Enable importing in projects with jQuery already loaded

add build script, package-lock, update import code to check for jQuery global availability first
in my project (Rails+React-Rails), the code was not working correctly because parollerJS was being added into the locally loaded module instead of the already available global jQuery instance

Thank you 👍 Could you please remove dependencies like uglify-js and commander thou - there is no need for command-line programs or parsers, minifiers in source files - each developer can use their own preferred tools locally.


1.12.x branch does not report app id or app version correctly

looking at the json reported - you can see it's wrong:

I expected to see something like:

Could you please tell me where do I find this json?


Deploy ALZ custom policies when using deploy_core_landing_zones

Community Note

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Please do not leave "+1" or "me too" comments, they generate extra noise for issue followers and do not help prioritize the request
If you are interested in working on this issue or have submitted a pull request, please leave a comment

Versions

terraform: 1.9.2
azure provider: 3.114
module: 6.0.0
Description
Describe the bug
I'm trying to deploy a custom management group hierarchy similar to es-lite. The Terraform plan gives those 2 errors:
Error: reading Policy Set Definition "Enforce-EncryptTransit_20240509": policy.SetDefinitionsClient#GetAtManagementGroup: Failure responding to request: StatusCode=404 -- Original Error: autorest/azure: Service returned an error. Status=404 Code="PolicySetDefinitionNotFound" Message="The policy set definition 'Enforce-EncryptTransit_20240509' could not be found."
Error: reading Policy Set Definition "Deploy-AUM-CheckUpdates": policy.SetDefinitionsClient#GetAtManagementGroup: Failure responding to request: StatusCode=404 -- Original Error: autorest/azure: Service returned an error. Status=404 Code="PolicySetDefinitionNotFound" Message="The policy set definition 'Deploy-AUM-CheckUpdates' could not be found."
Steps to Reproduce
main.tf
module "enterprise_scale" {
  source     = "Azure/caf-enterprise-scale/azurerm"
  version    = "6.0.0"

  default_location          = local.default_location
  deploy_core_landing_zones = false
  root_parent_id            = data.azurerm_client_config.core.tenant_id
  root_id                   = local.alz_root_id
  root_name                 = local.alz_root_name

  providers = {
    azurerm              = azurerm
    azurerm.connectivity = azurerm
    azurerm.management   = azurerm
  }

  archetype_config_overrides = {
    landing-zones = {
      archetype_id = "es_landing_zones"
      parameters = {
        Audit-AppGW-WAF = {
          effect = "Disabled"
        }
        Deny-IP-forwarding = {
          effect = "Audit"
        }
        Deny-MgmtPorts-Internet = {
          effect = "Audit"
        }
        Deploy-AzSqlDb-Auditing = {
          effect = "Audit"
        }
        Deploy-MDFC-DefSQL-AMA = {
          userWorkspaceResourceId = ""
        }
        Deploy-VM-Backup = {
          effect = "Disabled"
        }
        Deploy-VM-ChangeTrack = {
          effect = "Disabled"
        }
        Deploy-VM-Monitoring = {
          effect = "Audit"
        }
        Deploy-vmArc-ChangeTrack = {
          effect = "Disabled"
        }
        Deploy-vmHybr-Monitoring = {
          effect = "Disabled"
        }
        Deploy-VMSS-ChangeTrack = {
          effect = "Disabled"
        }
        Deploy-VMSS-Monitoring = {
          effect = "Audit"
        }
        Enable-DDoS-VNET = {
          effect = "Disabled"
        }
        Enforce-AKS-HTTPS = {
          effect = "Disabled"
        }
      }
      access_control = {}
    }
    platform = {
      archetype_id = "es_platform"
      parameters = {
        Deploy-VM-Backup = {
          effect = "Disabled"
        }
        Deploy-VM-ChangeTrack = {
          effect = "Disabled"
        }
        Deploy-VM-Monitoring = {
          effect = "Audit"
        }
        Deploy-vmArc-ChangeTrack = {
          effect = "Disabled"
        }
        Deploy-vmHybr-Monitoring = {
          effect = "Disabled"
        }
        Deploy-VMSS-ChangeTrack = {
          effect = "Disabled"
        }
        Deploy-VMSS-Monitoring = {
          effect = "Audit"
        }
      }
      access_control = {}
    }
  }

  custom_landing_zones = {
    "${local.alz_root_id}-platform" = {
      display_name               = "${upper(local.alz_root_name)} Platform"
      parent_management_group_id = local.alz_root_id
      subscription_ids           = []
      archetype_config = {
        archetype_id = "es_platform"
        parameters = {}
        access_control = {}
      }
    }
    "${local.alz_root_id}-platform-management" = {
      display_name               = "${upper(local.alz_root_name)} Platform Management"
      parent_management_group_id = "${local.alz_root_id}-platform"
      subscription_ids           = []
      archetype_config = {
        archetype_id   = "es_management"
        parameters     = {}
        access_control = {}
      }
    }
    "${local.alz_root_id}-landingzones" = {
      display_name               = "${upper(local.alz_root_name)} Landing Zones"
      parent_management_group_id = local.alz_root_id
      subscription_ids           = []
      archetype_config = {
        archetype_id = "es_landing_zones"
        parameters = {}
        access_control = {}
      }
    }
  }
}

It looks like the policy definitions are created on the management groups and will give an error when trying to use of the archetype without the associated management group. Is it possible to create all the policy and policy set definitions on the root management group that gets created?
Screenshots

Additional context

Have you tried creating the root manually as a custom MG and assigning the es_root archetype?  I think by disabling core landing zones, it also disables deploying root, so the initiatives you need aren’t available to the child MGs.


Incorrect number of item embeddings in sequence model

I'm kind of struggeling with calculating correct item-item-similarities with the sequence model.
What I try to do is the following: I fit the sequence model, then take the item_embeddings and run annoy to get item-similarities. This works fine with the factorization model.
However, with the sequence model, I cannot use 0 as an item-id as 0 is used by the to_sequence() method as a sequence padding value.
The number of items and therefore also the number of item_embeddings is determined by the maximum item-id + 1. If I start the item ids with 1 (to free 0 for the sequence padding), the model will assume that the number of items is 1 more than there actually is and create one more item_embeddings, which has no item reference. How can I handle this?

Off the top of my head, can you ignore the last embedding by slicing the embeddings array?

Ok, deleting the first item-embedding (index 0) does the trick. Thanks for the hint @maciejkula !


Do not create a new user if an IdpProfile exists.

Coverage increased (+0.04%) to 90.584% when pulling e89a411eb1aa78014b309b794152f7fabc8ac21b on akatsoulas:passwordless-registration into 3e2421cca4d75adb04a52e0126dba5b729ad1d43 on mozilla:master.


The result of the cascade operator is not expected

Consider the following example：
import 'package:vm_service_protos/vm_service_protos.dart';

void main() {
  var event = TrackEvent();
  var packet = TracePacket()
    ..trackEvent = event
    ..trackDescriptor = TrackDescriptor();

  assert(!event.isFrozen);
  assert(!packet.trackEvent.isFrozen);
}

The desired result is that the assertion succeeds.
But the current result is that:
dart --enable-asserts run example/issue.dart

Unhandled exception:
'file:///***/example/issue.dart': Failed assertion: line 10 pos 10: '!packet.trackEvent.isFrozen': is not true.
#0      _AssertionError._doThrowNew (dart:core-patch/errors_patch.dart:51:61)
#1      _AssertionError._throwNew (dart:core-patch/errors_patch.dart:40:5)
#2      main (file:///***/example/issue.dart:10:10)
#3      _delayEntrypointInvocation.<anonymous closure> (dart:isolate-patch/isolate_patch.dart:297:19)
#4      _RawReceivePort._handleMessage (dart:isolate-patch/isolate_patch.dart:184:12)

And when we remove the sixth line, the assertion succeeds again：
import 'package:vm_service_protos/vm_service_protos.dart';

void main() {
  var event = TrackEvent();
  var packet = TracePacket()
    ..trackEvent = event;

  assert(!event.isFrozen);
  assert(!packet.trackEvent.isFrozen);
}

dart info

If providing this information as part of reporting a bug, please review the information
below to ensure it only contains things you're comfortable posting publicly.

#### General info

- Dart 3.5.0-7.0.dev (dev) (Mon Apr 1 13:03:18 2024 -0700) on "macos_arm64"
- on macos / Version 14.1 (Build 23B74)
- locale is zh-Hans-CN

#### Process info

| Memory |  CPU | Elapsed time | Command line                                                                               |
| -----: | ---: | -----------: | ------------------------------------------------------------------------------------------ |
|  56 MB | 0.0% |        09:45 | dart devtools --machine --allow-embedding --dtd-uri ws:<path>/ZFkhxbMUwenWXcxb --port 9277 |
| 777 MB | 0.0% |        09:45 | dart language-server --protocol=lsp --client-id=VS-Code --client-version=3.85.20240327     |
|  32 MB | 0.0% |        09:45 | dart tooling-daemon --machine                                                              |
|  78 MB | 0.0% |        09:45 | flutter_tools.snapshot daemon                                                              |

I believe this is working as intended. It's unrelated to cascades, you'll get the same behavior from
var package = TracePacket();
package.trackEvent = event;
package.trackDescriptor = TrackDescriptor();

The reason the packet.trackEvent is frozen is that trackEvent and trackDescriptor are part of the same "one of" group, which means at most one of them can be set at any time. When you set trackDescriptor, it clears trackEvent.
When you then read trackEvent, it gives you a default value instead, since it can't return null, and that default value is a fresh frozen TrackEvent.
You can check this by adding:
 assert(!packet.hasTrackEvent());

and see that that assert succeeds. (Really should be a getter!)

@lrhn Thank you very much for your answer, and I have learned new knowledge about "one of" here.


Adding Signal Receiver to Flow Update

Fixes #3000

Thanks for this PR! Is there an easy way to test this bugfix / new behaviour?
Unfortunately the current test suite is not very exhaustive for urwid-related things - but we kinda want to not introduce more untested code.

