkin/builds/156529896, javadoc publishing is currently broken. This is an attempt to fix it.

Thanks! meant to ping you about this.


Adding trace logging to Environment Manager

This PR adds simple trace logging to Environment Manager.  Every API surface call has a simple trace logging method.


Expand explanation on pulseaudio crashing

Hi jess - I am hoping you might expand on the solution noted in closed issue #38 re; pulseaudio crashing (I would also echo how great your container work is!).  Am having same trouble as xinity, and am unclear how this was solved. I'm trying to get the Tor and pulseaudio containers to link (in virtualbox Centos 7, Mate VMs).  Like xinity, I have pulseaudio running on the host but have tried uninstalling it with no joy.  I think pulseaudio is crashing with same errors shown in #38.   Did solution "adapt my host config" mean changing the "HostConfig object" in a way that pierces the container and is there a URL you could suggest on how to do this? Does this introduce security issues by having the container use the host in this way and if so, is there another way to get my pulseaudio container to work? I'm using Docker 1.8.3. Thanks!

Hey I responded on the old issue you don't need a new one

Apologies (and thanks so much for the help)
Ted Barnes
Soholistic Networks
www.soholistic.net
(508) 277 9666
On 10/30/2015 10:44 AM, Jess Frazelle wrote:

Hey I responded on the old issue you don't need a new one
—
Reply to this email directly or view it on GitHub
https://github.com/jfrazelle/dockerfiles/issues/75#issuecomment-152544573.

No worries :)
On Fri, Oct 30, 2015 at 7:54 AM -0700, "senrabdet"<EMAIL_ADDRESS>wrote:
Apologies (and thanks so much for the help)
Ted Barnes
Soholistic Networks
www.soholistic.net
(508) 277 9666
On 10/30/2015 10:44 AM, Jess Frazelle wrote:

Hey I responded on the old issue you don't need a new one

—

Reply to this email directly or view it on GitHub

https://github.com/jfrazelle/dockerfiles/issues/75#issuecomment-152544573.

—
Reply to this email directly or view it on GitHub.


How to display in subview , not storyboard ?

I want to using UIViewController to create view, but i get Must call a designated initializer of the superclass 'MacawView' error.
How do i do to resolve ?
class MacawTextView: MacawView {

    required init?(coder aDecoder: NSCoder) {
        let text = Text(text: "Hello, World!", place: .move(dx: 145, dy: 100))
        super.init(node: text, coder: aDecoder)
    }

    init() {
        let rect  = CGRect(x: 100, y: 300, width: 100, height: 100)
        let text = Text(text: "Hello, World!", place: .move(dx: 145, dy: 100))
        super.init(node: text ,frame: rect)
    }
}

I have the same problem

Hi, @NasinLin, you can use init with a frame,  like here

Hi, @amarunko , I try it , but i can't work. I'm using SwiftUI to implement it.

Okay, did you try something like this?
class MacawTextView: MacawView {

    required init?(coder aDecoder: NSCoder) {
        let text = Text(text: "Hello, World!", place: .move(dx: 145, dy: 100))
        super.init(frame: CGRect.zero)
        node = text
    }

    init() {
        let rect  = CGRect(x: 100, y: 300, width: 100, height: 100)
        let text = Text(text: "Hello, World!", place: .move(dx: 145, dy: 100))
        super.init(frame: rect)
        node = text
    }
}

@amarunko yes, I try it but the screen display full black.
This is my code,
import SwiftUI
import Macaw

class MacawTextView: MacawView {

    required init?(coder aDecoder: NSCoder) {
        let text = Text(text: "Hello, World!", place: .move(dx: 145, dy: 100))
        super.init(frame: CGRect.zero)
        node = text
    }

    init() {
        let rect  = CGRect(x: 100, y: 20, width: 100, height: 100)
        let text = Text(text: "Hello, World!", place: .move(dx: 145, dy: 100))
        super.init(frame: rect)
        node = text
    }
}

struct TextsViewController: UIViewRepresentable {

    func makeUIView(context: Context) -> MacawTextView {
        return MacawTextView()
    }

    func updateUIView(_ activityIndicator: MacawTextView, context: Context) {

    }
}

struct SVGExample: View {
    var body: some View {
        VStack {
        TextsViewController()
        }
    }
}

struct SVGExample_Previews: PreviewProvider {
    static var previews: some View {
        SVGExample()
    }
}

Here - the black rectangle with the black text. You can try to use fill in constructor, for example - Color.red

@amarunko  It's ok , I try other example.  thanks

Checked this sample, with white text color it works fine. So closing this issue.


Update specs2-core to 4.12.9

Updates org.specs2:specs2-core from 4.12.6 to 4.12.9.
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Ignore future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "org.specs2", artifactId = "specs2-core" } ]

labels: test-library-update, semver-patch

Codecov Report

Merging #36 (593ddaf) into master (7841c84) will not change coverage.
The diff coverage is n/a.

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=======================================
  Coverage   31.78%   31.78%
=======================================
  Files          17       17
  Lines         258      258
  Branches        6        6
=======================================
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  Misses        176      176

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Update description of smsfilteractiontype.md to include restriction for AcceptImmediately action type

@Mike Jacobs, can you review this PR? Thanks.

#sign-off


FFmpegInteropX plays some videos with abnormal colors or black screen

I encountered a problem when using FFmpegInteropX to play some videos.
If I choose Automatic or ForceFFmpegSoftwareDecoder, the video color is abnormal and there is a mosaic phenomenon;
if I choose AutomaticSystemDecoder or ForceSystemDecoder, it will be black screen.
But if I use the system native player or ffplay to play, everything is normal.
The video information obtained by ffprobe is as follows:
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '.\Video.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 1
    compatible_brands: isommp41mp42
    creation_time   : 2022-08-06T01:13:14.000000Z
    copyright       :
    copyright-eng   :
  Duration: 00:02:37.03, start: 0.000000, bitrate: 9796 kb/s
  Stream #0:0[0x1](und): Video: hevc (Main 10) (hvc1 / 0x31637668), yuv420p10le(tv, bt2020nc/bt2020/arib-std-b67), 1920x1072, 9669 kb/s, 28.99 fps, 29 tbr, 600 tbn (default)
    Metadata:
      creation_time   : 2022-08-06T01:13:14.000000Z
      handler_name    : Core Media Video
      vendor_id       : [0][0][0][0]
    Side data:
      DOVI configuration record: version: 1.0, profile: 8, level: 4, rpu flag: 1, el flag: 0, bl flag: 1, compatibility id: 4
  Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 120 kb/s (default)
    Metadata:
      creation_time   : 2022-08-06T01:13:14.000000Z
      handler_name    : Core Media Audio
      vendor_id       : [0][0][0][0]

Thanks. I downloaded the video, but i won't be able to take a look until Monday.
Of the top of my head, here's some possible causes :
The video had some issues during muxing, so any GPU based decoding will fail or have weird results. You can try remuxing it (convert to another fornat and then back to hevc) and see if that fixes it.
GPU drivers might be bugged.
Some hidden edge case in our lib or ffmpeg.

I do not get any kind of artifacts with the sample file, no matter if I use hardware or software decoding. Have you checked that the sample still has those issues @yikuo123? If you cut samples using ffmpeg, it will do a remux the stream. This can often resolve issues with incorrectly muxed files, so some problems cannot be reproduced when creating samples with ffmpeg.
If the sample plays fine you your side as well, try to do a binary split of the file and see if the first chunk shows the issue. Often this works. Or upload the complete file, if it is possible. If you have WSL, it's very easy to split, e.g. split -b 100MB video.mov. Windows does not have any built in tools afaik.

Okay, looks like this is a HDR video and it currently does not play well when the display does not support HDR (or HDR is not enabled). Actually, on my PC this video looks a lot better when playing with our lib, compared to Windows 11 media player or MPC/VLC, because all other players do not use the HDR color info.
However, I need to disable forwarding the HDR metdatata when the display does not have HDR enabled. I have made a PR #339 for this. Please check if this works on your side as well. For me, the video now plays well, both with HDR enabled and disabled (but it looks a lot better with HDR enabled, of course).

Closing this. System decoders is legacy and not recommended anymore and playback works fine with ffmpeg hw decoding.


Does vllm support both CUDA 11.3 version and PyTorch 1.12?

Your current environment
The output of `python collect_env.py`

python3 collect_env.py
Collecting environment information...
PyTorch version: 1.12.1+cu113
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27

Python version: 3.10.14 (main, Apr 14 2024, 08:51:48) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.90.1.el7.x86_64-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: 11.3.109
CUDA_MODULE_LOADING set to:
GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB
Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn.so.8.9.7
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.9.7
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.9.7
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.9.7
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.9.7
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.9.7
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.9.7
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              72
On-line CPU(s) list: 0-71
Thread(s) per core:  2
Core(s) per socket:  18
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               85
Model name:          Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz
Stepping:            4
CPU MHz:             1199.890
CPU max MHz:         3700.0000
CPU min MHz:         1200.0000
BogoMIPS:            6000.00
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            25344K
NUMA node0 CPU(s):   0-17,36-53
NUMA node1 CPU(s):   18-35,54-71
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba rsb_ctxsw ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke md_clear spec_ctrl intel_stibp flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==1.12.1+cu113
[pip3] torchaudio==0.12.1+cu113
[pip3] torchvision==0.13.1+cu113
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: N/A
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity
GPU0     X      18-35,54-71     1

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

How you are installing vllm
CUDACXX=/usr/local/cuda-11/bin/nvcc pip3 install -e  .

