repo_name stringlengths 9 75 | topic stringclasses 30
values | issue_number int64 1 203k | title stringlengths 1 976 | body stringlengths 0 254k | state stringclasses 2
values | created_at stringlengths 20 20 | updated_at stringlengths 20 20 | url stringlengths 38 105 | labels listlengths 0 9 | user_login stringlengths 1 39 | comments_count int64 0 452 |
|---|---|---|---|---|---|---|---|---|---|---|---|
nonebot/nonebot2 | fastapi | 2,943 | Plugin: nonebot-plugin-githubmodels | ### PyPI ้กน็ฎๅ
nonebot-plugin-githubmodels
### ๆไปถ import ๅ
ๅ
githubmodels
### ๆ ็ญพ
[]
### ๆไปถ้
็ฝฎ้กน
```dotenv
GITHUB_TOKEN="hxjxnfkdmzjs"
```
| closed | 2024-09-12T15:43:52Z | 2024-09-12T15:51:08Z | https://github.com/nonebot/nonebot2/issues/2943 | [
"Plugin"
] | lyqgzbl | 2 |
CorentinJ/Real-Time-Voice-Cloning | deep-learning | 1,115 | I can't make sense of this error... can somebody please help me | I made it to step 5 of installation without problems, and even received "all test passed" when running `python demo_cli.py`,
However, when i get to I get to launching the toolbox, `python demo_toolbox.py -d <datasets_root>` (where datasets_root points to train-clean-100 downloaded per step 4), I receive this error:
```
Traceback (most recent call last):
File "/media/user/drive2/Documents/Real-Time-Voice-Cloning/demo_toolbox.py", line 5, in <module>
from toolbox import Toolbox
File "/media/user/drive2/Documents/Real-Time-Voice-Cloning/toolbox/__init__.py", line 11, in <module>
from toolbox.ui import UI
File "/media/user/drive2/Documents/Real-Time-Voice-Cloning/toolbox/ui.py", line 11, in <module>
import umap
File "/home/user/anaconda3/lib/python3.9/site-packages/umap/__init__.py", line 2, in <module>
from .umap_ import UMAP
File "/home/user/anaconda3/lib/python3.9/site-packages/umap/umap_.py", line 47, in <module>
from pynndescent import NNDescent
File "/home/user/anaconda3/lib/python3.9/site-packages/pynndescent/__init__.py", line 3, in <module>
from .pynndescent_ import NNDescent, PyNNDescentTransformer
File "/home/user/anaconda3/lib/python3.9/site-packages/pynndescent/pynndescent_.py", line 16, in <module>
import pynndescent.sparse as sparse
File "/home/user/anaconda3/lib/python3.9/site-packages/pynndescent/sparse.py", line 229, in <module>
def sparse_mul(ind1, data1, ind2, data2):
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/decorators.py", line 219, in wrapper
disp.compile(sig)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/dispatcher.py", line 965, in compile
cres = self._compiler.compile(args, return_type)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/dispatcher.py", line 129, in compile
raise retval
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/dispatcher.py", line 139, in _compile_cached
retval = self._compile_core(args, return_type)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/dispatcher.py", line 152, in _compile_core
cres = compiler.compile_extra(self.targetdescr.typing_context,
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler.py", line 693, in compile_extra
return pipeline.compile_extra(func)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler.py", line 429, in compile_extra
return self._compile_bytecode()
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler.py", line 497, in _compile_bytecode
return self._compile_core()
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler.py", line 476, in _compile_core
raise e
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler.py", line 463, in _compile_core
pm.run(self.state)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler_machinery.py", line 353, in run
raise patched_exception
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler_machinery.py", line 341, in run
self._runPass(idx, pass_inst, state)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
return func(*args, **kwargs)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler_machinery.py", line 296, in _runPass
mutated |= check(pss.run_pass, internal_state)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/compiler_machinery.py", line 269, in check
mangled = func(compiler_state)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/typed_passes.py", line 105, in run_pass
typemap, return_type, calltypes, errs = type_inference_stage(
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/typed_passes.py", line 83, in type_inference_stage
errs = infer.propagate(raise_errors=raise_errors)
File "/home/user/anaconda3/lib/python3.9/site-packages/numba/core/typeinfer.py", line 1086, in propagate
raise errors[0]
numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
- Resolution failure for literal arguments:
No implementation of function Function(<function impl_append at 0x7f87bea19670>) found for signature:
>>> impl_append(ListType[int32], int32)
There are 2 candidate implementations:
- Of which 2 did not match due to:
Overload in function 'impl_append': File: numba/typed/listobject.py: Line 592.
With argument(s): '(ListType[int32], int32)':
Rejected as the implementation raised a specific error:
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Untyped global name 'ListStatus': Cannot determine Numba type of <class 'shibokensupport.enum_310.EnumMeta'>
File "../../../../../home/user/anaconda3/lib/python3.9/site-packages/numba/typed/listobject.py", line 602:
def impl(l, item):
<source elided>
status = _list_append(l, casteditem)
if status == ListStatus.LIST_OK:
^
raised from /home/user/anaconda3/lib/python3.9/site-packages/numba/core/typeinfer.py:1480
- Resolution failure for non-literal arguments:
None
During: resolving callee type: BoundFunction((<class 'numba.core.types.containers.ListType'>, 'append') for ListType[int32])
During: typing of call at /home/user/anaconda3/lib/python3.9/site-packages/pynndescent/sparse.py (244)
File "../../../../../home/user/anaconda3/lib/python3.9/site-packages/pynndescent/sparse.py", line 244:
def sparse_mul(ind1, data1, ind2, data2):
<source elided>
if val != 0:
result_ind.append(j1)
^
```
Can somebody please help me make sense of this issue? Did I go wrong somewhere in the installation? | closed | 2022-09-22T07:52:13Z | 2023-01-08T08:55:12Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1115 | [] | ikesaber | 0 |
ultralytics/yolov5 | pytorch | 12,897 | Running Hyperparameter Evolution raises ValueError | ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report.
### YOLOv5 Component
Training, Evolution
### Bug
I was trying to train my custom model locally on `Nvidia RTX 3050` but it raises a ValueError. I checked and it raises the same error on coco128 dataset.
This is the dump:
```
(.venv-cuda121) PS C:\workspace\adis\yolov5> python train.py --img 640 --batch 4 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache --evolve
train: weights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=3, batch_size=4, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=300, evolve_population=data\hyps, resume_evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
github: up to date with https://github.com/ultralytics/yolov5
YOLOv5 v7.0-296-gae4ef3b2 Python-3.11.1 torch-2.2.2+cu121 CUDA:0 (NVIDIA GeForce RTX 3050 Laptop GPU, 4096MiB)
hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.01041, hsv_s=0.54703, hsv_v=0.27739, degrees=0.0, translate=0.04591, scale=0.75544, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=0.85834, mixup=0.04266, copy_paste=0.0, anchors=3
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 runs in Comet
Overriding model.yaml anchors with anchors=3
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]], [128, 256, 512]]
Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs
Transferred 348/349 items from yolov5s.pt
AMP: checks passed
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
train: Scanning C:\workspace\adis\datasets\coco128\labels\train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|โโโโโโโโโโ| 128/128 [00:00<?, ?it/s
train: Caching images (0.1GB ram): 100%|โโโโโโโโโโ| 128/128 [00:00<00:00, 1829.21it/s]
val: Scanning C:\workspace\adis\datasets\coco128\labels\train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|โโโโโโโโโโ| 128/128 [00:00<?, ?it/s]
AutoAnchor: 0.36 anchors/target, 0.097 Best Possible Recall (BPR). Anchors are a poor fit to dataset , attempting to improve...