@Kriechi  I searched on it but could not find a test for this. I guess we will have to wait untill some better support is provided by urwid.


when I config timeout value in yaml ,but this value was set in connect_timeout

Hello !!
when I config timeout value in yaml ,like :

spring:
redis:
host: <IP_ADDRESS>
port: 6379
timeout: PT40S
connect-timeout: PT50S

but when run Redisson.create, i found
 config.useSingleServer()
                .setAddress(prefix + redisProperties.getHost() + ":" + redisProperties.getPort())
                .setConnectTimeout(timeout)
                .setDatabase(redisProperties.getDatabase())
                .setPassword(redisProperties.getPassword());

timeout value is  40000
as we all know ,config has attributes like timeout, i don not know why use timeout value set into connect_timeout
version : 'org.redisson:redisson-spring-boot-starter:3.16.3'

Fixed in https://github.com/redisson/redisson/issues/4826


miniCPM run benchmark get error in iGPU

log:
python run.py
C:\Users\mi\miniconda3\lib\site-packages\torchvision\io\image.py:13: UserWarning: Failed to load image Python extension: ''If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
2024-05-21 20:54:40,883 - INFO - intel_extension_for_pytorch auto imported
C:\Users\mi\miniconda3\lib\site-packages\transformers\deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
  warnings.warn(
C:\Users\mi\miniconda3\lib\site-packages\torch\_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()
2024-05-21 20:54:46,922 - INFO - Converting the current model to sym_int4 format......
>> loading of model costs 19.866792400000122s and 2.544921875GB
<class 'transformers_modules.MiniCPM-2B-dpo-bf16.modeling_minicpm.MiniCPMForCausalLM'>
C:\Users\mi\miniconda3\lib\site-packages\transformers\generation\configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.8` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
C:\Users\mi\miniconda3\lib\site-packages\transformers\generation\configuration_utils.py:520: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.8` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(
2024-05-21 20:55:01,157 - WARNING - The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
2024-05-21 20:55:01,157 - WARNING - Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
Assertion failed: nb % SBS == 0, file dequantize.cpp, line 23

version:
ipex-llm                                2.1.0b20240519
intel-extension-for-pytorch             2.1.20+git4849f3b
pytorch-lightning                       2.2.2
pytorch-wpe                             0.0.1
rotary-embedding-torch                  0.5.3
torch                                   2.1.0a0+git7bcf7da
torch-complex                           0.4.3
torchaudio                              2.1.0+6ea1133
torchmetrics                            1.3.2
torchvision                             0.16.0a0+cxx11.abi

model:
MiniCPM-2B-dpo-fp32
MiniCPM-2B-dpo-bf16

Solved in 20240523 nightly build. Thanks


ERROR: Unable to find compatible target in system image.

This worked for me first time, but now gives me the following immediately:
julia> using PackageCompilerX
julia> create_sysimage(:ObjectDetector, sysimage_path="ObjectDetector.so")
ERROR: Unable to find compatible target in system image.

julia 1.3.0
I've also been trying out compile_incremental from PackageCompiler so maybe that's broken something?

Make sure to try with the default sysimage (if it has been replaced by PackageCompiler, re-download julia). And also, you can run with ENV["JULIA_DEBUG"] = "PackageCompilerX to get some debug outputs that might be useful.

Re-downloading julia fixed this. I guess revert() didn't fully revert back the sysimage.
Although my precompile statement functions are failing to load, while they run successfully when manually running precompile.jl.
[ Info: PackageCompilerX: creating system image object file, this might take a while...
┌ Error: failed to execute ObjectDetector.YOLO.v2_tiny_416_COCO(silent=true)
└ @ Main.anonymous none:24
┌ Error: failed to execute ObjectDetector.YOLO.v3_320_COCO(silent=true)
└ @ Main.anonymous none:24
┌ Error: failed to execute ObjectDetector.YOLO.v3_416_COCO(silent=true)
└ @ Main.anonymous none:24
┌ Error: failed to execute ObjectDetector.YOLO.v3_608_COCO(silent=true)
└ @ Main.anonymous none:24
┌ Error: failed to execute ObjectDetector.YOLO.v3_tiny_416_COCO(silent=true)
└ @ Main.anonymous none:24

Changing to silent=false shows that the functions get half-way through then crash (but I want the silent ones to be precompiled)
https://github.com/r3tex/ObjectDetector.jl/tree/packagecompilation/dev/compilation

I must have done a bad search and replace in the docs. https://github.com/KristofferC/PackageCompilerX.jl/commit/7766c7dcfa41f0029f2e8bfb0007dcabff10a4ad fixes that and with https://github.com/r3tex/ObjectDetector.jl/pull/20 I get a sysimage that works.
There are some precompilation statements that don't work from Zygote (might be because Zygote does some crazy stuff) but with the sysimage I get
julia> using ObjectDetector

julia> @time ObjectDetector.YOLO.v2_tiny_416_COCO(silent=true)
  1.692572 seconds (2.48 M allocations: 348.008 MiB, 7.20% gc time)
YOLO v2. Trained with DarkNet 0.2.0
WxH: 416x416   channels: 3   batchsize: 1
gridsize: 13x13   classes: 80   thresholds: Detect 0.2. Overlap 0.0

vs
julia> @time ObjectDetector.YOLO.v2_tiny_416_COCO(silent=true)
 17.446907 seconds (65.42 M allocations: 3.456 GiB, 4.58% gc time)
YOLO v2. Trained with DarkNet 0.2.0
WxH: 416x416   channels: 3   batchsize: 1
gridsize: 13x13   classes: 80   thresholds: Detect 0.2. Overlap 0.0

with default sysimage.

awesome! Thanks for fixing, testing and PR-ing!
It's great news that this works


Change network mode to l2tunnel by default for windows

Currently the default mode for the network in case of windows is
l2bridge. Changing it to l2tunnel by default with this change.

@saiyan86 I am running the aks-engine test suit from jenkins with this PR. I'll update here once that completes.


got the TypeError: Cannot read properties of undefined (reading 'id')

Thanks for this lib!
in the array, each object has an id field and it is unique.
item-key="id" specified
why can it be?

the problem was that I did not give the types of ts. as a result, the state was updated incorrectly.


KAFKA-7855: [WIP] Kafka Streams Maven Archetype quickstart fails to compile out of the box

In the LineSplit.java example, the untyped KStream is resolved to KStream<Object, Object>. value.split(..) then  fails to build, because value is of type Object.
Other possible solutions would be to add the type to the builder:
builder.<String, String>stream(...).flatmap(..).to(...), or builder.stream(..., Consumed.with(Serdes.String(), Serdes.String())).
This change matches the code in the tutorial.
WIP: It should be tested during compile that the Java resource files compiles. However, I haven't found any way to achieve this. Feedback appreciated!
Committer Checklist (excluded from commit message)

[x] Verify design and implementation
[ ] Verify test coverage and CI build status
[ ] Verify documentation (including upgrade notes)

@guozhangwang I am wondering why this was not detected via tests. Seems we don't have a test pipeline for this. I am not sure atm how hard it would be to get test coverage for this? Thoughts?

It is a bit hard.. note for architype project, it declares the streams jar as external dependency, so to test this we need to have a unit test that takes in the streams jar. Maybe we can have a system test style case, like StreamsUpgradeTest but it should not be part of o.a.k. package.

@guozhangwang What is the conclusion? We skip adding a test and merge?

Yeah although it is not ideal I felt it is very hard, if possible to add a test covering similar issues as part of AK, since it would involve adding a new procedure as part of unit test that 1) download an artifact, 2)  mvn compile on that artifact. We can probably add a script doing so and add it as part of a jenkins job, but would hard to be part of ./gradlew test cmd.

Thanks for the feedback! And sorry for the late reply, for some reason this conversation ended in my spam folder.
If you prefer, I can add the type to the builder - I see your point about the educational value.

cc @mjsax for final reviews.

I just tested this locally. It's does still not compile, because we also need to import KStream.
I leave it up to you how you fix it -- either add the import or get rid of KStream and add the type to the builder.

@aurlien Any update on this?

I will fix it tomorrow!

@mjsax I updated to use your recommended builder.<String, String> syntax.
However, I couldn't get the previous solution to fail, when compiling this project. I ran > mvn clean compile install -Dgpg.skip in the kafka/streams/quickstart/java directory. Am i doing something wrong?

The command you execute, creates the archetype. It only fails if you use the archetype, ie, you need to do a second step:
mvn archetype:generate \
    -DarchetypeCatalog=local \
    -DarchetypeGroupId=org.apache.kafka \
    -DarchetypeArtifactId=streams-quickstart-java \
    -DarchetypeVersion=2.3.0-SNAPSHOT \
    -DgroupId=streams.examples \
    -DartifactId=streams.examples \
    -Dversion=0.1 \
    -Dpackage=myapps

cd streams.examples

mvn compile

The creates a new project based on the archetype and you cannot compile this project.

Java8 Passed. Java11 failed with flaky tests.
Retest this please.

Thanks for the fix @aurlien!

Merged to trunk and cherry-picked to 2.2, 2.1, and 2.0.


For a 0.17.1 release

Codecov Report

Merging #510 (19d7fcf) into master (a2eee18) will decrease coverage by 0.54%.
The diff coverage is 50.00%.

@@            Coverage Diff             @@
##           master     #510      +/-   ##
==========================================
- Coverage   83.04%   82.49%   -0.55%
==========================================
  Files          37       37
  Lines        2914     2891      -23
==========================================
- Hits         2420     2385      -35
- Misses        494      506      +12

Impacted Files
Coverage Δ

src/MLJBase.jl
100.00% <ø> (ø)

src/resampling.jl
85.26% <50.00%> (-0.66%)
:arrow_down:

src/sources.jl
78.37% <0.00%> (-5.84%)
:arrow_down:

src/composition/models/inspection.jl
95.23% <0.00%> (-4.77%)
:arrow_down:

src/data/datasets.jl
86.11% <0.00%> (-2.78%)
:arrow_down:

src/measures/continuous.jl
97.36% <0.00%> (-2.64%)
:arrow_down:

src/measures/finite.jl
92.97% <0.00%> (-1.94%)
:arrow_down:

src/composition/learning_networks/inspection.jl
81.81% <0.00%> (-0.80%)
:arrow_down:

src/composition/learning_networks/nodes.jl
67.66% <0.00%> (-0.48%)
:arrow_down:

src/hyperparam/one_dimensional_range_methods.jl
91.76% <0.00%> (-0.38%)
:arrow_down:

... and 7 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update a2eee18...19d7fcf. Read the comment docs.

Codecov Report

Merging #510 (19d7fcf) into master (a2eee18) will decrease coverage by 0.54%.
The diff coverage is 50.00%.