Output:
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Obtaining file:///workspace/vllm
  Installing build dependencies ... done
  Checking if build backend supports build_editable ... done
  Getting requirements to build editable ... done
  Preparing editable metadata (pyproject.toml) ... done
Collecting cmake>=3.21 (from vllm==0.4.0.post1+cu113)
  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/be/2e/8f8daa34e6d743816f852c752da544c34ead6b4fbbdf547519430bdba590/cmake-3.29.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.7 MB)
......
Collecting exceptiongroup>=1.0.2 (from anyio<5,>=3.4.0->starlette<0.38.0,>=0.37.2->fastapi->vllm==0.4.0.post1+cu113)
  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b8/9a/5028fd52db10e600f1c4674441b968cf2ea4959085bfb5b99fb1250e5f68/exceptiongroup-1.2.0-py3-none-any.whl (16 kB)
Building wheels for collected packages: vllm
  Building editable for vllm (pyproject.toml) ... error
  error: subprocess-exited-with-error

  × Building editable for vllm (pyproject.toml) did not run successfully.
  │ exit code: 1
  ╰─> [5321 lines of output]
      /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.)
        device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),
      running editable_wheel
      creating /tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm.egg-info
      writing /tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm.egg-info/PKG-INFO
      writing dependency_links to /tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm.egg-info/dependency_links.txt
      writing requirements to /tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm.egg-info/requires.txt
      writing top-level names to /tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm.egg-info/top_level.txt
      writing manifest file '/tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm.egg-info/SOURCES.txt'
      reading manifest file '/tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm.egg-info/SOURCES.txt'
      reading manifest template 'MANIFEST.in'
      adding license file 'LICENSE'
      writing manifest file '/tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm.egg-info/SOURCES.txt'
      creating '/tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm-0.4.0.post1+cu113.dist-info'
      creating /tmp/pip-wheel-1owf_9s_/.tmp-lsswmwhe/vllm-0.4.0.post1+cu113.dist-info/WHEEL
      running build_py
      running build_ext
      -- The CXX compiler identification is GNU 7.5.0
      -- Detecting CXX compiler ABI info
      -- Detecting CXX compiler ABI info - done
      -- Check for working CXX compiler: /usr/bin/c++ - skipped
      -- Detecting CXX compile features
      -- Detecting CXX compile features - done
      -- Build type: RelWithDebInfo
      -- Target device: cuda
      -- Found Python: /usr/local/bin/python3 (found version "3.10.14") found components: Interpreter Development.Module
      -- Found python matching: /usr/local/bin/python3.
      -- Found CUDA: /usr/local/cuda (found version "11.3")
      -- The CUDA compiler identification is NVIDIA 11.3.109
      -- Detecting CUDA compiler ABI info
      -- Detecting CUDA compiler ABI info - done
      -- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped
      -- Detecting CUDA compile features
      -- Detecting CUDA compile features - done
      -- Found CUDAToolkit: /usr/local/cuda/include (found version "11.3.109")
      -- Performing Test CMAKE_HAVE_LIBC_PTHREAD
      -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed
      -- Looking for pthread_create in pthreads
      -- Looking for pthread_create in pthreads - not found
      -- Looking for pthread_create in pthread
      -- Looking for pthread_create in pthread - found
      -- Found Threads: TRUE
      -- Caffe2: CUDA detected: 11.3
      -- Caffe2: CUDA nvcc is: /usr/local/cuda/bin/nvcc
      -- Caffe2: CUDA toolkit directory: /usr/local/cuda
      -- Caffe2: Header version is: 11.3
      CMake Warning at /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/share/cmake/Caffe2/public/cuda.cmake:184 (message):
        Failed to compute shorthash for libnvrtc.so
      Call Stack (most recent call first):
        /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:87 (include)
        /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package)
        CMakeLists.txt:67 (find_package)

      -- USE_CUDNN is set to 0. Compiling without cuDNN support
      -- USE_CUSPARSELT is set to 0. Compiling without cuSPARSELt support
      -- Autodetected CUDA architecture(s):  8.0
      -- Added CUDA NVCC flags for: -gencode;arch=compute_80,code=sm_80
      CMake Warning at /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message):
        static library kineto_LIBRARY-NOTFOUND not found.
      Call Stack (most recent call first):
        /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:127 (append_torchlib_if_found)
        CMakeLists.txt:67 (find_package)

      -- Found Torch: /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/lib/libtorch.so
      -- CUDA supported arches: 7.0;7.5;8.0;8.6;8.9;9.0
      -- CUDA target arches: 80-real
      -- Punica target arches: 80-real
      -- Enabling C extension.
      -- Enabling moe extension.
       Please use Tensor.data_ptr<T>() instead. [-Wdeprecated-declarations]
         vllm::squeezellm::NUQ4MatMulKernel<<<blocks, threads, 0, stream>>>(
                                                                                ^
      /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:247:1: note: declared here
         T * data() const {
       ^ ~~
      /workspace/vllm/csrc/quantization/squeezellm/quant_cuda_kernel.cu:206:194: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated: Tensor.data<T>() is deprecated. Please use Tensor.data_ptr<T>() instead. [-Wdeprecated-declarations]
         vllm::squeezellm::NUQ4MatMulKernel<<<blocks, threads, 0, stream>>>(
                                                                                ^
      /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:247:1: note: declared here
         T * data() const {
       ^ ~~
      /workspace/vllm/csrc/quantization/squeezellm/quant_cuda_kernel.cu:206:238: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated: Tensor.data<T>() is deprecated. Please use Tensor.data_ptr<T>() instead. [-Wdeprecated-declarations]
         vllm::squeezellm::NUQ4MatMulKernel<<<blocks, threads, 0, stream>>>(
                                                                                ^
      /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:247:1: note: declared here
         T * data() const {
       ^ ~~
      [6/14] Building CUDA object CMakeFiles/_C.dir/csrc/moe_align_block_size_kernels.cu.o
      [7/14] Building CUDA object CMakeFiles/_C.dir/csrc/activation_kernels.cu.o
      [8/14] Building CUDA object CMakeFiles/_C.dir/csrc/pos_encoding_kernels.cu.o
      [9/14] Building CUDA object CMakeFiles/_C.dir/csrc/custom_all_reduce.cu.o
      /workspace/vllm/csrc/custom_all_reduce.cuh(110): warning: missing return statement at end of non-void function "vllm::upcast(vllm::array_t<T, N>) [with T=half, N=8]"
                detected during:
                  instantiation of "vllm::array_t<float, N> vllm::upcast(vllm::array_t<T, N>) [with T=half, N=8]"
      (173): here
                  instantiation of "P vllm::packed_reduce<P,ngpus,A>(const P **, int) [with P=vllm::array_t<half, 8>, ngpus=2, A=vllm::array_t<float, 8>]"
      (196): here
                  instantiation of "void vllm::cross_device_reduce_1stage<T,ngpus>(vllm::RankData *, vllm::RankSignals, volatile vllm::Signal *, T *, int, int) [with T=half, ngpus=2]"
      (459): here
                  instantiation of "void vllm::CustomAllreduce::allreduce(cudaStream_t, T *, T *, int, int, int) [with T=half]"
      /workspace/vllm/csrc/custom_all_reduce.cu(82): here

      /workspace/vllm/csrc/custom_all_reduce.cuh(124): warning: missing return statement at end of non-void function "vllm::downcast(vllm::array_t<float, O::size>) [with O=vllm::array_t<half, 8>]"
                detected during:
                  instantiation of "O vllm::downcast(vllm::array_t<float, O::size>) [with O=vllm::array_t<half, 8>]"
      (178): here
                  instantiation of "P vllm::packed_reduce<P,ngpus,A>(const P **, int) [with P=vllm::array_t<half, 8>, ngpus=2, A=vllm::array_t<float, 8>]"
      (196): here
                  instantiation of "void vllm::cross_device_reduce_1stage<T,ngpus>(vllm::RankData *, vllm::RankSignals, volatile vllm::Signal *, T *, int, int) [with T=half, ngpus=2]"
      (459): here
                  instantiation of "void vllm::CustomAllreduce::allreduce(cudaStream_t, T *, T *, int, int, int) [with T=half]"
      /workspace/vllm/csrc/custom_all_reduce.cu(82): here

      /workspace/vllm/csrc/custom_all_reduce.cuh(110): warning: missing return statement at end of non-void function "vllm::upcast(vllm::array_t<T, N>) [with T=nv_bfloat16, N=8]"
                detected during:
                  instantiation of "vllm::array_t<float, N> vllm::upcast(vllm::array_t<T, N>) [with T=nv_bfloat16, N=8]"
      (173): here
                  instantiation of "P vllm::packed_reduce<P,ngpus,A>(const P **, int) [with P=vllm::array_t<nv_bfloat16, 8>, ngpus=2, A=vllm::array_t<float, 8>]"
      (196): here
                  instantiation of "void vllm::cross_device_reduce_1stage<T,ngpus>(vllm::RankData *, vllm::RankSignals, volatile vllm::Signal *, T *, int, int) [with T=nv_bfloat16, ngpus=2]"
      (459): here
                  instantiation of "void vllm::CustomAllreduce::allreduce(cudaStream_t, T *, T *, int, int, int) [with T=nv_bfloat16]"
      /workspace/vllm/csrc/custom_all_reduce.cu(89): here