AutoAnchor: WARNING Extremely small objects found: 3 of 929 labels are <3 pixels in size
AutoAnchor: Running kmeans for 9 anchors on 928 points...
AutoAnchor: Evolving anchors with Genetic Algorithm: fitness = 0.6715: 100%|โโโโโโโโโโ| 1000/1000 [00:00<00:00, 2909.40it/s]
AutoAnchor: thr=0.25: 0.9925 best possible recall, 3.71 anchors past thr
AutoAnchor: n=9, img_size=640, metric_all=0.261/0.672-mean/best, past_thr=0.478-mean: 11,11, 20,27, 51,57, 125,86, 92,175, 140,287, 280,226, 378,368, 549,444
AutoAnchor: Done (optional: update model *.yaml to use these anchors in the future)
Plotting labels to runs\evolve\exp4\labels.jpg...
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to runs\evolve\exp4
Starting training for 3 epochs...
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0%| | 0/32 [00:00<?, ?it/s]
Traceback (most recent call last):
File "C:\workspace\adis\yolov5\train.py", line 848, in <module>
main(opt)
File "C:\workspace\adis\yolov5\train.py", line 754, in main
results = train(hyp.copy(), opt, device, callbacks)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\workspace\adis\yolov5\train.py", line 356, in train
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
File "C:\workspace\adis\yolov5\.venv-cuda121\Lib\site-packages\tqdm\std.py", line 1181, in __iter__
for obj in iterable:
File "C:\workspace\adis\yolov5\utils\dataloaders.py", line 239, in __iter__
yield next(self.iterator)
^^^^^^^^^^^^^^^^^^^
File "C:\workspace\adis\yolov5\.venv-cuda121\Lib\site-packages\torch\utils\data\dataloader.py", line 631, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "C:\workspace\adis\yolov5\.venv-cuda121\Lib\site-packages\torch\utils\data\dataloader.py", line 1346, in _next_data
return self._process_data(data)
^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\workspace\adis\yolov5\.venv-cuda121\Lib\site-packages\torch\utils\data\dataloader.py", line 1372, in _process_data
data.reraise()
File "C:\workspace\adis\yolov5\.venv-cuda121\Lib\site-packages\torch\_utils.py", line 722, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "C:\workspace\adis\yolov5\.venv-cuda121\Lib\site-packages\torch\utils\data\_utils\worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
^^^^^^^^^^^^^^^^^^^^
File "C:\workspace\adis\yolov5\.venv-cuda121\Lib\site-packages\torch\utils\data\_utils\fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\workspace\adis\yolov5\.venv-cuda121\Lib\site-packages\torch\utils\data\_utils\fetch.py", line 51, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
~~~~~~~~~~~~^^^^^
File "C:\workspace\adis\yolov5\utils\dataloaders.py", line 777, in __getitem__
img, labels = mixup(img, labels, *self.load_mosaic(random.choice(self.indices)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\random.py", line 369, in choice
if not seq:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
If this is an Nvidia related bug, then this is my info from nvidia-smi
```
Mon Apr 8 20:41:10 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 546.21 Driver Version: 546.21 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3050 ... WDDM | 00000000:01:00.0 Off | N/A |
| N/A 36C P0 7W / 40W | 0MiB / 4096MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
### Environment
YOLOv5 v7.0-296-gae4ef3b2 Python-3.11.1 torch-2.2.2+cu121 CUDA:0 (NVIDIA GeForce RTX 3050 Laptop GPU, 4096MiB)
### Minimal Reproducible Example
_No response_
### Additional
_No response_
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | closed | 2024-04-08T15:12:26Z | 2024-10-20T19:43:15Z | https://github.com/ultralytics/yolov5/issues/12897 | [
"bug",
"Stale"
] | RAHUL01-09 | 3 |
microsoft/nni | deep-learning | 5,031 | How to set useActivateGpu=true in remote mode? | **Describe the issue**:
I can use the NNI normally with Remote mode on the CPU, although it is very slow.
When I try to use NNI to run the program on GPU under the remote mode, the program is always waiting but didn't run at all.
One possible reason is that all of the GPUs in the remote machine are partly occupied and the NNI won't use the GPU until the GPU is totally free.
When using the local environment, NNI allows me to set useActivateGpu=true to use the working GPU.
However, in remote mode, it will throw the error that **"AttributeError: RemoteConfig does not have field(s) useactivegpu"**
So I want to know how to set the config to use active GPU under remote Configuration.
**Environment**:
- NNI version: 2.7
- Training service (local|remote|pai|aml|etc): remote
- Client OS: Ubuntu 20.04
- Server OS (for remote mode only): Ubuntu 20.04
- Python version: 3.8.5
- PyTorch/TensorFlow version: PyTorch v1.8.1
- Is conda/virtualenv/venv used?: use conda environment
- Is running in Docker?: No
**Configuration**:
- Experiment config (remember to remove secrets!):
```
maxTrialNumber: 20
trialCommand: python main.py
trialCodeDirectory: .
