text:
A way to do that with xml mapping ?

Hello and thanks for this great functionnality ! I can't understand why it's not in the native doctrine code. I'm asking if there is a way to deal with xml mapping? See you !

One thing you might find interesting (which i didn't know for long time) is that the discriminator map has become optional in doctrine. You only have to configure the inheritance type and the discriminator will be generated from the class name. (Even the discriminator column is not optional and defaults to a varchar called "dtype".)

Hello and sorry for my late reply. The problem with my configuration is that the childs entities have the same names of the parents. I know that's weird, example:
I have a abstract bundle and this bundle is used by a user and a organization (bundle), so we have AbstractFooBundle and UserFooBundle and OrganisationFooBundle.
In the AbstractFooBundle i have a Bar Entity inherited in UserFooBundle and OrganizationFooBundle, so the name is the same,Bar, the only difference is that inside UserFooBundle\Entity::Bar, there is a owner property connected to User Entity class in this case and in OrganizationFooBundle\Entity::Bar owner connected to Organization Entity class in this case.
I didn't found other method  to use your bundle, if i want to fix it i need to rename my classes, so Bar (in AbstractBundle), become UserBar (in UserFooBundle) and OrganizationBar (in OrganizationFooBundle), but i refuse it's ugly!
I also needed to unactive a function in doctrine about discrinator map (sorry i can tell you for the moment which one cause i don't have the code on my home machine, but i think that's the native behaviour you spoke to me).
So if you think about another architecture for my use case don't hesitate to free me from this problem.
Thanks a lot !

@Axxon Hi there! You can send me a PR with the code needed to enable XML mapping and I will be glad to extend this bundle with such a feature :)
However, annotations considered to be best mapping option in SF, maybe you should rewrite your codebase (if it's not too huge) instead?

Hello @init-penguin first sorry for my late response (but a lot change in my work), i don't need now the usage of your bundle, but in a near futur it's possible.
Also, annotations for me is a bad practice cause there don't permit a good separation of concern.
I keep in mind that doctrine will evolve and probably a lot change during this time, so we will see if this fonctionnality is coherent.

authors: Axxon Nemo64 init-penguin

repo: Levelab/doctrine-discriminator-bundle


text:
Availability on opam-repository

Is there a particular reason why this (very useful) library is not published on opam-repository?

FYI https://github.com/ocaml/opam-repository/pull/16519 but apparently we need to wait the support of ocaml 4.11 by js_of_ocaml to merge it . I'll this issue when this is sorted out.

Done !

source: gharchive/issue

authors: mlasson pveber

repo: LexiFi/ocaml-vdom

license: mit

license_type: permissive

license_source: bigquery


text:
Environment variable not set :SECRET_KEY

After a fresh install, I ran librephotos-cli clear_cache
Traceback (most recent call last):
  File "/usr/lib/librephotos/backend/manage.py", line 11, in <module>
	execute_from_command_line(sys.argv)
  File "/usr/lib/librephotos/.local/lib/python3.8/site-packages/django/core/management/init.py", line 401, in execute_from_command_line
	utility.execute()
  File "/usr/lib/librephotos/.local/lib/python3.8/site-packages/django/core/management/init.py", line 345, in execute
	settings.INSTALLED_APPS
  File "/usr/lib/librephotos/.local/lib/python3.8/site-packages/django/conf/init.py", line 82, in getattr
	self._setup(name)
  File "/usr/lib/librephotos/.local/lib/python3.8/site-packages/django/conf/init.py", line 69, in _setup
	self._wrapped = Settings(settings_module)
  File "/usr/lib/librephotos/.local/lib/python3.8/site-packages/django/conf/init.py", line 170, in init
	mod = importlib.import_module(self.SETTINGS_MODULE)
  File "/usr/lib/python3.8/importlib/init.py", line 127, in import_module
	return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
  File "<frozen importlib._bootstrap>", line 991, in _find_and_load
  File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 671, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 848, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/usr/lib/librephotos/backend/ownphotos/settings.py", line 26, in <module>
	raise NameError("Environment variable not set :" + envvar)
NameError: Environment variable not set :SECRET_KEY

Solution
Add the following line to /etc/librephotos/librephotos-backend.env
SECRET_KEY=$BASE_LOGS/secret.key

Ideally, this should be added to resources/etc/librephotos/librephotos-backend.env

https://github.com/LibrePhotos/librephotos-linux/blob/main/resources/bin/librephotos-backend
It should read out the secret key anyway, because of this entrypoint file. We either create a new one and read it, or we read an old one. I am not sure, why it's not working on your system.

repo: LibrePhotos/librephotos-linux

license: MIT

license_source: github-api


text:
new docker image for backend crashes

🐛 Bug Report

[ ] I am including my log files in zip format.
[x] I have looked for similar issues among all the former issues including closed ones.

What Operating system and version is LibrePhotos running on:
ubuntu
What architecture is LibrePhotos running on:
x64
How is LibrePhotos installed:
Docker
Description of issue:

statd: unrecognized service
Traceback (most recent call last):
File "/code/image_similarity/main.py", line 6, in
from retrieval_index import RetrievalIndex
File "/code/image_similarity/retrieval_index.py", line 1, in
import faiss
File "", line 259, in load_module
File "/usr/local/lib/python3.9/dist-packages/faiss-1.7.1-py3.9.egg/faiss/init.py", line 18, in
File "", line 259, in load_module
File "/usr/local/lib/python3.9/dist-packages/faiss-1.7.1-py3.9.egg/faiss/loader.py", line 65, in
File "", line 259, in load_module
File "/usr/local/lib/python3.9/dist-packages/faiss-1.7.1-py3.9.egg/faiss/swigfaiss.py", line 13, in
ImportError: cannot import name '_swigfaiss' from 'faiss' (/usr/local/lib/python3.9/dist-packages/faiss-1.7.1-py3.9.egg/faiss/init.py)
Traceback (most recent call last):
File "/code/manage.py", line 22, in
execute_from_command_line(sys.argv)
File "/usr/local/lib/python3.9/dist-packages/django/core/management/init.py", line 401, in execute_from_command_line
utility.execute()
File "/usr/local/lib/python3.9/dist-packages/django/core/management/init.py", line 377, in execute
django.setup()
File "/usr/local/lib/python3.9/dist-packages/django/init.py", line 24, in setup
apps.populate(settings.INSTALLED_APPS)
File "/usr/local/lib/python3.9/dist-packages/django/apps/registry.py", line 114, in populate
app_config.import_models()
File "/usr/local/lib/python3.9/dist-packages/django/apps/config.py", line 211, in import_models
self.models_module = import_module(models_module_name)
File "/usr/lib/python3.9/importlib/init.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "", line 1030, in _gcd_import
File "", line 1007, in _find_and_load
File "", line 986, in _find_and_load_unlocked
File "", line 680, in _load_unlocked
File "", line 855, in exec_module
File "", line 228, in _call_with_frames_removed
File "/code/api/models/init.py", line 1, in
from api.models.album_auto import AlbumAuto
File "/code/api/models/album_auto.py", line 4, in
from api.models.photo import Photo
File "/code/api/models/photo.py", line 7, in
from api.im2txt.sample import im2txt
File "/code/api/im2txt/sample.py", line 8, in
import torch
File "/usr/local/lib/python3.9/dist-packages/torch/init.py", line 196, in
from torch._C import *
ImportError: libtinfo.so.5: cannot open shared object file: No such file or directory

How can we reproduce it:
latest docker image

on this note. is it possible to maybe start tagging the docker images? so we can roll back to an older version. currently there is just the dev tag.

My updated container reproduces this behavior exactly as well.

There is now a new build online 👍
I agree, I should add tagging next.

It now works on ARM an x64

authors: derneuere phynias zemsten

repo: LibrePhotos/librephotos


text:
withdrawal handling in team league

fixes #465
when a player withdraws for team league they are

set unavailable for future rounds
if they are an alternate the SeasonPlayer is set inactive
if teams have not been created yet the SeasonPlayer is set inactive
A comment about the withdrawal is added to the SeasonPlayer
sending message to captains channel is skipped. This is handled in issue 448

One minor comment otherwise looks good, might want Lakin to double check though

source: gharchive/pull-request

authors: hopffgam mathgeek


text:
[OS X] Connecting and disconnecting external monitor sometimes causes top of LT window to be positioned offscreen

I haven't figured out an exact sequence of events to trigger this, but very often if I'm running Light Table on my laptop and moving to and from my desk, at some point I'll find that LT's title bar has been positioned above the top of my desktop, perhaps just underneath the menu bar. This makes it impossible to grab the window and move it. I end up having to resort to AppleScript to explicitly tell the window manager to reposition the window somewhere visible.
I'm running OS X 10.9.1 and I don't use LT in full-screen mode.

I experienced that a dozen of time (even on the atom-shell branch).
I have to remove the files in Application Support/LightTable to get LT on the screen

@MarcoPolo @BenjaminVanRyseghem Would you confirm whether you can consistently (or always) reproduce this by following the steps that you, @MarcoPolo, describe in your comment?

@kenny-evitt I am currently on vacations travelling, so I do not have a spare screen to try this out, sorry.
I will give it a try as soon as I am back home (somewhere next week)

@sgrimm @rzh @MarcoPolo @BenjaminVanRyseghem Would you confirm whether this still affects you running the latest version of Light Table (currently 0.8.0).

authors: BenjaminVanRyseghem kenny-evitt sgrimm

repo: LightTable/LightTable


text:
when the validation_step function returns a type defaultdict, TypeError: first argument must be callable or None occurs

🐛 Bug

both validation_step and validation_epoch_end are defined through overriding
when the validation_step function returns a type defaultdict, TypeError: first argument must be callable or None occurs.
returning types in List, Dict, OrderedDict do not give such error.
Error occurs from pytorch-lightning>=1.4.6
Error caused by PR #9261
Left a comment in https://github.com/PyTorchLightning/pytorch-lightning/pull/9261#discussion_r741130894

To Reproduce
import os

from collections import defaultdict

import torch
from torch.utils.data import DataLoader, Dataset

from pytorch_lightning import LightningModule, Trainer

class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len

class BoringModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def training_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("train_loss", loss)
        return {"loss": loss}

    def validation_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("valid_loss", loss)
        return defaultdict(float)

    def validation_epoch_end(self, outputs):
        return outputs

    def test_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("test_loss", loss)

    def configure_optimizers(self):
        return torch.optim.SGD(self.layer.parameters(), lr=0.1)

def run():
    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    val_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    test_data = DataLoader(RandomDataset(32, 64), batch_size=2)

    model = BoringModel()
    trainer = Trainer(
        default_root_dir=os.getcwd(),
        limit_train_batches=1,
        limit_val_batches=1,
        max_epochs=1,
    )
    trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
    trainer.test(model, dataloaders=test_data)

if __name__ == "__main__":
    run()

Expected behavior
  File "scripts/train.py", line 103, in fine_tune
    trainer.fit(model, dataloaders["train"], dataloaders["dev"])
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 552, in fit
    self._run(model)
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 922, in _run
    self._dispatch()
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 990, in _dispatch
    self.accelerator.start_training(self)
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
    self.training_type_plugin.start_training(trainer)
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 161, in start_training
    self._results = trainer.run_stage()
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1000, in run_stage
    return self._run_train()
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1035, in _run_train
    self._run_sanity_check(self.lightning_module)
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1122, in _run_sanity_check
    self._evaluation_loop.run()
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
    self.advance(*args, **kwargs)
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 110, in advance
    dl_outputs = self.epoch_loop.run(
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 111, in run
    self.advance(*args, **kwargs)
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 126, in advance
    output = recursive_detach(output, to_cpu=self.trainer.move_metrics_to_cpu)
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/utilities/memory.py", line 44, in recursive_detach
    return apply_to_collection(in_dict, torch.Tensor, detach_and_move, to_cpu=to_cpu)
  File "/root/.local/share/virtualenvs/zero/lib/python3.8/site-packages/pytorch_lightning/utilities/apply_func.py", line 109, in apply_to_collection
    return elem_type(OrderedDict(out))
TypeError: first argument must be callable or None