@@            Coverage Diff             @@
##           master     #510      +/-   ##
==========================================
- Coverage   83.04%   82.49%   -0.55%
==========================================
  Files          37       37
  Lines        2914     2891      -23
==========================================
- Hits         2420     2385      -35
- Misses        494      506      +12

Impacted Files
Coverage Δ

src/MLJBase.jl
100.00% <ø> (ø)

src/resampling.jl
85.26% <50.00%> (-0.66%)
:arrow_down:

src/sources.jl
78.37% <0.00%> (-5.84%)
:arrow_down:

src/composition/models/inspection.jl
95.23% <0.00%> (-4.77%)
:arrow_down:

src/data/datasets.jl
86.11% <0.00%> (-2.78%)
:arrow_down:

src/measures/continuous.jl
97.36% <0.00%> (-2.64%)
:arrow_down:

src/measures/finite.jl
92.97% <0.00%> (-1.94%)
:arrow_down:

src/composition/learning_networks/inspection.jl
81.81% <0.00%> (-0.80%)
:arrow_down:

src/composition/learning_networks/nodes.jl
67.66% <0.00%> (-0.48%)
:arrow_down:

src/hyperparam/one_dimensional_range_methods.jl
91.76% <0.00%> (-0.38%)
:arrow_down:

... and 7 more

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Meshlet fill cluster buffers rewritten

Objective

Make the meshlet fill cluster buffers pass slightly faster
Fix https://github.com/bevyengine/bevy/issues/15920 for meshlets
Added PreviousGlobalTransform as a required meshlet component to avoid extra archetype moves, slightly alleviating https://github.com/bevyengine/bevy/issues/14681 for meshlets
Enforce that MeshletPlugin::cluster_buffer_slots is not greater than 2^25 (glitches will occur otherwise). Technically this field controls post-lod/culling cluster count, and the issue is on pre-lod/culling cluster count, but it's still valid now, and in the future this will be more true.

Needs to be merged after https://github.com/bevyengine/bevy/pull/15846 and https://github.com/bevyengine/bevy/pull/15886
Solution

Old pass dispatched a thread per cluster, and did a binary search over the instances to find which instance the cluster belongs to, and what meshlet index within the instance it is.
New pass dispatches a workgroup per instance, and has the workgroup loop over all meshlets in the instance in order to write out the cluster data.
Use a push constant instead of arrayLength to fix the linked bug
Remap 1d->2d dispatch for software raster only if actually needed to save on spawning excess workgroups

Testing

Did you test these changes? If so, how?

Ran the meshlet example, and an example with 1041 instances of 32217 meshlets per instance. Profiled the second scene with nsight, went from 0.55ms -> 0.40ms. Small savings. We're pretty much VRAM bandwidth bound at this point.

How can other people (reviewers) test your changes? Is there anything specific they need to know?

Run the meshlet example

Changelog (non-meshlets)

PreviousGlobalTransform now implements the Default trait

This isn't a full fix for #15955, right? It only tackles the meshlet part of the problem.

Yeah. Only meshlets.

For further clarification, while working on this PR I discovered #15955. This PR fixes the bug for meshlets (by removing the code that caused it, as it's no longer needed). But we'll need a separate PR for an actual fix for the issue.

Perfect, thanks for the clarification. I've edited your PR description to make sure it doesn't accidentally get clobbered :)


DocVQA Code Release

Hi Minesh,
The DocVQA Paper & the challenge is very interesting work. It's a great step in the document understanding. In the paper, you mention the code is released on the RRC portal. However, I couldn't find any. Could you please help point out where the codebase is?
Best Regards
Tushar Jain
PS: I tried reaching out to you via your email address, however, you're "over quota" & unable to receive emails.

The baselines code using BERT will be made available in two day's time.
adding it here https://github.com/mineshmathew/DocVQA


enh: add screenshots

It would be nice to have some screenshots on the main README page.

Thank you for the suggestion, I have added some screenshots at the bottom of the README page.

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Hey can you make a version that is compatible with twilightmenu++?

How do I get this working on my DSi. I am using Twilight menu++ I also tried running it on DESmume but an error came up saying the the SDcard was missing or it was not patched. On the DSi I tried running it but it just showing a black screen.


document co-run zookeeper and bookie on same machine

Motivation
Issue: https://github.com/apache/pulsar/issues/3213
Modifications
update the readme to highlight this part
Result
After your change, what will change.

run integration tests


[spl-token-2022]: Allow serialisation/deserialisation for token-2022 extensions

Hey guys,  I am planning to add support for token-2022 indexing. In order to do this, I would like to have all the extensions support serialising/de-serialising. Is it ok if I create a PR to support this via the serde-trait feature-flag? Is there any reason not to do this?

Sounds good! Thanks!

Is there a way to get this in having solana-1.16 dependency? I see that the latest code requires 1.17?

Where do you see that requirement?
https://github.com/solana-labs/solana-program-library/blob/d97f735de7d435ef2207f20b6efd8631cefac345/token/program-2022/Cargo.toml#L26

When I specify the path locally
spl-token-2022 =  { path = "../../../solana/solana-program-library/token/program-2022" }

I see this error

all possible versions conflict with previously selected packages.

  previously selected package `solana-program v1.17.0`
    ... which satisfies dependency `solana-program = "=1.17.0"` of package `solana-sdk v1.17.0`
    ... which satisfies dependency `solana-sdk = "=1.17.0"` of package `solana-zk-token-sdk v1.17.0`
    ... which satisfies dependency `solana-zk-token-sdk = "^1.16.13"` of package `spl-pod v0.1.0 (/Users/nikhil/helius/solana/solana-program-library/libraries/pod)`
    ... which satisfies path dependency `spl-pod` of package `spl-tlv-account-resolution v0.4.0 (/Users/nikhil/helius/solana/solana-program-library/libraries/tlv-account-resolution)`
    ... which satisfies path dependency `spl-tlv-account-resolution` of package `spl-transfer-hook-interface v0.3.0 (/Users/nikhil/helius/solana/solana-program-library/token/transfer-hook-interface)`
    ... which satisfies path dependency `spl-transfer-hook-interface` of package `spl-token-2022 v0.9.0 (/Users/nikhil/helius/solana/solana-program-library/token/program-2022)`

The root of this conflict is that solana-zk-token-sdk v1.17.0 depends on solana-sdk v1.17.0, which in turn depends on solana-program v1.17.0.

Ah nuts. We'd have to go pin a lot of stuff to <1.17.0 and we don't typically do that in SPL unless there's a major problem. Are you guys unable to use 1.17.0?

Probably not. We're still in between upgrading to 1.16 and that's a heavy lift for us on our infra. Don't think we can use 1.17 now.
I did create a PR. Is there a way to hotfix it to an older release so we can use it with 1.16? Other alternate is to shadow all the extension interfaces on our codebase temporarily and use that to index the extensions till we upgrade to 1.17 but I would like to avoid this if possible.

Yes, we're hard-fixing to 1.16.15. Right now, we're using spl-token22-0.9. Will we get a release for the PR? Any idea when we can get it in?

bump @buffalojoec

Adding more serde support shouldn't be a breaking change, so I don't think we'll do a minor release. We'll probably just do a patch release.


Keep alive connectionstream

[One line description of your change]
Motivation:

Added KeepAlive support

Modifications:

Added support for starting a background task that sends KeepAlive frames at regular intervals
Added support to receive KeepAlive frames
Terminate KeepAlive background task if it doesn't hear back before maxLifetime is exceeded
Haven't added logic to store lastReceivedPosition

Result:

A connection that is opened must stay up with KeepAlive frames being exchanged between peers

Pushed latest changes. keepAlive will only be started from client end now. In ConnectionStreamHandler(), keepAlive task is started in channelActive() only when the SetupFrame is received in channelRead(). This works currently since ChannelActive() is not called up the chain when some of the intermediate handlers are removed. With this change, I feel we need a check like isKeepAlive started to make sure we don't have duplicate tasks started inadvertently (E.g. if context.fireChannelActive() is propagated up the stack)
One of the performance test is failing on my local machine. It has never run on my local and I see there are few changes in that test recently. I'll hold off on modifications until these changes are merged.

@dnadoba thanks for your help this morning. I made the changes and pushed the latest. I couldn't get the timeout test to work since the channel exits before I can read from it. Could you please take a look at what I might be missing? Thanks.


clarify match-filter LHS

Template

Before submitting a pull request make sure you have:

[x] At least skimmed through contributing guidelines including yt-dlp coding conventions
[ ] Searched the bugtracker for similar pull requests
[ ] Checked the code with flake8 and ran relevant tests

In order to be accepted and merged into yt-dlp each piece of code must be in public domain or released under Unlicense. Check one of the following options:

[x] I am the original author of this code and I am willing to release it under Unlicense
[ ] I am not the original author of this code but it is in public domain or released under Unlicense (provide reliable evidence)

What is the purpose of your pull request?

[ ] Fix or improvement to an extractor (Make sure to add/update tests)
[ ] New extractor (Piracy websites will not be accepted)
[ ] Core bug fix/improvement
[ ] New feature (It is strongly recommended to open an issue first)

Description of your pull request and other information

DESCRIPTION
Fixes #4263

This part of the docs is auto-generated from options.py. You should make the relevent change there and then run make Readme.md
I think https://github.com/yt-dlp/yt-dlp#filtering-formats is a better place to document this

#filtering-formats is a better place to document this

ahh I wouldn't have looked there if it wasn't working for me

#filtering-formats is a better place to document this

ahh I wouldn't have looked there if it wasn't working for me

Why? That is where the operators are documented


Built-in script isn't saved after it was edited

Godot version:
master
OS/device including version:
ubuntu 17.10
Issue description:
Scene built-in script isn't saved when:

you press Play(F5)
you press Play Scene(F6)
you Save All Scenes
you Save All (scripts)

It is saved when:

you Save Scene

Steps to reproduce:
Create a new project
Add a node
Add a built-in script
Run the project
Edit the built-in script
Run the project again
Comments
It seems to work in 3.0.2

It is indeed a duplicate, but there are some helpful info here that aren't in the original issue. It'd be much appreciated if you would copy them to the original issue. I'd close this for now. Thanks a lot.


fix: add null default to search handler in AbstractEfCustomFieldSearchService

friendly reminder: we will need a version upgrade. I didn't do it for this: #94 , because this PR was also open.


⚠ SceneTime has degraded performance

In 0e16853, SceneTime ($ST) experienced degraded performance:

HTTP code: 200
Response time: 1401 ms

Resolved: SceneTime performance has improved in 6ff5135 after 18 minutes.