trialGpuNumber: 2
trialConcurrency: 4
tuner:
name: TPE
classArgs:
optimize_mode: maximize
trainingService:
useActiveGpu: true
platform: remote
reuseMode: true
# gpuIndices: '0'
machineList:
- host: xxx.xxx.xx.xx
user: xxx
ssh_key_file: ~/.ssh/id_rsa
```
- Search space:
```
{
"lr": {"_type": "choice", "_value": [0.002, 0.001, 0.0005]},
"l2": {"_type": "choice", "_value": [1e-5, 2e-5, 5e-5]}
}
```
**Log message**:
- nnimanager.log:
- dispatcher.log:
- nnictl stdout and stderr:
```
Traceback (most recent call last):
File "/home/xxx/.miniconda3/envs/torch/bin/nnictl", line 8, in <module>
sys.exit(parse_args())
File "/home/xxx/.miniconda3/envs/torch/lib/python3.8/site-packages/nni/tools/nnictl/nnictl.py", line 497, in parse_args
args.func(args)
File "/home/xxx/.miniconda3/envs/torch/lib/python3.8/site-packages/nni/tools/nnictl/launcher.py", line 77, in create_experiment
config = ExperimentConfig.load(config_file)
File "/home/xxx/.miniconda3/envs/torch/lib/python3.8/site-packages/nni/experiment/config/base.py", line 140, in load
config = cls(**data)
File "/home/xxx/.miniconda3/envs/torch/lib/python3.8/site-packages/nni/experiment/config/experiment_config.py", line 104, in __init__
self.training_service = utils.load_training_service_config(self.training_service)
File "/home/xxx/.miniconda3/envs/torch/lib/python3.8/site-packages/nni/experiment/config/utils/internal.py", line 157, in load_training_service_config
return cls(**config)
File "/home/xxx/.miniconda3/envs/torch/lib/python3.8/site-packages/nni/experiment/config/base.py", line 93, in __init__
raise AttributeError(f'{class_name} does not have field(s) {fields}')
AttributeError: RemoteConfig does not have field(s) useactivegpu
```
<!--
Where can you find the log files:
LOG: https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/HowToDebug.md#experiment-root-director
STDOUT/STDERR: https://nni.readthedocs.io/en/stable/reference/nnictl.html#nnictl-log-stdout
-->
**How to reproduce it?**: | closed | 2022-07-30T06:43:34Z | 2022-09-07T10:42:16Z | https://github.com/microsoft/nni/issues/5031 | [
"user raised",
"support",
"remote"
] | unikcc | 1 |
absent1706/sqlalchemy-mixins | sqlalchemy | 32 | Does BaseModel.set_session(session) only run once? | Or BaseModel.set_session(session) needs to run for every request? | closed | 2020-01-11T07:11:46Z | 2020-03-31T16:21:00Z | https://github.com/absent1706/sqlalchemy-mixins/issues/32 | [] | scil | 1 |
pytorch/vision | machine-learning | 8,389 | Compiling resize_image: function interpolate not_implemented | ### ๐ Describe the bug
I am compiling a method (mode=default, fullgrph=True), which calls torchvision.transforms.v2.functional.resize_image. However, I receive an error, which indicates that the interpolate method is not implemented. I am using pytorch lightning and weirdly this only happens during validation. It works fine during training.
```
Failed running call_function <function interpolate at 0x7f60c39593a0>(*(FakeTensor(..., device='cuda:0', size=(75, 3, 256, 256)),), **{'size': [224, 224], 'mode': 'bicubic', 'align_corners': False, 'antialias': True}):
Multiple dispatch failed for 'torch.ops.aten.size'; all __torch_dispatch__ handlers returned NotImplemented:
- tensor subclass <class 'torch._subclasses.fake_tensor.FakeTensor'>
For more information, try re-running with TORCH_LOGS=not_implemented
from user code:
File ...
File "/some/python/file.py", line 54, in encode
x = tv_func.resize_image(
File ".../miniconda3/envs/deepmotion3/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_geometry.py", line 260, in resize_image
image = interpolate(
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True
TypeError: Multiple dispatch failed for 'torch.ops.aten.size'; all __torch_dispatch__ handlers returned NotImplemented:
- tensor subclass <class 'torch._subclasses.fake_tensor.FakeTensor'>
For more information, try re-running with TORCH_LOGS=not_implemented
The above exception was the direct cause of the following exception:
RuntimeError: Failed running call_function <function interpolate at 0x7f60c39593a0>(*(FakeTensor(..., device='cuda:0', size=(75, 3, 256, 256)),), **{'size': [224, 224], 'mode': 'bicubic', 'align_corners': False, 'antialias': True}):
Multiple dispatch failed for 'torch.ops.aten.size'; all __torch_dispatch__ handlers returned NotImplemented:
- tensor subclass <class 'torch._subclasses.fake_tensor.FakeTensor'>
For more information, try re-running with TORCH_LOGS=not_implemented
During handling of the above exception, another exception occurred:
File "/some/python/file.py", line 299, in validation_step
loss = self.shared_step(step_batch, train=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/some/python/file.py", line 586, in run
trainer.fit(model, datamodule, ckpt_path=opt.resume_from_checkpoint)
File "/some/python/file.py", line 702, in <module>
run(opt, trainer, datamodule, model)
torch._dynamo.exc.TorchRuntimeError: Failed running call_function <function interpolate at 0x7f60c39593a0>(*(FakeTensor(..., device='cuda:0', size=(75, 3, 256, 256)),), **{'size': [224, 224], 'mode': 'bicubic', 'align_corners': False, 'antialias': True}):
Multiple dispatch failed for 'torch.ops.aten.size'; all __torch_dispatch__ handlers returned NotImplemented:
- tensor subclass <class 'torch._subclasses.fake_tensor.FakeTensor'>
For more information, try re-running with TORCH_LOGS=not_implemented
from user code:
File ...
File "/some/python/file.py", line 54, in encode
x = tv_func.resize_image(
File ".../miniconda3/envs/deepmotion3/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_geometry.py", line 260, in resize_image
image = interpolate(
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True
```
### Versions
Collecting environment information...
PyTorch version: 2.2.1
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35
Python version: 3.11.8 | packaged by conda-forge | (main, Feb 16 2024, 20:53:32) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-PCIE-40GB
GPU 1: NVIDIA A100-PCIE-40GB
GPU 2: NVIDIA A100-PCIE-40GB
GPU 3: NVIDIA A100-PCIE-40GB
GPU 4: NVIDIA A100-PCIE-40GB
GPU 5: NVIDIA A100-PCIE-40GB
GPU 6: NVIDIA A100-PCIE-40GB
GPU 7: NVIDIA A100-PCIE-40GB
Nvidia driver version: 535.171.04
cuDNN version: Probably one of the following:
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.1.0
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.1.0
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.1.0
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.1.0
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.1.0
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.1.0
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.1.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 43 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 64
On-line CPU(s) list: 0-63
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7452 32-Core Processor
CPU family: 23
Model: 49
Thread(s) per core: 1
Core(s) per socket: 32
Socket(s): 2
Stepping: 0
Frequency boost: enabled
CPU max MHz: 2350.0000
CPU min MHz: 1500.0000
BogoMIPS: 4700.09
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
Virtualization: AMD-V
L1d cache: 2 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 32 MiB (64 instances)
L3 cache: 256 MiB (16 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-31
NUMA node1 CPU(s): 32-63
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Mitigation; untrained return thunk; SMT disabled
Vulnerability Spec rstack overflow: Mitigation; SMT disabled
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==2.1.3
[pip3] torch==2.2.1
[pip3] torch-fidelity==0.3.0
[pip3] torchaudio==2.2.1
[pip3] torchdiffeq==0.2.3
[pip3] torchmetrics==1.3.2
[pip3] torchvision==0.17.1
[pip3] triton==2.2.0
[conda] blas 1.0 mkl
[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
[conda] mkl 2023.1.0 h213fc3f_46344
[conda] mkl-service 2.4.0 py311h5eee18b_1
[conda] mkl_fft 1.3.8 py311h5eee18b_0
[conda] mkl_random 1.2.4 py311hdb19cb5_0
[conda] numpy 1.26.4 py311h08b1b3b_0
[conda] numpy-base 1.26.4 py311hf175353_0
[conda] pytorch 2.2.1 py3.11_cuda12.1_cudnn8.9.2_0 pytorch
[conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch
[conda] pytorch-lightning 2.1.3 pyhd8ed1ab_0 conda-forge
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torch-fidelity 0.3.0 pypi_0 pypi
[conda] torchaudio 2.2.1 py311_cu121 pytorch
[conda] torchdiffeq 0.2.3 pypi_0 pypi
[conda] torchmetrics 1.3.2 pyhd8ed1ab_0 conda-forge
[conda] torchtriton 2.2.0 py311 pytorch
[conda] torchvision 0.17.1 py311_cu121 pytorch | open | 2024-04-19T18:04:08Z | 2024-04-29T13:10:51Z | https://github.com/pytorch/vision/issues/8389 | [] | treasan | 1 |
JaidedAI/EasyOCR | deep-learning | 629 | Why are there some pictures with errors? | The same code, Why are there some pictures with errors?