      /workspace/vllm/csrc/custom_all_reduce.cuh(124): warning: missing return statement at end of non-void function "vllm::downcast(vllm::array_t<float, O::size>) [with O=vllm::array_t<nv_bfloat16, 8>]"
                detected during:
                  instantiation of "O vllm::downcast(vllm::array_t<float, O::size>) [with O=vllm::array_t<nv_bfloat16, 8>]"
      (178): here
                  instantiation of "P vllm::packed_reduce<P,ngpus,A>(const P **, int) [with P=vllm::array_t<nv_bfloat16, 8>, ngpus=2, A=vllm::array_t<float, 8>]"
      (196): here
                  instantiation of "void vllm::cross_device_reduce_1stage<T,ngpus>(vllm::RankData *, vllm::RankSignals, volatile vllm::Signal *, T *, int, int) [with T=nv_bfloat16, ngpus=2]"
      (459): here
                  instantiation of "void vllm::CustomAllreduce::allreduce(cudaStream_t, T *, T *, int, int, int) [with T=nv_bfloat16]"
      /workspace/vllm/csrc/custom_all_reduce.cu(89): here

      [10/14] Building CUDA object CMakeFiles/_C.dir/csrc/cache_kernels.cu.o
      /workspace/vllm/csrc/cache_kernels.cu(190): warning: variable "tgt_key_idx" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=float, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (267): here

      /workspace/vllm/csrc/cache_kernels.cu(195): warning: variable "tgt_value_idx" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=float, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (267): here

      /workspace/vllm/csrc/cache_kernels.cu(199): warning: variable "tgt_key" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=float, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (267): here

      /workspace/vllm/csrc/cache_kernels.cu(200): warning: variable "tgt_value" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=float, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (267): here

      /workspace/vllm/csrc/cache_kernels.cu(190): warning: variable "tgt_key_idx" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=uint16_t, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (269): here

      /workspace/vllm/csrc/cache_kernels.cu(195): warning: variable "tgt_value_idx" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=uint16_t, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (269): here

      /workspace/vllm/csrc/cache_kernels.cu(199): warning: variable "tgt_key" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=uint16_t, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (269): here

      /workspace/vllm/csrc/cache_kernels.cu(200): warning: variable "tgt_value" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=uint16_t, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (269): here

      /workspace/vllm/csrc/cache_kernels.cu(190): warning: variable "tgt_key_idx" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=__nv_bfloat16, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (271): here

      /workspace/vllm/csrc/cache_kernels.cu(195): warning: variable "tgt_value_idx" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=__nv_bfloat16, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (271): here

      /workspace/vllm/csrc/cache_kernels.cu(199): warning: variable "tgt_key" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=__nv_bfloat16, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (271): here

      /workspace/vllm/csrc/cache_kernels.cu(200): warning: variable "tgt_value" was declared but never referenced
                detected during instantiation of "void vllm::reshape_and_cache_kernel<scalar_t,cache_t,is_fp8_kv_cache>(const scalar_t *, const scalar_t *, cache_t *, cache_t *, const int64_t *, int, int, int, int, int, int, float) [with scalar_t=__nv_bfloat16, cache_t=uint8_t, is_fp8_kv_cache=true]"
      (271): here

      /workspace/vllm/csrc/cache_kernels.cu(287): warning: variable "idx" was declared but never referenced

      [11/14] Building CUDA object CMakeFiles/_C.dir/csrc/quantization/awq/gemm_kernels.cu.o
      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(282): warning: variable "j_factors1" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(283): warning: variable "row_stride2" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(284): warning: variable "split_k_iters" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(290): warning: variable "B_shared_warp" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(291): warning: variable "OC" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(53): warning: variable "scaling_factors_shared" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(54): warning: variable "zeros_shared" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(57): warning: variable "blockIdx_x" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(71): warning: variable "ld_zero_flag" was declared but never referenced

      /workspace/vllm/csrc/quantization/awq/gemm_kernels.cu(24): warning: function "vllm::awq::__pack_half2" was declared but never referenced

      [12/14] Building CUDA object CMakeFiles/_C.dir/csrc/quantization/gptq/q_gemm.cu.o
      [13/14] Building CUDA object CMakeFiles/_C.dir/csrc/attention/attention_kernels.cu.o
      /workspace/vllm/csrc/attention/attention_kernels.cu(654): warning: variable "thread_group_size" was declared but never referenced

      /workspace/vllm/csrc/attention/attention_kernels.cu(220): warning: variable "k_ptr" was declared but never referenced
                detected during:
                  instantiation of "void vllm::paged_attention_kernel<scalar_t,cache_t,HEAD_SIZE,BLOCK_SIZE,NUM_THREADS,IS_FP8_KV_CACHE,PARTITION_SIZE>(float *, float *, scalar_t *, const scalar_t *, const cache_t *, const cache_t *, int, float, const int *, const int *, int, const float *, int, int, int, float) [with scalar_t=float, cache_t=uint8_t, HEAD_SIZE=64, BLOCK_SIZE=8, NUM_THREADS=128, IS_FP8_KV_CACHE=true, PARTITION_SIZE=0]"
      (470): here
                  instantiation of "void vllm::paged_attention_v1_kernel<scalar_t,cache_t,HEAD_SIZE,BLOCK_SIZE,NUM_THREADS,IS_FP8_KV_CACHE>(scalar_t *, const scalar_t *, const cache_t *, const cache_t *, int, float, const int *, const int *, int, const float *, int, int, int, float) [with scalar_t=float, cache_t=uint8_t, HEAD_SIZE=64, BLOCK_SIZE=8, NUM_THREADS=128, IS_FP8_KV_CACHE=true]"
      (686): here
                  instantiation of "void paged_attention_v1_launcher<T,CACHE_T,BLOCK_SIZE,IS_FP8_KV_CACHE,NUM_THREADS>(at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, int, float, at::Tensor &, at::Tensor &, int, const std::optional<at::Tensor> &, float) [with T=float, CACHE_T=uint8_t, BLOCK_SIZE=8, IS_FP8_KV_CACHE=true, NUM_THREADS=128]"
      (767): here

      /workspace/vllm/csrc/attention/attention_kernels.cu(224): warning: variable "offset1" was declared but never referenced
                detected during:
                  instantiation of "void vllm::paged_attention_kernel<scalar_t,cache_t,HEAD_SIZE,BLOCK_SIZE,NUM_THREADS,IS_FP8_KV_CACHE,PARTITION_SIZE>(float *, float *, scalar_t *, const scalar_t *, const cache_t *, const cache_t *, int, float, const int *, const int *, int, const float *, int, int, int, float) [with scalar_t=float, cache_t=uint8_t, HEAD_SIZE=64, BLOCK_SIZE=8, NUM_THREADS=128, IS_FP8_KV_CACHE=true, PARTITION_SIZE=0]"
      (470): here
                  instantiation of "void vllm::paged_attention_v1_kernel<scalar_t,cache_t,HEAD_SIZE,BLOCK_SIZE,NUM_THREADS,IS_FP8_KV_CACHE>(scalar_t *, const scalar_t *, const cache_t *, const cache_t *, int, float, const int *, const int *, int, const float *, int, int, int, float) [with scalar_t=float, cache_t=uint8_t, HEAD_SIZE=64, BLOCK_SIZE=8, NUM_THREADS=128, IS_FP8_KV_CACHE=true]"
      (686): here
                  instantiation of "void paged_attention_v1_launcher<T,CACHE_T,BLOCK_SIZE,IS_FP8_KV_CACHE,NUM_THREADS>(at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, int, float, at::Tensor &, at::Tensor &, int, const std::optional<at::Tensor> &, float) [with T=float, CACHE_T=uint8_t, BLOCK_SIZE=8, IS_FP8_KV_CACHE=true, NUM_THREADS=128]"
      (767): here
......
                  instantiation of "void paged_attention_v2_launcher<T,CACHE_T,BLOCK_SIZE,IS_FP8_KV_CACHE,NUM_THREADS,PARTITION_SIZE>(at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, int, float, at::Tensor &, at::Tensor &, int, const std::optional<at::Tensor> &, float) [with T=__nv_bfloat16, CACHE_T=uint8_t, BLOCK_SIZE=32, IS_FP8_KV_CACHE=true, NUM_THREADS=128, PARTITION_SIZE=512]"
      (968): here

      ninja: build stopped: subcommand failed.
      Traceback (most recent call last):
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/editable_wheel.py", line 155, in run
          self._create_wheel_file(bdist_wheel)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/editable_wheel.py", line 357, in _create_wheel_file
          files, mapping = self._run_build_commands(dist_name, unpacked, lib, tmp)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/editable_wheel.py", line 280, in _run_build_commands
          self._run_build_subcommands()
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/editable_wheel.py", line 307, in _run_build_subcommands
          self.run_command(name)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/cmd.py", line 316, in run_command
          self.distribution.run_command(command)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/dist.py", line 967, in run_command
          super().run_command(command)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
          cmd_obj.run()
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/build_ext.py", line 91, in run
          _build_ext.run(self)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 359, in run
          self.build_extensions()
        File "<string>", line 187, in build_extensions
        File "/usr/local/lib/python3.10/subprocess.py", line 369, in check_call
          raise CalledProcessError(retcode, cmd)
      subprocess.CalledProcessError: Command '['cmake', '--build', '.', '--target', '_C', '-j', '72']' returned non-zero exit status 1.
      /tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/dist.py:988: _DebuggingTips: Problem in editable installation.
      !!

