[2120] Use equal in flapping test for better output

Context

Currently diff is truncated so can't see where the problem is.
https://github.com/DFE-Digital/manage-courses-backend/commit/02b61bd63d59a8ee3816ce791f99a00af663d019
https://dfe-ssp.visualstudio.com/Become-A-Teacher/_build/results?buildId=20565&view=ms.vss-test-web.build-test-results-tab

Changes proposed in this pull request

Use equal in flapping test for better output

Guidance to review
:ship: if the test still passes
Note that  match_array copes with differences in ordering but I don't
think that matters.
Checklist

[x] Make sure all information from the Trello card is in here
[ ] Attach to Trello card
[x] Rebased master
[x] Cleaned commit history
[x] Tested by running locally

 flapping json matcher test in recruitment_cycle_spec.rb


Add print statement for the console debugging in run_tests.py

$subject

@samvarankashyap bump

@samvarankashyap  bump ^^

Yes.


Flutter Android Studio Error - Unable to find bundled Java version error. Catalina 10.15.7

At a loss with solutions across boards for this Flutter doctor error: Unable to find bundled Java version error. Catalina 10.15.7
admin@Admins-MBP development % flutter doctor
Doctor summary (to see all details, run flutter doctor -v):
[✓] Flutter (Channel stable, 2.5.3, on Mac OS X 10.15.7 19H1519 darwin-x64,
locale en-US)
[✓] Android toolchain - develop for Android devices (Android SDK version 31.0.0)
[✓] Xcode - develop for iOS and macOS
[✓] Chrome - develop for the web
[✓] Android Studio (version 2020.3)
[!] Android Studio
✗ Unable to find bundled Java version.
[✓] IntelliJ IDEA Ultimate Edition (version 2021.2.3)
[✓] IntelliJ IDEA Community Edition (version 2021.2.3)
[✓] VS Code (version 1.61.2)
[✓] Connected device (1 available)

@mtmgit
Check this similar issue and underlying comments and see if any of them help to resolve your issue.

Without additional information, we are unfortunately not sure how to resolve this issue.
We are therefore reluctantly going to close this bug for now.
Please don't hesitate to comment on the bug if you have any more information for us; we will reopen it right away!
Thanks for your contribution.
Could everyone who still has this problem please file a new issue with the exact description of what happens, logs, and the output of flutter doctor -v.
All system setups can be slightly different, so it's always better to open new issues and reference related issues.


docs: loading/boostrap

describe architecture of loading content in client and server
describe Js Bootsrapping and move to development guide
describe how to use reactContext to share state on the server

Codecov Report

Merging #89 into next will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff            @@
##               next      #89   +/-   ##
=========================================
  Coverage     78.01%   78.01%
  Complexity      197      197
=========================================
  Files            30       30
  Lines           855      855
  Branches         72       72
=========================================
  Hits            667      667
  Misses          148      148
  Partials         40       40

Continue to review full report at Codecov.

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Not defined labels are not handled as Not available as it used to be

In the previous release the not defined labels where coloured grey as the points with no expression and labeled "Not available"
It should be like this https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-130148/results/tsne

However, now it shows like this:
https://wwwdev.ebi.ac.uk/gxa/sc/experiments/E-GEOD-130148/results?colourBy=inferred+cell+type+-+ontology+labels

I see this in all experiments I have observed so far.

I would prefer if we close this when this is merged to satisfaction.

All working well now on https://wwwdev.ebi.ac.uk/gxa/sc/experiments/E-GEOD-130148/results/tsne?colourBy=inferred+cell+type+-+ontology+labels as Not available shows up and in grey.


Update metadata description of branch spring boot3 spring.datasource.azure.passwordless-enabled

Sync change from PR:https://github.com/MicrosoftDocs/azure-dev-docs-pr/pull/3877

Hi, @hui1110 .
Is it ready for review now?

Why https://github.com/MicrosoftDocs/azure-dev-docs-pr/pull/3877 merge to main branch, but this PR merge to spring-boot-3 branch?

Why MicrosoftDocs/azure-dev-docs-pr#3877 merge to main branch, but this PR merge to spring-boot-3 branch?

Do you plan to merge to all 3 branches: main, spring-boot-3, connection-free?


support windows/arm64

This is in good shape and  works really well.
Can we add windows arm64 also to:
https://github.com/cogentcore/webgpu/tree/main/wgpu/lib/windows ?
Its possible now but was not a few months ago: https://github.blog/news-insights/product-news/arm64-on-github-actions-powering-faster-more-efficient-build-systems/
"These runners are available to our customers on our GitHub Team and Enterprise Cloud plans. We expect to begin offering Arm runners for open source projects by the end of the year."
You would need to go into your settings -> Actions -> runner groups -> then add an ARM runner :) This does mean for now, they are also not free in public repos.
jobs:
  build:
    strategy:
      matrix:
        os: [ubuntu-latest, ubuntu-arm-latest, windows-latest, macos-latest, macos-arm-latest]

    runs-on: ${{ matrix.os }}

You can test using UTM BTW if your on Mac, since its Arm also. But it can also emulate amd64 binaries.
https://github.com/utmapp/UTM

Thank you for the suggestion! We probably don't want to do this until they provide free runners for arm64. Also, do you know how much usage windows arm64 has in general? If there is not much usage and it can run amd64 binaries anyway, I'm not sure if we want to bloat the repository with more large object files. However, if there is decent usage, it is probably worth supporting this at some point. Also, thank you for linking to UTM; I didn't know about it!

Thank you for all of the information! We will definitely consider supporting windows/arm64 once GitHub makes those runners free.

January 2025

There is no inherent issue to supporting windows/arm64, it is just that we do not have the libwgpu_native.a file because we automatically compile them using GitHub Workflows, for which we cannot use windows/arm64. If you can build or find the libwgpu_native.a file for windows/arm64 for the correct version, we could upload it to add support for this. We are also planning to update to a new version of wgpu soon, so it may also make more sense to just wait until we do that and the free runners are available for this update; either way is fine.

We implemented support for Windows and Linux arm64 in the commits above by compiling the libwgpu_native.a files in UTM on our macOS computers. Windows and Linux are fully functional in our experience on UTM after we fixed several key annoying bugs with WebGPU (see 0779a62c5a1b0835178b6a60d4df9b45477bd4ae and https://github.com/cogentcore/core/commit/b1423587c8a35eaf98a872681d62457a0e159fdc), with fast performance in 2D and full but somewhat slow functionality in 3D. We will add these new arm64 platforms to the CD once they are supported, but the current approach works fine for now.
You can test this with the latest version of Cogent Core on the main branch (https://github.com/cogentcore/core/pull/1341 is required). Thank you for recommending UTM, which was key for this and will allow us to debug many platform-specific issues much easier going forward!


Fix error code for GET profile API

This will fix the regression bug that caused GET profile to return 500 instead of 404.

how to reproduce the failing test:

override with an invalid iam ID, e.g .iamID := "linkedin123"
https://github.com/jobsgowhere/jobsgowhere/blob/44718e731629dfb66a208f4baebc96fe871ad676/internal/person/controller.go#L34

override with api.GET("/profile", pc.GetProfile) to turn off API auth
https://github.com/jobsgowhere/jobsgowhere/blob/44718e731629dfb66a208f4baebc96fe871ad676/cmd/jobsgowhere.go#L37

restart backend server: go run main.go

send API request to GET http://localhost:8080/api/profile - see the 500 Interval Server Error with body:
{
  "error": "internal_error",
  "error_description": "An error occurred in the server, please retry after sometime. err=profile not found"
}

I think the root cause was introduced in https://github.com/jobsgowhere/jobsgowhere/pull/109/files#diff-4ac16bb69fd4610b064dcd749a372a00L35, where sql.ErrNoRows was originally suppressed
if err == sql.ErrNoRows {
	return nil, nil
}

was changed to
if err == sql.ErrNoRows {
	return nil, errors.New("profile not found")
}

which is logically correct (surface the error to calling function)
This surfaced up to
https://github.com/jobsgowhere/jobsgowhere/blob/44718e731629dfb66a208f4baebc96fe871ad676/internal/person/controller.go#L37-L39
Since the err was not nil, it threw http.StatusInternalServerError

@hanmd82 true, but for this use-case, we don't need to return any errors as such. If there is no record it should give 404. Let me know if you have any other recommendations?