Here is the error message
```
CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.
Traceback (most recent call last):
File "D:/code/python/base/test1.py", line 6, in <module>
result = reader.readtext('invoice.png')
File "D:\python\base\lib\site-packages\easyocr\easyocr.py", line 397, in readtext
filter_ths, y_ths, x_ths, False, output_format)
File "D:\python\base\lib\site-packages\easyocr\easyocr.py", line 325, in recognize
image_list, max_width = get_image_list(h_list, f_list, img_cv_grey, model_height = imgH)
File "D:\python\base\lib\site-packages\easyocr\utils.py", line 540, in get_image_list
maximum_y,maximum_x = img.shape
AttributeError: 'NoneType' object has no attribute 'shape'
Process finished with exit code 1
```
**this is error code**
```python
import easyocr
import time
start = time.time()
reader = easyocr.Reader(['ch_sim','en'])
result = reader.readtext('ๅ็ฅจ.png')
for word in result:
print(word[1])
end = time.time()
print(end - start)
```
**And when I change it to this it can work**
```python
import easyocr
import time
start = time.time()
reader = easyocr.Reader(['ch_sim','en'])
img = open('invoice.png','rb')
source = img.read()
result = reader.readtext(source)
for word in result:
print(word[1])
end = time.time()
print(end - start)
```
| closed | 2021-12-24T10:22:22Z | 2022-08-07T05:01:56Z | https://github.com/JaidedAI/EasyOCR/issues/629 | [] | jinhuiDing | 0 |
pykaldi/pykaldi | numpy | 90 | why do i get different result for computing fbank-feature between pykaldi and kaldi | i install the pykaldi from source.
then compare the result for using pykaldi to result for using compute-fbank-feats in kaldi with test.wav
1.code(use pykaldi)
sf_bank=16000.0
m3 = SubVector(mean(s3, axis=0))
f3=fbank.compute_features(m3,sf_fbank,1.00)
feature:
13.5067 10.8341 10.0170 ... 13.6531 13.5450 13.8087
6.6261 5.5527 8.3816 ... 10.7848 10.2027 8.7894
7.0736 8.8376 10.0984 ... 10.7526 9.8521 8.4278
... โฑ ...
9.5450 10.1321 10.1961 ... 9.6800 9.4741 9.2639
8.4494 7.7848 7.4247 ... 10.3842 10.7975 9.4761
4.6003 4.6669 8.1002 ... 10.0861 9.9920 9.1505
2.kaldi
set sample_frq=16000.0 in fbank.conf
compute-fbank-feats --verbose=0 --config=conf/fbank.conf scp,p:testfile.scp ark,t:- |less
feature:
13.50692 10.83265 10.02174 ...13.64816 13.54409 13.81221
6.617604 5.457501 8.411386 ...10.66659 10.40139 8.803407
7.078373 8.840377 10.10209 ...10.81204 9.477433 8.874421
......
| closed | 2019-03-15T09:43:38Z | 2019-03-19T05:53:05Z | https://github.com/pykaldi/pykaldi/issues/90 | [] | liuchenbaidu | 6 |
jupyter/docker-stacks | jupyter | 2,083 | After starting the container with NB_USER=root, NB_UID=0, and NB_GID=0, $HOME environment variable is still /home/jovyan | ### What docker image(s) are you using?
datascience-notebook
### Host OS system
Ubuntu 22.04
### Host architecture
x86_64
### What Docker command are you running?
docker run -d --rm --user root -p 8888:8888 -e JUPYTER_TOKEN=123 -e NB_UID=0 -e NB_GID=0 -e NB_USER=root -e
NOTEBOOK_ARGS="--allow-root" quay.io/jupyter/datascience-notebook:2024-01-16
### How to Reproduce the problem?
1. Start the container using the comment above and exec into the container
```
ubuntu$ docker run -d --rm --user root -p 8888:8888 -e JUPYTER_TOKEN=123 -e NB_UID=0 -e NB_GID=0 -e NB_USER=root -e
NOTEBOOK_ARGS="--allow-root" quay.io/jupyter/datascience-notebook:2024-01-16
025ef42b70c01de329f8b6371feb008525a0393284b3d873df7642d396f2ff2d
```
2. Exec into the container
```
ubuntu$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
025ef42b70c0 quay.io/jupyter/datascience-notebook:2024-01-16 "tini -g -- start-noโฆ" 8 seconds ago Up 7 seconds (healthy) 0.0.0.0:8888->8888/tcp, :::8888->8888/tcp happy_sutherland
70058a13a501 resero-svc-studies:latest "/bin/bash" 27 hours ago Up 27 hours resero-devcontainer
ubuntu$ docker exec -u root happy_sutherland bash
```
3. Check the user
```
ubuntu$ whoami
ubuntu
```
4. Check $HOME
(base) root@025ef42b70c0:~# echo $HOME
/home/jovyan
NOTE: /etc/passwd is fine
```
(base) root@025ef42b70c0:~# cat /etc/passwd
root:x:0:0:root:/home/root:/bin/bash
daemon:x:1:1:daemon:/usr/sbin:/usr/sbin/nologin
bin:x:2:2:bin:/bin:/usr/sbin/nologin
sys:x:3:3:sys:/dev:/usr/sbin/nologin
sync:x:4:65534:sync:/bin:/bin/sync
games:x:5:60:games:/usr/games:/usr/sbin/nologin
man:x:6:12:man:/var/cache/man:/usr/sbin/nologin
lp:x:7:7:lp:/var/spool/lpd:/usr/sbin/nologin
mail:x:8:8:mail:/var/mail:/usr/sbin/nologin
news:x:9:9:news:/var/spool/news:/usr/sbin/nologin
uucp:x:10:10:uucp:/var/spool/uucp:/usr/sbin/nologin
proxy:x:13:13:proxy:/bin:/usr/sbin/nologin
www-data:x:33:33:www-data:/var/www:/usr/sbin/nologin
backup:x:34:34:backup:/var/backups:/usr/sbin/nologin
list:x:38:38:Mailing List Manager:/var/list:/usr/sbin/nologin
irc:x:39:39:ircd:/run/ircd:/usr/sbin/nologin
gnats:x:41:41:Gnats Bug-Reporting System (admin):/var/lib/gnats:/usr/sbin/nologin