              ********************************************************************************
              An error happened while installing `vllm` in editable mode.

              The following steps are recommended to help debug this problem:

              - Try to install the project normally, without using the editable mode.
                Does the error still persist?
                (If it does, try fixing the problem before attempting the editable mode).
              - If you are using binary extensions, make sure you have all OS-level
                dependencies installed (e.g. compilers, toolchains, binary libraries, ...).
              - Try the latest version of setuptools (maybe the error was already fixed).
              - If you (or your project dependencies) are using any setuptools extension
                or customization, make sure they support the editable mode.

              After following the steps above, if the problem still persists and
              you think this is related to how setuptools handles editable installations,
              please submit a reproducible example
              (see https://stackoverflow.com/help/minimal-reproducible-example) to:

                  https://github.com/pypa/setuptools/issues

              See https://setuptools.pypa.io/en/latest/userguide/development_mode.html for details.
              ********************************************************************************

      !!
        cmd_obj.run()
      Traceback (most recent call last):
        File "/usr/local/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 353, in <module>
          main()
        File "/usr/local/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 335, in main
          json_out['return_val'] = hook(**hook_input['kwargs'])
        File "/usr/local/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 273, in build_editable
          return hook(wheel_directory, config_settings, metadata_directory)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 443, in build_editable
          return self._build_with_temp_dir(
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 395, in _build_with_temp_dir
          self.run_setup()
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 311, in run_setup
          exec(code, locals())
        File "<string>", line 380, in <module>
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/__init__.py", line 104, in setup
          return distutils.core.setup(**attrs)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 184, in setup
          return run_commands(dist)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 200, in run_commands
          dist.run_commands()
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 969, in run_commands
          self.run_command(cmd)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/dist.py", line 967, in run_command
          super().run_command(command)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
          cmd_obj.run()
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/editable_wheel.py", line 155, in run
          self._create_wheel_file(bdist_wheel)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/editable_wheel.py", line 357, in _create_wheel_file
          files, mapping = self._run_build_commands(dist_name, unpacked, lib, tmp)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/editable_wheel.py", line 280, in _run_build_commands
          self._run_build_subcommands()
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/editable_wheel.py", line 307, in _run_build_subcommands
          self.run_command(name)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/cmd.py", line 316, in run_command
          self.distribution.run_command(command)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/dist.py", line 967, in run_command
          super().run_command(command)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
          cmd_obj.run()
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/command/build_ext.py", line 91, in run
          _build_ext.run(self)
        File "/tmp/pip-build-env-ydewg39l/overlay/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 359, in run
          self.build_extensions()
        File "<string>", line 187, in build_extensions
        File "/usr/local/lib/python3.10/subprocess.py", line 369, in check_call
          raise CalledProcessError(retcode, cmd)
      subprocess.CalledProcessError: Command '['cmake', '--build', '.', '--target', '_C', '-j', '72']' returned non-zero exit status 1.
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
  ERROR: Failed building editable for vllm
Failed to build vllm
ERROR: Could not build wheels for vllm, which is required to install pyproject.toml-based projects

I have used ... to omit many similar output messages.
cuda and nvcc:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Mon_May__3_19:15:13_PDT_2021
Cuda compilation tools, release 11.3, V11.3.109
Build cuda_11.3.r11.3/compiler.29920130_0

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| 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 A100-SXM...  Off  | 00000000:AF:00.0 Off |                    0 |
| N/A   32C    P0    42W / 400W |  48770MiB / 81920MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

  ptxas /tmp/tmpxft_00000ba5_00000000-6_marlin_cuda_kernel.ptx, line 215; error   : Unknown modifier '.fractional'
 ptxas /tmp/tmpxft_00000ba5_00000000-6_marlin_cuda_kernel.ptx, line 215; fatal   : Parsing error near ':': syntax error
 ptxas fatal   : Ptx assembly aborted due to errors

Your cuda and nvcc version is too old. Please follow and read https://docs.vllm.ai/en/latest/getting_started/installation.html .

my computer can only max support cu113 + torch1.12.1, so how can  i install vllm

You can reach out to your admin to upgrade driver and cuda version.


Refactor, add unique IP logging to the datastore service

refactors the IP logging interceptor out of the TransactorService
adds IP logging to DatastoreService

👍 good call


Allow --config-path to be specified to ember-try commands.

This allows groups of configurations to be used with various commands.
For example, it would be nice in ember-test-helpers to group "legacy" Ember versions together and run them as a single matrix build with:
ember try:testall --config-path=config/legacy-scenarios.js

Then we could run current scenarios in another matrix build like:
ember try:testall --config-path=config/current-scenarios.js

@kategengler - Would love your thoughts here...

Sounds (and looks) good to me, thanks!


🐛 S2  react 图表宽高不能动态修改

描述：在 react 图表中，当 动态传入 宽高 给 图表时，宽高不发生改变

复现：
https://stackblitz.com/edit/react-da6yvk?file=src%2Fcom.jsx
版本：@antv/s2  1.12.2

ref #1159

:tada: This issue has been resolved in version @antv/s2-v1.13.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:

:tada: This issue has been resolved in version @antv/s2-react-v1.12.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Combine subqueries with outer joins

In this stackoverflow question I learned that subqueries can be joined only as inner joins.
How big of a restriction is that? I.e. could I fork esqueleto and implement it or would require fundamental changes?
(cf. my question on stackoverflow on the topic)

I don't see an easy way of supporting it. But I don't want to discourage you, maybe you'll approach this problem from a better angle than I did =).
BTW, subqueries are orthogonal to this issue.

(Pressed "Comment" too early.) Instead of "subqueries", I think it would be better to say "composition".


(fit vs fit_generator) and (sequential vs functional) same results?

I am trying to train a deep neural network using transfer learning in Keras with tensorflow. There are different ways to do that, if your data is small you can afford computing features using the pre-trained model for the entire data and then use those features to train and test a small network, this is good as you don't need to compute those features for each batch and at each epoch. However, if the data is large, it will be impossible to compute features for the entire data, in this case we use ImageDataGenerator, flow_from_directory and fit_generator. In this case features are computed each time fore each batch at each epoch which make things much slower. I was assuming that both approaches produce similar results in terms of accuracy and loss. The problem is that I took a small data-set and tried both approaches and got completely different results. I will appreciate if someone can tell if something is wrong in the provided code and/or why I am getting different results please?
Approach when having large data-set:
from keras.applications.inception_v3 import InceptionV3,preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model

datagen= ImageDataGenerator(preprocessing_function=preprocess_input)

train_generator = datagen.flow_from_directory('data/train',
                                              class_mode='categorical',
                                              batch_size=64,...)
vaild_generator = datagen.flow_from_directory('data/valid',
                                              class_mode='categorical',
                                              batch_size=64,...)

base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = Conv2D(filters = 128 , kernel_size = (2,2)) (x)
x = MaxPooling2D()(x)
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(2, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

model.compile(optimizer='rmsprop', loss='categorical_crossentropy',...)

model.fit_generator(generator = train_generator,
                    steps_per_epoch = len (train_generator),
                    validation_data = valid_generator ,
                    validation_steps = len(valid_generator),
                    ...)`

Approach when having small data-set:
from keras.applications.inception_v3 import InceptionV3,preprocess_input
from keras.models import Sequential
from keras.utils import np_utils

base_model = InceptionV3(weights='imagenet', include_top=False)
train_features = base_model.predict(preprocess_input(train_data))
valid_features = base_model.predict(preprocess_input(valid_data))

model = Sequential()
model.add(Conv2D(filters = 128 , kernel_size = (2,2),
                 input_shape=(train_features [1],
                              train_features [2],
                              train_features [3])))
model.add(MaxPooling2D())
model.add(GlobalAveragePooling2D())
model.add(Dense(1024, activation='relu'))
model.add(Dense(2, activation='softmax'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy',...)

model.fit(train_features, np_utils.to_categorical(y_train,2),
          validation_data=(valid_features, np_utils.to_categorical(y_valid,2)),
          batch_size=64,...)

I am having a similar issue. Did you ever solve this problem?


need Meteor#loginwith to login with other services

is it planned or already available?

Thanks for bringing it to notice.
It's not available right now but I'll implement it within some days.

There's a package called react-native-google-sign in that helps oAuth login in react-native. We need something like that for flutter. Anyway have tried redirect login for external services?