Environment
* CUDA:
        - GPU:
                - GeForce RTX 2080 Ti
                - GeForce RTX 2080 Ti
                - GeForce RTX 2080 Ti
                - GeForce RTX 2080 Ti
                - GeForce RTX 2080 Ti
                - GeForce RTX 2080 Ti
                - GeForce RTX 2080 Ti
                - GeForce RTX 2080 Ti
        - available:         True
        - version:           11.1
* Packages:
        - numpy:             1.21.2
        - pyTorch_debug:     False
        - pyTorch_version:   1.9.1+cu111
        - pytorch-lightning: 1.4.9
        - tqdm:              4.62.3
* System:
        - OS:                Linux
        - architecture:
                - 64bit
                - ELF
        - processor:         x86_64
        - python:            3.8.6
        - version:           #40~20.04.1-Ubuntu SMP Wed Jan 6 10:15:55 UTC 2021

Hi! Thanks for the detailed report.
While this is not fixed, can't you convert the defaultdict to a dict before returning it?
We need to add support and tests for passing defaultdict to apply_to_collection

It seems not fully fixed this bug currently with lightning==2.0.1.post0.
More specifically, we can write a snip of code to collect data manually:
def lit_collate(batch):
    items = defaultdict(list)

    for item in batch:
        for k, v in item.items():
            items[k].append(v)

    return items

It works well in this case, however, if we do a light modification to upper code:
def lit_collate(batch):
    items = defaultdict(list)

    for item in batch:
        for k, v in item.items():
            items[k].append(v)
    if "x" in items:
        items["x"] = torch.stack(items["x"])
    return items

If "x" exists, the error will raise again.

furthermore i'm seeing issues on lightning=2.2.1 using strategy="ddp" for trainer

Hello!
I am using torchgeo with a self-defined datamodule GeoDataModule.
torchgeo==0.6.0
pytorch==2.4
lightning==2.4.0
lightning-utilities==0.11.7
Similarly, my program crashes in the validation_step with the stacktrace:
Traceback (most recent call last):
File "check_slrum.py", line 59, in <module>
    trainer.fit(model=task, datamodule=module)
File "site-packages/lightning/pytorch/trainer/trainer.py", line 538, in fit
    call._call_and_handle_interrupt(
File "site-packages/lightning/pytorch/trainer/call.py", line 46, in _call_and_handle_interrupt
    return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "site-packages/lightning/pytorch/strategies/launchers/subprocess_script.py", line 105, in launch
    return function(*args, **kwargs)
File "site-packages/lightning/pytorch/trainer/trainer.py", line 574, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
File "site-packages/lightning/pytorch/trainer/trainer.py", line 981, in _run
    results = self._run_stage()
File "site-packages/lightning/pytorch/trainer/trainer.py", line 1023, in _run_stage
    self._run_sanity_check()
File "site-packages/lightning/pytorch/trainer/trainer.py", line 1052, in _run_sanity_check
    val_loop.run()
File "site-packages/lightning/pytorch/loops/utilities.py", line 178, in _decorator
    return loop_run(self, *args, **kwargs)
File "site-packages/lightning/pytorch/loops/evaluation_loop.py", line 135, in run
    self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)
File "site-packages/lightning/pytorch/loops/evaluation_loop.py", line 396, in _evaluation_step
    output = call._call_strategy_hook(trainer, hook_name, *step_args)
File "site-packages/lightning/pytorch/trainer/call.py", line 319, in _call_strategy_hook
    output = fn(*args, **kwargs)
File "site-packages/lightning/pytorch/strategies/strategy.py", line 410, in validation_step
    return self._forward_redirection(self.model, self.lightning_module, "validation_step", *args, **kwargs)
File "site-packages/lightning/pytorch/strategies/strategy.py", line 640, in __call__
    wrapper_output = wrapper_module(*args, **kwargs)
File "site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
File "site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
File "site-packages/torch/nn/parallel/distributed.py", line 1632, in forward
    inputs, kwargs = self._pre_forward(*inputs, **kwargs)
File "site-packages/torch/nn/parallel/distributed.py", line 1537, in _pre_forward
    moved_inputs, moved_kwargs = _to_kwargs(
File "site-packages/torch/distributed/utils.py", line 266, in _to_kwargs
    _recursive_to(inputs, target_device, use_side_stream_for_tensor_copies)
File "site-packages/torch/distributed/utils.py", line 155, in _recursive_to
    res = to_map(inputs)
File "site-packages/torch/distributed/utils.py", line 145, in to_map
    return list(zip(*map(to_map, obj)))
File "site-packages/torch/distributed/utils.py", line 149, in to_map
    return [type(obj)(i) for i in zip(*map(to_map, obj.items()))]
File "site-packages/torch/distributed/utils.py", line 149, in <listcomp>
    return [type(obj)(i) for i in zip(*map(to_map, obj.items()))]
peError: first argument must be callable or None

The inputs are indeed a defaultdict too. (defaultdict(<class 'list'> .... In my conda environment I do have
if isinstance(data, defaultdict):
            return elem_type(data.default_factory, OrderedDict(out))

introduced by PRQ #10316. Can someone help me out?

authors: MathiasBaumgartinger carmocca dangthatsright densechen jshin49

repo: Lightning-AI/lightning


text:
Resume training from the last checkpoint

🐛 Bug
What am I trying to do?

Create a ModelCheckpoint callback with save_last=True.
Interrupt training the model in the middle of an an epoch.
Restart training using the resume_from_checkpoint argument of the Trainer.

Expected Behavior
The Trainer is supposed to restore the entire training state (epoch, step, LR scheduler) according to the documentation here.
Actual Behavior
If the training is interrupted during an epoch, the ModelCheckpoint callback correctly saves the model and the training state. However, when we resume training, the training actually starts from the next epoch. So let's say we interrupted training when 20% of the first epoch had finished. When we resume training, the trainer actually starts from the second epoch, thereby skipping 80% of the steps of the first epoch.
Please reproduce using the BoringModel
https://colab.research.google.com/drive/1f32UPGA8eINrz17ykt9krCfjllI3krOI?usp=sharing
To Reproduce
Please follow the steps in the BoringModel above. I am not sure if the tensorboard snippets will be visible so I have taken screenshots here:
Clean Run

Interrupted Run

Environment

CUDA:

GPU:

Tesla T4

available:         True
version:           10.1

Packages:

numpy:             1.19.4
pyTorch_debug:     True
pyTorch_version:   1.7.0+cu101
pytorch-lightning: 1.1.2
tqdm:              4.41.1

System:

OS:                Linux
architecture:

64bit

processor:         x86_64
python:            3.6.9
version:           #1 SMP Thu Jul 23 08:00:38 PDT 2020

cc @borda @carmocca @justusschock @awaelchli @ninginthecloud

At least, CheckpointConnector save trainer's global_step and load it back to trainer's global_step.
Save:
https://github.com/PyTorchLightning/pytorch-lightning/blob/d20fd8e5ab1a52747fee2cd53290a679d8b726d0/pytorch_lightning/trainer/connectors/checkpoint_connector.py#L251
Load:
https://github.com/PyTorchLightning/pytorch-lightning/blob/d20fd8e5ab1a52747fee2cd53290a679d8b726d0/pytorch_lightning/trainer/connectors/checkpoint_connector.py#L143
The issue may be in training loop.

At least, CheckpointConnector save trainer's global_step and load it back to trainer's global_step.
Save:
https://github.com/PyTorchLightning/pytorch-lightning/blob/d20fd8e5ab1a52747fee2cd53290a679d8b726d0/pytorch_lightning/trainer/connectors/checkpoint_connector.py#L251
Load:
https://github.com/PyTorchLightning/pytorch-lightning/blob/d20fd8e5ab1a52747fee2cd53290a679d8b726d0/pytorch_lightning/trainer/connectors/checkpoint_connector.py#L143
The issue may be in training loop.

@Borda any updates?

@Borda any updates?

@sourabh-nutonomy regarding your scenario what do you expect to happen, continue at the same state (global step) as it was interrupted, e.g. 20% of the first epoch, or train the first epoch again?
One case is if you use shuffle for data there is no simple way how to just continue training unless you seed your training...

@sourabh-nutonomy regarding your scenario what do you expect to happen, continue at the same state (global step) as it was interrupted, e.g. 20% of the first epoch, or train the first epoch again?
One case is if you use shuffle for data there is no simple way how to just continue training unless you seed your training...

@Borda Yes, I understand that we cannot shuffle the data the same way without seeding the training. But I think that can be left to the users to figure out. They can either seed their training or they can ignore it if they want to. But I am expecting that the training restarts at the same global step when the interruption happened (so after 20% of the first epoch in the example). I can also describe my use-case which would explain the need to start at the same global step.
We currently use AWS spot instances to train our nets. They can go down anytime so we need to resume training from the same global step when the experiment restarts. If we start from the beginning of the epoch, in the worst case we can get stuck in an infinite loop where we keep retraining the same epoch (if the spot instances keep restarting). If we start from the next epoch (like what is happening currently), we would miss lots of gradient updates. Like we are missing 80% of steps in epoch 1 in the example we are discussing. Now if the experiment restarts multiple times, it will further exacerbate the problem.
Even when we run our experiments on our in-house cluster and not spot instances, our datasets are huge. So if we retrain the first epoch, we loose a few hours.

@Borda Yes, I understand that we cannot shuffle the data the same way without seeding the training. But I think that can be left to the users to figure out. They can either seed their training or they can ignore it if they want to. But I am expecting that the training restarts at the same global step when the interruption happened (so after 20% of the first epoch in the example). I can also describe my use-case which would explain the need to start at the same global step.
We currently use AWS spot instances to train our nets. They can go down anytime so we need to resume training from the same global step when the experiment restarts. If we start from the beginning of the epoch, in the worst case we can get stuck in an infinite loop where we keep retraining the same epoch (if the spot instances keep restarting). If we start from the next epoch (like what is happening currently), we would miss lots of gradient updates. Like we are missing 80% of steps in epoch 1 in the example we are discussing. Now if the experiment restarts multiple times, it will further exacerbate the problem.
Even when we run our experiments on our in-house cluster and not spot instances, our datasets are huge. So if we retrain the first epoch, we loose a few hours.

I see it would be great to allow it, but I would rather classify it as a feature instead of bug because this is expected behavior...
Just checking and there will be multiple places in the codebase that need to be changed accordingly
https://github.com/PyTorchLightning/pytorch-lightning/blob/9ebbfece5e2c56bb5300cfffafb129e399492469/pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py#L214

I see it would be great to allow it, but I would rather classify it as a feature instead of bug because this is expected behavior...
Just checking and there will be multiple places in the codebase that need to be changed accordingly
https://github.com/PyTorchLightning/pytorch-lightning/blob/9ebbfece5e2c56bb5300cfffafb129e399492469/pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py#L214

@Borda Can you confirm this will be part of Lightning 1.2? Any timeline on that? Not having this feature is blocking us from adopting Lightning.

@Borda Can you confirm this will be part of Lightning 1.2? Any timeline on that? Not having this feature is blocking us from adopting Lightning.

Unfortunately, it won't be available in 1.2 but we are prioritizing this feature for our next release!!

I got into this issue from the one requesting to be able to save every-n-steps link.
Restraining checkpointing at every epoch is hugely innocent, especially when the dataset is large, which is the main reason I chose pytorch-lighting to organise the experiments in a more systematical way.

