issue-682: Modify the ExceptionHandlersTest class.

Make the code easier to read.
Fixes #682

Make sure that:

[x] You have read the contribution guidelines.
[x] You submit test cases (unit or integration tests) that back your changes.
[x] Your local test passed mvn clean install -Dmaven.javadoc.skip=true.

Codecov Report

Merging #725 (873ef46) into master (398d1d6) will increase coverage by 0.03%.
The diff coverage is n/a.

@@             Coverage Diff              @@
##             master     #725      +/-   ##
============================================
+ Coverage     59.11%   59.14%   +0.03%
- Complexity     1091     1092       +1
============================================
  Files           237      237
  Lines          5461     5461
  Branches        604      604
============================================
+ Hits           3228     3230       +2
+ Misses         1956     1955       -1
+ Partials        277      276       -1

Impacted Files
Coverage Δ
Complexity Δ

.../java/org/dromara/soul/common/utils/UUIDUtils.java
87.50% <0.00%> (+6.25%)
13.00% <0.00%> (+1.00%)

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How to add a class using :wrap_with => { :class => 'control-label' }

I have
b.use :label, :wrap_with => { :class => 'control-label' }

in some of my wrappers. I just discovered that this actually does nothing. I would expect it to add  the specified class to my 'label' tag, but that is not the case.
Can anybody confirm if this a bug?
SimpleForm + Bootstrap Example has the very same :wrap_with options for wrappers, but from what I understood the only reason 'control-label' class is added there is the fact that it is explicitly specified below wrappers declarations:
config.label_class = 'control-label'

The above line causes 'control-label' class to be added to every label as opposed to adding it only to .form-horizontal styled forms. I don't think that there are any undesireable effects of this currently (because CSS selectors only include control-label class scoped under the form-horizontal one), but I believe that there should be a way to specify label class for every particular wrapper.

Great, thank you.


search keys in huge arrays/objects

http://code.google.com/p/kint/issues/detail?id=24

Is there a way to search all kind of content?
I miss that from search_krumo https://www.drupal.org/project/search_krumo

You can doubleclick on the plus sign next to any node and it expands all nodes - CTRL+F away.
This might be implemented at a later date, but there's literary hundreds of features with higher priority unfortunately.

Code for search is now done. https://www.drupal.org/project/search_kint
Could it be integrated as a plugin? Basically you just have to output the .js and .css files

@neoscopio How does this compare to https://github.com/jnvsor/kint/tree/2.x-dev feature wise?

@jnvsor Not sure if I understand you correctly, but main thing is a search box, and bonus is a "copy variable".
From what I see in your link that's not available.
1000 words:
https://www.drupal.org/files/project-images/search_kint_3.png

Path getting is in 2.x-dev:
Is your path getting done in javascript? 2.x-dev would be a bit more robust then (Handles things like name transforms and keys/properties with unprintable characters without breaking the path)
Took a look at the code for the search thingy. Looks for a dt:contains(query) and uses CSS to hide all the others right? Sweet. How hard do you think that would be to get working without jquery? How hard would it be to embed it in the result itself to reduce screen realestate use?

@raveren Nearly 5 years later but the preliminary version is looking good:

Pushed in bfdd22471510c9066e873475a6da9027f8a42cfd

You rock dude, I am away again from active development, glad to see Kint is in such skilled and buisy hands!


Confirm and if needed update gas fee for curve lending bot

Will you confirm that the current gas fee for curve lending bot is high enough and update if needed? It is  0.015 which is much lower than curve leverage (0.1)

When I deploy the bot, I set gas fee as 0.1. So it's fine.

https://paloma.explorers.guru/transaction/C1595D27E568E5ABA94F97FA21CF0973EF01841DF189594C955BC58EA8CDC128


Fixing a bug where Rails.version is potentially undefined.

Fixes #41 If a user is using certain gems (ActiveRecord, ActionMailer, etc) without the rails gem, in a standalone application, they encounter a situation where the Rails module is defined but doesn't have a version method, so improve the check before adding Rails.version to the headers.

I just gave you commit access, if you merge & cut a new bugfix release I'll push to Rubygems. Thanks for the patch!

Done, thanks!

Thanks Brett! 1.5.2 is up on Rubygems now.


Use excessEnumsTable to check enums at ingest time

why:  To prevent bad metrics from being ingested.
how:

Create in-memory excess enum metrics table which gets periodically read from Cassandra.
Use that table to check incoming metrics.
Fixed AstyanaxReader.getExcessEnumMetrics to return a set instead of a list.

You have a failing test in ExcessEnumsReaderIntegrationTest.  Need fixing.

@VinnyQ I can't reproduce the test failure.  how exactly is it failing for you?  and how are you running the integration tests?

@GeorgeJahad I generally run "mvn clean test integration-test" as shown in the README.  Looks fine now though, no more failing tests.  Must have been something weird on my laptop.

:+1: lgtm @GeorgeJahad


Method set in cache repository does not honor the PSR-16 SimpleCache contract

Laravel Version: 5.7
PHP Version: 7.2

Description:
Class Repository implements the set method whose contract is defined in Psr\SimpleCache\CacheInterface. By the contract, return value should be a boolean (true on success and false on failure). Yet, the implementation in the Repository does not return any value.
Normally, this doesn't cause any problems in Laravel, but it does when using Laravel's cache repository as a dependency in third-party libraries. To illustrate the problem, take a look at the following
code excerpt (part of the Manager class) from jeremykendall/php-domain-parser package:
/**
 * Returns true if the refresh was successful
 * @param string     $url the Public Suffix List URL
 * @param null|mixed $ttl the cache TTL
 * @return bool
 */
public function refreshRules(string $url = self::PSL_URL, $ttl = null): bool
{
    static $converter;
    $converter = $converter ?? new Converter();
    $data = json_encode($converter->convert($this->http->getContent($url)));

    return $this->cache->set($this->getCacheKey('PSL', $url), $data, $this->filterTtl($ttl) ?? $this->ttl);
}

Injecting Laravel's SimpleCache implementation to the Manager class, and calling this method results in a type error because the set method's return value is always void, when the expected value is a boolean.
I've checked all the implemented cache stores in Laravel and all of them have the ability to return a success status as a boolean:

ApcStore: uses https://secure.php.net/manual/en/function.apcu-store.php which returns a bool.
ArrayStore: can just return true.
DatabaseStore: uses https://github.com/laravel/framework/blob/master/src/Illuminate/Database/Query/Builder.php#L2542 which returns a bool.
FileStore: uses http://php.net/manual/en/function.file-put-contents.php which returns int on success or false on failure.
MemcachedStore: uses http://php.net/manual/en/memcached.set.php which returns a bool.
RedisStore: uses https://redis.io/commands/setex which returns string "OK" on success.

Ignoring the fact that these return values don't give enough info about the nature of failures when they occur, is there another reason why a return value for methods set, put, setMultiple, forever is not a boolean?

@driesvints , can  we close this, since https://github.com/laravel/framework/pull/26726 is merged?

Looks like, that is fixed in https://github.com/laravel/framework/pull/26726


⚠ ClamAV has degraded performance

In ffd543c, ClamAV (https://spamassassin.apache.org/) experienced degraded performance:

HTTP code: 200
Response time: 54 ms

Resolved: ClamAV performance has improved in 8a4507b.


Surface Pro 9 driver missing

Surface Pro 9 model 2038
https://www.microsoft.com/en-us/download/details.aspx?id=104680

+1

+2

Same.  No Surface Pro 9 Drivers listed for 21H2 and no drivers for any model for 22H2.
Any update on this?  Issue opened in November 2022.


#22 Updated dependencies + java 11 support

I think we can update github actions file .github/workflows/gradle.yml to use jdk11 instead of jdk8

Closes #22


Feature/validate filter additions

Additional validation steps and filters were added for enhanced quality control of genomic VCF files.

Many thanks for these valuable additions @MeganHolborn. I have resolved a conflict which I incidentally created via comments.


feat(xudttag): add xudt tags

https://github.com/Magickbase/ckb-explorer-public-issues/issues/620

How is it going @FrederLu

1、Suspicious and Out of Length Range label styles do not match the UI.

2、The spacing between the label and the copy above can be adjusted smaller.

1、Suspicious and Out of Length Range label styles do not match the UI.

2、The spacing between the label and the copy above can be adjusted smaller.

I will fix these today and publish it to mainnet if no functional bugs are found

1、Suspicious and Out of Length Range label styles do not match the UI.

2、The spacing between the label and the copy above can be adjusted smaller.

done


[yaml] Enhance YAML API docs

The current YAML API refernce docs look like the following:

This PR adds formatting to make the docs look more like the Sphinx-themed PyDocs used by the Python SDK:

This change not only adds some color and easier-to-read formatting , but also separates the transforms into individual pages to reduce clutter. This should hopefully make the user-experience a bit better.
UPDATE: the doc will remain as one page, but will include the new side panel and formatting changes.

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[ ] Update CHANGES.md with noteworthy changes.
[ ] If this contribution is large, please file an Apache Individual Contributor License Agreement.