@hanmd82 check the last commit. If it works then I will squash the commits.


feature request: return control key status

Hello. It wold be nice to know which control key is pressed with Enter (just Enter, Shift+Enter or Ctrl+Enter)
Something like:
choise = cli.launch()
if cli.lastCtrKey == Shift:
    # do some thing
else:
    return choice

or
choise = cli.launch(ctrl)
if ctrl == Shift:
    # do some thing
else:
    return choice

Can you be more specific on why this is a relevant feature? Eg, where this feature can be used, what problem did you encounter without this feature.


[Misc]: vLLM performs consistently poor as compared to HF TGI when tested with the DeepSeek Coder Model

Anything you want to discuss about vllm.
Hello Folks,
We are using the Deep Seek Coder model for code completions and chat completions. I did try to run the benchmark scripts for that model both for vLLM and TGI and I see that vLLM metrics are consistently poorer as compared to TGI.
Could you please review and comment on the setup ?
Bring up the servers:
MODEL="deepseek-ai/deepseek-coder-1.3b-instruct"

python -m vllm.entrypoints.openai.api_server \
    --model ${MODEL} \
    --swap-space 16 \
    --disable-log-requests

    (TGI backend)
    ./launch_tgi_server.sh ${MODEL} 8192

On the client side, run:
python benchmarks/benchmark_serving.py \
    --backend vllm \
    --model ${MODEL} \
    --dataset-name sharegpt \
    --dataset-path /home/anindya/ShareGPT_V3_unfiltered_cleaned_split.json \
    --request-rate 10 \
    --num-prompts 1000 \
    --save-result

python benchmarks/benchmark_serving.py \
    --backend tgi \
    --model ${MODEL} \
    --endpoint /generate_stream \
    --dataset-name sharegpt \
    --dataset-path /home/anindya/ShareGPT_V3_unfiltered_cleaned_split.json \
    --request-rate 10 \
    --num-prompts 1000 \
    --save-result

Results:
With DeepSeek Model vLLM Backend
============ Serving Benchmark Result ============
Successful requests:                     1000
Benchmark duration (s):                  182.67
Total input tokens:                      243330
Total generated tokens:                  186483
Request throughput (req/s):              5.47
Input token throughput (tok/s):          1332.10
Output token throughput (tok/s):         1020.90
---------------Time to First Token----------------
Mean TTFT (ms):                          6369.27
Median TTFT (ms):                        126.85
P99 TTFT (ms):                           33385.83
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          155.33
Median TPOT (ms):                        164.56
P99 TPOT (ms):                           265.79
==================================================

With DeepSeek Model TGI Backend
============ Serving Benchmark Result ============
Successful requests:                     1000
Benchmark duration (s):                  114.52
Total input tokens:                      243330
Total generated tokens:                  183272
Request throughput (req/s):              8.73
Input token throughput (tok/s):          2124.86
Output token throughput (tok/s):         1600.41
---------------Time to First Token----------------
Mean TTFT (ms):                          341.08
Median TTFT (ms):                        325.95
P99 TTFT (ms):                           789.21
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          26.39
Median TPOT (ms):                        25.78
P99 TPOT (ms):                           39.39
==================================================

Could you share the hardware you're benchmarking on? I will take a look and try to repro

Hi @ywang96 Its the standard AWS g5.4xlarge instance. Also, I ran multiple times to make sure the stats are consistent. I also did not enable flash-attention or quantization in any of them.
Also, vLLM does not support eetq quantization.

TTFT is shocking!

FYI: you can run https://github.com/vllm-project/vllm/blob/main/collect_env.py to report more environment information.

Hi @ywang96 Its the standard AWS g5.4xlarge instance. Also, I ran multiple times to make sure the stats are consistent.

@anindya-saha I'm actually a bit confused now because the plots you showed make more sense than the original results you posted in the issue.
Currently, vllm will always try to schedule requests whenever possible which lead to very low TTFT as expected but also affect TPOT because of prefill blocking decode.

Hey @ywang96, how does batch request happens in OpenAI Client?
Can you share some information? I want to see how increasing the batch size impacts the performance

Hey @ywang96, how does batch request happens in OpenAI Client? Can you share some information? I want to see how increasing the batch size impacts the performance

IMO for batch processing - benchmark with benchmark_throughput.py makes more sense.

Hi @ywang96 I ran many experiments when I logged the request. So the metrics was from there. Anyway, I reran the experiments again to create the charts today. I updated the original post with the metrics from the graph.

FYI: you can run https://github.com/vllm-project/vllm/blob/main/collect_env.py to report more environment information.
I believe something is wrong in your environment. It doesn't need 6369.27 ms to get the first token for just a 1.3b model.

This is the o/p from the env info:
Collecting environment information...
PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.29.0
Libc version: glibc-2.31

Python version: 3.10.13 (main, Feb 23 2024, 00:58:06) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-1056-aws-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A10G
Nvidia driver version: 535.161.07
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      48 bits physical, 48 bits virtual
CPU(s):                             16
On-line CPU(s) list:                0-15
Thread(s) per core:                 2
Core(s) per socket:                 8
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          AuthenticAMD
CPU family:                         23
Model:                              49
Model name:                         AMD EPYC 7R32
Stepping:                           0
CPU MHz:                            2324.061
BogoMIPS:                           5600.00
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          256 KiB
L1i cache:                          256 KiB
L2 cache:                           4 MiB
L3 cache:                           32 MiB
NUMA node0 CPU(s):                  0-15
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; safe RET
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save rdpid

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.1.2
[pip3] triton==2.1.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.0
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-15    0               N/A

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

@anindya-saha Could you give the latest vLLM package on PyPI a try? I couldn't quite repro your results on g5
============ Serving Benchmark Result ============
Successful requests:                     1000
Benchmark duration (s):                  115.06
Total input tokens:                      243330
Total generated tokens:                  183154
Request throughput (req/s):              8.69
Input token throughput (tok/s):          2114.82
Output token throughput (tok/s):         1591.82
---------------Time to First Token----------------
Mean TTFT (ms):                          42.66
Median TTFT (ms):                        37.20
P99 TTFT (ms):                           126.86
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          33.00
Median TPOT (ms):                        30.41
P99 TPOT (ms):                           85.07
==================================================

Below are the system info
INFO 04-13 09:49:21 pynccl.py:58] Loading nccl from library libnccl.so.2
PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.3.103
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A10G
Nvidia driver version: 535.129.03
cuDNN version: Could not collect
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
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             48
On-line CPU(s) list:                0-47
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7R32
CPU family:                         23
Model:                              49
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          1
Stepping:                           0
BogoMIPS:                           5599.99
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save rdpid
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          768 KiB (24 instances)
L1i cache:                          768 KiB (24 instances)
L2 cache:                           12 MiB (24 instances)
L3 cache:                           96 MiB (6 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-47
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; safe RET
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.1.2
[pip3] triton==2.1.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.0.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-47    0               N/A

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

@ywang96 I installed vLLM from source

Your results are quite similar to mine. Can you also publish the benchmark results from TGI from your same setup ?

@anindya-saha, if you'd accept some feedback, I was a bit confused as I saw this issue before and after your edit around 11 hours ago. After the edit, the issue title and description don't quite line up with the data. Personally, I find it helpful to append corrected data with context rather than edit-in-place.

@anindya-saha, if you'd accept some feedback, I was a bit confused as I saw this issue before and after your edit around 11 hours ago. After the edit, the issue title and description don't quite line up with the data. Personally, I find it helpful to append corrected data with context rather than edit-in-place.

Hi @AaronFriel That's a good feedback. However, after the edit the HF TGI metrics shows it is better than the vLLM metrics (except the TTFT). The graph also show the same. After the edit the issue description and the graph lines up, is that not what you feel ?

I think "consistently poor" suggests to me worse TTFT, TPOT and throughput, but it looks like vLLM and TGI may have made choices to optimize for one or the other. It'd help to clarify in the issue description that this is primarily about lower throughput.
Now that you're using vLLM latest, can you try enabling any of these features to optimize throughput?

enable_chunked_prefill
enable_prefix_caching
scheduler_delay_factor at 0.1, or other values in (0, 0.5]

Given that the performance gap is now only 16%, I think any of these could be sufficient to close the gap.

I think "consistently poor" suggests to me worse TTFT, TPOT and throughput, but it looks like vLLM and TGI may have made choices to optimize for one or the other. It'd help to clarify in the issue description that this is primarily about lower throughput.
Now that you're using vLLM latest, can you try enabling any of these features to optimize throughput?

enable_chunked_prefill
enable_prefix_caching
scheduler_delay_factor at 0.1, or other values in (0, 0.5]

Given that the performance gap is now only 16%, I think any of these could be sufficient to close the gap.