I think #6429 is a requirement for this. @carmocca @awaelchli @justusschock I'll update that issue with a more concrete proposal

What if you seed everything with a random number under the hood and then save this seed in the module's checkpoint?
Maybe that could keep runs stochastic but reproducible.

What if you seed everything with a random number under the hood and then save this seed in the module's checkpoint?

Sure, this is one of the things required to support this, but not the only one!

Any news on this one?

Still work in progress 📈

I have also just run into this issue. Hopefully support for it in the next release. 👍

Is resume training possible with the current version using the iter-based method? Is there a way to calculate near results in the current version? (Assuming that the seed is different and it is not reproduced)
Personally, the priority of this issue is quite high.

There are several PRs working on this, so far this is not yet supported, but we are actively working on it.

Probably due to the seed problem, the values are different, but it seems that quasi-resume training was possible.
Clean Run (20 epochs on CIFAR10 Training)
Validation Acc: 85.18

Interrupted Run (Interrupted in the middle of 10 epochs on CIFAR10 Training)
Validation Acc: 84.9

I'm using pytorch-lightning == 1.3.8, but with the current functionality, I can do this.

Fault tolerant training will be an experimental feature in 1.5. First version of the docs were recently added here. #9130 will track the progress of the remaining work.

Yes, I completely agree with you. Thanks a lot!

I think this feature is vital for fault tolerant training. Where is this currently at?

@AntreasAntoniou See this comment. https://github.com/PyTorchLightning/pytorch-lightning/issues/5325#issuecomment-913232625

The docs for fault-tolerance are here:
https://pytorch-lightning.readthedocs.io/en/stable/advanced/fault_tolerant_training.html
And there's a proposal to simplify the checkpoint management: https://github.com/PyTorchLightning/pytorch-lightning/issues/11912

Hi @carmocca , I looked at the link you posted. I can't find an example. How can I resume the training after a failure?

You want to set trainer.fit(..., ckpt_path="a_ckpt_to_resume_from").

Hi I'm still facing a KeyError: 'radam_buffer' on the Trainer.fit command on passing the ckpt_path of an interrupted checkpoint.
pytorch-forecasting==0.10.1
pytorch-lightning==1.8.1
Error trace:
`KeyError                                  Traceback (most recent call last)
/tmp/ipykernel_10535/1750406609.py in
4     train_dataloaders=train_dataloader,
5     val_dataloaders=val_dataloader,
----> 6     ckpt_path=best_model_path,
7 )
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
581         self.strategy._lightning_module = model
582         call._call_and_handle_interrupt(
--> 583             self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
584         )
585
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/call.py in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
36             return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
37         else:
---> 38             return trainer_fn(*args, **kwargs)
39
40     except _TunerExitException:
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in _fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
622             model_connected=self.lightning_module is not None,
623         )
--> 624         self._run(model, ckpt_path=self.ckpt_path)
625
626         assert self.state.stopped
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in _run(self, model, ckpt_path)
1055         # restore optimizers, etc.
1056         log.detail(f"{self.class.name}: restoring training state")
-> 1057         self._checkpoint_connector.restore_training_state()
1058
1059         self._checkpoint_connector.resume_end()
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py in restore_training_state(self)
294         if self.trainer.state.fn == TrainerFn.FITTING:
295             # restore optimizers and schedulers state
--> 296             self.restore_optimizers_and_schedulers()
297
298     def restore_precision_plugin_state(self) -> None:
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py in restore_optimizers_and_schedulers(self)
409                     " This is probably due to ModelCheckpoint.save_weights_only being set to True."
410                 )
--> 411             self.restore_optimizers()
412
413         if "lr_schedulers" not in self._loaded_checkpoint:
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py in restore_optimizers(self)
424
425         # restore the optimizers
--> 426         self.trainer.strategy.load_optimizer_state_dict(self._loaded_checkpoint)
427
428     def restore_lr_schedulers(self) -> None:
/opt/conda/lib/python3.7/site-packages/pytorch_lightning/strategies/strategy.py in load_optimizer_state_dict(self, checkpoint)
366         optimizer_states = checkpoint["optimizer_states"]
367         for optimizer, opt_state in zip(self.optimizers, optimizer_states):
--> 368             optimizer.load_state_dict(opt_state)
369             _optimizer_to_device(optimizer, self.root_device)
370
/opt/conda/lib/python3.7/site-packages/torch/optim/optimizer.py in load_state_dict(self, state_dict)
242         param_groups = [
243             update_group(g, ng) for g, ng in zip(groups, saved_groups)]
--> 244         self.setstate({'state': state, 'param_groups': param_groups})
245
246     def zero_grad(self, set_to_none: bool = False):
/opt/conda/lib/python3.7/site-packages/pytorch_forecasting/optim.py in setstate(self, state)
131     def setstate(self, state: dict) -> None:
132         super().setstate(state)
--> 133         self.radam_buffer = state["radam_buffer"]
134         self.alpha = state["alpha"]
135         self.k = state["k"]
KeyError: 'radam_buffer'`

@himanshudoi It looks like you are trying to reload an optimizer with a checkpoint that used a different optimizer class

authors: Alex-nutonomy AntreasAntoniou Borda GiuliaLanzillotta Keiku Wuziyi616 ananthsub awaelchli carmocca cn4750 edenlightning himanshudoi junjy007 justusschock mfoglio sourabh-nutonomy tarepan toliz


text:
fixes typing errors in rich_progress.py

What does this PR do?
Fixes mypy errors in pytorch_lightning/callbacks/progress/rich_progress.py for issue #13445.

Fixes #<issue_number>
Does your PR introduce any breaking changes? If yes, please list them.

Before submitting

[x] Was this discussed/approved via a GitHub issue? (not for typos and docs)
[x] Did you read the contributor guideline, Pull Request section?
[x] Did you make sure your PR does only one thing, instead of bundling different changes together?
[ ] Did you make sure to update the documentation with your changes? (if necessary)
[ ] Did you write any new necessary tests? (not for typos and docs)
[x] Did you verify new and existing tests pass locally with your changes?
[x] Did you list all the breaking changes introduced by this pull request?
[ ] Did you update the CHANGELOG? (not for typos, docs, test updates, or minor internal changes/refactors)

PR review
Anyone in the community is welcome to review the PR.
Before you start reviewing, make sure you have read the review guidelines. In short, see the following bullet-list:

[x] Is this pull request ready for review? (if not, please submit in draft mode)
[x] Check that all items from Before submitting are resolved
[x] Make sure the title is self-explanatory and the description concisely explains the PR
[ ] Add labels and milestones (and optionally projects) to the PR so it can be classified

Did you have fun?
Make sure you had fun coding 🙃

Some Test Pytorch full / pl-cpu (_, _, 1.9, ... runs gotAttributeError: 'EntryPoints' object has no attribute 'get'

@donlapark Don't worry about that one, it is caused by an unrelated third part. We will look into it

authors: awaelchli donlapark


text:
Support kwargs input for LayerSummary

What does this PR do?
Support kwargs input for LayerSummary #17676, and add unit test.

Before submitting

[x] Was this discussed/agreed via a GitHub issue? (not for typos and docs)
[x] Did you read the contributor guideline, Pull Request section?
[x] Did you make sure your PR does only one thing, instead of bundling different changes together?
[x] Did you make sure to update the documentation with your changes? (if necessary)
[x] Did you write any new necessary tests? (not for typos and docs)
[x] Did you verify new and existing tests pass locally with your changes?
Did you list all the breaking changes introduced by this pull request?
[x] Did you update the CHANGELOG? (not for typos, docs, test updates, or minor internal changes/refactors)

PR review
Anyone in the community is welcome to review the PR.
Before you start reviewing, make sure you have read the review guidelines. In short, see the following bullet-list:

Reviewer checklist

[ ] Is this pull request ready for review? (if not, please submit in draft mode)
[ ] Check that all items from Before submitting are resolved
[ ] Make sure the title is self-explanatory and the description concisely explains the PR
[ ] Add labels and milestones (and optionally projects) to the PR so it can be classified

@leng-yue CI is raising some issues. Looks like with_kwargs= does not exist before torch 2.0
Traceback (most recent call last):
  File "/Users/runner/work/lightning/lightning/tests/legacy/simple_classif_training.py", line 56, in <module>
    main_train(path_dir)
  File "/Users/runner/work/lightning/lightning/tests/legacy/simple_classif_training.py", line 46, in main_train
    trainer.fit(model, datamodule=dm)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 538, in fit
    call._call_and_handle_interrupt(
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py", line 42, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 577, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 962, in _run
    call._call_callback_hooks(self, "on_fit_start")
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py", line 189, in _call_callback_hooks
    fn(trainer, trainer.lightning_module, *args, **kwargs)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_summary.py", line 60, in on_fit_start
    model_summary = self._summary(trainer, pl_module)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_summary.py", line 74, in _summary
    return summarize(pl_module, max_depth=self._max_depth)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/utilities/model_summary/model_summary.py", line 462, in summarize
    return ModelSummary(lightning_module, max_depth=max_depth)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/utilities/model_summary/model_summary.py", line 201, in __init__
    self._layer_summary = self.summarize()
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/utilities/model_summary/model_summary.py", line 260, in summarize
    summary = OrderedDict((name, LayerSummary(module)) for name, module in self.named_modules)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/utilities/model_summary/model_summary.py", line 260, in <genexpr>
    summary = OrderedDict((name, LayerSummary(module)) for name, module in self.named_modules)
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/utilities/model_summary/model_summary.py", line 79, in __init__
    self._hook_handle = self._register_hook()
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/utilities/model_summary/model_summary.py", line 107, in _register_hook
    handle = self._module.register_forward_hook(hook, with_kwargs=True)
TypeError: Module.register_forward_hook() got an unexpected keyword argument 'with_kwargs'
Exception ignored in: <function LayerSummary.__del__ at 0x1300868c0>
Traceback (most recent call last):
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/utilities/model_summary/model_summary.py", line 84, in __del__
    self.detach_hook()
  File "/Users/runner/hostedtoolcache/Python/3.10.11/x64/lib/python3.10/site-packages/lightning/pytorch/utilities/model_summary/model_summary.py", line 115, in detach_hook
    if self._hook_handle is not None:
AttributeError: 'LayerSummary' object has no attribute '_hook_handle'

You need to pass it conditionally based on _TORCH_GREATER_EQUAL_2_0.
And for the 2.0 tests, the following are failing:
FAILED utilities/test_model_summary.py::test_example_input_array_types[example_input3-?-1] - AssertionError: assert [[1, 2, 3]] == ['?']
  At index 0 diff: [1, 2, 3] != '?'
  Full diff:
  - ['?']
  + [[1, 2, 3]]
FAILED utilities/test_model_summary.py::test_example_input_array_types[example_input3-?--1] - AssertionError: assert [[1, 2, 3]] == ['?']
  At index 0 diff: [1, 2, 3] != '?'
  Full diff:
  - ['?']
  + [[1, 2, 3]]

I removed the bug label because PyTorch added this functionality in newer versions and so the expectation can' be that Lightning was supposed to support this in the past. This is a new (and great) feature and extends the flexibility of the layer summary 🎉

authors: awaelchli carmocca leng-yue


text:
Using open-llama instead of LLaMA

I have tried to convert open-llama from https://huggingface.co/openlm-research/open_llama_7b_preview_200bt/tree/main, converting from transformers version to "normal" using convert_hf_checkpoint and then convert_checkpoint, it failed in later one at:
  converted["transformer.wte.weight"] = state_dict["tok_embeddings.weight"].to(dtype)
  converted["lm_head.weight"] = state_dict["output.weight"].to(dtype)
  converted["transformer.ln_f.scale"] = state_dict["norm.weight"].to(dtype)

Keys tok_embeddings.weight, output.weight and norm.weight are missing in state_dict. I tried avoiding that stating
converted = state_dict
but I got nothing, inference was broken. I am pretty much sure I was shooting in a dark with an empty gun. Any help is appreciated.