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R: @robertwb

Can you check https://github.com/apache/beam/actions/runs/11579341692/job/32235315129?pr=32825?

Test failures are pretty clearly unrelated, so I'm going to merge


Revenue only

Codecov Report

Merging #43 (f692979) into develop (ac23d94) will increase coverage by 0.85%.
The diff coverage is 88.40%.

:mega: This organization is not using Codecov’s GitHub App Integration. We recommend you install it so Codecov can continue to function properly for your repositories. Learn more
@@             Coverage Diff             @@
##           develop      #43      +/-   ##
===========================================
+ Coverage    84.95%   85.80%   +0.85%
===========================================
  Files           77       79       +2
  Lines         5038     5552     +514
===========================================
+ Hits          4280     4764     +484
- Misses         758      788      +30

Impacted Files
Coverage Δ

portfolyo/core/mixins/plot.py
14.75% <0.00%> (+0.11%)
:arrow_up:

portfolyo/core/mixins/text.py
26.19% <0.00%> (ø)

portfolyo/prices/convert.py
20.83% <0.00%> (-2.16%)
:arrow_down:

portfolyo/prices/hedge.py
14.28% <0.00%> (-0.61%)
:arrow_down:

portfolyo/tools/intersect.py
96.29% <ø> (ø)

portfolyo/testing/testing.py
62.26% <14.28%> (-31.49%)
:arrow_down:

portfolyo/core/pfline/base.py
62.35% <70.83%> (-0.07%)
:arrow_down:

portfolyo/core/pfline/flat.py
71.81% <71.81%> (ø)

portfolyo/core/pfline/nested.py
75.00% <75.00%> (ø)

tests/core/pfline/test_nested_helper.py
93.68% <86.04%> (ø)

... and 23 more

... and 3 files with indirect coverage changes
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[4.2.0][Dependency Upgrade] velocity-engine-core version Upgrade

Description
This issue is created to track the progress on velocity-engine-core version upgrade in APIM 4.2.0.
Affected Component
APIM
Version
4.2.0
Related Issues
No response
Suggested Labels
No response

PR : https://github.com/wso2/carbon-apimgt/pull/11797


Version 2.0

This PR should implement some changes discussed in #226:

[x] Remove deprecated connect/disconnect methods
[x] Remove deprecated filtered_messages and unfiltered_messages methods
[x] Switch to client-wide queue
[x] Rename id to identifier
[x] Remove deprecated message_retry_set Client argument
[x] Update README and documentation
[x] Add migration guide

I'm very fond of the idea to switch out paho with rumqtt, but I think that's better suited for a 3.0, otherwise this gets too big. I have too many ideas and too little time 😄
Feedback is welcome 😋

Awesome work.
Will take a look this weekend.

Really great to see all the effort put into this :smile: :+1: I'll review this weekend as well. :)

Great work! Everything seems good to me.

Thank you both for your valuable comments! 😊
There's another thing that I was not 100% sure about, maybe you have some ideas on that: I currently implemented the messages generator as Client.messages attribute. We could also implement it as Client.messages() method by accessing the current _messages() directly rather than calling and assigning it inside __init__.
I'm not sure if this is just a question of style or if there are other reasons for or against. I opted for Client.messages because that makes it feel more intuitive (to me) that only one generator can be used concurrently.
What do you think?

Client.messages makes sense for me.

Thank you both for your valuable comments! 😊
There's another thing that I was not 100% sure about, maybe you have some ideas on that: I currently implemented the messages generator as Client.messages attribute. We could also implement it as Client.messages() method by accessing the current _messages() directly rather than calling and assigning it inside __init__.
I'm not sure if this is just a question of style or if there are other reasons for or against. I opted for Client.messages because that makes it feel more intuitive (to me) that only one generator can be used concurrently.
What do you think?

I think it is easiest for our users to have Client.messages (as a property). However, this approach comes with some caveats. If two consumers iterate through Client.message concurrently then:

Each gets unique messages (there is no copying)
There is (to my knowledge) no fairness guarantees. That is, one consumer may get all the messages.
The routing model is essentially anycast.
If our end users want a different routing model (e.g., broadcast) then they have to build that themselves on top of aiomqtt.

The above may be surprising to our users. The easiest "work around" is just to write it very explicitly in the documentation. I don't think we should try to solve this routing problem. That's too difficult.

I agree that we shouldn't implement different routing models, that would probably build back the same complexity we had before :+1: Currently, something like this:
import asyncio
import aiomqtt

async def handle(client):
    async for message in client.messages:
        print(message.payload)

async def main():
    client = aiomqtt.Client("test.mosquitto.org")
    async with asyncio.TaskGroup() as tg:
        async with client:
            tg.create_task(handle(client))
            tg.create_task(handle(client))

asyncio.run(main())

fails with RuntimeError: anext(): asynchronous generator is already running; I think that's a good default.
Thanks for the great feedback! I'll merge and release this then 😊


QApplication::beep() broken on many systems. Use another API for BEL instead

As reported by @cqexbesd, BEL is not working, even though QApplication::beep() is being invoked. Googling for that turns out that this doesn't seem to always work and other people's fix was to simply not use it but other APIs, e.g. QMediaPlayer from QtMultimedia.
This seems as simple as:
QMediaPlayer player;

// ...

player.setMedia(QUrl::fromLocalFile("/usr/share/sounds/freedesktop/stereo/bell.oga"));
player.setVolume(50);

// ...

player.play();

But while being at it, why not making it (per profile) configurable, such that users can tweak what's about to be played when BEL is being processed.
NB: In case of BEL spam, a new sound must not be played if an old one is still playing.

Keep in mind that /usr/share/sounds/freedesktop/stereo/bell.oga is platform dependant

@uspasojevic96 that'll be configurable of course. that's the nice part about it :)

NB: We'd provide a default sounds - we have one already in the qml branch, we can simply use that as default sound, so it'll always work out of the box on all platforms.


deploy to Heroku button?

Couldn't figure out how to deploy to heroku, any chance this feature could be added for those of us less familiar with the service?

i'd love to have a feature like that. i tried to deploy the script on my own on heroku but the process was a little bit convoluted to me.
any help of experienced users would be appreciated.

@cboRD181 @Ekramhadidi I have added support for Heroku one click deploy button. (Checkout README)
Closing this issue for now. Please reopen it again if you face any difficulties to deploy on Heroku.


update: SCIM config for Okta

Describe your changes
Adds a warning to not select the import groups configuration.
Checklist

[ ] The first paragraph of the page is on one line.
[ ] The other lines have a line break at 90 characters.
[X] I checked the output.
[ ] I applied the style guide.
[ ] My links start with /docs/.

Preview: https://staceys-doc-1168.aiven-docs.pages.dev/docs/platform/howto/okta-user-provisioning-with-scim#configure-user-provisioning-for-okta


[Bug]: Unable to use vLLM for serving fine tuned mistral model

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

Collecting environment information...
PyTorch version: 2.2.1+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: Could not collect
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-5.10.213-201.855.amzn2.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: Tesla V100-SXM2-16GB
GPU 1: Tesla V100-SXM2-16GB
GPU 2: Tesla V100-SXM2-16GB
GPU 3: Tesla V100-SXM2-16GB

Nvidia driver version: 535.161.08
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:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             32
On-line CPU(s) list:                0-31
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
CPU family:                         6
Model:                              79
Thread(s) per core:                 2
Core(s) per socket:                 16
Socket(s):                          1
Stepping:                           1
CPU max MHz:                        3000.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           4600.04
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 pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single pti fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt
Hypervisor vendor:                  Xen
Virtualization type:                full
L1d cache:                          512 KiB (16 instances)
L1i cache:                          512 KiB (16 instances)
L2 cache:                           4 MiB (16 instances)
L3 cache:                           45 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-31
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                 Mitigation; PTE Inversion
Vulnerability Mds:                  Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, STIBP disabled, RSB filling
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.19.3
[pip3] torch==2.2.1
[pip3] triton==2.2.0
[pip3] vllm-nccl-cu12==<IP_ADDRESS>.3.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	GPU1	GPU2	GPU3	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	NV1	NV1	NV2	0-31	0		N/A
GPU1	NV1	 X 	NV2	NV1	0-31	0		N/A
GPU2	NV1	NV2	 X 	NV2	0-31	0		N/A
GPU3	NV2	NV1	NV2	 X 	0-31	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