I see what you mean. Yes, we are mainly concerned with throughput or latency. TTFT is not a major concern for our use case right now.
I did not want to try any optimization on vLLM yet because I wanted to see how vLLM and TGI compare neck to neck without any optimization and using the vanilla OOTB benchmark scripts provided in the vLLM repo itself. If that shows quite good positive sign already then I can make a case for engineers to allocate time and investigate more with other parameters to bring it down even further.
If I enable any kind of optimization I have to enable similar optimization in the HF TGI server too to make the benchmark study comparable scenarios, which will be quite time consuming.
I just wanted to check do you see the same behavior with this particular deepseek-ai/deepseek-coder-1.3b-instruct  model that TGI HF performs better w.r.t. throughput as compared to vLLM.

@anindya-saha Here are the results I get from TGI, and I actually used their latest version as well (v2.0.0)
============ Serving Benchmark Result ============
Successful requests:                     1000
Benchmark duration (s):                  112.05
Total input tokens:                      243330
Total generated tokens:                  184459
Request throughput (req/s):              8.92
Input token throughput (tok/s):          2171.58
Output token throughput (tok/s):         1646.19
---------------Time to First Token----------------
Mean TTFT (ms):                          329.45
Median TTFT (ms):                        308.16
P99 TTFT (ms):                           702.44
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          24.37
Median TPOT (ms):                        24.58
P99 TPOT (ms):                           34.13
==================================================

One thing I'll have to point out regarding the throughput difference: If your overall concern is just throughput (how many requests you can process over a certain period of time), then benchmark_request.py is a better benchmark to run.
IMO LLM online inference generally speaking is a lot more complicated than just throughput & latency because of its unique autoregressive nature. For example, one could argue that if the use case is simply generating a few tokens as labels, then TTFT will be significantly more important here. For the ShareGPT dataset, each request has different input and output lengths setting to reflect what it might look like if it's serving a conversation use case.
Therefore, another suggestion I have here is benchmarking on your own dataset. Initially I thought about adding a data registry so that people can port their own dataset to the benchmark script (I didn't get time to do it unfortunately), but at least I hope the script itself is easy enough to follow and modify to add your own dataset.

I did not want to try any optimization on vLLM yet because I wanted to see how vLLM and TGI compare neck to neck without any optimization and using the vanilla OOTB benchmark scripts provided in the vLLM repo itself. If that shows quite good positive sign already then I can make a case for engineers to allocate time and investigate more with other parameters to bring it down even further.

Definitely, but I think it's important to understand that your optimization goal (maximum throughput) may not be the vLLM projects, it may not be a "bug" for vLLM to default to lower TTFT and lower throughput, and TGI to default to higher TTFT for higher throughput.
I don't know if this project has expressly written down their optimization goals, but that's why options like the ones I shared exist.

I don't know if this project has expressly written down their optimization goals, but that's why options like the ones I shared exist.

@ywang96 I couldn't able to understand as `benchmark_throughput.py' usage the ofline serving as compared to the 'benchmark_serving.py'.
If I want to use the online serving at prod then it doesn't make sense to compare with ofline serving.

@anindya-saha Here are the results I get from TGI, and I actually used their latest version as well (v2.0.0)
============ Serving Benchmark Result ============
Successful requests:                     1000
Benchmark duration (s):                  112.05
Total input tokens:                      243330
Total generated tokens:                  184459
Request throughput (req/s):              8.92
Input token throughput (tok/s):          2171.58
Output token throughput (tok/s):         1646.19
---------------Time to First Token----------------
Mean TTFT (ms):                          329.45
Median TTFT (ms):                        308.16
P99 TTFT (ms):                           702.44
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          24.37
Median TPOT (ms):                        24.58
P99 TPOT (ms):                           34.13
==================================================

One thing I'll have to point out regarding the throughput difference (3% in this case): If your overall concern is just throughput (how many requests you can process over a certain period of time), then benchmark_throughput.py is a better benchmark to run since all requests are sent at time 0.
IMO LLM online inference generally speaking is a lot more complicated than just throughput & latency because of its unique autoregressive nature. For example, one could argue that if the use case is simply generating a few tokens as labels, then TTFT will be significantly more important here. For the ShareGPT dataset, each request has different input and output lengths setting to reflect what it might look like if it's serving a conversation use case.
Therefore, another suggestion I have here is benchmarking on your own dataset that best reflects your use case. Initially I thought about adding a data registry when I refactored benchmark_serving.py so that people can port their own dataset to the benchmark script (I didn't get time to do it unfortunately), but at least I hope the script itself is easy enough to follow and modify to add your own dataset

Thank you for the suggestions. I will revisit them.  Also, the metrics that you posted for HF TGI is similar to what I seeing as well.
I am making an application for Code Completion and also another product for Chat. During inference I need to choose an inference server to host the models. What should I optimize for Latency, Throughput or TTFT ? When you have a chance, I wanted to get your general suggestions and if you have insights on them.
Personally I feel the following:
Code Completion: Optimize for latency to ensure that suggestions appear swiftly as the user types, enhancing the feeling of immediate responsiveness.
Chat Application: Focus primarily on latency and throughput. Latency is critical for a fast conversational experience, and throughput is important to manage multiple users and conversations concurrently without performance bottlenecks.
I am curious to know your thoughts on them.

Personally I feel the following:
Code Completion: Optimize for latency to ensure that suggestions appear swiftly as the user types, enhancing the feeling of immediate responsiveness.
Chat Application: Focus primarily on latency and throughput. Latency is critical for a fast conversational experience, and throughput is important to manage multiple users and conversations concurrently without performance bottlenecks.
I am curious to know your thoughts on them.

@anindya-saha One distinction between the two is whether the outputs are streamed.
For code completion, it probably will not be streamed, so end-to-end latency (TTFT + TPOT * # of output tokens - 1) is what you would care about.
For chat application similar to chatgpt where the outputs are streamed, I would actually argue that TTFT is much more significant than TPOT usually since it's the user perceived latency of model response. Take one extreme example: Imagine you have to add 10 seconds to the end-to-end latency of a 100-token streaming output, would you add it to the TTFT, or TPOT by spread out over the 99 tokens?
Of course, the metric to optimize will always depend on the goal of your use case, but I hope the serving benchmark can provide enough insights for you to play around with!


Add TextStypes Class

I believe the SpongePowered team should add a TextStyles class. This class would be used to make font obfuscated, bold or in italics. If I were to create a bold "Hello World" message in GREEN, this is what I would do:
public Message createExampleMessage() {
    return Messages.builder("Hello World").style(TextStyles.BOLD).color(TexttColors.GREEN).build();
}

https://github.com/SpongePowered/SpongeAPI/blob/master/src/main/java/org/spongepowered/api/text/format/TextStyles.java

How could I implement this into a Message? I dosen't say on the documentation!

Check out the Text API 2. #425

Also, you need to use a MessageBuilder.


stream.pipe(request) Error: socket hang up

Hi:
I have a problem，
code:
var stream = fs.createWriteStream(path.join(__dirname, './test.json'));
request = new Request(method, url);
stream.pipe(request);

Upload about 100 K file no problem
But there will be a mistake in more than 200 K
Error is
{ Error: socket hang up
    at createHangUpError (_http_client.js:254:15)
    at Socket.socketCloseListener (_http_client.js:286:23)
    at emitOne (events.js:101:20)
    at Socket.emit (events.js:188:7)
    at TCP._handle.close [as _onclose] (net.js:498:12) code: 'ECONNRESET', response: undefined }

After the examination, no problems were found, can you help me?

Can you verify it happens with other servers? It looks like a connection closed by the server, so the problem may be servers-side, and not in superagent.

Thank you very much for your answer.
I check on the server, the server did not receive the next data of the client, Content-length and the received content of the length does not match, the server waiting for timeout to actively disconnect the link, and then the client throws an exception.
I try to set highWaterMark larger than sending files, and like this, requests are successful
let stream = fs.createReadStream(file, {
    highWaterMark: 300000
})
request = new Request(method, url);
stream.pipe(request);

The server should not receive the complete stream, if I do not set highWaterMark, but use default values, can solve this problem?
Do you have any ideas about this question?

Your server may not be expecting to receive unstructured POST. Try using request.post().attach('file', stream) instead.

But my interface is PUT. Is there any other way?

If it's put then it should work. Are you setting content-length?

yes, I set up Content-Length, and it was right, but I didn't know why stream was interrupted.