OpenLLaMA used https://github.com/young-geng/EasyLM to train their checkpoint. It's likely that there's a mismatch in the weights. We would need a specific conversion for it.
The current convert_hf_checkpoint code is tailored to weights generated by the original LLaMA implementation from https://github.com/facebookresearch/llama

Seems to work fine with the latest weights with 300bt

$ python generate.py --prompt "what is the meaning of life?"
Loading model ...
Time to load model: 22.46 seconds.
Global seed set to 1234
what is the meaning of life? What is the meaning of life? What does it mean to be happy? How do I choose my life goals? What makes a good life? How do I become an achievement-oriented
person? What is the secret to happiness? How do I achieve
Time for inference 1: 3.50 sec total, 14.27 tokens/sec
Memory used: 13.52 GB

Oh nice! Can you confirm the steps you ran?

@lantiga I just copied the transformer weights are ran the convert script
python scripts/convert_hf_checkpoint.py --model_size 7B

Awesome, thank you! We'll add to the howto and feature this in the README

authors: carmocca kstevica lantiga vihangd

repo: Lightning-AI/lit-llama


text:
windows

Can you modify the code so that it can read the data set correctly under the windows system?

sorry, i do not have a windows machine.

repo: LikeLy-Journey/SegmenTron


text:
Crashes on 1.19

Not really sure what's causing the problem here - I have the appropriate mods listed on the CurseForge page...
Description: Initializing game

java.lang.RuntimeException: Could not execute entrypoint stage 'main' due to errors, provided by 'endgoblintraders'!
	at net.fabricmc.loader.impl.entrypoint.EntrypointUtils.lambda$invoke0$0(EntrypointUtils.java:51)
	at net.fabricmc.loader.impl.util.ExceptionUtil.gatherExceptions(ExceptionUtil.java:33)
	at net.fabricmc.loader.impl.entrypoint.EntrypointUtils.invoke0(EntrypointUtils.java:49)
	at net.fabricmc.loader.impl.entrypoint.EntrypointUtils.invoke(EntrypointUtils.java:35)
	at net.fabricmc.loader.impl.game.minecraft.Hooks.startClient(Hooks.java:52)
	at net.minecraft.class_310.<init>(class_310.java:443)
	at net.minecraft.client.main.Main.main(Main.java:198)
	at net.fabricmc.loader.impl.game.minecraft.MinecraftGameProvider.launch(MinecraftGameProvider.java:461)
	at net.fabricmc.loader.impl.launch.knot.Knot.launch(Knot.java:74)
	at net.fabricmc.loader.impl.launch.knot.KnotClient.main(KnotClient.java:23)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:568)
	at org.multimc.impl.OneSixLauncher.invokeMain(OneSixLauncher.java:104)
	at org.multimc.impl.OneSixLauncher.launchWithMainClass(OneSixLauncher.java:175)
	at org.multimc.impl.OneSixLauncher.launch(OneSixLauncher.java:185)
	at org.multimc.EntryPoint.listen(EntryPoint.java:127)
	at org.multimc.EntryPoint.main(EntryPoint.java:57)
Caused by: java.lang.RuntimeException: java.lang.reflect.InvocationTargetException
	at me.shedaniel.autoconfig.util.Utils.constructUnsafely(Utils.java:49)
	at me.shedaniel.autoconfig.serializer.Toml4jConfigSerializer.createDefault(Toml4jConfigSerializer.java:84)
	at me.shedaniel.autoconfig.serializer.Toml4jConfigSerializer.deserialize(Toml4jConfigSerializer.java:78)
	at me.shedaniel.autoconfig.ConfigManager.load(ConfigManager.java:92)
	at me.shedaniel.autoconfig.ConfigManager.<init>(ConfigManager.java:53)
	at me.shedaniel.autoconfig.AutoConfig.register(AutoConfig.java:66)
	at com.jab125.egt.EGobT.onInitialize(EGobT.java:42)
	at net.fabricmc.loader.impl.entrypoint.EntrypointUtils.invoke0(EntrypointUtils.java:47)
	... 16 more
Caused by: java.lang.reflect.InvocationTargetException
	at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
	at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:77)
	at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
	at java.base/java.lang.reflect.Constructor.newInstanceWithCaller(Constructor.java:499)
	at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:480)
	at me.shedaniel.autoconfig.util.Utils.constructUnsafely(Utils.java:47)
	... 23 more
Caused by: java.lang.NoSuchFieldError: field_25407
	at com.jab125.egt.config.EndGoblinTradersConfig$EndGoblinTrader.<init>(EndGoblinTradersConfig.java:28)
	at com.jab125.egt.config.EndGoblinTradersConfig.<init>(EndGoblinTradersConfig.java:17)
	... 29 more

A detailed walkthrough of the error, its code path and all known details is as follows:
---------------------------------------------------------------------------------------

-- Head --
Thread: Render thread
Stacktrace:
	at net.fabricmc.loader.impl.entrypoint.EntrypointUtils.lambda$invoke0$0(EntrypointUtils.java:51)
	at net.fabricmc.loader.impl.util.ExceptionUtil.gatherExceptions(ExceptionUtil.java:33)
	at net.fabricmc.loader.impl.entrypoint.EntrypointUtils.invoke0(EntrypointUtils.java:49)
	at net.fabricmc.loader.impl.entrypoint.EntrypointUtils.invoke(EntrypointUtils.java:35)
	at net.fabricmc.loader.impl.game.minecraft.Hooks.startClient(Hooks.java:52)
	at net.minecraft.class_310.<init>(class_310.java:443)

-- Initialization --
Details:
	Modules:
Stacktrace:
	at net.minecraft.client.main.Main.main(Main.java:198)
	at net.fabricmc.loader.impl.game.minecraft.MinecraftGameProvider.launch(MinecraftGameProvider.java:461)
	at net.fabricmc.loader.impl.launch.knot.Knot.launch(Knot.java:74)
	at net.fabricmc.loader.impl.launch.knot.KnotClient.main(KnotClient.java:23)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:568)
	at org.multimc.impl.OneSixLauncher.invokeMain(OneSixLauncher.java:104)
	at org.multimc.impl.OneSixLauncher.launchWithMainClass(OneSixLauncher.java:175)
	at org.multimc.impl.OneSixLauncher.launch(OneSixLauncher.java:185)
	at org.multimc.EntryPoint.listen(EntryPoint.java:127)
	at org.multimc.EntryPoint.main(EntryPoint.java:57)