How would you like to use vllm
Hi Team,
I am trying to serv a fine tuned model ([TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](TheBloke/Mistral-7B-Instruct-v0.2-GPTQ · Hugging Face)) and getting into some issue.
Appreciate if you can educate me on how to fix it?
Error
+ python3 -m vllm.entrypoints.openai.api_server --tensor-parallel-size 4 --worker-use-ray --host <IP_ADDRESS> --port 8080 --model TheBloke/Mistral-7B-Instruct-v0.2-GPTQ --served-model-name mistral --dtype float16 --tokenizer TheBloke/Mistral-7B-Instruct-v0.2-GPTQ --enable-lora --lora-modules tuned-model=/model/v1 -q gptq
INFO 05-08 00:44:27 api_server.py:151] vLLM API server version 0.4.1
INFO 05-08 00:44:27 api_server.py:152] args: Namespace(host='<IP_ADDRESS>', port=8080, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, served_model_name=['mistral'], lora_modules=[LoRA(name='tuned-model', local_path='/model/v1')], chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='TheBloke/Mistral-7B-Instruct-v0.2-GPTQ', tokenizer='TheBloke/Mistral-7B-Instruct-v0.2-GPTQ', skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='float16', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', worker_use_ray=True, pipeline_parallel_size=1, tensor_parallel_size=4, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=5, disable_log_stats=False, quantization='gptq', enforce_eager=False, max_context_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=True, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', max_cpu_loras=None, device='auto', image_input_type=None, image_token_id=None, image_input_shape=None, image_feature_size=None, scheduler_delay_factor=0.0, enable_chunked_prefill=False, speculative_model=None, num_speculative_tokens=None, speculative_max_model_len=None, model_loader_extra_config=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
WARNING 05-08 00:44:28 config.py:1011] Casting torch.bfloat16 to torch.float16.
WARNING 05-08 00:44:28 config.py:169] gptq quantization is not fully optimized yet. The speed can be slower than non-quantized models.
2024-05-08 00:44:30,137	INFO worker.py:1749 -- Started a local Ray instance.
INFO 05-08 00:44:31 llm_engine.py:98] Initializing an LLM engine (v0.4.1) with config: model='TheBloke/Mistral-7B-Instruct-v0.2-GPTQ', speculative_config=None, tokenizer='TheBloke/Mistral-7B-Instruct-v0.2-GPTQ', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=4, disable_custom_all_reduce=False, quantization=gptq, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0)
INFO 05-08 00:44:42 utils.py:608] Found nccl from library /root/.config/vllm/nccl/cu12/libnccl.so.2.18.1
(RayWorkerWrapper pid=2115) INFO 05-08 00:44:42 utils.py:608] Found nccl from library /root/.config/vllm/nccl/cu12/libnccl.so.2.18.1
INFO 05-08 00:44:42 selector.py:65] Cannot use FlashAttention backend for Volta and Turing GPUs.
INFO 05-08 00:44:42 selector.py:33] Using XFormers backend.
(RayWorkerWrapper pid=2115) INFO 05-08 00:44:42 selector.py:65] Cannot use FlashAttention backend for Volta and Turing GPUs.
(RayWorkerWrapper pid=2115) INFO 05-08 00:44:42 selector.py:33] Using XFormers backend.
INFO 05-08 00:44:44 pynccl_utils.py:43] vLLM is using nccl==2.18.1
(RayWorkerWrapper pid=2115) INFO 05-08 00:44:44 pynccl_utils.py:43] vLLM is using nccl==2.18.1
INFO 05-08 00:44:49 utils.py:115] generating GPU P2P access cache for in /root/.config/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
INFO 05-08 00:44:50 utils.py:129] reading GPU P2P access cache from /root/.config/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
(RayWorkerWrapper pid=2115) INFO 05-08 00:44:50 utils.py:129] reading GPU P2P access cache from /root/.config/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
(RayWorkerWrapper pid=2273) INFO 05-08 00:44:42 utils.py:608] Found nccl from library /root/.config/vllm/nccl/cu12/libnccl.so.2.18.1 [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)
(RayWorkerWrapper pid=2273) INFO 05-08 00:44:42 selector.py:65] Cannot use FlashAttention backend for Volta and Turing GPUs. [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) INFO 05-08 00:44:42 selector.py:33] Using XFormers backend. [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) INFO 05-08 00:44:44 pynccl_utils.py:43] vLLM is using nccl==2.18.1 [repeated 2x across cluster]
INFO 05-08 00:44:51 weight_utils.py:193] Using model weights format ['*.safetensors']
(RayWorkerWrapper pid=2115) INFO 05-08 00:44:51 weight_utils.py:193] Using model weights format ['*.safetensors']
INFO 05-08 00:45:02 model_runner.py:173] Loading model weights took 1.0413 GB
(RayWorkerWrapper pid=2115) INFO 05-08 00:45:03 model_runner.py:173] Loading model weights took 1.0413 GB
(RayWorkerWrapper pid=2273) INFO 05-08 00:44:50 utils.py:129] reading GPU P2P access cache from /root/.config/vllm/gpu_p2p_access_cache_for_0,1,2,3.json [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) INFO 05-08 00:44:51 weight_utils.py:193] Using model weights format ['*.safetensors'] [repeated 2x across cluster]
ERROR 05-08 00:45:04 worker_base.py:157] Error executing method determine_num_available_blocks. This might cause deadlock in distributed execution.
ERROR 05-08 00:45:04 worker_base.py:157] Traceback (most recent call last):
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 149, in execute_method
ERROR 05-08 00:45:04 worker_base.py:157]     return executor(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
ERROR 05-08 00:45:04 worker_base.py:157]     return func(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 138, in determine_num_available_blocks
ERROR 05-08 00:45:04 worker_base.py:157]     self.model_runner.profile_run()
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
ERROR 05-08 00:45:04 worker_base.py:157]     return func(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 927, in profile_run
ERROR 05-08 00:45:04 worker_base.py:157]     self.execute_model(seqs, kv_caches)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
ERROR 05-08 00:45:04 worker_base.py:157]     return func(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 848, in execute_model
ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = model_executable(**execute_model_kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 360, in forward
ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = self.model(input_ids, positions, kv_caches,
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 286, in forward
ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states, residual = layer(
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 224, in forward
ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = self.input_layernorm(hidden_states)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs)
ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/layernorm.py", line 59, in forward
ERROR 05-08 00:45:04 worker_base.py:157]     out = torch.empty_like(x)
ERROR 05-08 00:45:04 worker_base.py:157] RuntimeError: CUDA error: no kernel image is available for execution on the device
ERROR 05-08 00:45:04 worker_base.py:157] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
ERROR 05-08 00:45:04 worker_base.py:157] For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
ERROR 05-08 00:45:04 worker_base.py:157] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
ERROR 05-08 00:45:04 worker_base.py:157]
Traceback (most recent call last):
  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 159, in <module>
    engine = AsyncLLMEngine.from_engine_args(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 361, in from_engine_args
    engine = cls(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 319, in __init__
    self.engine = self._init_engine(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 437, in _init_engine
    return engine_class(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 160, in __init__
    self._initialize_kv_caches()
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 236, in _initialize_kv_caches
    self.model_executor.determine_num_available_blocks())
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/ray_gpu_executor.py", line 199, in determine_num_available_blocks
    num_blocks = self._run_workers("determine_num_available_blocks", )
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/ray_gpu_executor.py", line 318, in _run_workers
    driver_worker_output = self.driver_worker.execute_method(
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 158, in execute_method
    raise e
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 149, in execute_method
    return executor(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 138, in determine_num_available_blocks
    self.model_runner.profile_run()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 927, in profile_run
    self.execute_model(seqs, kv_caches)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 848, in execute_model
    hidden_states = model_executable(**execute_model_kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 360, in forward
    hidden_states = self.model(input_ids, positions, kv_caches,
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 286, in forward
    hidden_states, residual = layer(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 224, in forward
    hidden_states = self.input_layernorm(hidden_states)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/layernorm.py", line 59, in forward
    out = torch.empty_like(x)
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157] Error executing method determine_num_available_blocks. This might cause deadlock in distributed execution.
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157] Traceback (most recent call last):
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 149, in execute_method
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return executor(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return func(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 138, in determine_num_available_blocks
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     self.model_runner.profile_run()
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return func(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 927, in profile_run
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     self.execute_model(seqs, kv_caches)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return func(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 848, in execute_model
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = model_executable(**execute_model_kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 360, in forward
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = self.model(input_ids, positions, kv_caches,
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 286, in forward
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states, residual = layer(
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 224, in forward
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = self.input_layernorm(hidden_states)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/layernorm.py", line 59, in forward
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]     out = torch.empty_like(x)
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157] RuntimeError: CUDA error: no kernel image is available for execution on the device
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157] For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
(RayWorkerWrapper pid=2115) ERROR 05-08 00:45:04 worker_base.py:157]
(RayWorkerWrapper pid=2211) INFO 05-08 00:45:03 model_runner.py:173] Loading model weights took 1.0413 GB [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157] Error executing method determine_num_available_blocks. This might cause deadlock in distributed execution. [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157] Traceback (most recent call last): [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 149, in execute_method [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     return executor(*args, **kwargs) [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context [repeated 6x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     return func(*args, **kwargs) [repeated 6x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 138, in determine_num_available_blocks [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     self.model_runner.profile_run() [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 927, in profile_run [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     self.execute_model(seqs, kv_caches) [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 848, in execute_model [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = model_executable(**execute_model_kwargs) [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl [repeated 8x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     return self._call_impl(*args, **kwargs) [repeated 8x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl [repeated 8x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     return forward_call(*args, **kwargs) [repeated 8x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/layernorm.py", line 59, in forward [repeated 8x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = self.model(input_ids, positions, kv_caches, [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states, residual = layer( [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     hidden_states = self.input_layernorm(hidden_states) [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]     out = torch.empty_like(x) [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157] RuntimeError: CUDA error: no kernel image is available for execution on the device [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157] For debugging consider passing CUDA_LAUNCH_BLOCKING=1. [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. [repeated 2x across cluster]
(RayWorkerWrapper pid=2273) ERROR 05-08 00:45:04 worker_base.py:157]  [repeated 2x across cluster]
[W CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]

this has not been addressed yet in 0.4.2 nor torch==2.3.0, so one immediate workaround is to downgrade to vllm==0.4.0 torch==2.1.2

this has not been addressed yet in 0.4.2 nor torch==2.3.0, so one immediate workaround is to downgrade to vllm==0.4.0 torch==2.1.2