When I upload stream, I use require ('http').Request, and the upload is successful. I think what SuperAgent did when uploading stream, see the code has nothing to find, and I will continue to follow this problem.
code:
var http = require('http');

function stream (req, callback) {
    var options = {
        host: req.headers.Host,
        port: req.port,
        method: req.method,
        headers: req.headers,
        path: req.path
    }
    return http.request(options, function (httpResp) {
        callback(httpResp);
        httpResp.emit('headers', httpResp.statusCode, httpResp.headers);
    });
}
module.exports = stream;

readStream.pipe(stream);

I had the same issue, solved in the same way - using http.request instead. I'm using node 10.6.0

I think https://github.com/visionmedia/superagent/issues/1654 might be the root cause


Add caching status page

Add a hidden page where you show the content of the caching classes

This is moved to backlog again.  First, it's better to implement memcache and take the content from there.


Match for homograph

Maybe I missed some code, I don't see the match part.
I personally used FLANN of opencv to match the two v_boat pic in the demo,  the result is as follow.
I would appreciate it if can guide me to the match part in the project.

Did you extract the patch descriptor of each keypoint correctly? We use HardNet descriptor for image matching.


Preview trc files specified in meters is incorrect

Steps to reproduce
Reported by BJ Fregly student, converted file to mm and it worked.
Expected result
Actual result
(include screen grabs where appropriate)
Environment and GUI version

Should we put this on the 4.0 path?

Definitely

Can you post the .trc file that revealed the issue?

drive_kick1_fixed_reordered_scaled.txt

I was able to verify that this is a marker preview (associate motion) issue, by running IK with the data and the Rajagopal model.

As of PR #1012 (artifact OpenSim-4.0-2018-10-12-ba06a01b) we are able to preview both markers in m (red) and in mm (blue) below and seems to have no effect on GRFs (green) even though all conversion is now multiplicative (as a conversion factor should be).


get source from parent container when corgi download_url invalid

Generally LGTM. While checking the data on stage, found so many data in task params, especially the component data, filed OLCS-582 to track.


allow 1 retry for failing tests

as described here, lets see if this can help out at all.
My gut tells me that this is not a good idea to do in practice, but false failure rate due to known flaky tests seems pretty bad lately. It could be potentially scoped to druid-api, druid-common, druid-processing, druid-server, druid-indexing-service, druid-kafka-indexing-service to cover all currently open issues with the flaky-test label.
I'd much prefer if we could just set an annotation on the actual tests that we know to be lame and only allow this functionality on those, but I haven't turned up anything that does that yet. Idealistically we fix them all, but on the other hand some of these have been failing on occasion for years now, that I know myself and many other have spent a fair chunk of time trying to resolve to no avail. Many of these tests are covering rather complicated behaviors and interactions, so are generally worth putting up with flaky behavior for the value they provide, so disabling them isn't an option either.
I would say on the upside, this option will hopefully result in less time spent re-triggering the same (mostly curator-test based) tests over and over until they pass. An obvious downside is lower chance of once and for all resolving flaky tests since catching new ones requires checking the test results manually and that it's easier to ignore existing issues if they don't explode on you often.
Additionally, this might not help at all, since retries might push us over the timeout limit. I suggest we re-run the tests on this PR many times to check if it's effective at all. Also, this option is only for junit 4.x, just to add to the pile of things to consider.

1 might not be enough, but let's see if this PR passes tests with 1.

@gianm @clintropolis i dont see travis-ci tests running

I think we should think about 3-5 retries

There is currently a partial outage of travis affecting github sync https://www.traviscistatus.com

Updated to 3

👍  thanks @clintropolis!


No Icon

Hi,
I'm making a template (and how to video) for unraid.
While everything should work as it is, it's nicer if I can add an icon.
Would you be able to create one we could use?
Thanks!

What about this?
I'm not a good designer, so dall-e prepared this one :)


pod issue investigator

Main Changes:

Refactored code that generates crashloop_backoff finding blocks to their own file

src/robusta/core/playbooks/pod_utils/crashloop_utils.py is from playbooks/robusta_playbooks/restart_loop_reporter.py

Refactored code that generates imagepull_backoff finding blocks to their own file

src/robusta/core/playbooks/pod_utils/imagepull_utils.py is from playbooks/robusta_playbooks/image_pull_backoff_enricher.py

Added pending_pod_enricher that investigates why a pod is pending, in the same way imagepull_backoff works

Added enricher pod_issue_investigator that investigates if a pod is

crashlooping (or crashed recently)
imagepulled backoffing
pending
if it is then it creates the relevant findings (for crashloopbackoff and imagepullbackoff it will by from their code)

Tested on

Deployments with image pulls
Deployments with crashpods
10 different types of pending pods

Future improvements:
Add OOMKill detection as part of its investigation


Typescript error for algToKeyUriAlg in LoginConfigTotp.tsx

Hello,
first of all: awesome project!
Typescript is not happy with this line:
https://github.com/keycloakify/keycloakify/blob/a02c38ac4502163935a4c36fcf3efdbb52892a6d/src/login/pages/LoginConfigTotp.tsx#L19
The error: TS2702: 'KcContext' only refers to a type, but is being used as a namespace here.
I think it should be:
const algToKeyUriAlg: Record<typeof kcContext["totp"]["policy"]["algorithm"], string> = {

What do you think?

By the way, the page LoginConfigTotop is crashing because it try to call "map()" on totp.policy.supportedApplications, but supportedApplications is directly under totp.
I think it should be defined like this in keycloakify/src/login/kcContext/KcContext.ts
        totp: {
            enabled: boolean;
            manualUrl: string;
            policy: {
                algorithm: "HmacSHA1" | "HmacSHA256" | "HmacSHA512";
                digits: number;
                lookAheadWindow: number;
            } & (
                | {
                      type: "totp";
                      period: number;
                  }
                | {
                      type: "hotp";
                      initialCounter: number;
                  }
            );
            qrUrl: string;
            supportedApplications: string[];
            totpSecret: string;
            totpSecretEncoded: string;
            totpSecretQrCode: string;
            otpCredentials: { id: string; userLabel: string }[];
        }

Here a copy of the totp object when I debug:
{
    "enabled": false,
    "totpSecretEncoded": "...",
    "totpSecret": "...",
    "manualUrl": "https://....",
    "supportedApplications": [
        "totpAppGoogleName",
        "totpAppFreeOTPName"
    ],
    "totpSecretQrCode": "....",
    "policy": {
        "initialCounter": "0",
        "type": "totp",
        "codeReusable": false,
        "lookAheadWindow": "1",
        "algorithm": "HmacSHA1",
        "period": "30",
        "algorithmKey": "SHA1",
        "digits": "6"
    },
    "qrUrl": "https://....",
    "otpCredentials": []
}

Sure, but I also hit #262, @sukso96100 seems to be working on it.
Should I only fix this one or try to do both in the same PR?

If you could do both it would be awesome

That's a weird issue :sweat_smile:
It only happen when ejecting the page, because KcContext is not exported as a Namespace anymore but like this in a keycloakified app:
export type KcContext = NonNullable<typeof kcContext>;

Yes that's exatly it.
It's because users are enable to extend the KcContext so they can have corect typing for their custom plugin or implement unsupported page themselvs without having to submit a PR.
Thanks for working on a PR!


Metrics for processing, graphing, database access, network, etc...

It'd be nice if we had more metrics on various parts of turbot as well as monitoring of postgresql database usage.

Also if we could integrate with a service like datadog or something similar we could have persistent metrics dashboards and alerting.


Registering for next ceremonies

Yes, this task took a bit longer because I found it strange that the application would record 10 notifications every time it was opened. I was curious about what would happen if a new record was made with the same ID, and according to my research, the new record would replace the old one. Still, I prefer to lower the number of notifications to 3-5, since 10 would be about 3 months.
Check the translation of the title and content values that I am using:
Title(en) : "Registration for the gathering has started"
Content(en) : "Register now, hurry up!"

Title(de) : "Die Anmeldung für das Treffen hat begonnen"
Content(de) : "Melden Sie sich jetzt an, verpassen Sie keine Gelegenheit!"

Title(fr) : "L'inscription pour la réunion a commencé"
Content(fr) : "Inscrivez-vous maintenant, ne manquez pas cette occasion!"

Title(ru) : "Регистрация на встречу началась"
Content(ru) : "Зарегистрируйтесь сейчас, не упустите шанс!"