Code for current version of Web interface

Hi,
Silly question, but where is the code for the current version of the Web interface? Haven't been able to find it.
Thanks!

Hi !
I think you're looking for this : https://github.com/meilisearch/MeiliSearch/blob/release-v0.20.0/meilisearch-http/public/interface.html
With the v0.21, this file has been removed, and the old dashboard replaced by this one.
Don't hesitate if you have any other question 😀

awesome, thank you:)


Let the user specify the subset of cbmc-batch jobs to be run

Additionally, check for cbmc, cbmc-batch and aws. Plus minor fixes.
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

@justinboswell When I click on "Details" next to any of the CBMC Batch tests in the code checks above, the S3 bucket I'm directed to is access restricted. It would be great if I can get access to those S3 buckets. I'm interested in looking at the .goto files that are generated in there.

@justinboswell It would be very useful for me if I can get access to the s3 bucket in which the regression test results of this PR are stored. For example, following the link from the checks reported above, one of them is
https://s3.console.aws.amazon.com/s3/buckets/567564403995-us-west-2-proofs/aws_array_list_clear-20200309-171543/out/
When I try to open it from my AWS account, I'm denied access. How can I get access to it?

This is a limitation of CodeBuild at present, unfortunately, it does not allow public access to build logs, and the CBMC runner mirrors that policy. We've raised the issue internally, and it's being worked on. I will forward this on to the internal team that manages CBMC for us and see if they can do anything with that S3 bucket.


Simplify conda env yaml files and combine into single env

Summary of changes

Add prod.yml

This file is a combination of proc.yml with pub.yml
Add pip section with esgcet>=5.2.0
Remove autocurator as a dependency

Replaced by esgcet>=5.2.0 with the -xarray flag
Closes #143

Update dependencies using looser constraints

Remove pub.yml and proc.yml
Remove ci.yml -- this repo has no GitHub Actions CI/CD workflows to run builds

Action Items

[ ] Build single datasm environment using prod.yml (works on my end -- Tom)
[ ] Add or remove dependencies based on needs of datasm
[ ] Try publishing with esgcet>=5.2.0 and the -xarray command which replaces autocurator

@chengzhuzhang Do you feel it is OK to test the resulting "prod" environment with a publication?  Nothing (at present) is publication-authorized, and (AFAIK) the only way to test egspublish is to publish ;).  I can retract/delete an E3SM set (and only retract a CMIP6 set).
@tomvothecoder
I have built a new environment based upon the prod.yml.   It includes "- e3sm_to_cmip >=1.9.1", but issuing
e3sm_to_cmip --version
produces
Traceback (most recent call last):
  File "/home/bartoletti1/mambaforge/envs/dsm_prod/bin/e3sm_to_cmip", line 6, in <module>
    from e3sm_to_cmip.__main__ import main
  File "/home/bartoletti1/mambaforge/envs/dsm_prod/lib/python3.10/site-packages/e3sm_to_cmip/__main__.py", line 35, in <module>
    np.warnings.filterwarnings("ignore")
  File "/home/bartoletti1/mambaforge/envs/dsm_prod/lib/python3.10/site-packages/numpy/__init__.py", line 328, in __getattr__
    raise AttributeError("module {!r} has no attribute "
AttributeError: module 'numpy' has no attribute 'warnings'. Did you mean: 'hanning'?

Also, "pip install ."(datasm) returns:
ERROR: Package 'datasm' requires a different Python: 3.10.12 not in '<3.10,>=3.8'  (how do I investigate?)
Finally, if I "pip install ."(e3sm_to_cmip), to obtain v1.10.0rc1, the e2c version works:
Successfully installed e3sm-to-cmip-1.10.0rc1
(dsm_prod) -bash-4.2$ e3sm_to_cmip --version
e3sm_to_cmip 1.10.0rc1

@tomvothecoder @chengzhuzhang
Datasm has a "setup.py" (written by Sterling I suppose) that I have never knowingly invoked, and it appears to be the source of the error Python: 3.10.12 not in '<3.10,>=3.8'
setup(
    author="datasm developers",
    python_requires=">=3.8,<3.10",
    classifiers=[
        "Development Status :: 3 - Alpha",
        "Intended Audience :: Developers",
        "License :: OSI Approved :: MIT License",
        "Natural Language :: English",
        "Programming Language :: Python :: 3",
        "Programming Language :: Python :: 3.8",
    ],
    description="A cli utility to automate complex nested workflows for publishing E3SM data to ESGF.",
    license="MIT License",
    include_package_data=True,
    keywords=["datasm"],
    name="datasm",
    packages=find_packages(include=["datasm", "datasm.*"]),
    package_dir={"datasm": "datasm"},
    entry_points={
        "console_scripts": [
            "datasm = datasm.__main__:main",
        ],
    },
    test_suite="tests",
    # TODO: Update this URL once the repo is renamed.
    url="https://github.com/E3SM-Project/esgfpub",
    version="0.1.0",
    zip_safe=False,
    cmdclass={
        "clean": CleanCommand,
    },
)

I assume pip reads this file.  (# TODO -lol)
Can I simply change the line python_requires=">=3.8,<3.10", or must I recalculate these limits somehow?

@tomvothecoder @chengzhuzhang
Datasm has a "setup.py" (written by Sterling I suppose) that I have never knowingly invoked, and it appears to be the source of the error Python: 3.10.12 not in '<3.10,>=3.8'
setup(
    author="datasm developers",
    python_requires=">=3.8,<3.10",
    classifiers=[
        "Development Status :: 3 - Alpha",
        "Intended Audience :: Developers",
        "License :: OSI Approved :: MIT License",
        "Natural Language :: English",
        "Programming Language :: Python :: 3",
        "Programming Language :: Python :: 3.8",
    ],
    description="A cli utility to automate complex nested workflows for publishing E3SM data to ESGF.",
    license="MIT License",
    include_package_data=True,
    keywords=["datasm"],
    name="datasm",
    packages=find_packages(include=["datasm", "datasm.*"]),
    package_dir={"datasm": "datasm"},
    entry_points={
        "console_scripts": [
            "datasm = datasm.__main__:main",
        ],
    },
    test_suite="tests",
    # TODO: Update this URL once the repo is renamed.
    url="https://github.com/E3SM-Project/esgfpub",
    version="0.1.0",
    zip_safe=False,
    cmdclass={
        "clean": CleanCommand,
    },
)

I added a setup.py awhile ago for the chance that we make datasm a Python package hosted on Anaconda. I don't think we are going down that path though.

I assume pip reads this file. (# TODO -lol)

Yes, pip reads this file when you invoke pip install . (Anaconda also uses this file to build conda packages).

Can I simply change the line python_requires=">=3.8,<3.10", or must I recalculate these limits somehow?

I set the constraint to python_requires=">=3.8,<3.10" based on the limitations of the conda proc.yml and pub.yml environments.
The line should be updated to python_requires=">=3.9". The new conda environment (prod.yml) has much more flexibility with the Python version now. We want Python >=3.9 because Python 3.8 is being phased out.

@tomvothecoder  Thanks Tom!  I've managed a conda/mamba build (and datasm_install) having made the adjustments.  (Full-up testing is another matter, but will  require a bit more time.)  ASIDE:  When I recently did "pip install", I added "-vvv" to get more information, and was surprised to see a syntax error in a module that had long ago been "git removed".  I poked around and found that "build" and "datasm-egg-info" listed old files.  What process is supposed to update these?

@tomvothecoder Thanks Tom! I've managed a conda/mamba build (and datasm_install) having made the adjustments. (Full-up testing is another matter, but will require a bit more time.) ASIDE: When I recently did "pip install", I added "-vvv" to get more information, and was surprised to see a syntax error in a module that had long ago been "git removed". I poked around and found that "build" and "datasm-egg-info" listed old files. What process is supposed to update these?

I have not encountered the issue of old files being present in those directories so I'm not exactly sure how they are handled. I think they usually get overwritten after running pip install ..
Are those directories in the root of the datasm repo and generated after running pip install .? If so, you can probably safely delete them.

From email chain 9/11/23:
Tony received this error when building and using the prod.yml file:

Traceback (most recent call last):
  File "/home/bartoletti1/mambaforge/envs/dsm_prod_test2/bin/esgpublish", line 8, in <module>
    sys.exit(main())
  File "/home/bartoletti1/mambaforge/envs/dsm_prod_test2/lib/python3.10/site-packages/esgcet/pub_internal.py", line 83, in main
    pub = pub_args.get_args()
  File "/home/bartoletti1/mambaforge/envs/dsm_prod_test2/lib/python3.10/site-packages/esgcet/args.py", line 46, in get_args
    parser.add_argument("--version", action="version", version=f"esgpublish v{esgcet.__version__}",help="Print the version and exit")
NameError: name 'esgcet' is not defined

For what its worth,  my environment (dsm_prod_test2) lists this esgcet:

(dsm_prod_test2) -bash-4.2$ mamba list | grep esgcet
esgcet                    5.2.0                    pypi_0    pypi

Sasha replied that the esgcet/args.py is missing an import statement.

Great, this is a big W and should make your life easier.
Thanks @TonyB9000.

Just a side note - during publication of the v2 Large Ensemble datasets, the publisher balked at the CFmon variables.  Sasha recognized the problem and was to address it - but it necessitated reverting to the "pub-only" environment (older publisher), albeit only for the CFmon publications.  I am ignorant of the range of specific checks that the publisher makes during publication.

@TonyB9000 could you submit an issue to https://github.com/ESGF/esg-publisher so that this won't slip?

@chengzhuzhang Done (Issue #226)


Add release for Apple Silicon

# electron-builder.yml

mac:
  target:
    - target: dmg
      arch:
        - x64
        - arm64

This configuration didn't work when building the app on Github Actions, my very uninformed guess is because in release.yml we specify macos-latest as the matrix.os strategy and that only applies to the x86 version.
Reopening it because it's not fixed.

https://github.com/fmaclen/canutin-desktop/pull/216


Fixed self-send display issue

removed unnecessary spacing
parsed the values of the UTXO incase we have multiple matches. we were concatenating the values and not adding them.

Neat, merging!


Remove version: Jackett.Jackett version 0.20.3160

404 - release deleted
Microsoft Reviewers: Open in CodeFlow

URL: https://github.com/Jackett/Jackett/releases/download/v0.20.3160/Jackett.Installer.Windows.exe
Response Error: The remote server returned an error: (404) Not Found.
(Automated message - build 918)


QML software, running filament, if it runs for a long time, such as night, the software will crash

This bug report doesn't follow the issue template and doesn't have repro steps nor any actionable information.