-- System Details --
Details:
	Minecraft Version: 1.19
	Minecraft Version ID: 1.19
	Operating System: Linux (amd64) version 5.18.9-arch1-1
	Java Version: 17.0.3, N/A
	Java VM Version: OpenJDK 64-Bit Server VM (mixed mode), Oracle Corporation
	Memory: 1488092624 bytes (1419 MiB) / 4408213504 bytes (4204 MiB) up to 8589934592 bytes (8192 MiB)
	CPUs: 12
	Processor Vendor: AuthenticAMD
	Processor Name: AMD Ryzen 5 3600 6-Core Processor
	Identifier: AuthenticAMD Family 23 Model 113 Stepping 0
	Microarchitecture: unknown
	Frequency (GHz): -0.00
	Number of physical packages: 1
	Number of physical CPUs: 6
	Number of logical CPUs: 12
	Graphics card #0 name: TU104 [GeForce RTX 2070 SUPER]
	Graphics card #0 vendor: NVIDIA Corporation (0x10de)
	Graphics card #0 VRAM (MB): 288.00
	Graphics card #0 deviceId: 0x1e84
	Graphics card #0 versionInfo: unknown
	Virtual memory max (MB): 12069.10
	Virtual memory used (MB): 6914.59
	Swap memory total (MB): 4096.00
	Swap memory used (MB): 211.00
	JVM Flags: 2 total; -Xms512m -Xmx8192m
	Fabric Mods:
		additionaladditions: Additional Additions 5.0.1
			libgui: LibGui 6.0.0-beta.5+1.19-pre1
				jankson: Jankson 4.1.1+j1.2.1
					blue_endless_jankson: jankson 1.2.1
				libninepatch: LibNinePatch 1.1.0
		appleskin: AppleSkin 2.4.0+mc1.19
		architectury: Architectury 5.7.28
		badpackets: Bad Packets 0.1.2
		balm-fabric: Balm 4.3.0+0
		betteradvancements: Better Advancements 0.2.1.134
		betteranimalsplus: Better Animals Plus 1.19-11.0.6
		betterf3: BetterF3 1.3.1
		blossom: Blossom 1.0.5
		bookshelf: Bookshelf 14.0.5
		byg: Oh The Biomes You'll Go 2.0.0.5
		charm: Charm 4.1.2
			com_moandjiezana_toml_toml4j: toml4j 0.7.2
		cleancut: CleanCut 1.19-5.0-fabric
		cloth-api: Cloth API 4.0.65
			cloth-basic-math: cloth-basic-math 0.6.1
			cloth-client-events-v0: Cloth Client Events v0 4.0.65
			cloth-common-events-v1: Cloth Common Events v1 4.0.65
			cloth-scissors-api-v1: Cloth Scissors API v1 4.0.65
			cloth-utils-v1: Cloth Utils v1 4.0.65
		cloth-config: Cloth Config v6 7.0.72
		clumps: Clumps 9.0.0+3
		controlling: Controlling For Fabric 10.0+1
		cookingforblockheads: Cooking for Blockheads 13.1.2
		croptopia: Croptopia 2.0.6
			com_typesafe_config: config 1.4.1
			io_leangen_geantyref_geantyref: geantyref 1.3.11
			org_spongepowered_configurate-core: configurate-core 4.1.2
			org_spongepowered_configurate-hocon: configurate-hocon 4.1.2
		darkpaintings: DarkPaintings 11.0.2
		darkutils: DarkUtilities 11.0.2
		ecologics: Ecologics 2.1.1
		enchdesc: EnchantmentDescriptions 11.0.5
		endgoblintraders: End Goblin Traders 1.4.4
		essential: Essential 3160+feature-fabric-1.19
		essential-container: essential-container 1.0.0
			essential-loader: essential-loader 1.0.0
		fabric: Fabric API 0.56.3+1.19
			fabric-api-base: Fabric API Base 0.4.9+e62f51a3a9
			fabric-api-lookup-api-v1: Fabric API Lookup API (v1) 1.6.7+9ff28f40a9
			fabric-biome-api-v1: Fabric Biome API (v1) 9.0.14+b2a4a624a9
			fabric-blockrenderlayer-v1: Fabric BlockRenderLayer Registration (v1) 1.1.18+9ff28f40a9
			fabric-command-api-v1: Fabric Command API (v1) 1.2.6+f71b366fa9
			fabric-command-api-v2: Fabric Command API (v2) 2.1.2+0d55f585a9
			fabric-commands-v0: Fabric Commands (v0) 0.2.23+df3654b3a9
			fabric-containers-v0: Fabric Containers (v0) 0.1.27+df3654b3a9
			fabric-content-registries-v0: Fabric Content Registries (v0) 3.1.1+82552130a9
			fabric-convention-tags-v1: Fabric Convention Tags 1.0.8+37622d24a9
			fabric-crash-report-info-v1: Fabric Crash Report Info (v1) 0.2.3+bd0a0d4aa9
			fabric-data-generation-api-v1: Fabric Data Generation API (v1) 5.1.1+a680b9b4a9
			fabric-dimensions-v1: Fabric Dimensions API (v1) 2.1.27+72da3b3da9
			fabric-entity-events-v1: Fabric Entity Events (v1) 1.4.16+9ff28f40a9
			fabric-events-interaction-v0: Fabric Events Interaction (v0) 0.4.26+9ff28f40a9
			fabric-events-lifecycle-v0: Fabric Events Lifecycle (v0) 0.2.25+df3654b3a9
			fabric-game-rule-api-v1: Fabric Game Rule API (v1) 1.0.19+18990361a9
			fabric-item-api-v1: Fabric Item API (v1) 1.5.4+767db26ea9
			fabric-item-groups-v0: Fabric Item Groups (v0) 0.3.24+9ff28f40a9
			fabric-key-binding-api-v1: Fabric Key Binding API (v1) 1.0.18+9ff28f40a9
			fabric-keybindings-v0: Fabric Key Bindings (v0) 0.2.16+df3654b3a9
			fabric-lifecycle-events-v1: Fabric Lifecycle Events (v1) 2.1.0+33fbc738a9
			fabric-loot-api-v2: Fabric Loot API (v2) 1.0.2+9e7660c6a9
			fabric-loot-tables-v1: Fabric Loot Tables (v1) 1.1.2+9e7660c6a9
			fabric-message-api-v1: Fabric Message API (v1) 1.0.1+513f4a59a9
			fabric-mining-level-api-v1: Fabric Mining Level API (v1) 2.1.9+33fbc738a9
			fabric-models-v0: Fabric Models (v0) 0.3.15+9ff28f40a9
			fabric-networking-api-v1: Fabric Networking API (v1) 1.0.27+7fe97409a9
			fabric-networking-v0: Fabric Networking (v0) 0.3.14+df3654b3a9
			fabric-object-builder-api-v1: Fabric Object Builder API (v1) 4.0.6+9ff28f40a9
			fabric-particles-v1: Fabric Particles (v1) 1.0.8+dc39553aa9
			fabric-registry-sync-v0: Fabric Registry Sync (v0) 0.9.17+23c4cfefa9
			fabric-renderer-api-v1: Fabric Renderer API (v1) 1.0.8+9ff28f40a9
			fabric-renderer-indigo: Fabric Renderer - Indigo 0.6.9+1f92344ea9
			fabric-renderer-registries-v1: Fabric Renderer Registries (v1) 3.2.18+df3654b3a9
			fabric-rendering-data-attachment-v1: Fabric Rendering Data Attachment (v1) 0.3.12+9ff28f40a9
			fabric-rendering-fluids-v1: Fabric Rendering Fluids (v1) 3.0.5+9ff28f40a9
			fabric-rendering-v0: Fabric Rendering (v0) 1.1.20+df3654b3a9
			fabric-rendering-v1: Fabric Rendering (v1) 1.10.14+9ff28f40a9
			fabric-resource-conditions-api-v1: Fabric Resource Conditions API (v1) 2.0.9+e62f51a3a9
			fabric-resource-loader-v0: Fabric Resource Loader (v0) 0.5.4+db8596f1a9
			fabric-screen-api-v1: Fabric Screen API (v1) 1.0.23+9ff28f40a9
			fabric-screen-handler-api-v1: Fabric Screen Handler API (v1) 1.2.6+9ff28f40a9
			fabric-textures-v0: Fabric Textures (v0) 1.0.18+9ff28f40a9
			fabric-transfer-api-v1: Fabric Transfer API (v1) 2.0.9+e62f51a3a9
			fabric-transitive-access-wideners-v1: Fabric Transitive Access Wideners (v1) 1.1.1+9e7660c6a9
		fabricloader: Fabric Loader 0.14.8
		farmersdelight: Farmer's Delight 1.19-0.3.0
		farmingforblockheads: Farming for Blockheads 11.0.1
		foodplusid: Food+ 1.4.1
		fpsreducer: FPS Reducer 1.19-2.0.1
		ftbbackups2: FTB Backups 2 1.0.12
		ftbchunks: FTB Chunks 1900.3.7-build.154
		ftblibrary: FTB Library 1900.3.7-build.131
		ftbteams: FTB Teams 1900.2.7-build.56
		ftbultimine: FTB Ultimine 1900.3.4-build.58
		goblintraders: Goblin Traders 1.5.2
		indium: Indium 1.0.6+mc1.19
		inventoryhud: Inventory HUD + 3.4.2
		iris: Iris 1.2.5
			org_anarres_jcpp: jcpp 1.4.14
		ironchests: Iron Chests: Restocked 3.0.2
		java: OpenJDK 64-Bit Server VM 17
		jei: Just Enough Items 11.0.0.206
		lambdynlights: LambDynamicLights 2.1.2+1.19
			pride: Pride Lib 1.1.2+1.19
			spruceui: SpruceUI 4.0.0+1.19
		lithium: Lithium 0.8.0
		mcwbridges: Macaw's Bridges 2.0.3
		mcwdoors: Macaw's Doors 1.0.6
		mcwpaintings: Macaw's Paintings 1.0.3
		minecraft: Minecraft 1.19
		modmenu: Mod Menu 4.0.0
		more-axolotls: More Axolotls 1.0.2
		moreberries: More Berries 1.5.1
		morevillagers: More Villagers 4.0.2
		mousetweaks: Mouse Tweaks 2.22
		myloot: myLoot 3.0-1.19
			omega-config: OmegaConfig 1.2.3-1.18.1
		naturescompass: Nature's Compass 1.19-2.0.4-fabric
		phosphor: Phosphor 0.8.1
		repurposed_structures: Repurposed Structures 6.0.7+1.19
			midnightlib: MidnightLib 0.5.2
		right-click-harvest: Right-Click-Harvest 1.2.0
		simple_backpack: SimpleBackpack 1.3.16
		sodium: Sodium 0.4.2+build.16
			org_joml_joml: joml 1.10.4
		spawnermod: Enhanced Mob Spawners 1.1.4
		spoornpacks: SpoornPacks 4.3.1-1.19
			com_fasterxml_jackson_core_jackson-annotations: jackson-annotations 2.13.1
			com_fasterxml_jackson_core_jackson-core: jackson-core 2.13.1
			com_fasterxml_jackson_core_jackson-databind: jackson-databind 2.13.1
		terrablender: TerraBlender 2.0.0.113
			com_electronwill_night-config_core: core 3.6.4
			com_electronwill_night-config_toml: toml 3.6.4
		thonkutil: ThonkUtil 2.15.3+1.19
			thonkutil-base: ThonkUtil Base 1.13.1+575521ba57
			thonkutil-capes-v1: ThonkUtil Capes (v1) 1.4.1+7ce8ef6257
			thonkutil-coords-v1: ThonkUtil Coords (v1) 1.1.1+8ff533c957
			thonkutil-customization-v1: ThonkUtil Customization (v1) 1.1.1+8ff533c957
			thonkutil-legacy: ThonkUtil Legacy 1.1.1+5d4263f557
			thonkutil-modchecker-v1: ThonkUtil ModChecker (v1) 1.1.1+8ff533c957
			thonkutil-potions-v0: ThonkUtil Potions (v0) 1.5.1+8ff533c957
			thonkutil-titlescreen-v1: ThonkUtil TitleScreen (v1) 1.2.1+8ff533c957
			thonkutil-trades-v1: ThonkUtil Trades (v1) 1.2.1+8ff533c957
		toms_storage: Tom's Simple Storage Mod 1.3.5
		torohealth: ToroHealth Damage Indicators 1.19-fabric-1
		trinkets: Trinkets 3.4.0
			cardinal-components-base: Cardinal Components API (base) 5.0.0-beta.1
			cardinal-components-entity: Cardinal Components API (entities) 5.0.0-beta.1
		universal-graves: Universal Graves 2.1.0+1.19
			common-protection-api: Common Protection API 1.0.0
			fabric-permissions-api-v0: fabric-permissions-api 0.1-SNAPSHOT
			hologram-api: Hologram API 0.2.2+1.19
			placeholder-api: Placeholder API 2.0.0-beta.6+1.19
			polymer: Polymer (Core) 0.2.2+1.19
				polymer-registry-sync-manipulator: Polymer Registry Sync Manipulator 0.0.1+1.19
			server_translations_api: Server Translations API 1.4.16+1.19
				packet_tweaker: Packet Tweaker 0.3.0+1.18.2
			sgui: sgui 1.1.1+1.19
		waystones: Waystones 11.0.1
		wthit: wthit 5.4.3
	Launched Version: 1.19
	Backend library: LWJGL version 3.3.1 SNAPSHOT
	Backend API: Unknown
	Window size: <not initialized>
	GL Caps: Using framebuffer using OpenGL 3.2
	GL debug messages: <disabled>
	Using VBOs: Yes
	Is Modded: Definitely; Client brand changed to 'fabric'
	Type: Client (map_client.txt)

@Jab125 Then you might have a problem.
https://www.curseforge.com/minecraft/mc-mods/end-goblin-traders-fabric/files/3682602
Latest build is listed as 1.19 compatible

repo: LimeAppleBoat/EndGoblinTraders


text:
🛑 SearXNG is down

In 0e9c548, SearXNG (https://searxng.linerly.tk) was down:

HTTP code: 0
Response time: 0 ms

Resolved: SearXNG is back up in 3bcbdad.

repo: Linerly/status


text:
🛑 LinerlyBot (Discord) is down

In 2ebe557, LinerlyBot (Discord) (https://linerlybot-discord.linerly.tk) was down:

HTTP code: 0
Response time: 0 ms

Resolved: LinerlyBot (Discord) is back up in eb2f7a4.


text:
🛑 SearXNG is down

In 5283b5a, SearXNG (https://searxng.linerly.repl.co) was down:

HTTP code: 0
Response time: 0 ms

Resolved: SearXNG is back up in ca31a2f.


text:
Trying to upgrade rust results in a crash

Please fill out the issue checklist below and provide all the requested information.

[ ] ran a brew command and reproduced the problem with multiple formulae? If it's a problem with a single, official formula (not cask) please file this issue at Linuxbrew/homebrew-core: https://github.com/Linuxbrew/homebrew-core/issues/new/choose. If it's a brew cask problem please file this issue at https://github.com/Homebrew/homebrew-cask/issues/new/choose. If it's a tap (e.g. Homebrew/homebrew-php) problem please file this issue at the tap.
[x] ran brew update and can still reproduce the problem?
[x] ran brew doctor, fixed all issues and can still reproduce the problem?
[x] ran brew config and brew doctor and included their output with your issue?

brew doctor
Please note that these warnings are just used to help the Homebrew maintainers
with debugging if you file an issue. If everything you use Homebrew for is
working fine: please don't worry or file an issue; just ignore this. Thanks!

Warning: "config" scripts exist outside your system or Homebrew directories.
`./configure` scripts often look for *-config scripts to determine if
software packages are installed, and what additional flags to use when
compiling and linking.