Can you please explain what do you mean by redirect login for external services?

login style option redirect as per Docs


Inject js onLoadStarted

how to inject js onLoadStarted?
i have tried, using page.RunScript but notworking,
need detect window.alert on phantomjs,

PhantomJS does not support certain alert and confirm boxes. Have you tried page.RunScript with other webdrivers? Can you provide example code that shows page.RunScript behaving incorrectly?

ok, thanks @sclevine , i have tried with chrome and working well,

Great to hear :)


Webinar: OGC API Features as an INSPIRE Download service

To learn more about the OGC API and INSPIRE Download Service
https://www.youtube.com/watch?v=y__2LDQVPUo

Use cases
https://github.com/search?q=API4INSPIRE
https://wg-brgm.k8s.ilt-dmz.iosb.fraunhofer.de/servlet/is/105/


dotnet initialization error

Problem encountered on https://dotnet.microsoft.com/en-us/learn/aspnet/hello-world-tutorial/install
Operating System: windows
Provide details about the problem you're experiencing. Include your operating system version, exact error message, code sample, and anything else that is relevant.

What is the exact error you are running into?


feat(tutorial): complete step 4 - sophia antipolis

Closes #
{{short description}}
Changelog
New

{{new thing}}

Changed

{{change thing}}

Removed

{{removed thing}}

I have back-tracked the merge that messed up eveything.

👍 Looks good - 🎉 You are one step closer to completion of the Carbon React tutorial and earning the digital credential.


Initial vault unseal support

Remove unwanted go:buildline
Add havePod and execInPod functions
Fix up quay repository
Add insecureVaultUnseal boolean to the operator
Add vault.go with the needed code to unseal the vault
Wire the vault unsealing code into the main controller

Thanks, I retested everything:

Deployed with insecureManagedVault: false -> deployed correctly
Changed insecureManagedVault to true and the vault got correctly unsealed"
oc exec -it -n vault vault-0 -- bash -c 'vault status -format=json'
{
"type": "shamir",
"initialized": true,
"sealed": false,
"t": 3,
"n": 5,
"progress": 0,
"nonce": "",
"version": "1.10.3",
"migration": false,
"cluster_name": "vault-cluster-a2a2e7ca",
"cluster_id": "8f86e549-ff70-2b0b-034c-4b4d5b1924d7",
"recovery_seal": false,
"storage_type": "file",
"ha_enabled": false,
"active_time": "0001-01-01T00:00:00Z"
}


Add multicore write demo to regressions

Adding a multicore test to regressions.

write_demo probably isn't the right name here.  Maybe mc_sanity?

LGTM! Merge if ready


Simple Curl Request Function Translation

Hello everyone, I am trying to translate a curl request to a rust code. I am creating an API for rust.
I need help to get started on this one.
The original curl request looks like this :
curl --user $rpcuser:$rpcpassword --data-binary '{"jsonrpc": "1.0", "id":"curltest", "method": "getwalletinfo", "params": [] }' -H 'content-type: text/plain;' http://<IP_ADDRESS>:$rpcport/
Below is my code translated
extern crate reqwest;
use reqwest::header::*;

fn main() -> Result<(), reqwest::Error> {
    let mut headers = HeaderMap::new();
    headers.insert(CONTENT_TYPE, "text/plain;".parse().unwrap());

    let res = reqwest::Client::new()
        .post("http://<IP_ADDRESS>:8158/")
        .basic_auth("user2509089925", Some("passf2e2f61a68e19ce65fe8211aa5d42163fb5c507c8d8d63dabd2c3f66c5358527eb"))
        .headers(headers)
        .body("{\"jsonrpc\": \"1.0\", \"id\":\"curltest\", \"method\": \"getwalletinfo\", \"params\": [] }")
        .send()?
        .text()?;
    println!("{}", res);
    Ok(())
}

Errors I am getting
error[E0433]: failed to resolve: use of undeclared type or module HeaderMap
--> src\main.rs:5:23
|
5 |     let mut headers = HeaderMap::new();
|                       ^^^^^^^^^ use of undeclared type or module HeaderMap
error[E0425]: cannot find value CONTENT_TYPE in this scope
--> src\main.rs:6:20
|
6 |     headers.insert(CONTENT_TYPE, "text/plain;".parse().unwrap());
|                    ^^^^^^^^^^^^ not found in this scope
help: possible candidate is found in another module, you can import it into scope
|
2 | use http::header::CONTENT_TYPE;
|
warning: unused import: reqwest::header::*
--> src\main.rs:2:5
|
2 | use reqwest::header::*;
|     ^^^^^^^^^^^^^^^^^^
|
= note: #[warn(unused_imports)] on by default
error: aborting due to 2 previous errors
Some errors have detailed explanations: E0425, E0433.

extern crate reqwest;

fn main() -> Result<(), reqwest::Error> {
    let res = reqwest::Client::new()
        .post("http://<IP_ADDRESS>:8158/")
        .basic_auth("user2509089925", Some("passf2e2f61a68e19ce65fe8211aa5d42163fb5c507c8d8d63dabd2c3f66c5358527eb"))
        .header("content-type", "text/plain")
        .body("{\"jsonrpc\": \"1.0\", \"id\":\"curltest\", \"method\": \"getwalletinfo\", \"params\": [] }")
        .send()?
        .text()?;
    println!("{}", res);
    Ok(())
}

Thanks. I was using an old version of the request. I updated my cargo to reqwest ="0.9.22" and it's working good. Thanks for the quick reply seanmonster. I appreciate your time and effort.


Prerender during build

#4112.

[x] Add new config.kit.prerender.default option (defaults to false)
[x] Prerender during build, and pass builder.prerendered object to adapters
[x] Implement builder.writePrerendered(dest, { fallback }) method
[x] Include default option in builder.prerendered, so e.g. adapter-static can warn if default was false when there's no fallback
[x] Use prerendered pages in svelte-kit preview
[x] Make prerendered available to service workers
[x] Update all adapters
[x] Update docs

Please don't delete this checklist! Before submitting the PR, please make sure you do the following:

[x] It's really useful if your PR references an issue where it is discussed ahead of time. In many cases, features are absent for a reason. For large changes, please create an RFC: https://github.com/sveltejs/rfcs
[x] This message body should clearly illustrate what problems it solves.
[x] Ideally, include a test that fails without this PR but passes with it.

Tests

[ ] Run the tests with pnpm test and lint the project with pnpm lint and pnpm check

Changesets

[ ] If your PR makes a change that should be noted in one or more packages' changelogs, generate a changeset by running pnpx changeset and following the prompts. All changesets should be patch until SvelteKit 1.0

The UI for linking issues is broken, because GitHub is a modern app built with 00's technology, but this will close the following issues:

#4112
#3792
#2271
#923


Repo Owner and Repo Name as Property

Refer to multiple Repo's
The Repo in the settings may be sufficient as default.
Might it be possible to override this settings in the properties of the note referring to an issue?
So pointing to multiple repos within the Obsidian Note would be a great help.

Thanks for the suggestion @idwkn. I will introduce this feature in the next minor release!
Check the kanban for all updates

Prototype: https://github.com/GabAlpha/obsidian-githobs/discussions/14

Would be very useful that we could define that a whole folder is a determined github repository.
My usecase is the following: I have a personal vault where I organize all projects and researchs I am doing, so I have folder for each project, and each project is a different github repository, so when creating an issue inside that folder, I would need it to create the issue for that project.


Replicas discovery

AuroraReplicasDiscoverer makes is easy to fetch direct urls to all replicas. It could be handy when you don't want to depend on default AWS load balancing.

https://github.com/atlassian-labs/db-replica/pull/102


Short circuit the deleteSegments method when segmentNames is empty

Avoid unnecessary checks.

Codecov Report
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Server TID support, #40

Implementation for #40
Keeps a socket with each client's details so we can reply over the correct port.

This isn't ready yet, I'm adding #39 to this becuase it's the same file. Will reopen when done.


update dgenerate

Can you update dgenerate for new models?
Spandrel now supports many more models.
https://github.com/chaiNNer-org/spandrel
degenerate is a very nice program. I use it constantly.
Please continue your support.

I will take a look, this should be fairly simple to do
I do recall that I needed to possibly modify my usage of spandrel to not use tiling for certain models
I have become quite busy

Thank you. I'm waiting for degenerate's new versions.

Thank you. I'm waiting for degenerate's new versions(for new models).
whenever the (https://github.com/chaiNNer-org/spandrel/tree/main/libs/spandrel/spandrel) to (C:\Program Files\dgeneratet_internal\spandrel) folder is changed new models work. Can you develop dgenerate this way? Thus, there is no need to constantly update dgenerate(for new models).

Due to the way PyInstaller creates the environment under _internal, and the generated executables interactions with the environment when loading modules, the launcher will become unhappy about loading the module with version differences or renamed / now missing sources etc. Unfortunately it cannot be made to work that way for the windows installer, at least with PyInstaller.
It could be made to work that way if spandrel features could be loaded as an accessory module, with the user installing it manually into an environment of their own creation. For instance in the simple case, if you install dgenerate into it's own python environment instead of using the Windows installer, and then forcefully upgrade the spandrel package to 0.3.1, that would probably work (from what I am observing now) in the short term, as long as no major API changes actually occurred in spandrel. In the long term it has to be manually updated (in every case) to ensure that the dependency is still written against correctly with version changes, spandrel seems to be in fairly active development so something breaking with the API could happen frequently.
I will bump spandrel to latest and release a new installer once I ensure it is still working.
On Windows, the entire environment must be crafted carefully and frozen for this software to "just work" without any issues, and it just barely works as is, so I would imagine any tampering of dependencies in the _internal environment created by PyInstaller will cause it to tip into the "don't work" state very quickly. The windows installer is a convenience that trades off entirely the users ability to control the python environment / dependencies in favor of something that will just work when it is installed.