Valid is false

I tried to run your code. and it doesn't work for me:

Valid is false
publicKey isn't used.

Can you help me with first? Thanks

I found a solution. It needs to do some changes in KeyFunc for jwt.Parse:

Change a signing method to jwt.SigningMethodRSA.
This function has to return a pointer to public key.

Just for information.
if _, ok := token.Method.(*jwt.SigningMethodRSA); !ok {
	return nil, fmt.Errorf("There was an error")
}
return &signKey.PublicKey, nil


Add $Glizzy Gun collection

Added meta.json and inscriptions.json files for $Glizzy Gun collection.

Please don't use a website link as icon. It should be a link of an image. You may also use one of your inscription as icon. But the code should be change to "inscription_icon" @IBW3B

Thanks! Juste fixed.


buffer: add log for time periods of restored chunks which may be broken

Which issue(s) this PR fixes:
Partial fix for #3970
What this PR does / why we need it:
Add logs of possible time periods of buffer corruption during buffer resumimg.
When one of the remaining chunk files is broken at the resuming, this logs created_at and modified_at of other files undetected.
Since a broken chunk file was found, it is possible that other files remaining at the time of resuming were also broken. Here is the list of the files.
  /path/buffer.bxxxx.log: created_at=2023-01-26 18:08:16 +0900 modified_at=2023-01-26 18:08:17 +0900
  /path/buffer.qxxxx.log: created_at=2023-01-26 18:08:16 +0900 modified_at=2023-01-26 18:08:17 +0900
  {...}

Fluentd can detect the corruption when the meta-data is broken, but can't when the chunk file is broken.
So if one corruption is detected, we should assume that other files may also be broken.
This allows us to know other possible time periods of corruption.
Docs Changes:
Not needed.
Release Note:
Add logs for time period of restored buffer possibly broken.
How to Reproduce

Prepare a config:

<source>
  @type sample
  tag test.a
</source>

<source>
  @type sample
  tag test.b
</source>

<source>
  @type sample
  tag test.c
</source>

<match test.**>
  @type file
  path /test/fluentd/log/${tag}/%Y%m%d/fluentd.log
  append true
  add_path_suffix false
  <buffer time,tag>
    @type file
    path /test/fluentd/buffer
    timekey 24h
    flush_mode interval
    flush_interval 10s
    overflow_action drop_oldest_chunk
  </buffer>
</match>

Start the fluentd, and you can get 3 chunk files.
Stop the fluentd so that the 3 files remain.
Break the one of meta data:

$ head -c 89 /dev/zero > buffer.b5f32716d72fc5d71592aa89c5865fd48.log.meta

Restart the fluentd, and you can see the following logs:

[info]: #0 fluent/log.rb:330:info: starting fluentd worker pid=920781 ppid=920761 worker=0
[debug]: #0 fluent/log.rb:309:debug: restoring buffer file: path = /test/fluentd/buffer/buffer.b5f32716d7292f8138b36fd759abf7207.log
[debug]: #0 fluent/log.rb:309:debug: restoring buffer file: path = /test/fluentd/buffer/buffer.b5f32716d72fc5d71592aa89c5865fd48.log
[error]: #0 fluent/log.rb:372:error: found broken chunk file during resume. Deleted corresponding files: path="/test/fluentd/buffer/buffer.b5f32716d72fc5d71592aa89c5865fd48.log" mode=:staged err_msg="staged meta file is broken. no implicit conversion of Symbol into Integer"
[debug]: #0 fluent/log.rb:309:debug: restoring buffer file: path = /test/fluentd/buffer/buffer.b5f32716d734618fef772d3ae48fd577a.log
[info]: #0 fluent/log.rb:330:info: Since a broken chunk file was found, it is possible that other files remaining at the time of resuming were also broken. Here is the list of the files.
[info]: #0 fluent/log.rb:330:info:   /test/fluentd/buffer/buffer.b5f32716d7292f8138b36fd759abf7207.log: created_at=2023-01-26 18:08:16 +0900 modified_at=2023-01-26 18:08:17 +0900
[info]: #0 fluent/log.rb:330:info:   /test/fluentd/buffer/buffer.b5f32716d734618fef772d3ae48fd577a.log: created_at=2023-01-26 18:08:16 +0900 modified_at=2023-01-26 18:08:17 +0900
[debug]: #0 fluent/log.rb:309:debug: buffer started instance=3000 stage_size=216 queue_size=0
[info]: #0 fluent/log.rb:330:info: fluentd worker is now running worker=0

This allows us to know other possible time periods of corruption.

Thanks for merging!


feat(js): add updateText api that calc text diff automatically

Could this be implemented from Rust side? From what I could get I would need to manually provide the diffs to populate the history for loro, kinda difficult to use.

Yes. I'll provide that in the Rust Loro crate

This is simple enough where I would argue it should be an example in the documentation rather than a built-in solution.
A separate package with this example could also be made for convenience but that is IMO not required for the 5 lines of code that it takes to implement.

There are two downsides with including this in the core packages directly:

it increases the package size (which matters more on web)
there are different diffing algorithms with various trade-offs (e.g. histogram diff or, for code, something like https://github.com/Wilfred/difftastic could be more appropriate)

Problem 1 is solvable on the web by relying on tree-shaking, but this is relatively fragile as the optimization is hard to test for and enforce. If the implementation is moved to Rust, the package size problem is solvable in Cargo using optional features, but I'm not sure how that would play with WebAssembly builds. Tree-shaking does not work with WebAssembly as the JS bundlers can't see into the code AFAIK.
To solve problem 2 there will need to be documentation or people will be forced to read the source code for the native updateText API to figure out how to apply machine-generated updates.


How to add new togle button in ui-grid

I want to add new toggle button in table how to i add this in row

Check the scope tutorial, it has a button in it.  http://ui-grid.info/docs/#/tutorial/305_appScope

hi i am already show this tutorial. i want to add new botton in every row
 Edit  like this

Best Regards,
Gajendrasinh Zala
On Sat, Mar 21, 2015 at 1:51 AM, Paul<EMAIL_ADDRESS>wrote:

Closed #3066 https://github.com/angular-ui/ng-grid/issues/3066.
—
Reply to this email directly or view it on GitHub
https://github.com/angular-ui/ng-grid/issues/3066#event-260766588.

That's what the tutorial does, right?

WOW.... I;m working on this now. I searched a lot about how to add a button at the end of a row. Finally I got and now i'm working.
Thanks a lot.

Have a problem. In that example, there's Name column. I want to pass name in the showMe() function as a parameter.  A follows :
ng-click="grid.appScope.showMe(name)"
How can I do this ? Then I can access it from the controller. Imagine if I want to update or remove a row, I have to send the ID. Like that.

@PaulL1  Please help me on this.


TensorFlow 2.10.0 not compatible with TensorRT 8.4.3

Click to expand!
Issue Type
Build/Install
Source
binary
Tensorflow Version
2.10.0
Custom Code
No
OS Platform and Distribution
Ubuntu 22.04 LTS
Mobile device
No response
Python version
3.10.4
Bazel version
No response
GCC/Compiler version
No response
CUDA/cuDNN version
cuda 11.7
GPU model and memory
NVIDIA GeForce RTX 3090
Current Behaviour?
I cannot use TensorRT 8 with the latest version of TensorFlow and CUDA.

Standalone code to reproduce the issue
import tensorflow as tf

Relevant log output
2022-09-13 11:06:57.075736: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-09-13 11:06:57.075769: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-09-13 11:06:57.075772: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

https://github.com/tensorflow/tensorflow/issues/57671

I see the same cuBLAS issue too. Any progress with TensorRT or cuBLAS issues?
Thanks for your time.

I know, it's only optional but I want to use Nvidia's TensorRT capabilities.
Thanks.

Welcome :D

Until it's not solved I don't close this issue.
Thanks for understanding.

So after spending 2 days on this issue... Here's a solution/workaround to get TF2.10 running with TRT on Ubuntu 20.04 (assuming 22 schould work aswell):
As the Error mentions
Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
-> so you need TensorRT 7.X ...
to get it installed you have to differ some actions from official Step-by-step instructions as in: https://www.tensorflow.org/install/pip

Sys. Req. -> guidelike
install Miniconda/Anaconda -> guidelike
Create a conda environment -> differs
a) you need python 3.8 to install nvidia-tensorrt 7.x later on, otherwise pip won't found this specific version
conda create --name tf-py38 python=3.8
b) conda activate tf-py38
GPU setup -> guidelike, follow as is (nvidia-smi,conda install etc.)
(4,5.) Install tensorrt -> Extra step
pip-install tensorrt as in: https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-723/install-guide/index.html#installing-pip:

pip install --upgrade setuptools pip
pip install nvidia-pyindex
check for desired specific tensorrt-versions -> pip install nvidia-tensorrt==
py3.10 -> available from 8.4.0.+
py3.9 -> available from 8.0.+
py3.8 -> available from 7.2.2.+
install your favorite version e.g. -> pip install nvidia-tensorrt==<IP_ADDRESS>
verify -> python3 -c "import tensorrt; print(tensorrt.__version__); assert tensorrt.Builder(tensorrt.Logger())"
Configure the system paths once again as before to contain tensorrt path:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/
or with recommended automation:
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

install Tensorflow and follow remaining steps

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" -> no more missing 'libnvinfer.so.7' and 'libnvinfer_plugin.so.7'
Hope it helps... Best Regards
DiDaMain

Hi, Could you please try with the nightly version, cublas issue seems to be fixed with the PR here https://github.com/tensorflow/tensorflow/pull/56691, but it is not available in 2.10.0.

Hi @sachinprasadhs
Yes, it's fixed! Tks!
$ python3 -c "import tensorrt; print(tensorrt.__version__); assert tensorrt.Builder(tensorrt.Logger())"
<IP_ADDRESS>
$ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2022-09-21 09:49:24.963742: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-09-21 09:49:26.095035: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-09-21 09:49:26.117763: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-09-21 09:49:26.117901: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

You can try with the latest version, if it is working as intended, I would leave it as it is.

You can switch back to TensorRT <IP_ADDRESS> with tensorflow nightly version, until Tensorflow 2.11.0 is available, which would fix the dependency issues.

Ok. I've done that.
Tks.