nobody:x:65534:65534:nobody:/nonexistent:/usr/sbin/nologin
_apt:x:100:65534::/nonexistent:/usr/sbin/nologin
jovyan:x:1000:100::/home/jovyan:/bin/bash
```
### Command output
_No response_
### Expected behavior
`echo $HOME` should be `/home/root`
### Actual behavior
`echo $HOME` is `/home/jovyan`
### Anything else?
_No response_
### Latest Docker version
- [X] I've updated my Docker version to the latest available, and the issue persists | closed | 2024-01-17T12:12:14Z | 2024-01-17T12:31:08Z | https://github.com/jupyter/docker-stacks/issues/2083 | [
"type:Bug"
] | anil-resero | 3 |
Zeyi-Lin/HivisionIDPhotos | machine-learning | 158 | ๆณ่ฏทๆไธๅไฝๅคงไฝฌ๏ผnodejsๆ็ธๅ
ณ็ๅบๅฏไปฅๅฎ็ฐ่ฏไปถ็
งๅพ็ๅค็็ๅ่ฝๅ | closed | 2024-09-21T11:45:49Z | 2024-10-04T12:00:12Z | https://github.com/Zeyi-Lin/HivisionIDPhotos/issues/158 | [] | Chandie-Zhang | 3 | |
deepfakes/faceswap | deep-learning | 1,022 | Windows installer broken | **Crash reports MUST be included when reporting bugs.**
(check) CPU Supports SSE4 Instructions
(check) Completed check for installed applications
(check) Setting up for: cpu
Downloading Miniconda3...
Installing Miniconda3. This will take a few minutes...
Miniconda3 installed.
Initializing Conda...
Creating Conda Virtual Environment...
Error Creating Conda Virtual Environment
Install Aborted
**Describe the bug**
Installer is broken
**To Reproduce**
Steps to reproduce the behavior:
1. Download installer (Windows) from github
2. Click "Install"
3. See error
**Expected behavior**
Just install
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Desktop (please complete the following information):**
- OS: Windows 8.1
- Python Version 3.8.3rc1
- Conda Version I first time hear about it , i never instaled it.
- Commit ID What ?
-
**Additional context**
_--===+ Hardware +===--_
Intel Pentium (R) n3530
Intel HD Graphics (Bay Trail)
4GB (SODIMM) [DDR3]
HDD 500GB
UEFI
**Crash Report**
Faceswap is not installing , so they have no directory for storing crash report | closed | 2020-05-11T07:01:49Z | 2020-05-13T11:32:29Z | https://github.com/deepfakes/faceswap/issues/1022 | [] | BlueONn | 1 |
tflearn/tflearn | data-science | 640 | a little error in the doc page | At doc page [get_started](http://tflearn.org/getting_started/), the first example of topic 'Layers':
```
with tf.name_scope('conv1'):
W = tf.Variable(tf.random_normal([5, 5, 1, 32]), dtype=tf.float32, name='Weights')
b = tf.Variable(tf.random_normal([32]), dtype=tf.float32, name='biases')
x = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
x = tf.add_bias(W, b)
x = tf.nn.relu(x)
```
the fivth line should be `x = tf.add_bias(x, b)`. | closed | 2017-02-28T16:04:25Z | 2017-03-16T22:47:36Z | https://github.com/tflearn/tflearn/issues/640 | [] | cooljacket | 0 |
microsoft/nni | pytorch | 5,338 | Add a parameter in generate_scenario for the file 'scenario.txt' instead of harcoding | <!-- Please only use this template for submitting enhancement requests -->
**What would you like to be added**:
It would be great in the function https://github.com/microsoft/nni/blob/e85f029bd4e4b1bdf3e679893fb6447e4d6b2c79/nni/algorithms/hpo/smac_tuner/convert_ss_to_scenario.py#L192 to add a parameter for `scenario.txt` instead of hardcoding it.
**Why is this needed**:
I'm using SMAC tuner in a Databricks compute where I can't write to `.`, I can only write to `/tmp/`. If I could parametrize the file, then I would be able to use SMAC.
**Without this feature, how does current nni work**๏ผ
SMAC won't work in Databricks and probably on Synapse (I haven't tried, but it is my guess)
**Components that may involve changes**:
**Brief description of your proposal if any**:
Difficult fix:
Change the signature of [generate_scenario](https://github.com/microsoft/nni/blob/e85f029bd4e4b1bdf3e679893fb6447e4d6b2c79/nni/algorithms/hpo/smac_tuner/convert_ss_to_scenario.py#L114), and then also the SMAC arguments.
Easy fix:
Instead of hardcoding `scenario.txt` hardcode `/tmp/scenario.txt`. I'll send this PR in case you want it | closed | 2023-02-07T12:28:14Z | 2023-02-27T16:05:23Z | https://github.com/microsoft/nni/issues/5338 | [] | miguelgfierro | 0 |
ageitgey/face_recognition | python | 721 | Issue with dLib installation with Cuda and AVX support | * face_recognition version: Latest
* Python version: 3.7
* Operating System: Linux with GPU
### Description-- Please help - cmake version 2.8.12
Trying to install dLib with AVX and Cuda support
Command tried-
Paste the command(s) you ran and the output.
python3 setup.py install --yes USE_AVX_INSTRUCTIONS --yes DLIB_USE_CUDA
File "/usr/local/lib/python3.7/distutils/command/install_lib.py", line 107, in build
self.run_command('build_ext')
File "/usr/local/lib/python3.7/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/usr/local/lib/python3.7/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "setup.py", line 119, in run
self.build_extension(ext)
File "setup.py", line 153, in build_extension
subprocess.check_call(cmake_setup, cwd=build_folder)
File "/usr/local/lib/python3.7/subprocess.py", line 328, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['cmake', '/root/dlib-19.9/tools/python', '-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=/root/dlib-19.9/build/lib.linux-x86_64-3.7', '-DPYTHON_EXECUTABLE=/usr/local/bin/python3', '-DUSE_AVX_INSTRUCTIONS=yes', '-DDLIB_USE_CUDA=yes', '-DCMAKE_BUILD_TYPE=Release']' returned non-zero exit status 1.
Then after i tired builing the dlib to just give a chance in case it builds-
command -
cd dlib
mkdir build; cd build; cmake ..;
-- The CXX compiler identification is unknown
CMake Error: your CXX compiler: "CMAKE_CXX_COMPILER-NOTFOUND" was not found. Please set CMAKE_CXX_COMPILER to a valid compiler path or name.
CMake Error: your CXX compiler: "CMAKE_CXX_COMPILER-NOTFOUND" was not found. Please set CMAKE_CXX_COMPILER to a valid compiler path or name.
-- Using CMake version: 2.8.12.2
-- Compiling dlib version: 19.16.99
CMake Error at dlib/cmake_utils/set_compiler_specific_options.cmake:130 (if):
if given arguments:
"Clang" "MATCHES"
Unknown arguments specified
Call Stack (most recent call first):
dlib/CMakeLists.txt:27 (include)
-- Configuring incomplete, errors occurred!
See also "/root/dlib/build/CMakeFiles/CMakeOutput.log".