"But I was able to test and it worked with final scheduledDate = DateTime.now().add(Duration(minutes: i+1));"
  /// This method schedules reminders for ceremonies that will occur in a certain period of time.
  static Future<void> scheduleCeremoniesReminders(
    int? nextRegisteringPhase,
    int? currentCeremonyIndex,
    int? ceremonyCycleDuration,
    TranslationsEncointer dic,
  ) async {
    // Check if all required variables are defined
    if (nextRegisteringPhase != null && currentCeremonyIndex != null && ceremonyCycleDuration != null) {
      for (var i = 0; i < 10; i++) {
        // calculate the scheduled date by adding i*ceremonyCycleDuration to nextRegisteringPhase
        final scheduledDate = DateTime.fromMillisecondsSinceEpoch(nextRegisteringPhase + i * ceremonyCycleDuration);
        final scheduledDateTest = DateTime.now().add(Duration(minutes: i+1)); // just for testing
        await NotificationPlugin.scheduleNotification(
          currentCeremonyIndex + i,
          dic.registeringPhaseReminderTitle,
          dic.registeringPhaseReminderContent,
          scheduledDate,
        );
      }
    }
  }

Also, I am very pleased that you took extra time to test this well!


Encode the cardinal product of facet fields when it is small

Right now facets are encoded as a list of facet term ordinal in a multiple valued fast field.
This is neat... but if the number of facets is small, it is much more efficient to store a single facet field that contains
the "facet set". This is especially relevant for logs, where some of the facets values might be not independent at all (e.g.
log level and logging class).
Ideally we should detect this situation in the serializer and encode the facets in a different fashion in that case.

Closing... This is not something we want.


Prompt the user to create a ticket, if none exist, Resolves #14

Related Issue
Closes #14
Type of Change

[ ] New Feature
[x] Bug Fix
[ ] Code Refactor
[ ] Documentation Update
[ ] Other (please specify):

Description of Change
Added a card with similar style as of ticket which displays, "No Tickets Found" and clicking on the card it redirects to page to create new ticket. This will display only when no ticket exists.
Implementation Details
Added a card with similar style as of ticket which displays, "No Tickets Found" and clicking on the card it redirects to page to create new ticket. This will display only when no ticket exists ie when ticket array is empty.
Demo
https://github.com/user-attachments/assets/354ec0fe-e7f4-4de5-b486-f3270116d041

Kindly review this PR @AyushDharDubey

Kindly review this PR @AyushDharDubey

Putting all the information in the card , that is , no tickets found and as well as create ticket now , feels a bit confusing for a first time user. To improve the user experience, show the message of no tickets found in the card but have a button for "create your first ticket now" so that they know what to click to get started. You can try any other suitable way to improve the user experience as well ,if you wish to.

Hi @himani-r5 mam
As said i have added a button to create ticket in the card itself.
https://github.com/user-attachments/assets/1866a1cb-71c0-4f13-bfb6-693c804124b7
Kindly resolve the issue by merging the PR.

Sir @AyushDharDubey
I have made all the changes as said.
Kindly move on by merging the PR.

@MadhavDhall i feel it would be better if the NoTicketExists component would be rendered even before the view by section
i.e the check
        {tickets.length === 0 ? <NoTicketExists /> :

instead of being at line 66 might better be at line 50
apart from this, please make sure that your commit messages clearly reflect your changes.
thank you

Hi @AyushDharDubey sir
I have made all changes as suggested.
Kindly merge the PR.

Kindly merge the PR.

@MadhavDhall, the maintainers are here to merge the PR only, and it will ultimately be merged once all necessary reviews are completed, but first we need to ensure we are satisfied with the changes before proceeding

The contents of the card might look better if aligned to the center.
@himani-r5 @MadhavDhall, what do you say?

The contents of the card might look better if aligned to the center.
@himani-r5 @MadhavDhall, what do you say?
i mean on wider displays the card looks this

Agreed sirji.

The contents of the card might look better if aligned to the center.
@himani-r5 @MadhavDhall, what do you say?
i mean on wider displays the card looks this

Yes they would … having a better UI wont hurt 🥹

@AyushDharDubey @himani-r5, as said i have removed unnecessary line and aligned card's content at center. Anything else you feel?

Good work @MadhavDhall,
the PR is looking good enough and is ready to be merged


where is the data importance sampling in the code？

Hi, I am interested in your work.
Could you please share the data importance sampling code with me?

As there have been no further updates in these months, I will now close this issue. If you have any other questions, please feel free to re-open it and we will be happy to assist you.


Enable maps to work as inputs

Add name prop and enable selected choropleth to function as inputs.

One more thing to do, which is extend this to the echarts maps components


DiffText

We already have lots of showt and showtl variants for showing things directly to strict or lazy Text. This works really well for particular scenarios (e.g., #21), but for larger data structures, I fear that directly using Text incurs a performance hit. Perhaps we ought to introduce a similar trick to ShowS, i.e.:
type DiffText = Text -> Text

And base our showt combinators off of that?

This probably isn't worth it. After all, Builder is itself an effective difference list of lazy Texts, so it seems silly to introduce yet another difference list type.
I'll close this for now. If you want this, please reopen.


[BEAM-3484] Fix split issue in HadoopInputFormatIOIT

When using DBInputFormat to fetch data from RDBMS, Beam parallelises the process by using LIMIT and OFFSET clauses of SQL query to fetch different ranges of records (as a split) by different workers. By default, RDBMS doesn't guarantee predicted order of results and for the same query it can be different every time. So, it can cause duplicates or missing of some rows in final result.
To guarantee the same order and proper split of results the client must order them by one or more keys (either PRIMARY or UNIQUE). It can be done by setting configuration option in Hadoop configuration.

Follow this checklist to help us incorporate your contribution quickly and easily:

[x] Make sure there is a JIRA issue filed for the change (usually before you start working on it).  Trivial changes like typos do not require a JIRA issue.  Your pull request should address just this issue, without pulling in other changes.
[x] Format the pull request title like [BEAM-XXX] Fixes bug in ApproximateQuantiles, where you replace BEAM-XXX with the appropriate JIRA issue.

[x] Run mvn clean verify to make sure basic checks pass. A more thorough check will be performed on your pull request automatically.
[ ] If this contribution is large, please file an Apache Individual Contributor License Agreement.

R: @iemejia @chamikaramj

@lgajowy can you please help us validate if the proposed fix by @aromanenko-dev fix the issue. In our local tests it does but better to double check with you since you reported it.

@iemejia Done

Run Java Gradle PreCommit

Run Java PreCommit

This looks cool. :)
@iemejia I'll verify this as you asked.

Run the IT on this branch for 5_000_000 records, twice. Both tests succeeded and I no longer see the issue. Thanks!

Thanks @aromanenko-dev for finding the root issue + fixing it. Also thanks @lgajowy for reporting it and for con verifying that it works.

@lgajowy Thank you for testing this!
@iemejia  Thank you review and merging!


Store the cumulative change history of all document editions as each new edition is created

We have previously created a JSON column in which to store the change history of all editions of a document.  We have also added validation of the fields and structure of the JSON data to be stored.
We now need to ensure that as each new edition is created - the change history is updated with the superseded edition's change note(s) - if appropriate - and store them with the newly created edition.
This change adds that functionality by introducing a service object (CreateNextEditionService) and updating the existing code where required.
Trello: https://trello.com/c/xMRotBoK/1460-create-a-changehistory-json-column-on-metadatarevisions-and-populate-it-when-new-editions-are-added

@kevindew @1pretz1 Thanks for all the comments and help on this.  Hopefully, we're close!


Build and run a game from the Defold Editor

Right now this is only possible with bob because of known limitations of Defold. But after implementing https://github.com/defold/defold/issues/6604, it will be possible to run an editor build from VSCode.

Do we need it in this extension? Anyway, alredy implemented in mikatuo.vscode-defold-ide.


Octopod Plugin + Octopod IOS: Get 10 Push-Messages for every status (25%, 50%, 75%)

Hi...
I'm using Octoprint with Octopod Plugin and Octopod-Client on Iphone XR and Apple Watch.
I only got one printer "Anycubic I3 Mega" connected. Every status update (25%, 50%, 75%) fires 10 push notifications, that immediatly appears on Iphone and Apple Watch, which is annoying. The completion message finally comes twice. One with printer name in head of message, the last without a printer name.
Look at the screen shot:

The configuration page in plugin:

Is this just a configuration problem?
Thank you...
Regards,
Gosarianer

Ouch! The only reason I can think of for getting multiple notifications is that in OctoPrint in the config.yaml file somehow the OctoPod app got registered multiple times. Open that file and under plugins -> octopod -> tokens you can delete all the entries that appear there that start with a -.  Restart OctoPrint and open again the OctoPod app on your phone. Make sure to have a backup of config.yaml before making any changes.
If you figure out the steps that produced this situation let me know and I will see if there is anything I can do to prevent it.
There is already a ticket that will let you see all registered devices (i.e. tokens) with this plugin so you can delete then from the UI instead of manually modifying this file.
Thanks,
Gaston

Hi @Gosarianer, I will close this ticket since release 0.2.7 of OctoPod plugin for OctoPrint now supports https://github.com/gdombiak/OctoPrint-OctoPod/issues/43 which lets you control and delete duplicates.