The same error while using Meta-Llama-3-8B-Instruct with enable_lora=True. When I downgrade to vllm==0.4.0 and torch==2.1.2, a new error occurred due to the large size of Llama-3 vocabulary:
ValueError: When using LoRA, vocab size must be 32000 >= vocab_size <= 33024

@OnewayLab Was this resolved later? I am experience same problem.

this has not been addressed yet in 0.4.2 nor torch==2.3.0, so one immediate workaround is to downgrade to vllm==0.4.0 torch==2.1.2

The same error while using Meta-Llama-3-8B-Instruct with enable_lora=True. When I downgrade to vllm==0.4.0 and torch==2.1.2, a new error occurred due to the large size of Llama-3 vocabulary:
ValueError: When using LoRA, vocab size must be 32000 >= vocab_size <= 33024

I meet the same error.

I get the same error with Mistral + loras.
base_model.model.lm_head.weight is unsupported LoRA weight


Don't work after upgrade to 0.7.4-beta

Hello, I have updated to the latest BETA version and the integration has stopped working right now, none of the sensors shows me data, I have a LYWSD03MMC and so far everything is OK, I have downloaded the version and it has worked correctly again .

Thank you for your work!

Thanks for reporting. That is really strange, it works fine on my end. I will revert this import, and look into this.

Aha, found it. This LIGHT_LUX component is introduced in 0.116. So it will work fine after updating to core 0.116

Yes, I am still on version 0.115.6 as you can see in my screenshot, for now I will not upload because there are more devices that cause me problems and my knowledge does not allow me to fix it. thanks for the answers

I've reverted the LIGHT_LUX import. I've decided to wait a little while before making this change, to make sure that most users are on 0.116. So it should be safe for you to update to 0.7.4 when it becomes stable.

0.7.4 final is released today, which fixes this issue. Note that this change will be made later when HA core 0.118 is released.


Neo4J RETRIEVER Integration [FEATURE]

Integration of Neo4J retriever would be amazing to help create knowledge graph-based RAG-s.
https://python.langchain.com/docs/integrations/vectorstores/neo4jvector

Duplicate of 1237


Questions regarding privacy and project motivation

Having read though a couple of issues here, researching on the net and generally reading news bits about N1, I'm increasingly worried. In most cases, there is absolutely no hint at the fact that Nylas is actually storing all of my email content along with my login credentials. A user just downloading the application and entering user information is not give notice that this is actually happening and where the data is actually stored. This popped up in my mind when I started the software, so it may be possible that somewhere down the road of the setup some notice is given. This has extremely serious implications regarding privacy and self-determined data distribution, not at least because world-wide there are many different legislations regulating what data may be stored, when, how and by whom. Therefore, I have some questions regarding your stance towards those issues, especially considering https://github.com/nylas/N1/issues/38#issuecomment-147115507 where @bengotow repeats that "Long term, we envision folks using our hosted version of the sync engine."

Is it a business idea that you are eventually going to start charging people for that functionality at some point?
Why would you be interested in getting access to all my email and login credentials in the first place? Certainly "ease of use" cannot be the main contributing factor here. Storing so much third-party data must be a disaster waiting to happen as long as you don't have the resources of a company like Google. Having so much data centralized at one location creates a very compelling target.
What about people living in other countries, like Germany, where privacy protection laws explicitly prohibit exporting data to other countries without definite user consent. The issue gets even more complicated for a freelancer or small agency, for example, being contracted by a large corporation. Usually NDAs and contractual fine print prohibits transfer of data out of the residing country. I am aware that this also applies to webmail providers like Google Mail and in the real world is treated with a rather cavalier attitude. You certainly don't follow that attitude, do you? So what's your position on this?
What assurances does a user have regarding the safety of his personal data?
What reasonable course of action does a EU citizen have for finding out if you were served with a request for access to personal data? It is my impression that being US-based, in most cases you are bound by law to comply with such enforcement requests.
What is your policy on defending the users' privacy and data against such requests?

I'm looking forward to being enlightened on those topics. The project itself is commendable and I myself would love to use it, however until all of those points have been cleared up, I'm advising against signing up.

Hey @herrbischoff — thanks for checking out N1! You can find information about our security policies here: https://nylas.com/security. We do not sell or ever plan to sell user data or "aggregate email data" (ala Context.io) —there's a bit more information about why N1 is built the way it is here: https://nylas.com/N1/faq.
At the end of the day, we do store your mail data in the same way Mailbox, Accompli, and others have in the past. It allows us to build something better than IMAP / Exchange, and will make some awesome features possible in the future, like priority inbox for Exchange and great search. We tried to find ways around storing mail data, since it's costly for us and needs to be secured with great care, but it's core to building a mail client to rival Gmail and others, and essential for doing anything on mobile. (Someday!)
At the end of the day, N1 will always be free and open-source. We're committed to building a great foundation on modern technologies. Of course, we have to make money eventually - we plan to monetize N1 by building a "plugin store" into the app and selling subscription plugins and extensions. I expect most of these will be serious productivity-oriented plugins that tie N1 to CRMs, mail merge tools, etc.
Hope that helps—

Also just to add, if you have specific legal questions related to N1 or Nylas, feel free to email<EMAIL_ADDRESS>

@bengotow: Thanks for the answers so far, which in essence really do resolve just one question. Please see my additions below.
@grinich: why did you close this issue already? Most of my questions still remain unanswered and I believe that this should be something discussed openly. In fact, if your platform is as secure and considerate as you claim, a discussion on this topic should only increase your perceived trust by users. The N1 code, as is, ties into your hosted service and therefore the implications of this behavior need to be laid out.

Regarding https://nylas.com/security "Nylas is Safe Harbor Compliant for both US-EU and US-Swiss standards[...]". You must be aware that as of October 6th, 2015 the Safe Harbor agreements of 2000 have been declared invalid by the European Court of Justice (see: https://www.ftc.gov/tips-advice/business-center/privacy-and-security/u.s.-eu-safe-harbor-framework). Are you going to take action to ensure EU users are granted their extended rights to privacy?
Since you appear to run your service on AWS, the easiest course of action to get around some of my raised issues would be to enable running instances exclusively in EU data centers, which are located Ireland and Germany. Is this something you will consider?

These questions are still open:

Why would you be interested in getting access to all my email and login credentials in the first place? Certainly "ease of use" cannot be the main contributing factor here. Storing so much third-party data must be a disaster waiting to happen as long as you don't have the resources of a company like Google. Having so much data centralized at one location creates a very compelling target.
What about people living in other countries, like Germany, where privacy protection laws explicitly prohibit exporting data to other countries without definite user consent. The issue gets even more complicated for a freelancer or small agency, for example, being contracted by a large corporation. Usually NDAs and contractual fine print prohibits transfer of data out of the residing country. I am aware that this also applies to webmail providers like Google Mail and in the real world is treated with a rather cavalier attitude. You certainly don't follow that attitude, do you? So what's your position on this?
What assurances does a user have regarding the safety of his personal data?
What reasonable course of action does a EU citizen have for finding out if you were served with a request for access to personal data? It is my impression that being US-based, in most cases you are bound by law to comply with such enforcement requests.
What is your policy on defending the users' privacy and data against such requests?

Thanks in advance for taking the time to answer those remaining questions.

@bengotow @grinich Any news on this?

hi @herrbischoff -- I closed it because we use this repo to track bugs related to the code development of N1
Can you send me a note via<EMAIL_ADDRESS>and I can Skype you? I don't have a ton of time and I think taking this synchronous would be way faster at answering your concerns.

I am interested in these answers as well! Please keep the discussion public.

@herrbischoff Where you able to speak with Grinich?
The near evasiveness of this overwhelmingly important issue is troubling at best. This should not be closed until the questions are answered or we are pointed towards sufficiently thorough answers somewhere else.
You offer no public forum, so you might just have to deal with this topic be handled here.

I'm as well really interested in this topic and some more insightful answer from the Nylas Team.
Please keep this discussion open!