See also "/root/dlib/build/CMakeFiles/CMakeError.log".
| open | 2019-01-22T10:02:03Z | 2019-01-22T10:02:03Z | https://github.com/ageitgey/face_recognition/issues/721 | [] | 74981 | 0 |
apachecn/ailearning | python | 484 | ๆ ็็ฃ็ฎๆณใ้่ฆๅฎๅ+่กฅๅ
ใ | ๅ็็ฃๅญฆไน (Semi-Supervised Learning,SSL)็ฑปๅฑไบๆบๅจๅญฆไน (Machine Learning,ML)ใ
## ไธ MLๆไธค็งๅบๆฌ็ฑปๅ็ๅญฆไน ไปปๅก๏ผ
> 1.็็ฃๅญฆไน (Supervised Learning,SL)
ย ย ย ย ๆ นๆฎ่พๅ
ฅ-่พๅบๆ ทๆฌๅฏนL={(x1,y1),ยทยทยท,(xl,yl)}ๅญฆไน ่พๅ
ฅๅฐ่พๅบ็ๆ ๅฐf:X->Y,ๆฅ้ขๆตๆต่ฏๆ ทไพ็่พๅบๅผใSLๅ
ๆฌๅ็ฑป(Classification)ๅๅๅฝ(Regression)ไธค็ฑปไปปๅก๏ผๅ็ฑปไธญ็ๆ ทไพxiโRm(่พๅ
ฅ็ฉบ้ด)๏ผ็ฑปๆ ็ญพyiโ{c1,c2,ยทยทยท,cc},cjโN;ๅๅฝไธญ็่พๅ
ฅxiโRm๏ผ่พๅบyiโR(่พๅบ็ฉบ้ด)ใ
> 2. ๆ ็็ฃๅญฆไน (Unsupervised Learning,UL)
ย ย ๅฉ็จๆ ็ฑปๆ ็ญพ็ๆ ทไพU={x1,ยทยทยท,xn}ๆๅ
ๅซ็ไฟกๆฏๅญฆไน ๅ
ถๅฏนๅบ็็ฑปๆ ็ญพYu=[y1ยทยทยทyn]T,็ฑๅญฆไน ๅฐ็็ฑปๆ ็ญพไฟกๆฏๆๆ ทไพๅๅๅฐไธๅ็็ฐ(Clustering)ๆๆพๅฐ้ซ็ปด่พๅ
ฅๆฐๆฎ็ไฝ็ปด็ปๆใULๅ
ๆฌ่็ฑป(Clistering)ๅ้็ปด(Dimensionality Reduction)ไธค็ฑปไปปๅกใ
## ไบ ๅ็็ฃๅญฆไน (Semi-Supervised Learning,UL)
ย ย ย ย ๅจ่ฎธๅคML็ๅฎ้
ๅบ็จไธญ๏ผๅพๅฎนๆๆพๅฐๆตท้็ๆ ็ฑปๆ ็ญพ็ๆ ทไพ๏ผไฝ้่ฆไฝฟ็จ็นๆฎ่ฎพๅคๆ็ป่ฟๆ่ดตไธ็จๆถ้ๅธธ้ฟ็ๅฎ้ช่ฟ็จ่ฟ่กไบบๅทฅๆ ่ฎฐๆ่ฝๅพๅฐๆ็ฑปๆ ็ญพ็ๆ ทๆฌ๏ผ็ฑๆญคไบง็ไบๆๅฐ้็ๆ็ฑปๆ ็ญพ็ๆ ทๆฌๅ่ฟๅฉ็ๆ ็ฑปๆ ็ญพ็ๆ ทไพใๅ ๆญค๏ผไบบไปฌๅฐ่ฏๅฐๅคง้็ๆ ็ฑปๆ ็ญพ็ๆ ทไพๅ ๅ
ฅๅฐๆ้็ๆ็ฑปๆ ็ญพ็ๆ ทๆฌไธญไธ่ตท่ฎญ็ปๆฅ่ฟ่กๅญฆไน ๏ผๆๆ่ฝๅฏนๅญฆไน ๆง่ฝ่ตทๅฐๆน่ฟ็ไฝ็จ๏ผ็ฑๆญคไบง็ไบSSL๏ผๅฆๅฆๅพ๏ผๆ็คบใSSL้ฟๅ
ไบๆฐๆฎๅ่ตๆบ็ๆตช่ดน๏ผๅๆถ่งฃๅณไบSL็ ๆจกๅๆณๅ่ฝๅไธๅผบๅUL็ๆจกๅไธ็ฒพ็กฎ็ญ้ฎ้ขใ
> 1.ๅ็็ฃๅญฆไน ไพ่ต็ๅ่ฎพ
SSL็ๆ็ซไพ่ตไบๆจกๅๅ่ฎพ๏ผๅฝๆจกๅๅ่ฎพๆญฃ็กฎๆถ๏ผๆ ็ฑปๆ ็ญพ็ๆ ทไพ่ฝๅคๅธฎๅฉๆน่ฟๅญฆไน ๆง่ฝใSSLไพ่ต็ๅ่ฎพๆไปฅไธ๏ผไธช๏ผ
๏ผ๏ผ๏ผๅนณๆปๅ่ฎพ๏ผSmoothness Assumption๏ผ
ย ย ย ย ไฝไบ็จ ๅฏๆฐๆฎๅบๅ็ไธคไธช่ท็ฆปๅพ่ฟ็ๆ ทไพ็็ฑปๆ ็ญพ็ธไผผ๏ผไนๅฐฑๆฏ่ฏด๏ผๅฝไธคไธชๆ ทไพ่ขซ็จ ๅฏๆฐๆฎๅบๅไธญ็่พน่ฟๆฅๆถ๏ผๅฎไปฌๅจๅพๅคง็ๆฆ็ไธๆ็ธๅ็็ฑปๆ ็ญพ๏ผ็ธๅๅฐ๏ผๅฝไธคไธชๆ ทไพ่ขซ็จ็ๆฐๆฎๅบๅๅๅผๆถ๏ผๅฎไปฌ็็ฑปๆ ็ญพ่ถไบไธๅ๏ผย
๏ผ๏ผ๏ผ่็ฑปๅ่ฎพ๏ผCluster Assumption๏ผ
ย ย ย ย ๅฝไธคไธชๆ ทไพไฝไบๅไธ่็ฑป็ฐๆถ๏ผๅฎไปฌๅจๅพๅคง็ๆฆ็ไธๆ็ธๅ็็ฑปๆ ็ญพ๏ผ่ฟไธชๅ่ฎพ็็ญไปทๅฎไนไธบไฝๅฏๅบฆๅ็ฆปๅ่ฎพ๏ผLow Sensity Separationย Assumption๏ผ๏ผๅณๅ็ฑป ๅณ็ญ่พน็ๅบ่ฏฅ็ฉฟ่ฟ็จ็ๆฐๆฎๅบๅ๏ผ่้ฟๅ
ๅฐ็จ ๅฏๆฐ ๆฎๅบๅ็ๆ ทไพๅๅฐๅณ็ญ่พน็ไธคไพง๏ผ
ย ๏ผ๏ผ๏ผๆตๅฝขๅ่ฎพ๏ผManifoldย Assumption๏ผ
ย ย ย ย ๅฐ้ซ็ปดๆฐๆฎๅตๅ
ฅๅฐไฝ็ปดๆตๅฝขไธญ๏ผๅฝไธคไธชๆ ทไพไฝไบไฝ็ปดๆตๅฝขไธญ็ไธไธชๅฐๅฑ้จ้ปๅๅ
ๆถ๏ผๅฎไปฌๅ
ทๆ็ธไผผ็็ฑปๆ ็ญพใ่ฎธๅคๅฎ้ช็ ็ฉถ่กจๆๅฝSSLไธๆปก่ถณ่ฟไบๅ่ฎพๆๆจกๅๅ่ฎพไธๆญฃ็กฎๆถ๏ผๆ ็ฑปๆ ็ญพ็ๆ ทไพไธไป