Hi, any progress on this one?
Latest nightly still appears to be looking for libnvinfer.so.7 :
Successfully installed tf-nightly-2.11.0.dev20221003

2022-10-03 09:54:22.740813: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-10-03 09:54:45.914148: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory

Kind regards

Same issue as earlier commenters with tensorflow 2.10.0 and @pberndt with tf-nightly-2.11.0.dev20221003 although the cublas error is gone as reported in earlier comments.
Downgraded to 2.9.0 and I'm not seeing any libnvinfer.so.7 warnings.
It was either that or downgrading to Python 3.8 to install TensorRT 7 since it doesn't seem to support later python versions.
The only related topics I can find are about TensorRT 8 breaking compilation on some earlier versions of Tensorflow but unclear if that has any relation to this.
OS: Ubuntu 22.04
Python: 3.10.6
CUDA: 11.7
TensorRT: 8.4.3
To note: TensorRT was installed via pip since there is no .deb package for Ubuntu 22.04 at the moment and python3-libnvinfer-dev package installation would fail since it expects Python < 3.9
Also, TensorRT is compiled for CUDA 11.6 and not 11.7 - although Nvidia reports it as compatible in their documentation
Best regards

I had a similar result to avocadotruck94: I downgraded from tensorflow 2.10 to 2.9, and the warnings disappeared.
System Config:

Operating System: Ubuntu 20.04 (Focal Fossa)
GPU: GeForce RTX 3080 (Notebook)
nvidia-driver: 510.47.03
cuDNN: <IP_ADDRESS>
CUDA/cudatoolkit: 11.6
tensorrt: 8.4.1

Environment Config:

cudatoolkit: 11.7
cudnn: <IP_ADDRESS>
tensorflow: 2.10

Under that environment config, the following happens:
>> import tensorflow as tf
2022-10-17 18:31:14.944918: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-10-17 18:31:15.018686: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2022-10-17 18:31:15.038260: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2022-10-17 18:31:15.413887: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/x86_64-linux-gnu/:/usr/local/cuda-11.6/bin/
2022-10-17 18:31:15.413923: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/x86_64-linux-gnu/:/usr/local/cuda-11.6/bin/
2022-10-17 18:31:15.413927: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
>>>

If this is changed to:

cudatoolkit: 11.7
cudnn: <IP_ADDRESS>
tensorflow: 2.9

The following happens instead:
>>> import tensorflow as tf
2022-10-17 18:33:30.113392: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
>>>

Which seems pretty inconspicuous.
Also, the following line didn't throw any errors (more of a sanity check):
>>> from tensorflow.python.compiler import tensorrt
>>>

Which makes it sound like it tensorflow 2.9 is finding the tensorrt libraries properly.
In my case, since I haven't found a package offering "libnvinfer_plugin.so.7", I think I am going to stick with a downgraded tensorflow (since none of the above errors/warnings were reported).  Plus, that has made me suspicious that there is some kind of hard coded/bad version detection present.
Hope that helps

I solve this problem by downgrading the tf to 2.9.2

I have the same issue with tensorflow 2.11.0.
$ python3
Python 3.10.6 (main, Nov  2 2022, 18:53:38) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2022-11-25 10:46:42.560645: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-11-25 10:46:43.462814: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /tools/torch_git/install/lib:/tools/torch_git/install/lib:/usr/local/lib:/usr/local/cuda/lib64
2022-11-25 10:46:43.462910: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /tools/torch_git/install/lib:/tools/torch_git/install/lib:/usr/local/lib:/usr/local/cuda/lib64
2022-11-25 10:46:43.462921: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

on Ubuntu 22.04, python 3.10.4, Nvidia driver 515.65.01, cuda 11.2.2, cudnn <IP_ADDRESS>
I tried the workaround proposed by @didamain but the only tensorrt versions displayed by pip install nvidia-tensorrt== are 0.0.1.dev4, 0.0.1.dev5, <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>
I have no issue when installing tensorflow 2.9.3.

Duplicates #57671?

Probably yes.
I have the same issue with TF 2.11.0 and TensorRT 8.5.1-1+cuda11.8.

Hi @rishikasinha-tf, Could you please take a look?

Hi @markub3327, it seems the issue is encountered in TF 2.11.0, too. Could you please add the fix to the master branch? One option to test the fix is using tf-nightly. After that we can cherry pick to the r2.11 branch. FYI @reedwm

Same issue here with tensorflow 2.11.0 and TensorRT <IP_ADDRESS>

pip install nvidia-pyindex (NVIDIA's version of PyPi) will give you the necessary nvidia-tensorrt versions. But this doesn't solve the TensorRT 8 not found issue. At least not for me. (I'm not using Conda to manage environments.) Extremely annoying I have to downgrade to RT7 and Py38 (or completely mess with $LD_LIBRARY_PATH just to keep Cuda bindings. I'm not moving to nightly either. This should "just work" using pip install *, poetry install *`, etc.
And why is this issue closed anyway? It's not solved. Not in v2.10. And not in v2.11. Not in the official release versions, anyway.

I have the same issue with tensorflow 2.11.0.
$ python3
Python 3.10.6 (main, Nov  2 2022, 18:53:38) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2022-11-25 10:46:42.560645: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-11-25 10:46:43.462814: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /tools/torch_git/install/lib:/tools/torch_git/install/lib:/usr/local/lib:/usr/local/cuda/lib64
2022-11-25 10:46:43.462910: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /tools/torch_git/install/lib:/tools/torch_git/install/lib:/usr/local/lib:/usr/local/cuda/lib64
2022-11-25 10:46:43.462921: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

on Ubuntu 22.04, python 3.10.4, Nvidia driver 515.65.01, cuda 11.2.2, cudnn <IP_ADDRESS>
I tried the workaround proposed by @didamain but the only tensorrt versions displayed by pip install nvidia-tensorrt== are 0.0.1.dev4, 0.0.1.dev5, <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>
I have no issue when installing tensorflow 2.9.3.

pip install nvidia-pyindex (NVIDIA's version of PyPi) will give you the necessary nvidia-tensorrt versions. But this doesn't solve the TensorRT 8 not found issue. At least not for me. (I'm not using Conda to manage environments.) Extremely annoying I have to downgrade to RT7 and Py38 (or completely mess with $LD_LIBRARY_PATH just to keep Cuda bindings. I'm not moving to nightly either. This should "just work" using pip install *, poetry install *`, etc.
And why is this issue closed anyway? It's not solved. Not in v2.10. And not in v2.11. Not in the official release versions, anyway.

I'm waiting for the stable release of 2.12.0, there will be TensorRT 8 working.
Until that, I'm using TF 2.9.

How do you do to instal TF 2.9 ?
I have the same issues but I don't succeed in fixing them !

@jthibaut this fix by @didamain works perfectly. Scroll up and you'll see his comment with detailed instructions. Try this only if tf-nightly doesn't work for you. Good luck.

@jthibaut please run this from terminal with your environment activates, this will add all the libraries installed by tesnorrt to path as they do not get added automatically:

echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cublas/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cuda_runtime/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cuda_nvrtc/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cudnn/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/tensorrt/'> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

@jthibaut what did you use to install tesnorrt? In case you used @didamain workaround, you need to do this:
Run this from terminal with your environment activated, this will add all the libraries installed by tesnorrt to path as they do not get added automatically, you can also swap out the python3.8 with your python vresion:

echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cublas/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cuda_runtime/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cuda_nvrtc/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cudnn/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/tensorrt/'> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

If you used pip install tensorrt, the paths may be a little different:

echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/'> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

basically you can go check in the site-packages folder where tensorrt is and add it to path accordingly.

I have the following message after the last command:
bash: /home/jacquin/miniconda3/etc/conda/activate.d/env_vars.sh: Aucun fichier ou dossier de ce type
Translated:
`bash: /home/jacquin/miniconda3/etc/conda/activate.d/env_vars.sh: No file or no directory of tthis type
`

@jthibaut I just saw you are not using miniconda. You can try this from your home directory:
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/jacquin/.local/lib/
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: /home/jacquin/.local/lib/python3.10/site-packages/tensorrt/'>> ~/. bashrc

@learning-to-play I believe it is closed by mistake - the issue still persists

@mshavliuk , It will be part of Tensorflow 2.12, till then you can use nightly version.

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/

ubuntu 22.04
cuda 12.0
tensorrt 8.5.0
cudnn 8.7.0
tf-nightly
the error still persists

@sachinprasadhs , tf-nightly-2.12.0.dev20230114, Ubuntu 22.04.1 LTS, Python 3.10.8, miniconda - the issue persists.
$ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2023-01-14 22:45:48.016946: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-14 22:45:48.114866: E tensorflow/tsl/lib/monitoring/collection_registry.cc:81] Cannot register 2 metrics with the same name: /tensorflow/core/bfc_allocator_delay
2023-01-14 22:45:48.483636: W tensorflow/tsl/platform/default/dso_loader.cc:66] Could not load dynamic library 'libnvinfer.so.8'; dlerror: libnvinfer.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/max/miniconda3/lib/:/lib/:/home/max/miniconda3/envs/project/lib/
2023-01-14 22:45:48.483677: W tensorflow/tsl/platform/default/dso_loader.cc:66] Could not load dynamic library 'libnvinfer_plugin.so.8'; dlerror: libnvinfer_plugin.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/max/miniconda3/lib/:/lib/:/home/max/miniconda3/envs/project/lib/
2023-01-14 22:45:48.483684: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2023-01-14 22:45:49.083557: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:997] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-01-14 22:45:49.086483: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:997] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-01-14 22:45:49.086598: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:997] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

ubuntu 22.04
cuda 12.0
tensorrt 8.5.0
cudnn 8.7.0
tf-nightly
the error still persists

I know, it's only optional but I want to use Nvidia's TensorRT capabilities.
Thanks.

How is it going?

I know, it's only optional but I want to use Nvidia's TensorRT capabilities.
Thanks.

How is it going?

https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/rel-22-12.html#rel-22-12

How is it going?

https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/rel-22-12.html#rel-22-12

@johnnynunez thank you.
Although I'm still not deploying containers that seems very good at least as a reference for my custom environment building.
I still want to control a customized environment with CONDA and pip on Ubuntu 22 bare metal.