Having additional scripts in your path can confuse software installed via
Homebrew if the config script overrides a system or Homebrew provided
script of the same name. We found the following "config" scripts:
  /home/omer/.pyenv/shims/python3-config
  /home/omer/.pyenv/shims/python3.7m-config
  /home/omer/.pyenv/shims/python3.7-config
  /home/omer/.pyenv/shims/python-config

HOMEBREW_VERSION: 1.8.1
ORIGIN: https://github.com/Linuxbrew/brew
HEAD: 4c4ed05a7d5755ed8c7e9f974959276216c5cb26
Last commit: 16 hours ago
Core tap ORIGIN: https://github.com/Linuxbrew/homebrew-core
Core tap HEAD: d5d3078fe2f492c8de697c43e5e3ad9d13db9ecb
Core tap last commit: 16 hours ago
HOMEBREW_PREFIX: /home/linuxbrew/.linuxbrew
HOMEBREW_CACHE: /home/omer/.cache/Homebrew
HOMEBREW_DEV_CMD_RUN: 1
CPU: octa-core 64-bit skylake
Homebrew Ruby: 2.5.1 => /usr/bin/ruby2.5
Clang: N/A
Git: 2.19.1 => /home/linuxbrew/.linuxbrew/bin/git
Curl: 7.58.0 => /usr/bin/curl
Kernel: Linux 4.15.0-38-generic x86_64 GNU/Linux
OS: Ubuntu 18.04.1 LTS (bionic)
Host glibc: 2.27
/usr/bin/gcc: 7.3.0
glibc: N/A
gcc: 5.5.0_4
xorg: N/A

To help us debug your issue please explain:

What you were trying to do (and why)
brew upgrade
What happened (include command output)

brew upgrade
Updating Homebrew...
==> Auto-updated Homebrew!
Updated Homebrew from f976d4242 to 4c4ed05a7.
Updated 1 tap (homebrew/core).
==> New Formulae
gitmoji                                  healpix
==> Updated Formulae
rust ✔                                   kubeless
ansifilter                               kubernetes-service-catalog-client
ark                                      libtensorflow
bison                                    libtiff
bitwarden-cli                            lumo
botan                                    lxc
cargo-completion                         mosquitto
chronograf                               mps-youtube
cling                                    nasm
confluent-oss                            netdata
dash                                     php
doitlive                                 php@7.1
emscripten                               quicktype
eslint                                   scrcpy
exploitdb                                shfmt
firebase-cli                             skaffold
gitversion                               sops
goreleaser                               syncthing
hugo                                     tunnel
i2pd                                     wskdeploy
krakend                                  yle-dl

==> Upgrading 1 outdated package:
rust 1.30.0 -> 1.30.1
==> Upgrading rust
==> Downloading https://static.rust-lang.org/dist/rustc-1.30.1-src.tar.gz
######################################################################## 100.0%
==> ./configure --prefix=/home/linuxbrew/.linuxbrew/Cellar/rust/1.30.1 --release
==> make
==> make install
==> Downloading https://static.rust-lang.org/dist/2018-10-12/cargo-0.30.0-x86_64
Already downloaded: /home/omer/.cache/Homebrew/downloads/24083ea93fbc9f3d294c3969a45d313836941c621f49000455a0caf965e2d793--cargo-0.30.0-x86_64-unknown-linux-gnu.tar.gz
==> ./install.sh --prefix=/tmp/rust-20181112-12016-tip4g2/rustc-1.30.1-src/cargo
==> Cloning https://github.com/rust-lang/cargo.git
Updating /home/omer/.cache/Homebrew/rust--cargo--git
From https://github.com/rust-lang/cargo
 * [new tag]         0.31.1     -> 0.31.1
 * [new tag]           0.1.0      -> 0.1.0
 * [new tag]           0.10.0     -> 0.10.0
 * [new tag]           0.11.0     -> 0.11.0
 * [new tag]           0.12.0     -> 0.12.0
 * [new tag]           0.13.0     -> 0.13.0
 * [new tag]           0.14.0     -> 0.14.0
 * [new tag]           0.2.0      -> 0.2.0
 * [new tag]           0.3.0      -> 0.3.0
 * [new tag]           0.31.0     -> 0.31.0
 * [new tag]           0.4.0      -> 0.4.0
 * [new tag]           0.5.0      -> 0.5.0
 * [new tag]           0.6.0      -> 0.6.0
 * [new tag]           0.7.0      -> 0.7.0
 * [new tag]           0.8.0      -> 0.8.0
 * [new tag]           0.9.0      -> 0.9.0
 * [new tag]           v0.0.1-pre -> v0.0.1-pre
==> Checking out tag 0.31.1
Previous HEAD position was 524a578d Auto merge of #5867 - ehuss:test-example-help, r=alexcrichton
HEAD is now at a1a4ad37 Auto merge of #6253 - ehuss:backport-msys-progress, r=alexcrichton
HEAD is now at a1a4ad37 Auto merge of #6253 - ehuss:backport-msys-progress, r=alexcrichton
==> cargo install --root /home/linuxbrew/.linuxbrew/Cellar/rust/1.30.1 --path .
==> Downloading https://github.com/racer-rust/racer/archive/2.0.14.tar.gz
Already downloaded: /home/omer/.cache/Homebrew/downloads/73aa48eccbd3a7fc42ea3de581301e54abd6bce79650133436fb48a07cced1ad--racer-2.0.14.tar.gz
==> cargo install --root /home/linuxbrew/.linuxbrew/Cellar/rust/1.30.1/libexec -
Error: wrong number of arguments (given 1, expected 2)
Please report this bug:
  https://github.com/Linuxbrew/brew/wiki/troubleshooting
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/exceptions.rb:548:in `block in initialize'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/exceptions.rb:557:in `map'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/exceptions.rb:557:in `initialize'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/utils/popen.rb:10:in `new'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/utils/popen.rb:10:in `safe_popen_read'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/os/linux/elf.rb:134:in `needed_libraries_using_readelf'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/os/linux/elf.rb:111:in `needed_libraries'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/os/linux/elf.rb:87:in `initialize'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/os/linux/elf.rb:150:in `new'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/os/linux/elf.rb:150:in `metadata'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/os/linux/elf.rb:158:in `dynamically_linked_libraries'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/linkage_checker.rb:98:in `block in check_dylibs'
/usr/lib/ruby/2.5.0/pathname.rb:565:in `block in find'
/usr/lib/ruby/2.5.0/find.rb:49:in `block (2 levels) in find'
/usr/lib/ruby/2.5.0/find.rb:48:in `catch'
/usr/lib/ruby/2.5.0/find.rb:48:in `block in find'
/usr/lib/ruby/2.5.0/find.rb:43:in `each'
/usr/lib/ruby/2.5.0/find.rb:43:in `find'
/usr/lib/ruby/2.5.0/pathname.rb:565:in `find'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/linkage_checker.rb:91:in `check_dylibs'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/extend/os/linux/linkage_checker.rb:23:in `check_dylibs'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/linkage_checker.rb:25:in `initialize'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/formula_installer.rb:638:in `new'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/formula_installer.rb:638:in `block in finish'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/cache_store.rb:15:in `use'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/formula_installer.rb:635:in `finish'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/cmd/upgrade.rb:164:in `upgrade_formula'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/cmd/upgrade.rb:109:in `block in upgrade_formulae'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/cmd/upgrade.rb:106:in `each'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/cmd/upgrade.rb:106:in `upgrade_formulae'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/cmd/upgrade.rb:84:in `upgrade'
/home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/brew.rb:89:in `<main>'

What you expected to happen
Upgrade should complete successfully.
Step-by-step reproduction instructions (by running brew commands)
No idea how to reproduce this.

I have seen this error multiple times. I'm not sure where it occurs exactly.

repo: Linuxbrew/brew


text:
glibc: Requires gcc ≥ 4.4. These critical programs are missing or too old: gcc

I am unable to install gcc. Installing gcc pulls in glibc as dependency which then fails with too old gcc.
~$ brew install gcc
==> Installing dependencies for gcc: binutils, linux-headers, glibc, gmp, mpfr, libmpc, isl
==> Installing gcc dependency: binutils
worked
==> Installing gcc dependency: linux-headers
worked
==> Installing gcc dependency: glibc
==> Downloading http://ftpmirror.gnu.org/glibc/glibc-2.19.tar.bz2
Already downloaded: /omd/sites/test/.cache/Homebrew/glibc-2.19.tar.bz2
==> ../configure --disable-debug --disable-dependency-tracking --disable-silent-rules --prefix=/opt/omd/sites/test/.linuxbrew/Cellar/glibc/2.19 --enable-obsolet
Last 15 lines from /omd/sites/test/.cache/Homebrew/Logs/glibc/01.configure:
checking version of gmake... 3.81, ok
checking for gnumsgfmt... no
checking for gmsgfmt... no
checking for msgfmt... msgfmt
checking version of msgfmt... 0.17, ok
checking for makeinfo... makeinfo
checking version of makeinfo... 4.12, ok
checking for sed... sed
checking version of sed... 4.1.5, ok
checking for gawk... gawk
checking version of gawk... 3.1.6, ok
checking for nm... nm
configure: error:
*** These critical programs are missing or too old: gcc
*** Check the INSTALL file for required versions.

checking for gcc... /usr/bin/gcc-4.3 -B/home/test/.linuxbrew/Cellar/binutils/2.25.1/bin/
checking version of /usr/bin/gcc-4.3 -B/home/test/.linuxbrew/Cellar/binutils/2.25.1/bin/... 4.3.4, bad

The homepage says minimum gcc is 4.2. Is this still true?
~$ brew config
HOMEBREW_VERSION: 0.9.5
ORIGIN: git://github.com/Homebrew/linuxbrew.git
HEAD: 46013be9b4019b3186ceacf39e0acb636382a269
Last commit: 9 hours ago
HOMEBREW_PREFIX: /home/test/.linuxbrew
HOMEBREW_REPOSITORY: /home/test/.linuxbrew
HOMEBREW_CELLAR: /home/test/.linuxbrew/Cellar
HOMEBREW_BOTTLE_DOMAIN: https://linuxbrew.bintray.com
CPU: 8-core 64-bit ivybridge
OS X: 0-x86_64
Xcode: N/A
CLT: N/A
Clang: N/A
X11: 2.7.8 => /usr
System Ruby: 1.8.7-p72 => /usr/bin/ruby
Perl: /usr/bin/perl
Python: /usr/bin/python => /usr/bin/python2.6
Ruby: /usr/bin/ruby
Java: N/A

Interesting. I didn't know that GCC 4.3 is not able to build glibc. GCC 4.4 definitely works. I have tested that. Which distribution of Linux are you using?
A workaround is to set
export HOMEBREW_BUILD_FROM_SOURCE=1

That did not change anything. The error is the same with HOMEBREW_BUILD_FROM_SOURCE set.
This is a sles 11 sp1 (64bit)

Try brew install gcc --without-glibc.

I too have this issue on a RHEL 5 system.  I actually have access to gcc 4.8.3 (through GNU modules) but can't figure out how to get Linuxbrew to use it... maybe reinstall Linux brew setting CC and CXX to point to 4.8.3?

@zbeekman Create symlinks for gcc like so: https://github.com/Linuxbrew/linuxbrew/wiki/Standalone-Installation#create-symlinks-for-gcc
Linuxbrew ignores CC and CXX.

@sni Did the above workaround work for you?