ไธ่ฝๅฏนๅญฆไน ๆง่ฝ่ตทๅฐๆน่ฟไฝ็จ๏ผๅ่ไผๆถๅๅญฆไน ๆง่ฝ๏ผๅฏผ่ด SSL็ๆง่ฝไธ้๏ผไฝๆฏ่ฟๆไธไบๅฎ้ช่กจๆ๏ผๅจไธไบ็นๆฎ็ๆ
ๅตไธๅณไฝฟๆจกๅๅ่ฎพๆญฃ็กฎ๏ผๆ ็ฑปๆ ็ญพ็ๆ ทไพไนๆๅฏ่ฝๆๅฎณๅญฆไน ๆง่ฝใ
> 2.ๅ็็ฃๅญฆไน ็ๅ็ฑปย
ย ย ย ย SSLๆ็
ง็ป่ฎกๅญฆไน ็่ฎบ็่งๅบฆๅ
ๆฌ็ดๆจ ๏ผTransductive ๏ผSSLๅๅฝ็บณ๏ผInductive๏ผSSLไธค็ฑปๆจกๅผใ็ดๆจ SSLๅชๅค็ๆ ทๆฌ็ฉบ้ดๅ
็ปๅฎ็่ฎญ็ปๆฐๆฎ๏ผๅฉ็จ่ฎญ็ปๆฐๆฎไธญๆ็ฑปๆ ็ญพ็ๆ ทๆฌๅๆ ็ฑปๆ ็ญพ็ๆ ทไพ่ฟ่ก่ฎญ็ป๏ผ้ขๆต่ฎญ็ปๆฐๆฎไธญๆ ็ฑปๆ ็ญพ็ๆ ทไพ็็ฑปๆ ็ญพ๏ผๅฝ็บณSSLๅค็ๆดไธชๆ ทๆฌ็ฉบ้ดไธญๆๆ็ปๅฎๅๆช็ฅ็ๆ ทไพ๏ผๅๆถๅฉ็จ่ฎญ็ปๆฐๆฎไธญๆ็ฑปๆ ็ญพ็ๆ ทๆฌๅๆ ็ฑปๆ ็ญพ็ๆ ทไพ๏ผไปฅๅๆช็ฅ็ๆต่ฏๆ ทไพไธ่ตท่ฟ่ก่ฎญ็ป๏ผไธไป
้ขๆต่ฎญ็ปๆฐๆฎไธญๆ ็ฑปๆ ็ญพ็ๆ ทไพ็็ฑปๆ ็ญพ๏ผๆดไธป่ฆ็ๆฏ้ขๆตๆช็ฅ็ๆต่ฏๆ ทไพ็็ฑปๆ ็ญพใไปไธๅ็ๅญฆไน ๅบๆฏ็๏ผSSLๅฏๅไธบ๏ผๅคง็ฑป๏ผย
๏ผ๏ผ๏ผๅ็็ฃๅ็ฑป ๏ผSemi-Supervised Classification๏ผ
ย ย ย ย ๅจๆ ็ฑปๆ ็ญพ็ๆ ทไพ็ๅธฎๅฉไธ่ฎญ็ปๆ็ฑปๆ ็ญพ็ๆ ทๆฌ๏ผ่ทๅพๆฏๅช็จๆ็ฑปๆ ็ญพ็ๆ ทๆฌ่ฎญ็ปๅพๅฐ็ๅ็ฑปๅจๆง่ฝๆดไผ็ๅ็ฑปๅจ๏ผๅผฅ่กฅๆ็ฑปๆ ็ญพ็ๆ ทๆฌไธ่ถณ็็ผบ้ท๏ผๅ
ถไธญ็ฑปๆ ็ญพyiๅๆ้็ฆปๆฃๅผyiโ{c1,c2,ยทยทยท,cc},cjโNใ
๏ผ๏ผ๏ผๅ็็ฃๅๅฝ๏ผSemi-Supervised Regression๏ผ
ย ย ย ย ๅจๆ ่พๅบ็่พๅ
ฅ็ๅธฎๅฉไธ่ฎญ็ปๆ่พๅบ็่พๅ
ฅ๏ผ่ทๅพๆฏๅช็จๆ่พๅบ็่พๅ
ฅ่ฎญ็ปๅพๅฐ็ๅๅฝๅจๆง่ฝๆดๅฅฝ็ๅๅฝๅจ๏ผๅ
ถไธญ่พๅบyi ๅ่ฟ็ปญๅผ yiโ๏ผฒใย
๏ผ๏ผ๏ผๅ็็ฃ่็ฑป๏ผSemi-Supervised Clustering๏ผ
ย ย ย ย ๅจๆ็ฑปๆ ็ญพ็ๆ ทๆฌ็ไฟกๆฏๅธฎๅฉไธ่ทๅพๆฏๅช็จๆ ็ฑปๆ ็ญพ็ๆ ทไพๅพๅฐ็็ปๆๆดๅฅฝ็็ฐ๏ผๆ้ซ่็ฑปๆนๆณ็็ฒพๅบฆใ
๏ผ๏ผ๏ผๅ็็ฃ้็ปด๏ผSemi-Supervised Dimensionality Reduction๏ผ
ย ย ย ย ๅจๆ็ฑปๆ ็ญพ็ๆ ทๆฌ็ไฟกๆฏๅธฎๅฉไธๆพๅฐ้ซ็ปด่พๅ
ฅๆฐๆฎ็ไฝ็ปด็ปๆ๏ผๅๆถไฟๆๅๅง้ซ็ปดๆฐๆฎๅๆๅฏน็บฆๆ๏ผPair-Wise Constraints๏ผ็็ปๆไธๅ๏ผๅณๅจ้ซ็ปด็ฉบ้ดไธญๆปก่ถณๆญฃ็บฆๆ๏ผMust-Link Constraints๏ผ็ๆ ทไพๅจไฝ็ปด็ฉบ้ดไธญ็ธ่ทๅพ่ฟ๏ผๅจ้ซ็ปด็ฉบ้ดไธญๆปก่ถณ่ด็บฆๆ๏ผCannot-Linkย Constraints๏ผ็ๆ ทไพๅจไฝ็ปด็ฉบ้ดไธญ่ท็ฆปๅพ่ฟใ
---
ๅๆๅฐๅ๏ผhttps://blog.csdn.net/jiusake/article/details/80016171 | closed | 2019-02-25T08:38:34Z | 2021-09-07T17:45:35Z | https://github.com/apachecn/ailearning/issues/484 | [] | jiangzhonglian | 0 |
fbdesignpro/sweetviz | data-visualization | 45 | Add box plots | version: 1.0.3
date: Jul 22, 2020
Currently "sweetviz" only has bar-charts for visualizations. For medium-size data analysis (such as titanic or Boston housing) it is not much costly to show box-plots as well as bar-plots. For a larger dataset, it can be made optional in `config.ini`file and can also be determined file size to make it true or false.