Hi folks. I never got a follow-up from @herrbischoff and found a free hour to write this up.
Here’s a few answers to hopefully shed light on our cloud sync system. Thanks for your patience!
We’re planning to start charging people to use our hosted cloud sync, since it costs us money to provide the infrastructure. We're not looking at other business models that sell the data (even aggregate data). The price will be pretty reasonable-- think GitHub for private repos, or a DigitalOcean droplet. Of course, you can always run the sync engine yourself since it’s open source GPL free software.
The Nylas N1 product depends on our cloud sync system in order to provide features like snoozing, send later, and more collaboration tools we plan to launch later this year. You can’t reasonably build these things without having access to the data layer from a server component. It's also the "heavy lifting" part of the system, and allows N1 to be faster and compatible with more providers.
This isn't uncommon. Most modern email apps use a cloud sync component. e.g. Mailbox, Accompli, Cloud Magic, Polymail, and Spark by Riddle.
We know this architecture has different security implications, and we’ve worked from the start to make a secure system. You can read what we've published here: https://nylas.com/security
Regarding international users: Nylas is an American company based in San Francisco, with our primary data center in the USA. This means we are subject to any and all US laws and regulations.
We recognize the need for some users to have data stored outside the US, but as a small company we can’t actively manage this right now (for either N1 or the API platform customers). We definitely plan to someday open a data center in Europe (& elsewhere!), but we don’t have the volume right now to justify spending time on it.
We encourage folks who have this as a hard requirement to instead run the open source sync system themselves on a hosting provider they trust. It doesn't phone home or transmit data to us in any way.
We know the cloud sync architecture we’ve picked can’t satisfy everyone’s needs. And that’s fine— there are other products out there with different pros and cons. It’s our hope that by open sourcing our code under a libre license, and also being straightforward about the business we’re building, others who can't use N1 will still be able to benefit somewhat from our work. Even if that's only to experiment with the platform, or check out a reference-implementation of a large JavaScript desktop app. (I think we might be the the biggest React+Flux based app built on Electron!)
I can say right how that we haven’t received any government or law enforcement request for data. This actually makes sense, since we’re not the primary mail store. These requests instead go to the mail provider (i.e. Gmail, Yahoo, MSFT, your ISP, etc.).
Probably the biggest “assurance” you have for us safely storing  data is that we have the same thing at stake as our users. Any breech would be extremely damaging to Nylas and our future as a company. It’s critically important for us to do everything we can to prevent that.
On the other hand, none of this will matter unless we continue to build a product that people love and want to use every day. The majority of our team spends their time creating new features, improving performance, fixing bugs, and generally working to make N1 the best email app out there.
Right now we have over 500 issues on GitHub and tens of thousands of developers actively experimenting with the N1 platform. I hope you won’t mistake evasiveness for instead simply not having enough time to talk with everyone.
And if you know folks who want to work on this, we are actively hiring! :smile:  https://jobs.lever.co/nylas

Dear all,
Thanks to @herrbischoff for getting the ball rolling.
I just discovered Nylas, and it looks like a tremendous product.
Here's the exhaustive list of what I would need to stick with it, by order of priority:

Clear privacy statement
Important / Starred / Unread
Multilingual (or disabled) spell checking

Please keep the discussion public.

hi @briatte -- What questions do you have that haven't been answered above?
We're working on an important/starred/unread view and also multilingual spell checking.

Thanks for the kind words!

You're welcome.

What questions do you have that haven't been answered above?

I didn't have any questions in the beginning: I just did not know what I was signing when I started using the app. I found this thread by serendipity. The issues it raises and answers should be shown in a plain-language statement when the app is launched for the first time, or better, every time that a new account is added. At least in my opinion.
Will have a look at the thread you mentioned. Looking forward to the next release in all cases.

I just found Nylan.  It looks great but I am concerned like others.  It would be nice if this was opt in feature.  I am not sure why this issue is closed, that doesn't seem very open.  I am glad I found it before downloading.

For other folks referencing this thread, I definitely recommend you also check out our help center article for Why does Nylas N1 sync email via the cloud?.

Just to post a quick update here: our new version of Nylas Mail (announced here) now syncs data locally and directly with your mail provider.

@grinich but still phones home, right?

@useretail  does it or not, that is my question! any idea? thx
Dan

@skywalker89 of course it does. see (in)complete list at #1849

ok thx...moved back to TB

@grinich thanks for taking the time to explain all this.
Accessing user data to enable the described features makes sense. The thing is, there is a ton of users who want to own their data and no matter what features you add, they won't change their mind. Personally I would pay money for an awesome email client that doesn't phone home, I believe many others would. You see, a lot of people feel that giving up their data is actually a cost, a much much bigger cost than paying for software.
If I may propose a solution that would make a lot of people happy: add a switch on the app so people who value their data, can just disable all features that raise privacy concerns. Singing up to the sync engine and giving you personal data and login credentials should be absolutely optional. Search will be less powerful, especially on mobile, but for many this is a tiny price to pay. Give users options that matter to them and they'll love you and the product for it. For every message you get here on this thread, there is a big crowed behind who is less vocal, but still abstains from the product. Why miss out on all these people? Users who value their privacy is a big market, it can pay dividends to address it.
Thanks

@grinich btw, is there a link by any chance that explains how to run the open source sync system on our own host?
Thank you

Installed. Setup 7 accounts. Enjoyed for ~5 minutes. Couldn't empty Trash or Spam. Checked #599 . Facepalmed. Uninstalled. Got back to AirMail.


Pr fix remove made from legislation url

config/locals/reference.yml has a list of URL that are used to link the application to legislation.
When discussing it with the designers they explained it should point to the latest version of the legislation and not the original version. However, some of the URLs are pointing to the original versions of the legislation.
For example reference.yml points to the original '/made' version the URL will be changed by removing '/made'
 url: https://www.legislation.gov.uk/uksi/2015/596/schedule/2/part/1/crossheading/class-g-chimneys-flues-etc-on-a-dwellinghouse/made

Heads up I've updated the changes to include CLASS B which also is wrong.


pgbackrest: init at 2.40

Description of changes
https://pgbackrest.org/ -- Reliable PostgreSQL Backup & Restore
Things done

Built on platform(s)

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

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

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

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

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

[x] Fits CONTRIBUTING.md.

I don't have the time to do a full review right now, but the package builds and runs fine.

@zaninime are you still interested in working on this PR?

@bbigras yes. I can find some time to bring it to 2.41.


kernel: deprecate DEVICE_INIT

Devices need an API struct set at build time in order to be
usable from user mode. Deprecate the old DEVICE_INIT macro,
which doesn't do this.
Signed-off-by: Andrew Boie<EMAIL_ADDRESS>

Codecov Report

Merging #11847 into master will increase coverage by 46.26%.
The diff coverage is n/a.

@@             Coverage Diff             @@
##           master   #11847       +/-   ##
===========================================
+ Coverage   51.46%   97.72%   +46.26%
===========================================
  Files         231        4      -227
  Lines       28737       44    -28693
  Branches     7149       10     -7139
===========================================
- Hits        14789       43    -14746
+ Misses      11130        0    -11130
+ Partials     2818        1     -2817

Impacted Files
Coverage Δ

subsys/net/lib/websocket/websocket.c

include/atomic.h

include/net/websocket.h

subsys/net/ip/nbr.h

subsys/net/ip/net_mgmt.c

subsys/net/lib/coap/coap_link_format.c

kernel/queue.c

lib/rbtree/rb.c

subsys/net/lib/mqtt_sock/mqtt_os.h

subsys/net/ip/net_if.c

... and 216 more

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Include object path in properties

I can not tell you how many times I have needed Object Path, i.e. which folder is it in.

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

ID: 7884089c-5ea7-b495-b0e1-06035e2ce281
Version Independent ID: 42ae40d7-3a20-7ab8-faff-77e27c33bfaa
Content: Get-CMCollection - Configuration Manager
Content Source: sccm-ps/ConfigurationManager/Get-CMCollection.md
Product: configuration-manager
Technology: configmgr-other
GitHub Login: @aczechowski
Microsoft Alias: aaroncz

Thanks @Barleyologist for taking the time to provide feedback on Configuration Manager! This is an interesting feature idea, and I want to make sure it gets directly into the hands of the engineering team. Fortunately, we've got dedicated channels for that very purpose. :) See the Product feedback section in our docs. I think it’s important that you submit it directly, because that allows you to receive notifications and more closely monitor the progress. I'm closing this issue as there's nothing actionable for docs at this time.


Why is training so slow

4900 images and batchsize：64  gpu：2080TI

Because the workers in dataloader is 0. I denote this in my train code. If I add workers, it will report some errors when I use multiscale training trick. I have no idea how to solve this problems.
---Original---
From<EMAIL_ADDRESS>Date: Sat, May 23, 2020 13:54 PM
To<EMAIL_ADDRESS>Cc<EMAIL_ADDRESS>Subject: [yjh0410/yolov2-yolov3_PyTorch] Why is training so slow (#34)
4900 images and batchsize：64  gpu：2080TI
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub, or unsubscribe.

And if you don't plan to try multi scale training, you'd better set num_workers as 8 or more to accelerate training.
---Original---
From<EMAIL_ADDRESS>Date: Sat, May 23, 2020 13:54 PM
To<EMAIL_ADDRESS>Cc<EMAIL_ADDRESS>Subject: [yjh0410/yolov2-yolov3_PyTorch] Why is training so slow (#34)
4900 images and batchsize：64  gpu：2080TI
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub, or unsubscribe.