So after spending 2 days on this issue... Here's a solution/workaround to get TF2.10 running with TRT on Ubuntu 20.04 (assuming 22 schould work aswell):
As the Error mentions Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory -> so you need TensorRT 7.X ... to get it installed you have to differ some actions from official Step-by-step instructions as in: https://www.tensorflow.org/install/pip

Sys. Req. -> guidelike

install Miniconda/Anaconda -> guidelike

Create a conda environment -> differs
a) you need python 3.8 to install nvidia-tensorrt 7.x later on, otherwise pip won't found this specific version
conda create --name tf-py38 python=3.8
b) conda activate tf-py38

GPU setup -> guidelike, follow as is (nvidia-smi,conda install etc.)
(4,5.) Install tensorrt -> Extra step
pip-install tensorrt as in: https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-723/install-guide/index.html#installing-pip:

pip install --upgrade setuptools pip
pip install nvidia-pyindex
check for desired specific tensorrt-versions -> pip install nvidia-tensorrt==
py3.10 -> available from 8.4.0.+
py3.9 -> available from 8.0.+
py3.8 -> available from 7.2.2.+
install your favorite version e.g. -> pip install nvidia-tensorrt==<IP_ADDRESS>
verify -> python3 -c "import tensorrt; print(tensorrt.__version__); assert tensorrt.Builder(tensorrt.Logger())"
Configure the system paths once again as before to contain tensorrt path:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/
or with recommended automation:
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

install Tensorflow and follow remaining steps

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" -> no more missing 'libnvinfer.so.7' and 'libnvinfer_plugin.so.7'
Hope it helps... Best Regards DiDaMain

i don't understand the export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/ part.

i don't understand the export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/
part. could you please tell me exactly which path i have to export and where,

i don't understand the export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.8/site-packages/tensorrt/ part. could you please tell me exactly which path i have to export and where,

because you install cuda and tensorrt prebinaries from anaconda. Conda prefix is where your SO path.
Ubuntu is like /home/user/...
windows is like C:\ProgramFiles...

cudnn 8.8 is out for cuda 12.0. Any news for tensorrt

So after spending 2 days on this issue... Here's a solution/workaround to get TF2.10 running with TRT on Ubuntu 20.04 (assuming 22 schould work aswell):

After spending much more than two days trying to solve the same problem and now learning that Tensorflow still doesn't support the latest versions of Python, CUDA Toolkit and TensorRT, this solution with the "locked" versions worked perfectly for me with Linux Mint 21.1, I thank you infinitely!🙇🏻♂

cd /usr/lib/x86_64-linux-gnu
ln -s libnvinfer.so.8 libnvinfer.so.7
ln -s libnvinfer_plugin.so.8 libnvinfer_plugin.so.7
ln -s libnvonnxparser.so.8 libnvonnxparser.so.7
ln -s libnvparsers.so.8 libnvparsers.so.7

work for me.
python 3.10 with tensorrt 8+
cd /usr/lib/x86_64-linux-gnu
ln -s libnvinfer.so.8 libnvinfer.so.7
ln -s libnvinfer_plugin.so.8 libnvinfer_plugin.so.7
ln -s libnvonnxparser.so.8 libnvonnxparser.so.7
ln -s libnvparsers.so.8 libnvparsers.so.7

work for me. python 3.10 with tensorrt 8+
cd /usr/lib/x86_64-linux-gnu
ln -s libnvinfer.so.8 libnvinfer.so.7
ln -s libnvinfer_plugin.so.8 libnvinfer_plugin.so.7
ln -s libnvonnxparser.so.8 libnvonnxparser.so.7
ln -s libnvparsers.so.8 libnvparsers.so.7

I will add:
with cuda 12
cd /usr/local/cuda-12.0/lib64
ln -s libcufft.so.11 libcufft.so.10
ln -s libcusparse.so.12 libcusparse.so.11

Now it looks fine .... Thanks.

(tf) kai@PC:~$ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2023-04-23 10:19:08.001875: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2023-04-23 10:19:08.310932: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-23 10:19:10.796140: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-04-23 10:19:10.948047: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-04-23 10:19:10.948430: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Hi @markub3327 ,
Thanks for coming back and confirming. Could you please spare some time to close the issue if resolved. Thanks!

using python 3.10, tf 2.12:
pip install nvidia-tensorrt
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: nvidia-tensorrt in ./miniconda3/lib/python3.10/site-packages (99.0.0)
Requirement already satisfied: tensorrt in ./miniconda3/lib/python3.10/site-packages (from nvidia-tensorrt) (8.6.1)

but
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
**2023-07-28 12:08:22.549302: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT**
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU')]

Tried:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/python3.10/site-packages/tensorrt/

No success:
python3 -c "import tensorrt; print(tensorrt.__version__); assert tensorrt.Builder(tensorrt.Logger())"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/h21/luas6629/miniconda3/lib/python3.10/site-packages/tensorrt/__init__.py", line 18, in <module>
    from tensorrt_bindings import *
ModuleNotFoundError: No module named 'tensorrt_bindings'


Create FastAI starter project and source repository

Which area is this issue related to?

/kind task
/area registry
Issue Description

Create FastAI starter project and source repository under https://github.com/devfile-samples
See example Tensorflow v2 starter project for reference.
Project Dependencies

fastai==2.7.15
torch==2.3.0
torchvision==0.18.0
torchaudio==2.3.0

During both the runtime of the init command and the deployment container image build, everywhere this dependency is installed, it needs to pick one of the following index urls to use based on the chosen Compute Platform (CPU/GPU):

CUDA 11.8 (Nvidia GPU) - https://download.pytorch.org/whl/cu118
CUDA 12 (Nvidia GPU) - None
CPU - https://download.pytorch.org/whl/cpu
ROCm (AMD GPU) - https://download.pytorch.org/whl/rocm6.0

Acceptance Criteria

[ ] Create starter project source repository
[ ] Initialize FastAI starter project that creates the MNIST model then saves and loads in

This lets someone override for loading in their own model to use

This issue can extend off the pytorch starter project work https://github.com/devfile/api/issues/1583


Move loose Elixir modifiers into folder structure

These were in flight PR's when @supernintendo organized the Elixir modifiers.

Thanks!


fix compiler warnings

PS - someone on tensile side should probably see if the type instantiation should be the same on windows and linux (i.e. long or long long)).  Unrelated to fixing the warning but could be a problem down the road.

@nakajee thanks for taking this over, I assume I can leave it to you to merge as you will know what failures are the norm and can be ignored?

@nakajee thanks for taking this over, I assume I can leave it to you to merge as you will know what failures are the norm and can be ignored?

I can handle merge.
I am still waiting for CI tests.
Fails on extended 906 is expected (will be fixed with #1617).
Not sure why 908 taking so long time.


TextFieldTapRegion close keyboard when node still in focus

Steps to reproduce

Use code example below
Run using latest Flutter Beta (3.22.0-0.1.pre)

Expected results
Keyboard should still showed as text fields is still in focus, this behavior is correct on beta 3.21, but got issue on beta 3.22.
Actual results
Keyboard close after press/do some action on the TextFieldTapRegion
Code sample
Code sample
import 'package:flutter/material.dart';

void main() {
  runApp(const MyApp());
}

class MyApp extends StatelessWidget {
  const MyApp({super.key});

  // This widget is the root of your application.
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Tap Region',
      theme: ThemeData(
        colorScheme: ColorScheme.fromSeed(seedColor: const Color(0xFF263238)),
        useMaterial3: true,
      ),
      home: const MyHomePage(),
    );
  }
}

class MyHomePage extends StatefulWidget {
  const MyHomePage({super.key});

  @override
  State<MyHomePage> createState() => _MyHomePageState();
}

class _MyHomePageState extends State<MyHomePage> {
  final TextEditingController _textController = TextEditingController();
  final FocusNode _textFocus = FocusNode();

  @override
  void dispose() {
    _textController.dispose();
    _textFocus.dispose();
    super.dispose();
  }

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        backgroundColor: Theme.of(context).colorScheme.inversePrimary,
        title: const Text('Tap Region Issue'),
      ),
      body: Column(
        crossAxisAlignment: CrossAxisAlignment.start,
        mainAxisAlignment: MainAxisAlignment.start,
        children: <Widget>[
          Expanded(
            child: Column(
              crossAxisAlignment: CrossAxisAlignment.start,
              mainAxisAlignment: MainAxisAlignment.start,
              children: <Widget>[
                const Text(
                  'This is to demo Tap Region issue',
                ),
                const SizedBox(height: 10,),
                TextFormField(
                  controller: _textController,
                  focusNode: _textFocus,
                  showCursor: true,
                  onTap: (() {
                    // set state so we will rebuild the bottom bar
                    setState(() {
                    });
                  }),
                ),
              ],
            ),
          ),
          _buildBottomBar(),
        ],
      ),
    );
  }

  Widget _buildBottomBar() {
    // if text focus, build the bottom bar
    if (_textFocus.hasFocus) {
      return TextFieldTapRegion(
        child: Container(
          height: 40,
          width: double.infinity,
          color: Colors.grey[600],
          padding: const EdgeInsets.all(5),
          child: Row(
            crossAxisAlignment: CrossAxisAlignment.center,
            mainAxisAlignment: MainAxisAlignment.spaceEvenly,
            children: <Widget>[
              _button(text: '😊'),
              _button(text: '😔'),
              _button(text: '🙏'),
              _button(text: '🤗'),
              _button(text: '😁'),
              _button(text: '😂'),
              _button(text: '🥰'),
              _button(text: '👍'),
            ],
          ),
        ),
      );
    }

    // return sized box if text is not focused
    return const SizedBox.shrink();
  }

  Widget _button({required String text}) {
    return InkWell(
      onTap: (() {
        _textController.text = _textController.text + text;
      }),
      child: Container(
        width: 30,
        height: 30,
        color: Colors.grey[300],
        child: Text(
          text
        ),
      ),
    );
  }
}

Screenshots or Video

Screenshots / Video demonstration, as you can see the keyboard is close after pressing one of the emoticon

Logs
Logs
No logs

Flutter Doctor output
Doctor output
Flutter 3.22.0-0.1.pre • channel beta • https://github.com/flutter/flutter.git
Framework • revision 29babcb32a (7 days ago) • 2024-04-03 17:17:04 -0500
Engine • revision 97550907b7
Tools • Dart 3.4.0 (build 3.4.0-282.1.beta) • DevTools 2.34.2
PS E:\Project\tap_region_issue> flutter doctor
Doctor summary (to see all details, run flutter doctor -v):
[✓] Flutter (Channel beta, 3.22.0-0.1.pre, on Microsoft Windows [Version 10.0.22631.3374], locale en-SG)
[✓] Windows Version (Installed version of Windows is version 10 or higher)
[!] Android toolchain - develop for Android devices (Android SDK version 33.0.2)
    ✗ cmdline-tools component is missing
      Run `path/to/sdkmanager --install "cmdline-tools;latest"`
      See https://developer.android.com/studio/command-line for more details.
    ✗ Android license status unknown.
      Run `flutter doctor --android-licenses` to accept the SDK licenses.
      See https://flutter.dev/docs/get-started/install/windows#android-setup for more details.
[✓] Chrome - develop for the web
[✓] Visual Studio - develop Windows apps (Visual Studio Build Tools 2019 16.11.26)
[✓] Android Studio (version 2022.2)
[✓] VS Code (version 1.88.0)
[✓] VS Code, 64-bit edition (version 1.87.2)
[✓] Connected device (3 available)
[✓] Network resources

! Doctor found issues in 1 category.