I will test it and offer some suggestions.

error
image-process: error: [Errno 13] Permission denied: '.\.lock'
I use this command.
dgenerate --sub-command image-process tmp2.jpg --output tmp2u.png -al 1 --processors "upscaler;model=models/4x_Real_SSDIR_DAT_GAN.pth"

Thank you. It's perfect. it works.
I have tested. I will have some suggestions.
1- dgenerate 1.5x-2x slower than chainner. Can it be accelerated?
2- I am using this command.
dgenerate --sub-command image-process tmp2.jpg --output tmp2u.png -al 1 --processors "upscaler;model=models/4x_Real_SSDIR_DAT_GAN.pth"
Can a dgenerate version be made just for only upsclar models? In this way, it eliminates other unused features, speeds up and reduces its size.
3- only support (-d cuda:0) other -d cuda:1,2,3 error 'CUDA device ordinal 1 is invalid, no such device exists'
4- Could there be GPU selection support? -g gpu-id gpu device to use (default=auto) can be 0,1,2 for multi-gpu
5-Gray models do not work as in previous versions. for example this model.
004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth

I can think of a few reasons for it to be slower upfront, I will need to investigate if the tiling can be optimized any further though I am guessing chainner has a more efficient implementation of tiling.  One of the other reasons could be that dgenerate has a very slow start up time due to imports of large dependencies such as torch and friends, numpy, large machine learning librarys etc, that support its other features. As you have identified, dgenerate does many other things and it has a lot of large machine learning dependencies, upon a fresh install it will take even longer to bytecode compile all of this on the first execution.  chainner will not have such a problem with it's server client architecture, which allows those libraries to be imported once when the server launches and then used repeatedly in the same process.  This can be somewhat mitigated for batch operations by using config files, which are read in to dgenerate from STDIN and described in the readme and amongst the examples in the example folder, multiple operations can be preformed within the same process this way, without having to spin up a new process and incur the overhead of importing dependencies over again. I had it in mind at some point to allow dgenerate to be used as a server which takes requests as a background process, this theoretically could already be done now by talking to it over STDIN with a pipe, but I have not written any other software to facilitate talking to it this way.

The upscaler portion of dgenerate was implemented primarily to allow the upscaling of AI generated content produced using the various diffusion algorithms that dgenerate supports, as a post process capability on top of its primary feature set (creating AI generated images). I thought the image processing functionality internal to dgenerate would be useful as a stand alone feature, enabling upscaling and other processing of arbitrary images, so I added a way to use it in this manner on any image, I am glad this has been useful! The image-process sub-command and \image_process config directive uses a lot of reusable functionality I wrote for dealing with multimedia content in a generic fashion and handling alignment and resizing (images/videos) etc. mediainput.py & mediaoutput.py which is in turn very intertwined and dependent with a couple other modules within dgenerate, it could be extracted into a standalone program with quite a bit of effort, however it will still depend on some very large dependencies such as torch and numpy etc, so I am not sure if it would be worth it as the dependencies are mostly what contribute to all of the bloat.  upscaler.py is the image processor plugin which handles the upscaler functionality. It might be easier to steal this code from the upscaler plugin for standalone use than to refactor the image-process subcommand out of dgenerate, however the plugin itself does not handle any of the alignment or naive resizing functionality.

If am understanding correctly, you are having trouble specifying a GPU during upscaling? this can be accomplished in one of two ways demonstrated in the code box below. currently I am working on a machine with two 1080TI GPU installed and this works for me. You may need to inspect the value of your CUDA_VISIBLE_DEVICES environmental variable if this is not working on a machine which has multiple GPUs installed, see nvidias website for details.  Only if you have multiple GPUs installed in your machine that are reported as independent devices by the nvidia-smi command, can you assign jobs to a different GPU, otherwise if the device is not visible for some reason, you will get an error stating that the device ordinal does not exist. Only one GPU can ever be specified, I.E. the job cannot be distributed across multiple GPUs, the job will happen all on the specified GPU. Note that cuda:0 indicates the first GPU installed in the system. A single upscaling task being distributed across multiple GPUs is not something spandrel implements, and would be difficult to implement, however I am not sure if this is what you are asking about. If your machine encounters an error with either of the following commands and you are sure that you have at least 2 independent GPUs installed in your system, could you post the dgenerate error output, the value of CUDA_VISIBLE_DEVICES in your environment, and the output of the nvidia-smi command?

# Run the 2nd GPU on the system

dgenerate --sub-command image-process tmp2.jpg --output tmp2u.png -al 1 --processors "upscaler;model=4x_Real_SSDIR_DAT_GAN.pth" --device cuda:1

# Or use a plugin argument

dgenerate --sub-command image-process tmp2.jpg --output tmp2u.png -al 1 --processors "upscaler;device=cuda:1;model=4x_Real_SSDIR_DAT_GAN.pth"

I see what is happening is that the model is being fed an RGB image by dgenerate when it expects an single channel grayscale image, I believe I noticed this problem with DDColor which was one of the model architectures I wanted to test that uses tiling internally.  The code for upscaling is hard coded for 3 channel images currently and I will investigate generifying it based on the instantiated upscaler models desired number of color channels.  It will require an in memory conversion to the correct number of channels IE grayscale when using those models. It may take me some time unfortunately even though it is a small fix, due to life :) but I will try to look at it as I am interested in having every model architecture working.

I am unsure what you mean by portable, but if you mean a single executable I am not sure it is possible with the current state of PyInstaller as a project.  It has a terrible time trying to handle the machine learning dependencies in the install folder configuration as is.  But also the executable would contain all of torch and numpy, etc. and be around as enormous as the existing install folder.  It is really hard to escape this bloat for a python ML project, the size of the dependencies required to make even just the upscaler work such as torch, numpy, possibly OpenCV are ridiculously large and incur a huge startup cost for the application. I wish it were not like this :)

Per .5
Model architectures expecting single channel images (IE Grayscale), including DDColor image colorization models and SwinIR gray variants etc. should be fixed by this change in the upscaler plugin:
7f11dbb - upscaler.py
Incoming images which are detected to already be grayscale are simply reshaped to a single channel to conform to what the model wants as input. Image processor plugins always receive images as RGB so this reshaping / conversion is necessary.
Incoming images which are detected to be full color images are converted from color to grayscale in the process of being reshaped.
In addition, a boolean upscaler plugin option force-tiling has been added. Model architectures such as SCUNet and MixDehazeNet are no longer tiled by default unless forced by the user, as it is discouraged by the model implementation. All other model architectures where tiling is fully supported undergo external image tiling.
Architectures which tile internally such as DDColor from spandrel extra architectures no longer
undergo any external tiling and cannot be forced to undergo external tiling.  DDColor is the only
implementation with internal tiling currently.
Version bump to 3.2.0 for technically "new features"

Per. 1
Attempt optimizations to tiled scaling c170807

Can you tell me your e-mail? Let's discuss some things via e-mail.

3-  'CUDA_VISIBLE_DEVICES in your environment and nvidia-smi command'
I don't know how to do it or the commands.
The graphics card feature I use is in the picture.
For nccn models I use this Universal-NCNN-Upscaler.
https://github.com/TNTwise/Universal-NCNN-Upscaler
https://github.com/TNTwise/SPAN-ncnn-vulkan
Can dgenerate run ncnn files (. param . bin), what is the command?
6- I didn't mean "if you mean a single executable".
I meant the installer file (dgenerate.msi). Not a single exe. In the same way, there will be a dgenerate and _internal folder.
It can work without installing Python and others. You can make all the necessary settings in a .bat file (install.bat) for portable.

error for cuda:1

It appears your system has a singular GTX 1660 installed (or recognized currently by the system) in addition to intel UHD integrated graphics
The only device that supports CUDA on your system is the one GTX 1660 (device ordinal 0) which is why the other GPU is not visible to torch
Only devices listed by nvidia-smi will be compatible with acceleration with dgenerate
spandrel does not support models in NCNN format, spandrel is centered around running models with torch on Nvidia hardware / CUDA where as NCNN is made from the ground up to run on multiple types of hardware particularly mobile platforms and embedded devices
It seems there is a way convert directly from models compatible with chainner (spandrel) into NCNN for optimized use on other devices. I am not sure if there is a tool to convert NCNN models back to torch models however. It might be likely that the NCNN models you are using have a Torch format counterpart from which they were converted from, which will in turn work with dgenerate
The NCNN models might be able to have acceleration through Intel UHD graphics however this is currently not something dgenerate can do
A zip file of the PyInstaller environment itself with the generated executable would suffice for the described portable installation, it could be unzipped and run that way without the need for installer. Is this what you mean?