Unfortunatly this doesn't work either:
~$ brew install gcc --without-glibc
...
==> Installing gcc
==> Downloading http://ftpmirror.gnu.org/gcc/gcc-5.3.0/gcc-5.3.0.tar.bz2
Already downloaded: /home/test/.cache/Homebrew/gcc-5.3.0.tar.bz2
==> ../configure --prefix=/home/test/.linuxbrew/Cellar/gcc/5.3.0  --enable-languages=c,c++,objc,obj-c++,fortran --program-suffix=-5 --with-gmp=/opt/omd
==> make bootstrap
Last 15 lines from /home/test/.cache/Homebrew/Logs/gcc/02.make:
make[3]: *** [graphite-blocking.o] Error 1
make[3]: *** [graphite-isl-ast-to-gimple.o] Error 1
make[3]: *** [graphite-dependences.o] Error 1
make[3]: *** [graphite-interchange.o] Error 1
/bin/sh ../../gcc/../move-if-change tmp-attrtab.c    insn-attrtab.c
/bin/sh ../../gcc/../move-if-change tmp-dfatab.c     insn-dfatab.c
/bin/sh ../../gcc/../move-if-change tmp-latencytab.c insn-latencytab.c
echo timestamp > s-attrtab
rm gcc.pod
make[3]: Leaving directory `/tmp/gcc20160217-30409-1yd8s87/gcc-5.3.0/build/gcc'
make[2]: *** [all-stage1-gcc] Error 2
make[2]: Leaving directory `/tmp/gcc20160217-30409-1yd8s87/gcc-5.3.0/build'
make[1]: *** [stage1-bubble] Error 2
make[1]: Leaving directory `/tmp/gcc20160217-30409-1yd8s87/gcc-5.3.0/build'
make: *** [bootstrap] Error 2

Updating gcc from the sles package repository is not an option. Isn't this the hole purpose of linuxbrew? :-)

Yes, yes it is. Bootstrapping from >10 year-old systems is tricky though. It doesn't always work smoothly, but I'm working on it. CentOS 5 has GCC 4.1 by default, but can also optionally install GCC 4.4 sudo yum install gcc44 gcc44-c++. I was hoping that SLES 11 had something similar.
There's not enough info in the above log. Please report brew gist-logs gcc

Here it is: https://gist.github.com/anonymous/3fac881b110ce98c8b97
I fixed that one by removing the trailing comma in .linuxbrew/opt/isl/include/isl/ctx.h line 83
Then it fails with "cannot find -lz" when trying to link. I will continue tomorrow...
Btw, is there a list of install options like --without-glibc, i was unable to find them in the manpage.

Btw, is there a list of install options like --without-glibc, i was unable to find them in the manpage.

See brew options gcc

Then it fails with "cannot find -lz" when trying to link. I will continue tomorrow...

Please report ls /lib64/libz*
Try applying this patch:
diff --git a/Library/Formula/gcc.rb b/Library/Formula/gcc.rb
index 17dd83d..a7b17db 100644
--- a/Library/Formula/gcc.rb
+++ b/Library/Formula/gcc.rb
@@ -131,7 +131,7 @@ class Gcc < Formula
       "--with-isl=#{Formula["isl"].opt_prefix}",
     ]
     args += [
-      "--with-system-zlib",
+      ("--with-system-zlib" if OS.mac?),
       "--enable-libstdcxx-time=yes",
       "--enable-stage1-checking",
       "--enable-checking=release",

I fixed that one by removing the trailing comma in .linuxbrew/opt/isl/include/isl/ctx.h line 83

Please report this issue upstream to the authors of isl if you can.

authors: sjackman sni zbeekman

repo: Linuxbrew/linuxbrew


text:
AutoPlay 有bug

demo里面的BannerViewActivity自动滚动不行，不会自动

把 mBannerView.setCircle(false) 改成 mBannerView.setCircle(true)

把 mBannerView.setCircle(false) 改成 mBannerView.setCircle(true)
注释掉以下两行,是mBannerView.setCircle(false)时使用的
// mIndicatorDefault.setViewPager(mBannerView.getViewPager(),false);
// mIndicatorNotAnim.setViewPager(mBannerView.getViewPager(),false);

这么做是生效了，但是用在adapter刷新数据后就卡片不动了，指示器还在走，请问是为什么啊

authors: LinweiJ loechove lwlbeng

repo: LinweiJ/ViewPagerIndicator


text:
关于训练配置

你好作者！我尝试用自己的数据复现你的实验，但是报错torch.OutOfMemoryError: CUDA out of memory. 请问是因为硬件配置的问题吗？方便说一下你的硬件配置吗？

你好。这个问题的原因很可能是机器显存不足。你可以将batch_size调小一点试试，例如，你可以先尝试将batch_size设置为2。如果这样能够正常运行，你再尝试逐渐调大你的batch_size。此外，你还可以将image_size裁剪的小一些。当然，也可以使用torch.cuda.empty_cache()清理一下cuda的缓存试试。  如果上述操作都不能解决问题，建议你换一个有更大显存的显卡试试。

好的谢谢！

repo: LiuFei-AHU/MPSFFA


text:
Nextjs build can't show the page

Nextjs version:  13.4.19
destack version: 2.1.2
issue:
when I build the project and start it, the page can't show the page that i create in the dev environment.

Having the same problem, i believe is not finding the theme

@Cobb-Zhang @franciscoarruda  I had a look on the issue.
From the screenshot it seemed there was an unnecessary request to the development server. I just pushed a fix for that  https://github.com/LiveDuo/destack/commit/565b886bfa105bad9458dc4033235489fead20fe.
I suggest you update to destack 2.1.3 and start your project again. If the problem persists I would need more info to figure it out. Maybe a repo that reproduces the problem and more details about your OS and the node/npm versions you are using.

I'm using Destack-Starter.
I tried again from the start:

Downloaded Zip
Installed dependencies (npm)
Run build
Run start

The error that shows is:

Yes i did run build and the run start

I believe the error comes from this function, not sure

It should do. Although I don't get the error, I noticed that when I delete data/default.json then this exact issue appears.

@franciscoarruda It was an issue with Windows that had some differences from unix based systems such as Mac and Linux.
Should be fixed by https://github.com/LiveDuo/destack/commit/eb7ba180794b449223b7cc46f28c5d4edcdb632e and https://github.com/LiveDuo/destack/commit/506ed94c9a1a3479a5de1607987431076631728f.
I tested it on a Windows machine and it works fine. If you still experience issues let me know.

Fixed 👌🫡
Nice project btw !

Thanks!

@LiveDuo  Fixed，good job ！This project is really convenient！

Just as you say, update the lastest version which can fix the problem.

authors: Cobb-Zhang LiveDuo franciscoarruda

repo: LiveDuo/destack


text:
Theory question on warmup

Due to the lack of samples in the early stage, the adaptive learning rate has an
undesirably large variance, which leads to suspicious/bad local optima -- pg. 3

Does this apply when feeding the same dataset in a different configuration? Namely, I'm training a timeseries (16-channel EEG) CNN-LSTM classifier, and vary the input timesteps across epochs for the same model. While the information source probability distribution remains identical, what the neural net effectively "sees" differs substantially between, say, 13500 and 216000 timesteps.
This considered, is warmup for the first epoch of every new timesteps setting advisable? Thanks

Thanks for asking :- ) But I'm sorry that i'm not sure i understand this question. It will be better if you can elaborate more : -)

@LiyuanLucasLiu Sure: my model architecture is CNN --> LSTM - both are able to deal with variable timesteps. Instead of training to classify N timesteps, I vary timesteps as a form of 'warmup' and data augmentation before reaching N - so if N=216000, for example, I start with timesteps=216000//16, then //8, //4, //2, and finally 216000. While the data's the same, the information that the model receives per update/batch is different - thus, I'd suspect the optimizer would benefit from warmup on each new configuration to lower variance.
However, I personally experimented with RAdam vs Adam and RNAdam vs NAdam - the results are: R's substantially lower validation loss variance and prevent spikes, but non-R's yield better final loss and F1-score.
What I did not test: Adam/Nadam with warmup, or RAdam/RNAdam without warmup. It's possible that lower variance isn't beneficial in the long run, similar to SGD - due to converging to local minima prematurely.
Lastly: it's possible the implementations were off, or that my warmup wasn't configured properly (one full epoch or 1/2 epoch). But as is, R was bested by non-R consistently across many hyperparameters.

Thanks for letting us know : -) In this situation, I don't think there is a firm answer to the question. From my point of view, these stages are similar to each other, and the gradient statistics should be relative similar.
From the practical point of view, I think warmup may not be really necessary, since the vanilla adam works reasonable well -- it means the variance issue is not very serious (as in Figure 9 in the paper, the magnitude of the variance can be negligible in some situation). In this situation, I would recommend to:

use RAdam / RNAdam without warmup: using additional warmup may not be necessary, since the vanilla Adam works well;
tune the hyper-parameters (especially learning rates / learning rate scheduler) a little bit;

I myself didn't use tensorflow (before pytorch, i mainly use torch + lua XD), and i'm not 100% sure that it is correct (also, I just fixed a bug in my pytorch implementation, as in: this commit).
Hope it helps.

@LiyuanLucasLiu Understood - thanks for the response, and for your work

authors: LiyuanLucasLiu OverLordGoldDragon

repo: LiyuanLucasLiu/RAdam


text:
qq官方适配器不响应

我看说适配了官方的qq机器人，但我在使用的时候发现官方机器人的消息并没有响应，看了下日志显示跳过了消息处理，了解了一下说这种情况是不支持当前适配器的消息响应，跳过了消息处理，想问下是插件的qq官方适配器还没有做好吗？
注：使用的是官方的qq频道的机器人

我这边是可以正常用的

我在网上找了一些其他插件类似的问题，基本原因好像都是插件没有适配对应的适配器

还有的说是事件类型不正确

解决了，我在消息类型中添加了一个MessageEvent类型后解决了，果然还是事件类型的问题

authors: JaniQuiz Ljzd-PRO

repo: Ljzd-PRO/nonebot-plugin-mystool


text:
Keep it concise. Port to javascript

The language has become a little too complex than I expected. In the beginning, I only want these features:

inputs in various data types: decimal, hex, binary string and bytes.
define new words, compiled to opcodes.
unroll do..loop

Then, other features are added:

interpreter
literal
macros

Things began to become more and more complicated and gradually lost its initial meaning. I realized the key is just "compile to opcodes" not "a language can do anything off-chain". In fact, using simple string replacing we can inject the off-chain data into the MiniForth:
: word1
  {{ arg }} dup +
;

Just replace the {{ arg }} with any off-chain computed data, people can use any programming language to do the off-chain stuff.
Now I will focus on the 3 initial goals above, and port the compiler into JS. (the bsv.js has a great interpreter we can use it).

https://github.com/Ljzn/mini_forth/blob/master/j/mini.pegjs


text:
🛑 invest is down

In 7b3f0ff, invest (https://blog-pos-17/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: invest is back up in 1eb93a6.

repo: LloydGain/UppTIME


text:
🛑 invest is down

In f03eeec, invest (https://blog-pos-17/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: invest is back up in 1f6d403.


text:
A bunch of improvements

New Helpers

d3-arc-tween
d3-attr-tween
d3-select

Added to README

d3-select
d3-call
d3-select-all
d3-join

Added Integration Tests

d3-attr
d3-call
d3-join
d3-select-all
d3-select
d3-transition

Other

Removed log-debug

/cc @zigahertz

@taras thanks so much! merging this now, and bumping the version

authors: taras zigahertz


text:
Change Map Style

Hello,
Is it possible to define the map styles? For example: http://snazzymaps.com/
Thank you,
Miguel

Hi Corvis, how about the map type? https://developers.google.com/maps/documentation/javascript/maptypes

authors: MulaiTeam mdmoura

repo: Logicify/jquery-locationpicker-plugin


text:
[Help] 中文批量tts问题

Describe the problem you confuse
超过100字节的文本很大可能就不成功，像六脉神剑一样时灵时不灵的。请问可能是什么问题？有没有什么解决方法？先谢谢作者，除去这个还是非常好用的。

更新之后有报错信息了，报错是
转换失败 Error: Error invoking remote method 'speech': AxiosError: Request failed with status code 429

感觉好像找到问题了，没转录的应该是没读取完成，时间有点长，十条100字节左右的文本需要等待10分钟左右才能全部读取完...

repo: LokerL/tts-vue


text:
🛑 jpvir is down

In 5091370, jpvir ($JPVIR) was down:

HTTP code: 0
Response time: 0 ms

Resolved: jpvir is back up in cae2064.

repo: LonelyJupiter/UPPTIME


text:
Task 4 hash function

Fixes #4
Creates a class that generates a hash for URL shortening. All the work is being done in the utils and test folder.
Start the unit test watcher by typing the following command:
npm run test:watch

@panoz7 I'm going to go ahead and merge this and try incorporating your work from https://github.com/panoz7/urly-poc

repo: LonnyGomes/urly


text:
Poping particles shadows on dynamic mesh

Your addon's effect is so great.But when i try to generate some procedural mesh
https://imgur.com/euTGxR3
There are some particles shadow will pop out and appear randomly.(From the picture it seems aceptable ,by if run in video it looks very anoying )
Is there any way i can avoid this issue happen.The shadow will follow the direction light.And
I found it's related to the voxel?When the resolution is low,the particle shadow will become bigger.
This issue seems won't happen in the static mesh.