Finetuning LLama 2 Code Instruct without using Hugging Face

I tried to finetune CodeLlama 7b Instruct by downloading weights through official repository
Folder Structure of Folder Containing CodeLlama Instruct Weights

params.json contents

Torch Tune Command  in google colab to Run FineTuning Job Using qLoRa
tune run lora_finetune_single_device --config llama2/7B_qlora_single_device checkpointer.checkpoint_dir=/llama/CodeLlama-7b-Instruct/ tokenizer.path=lama/CodeLlama-7b-Instruct/tokenizer.model checkpointer.output_dir=/content/drive/llama/ checkpointer.checkpoint_files=[consolidated.00.pth] dtype=fp 32
Issues Faced
1 num_attention_heads not found when i rename params.json to config.json
2 For unaltered codellama weighs folder i faced .config.json not found issue
How to address num_attention_heads issue ?

@sgupta1007 Thanks for filing this issue! Good question. So there are a couple of things going on here:

Checkpoint Format. Since you've downloaded from the original Meta repo, you'll need to update the checkpointer to the one that is able to understand this format. This is because the default checkpointer in the config understands the HF formatted checkpoint. You can do this by adding checkpointer=torchtune.utils.FullModelMetaCheckpointer to the command you have above. Take a look at the checkpointer deepdive for more information on checkpoint formats and how the checkpointer handles them.

Change in Vocab Size. Even if you make the change above, you'll hit into the issue where the checkpoint weights dont match the shape of the model. This is because the base llama2 7B model supports a vocab_size of 32000 while code-llama 7B has a vocab_size of 32016. This should be a pretty simple change to add the code llama 7B model builders similar to this and this. Note that the max_seq_len for code-llama is also higher - 16384  compared to the 4096 of the base model. If you'd be up for making the change, I'd be happy to review the code. Or I can add in a PR on Monday morning.

Hope this helps!

I picked this up, hope it's not poor ettiquette :) https://github.com/pytorch/torchtune/pull/847

Happy to say this is implemented @sgupta1007  :) Would it be appropriate to close this issue?

@SalmanMohammadi I am still getting the error
Torchtune command and error are given below
Torchtune modified Command
!tune run lora_finetune_single_device --config llama2/7B_qlora_single_device checkpointer=torchtune.utils.FullModelMetaCheckpointer checkpointer.checkpoint_dir=/codellama-main/llama/CodeLlama-7b-Instruct/ tokenizer.path=/codellama-main/llama/CodeLlama-7b-Instruct/tokenizer.model checkpointer.output_dir=/llm/ checkpointer.checkpoint_files=[consolidated.00.pth] dtype=fp32 model=torchtune.models.llama2.code_llama2_7b
Error
ModuleNotFoundError("No module named 'torchtune.models.llama2.code_llama2_7b'")
Are you sure that 'code_llama2_7b' is importable from module 'torchtune.models.llama2'?

Could you try the following command?
!tune run lora_finetune_single_device --config code_llama2/7B_qlora_single_device checkpointer=torchtune.utils.FullModelMetaCheckpointer checkpointer.checkpoint_dir=/codellama-main/llama/CodeLlama-7b-Instruct/ tokenizer.path=/codellama-main/llama/CodeLlama-7b-Instruct/tokenizer.model checkpointer.output_dir=/llm/ checkpointer.checkpoint_files=[consolidated.00.pth] dtype=fp32

code_llama2 models and recipe configs are under their own folder, rather than under llama2/. I hadn't tried it with the ...MetaCheckpointer but hopefully it works.

Still Facing the Error
FileNotFoundError: [Errno 2] No such file or directory: '/content/code_llama2/7B_qlora_single_device'

My apologies, code_llama2 isn't in the latest release! You can follow the process here to install from source to get it working. Let me know how you get on.


Solution Card: Too many buttons

PR includes

[x] Bug Fix

Related issues
Resolve #865

https://github.com/user-attachments/assets/fb7ded04-d756-4484-a845-4c84b3f79a52


Minor Improvements

Added "Show Conflicts" toggle
Fixed resize bug
Minor refactoring to clean up dash_html.py & app.py

Not sure if this is easily fixable, but it'd be nice to have the conflicts button switch between the two graphs as smoothly as the sort button. Can both images be rendered at start and just hidden in the same was as the sorted ones?

The reason this was happening was because there's a loading wrapper around the graph which is necessary for switching between scenarios and for first page load but makes it look awkward when switching between show/hide conflicts. I found that Dash Loading has a cool feature that lets you delay the start of the load so by setting it to 200ms it doesn't show the loading icon for short loads. Using this feature required a Dash upgrade though so I've upgraded the requirement to 2.17.1
https://github.com/dwave-examples/flow-shop-scheduling/pull/5/commits/6168ff69af2cd4e620ed759fb9bc9eb189c14862

The legend now has a conflicts column (adding an extra item for each "conflicted" colour) which I don't think is necessary. It looks better, and to me clearer, just to have the colours in the legend.

@thisac I haven't been able to find a solution for this. Do you have any ideas?


A bug in latest litellm.

A small fix for you. The latest litellm has at least one bug in it. Specifically version: 1.28.13 seems to fail with:
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/usr/local/lib/python3.8/dist-packages/txtai/__init__.py", line 8, in <module>
    from .app import Application
  File "/usr/local/lib/python3.8/dist-packages/txtai/app/__init__.py", line 5, in <module>
    from .base import Application, ReadOnlyError
  File "/usr/local/lib/python3.8/dist-packages/txtai/app/base.py", line 12, in <module>
    from ..embeddings import Documents, Embeddings
  File "/usr/local/lib/python3.8/dist-packages/txtai/embeddings/__init__.py", line 5, in <module>
    from .base import Embeddings
  File "/usr/local/lib/python3.8/dist-packages/txtai/embeddings/base.py", line 17, in <module>
    from ..graph import GraphFactory
  File "/usr/local/lib/python3.8/dist-packages/txtai/graph/__init__.py", line 5, in <module>
    from .base import Graph
  File "/usr/local/lib/python3.8/dist-packages/txtai/graph/base.py", line 14, in <module>
    from .topics import Topics
  File "/usr/local/lib/python3.8/dist-packages/txtai/graph/topics.py", line 5, in <module>
    from ..pipeline import Tokenizer
  File "/usr/local/lib/python3.8/dist-packages/txtai/pipeline/__init__.py", line 12, in <module>
    from .llm import *
  File "/usr/local/lib/python3.8/dist-packages/txtai/pipeline/llm/__init__.py", line 5, in <module>
    from .factory import GenerationFactory
  File "/usr/local/lib/python3.8/dist-packages/txtai/pipeline/llm/factory.py", line 8, in <module>
    from .litellm import LiteLLM
  File "/usr/local/lib/python3.8/dist-packages/txtai/pipeline/llm/litellm.py", line 7, in <module>
    import litellm as api
  File "/usr/local/lib/python3.8/dist-packages/litellm/__init__.py", line 590, in <module>
    from .llms.bedrock import (
  File "/usr/local/lib/python3.8/dist-packages/litellm/llms/bedrock.py", line 285, in <module>
    class AmazonMistralConfig:
  File "/usr/local/lib/python3.8/dist-packages/litellm/llms/bedrock.py", line 301, in AmazonMistralConfig
    stop: Optional[list[str]] = None

You might want to consider locking down these dependencies a bit more to avoid these issues? I don't typically use setup.py, so I am unsure what the best method is for doing so when you are using setup.py, but I've had a lot of success with pip-tools for doing this at work. Specifically this set of commands:
python -m piptools compile cloudfunction/dev-requirements.in
python -m piptools compile cloudfunction/requirements.in
python -m piptools sync cloudfunction/dev-requirements.txt cloudfunction/requirements.txt

This is the 2nd time this has happened with litellm in the last few weeks. Normally, I don't have issues like this and pinning to a specific release has it's own set of challenges.
I just filed an issue upstream.