As for emailing, I wish for issue discussions to remain on the repository as opposed to email, so that the discussions can be referenced by other users who might benefit from the information

A zip file of the PyInstaller environment itself with the generated executable would suffice for the described portable installation, it could be unzipped and run that way without the need for installer. Is this what you mean?
yes that's what I meant.
There will only be dgenerate.exe, installset.bat and _internal folder in the zip.

I can create a two part zip file in the installer build prior to packaging that can be distributed this way, unzip and run from the directory.
You will need to open a command prompt in the directory of the dgenerate executable in order to run it, unless it is added to your system PATH. Or I suppose refer to it by its full path in a BAT file.
Currently all the installer does is install it and add its location to your system PATH variable for convenience so that it can be run from the command line anywhere without additional steps

New release should fix grayscale model issues, I have included zips of the raw executable/environment

I have tested.
1- I did not see any acceleration in v3.2.0 compared to other versions.
Also, when we click on dgenerate, there is a wait while loading at startup.
2- If it is not run as administrator, as in other versions, it gives this error.
image-process: error: [Errno 13] Permission denied: '.\.lock'
3- It gives this error in the picture. for 'dn_grl_base_c3.ckpt'

Some models do not work in dgenerate dn_grl_base_c3.ckpt, jpeg_grl_small_c3.ckpt.
others dn_grl_base_c3s50.ckpt... works.
all models work in chainner.
you can download it from here
https://github.com/ofsoundof/GRL-Image-Restoration/releases/

I compared the png outputs of dgenerate and chainner. not the same. Even though the tile sizes are the same. Is it due to overlap? I tried different overlap values but could not equalize them.

It seems that model expects 4 channel input, IE something other than RGB.  I will need to dig through the source or find some documentation on what sort of color data it wants, I had really only considered the simple case of RGB images since that is mostly what I am working with
multithreaded processing for tiles is potentially possible but I am not sure if/when I can get around to testing out something like that, since I generally run the upscaler in batch unattended I hadn’t put an emphasis on speeding it up. From the memory overhead on the GPU on my machine it could potentially process 3 tiles simultaneously. I am unsure what this would entail because I haven’t investigated how thread safe running the models are, I am guessing they would need their own instance for each thread and to be completely isolated
I have not compared the output of chainners upscaling with dgenerate so far so I will see if I can do that

\.lock in the output directory of dgenerate is used as a method of preventing multiple dgenerate processes from interfering with eachothers output if running simultaneously on different GPUs as a separate process.
If this file exists or is stuck existing as owned by administrator you can safely delete it manually when dgenerate is not running
I would not run dgenerate as administrator, or with an output location where administrator privileges are required to write

models such as dn_grl_base_c1.ckpt from https://github.com/ofsoundof/GRL-Image-Restoration/releases/ seem to expect 2 channels even, off the top of my head I am not familiar with what sort of data it could be wanting as input :)
I will have to research

Chainner simply treats the 4th channel for dn_grl_base_c3.ckpt as the alpha channel of the image, as all 1s (complete opacity in the alpha channel), from what I am seeing by testing all 0.0s VS all 1.0s output in dgenerate and comparing the results, this is possibly incorrect use of the model by chainner. The result is very blurry at 1.0 opacity and not blurry at 0.0, this may be some additional image data that is meant to be consumed by the model which is not an alpha/opacity value that is not accounted for by chainner? it could be a depth value or some map that is supposed to be calculated from the input image.  It is a bit beyond the scope of dgenerate to provide this data, so I may divert to what chainner is doing and just fill it with 1.0s

I tried these in dgenerate for chainner tile size 512
4x_Real_SSDIR_DAT_GAN.pth;tile=512;overlap=8
4x_Real_SSDIR_DAT_GAN.pth;tile=512;overlap=16
4x_Real_SSDIR_DAT_GAN.pth;tile=512;overlap=32
These did not match exactly.
4x_Real_SSDIR_DAT_GAN.pth;tile=512;overlap=64
99% match.
I tried these in dgenerate for chainner tile size 256
4x_Real_SSDIR_DAT_GAN.pth;tile=256;overlap=1
4x_Real_SSDIR_DAT_GAN.pth;tile=256;overlap=2
4x_Real_SSDIR_DAT_GAN.pth;tile=256;overlap=8
4x_Real_SSDIR_DAT_GAN.pth;tile=256;overlap=12
4x_Real_SSDIR_DAT_GAN.pth;tile=256;overlap=16
4x_Real_SSDIR_DAT_GAN.pth;tile=256;overlap=32
4x_Real_SSDIR_DAT_GAN.pth;tile=256;overlap=64
I couldn't match it in any way.
tile size 256 chainner

256;overlap=32 dgenerate

For 2 channel models such as dn_grl_base_c1.ckpt I receive this error in chainner, I am guessing it does not attempt to down convert. I am guessing if I provide the correct image format to begin with it would work, but I am not sure what that format is.

I will try to investigate the output differences later tonight, it is difficult to tell with the naked eye what might be going on, it could be a combination of tiling algorithm differences, differences in image loading & saving

Try this it works.
https://github.com/chaiNNer-org/chaiNNer-nightly/releases/download/2024-04-05/chaiNNer-windows-latest-nightly.2024-04-05.zip
Calculate and display the differences between two images
https://www.imageen.com/files/demos/run/ImageAnalysis/ImagesDiff/ImagesDiff.zip

The 2 most important things for me (1.) are speeding up dgenerate and reducing the size (reducing the size of the _internal folder).
(2.) Can you make a special version that only runs .pth and .ckpt (models) files?
In addition, this way it will be able to sort out many things.

Currently I do not have the bandwidth to refactor part of this project to isolate out just this feature into one tool and then maintain it, however I am not opposed to the code being forked or borrowed in order to do this!
Though I am interested in supporting more models if I can find the information on how they are meant to be used
After testing a smaller image 256x256, in which no tiling occurred, and testing the same image in chainner under the same condition (no tiling)
The image outputs still differed in file size and similarity between dgenerate and chainner.
This sort of leads me to believe that at least part of the difference is caused by something occurring during the loading of the image into memory and or its conversion into a Tensor before processing, that is minutely different than what is going on in the chainner code base.
These differences would compound during tiling (especially at lower tile sizes) and processing of the image as well as during encoding while saving them to disk, especially for JPG and lossy formats.  The method of blending tiles preformed by dgenerate is also different than chainner from looking through their backend code.
I am not sure it is worth pursuing completely identical outputs unless there is serious artifacting since the outputs are very comparable visually

https://github.com/sekiju/sr-core
you can check this out. It gives the same equal output as chainner. but it works very very slowly.
You can also check this out.
https://github.com/muslll/neosr

Updated https://github.com/chaiNNer-org/spandrel/tree/main/libs/spandrel
for Add support for SAFMN_BCIE.
You can add it to dgenerate.

Must wait for their release and the package being published to PyPi

I follow you, you are making big changes.

If tile=auto, what will be the overlap value?

It will be the default value mentioned by ‘’dgenerate —image-processor-help upscaler’’ which is 32. I neglected to check the condition of overlap potentially being more than the auto estimated optimal tile size but that should be unlikely to happen at least, with the amount of memory on most modern cards :)


Turkish language and git-updater support

Hello @cloudpanel-io
For those who want to use varnish cache with cloudpanel from Turkey, I translated the plugin into Turkish and I have added the headers requested by the git-updater plugin.

Thanks a lot @mertcangokgoz


panic: qtls.ConnectionState not compatible with tls.ConnectionState

To Reproduce
Step by step instructions to reproduce the behavior:

run build.sh
try to start the media repo

Expected behavior

the media repo start up

Describe the bug

the repo crashes with:

panic: qtls.ConnectionState not compatible with tls.ConnectionState
goroutine 1 [running]:
github.com/lucas-clemente/quic-go/internal/handshake.init.1()
/opt/matrix/matrix-media-repo/go/pkg/mod/github.com/lucas-clemente/quic-go@v0.13.1/internal/handshake/unsafe.go:17 +0x12e

Details
Version of Node.js:
PLEASE NOTE: Only stable LTS versions (10.x and 12.x) are fully supported but we will do our best with newer versions.
15.2.1
Server Operating System:
The server (which might be your dev laptop) on which Apostrophe is running. Linux? MacOS X? Windows? Is Docker involved?
Linux
Additional context:

Add any other context about the problem here. If the problem is specific to a browser, OS or mobile device, specify which.

Screenshots
If applicable, add screenshots to help explain your problem.

Duplicate of https://github.com/turt2live/matrix-media-repo/issues/278


[rv_dm] CSR test failed

@sriyerg
As discussed, the RV_DM csr_rw test are failing in some seeds. The CI in this PR #13822 happens to get this error too.
Please take a look. Or let me know if it's OK to exclude checking the DMI CSR. I can make a PR to fix it.

UVM_ERROR (csr_utils_pkg.sv:435) [csr_utils::csr_rd_check] Check failed obs == exp (* [*] vs * [*]) Regname: jtag_dtm_ral.dmi has 1 failures:

Test rv_dm_jtag_dtm_csr_rw has 1 failures.