I used yolov2 training, not multi-scale, and a batch spent nearly one minute in training. An epoch has many batches. So I feel strange. Is it because the code parses XML.

No multi scale？Aha！You can set num_workers as 8(default=8).
Do you set --cuda to use GPU？ Its default value is False.
---Original---
From<EMAIL_ADDRESS>Date: Sat, May 23, 2020 14:14 PM
To<EMAIL_ADDRESS>Cc: "Comment"<comment@noreply.github.com>;"Jianhua<EMAIL_ADDRESS>Subject: Re: [yjh0410/yolov2-yolov3_PyTorch] Why is training so slow (#34)
I used yolov2 training, not multi-scale, and a batch spent nearly one minute in training. An epoch has many batches. So I feel strange. Is it because the code parses XML.
—
You are receiving this because you commented.
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Sorry, I didn't read the code carefully. Now it's fast to use CUDA. thank you

You're welcome！！！(๑>؂<๑）
---Original---
From<EMAIL_ADDRESS>Date: Sat, May 23, 2020 14:27 PM
To<EMAIL_ADDRESS>Cc: "Comment"<comment@noreply.github.com>;"Jianhua<EMAIL_ADDRESS>Subject: Re: [yjh0410/yolov2-yolov3_PyTorch] Why is training so slow (#34)
Sorry, I didn't read the code carefully. Now it's fast to use CUDA. thank you
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No multi scale？Aha！You can set num_workers as 8(default=8).  Do you set --cuda to use GPU？ Its default value is False.
…
---Original--- From<EMAIL_ADDRESS>Date: Sat, May 23, 2020 14:14 PM To<EMAIL_ADDRESS>Cc: "Comment"<comment@noreply.github.com>;"Jianhua<EMAIL_ADDRESS>Subject: Re: [yjh0410/yolov2-yolov3_PyTorch] Why is training so slow (#34) I used yolov2 training, not multi-scale, and a batch spent nearly one minute in training. An epoch has many batches. So I feel strange. Is it because the code parses XML. — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.

SET cuda TRUE

2080ti x 1
ubuntu 16.04
python 3.6
pytorch 1.5.0
cpu cores  :8
cpu processor num :16

when i use cpu to train the model ,the training is sooooooo slow, such as the blow info...
[Epoch 1/250][Iter 0/517][lr 0.000000][Loss: obj 210.70 || cls 4.88 || bbox 8.88 || total 224.46 || size 416 || time: 47.35]
but when i set cuda True, the training speed up  obivous!
Epoch 1/250][Iter 10/517][lr 0.000000][Loss: obj 212.88 || cls 7.12 || bbox 9.07 || total 229.07 || size 416 || time: 2.20]

SET CUDA TRUE To use GPU!
envs

2080ti x 1
ubuntu 16.04
python 3.6
pytorch 1.5.0
cpu cores  :8
cpu processor num :16

without GPU
when i use cpu to train the model ,the training is sooooooo slow, such as the blow info...
[Epoch 1/250][Iter 0/517][lr 0.000000][Loss: obj 210.70 || cls 4.88 || bbox 8.88 || total 224.46 || size 416 || time: 47.35]
use GPU
but when i set cuda True, the training speed up  obivous!
Epoch 1/250][Iter 10/517][lr 0.000000][Loss: obj 212.88 || cls 7.12 || bbox 9.07 || total 229.07 || size 416 || time: 2.20]

Thanks for your advice！HAHA！
The reason why I didn't set cuda as True is that I wanna indirectly remind everyone who plans to try this project to use GPU to train the model. If no GPU，it's a little hard to study object detection.

SET CUDA TRUE To use GPU!
envs

2080ti x 1
ubuntu 16.04
python 3.6
pytorch 1.5.0
cpu cores  :8
cpu processor num :16

without GPU
when i use cpu to train the model ,the training is sooooooo slow, such as the blow info...
[Epoch 1/250][Iter 0/517][lr 0.000000][Loss: obj 210.70 || cls 4.88 || bbox 8.88 || total 224.46 || size 416 || time: 47.35]
use GPU
but when i set cuda True, the training speed up obivous!
Epoch 1/250][Iter 10/517][lr 0.000000][Loss: obj 212.88 || cls 7.12 || bbox 9.07 || total 229.07 || size 416 || time: 2.20]

Thanks for your advice！HAHA！
The reason why I didn't set cuda as True is that I wanna indirectly remind everyone who plans to try this project to use GPU to train the model. If no GPU，it's a little hard to study object detection.

SET CUDA TRUE To use GPU!
envs

2080ti x 1
ubuntu 16.04
python 3.6
pytorch 1.5.0
cpu cores  :8
cpu processor num :16

without GPU
when i use cpu to train the model ,the training is sooooooo slow, such as the blow info...
[Epoch 1/250][Iter 0/517][lr 0.000000][Loss: obj 210.70 || cls 4.88 || bbox 8.88 || total 224.46 || size 416 || time: 47.35]
use GPU
but when i set cuda True, the training speed up obivous!
Epoch 1/250][Iter 10/517][lr 0.000000][Loss: obj 212.88 || cls 7.12 || bbox 9.07 || total 229.07 || size 416 || time: 2.20]

Thanks for your advice！HAHA！
The reason why I didn't set cuda as True is that I wanna indirectly remind everyone who plans to try this project to use GPU to train the model. If no GPU，it's a little hard to study object detection.

Your purpose is good, but I still hope the cuda default setting is True, thx!!


branches in the repo

We have about 55 branches in the repo:
 git ls-remote origin | grep refs/head
76a30522421f06ea509104c2bc9b50c26032c29e        refs/heads/api-compat-link
f91430e7aec6988c522da026b860b526f27c2166        refs/heads/bobcatfish-patch-1
f74bdaead119181e93cfeefab6337b0baef63f71        refs/heads/codeql
a7babbf4c0997708085f2b717f98998f121d142c        refs/heads/dependabot/go_modules/github.com/jenkins-x/go-scm-1.13.12
d32320a0dcf078a7f64f95f7fd2f3703e616c633        refs/heads/dependabot/go_modules/k8s.io/apimachinery-0.25.8
bfd676c025c679a1152607d2307384fbcffb3252        refs/heads/imjasonh-patch-1
9f971c41f669dfe3559518206a5e78766e76778f        refs/heads/imjasonh-patch-2
34fa1c0128f7d0df7c755e1c975d23b1f5e1833b        refs/heads/main
c1500fab83b09edadefb38bb8920a0c837d8f32b        refs/heads/release-0.1
b3c2b788a87a3983e44bd127f376795907c58075        refs/heads/release-21-0
0de7e2179090e0e2852ed5f12635137f36b4ee58        refs/heads/release-v0.10.x
821ac4d41f7ac8852cc51a3942b0fa171315a67e        refs/heads/release-v0.11.x
a54dd3984affab47f3018852e61a1a6f9946ecfa        refs/heads/release-v0.12.x
3a0cdba56ecb64069b12cbf12936223ab0f67e38        refs/heads/release-v0.13.0x
a4836a97f344c275cdf539288c75b7353a2e89f2        refs/heads/release-v0.14.x
92cf75ff742913f2ccecc27f6bb76ab0085833ee        refs/heads/release-v0.15.x
c1e10c767aa871fbec31551adb07d182beec6bcf        refs/heads/release-v0.16.x
a164ba3ef1f1cdbf4f8aa2bf8b5ecc89d3268dba        refs/heads/release-v0.17.x
f05ae9ebe08f0cc24815cb73f986856aa8633610        refs/heads/release-v0.18.x
e7425154434fe2a3c240216034f0832fdc51b0bf        refs/heads/release-v0.19.x
3d178f0fb90a64c0af995da1839f80dea3381c97        refs/heads/release-v0.20.x
787ad0ecdbebd8b503b7780ffe2a44e706146b39        refs/heads/release-v0.21.x
52f10075658f719492d63f404e5d05516ec1fe92        refs/heads/release-v0.22.x
bd1abcbeada651f28c6f3558332e9177d07d8fac        refs/heads/release-v0.23.x
36026c1bcdd2a53a037b12c659e5e105fd0fd4d9        refs/heads/release-v0.24.x
17c1c848c36cc633143f3821512ddd94df9eae90        refs/heads/release-v0.25.x
18c9bfae3a0e457441fcf17dedfdef49ea821f87        refs/heads/release-v0.27.x
e5c33d95d5bc1f27b0e94c9797cca231fcf3fcf6        refs/heads/release-v0.28.x
e68808a503237f28d70b15375c06219bab7e6a2c        refs/heads/release-v0.29.x
e0fed7f44ead371308a269b93c0cd0ef6cb8b221        refs/heads/release-v0.3.0
2f41321717beb9126e7ab2b1ae9f0fa51a1a9751        refs/heads/release-v0.30.x
b76cb0a2402492f6a116fc6103277026a8155b51        refs/heads/release-v0.31.x
2057c9dc65866118c45354c6b79a2b0bc7563f3b        refs/heads/release-v0.32.x
6b5710c3242ce7c3e1e8b876b7d35316449f617a        refs/heads/release-v0.33.x
fea15d7282b9fd571b97749442781753e2f82d29        refs/heads/release-v0.34.x
2bbc458c232820ae865e6a8254cdc1f4027f974b        refs/heads/release-v0.35.x
2da9a58c7e773c2068d3fccec84719f83488e1d8        refs/heads/release-v0.36.x
1759eabb937cb3b4c3f9d8327efb796d2fdd1f07        refs/heads/release-v0.37.x
b109054304f5a1db12910c71874eb9c7d896fb05        refs/heads/release-v0.38.x
6a789d4d48c0ed23e2c4607cb5d1519f2f64e569        refs/heads/release-v0.39.x
4003f7211e03122cd314816be8554bf27beb1898        refs/heads/release-v0.40.x
987d00a7a57a4051a2d615f0f1ac9ae7e92e5f72        refs/heads/release-v0.41.x
c12d43d5fe9c455b06b64eb3fc69aac711c78ade        refs/heads/release-v0.42.x
466db395ede35f9fad39c6b54da3ded31cf86571        refs/heads/release-v0.43.x
0e9e4b9daf9f54dc131533edf41c569c31e49926        refs/heads/release-v0.44.x
268b94bd749d2371634656f472e031ca1700a463        refs/heads/release-v0.45.x
086e76abb81d2d35e341553e501410790b0797d8        refs/heads/release-v0.46.x
f8232b8b73d6996515f7672c7acd2decf5046c93        refs/heads/release-v0.5.x
5fa70add12ede0ac3b14f90563eb2d6626adecc5        refs/heads/release-v0.6.x
1db2b54a1684a34232e8505c4af15824b7c4668f        refs/heads/release-v0.9.x
fad2bef872c3699448b0dbfb46cea85499855bba        refs/heads/release-v0.9.x-jasonhall
486be668f009d79c0f663eedf3cbd0a9a72cdc76        refs/heads/revert-5361-main
dcc7e55a5f077b1b3f4a403504da6b053feb44e3        refs/heads/v0-21-0
9a7a331134857da05889cde94e35b56f5be3f65f        refs/heads/v0.31.x
131a0ffa75e941493f2073354ae346762e1ad27d        refs/heads/v0.9.2-release