Really sorry about this, I'm the co-maintainer of litellm. This was caused because our AWS account was disabled and we could not run ci/cd testing for bedrock models, so this did not get caught
Open to suggestions on how we can improve @davidmezzetti @thealmightygrant

This will be fixed upstream.

how do i repro this @davidmezzetti ?
pip install litellm works fine

Perhaps it's Python 3.8 specific?

@ishaan-jaff I just tried it in Python 3.9 and it works. It's Python 3.8 specific and the last instance was also related to Python 3.8. Luckily 3.8 is EOL later this year but I like to support the minimum version of Python for txtai.
This following OS issue appears to be a similar problem. They recommend using the typing List to support 3.8.
https://stackoverflow.com/questions/75202610/typeerror-type-object-is-not-subscriptable-python


Added troubleshooting section to kubernetes hosting docs.

Docs Build status updates of commit 9ab39f3:
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Note: Broken links written as relative paths are included in the above build report. For broken links written as absolute paths or external URLs, see the broken link report.
Note: Your PR may contain errors or warnings unrelated to the files you changed. This happens when external dependencies like GitHub alias, Microsoft alias, cross repo links are updated. Please use these instructions to resolve them.
For any questions, please:Try searching the docs.microsoft.com contributor guidesPost your question in the Docs support channel

Thank you, @kuskmen!

Docs Build status updates of commit b382a9f:
:x: Validation status: errors
Please follow instructions here which may help to resolve issue.

File
Status
Preview URL
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:x:Error

Details

Line 0, Column 0: [Error-config-not-found] Can't find docfx config file in 'W:\rqj2-s'.

For more details, please refer to the build report.
If you see build warnings/errors with permission issues, it might be due to single sign-on (SSO) enabled on Microsoft's GitHub organizations. Please follow instructions here to re-authorize your GitHub account to Docs Build.
Note: Broken links written as relative paths are included in the above build report. For broken links written as absolute paths or external URLs, see the broken link report.
Note: Your PR may contain errors or warnings unrelated to the files you changed. This happens when external dependencies like GitHub alias, Microsoft alias, cross repo links are updated. Please use these instructions to resolve them.
For any questions, please:Try searching the docs.microsoft.com contributor guidesPost your question in the Docs support channel


[QUESTION] - How many loop for DAY1-pt2 ?

I run my script (very similar to yours) and it's looping many times but is a bit weird, how many loop did you do before found the duplicate frequence?

I don't have an exact number, I actually have a while loop running until I find the duplicate.
What seems to be happening with your solution?

Actually I don't know...
This operation seems to be quite long! (that's why I would know how many loops ahahah)

Hmm, at best guess, it looks like an infinite loop! Does it just freeze at the point in your screenshot?

Still running, It's only a screen, but it takes too long to process data
(but not an infinite loop, I've tested it with a smaller array)... that's
why I was curious to know ho many loop.
On Wed, 5 Dec 2018 at 19:49, Adrienne Tacke<EMAIL_ADDRESS>wrote:

Hmm, at best guess, it looks like an infinite loop! Does it just freeze at
the point in your screenshot?
—
You are receiving this because you authored the thread.
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Ok solved doing a for loop instead forEach (don't know why... I will spend next days studying why, sigh)

I'm glad you resolved it somehow!


Partial response on unwrap_or_default

Closes #368
Changes:

We filter out errors in mapping but we return all the correct values in one page

Timebox exceeded


how can i run the demo of ble_mesh_fast_provision (IDFGH-1522)

when i run the demo ( i do not modify anything), the client log tell me :
BLE_MESH: Device in queue not support PB-GATT

loop the log, and try link PB-ADV again and again.

Hi isDerek,
1. We have detailed documentation for this demo (in the engineering path) and we have tested it many times.
2.  You also check the selection:  Componet config--》Bluetooth--》Bluetooth controller--》BLE full scan feature supported.
3.   You also check the selection:    Component config--"Bluetooth --" BLuetooth controller--"Scan Duplicate Type (Scan Duplicate By Device Address And Advertising Date)

@isDerek Thanks for reporting the issue. For such question, we recommend you submit a post on our forum at forum, will close this issue now. Thanks.


Comment:Step-Basic-Db-connector

Comment:Step-Basic-Db-connector
Can mention about installing dependencies

That is mentioned at https://docs.feathersjs.com/guides/step-by-step/preliminaries.html#install-dependencies-used-by-the-examples
Please feel free to reopen this issue if I have misunderstood something.


Update MHR tile image and text

Issue #: https://github.com/bcgov/entity/issues/12068
Description:

Update MHR tile Image and Description text

/gcbrun

Temporary Url for review: https://yfthig-dev--pr-82-sxy3jpe8.web.app

UXA - Text/image looks good for non-staff user. Does this PR include image/text on Staff dashboard as well?

UXA - Text/image looks good for non-staff user. Does this PR include image/text on Staff dashboard as well?

This task was to update tiles for Public User. I haven't done anything to Staff.


MultiMesh does not work in C#

Godot version:
v3.2.2.beta.mono.official
v3.2.1.stable.mono.official
OS/device including version:
MacOS 10.13.6
Issue description:
For some reason, I can't render meshes using MultiMesh when using C#. Pretty much the same code works as expected in GDScript. I've tried to setup MultiMesh using the editor's interface and from the script as in the example - same result.
Steps to reproduce:

Create a simple scene - Spatial -> MultiMeshInstance, configure the multi mesh
Write logic to populate the multimesh via SetInstanceTransform method in C# - does not display anything.
Write the same logic in GDScript - works like a charm.

Minimal reproduction project:
multimesh-test.zip

Hope that makes sense, thank you

The Basis default constructor initializes all fields to 0f, which is different to GDScript. Use Basis.Identity to get the same value.
This is due to a limitation with struct default constructors in C#. The same applies for a few other types, see: https://docs.godotengine.org/en/stable/getting_started/scripting/c_sharp/c_sharp_differences.html?#basis
I haven't tested the uploaded project so there may be other issues.

Hi @neikeq
Thank you for your reply, indeed Basis.Identity was the key and it works now, didn't notice this difference (although I've read the document your linked🤦 )
I've created a small PR to update the example: https://github.com/godotengine/godot-docs/pull/3624
Closing this one, as there is no issue, thank you again.


Known vulnerabilities affecting docker images (CVE-2019-18276)

Automatic scanning of 4.4 and 4.2.9 when I tried to pull into my companies artifactory shows that they are affected by CVE-2019-18276:

An issue was discovered in disable_priv_mode in shell.c in GNU Bash through 5.0 patch 11. By default, if Bash is run with its effective UID not equal to its real UID, it will drop privileges by setting its effective UID to its real UID. However, it does so incorrectly. On Linux and other systems that support "saved UID" functionality, the saved UID is not dropped. An attacker with command execution in the shell can use "enable -f" for runtime loading of a new builtin, which can be a shared object that calls setuid() and therefore regains privileges. However, binaries running with an effective UID of 0 are unaffected.

Seems to be an issue with the pre-built base image? I'm just getting started with docker (so this may be a dumb question), but is this something you can patch or do you need to choose a new base image?

https://security-tracker.debian.org/tracker/CVE-2019-18276
The package is still vulnerable/unfixed so there's nothing actionable for us to do. Also the Debian security team notes it is a "negligible security impact" and the urgency is "unimportant"
See also https://github.com/docker-library/faq#why-does-my-security-scanner-show-that-an-image-has-cves
And https://github.com/docker-library/postgres/issues/286#issuecomment-302512767 docker-library/openjdk#161, docker-library/openjdk#112, docker-library/postgres#286, docker-library/drupal#84, docker-library/official-images#2740, docker-library/ruby#117, docker-library/ruby#94, docker-library/python#152, docker-library/php#242, docker-library/buildpack-deps#46, docker-library/openjdk#185.
A CVE doesn't imply having an actual vulnerability, and often is even a false positive (given how most distributions handle versioning/security updates in stable releases). If there are actionable items we can resolve, we're happy to do so (and do so actively). We update all Debian based images to include any updates in apt packages at least monthly (we regenerate the base images and then rebuild all dependent images).