0.rv_dm_jtag_dtm_csr_rw.1
Line 53, in log /azp/agent/_work/1/s/scratch/HEAD/rv_dm-sim-vcs/0.rv_dm_jtag_dtm_csr_rw/out/run.log
  UVM_ERROR @  12021752 ps: (csr_utils_pkg.sv:435) [csr_utils::csr_rd_check] Check failed obs == exp (0 [0x0] vs<PHONE_NUMBER>00 [0x2663715d5c]) Regname: jtag_dtm_ral.dmi
  UVM_INFO @  12021752 ps: (uvm_report_catcher.svh:705) [UVM/REPORT/CATCHER]
  --- UVM Report catcher Summary ---

Fixed in #13920.

Thanks @sriyerg


⚠ ClamAV has degraded performance

In 0e3af56, ClamAV (https://spamassassin.apache.org/) experienced degraded performance:

HTTP code: 200
Response time: 72 ms

Resolved: ClamAV performance has improved in 5c304ec after 5 minutes.


Abandon FindBugs

FindBugs is no longer maintained.
https://mailman.cs.umd.edu/pipermail/findbugs-discuss/2016-November/004321.html
And we have had unnecessary dependency on com.google.code.findbugs:annotations:3.0.0 for FindBugs.
We're not effectively using FindBugs. It's time to abandon FindBugs. We may enable SpotBugs instead in the future.

@sakama Can you have a look?

Thanks!


feat: update with master

Description
This PR updates next-major-spec with master.

Remember to merge this as regular commit.

/help

I think the best still is keeping a PR always opened from next-major to master and tell @asyncapi-bot to keep it up-to-date.
Right @derberg ?

Its not possible because of conflicts in package version. Have to happen manually.

@smoya you will have to change the settings of the branch and then change it back 🙂

I thought that wasnt allowed to me. But it is. So proceeding.
However, better to find a long term solution, if any. Will create an issue later to discuss about this.

Its not possible because of conflicts in package version. Have to happen manually.

Yeah, right. The never ending story.

@jonaslagoni
I dont fully get why squash and merge works but Rebase doesnt. Do you believe there are conflicts? Can you rebase and push?


How to use srcset

Hi. Is it possible to use img srcset attribute? I use this template and data.

data: "img/picture-sad.png 132w<EMAIL_ADDRESS>264w"
But html output looks like this:
<img class="sad_face" srcset="img/picture-sad.png" 132w,=""<EMAIL_ADDRESS>264w="">

Hi @StanislavMayorov,
what you're seeing here is the default HTML escaping of mustache as described in the Variables section. If you want to explicitly avoid that safety, you can use triple mustaches: {{{ sad_face_srcset }}}.
Does that answer your question?

Yes. Thank you.


Update pubspec in preparation for publication

Upstream dependencies should be ready now.

http hasn't been published, my mistake.

http hasn't been published, my mistake.


Various auth tweaks and fixes

This should unblock the front end team! Although maybe someone else should test it out before this gets merged :sweat_smile:

Thanks! Just want to get some back end eyes on this tomorrow before merging


Add Red Hat Enterprise Linux Update free-courses-en.md

Hacktoberfest notes:

due to volume of submissions, we may not be able to review PRs that do not pass tests and do not have informative titles.
please read our contributing guidelines
be sure to check the output of Travis-CI for linter errors
if this is your first open source contribution, make sure it's not your last!

What does this PR do?
Add Red Hat Enterprise Linux Technical Overview course on Udemy.
For resources
Understand the applications of Linux at a high level.
Practice basic Linux techniques and system administration tasks.
Students will be prepared to take more advanced courses and including those leading to the RHCSA exam
Description
In this official Red Hat® training course, Steve Bonneville, a technical curriculum architect for Red Hat Training, will teach students the basics of Linux® and demonstrate basic practical techniques of Linux use and system administration tasks for professionals new to the operating system.
Why is this valuable (or not)?
It helps one get started with Red Hat Linux by giving a stronghold on fundamentals.
How do we know it's really free?
The course on Udemy is listed under the free category(without certification).
For book lists, is it a book? For course lists, is it a course? etc.
It is a course to get started with red hat linux.
Checklist:

[x] Not a duplicate
[x] Included author(s) if appropriate
[x] Lists are in alphabetical order
[x] Needed indications added (PDF, access notes, under construction)

Thanks, and welcome to free-programming-books!


"Uncompleting" a reminder in edit mode is not possible

Looks like it's overwritten when navigating back to the "Done" list?

Confirmed. If you edit a completed reminder and change it to not done, it is saved right after when navigated to the list view. Likely because the CheckedChanged method is triggered when all checkboxes come into view and are checked.
Can Mode=TwoWay on the checkbox be changed to something that solves the problem? The reminder should never be saved unless the user (View) updates it.


Policy Subnets should have a Network Security Group

Describe the bug
I tried to only implement this policy definition, but it gives an error about that its not an array. I really like this policy and only wanted to deploy this specific policy definition
[excludedSubnets: {](javascript:void(0))
type: &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Array&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;,
[metadata: {](javascript:void(0))
displayName: &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Excluded Subnets&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;,
description: &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;Array of subnet names that are excluded from this policy&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;
},
[defaultValue: [](javascript:void(0))
&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;GatewaySubnet&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;,
&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;AzureFirewallSubnet&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;,
&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;AzureFirewallManagementSubnet&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;
]

}
Steps to reproduce
Assign policy to management group.
1.
2.
Screenshots

Hey @guidovbrakel,
Thanks for the issue. I'm afraid my JavaScript knowledge is not that high so I can only assist here from an Azure/ARM perspective.
The individual policy definition is available here: https://github.com/Azure/Enterprise-Scale/blob/main/src/resources/Microsoft.Authorization/policyDefinitions/Deny-Subnet-Without-Nsg.json

Note: you will need to remove the double [[ escaping in places it occurs and replace with a single [. This is becuase of the way we handle the larger deployment of all the polices.

It is also possible to deploy this policy only using the ALZ-Bicep customPolicyDefinitions.bicep module and just remove the unwanted policy objects from the following arrays:

varCustomPolicyDefinitionsArray (line 12)
varCustomPolicySetDefinitionsArray (line 440)

Then it will just deploy the policy you leave in the arrays as per the deployment guidance here

P.S. this version of the definition in ALZ Bicep is already "unescaped": https://github.com/Azure/ALZ-Bicep/blob/main/infra-as-code/bicep/modules/policy/definitions/lib/policy_definitions/policy_definition_es_deny_subnet_without_nsg.json

Let us know if this helps you 👍

Closing as stale


UP024 fixer replaces wrong members with "as" alias import

$ ruff --isolated  --select UP foo.py
foo.py:5:7: UP024 Replace aliased errors with `OSError`
foo.py:6:8: UP024 Replace aliased errors with `OSError`
Found 2 error(s).
2 potentially fixable with the --fix option.
$ cat foo.py
from socket import error as SocketError
from events import fire, error

try:
        fire(error())
except SocketError:
        print()
$ ruff --isolated  --select UP --fix foo.py
Found 2 error(s) (2 fixed, 0 remaining).
$ cat foo.py
from socket import error as SocketError
from events import fire, error

try:
        fire(OSError())
except OSError:
        print()

fire(error()) must not be replaced
the leftover import is now useless

We need to verify that it's a builtin.

Wait, sorry, what's incorrect here? The unused import will get removed if you enable unused import checks. And this rewrite is correct:
>>> from socket import error as SocketError
>>> SocketError
<class 'OSError'>

Ah, wait, misread the code.

Though it's not obvious, this is the same underlying issue as #1690. Which is that our import tracking has a few limitations we need to rework.


Codes in C++, Java, PHP

Why:

Closes #54 , #75 , #97 , #289 , #338 , #360 , #384 , #440 , #459 , #515 , #561 , #886
What's being changed:

Hey @Mahak008,
You are pushing from the master branch. Please create a branch for your solutions, and you can refer steps from make changes.
For linking more than one issues,
It should look like below
Closes #1
Closes #2
Closes #3
..... so on

Thank you!

 Please create a branch for your solutions, and you can refer steps from make changes.
For linking more than one issues, It should look like below
Closes #1
Closes #2
Closes #3
..... so on

Thank you!

@victoriacheng15 Can you elaborate what exactly to be done for getting my PR merged.


fix: Lineage bug and tracking granularity

Summary of Changes
Fix lineage bugs

Not loading upstream/downstream on page navigation
"0" showing up when column lineage is missing

Tracking

Add click tracking for table, dashboard, and user list item components
Add upstream/downstream direction to lineage related events

Documentation

CheckList
Make sure you have checked all steps below to ensure a timely review.

[ ] PR title addresses the issue accurately and concisely, including a title prefix.
[ ] PR includes a summary of changes.
[ ] My commits follow the guidelines from "How to write a good git commit message"

👍


Manage width size custom cell after "reloadMenu" method

Hi,
I'm trying to call the method "reloadMenu" when I update the title of a cell. The title is correctly updated, but it seems the method "pagingViewController(_ pagingViewController: PagingViewController, widthForPagingItem pagingItem: T, isSelected: Bool) " is not called again, so the width of the view doesn't change according the new title. (to be able to have a size equals to the width of the title)
Any ideas?
Thank you,

Ops, closed the issue by mistake.