Currently, we do not have any formal process of how many branches should we maintain at any given time. This is a lower priority cleanup but just wanted to bring awareness of having many stale branches in the repo.
Can we delete branches owned by some of the pipeline maintainers?
 git ls-remote origin | grep refs/head | grep jason
bfd676c025c679a1152607d2307384fbcffb3252        refs/heads/imjasonh-patch-1
9f971c41f669dfe3559518206a5e78766e76778f        refs/heads/imjasonh-patch-2
fad2bef872c3699448b0dbfb46cea85499855bba        refs/heads/release-v0.9.x-jasonhall

@imjasonh, thoughts?
git ls-remote origin | grep refs/head | grep bobcatfish
f91430e7aec6988c522da026b860b526f27c2166        refs/heads/bobcatfish-patch-1

@bobcatfish thoughts?
We might be able to propose some guidelines on maintaining branches based on the release process:
https://github.com/tektoncd/pipeline/blob/main/releases.md
How about we only maintain the branches until the oldest EOL which is 0.37 i.e. maintain branches from 0.37 to the current latest 0.46?

@pritidesai what do you mean by "maintaining" the branches ? We shouldn't necessarily thing of "maintaining" branches but maintaining releases — and today, with LTS, we kind-of have those guidelines.
I deleted the "personal" branches (patch & co) from Jason and Christe 😛

thanks @vdemeester 😄

what do you mean by "maintaining" the branches ? We shouldn't necessarily thing of "maintaining" branches but maintaining releases — and today, with LTS, we kind-of have those guidelines.

sorry for the confusion 😄 "maintaining" as in create a branch for each release and update them with the patches (which is what we follow) in addition to clean them up when no longer supported based on the release guidelines.

thanks @vdemeester smile

what do you mean by "maintaining" the branches ? We shouldn't necessarily thing of "maintaining" branches but maintaining releases — and today, with LTS, we kind-of have those guidelines.

sorry for the confusion smile "maintaining" as in create a branch for each release and update them with the patches (which is what we follow) in addition to clean them up when no longer supported based on the release guidelines.

So I would definitely not want to clean them up (aka removing them). The main reason for this is, even if we "don't" support those release anymore (aka not cherry-picking to it, …), some people or tools might rely on those being available. As an example, OpenShift Pipelines do track release branches, and our support cycle is larger than the LTS timeframe, which means, we may carry patches, but we will rely on the release branch to still be there. (Note: technically, we could rely on tags, but in practice, right now, one of the tool we use, doesn't work properly with them for some arcane reason).
Branches in git are very cheap, removing them from a remote is almost solely cosmetic.

We could consider removing branches for releases that are not maintained anymore - it is cosmetic, but still would be nice to have.

We could consider removing branches for releases that are not maintained anymore - it is cosmetic, but still would be nice to have.

As said above, I would rather not and I am not even sure what we would gain to do so. Also, the fact that we do not support a release any more, doesn't mean no one is depending on it, or tracking the release branch. As an example, OpenShift Pipelines, fetch sources from the release branches and, as we have a support time that is longer than LTS (more than a year), we are usually still fetching branches of version not supported any more by upstream. Removing these from the repository (upstream remote) would be a problem for us.
As said, branches in git are very cheap, and I don't really see any reason to remove them.


refactor createAPIServerClient for easier integration with 3rd party …

…kubelet extensions, e.g. kubernetes-mesos
xref https://github.com/mesosphere/kubernetes-mesos/issues/332
xref #8882 -- this PR is a prerequisite for kubernetes-mesos integration
/cc @davidopp @guenter @bgrant0607 @thockin

cc @dchen1107

not sure why shippable is failing, can't see the build logs there (shippable just hangs)

Some integration test is failing. I'll re-run shippable to see if it's a flake.
F0608 22:53:13.316182   26960 integration.go:800] FAILED: unexpected endpoints: timed out waiting for the condition
!!! Error in ./hack/test-integration.sh:49
  '"${KUBE_OUTPUT_HOSTBIN}/integration" --v=${LOG_LEVEL} --api-version="$1" --max-concurrency="${KUBE_INTEGRATION_TEST_MAX_CONCURRENCY}"' exited with status 255
Call stack:
  1: ./hack/test-integration.sh:49 runTests(...)
  2: ./hack/test-integration.sh:63 main(...)
Exiting with status 1

change looks fine, re-running shippable (was a flake)

I don't know why this is failing shippable but it does not seem to be a flake. The test that is failing is the second test in "script" namely
PATH=$HOME/gopath/bin:./third_party/etcd:$PATH ./hack/test-cmd.sh

Here is the output from the failed run:
tput: No value for $TERM and no -T specified
tput: No value for $TERM and no -T specified
tput: No value for $TERM and no -T specified
tput: No value for $TERM and no -T specified
tput: No value for $TERM and no -T specified

etcd -data-dir /tmp/test-etcd.AeUIwp --bind-addr <IP_ADDRESS>:4001 >/dev/null 2>/dev/null

Waiting for etcd to come up.
+++ [0609 17:47:29] etcd:
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
  0     0    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0
100    87  100    87    0     0     68      0  0:00:01  0:00:01 --:--:--    68
{"action":"set","node":{"key":"/_test","value":"","modifiedIndex":3,"createdIndex":3}}
+++ [0609 17:47:31] Running kubectl with no options
kubectl controls the Kubernetes cluster manager.

Find more information at https://github.com/GoogleCloudPlatform/kubernetes.

Usage:
  kubectl [flags]
  kubectl [command]

Available Commands:
  get            Display one or many resources
  describe       Show details of a specific resource
  create         Create a resource by filename or stdin
  update         Update a resource by filename or stdin.
  delete         Delete a resource by filename, stdin, resource and ID, or by resources and label selector.
  namespace      SUPERCEDED: Set and view the current Kubernetes namespace
  logs           Print the logs for a container in a pod.
  rolling-update Perform a rolling update of the given ReplicationController.
  scale          Set a new size for a Replication Controller.
  exec           Execute a command in a container.
  port-forward   Forward one or more local ports to a pod.
  proxy          Run a proxy to the Kubernetes API server
  run            Run a particular image on the cluster.
  stop           Gracefully shut down a resource by id or filename.
  expose         Take a replicated application and expose it as Kubernetes Service
  label          Update the labels on a resource
  config         config modifies kubeconfig files
  cluster-info   Display cluster info
  api-versions   Print available API versions.
  version        Print the client and server version information.
  help           Help about any command

Flags:
      --alsologtostderr=false: log to standard error as well as files
      --api-version="": The API version to use when talking to the server
      --certificate-authority="": Path to a cert. file for the certificate authority.
      --client-certificate="": Path to a client key file for TLS.
      --client-key="": Path to a client key file for TLS.
      --cluster="": The name of the kubeconfig cluster to use
      --context="": The name of the kubeconfig context to use
  -h, --help=false: help for kubectl
      --insecure-skip-tls-verify=false: If true, the server's certificate will not be checked for validity. This will make your HTTPS connections insecure.
      --kubeconfig="": Path to the kubeconfig file to use for CLI requests.
      --log-backtrace-at=:0: when logging hits line file:N, emit a stack trace
      --log-dir=: If non-empty, write log files in this directory
      --log-flush-frequency=5s: Maximum number of seconds between log flushes
      --logtostderr=true: log to standard error instead of files
      --match-server-version=false: Require server version to match client version
      --namespace="": If present, the namespace scope for this CLI request.
      --password="": Password for basic authentication to the API server.
  -s, --server="": The address and port of the Kubernetes API server
      --stderrthreshold=2: logs at or above this threshold go to stderr
      --token="": Bearer token for authentication to the API server.
      --user="": The name of the kubeconfig user to use
      --username="": Username for basic authentication to the API server.
      --v=0: log level for V logs
      --validate=false: If true, use a schema to validate the input before sending it
      --vmodule=: comma-separated list of pattern=N settings for file-filtered logging

Use "kubectl help [command]" for more information about a command.