On the VXGI script in the inspector, you can enable "Visualize Mipmap" property to see where are those voxels at. You can then figure it out or post me a picture with mipmap visuallized so I can figure it out for myself.

https://imgur.com/K4CSUpJ
What does it means? i saw some colored voxels pop out.Is it because i generate some useless vertex in the mesh?

https://imgur.com/X2DpSYC
https://imgur.com/HrTseoE
But it seems possible,because i have made a clamp function to cut the mesh to avoid it out of the section

You can switch to wireframe mode in Unity to see if there are "tiny" triangles there.

My guess is that this is a precision error on the GPU side because of extreme steep surface (comparing at one point to another).

ok, thank u  for your suggestions.I have checked my mesh is fine.And i found a temporary solution~When i rotate the plane a little bit.Like set the Rotation (0.1,0,0),the strange particle shadow will disappear.I don't figure out why

Based on the irregularity of the mesh, I guess this is because the steepness of the slope relative to the projection used in voxelization that causes floating point errors. I will think of some way to improve it later on.

authors: Looooong Wenzy--

repo: Looooong/Unity-SRP-VXGI


text:
Hamersley Leaflet notebook

--- I got the following to work thusly, giving an error with the old version.
vtk = True
if vtk:
vtkexp = gp._plot.export_to_vtk(geo_model, path=vtk_path, name=test_data_name + '.vtk', voxels=False, block=None,
surfaces=True)

Miguel says it is a fix that he needs to make, but that will get it to work for the current version.

sure, as with LoopStructural, its better if the modelling engine takes over the export task (making it an import) since the goalposts keep moving! I'll put this in the notebooks.

authors: bluetyson markjessell


text:
Lootr Chests on New World Creation

So I am having an issue where the Lootr chests that spawn in the village I spawn at, do not open and say there is no loot table available. If I move about, say for example, 500 blocks to a new village, the chests work as they should. It is only the spawn chests in and around an area of about 100-400 blocks that seem to be affected.
I cannot open them, I have tried to refresh them all with game commands, and they still remain unopenable. I spawned in several chests and they all are afflicted with this.
I don't know what to do, please advise.

What version of Minecraft are you playing, what mod loader and what version of Lootr are you using?
The "no loot table" available is a standard part of the Jade interface. There's Jade Integrations with Lootr, but I forget exactly which mod provides it.
However, please keep in mind that there is spawn protection, and blocks don't function the same within the spawn protection area. If your village is indeed the spawn area, check to make sure that spawn protection isn't an issue; try opening them in creative mode, for example. This would potentially have the effect of not being able to open containers.

What version of Minecraft are you playing, what mod loader and what version of Lootr are you using?
The "no loot table" available is a standard part of the Jade interface. There's Jade Integrations with Lootr, but I forget exactly which mod provides it.
However, please keep in mind that there is spawn protection, and blocks don't function the same within the spawn protection area. If your village is indeed the spawn area, check to make sure that spawn protection isn't an issue; try opening them in creative mode, for example. This would potentially have the effect of not being able to open containers.

I am on Forge, 1.20.1, the modloader I am using is Prism,  and I am using 0.7.34.87 Lootr version. I will look and see if I can fidn the Jade integration modd for Lootr and report back if that helped the issue!

So I unenabled Jade and Jade Addons to see if that was the issue, and unfortunately the same issue is still happening when I try to open chests in my spawned village area. Everything that I can interact with i.e blocks, crafting tables and other ui based blocks, are working. It's just the Lootr chests in my spawn point that don't work.
Update: I also removed village spawn point mod thinking there might be incompatibility, and its still not working.

So to clarify what I meant:

Jade and Jade Addons are not going to be the cause of the problem. If you haven't opened the chest yet, you will get "loot not generated" regardless (I believe)
The default "loot not generated" whenever looking at a Lootr chest is generally a result of the mod seeing that the container has a loot table still, which, for Vanilla containers, means that loot is not generated: in Vanilla, when it generates loot, it removes the loot table to prevent it from generating again. Lootr does not remove the loot table.
I'm not sure how the Village spawn mod functions, but if it ensures that spawn is placed at a village, that also means spawn protection will be applied to that village. Removing the Village spawn mod would not have any effect.

If I move about, say for example, 500 blocks to a new village, the chests work as they should.

The question is here whether or not spawn protection is applying in this area. Spawn protection generally prevents anyone who doesn't have permission from interacting with blocks, but only applies to a specific area, and should not do anything in single-player (you appear to be in single player).
Are you able to open these chests in creative mode? That should bypass any potential spawn protection.
Are there any other mods installed that could potentially affect things?
The fact that chests are working outside of this area leads me to believe that Lootr isn't the issue.
If you aren't in single-player, you can disable spawn protection in the server properties and that should fix it.
Otherwise, if you could upload a copy of your logs when you try to interact with the blocks, that would be helpful. Further, using /data get block while looking at a chest and then using the  key to complete the coordinates and taking a screenshot of the result would help me diagnose the issue further.

authors: lemonelda noobanidus

repo: LootrMinecraft/Lootr


text:
🛑 Ronz AMOGUS Bot is down

In 23f3937, Ronz AMOGUS Bot ($RONZAMOGUS_BOT_URL) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Ronz AMOGUS Bot is back up in cb21561.

repo: LordRonz/status


text:
🛑 Ronz AMOGUS Bot is down

In 2d07f35, Ronz AMOGUS Bot ($RONZAMOGUS_BOT_URL) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Ronz AMOGUS Bot is back up in c419037.


text:
“说话”功能有http 接口可调用吗

想通过http请求去触发，让桌宠讲话，有可以实现的方法吗

宠物聊天功能本来用的好好的，更新之后无法使用，更新明明说的是修复400问题，但是这次更新修没修复我不知道，但是原来能用的用不了了，我刚跟身边的朋友推荐这游戏就很尴尬

宠物聊天功能本来用的好好的，更新之后无法使用，更新明明说的是修复400问题，但是这次更新修没修复我不知道，但是原来能用的用不了了，我刚跟身边的朋友推荐这游戏就很尴尬

发下聊天内容给我,我研究下看看

authors: Kylin93CN LorisYounger kylinZhang-pr

repo: LorisYounger/VPet


text:
link play error, can't select any song above v4.4.

I'm using 5.5.8c. I have all song packs at the server for v5.5.8, and I downloaded all the songs and can play them in single play. However, in Link Play, when I try to play songs that were released after v4.4, the games shows the screen that I don't own the song, even though I have already purchased it.
I saw a bug report that had the similar issue with me, and you said that it's the client's problem at that report. If it's the client's problem, could you speficially write what is wrong with the client, so I could fix it? If it's the server's problem, I'll hope you could fix this problem soon.

I have known the problem. The new difficulty changes something in Link Play. I need some time to adjust the server.

it's good to hear that you are aware of this problem, I hope you could fix this problem. Thanks, I'll end this issue.

authors: Lost-MSth rlarhsid

repo: Lost-MSth/Arcaea-server


text:
svg renderer

overview
it seems like this repo is completely based on the canvas renderer, as it requires a canvas element to function properly. are you planning to integrate the svg renderer as well at some point?
type

[x] new feature
[ ] changes to existing features

motivation
i prefer this repo over the player component, because it is not designed as a web component and does not encapsulate its functionality within a custom <dotlottie-player> element, which unfortunately creates multiple issues on my side with my bundler and code splitting.
so this new repo seems to be making all my dreams come true. however, some of my animations are being played back filling the whole viewport, which creates performance issues and low frame rates. the svg renderer would eliminate these issues.
labels

[x] Add the type: feature request label to this issue.

Hi @jaro-io
First off, thank you for taking the time to create an issue and share your thoughts on our dotlottie-web.
Regarding your query about integrating an SVG renderer: currently, it's not in our development plan to include this feature. The primary reason is our choice to use thorvg as our rendering engine. Thorvg is a highly efficient, low-level renderer that outputs to a frame buffer, which we then patch directly to the canvas. This approach significantly enhances performance compared to other web-based Lottie players, making our package more performant overall.
Your concern about performance issues and low frame rates, especially when animations fill the entire viewport, is valid. It sounds like these might be related to the lack of fixed width and height set on the canvas. To better understand and address this issue, could you please provide a codesandbox example? This would allow us to investigate more thoroughly and potentially find a workaround within the current framework.
While we understand the appeal of an SVG renderer, our current focus is on leveraging thorvg to its fullest to ensure optimal performance. We'll keep your suggestion in mind for future updates, though!
Thank you

thank you so much for this detailed answer @theashraf! 🌻
i tried it with fixed width and height, but it did not improve the frame rate on my side.
unfortunately i can’t share the lottie file as it’s for a client website that is not live yet. so i can’t provide a codesandbox example as of now. i might be able to do so later on.
are you aware of any other dotlottie libraries that are not designed as a web component and use an svg renderer? i could only find this one so far. all the others make use of the custom <dotlottie-player> tag to initialise the module, which unfortunately does not fit my setup.
i would of course love to be able to use dotlottie over the regular lottie files. it’s just amazing how much smaller they are.
all the best!
🍂✨

@jaro-io If you are using a high screen resolution, it's best to let DotLottie manage the width and height. DotLottie optimizes the size of the canvas drawing area based on the device pixel ratio to prevent blurry animations. However, for this to work effectively, ensure that the canvas element fully occupies the width and height of its container.
<div style="width: 300px; height: 300px">
     <canvas id="dotLottie-canvas" style="width: 100%; height: 100%"></canvas>
</div>

This approach is demonstrated in the following CodeSandbox example:
DotLottie Web - Controlling Animation Playback
Regarding the package you mentioned, there is one available - @dotlottie/common. However, be aware that it is not stable, is likely to be deprecated soon, and lacks documentation. It's used internally in both @dotlottie/player-component and @dotlottie/react-player so use it cautiously.
Thank you.

@theashraf I have an animation that renders correctly with the player from @dotlottie/react-player as it has text but when I try to use @lottiefiles/dotlottie-react the text overflows the container. Is this related to being rendered in svg vs canvas? If yes, is there a way to use @lottiefiles/dotlottie-react and make sure that the animation works correctly?
Check the example here, Animation Src.

authors: PulkitBanta jaro-io theashraf

repo: LottieFiles/dotlottie-web


text:
Dependency is missing

PureComponent, that is used in /src/lib/Preview.js, is appeared only in react 15.3.0 https://github.com/facebook/react/blob/master/CHANGELOG.md#1530-july-29-2016. So I believe you should add dependency to "react": "^15.3.0"

Hey,
While it's true that PureComponent was introduced in 15.3.0, I chose to use "react": "*" so you could use any version of React that you want.
But you are right, using "react": ">=15.3.0" would be more correct.
However, I am wondering if the use of PureComponent is actually relevant or just a waste of resource.

repo: LouisBrunner/react-dnd-multi-backend


text:
Improve configuration processing

Improve configuration processing
app.config('app') should return dict of everything under app.
Handle lists better!

Done

repo: LowieHuyghe/edmunds