Make raw.macro a normal dependency

This PR changes the raw.macro dependency (which was introduced in https://github.com/primer/components/pull/489) to be a normal dependency.
Even if this npm package is used for build purposes, it needs to get installed when installing @primer/components in order to make babel build correctly the Dialog component.
If this is not the right approach, maybe it would be good to add a mention about installing raw.macro on the project Readme.
Merge checklist
None of this checks are applicable:

[x] Changed base branch to release branch
[x] Add or update TypeScript definitions (index.d.ts) if necessary
[x] Tested in Chrome
[x] Tested in Firefox
[x] Tested in Safari
[x] Tested in Edge

Thanks for the PR, @rafeca! 😸
Are you seeing an error message related to raw.macro?
I ask because users shouldn't have to know about raw.macro. It's an implementation detail. In fact, user shouldn't have to worry about compiling @primer/components with Babel at all. We compile the code with Babel before publishing to npm. All usages of raw.macro (e.g. raw()) are replaced with strings at build time, before the package is published. I looked through the 13.2.0 build files and couldn't find any references to raw.macro. The one place we use raw() was replaced by a string: https://unpkg.com/browse/<EMAIL_ADDRESS>

@rafeca How are you importing the Dialog component?

Hey @colebemis ! Thanks for the quick response! We're importing the components directly from the src folder (e.g import Dialog from "@primer/components/src/Dialog";, so I guess that's not the right way to import them...
We should import them directly from @primer/components to get the transformed ones, but then to avoid including all unused components we have to rely on the bundler doing proper tree shaking (which I guess it's fine).
Anyways, this PR is not relevant 😅

Hey @rafeca! We built it to assume that tree shaking would be set up properly in the consumers application, but we are looking into exporting a bundle for each component to make this a bit easier on consumers!


[Fix][UI Next][V1.0.0-Alpha] Fix missing pagination in version info table.

Purpose of the pull request
close #8740

Brief change log

Verify this pull request

This pull request is code cleanup without any test coverage.
(or)
This pull request is already covered by existing tests, such as (please describe tests).
(or)
This change added tests and can be verified as follows:

LGTM


[enhancement] Make expr and scalar operator convert more easily

What type of PR is this：

[ ] bug
[ ] feature
[x] enhancement
[ ] refactor
[ ] others

Currently, when exprs and  scalar operators transform each other, we need  a lot of contexts, which is hard to use, this PR is in order to Make expr and scalar operator convert more easily.

[FE PR Coverage check]
:disappointed: fail : 59 / 94 (62.77%)
file detail

path
covered line
new line
coverage

:large_blue_circle:
com/starrocks/analysis/SlotDescriptor.java
0
7
00.00%

:large_blue_circle:
com/starrocks/sql/optimizer/transformer/SqlToScalarOperatorTranslator.java
0
6
00.00%

:large_blue_circle:
com/starrocks/sql/analyzer/ExpressionAnalyzer.java
0
10
00.00%

:large_blue_circle:
com/starrocks/sql/plan/ScalarOperatorToExpr.java
59
71
83.10%

I recommend extracting the logic of mutual conversion separately, rather than mixing it in ExpressionAnalyzer

The logic is very clear, no any mixing.

run starrocks_be_unittest

run starrocks_clang-format

[FE PR Coverage check]
:disappointed: fail : 59 / 94 (62.77%)
file detail

path
covered line
new line
coverage

:large_blue_circle:
com/starrocks/analysis/SlotDescriptor.java
0
7
00.00%

:large_blue_circle:
com/starrocks/sql/optimizer/transformer/SqlToScalarOperatorTranslator.java
0
6
00.00%

:large_blue_circle:
com/starrocks/sql/analyzer/ExpressionAnalyzer.java
0
10
00.00%

:large_blue_circle:
com/starrocks/sql/plan/ScalarOperatorToExpr.java
59
71
83.10%


I get an error from measuring width

This is my code.
if 'screen_setting' not in st.session_state:
    x = streamlit_js_eval(js_expressions='window.innerWidth', key='WIDTH',  want_output = True)
    sleep(2)
    if x<662:
        st.session_state.screen_setting='mobile'
    if x>=662:
        st.session_state.screen_setting='pc'

And always I get this error. The code itself is working properly, but why is this happening?
Traceback (most recent call last):

  File "/home/adminuser/venv/lib/python3.11/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 584, in _run_script

    exec(code, module.__dict__)

  File "/mount/src/neri/streamlit_app.py", line 17, in <module>

    if x<662:

       ^^^^^

TypeError: '<' not supported between instances of 'NoneType' and 'int'

The following code works on my machine:
if 'screen_setting' not in st.session_state:
        if (x := streamlit_js_eval(js_expressions='window.innerWidth', key='WIDTH',  want_output = True)) is not None:

            if x<662:
                st.session_state.screen_setting='mobile'
            if x>=662:
                st.session_state.screen_setting='pc'

            st.write(st.session_state.screen_setting)

or slightly more concise:
if 'screen_setting' not in st.session_state:
        if (x := streamlit_js_eval(js_expressions='window.innerWidth', key='WIDTH',  want_output = True)) is not None:

            st.session_state.screen_setting='mobile' if x<662 else 'pc'

            st.write(st.session_state.screen_setting)

Yeah right? Mine and yours works with no problem, but the thing is it shows really brief error screen and disappears real quick, and works. Maybe this error is from streamlit cloud? idk

Yeah right? Mine and yours works with no problem, but the thing is it shows really brief error screen and disappears real quick, and works. Maybe this error is from streamlit cloud? idk

The streamlit_js_eval function briefly returns None before it is updated with the actual value. You need to consider that situation in your code so that it does not cause an exception. The second if in my code is for that reason.

I tried this one and it still shows me a brief error message. Can I avoid it completely?
if 'screen_setting' not in st.session_state:
    x = streamlit_js_eval(js_expressions='window.innerWidth', key='WIDTH', want_output=True)
    try:
        if x < 662:
            st.session_state.screen_setting = 'mobile'
        else:
            st.session_state.screen_setting = 'pc'
    except Exception as e:
        pass

So I tried your codes without changing anything again but it still shows brief error and the error screen disappears. And that error messages are constantly updated after 5 seconds..

@atfot You are likely just deferring the issue to further down in your code (i.e. that line when you check if screen_setting is pc).
I'm not sure what else can be done, but if yu need that session state variable to have a value, maybe set a default value for it (pc), and then let SJE change it to the actual value after this short delay.


Condition without brackets indent problem

Describe the bug
Yes it works correctly and compile correctly:
http://www.noelshack.com/2021-12-2-1616497276-screen-shot-2021-03-23-at-11-38-51-am.png
But the actually the norm want it:
http://www.noelshack.com/2021-12-2-1616497276-screen-shot-2021-03-23-at-11-39-06-am.png
The norm pdf specify nothing for that case.

The pdf is updated and will be on the intra soon. I apologize for the delay


Async-Executor v1.9.0 Selected by Cargo Locks up the engine

Bevy version
Bevy 0.12.1
[Optional] Relevant system information
macOs Sonoma
What you did
Shared a Project without Cargo.lock checked in.  A new Cargo.lock resolved by cargo Resolves to Async-Executor 1.9.0, which locks up the game at the very start.  This is very hard to bisect when many things are different in Cargo.lock
Additional information
Either something changed with async-executor that is more than a small bump (not exactly MINOR in semver) or perhaps there is a bug.  Maybe bevy can lock the dependency.
Temporary Fix
For those who might also run into this, we are explicitly pinning async-executor to 1.8.0 in our own Cargo.

async-executor 1.9.0 has been yanked for now. New builds shouldn't be pulling it anymore. Going to continue to investigate why this happened, as this was a bug. In retrospect, having been working on both projects, I should have validated changes to async-executor worked with Bevy.

Closing this out for now as async_executor has yet to publish a new version, but the issue has been fixed upstream. A new version has not been published due to a major regression in performance due to higher contention.


Create scarletmail.txt

Added Rutgers University's email. Example<EMAIL_ADDRESS>

@MazaArt I am sorry, the domain rutgers.edu is removed from our trusted domains list because the school allows to register an email address there even if you are not a student of the school. If you are the student of this school, please apply with official document (OFFICIAL DOCUMENT tab at https://www.jetbrains.com/shop/eform/students).


:robot: Triggering CI on branch 'release-next' after synching to openshift/master

/test 4.5-e2e

OCF Webhook is merging this PR


Anchor build issue

When I am using anchor build  for my program , it is showing me this error. I have entered the solana binaries to my path variable insidre environment correct.
error: not a directory: '/home/muhammad/.local/share/solana/install/releases/1.17.17/solana-release/bin/sdk/sbf/dependencies/platform-tools/rust/bin'

Looks like something went wrong during your Solana installation. I'd try removing the current version and re-install with
solana-install init 1.17.0

yes it worked when I installed the previous version and and updated the PATH on ~/.bashrc
like this
export PATH="$PATH:/home/muhammad/.local/share/solana/install/releases/1.17.0/solana-release/bin/sdk/sbf/dependencies/platform-tools/rust/bin"