+++ [0609 17:47:31] Starting kubelet in masterless mode
W0609 17:47:31.259440   21214 server.go:253] failed to set oom_score_adj to -900: write /proc/self/oom_score_adj: permission denied
no api servers specified
!!! [0609 17:47:36] Timed out waiting for http://<IP_ADDRESS>:10248/healthz
!!! Error in ./hack/test-cmd.sh:43
  'return 1' exited with status 1
Call stack:
  1: ./hack/test-cmd.sh:43 main(...)
Exiting with status 1

Here is the first part of the output from a run (of a different PR) that passes:
tput: No value for $TERM and no -T specified
tput: No value for $TERM and no -T specified
tput: No value for $TERM and no -T specified
tput: No value for $TERM and no -T specified
tput: No value for $TERM and no -T specified

etcd -data-dir /tmp/test-etcd.BmRWQq --bind-addr <IP_ADDRESS>:4001 >/dev/null 2>/dev/null

Waiting for etcd to come up.
+++ [0609 19:08:38] etcd:
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
  0     0    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0
100    87  100    87    0     0     68      0  0:00:01  0:00:01 --:--:--    68
{"action":"set","node":{"key":"/_test","value":"","modifiedIndex":3,"createdIndex":3}}
+++ [0609 19:08:39] Running kubectl with no options
kubectl controls the Kubernetes cluster manager.

Find more information at https://github.com/GoogleCloudPlatform/kubernetes.

Usage:
  kubectl [flags]
  kubectl [command]

Available Commands:
  get            Display one or many resources
  describe       Show details of a specific resource
  create         Create a resource by filename or stdin
  update         Update a resource by filename or stdin.
  delete         Delete a resource by filename, stdin, resource and ID, or by resources and label selector.
  namespace      SUPERCEDED: Set and view the current Kubernetes namespace
  logs           Print the logs for a container in a pod.
  rolling-update Perform a rolling update of the given ReplicationController.
  scale          Set a new size for a Replication Controller.
  exec           Execute a command in a container.
  port-forward   Forward one or more local ports to a pod.
  proxy          Run a proxy to the Kubernetes API server
  run            Run a particular image on the cluster.
  stop           Gracefully shut down a resource by id or filename.
  expose         Take a replicated application and expose it as Kubernetes Service
  label          Update the labels on a resource
  config         config modifies kubeconfig files
  cluster-info   Display cluster info
  api-versions   Print available API versions.
  version        Print the client and server version information.
  help           Help about any command

Flags:
      --alsologtostderr=false: log to standard error as well as files
      --api-version="": The API version to use when talking to the server
      --certificate-authority="": Path to a cert. file for the certificate authority.
      --client-certificate="": Path to a client key file for TLS.
      --client-key="": Path to a client key file for TLS.
      --cluster="": The name of the kubeconfig cluster to use
      --context="": The name of the kubeconfig context to use
  -h, --help=false: help for kubectl
      --insecure-skip-tls-verify=false: If true, the server's certificate will not be checked for validity. This will make your HTTPS connections insecure.
      --kubeconfig="": Path to the kubeconfig file to use for CLI requests.
      --log-backtrace-at=:0: when logging hits line file:N, emit a stack trace
      --log-dir=: If non-empty, write log files in this directory
      --log-flush-frequency=5s: Maximum number of seconds between log flushes
      --logtostderr=true: log to standard error instead of files
      --match-server-version=false: Require server version to match client version
      --namespace="": If present, the namespace scope for this CLI request.
      --password="": Password for basic authentication to the API server.
  -s, --server="": The address and port of the Kubernetes API server
      --stderrthreshold=2: logs at or above this threshold go to stderr
      --token="": Bearer token for authentication to the API server.
      --user="": The name of the kubeconfig user to use
      --username="": Username for basic authentication to the API server.
      --v=0: log level for V logs
      --validate=false: If true, use a schema to validate the input before sending it
      --vmodule=: comma-separated list of pattern=N settings for file-filtered logging

Use "kubectl help [command]" for more information about a command.

+++ [0609 19:08:40] Starting kubelet in masterless mode
W0609 19:08:40.174835   21324 server.go:253] failed to set oom_score_adj to -900: write /proc/self/oom_score_adj: permission denied
I0609 19:08:40.175367   21324 manager.go:126] cAdvisor running in container: "/docker/a19f3a853d32848c1ba2ef3f47cd5779fe39cc349c9c828c3dbc9d79c4a912d8"
I0609 19:08:40.176483   21324 fs.go:88] Filesystem partitions: map[/dev/disk/by-label/DOROOT:{mountpoint:/ds major:253 minor:1}]
I0609 19:08:40.239154   21324 machine.go:224] Couldn't collect info from any of the files in "/etc/machine-id,/var/lib/dbus/machine-id"
I0609 19:08:40.239358   21324 manager.go:147] Machine: {NumCores:16 CpuFrequency:2399998 MemoryCapacity:50650255360 MachineID: SystemUUID:847B5603-62B4-43E4-92D9-F11DD1B48A53 BootID:27d64fa5-fd87-43bd-9711-62d923438109 Filesystems:[{Device:/dev/disk/by-label/DOROOT Capacity:507172954112}] DiskMap:map[253:0:{Name:vda Major:253 Minor:0 Size:515396075520 Scheduler:none}] NetworkDevices:[{Name:eth0 MacAddress:02:42:ac:11:09:96 Speed:10000 Mtu:1500}] Topology:[{Id:0 Memory:50650255360 Cores:[{Id:0 Threads:[0] Caches:[]}] Caches:[]} {Id:1 Memory:0 Cores:[{Id:0 Threads:[1] Caches:[]}] Caches:[]} {Id:2 Memory:0 Cores:[{Id:0 Threads:[2] Caches:[]}] Caches:[]} {Id:3 Memory:0 Cores:[{Id:0 Threads:[3] Caches:[]}] Caches:[]} {Id:4 Memory:0 Cores:[{Id:0 Threads:[4] Caches:[]}] Caches:[]} {Id:5 Memory:0 Cores:[{Id:0 Threads:[5] Caches:[]}] Caches:[]} {Id:6 Memory:0 Cores:[{Id:0 Threads:[6] Caches:[]}] Caches:[]} {Id:7 Memory:0 Cores:[{Id:0 Threads:[7] Caches:[]}] Caches:[]} {Id:8 Memory:0 Cores:[{Id:0 Threads:[8] Caches:[]}] Caches:[]} {Id:9 Memory:0 Cores:[{Id:0 Threads:[9] Caches:[]}] Caches:[]} {Id:10 Memory:0 Cores:[{Id:0 Threads:[10] Caches:[]}] Caches:[]} {Id:11 Memory:0 Cores:[{Id:0 Threads:[11] Caches:[]}] Caches:[]} {Id:12 Memory:0 Cores:[{Id:0 Threads:[12] Caches:[]}] Caches:[]} {Id:13 Memory:0 Cores:[{Id:0 Threads:[13] Caches:[]}] Caches:[]} {Id:14 Memory:0 Cores:[{Id:0 Threads:[14] Caches:[]}] Caches:[]} {Id:15 Memory:0 Cores:[{Id:0 Threads:[15] Caches:[]}] Caches:[]}]}
I0609 19:08:40.240674   21324 manager.go:154] Version: {KernelVersion:3.13.0-43-generic ContainerOsVersion:Ubuntu precise (12.04.5 LTS) DockerVersion:Unknown CadvisorVersion:0.14.0}
I0609 19:08:40.243968   21324 plugins.go:69] No cloud provider specified.

It looks like they diverge starting at the message "no api servers specified"
I didn't have a chance to look into it yet, but it seems that this PR is causing that.

You might want to try running that test manually on the command line to debug this.

Thanks, will take a look.
On Tue, Jun 9, 2015 at 5:05 PM, David Oppenheimer<EMAIL_ADDRESS>wrote:

You might want to try running that test manually on the command line to
debug this.
—
Reply to this email directly or view it on GitHub
https://github.com/GoogleCloudPlatform/kubernetes/pull/9423#issuecomment-110503910
.

Can you squash your two commits, and we'll let the tests run again, and then I think it will be OK?

ok, squashed

LGTM