LGTM

Thanks for the report! This is a known issue being tracked at https://github.com/flutter/flutter/issues/145601. Closing this issue as a duplicate, so please follow up on it for further updates.


German Translation

Hello. Please take a look at the following translation:
"Send us an email" = "Senden Sie uns eine E-Mail";
"Open an issue on GitHub" = "Öffnen Sie ein Problem auf GitHub";
"Clear console" = "Konsole löschen";
"Kill Process" = "Kill Process";
"Sync" = "Sync";
"Synchronize Changes" = "Änderungen synchronisieren";
"You don't have any open editor." = "Sie haben keinen offenen Editor.";
"This will pull remote changes down to your local repository and then push local commits to the upstream branch. Conflicts will be merged and overriden by the local copy." = "Dadurch werden Remote-Änderungen in Ihr lokales Repository übertragen und lokale Commits an den Upstream-Zweig weitergeleitet. Konflikte werden von der lokalen Kopie zusammengeführt und überschrieben.";
"Remote Credentials (For Git pull, push and clone)" = "Remote-Anmeldeinformationen (für Git ziehen, drücken und klonen)";
"Password / Personal Access Token" = "Passwort / Personal Access Token";
"User Name" = "Benutzername";
"Show hidden files" = "Versteckte Dateien anzeigen";
"Erase" = "Erase"; = "Erase";
"This will erase all user settings, including author identity and credentials." = "This will erase all user settings, including author identity and credentials."; = "Dadurch werden alle Benutzereinstellungen gelöscht, einschließlich der Autorenidentität und der Anmeldeinformationen.";
"Erase all settings" = "Alle Einstellungen löschen";
"Release Notes" = "Release Notes";

"New File" = "Neue Datei";
"Open File" = "Open File";
"Open Folder" = "Open Folder";
"Open Editors" = "Open Editors";
"EXPLORER" = "EXPLORER";
"SEARCH" = "SEARCH";
"Search" = "Suche";
"Results" = "Ergebnisse";
"Search Text" = "Suchtext";
"Search File" = "Search File";
"Quick add" = "Quick add";
"Custom" = "Custom";
"Where" = "Where";
"Current Folder" = "Aktueller Ordner";
"Add File" = "Add File";
"INPUT" = "INPUT";
"PROBLEMS" = "PROBLEME";
"OUTPUT" = "OUTPUT";
"Options" = "Options";
"Close All" = "Close All";
"Preview in Safari" = "Vorschau in Safari";
"Mini Map" = "Mini Map";
"Line Numbers" = "Zeilennummern";
"OTHERS" = "OTHERS";
"Tab Size" = "Tab Size";

"Settings" = "Einstellungen";
"General" = "Allgemein";
"Editor" = "Editor";
"Compiler" = "Compiler";
"Accent" = "Akzent";
"Font Size" = "Schriftgröße";
"Console Font Size" = "Konsolenschriftgröße";
"Bracket Completion" = "Bracket Completion";
"Theme" = "Theme";
"Rate Code App" = "Rate Code App";
"About" = "About";
"Version" = "Version";

"Done" = "Fertig";

"Recent" = "Recent";
"Show in Files App" = "In Dateien anzeigen App";
"Copy Relative Path" = "Relativen Pfad kopieren";
"Delete" = "Löschen";
"Color Scheme" = "Farbschema";
"Dark Theme" = "Dark Theme";
"Light Theme" = "Light Theme";
"Text Wrap" = "Text Wrap";
"Render Whitespace" = "Render Whitespace";

"Share" = "Share";
"Rename" = "Umbenennen";
"Assign as workplace folder" = "Als Arbeitsplatzordner zuweisen";
"Select for compare" = "Zum Vergleich auswählen";
"Compare with selected" = "Vergleiche mit ausgewählten";
"Version Control" = "Versionskontrolle";
"Author Identity" = "Author Identity";
"Authentication" = "Authentifizierung";
"Name" = "Name";
"Email address" = "E-Mail-Adresse";
"Save" = "Speichern";
"Close Editor" = "Editor schließen";
"Zoom in" = "Vergrößern";
"Zoom out" = "Verkleinern";
"Show Explorer" = "Show Explorer";
"Show Search" = "Show Search";
"Show Source Control" = "Show Source Control";
"User Settings" = "Benutzereinstellungen";
"Show Panel" = "Show Panel";
"Run Code" = "Run Code";
"New Folder" = "Neuer Ordner";

"Source Control" = "Source Control";
"The folder currently opened doesn't have a git repository." = "Der aktuell geöffnete Ordner hat kein Git-Repository.";
"Example: https://github.com/weichsel/ZIPFoundation.git" = "Beispiel: https://github.com/weichsel/ZIPFoundation.git";
"Initialize Repository" = "Repository initialisieren";
"Clone Repository" = "Clone Repository";
"TERMINAL" = "TERMINAL";
"Keyboard Toolbar" = "Tastatursymbolleiste";
"Always Open In New Tab" = "Immer in neuem Tab öffnen";
"Changes" = "Änderungen";
"There are no staged changes." = "Es gibt keine inszenierten Änderungen.";
"Message (⌘Enter to commit)" = "Nachricht (⌘Eingabe festschreiben)";
"Staged Changes" = "Inszenierte Änderungen";
"No changes are made in the working directory" = "Im Arbeitsverzeichnis werden keine Änderungen vorgenommen";
"Git checkout: Uncommitted Changes" = "Git checkout: Uncommitted Changes";
"Uncommited changes will be lost. Do you wish to proceed?" = "Nicht festgeschriebene Änderungen gehen verloren. Möchten Sie fortfahren?";
"Commit message cannot be empty" = "Commit-Nachricht darf nicht leer sein";
"Commit succeeded" = "Commit erfolgreich";
"Pushing to remote" = "Pushing to remote";
"Push succeeded" = "Push erfolgreich";
"No changes are made in this file" = "In dieser Datei werden keine Änderungen vorgenommen";

"Smooth Scrolling" = "Smooth Scrolling";
"Stage All Changes" = "Stage All Changes";
"Push" = "Push";
"Fetch" = "Fetch";

Hello, thank you very much for the quick response. I made some adjustments but couldn't copy it out of the Code App. So I had to create and pack an html file. I hope that's okay, otherwise I'll do it again tonight.
german_translation.zip

Hello, thank you very much for the quick response. I made some adjustments but couldn't copy it out of the Code App. So I had to create and pack an html file. I hope that's okay, otherwise I'll do it again tonight.
german_translation.zip

That's okay! Thanks for the review. I will include the translation in the coming update.

Added in TestFlight build.


Building alpha versions, 0.13.0

So from what I understand building alphas with this module is not officially supported but is there a workaround?
version: '0.13.0-alpha4',

Error: Unable to download nodewebkit.

See #275. Any help would appreciated.


Change continuous to numerical in datasets, function names, etc

Follow-up to fix fall-out from #98. We're relying on a string comparison to check data types for columns set in dataset JSON files. The comparison string was changed from "continuous" to "numerical" for display purposes; this PR updates all the other instances of "continuous" to "numerical" so the type checking will still work.

Thank you!


docs: update instructions for test campaigns

Several minor fixes. I'll leave comments inline for parts I'm not really sure.

Unsure 3/3:
https://github.com/xlab-uiuc/acto/blob/b0f22401723536d47f9e64d0b76dc15e7db28e21/test_campaign.md?plain=1#L106
Just to double check: here num-workers is 8, not 12?

Unsure 3/3:
https://github.com/xlab-uiuc/acto/blob/b0f22401723536d47f9e64d0b76dc15e7db28e21/test_campaign.md?plain=1#L106
Just to double check: here num-workers is 8, not 12?

Right, we run everything according to the paper

Ohhh my bad. I just realized my commands last night already has this number mistake. I was checking against that thus the confusion. Never mind

Just to double check: here num-workers is 8, not 12?

Ohhh my bad. I just realized my commands last night already has this number mistake. I was checking against that thus the confusion. Never mind

I also found the root of all this, shall we update it here too?
https://github.com/xlab-uiuc/acto/blob/85e133e78d04032a6e998dbfbb20a4768b3d6865/scripts/runbatch_5.sh#L3

Just to double check: here num-workers is 8, not 12?

Ohhh my bad. I just realized my commands last night already has this number mistake. I was checking against that thus the confusion. Never mind

I also found the root of all this, shall we update it here too?
https://github.com/xlab-uiuc/acto/blob/85e133e78d04032a6e998dbfbb20a4768b3d6865/scripts/runbatch_5.sh#L3

Sure, but this script is quite out dated, we might want to just fix everything at once. For now it's fine

I just realized this is not a missing hyphen problem but perhaps some editor/input method's features that turn double hyphens -- into an en dash –.
https://github.com/xlab-uiuc/acto/blob/85e133e78d04032a6e998dbfbb20a4768b3d6865/README.md?plain=1#L204
I actually caught more and will push the fix soon. I suggest we turn off relevant features and turn on non-ascii detection. @tianyin @tylergu
Ref https://en.wikipedia.org/wiki/Dash


To solve ReleaseBuild Error for verifyreleaseresources failed to execute aapt

When you are going do a releaseBuild you will see this issue,  I was juggling with it for more than 4 hours. as I'm not a android developer by itself. So it was too hard for me to figure out the issue, Then i was thinking what could be the issue. It was very simple! Then I tried to collect information about releasing a package and all that . then compared with a valid package. and got the culprit!
FAILURE: Build failed with an exception.

* What went wrong:
Execution failed for task ':react-native-html-to-pdf:verifyReleaseResources'.
> com.android.ide.common.process.ProcessException: Failed to execute aapt

Please merge and pull this package ASAP it's so frustrating stuff.

Fixed by another PR. https://github.com/christopherdro/react-native-html-to-pdf/pull/125