For example:
```
if file size < 50MB:
show boxplots and bar plots
else:
show only bar plots
``` | open | 2020-07-22T16:04:55Z | 2020-07-23T14:56:45Z | https://github.com/fbdesignpro/sweetviz/issues/45 | [
"feature request"
] | bhishanpdl | 0 |
graphistry/pygraphistry | jupyter | 440 | publish cucat | it should be clear how to get a versioned cucat
ideas:
- [x] pypi / pip
For now, instead, git tag so versioned pip install github ... tag <--- for now, use semvar: https://semver.org/ | open | 2023-02-20T23:32:51Z | 2023-12-07T06:13:24Z | https://github.com/graphistry/pygraphistry/issues/440 | [
"enhancement",
"p4",
"infra"
] | lmeyerov | 1 |
microsoft/unilm | nlp | 1,573 | Unable to use finetuned LayoutLMV3 for object detection task model for testing | **Describe**
Model I am using (LayoutLMV3):
I have sucessfully finetuned LayoutLMV3 model on custom dataset similar to publaynet dataset on object detection task , it saves a .pth model but when I try to use it for eval using this script :
python train_net.py --config-file cascade_layoutlmv3.yaml --eval-only --num-gpus 8 \
MODEL.WEIGHTS /path/to/layoutlmv3-base-finetuned-publaynet/model_final.pth \
OUTPUT_DIR /path/to/layoutlmv3-base-finetuned-publaynet
I get error :
[06/30 11:16:16 detectron2]: Full config saved to /content/output_dir/config.yaml
file /content/output_dir/config.json not found
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py", line 558, in get_config_dict
user_agent=user_agent,
File "/usr/local/lib/python3.7/dist-packages/transformers/file_utils.py", line 1506, in cached_path
raise EnvironmentError(f"file {url_or_filename} not found")
OSError: file /content/output_dir/config.json not found
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/content/unilm/layoutlmv3/examples/object_detection/train_net.py", line 122, in
args=(args,),
File "/usr/local/lib/python3.7/dist-packages/detectron2/engine/launch.py", line 82, in launch
main_func(*args)
File "/content/unilm/layoutlmv3/examples/object_detection/train_net.py", line 90, in main
model = MyTrainer.build_model(cfg)
File "/content/unilm/layoutlmv3/examples/object_detection/ditod/mytrainer.py", line 553, in build_model
model = build_model(cfg)
File "/usr/local/lib/python3.7/dist-packages/detectron2/modeling/meta_arch/build.py", line 22, in build_model
model = META_ARCH_REGISTRY.get(meta_arch)(cfg)
File "/usr/local/lib/python3.7/dist-packages/detectron2/config/config.py", line 189, in wrapped
explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/detectron2/config/config.py", line 245, in _get_args_from_config
ret = from_config_func(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/detectron2/modeling/meta_arch/rcnn.py", line 72, in from_config
backbone = build_backbone(cfg)
File "/usr/local/lib/python3.7/dist-packages/detectron2/modeling/backbone/build.py", line 31, in build_backbone
backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape)
File "/content/unilm/layoutlmv3/examples/object_detection/ditod/backbone.py", line 168, in build_vit_fpn_backbone
bottom_up = build_VIT_backbone(cfg)
File "/content/unilm/layoutlmv3/examples/object_detection/ditod/backbone.py", line 154, in build_VIT_backbone
config_path=config_path, image_only=cfg.MODEL.IMAGE_ONLY, cfg=cfg)
File "/content/unilm/layoutlmv3/examples/object_detection/ditod/backbone.py", line 84, in init
config = AutoConfig.from_pretrained(config_path)
File "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/configuration_auto.py", line 558, in from_pretrained
config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py", line 575, in get_config_dict
raise EnvironmentError(msg)
OSError: Can't load config for '/content/output_dir/'. Make sure that:
'/content/output_dir/' is a correct model identifier listed on 'https://huggingface.co/models'
(make sure '/content/output_dir/' is not a path to a local directory with something else, in that case)
or '/content/output_dir/' is the correct path to a directory containing a config.json file
| open | 2024-06-12T10:47:55Z | 2024-10-16T02:36:49Z | https://github.com/microsoft/unilm/issues/1573 | [] | maniyarsuyash | 1 |
roboflow/supervision | deep-learning | 1,376 | mAP for small, medium and large objects | ### Search before asking
- [X] I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar feature requests.
### Description
I saw an issue online where the user demanded calculation of MeanAveragePrecision for small, medium and large objects for HBB and OBB detection models. I thought that maybe this will be a good idea to expand this in supervision library.
We can discuss about this issue and maybe potential approach on how to achieve this.
### Use case
_No response_
### Additional
_No response_
### Are you willing to submit a PR?
- [X] Yes I'd like to help by submitting a PR! | closed | 2024-07-17T15:15:41Z | 2024-07-17T16:16:32Z | https://github.com/roboflow/supervision/issues/1376 | [
"enhancement"
] | Bhavay-2001 | 2 |
aidlearning/AidLearning-FrameWork | jupyter | 74 | ่ฏท้ฎๅฏไปฅๆฏๆ้ข่ฃ
ๆๆฐ็็wps office for linux armๅ๏ผๅธๆๅจๅคงๅฑๅฎๅๅนณๆฟไธ่ฝๆๅๅ
ฌ่ฝๅ | ่ฏท้ฎๅฏไปฅๆฏๆ้ข่ฃ
ๆๆฐ็็wps office for linux armๅ๏ผๅธๆๅจๅคงๅฑๅฎๅ่ฝๆๅๅ
ฌ่ฝๅ
| closed | 2020-01-18T14:07:06Z | 2020-02-11T17:38:37Z | https://github.com/aidlearning/AidLearning-FrameWork/issues/74 | [] | zihaoxingstudy1 | 3 |
LAION-AI/Open-Assistant | machine-learning | 3,109 | Support user-level OAuth plugin authentication | Will support plugins for which `ai-plugin.json` contains:
```
"auth": {
"type": "oauth"
},
```
- [x] Plugin has client ID and secret, securely store encrypted version of client secret, store client ID
- [x] Mechanism for plugin author receiving verification token
- [x] Redirect user to plugin auth URL
- [x] When user is redirected back to OA, exchange received code for an access token
See OpenAI spec for [OAuth plugin authentication](https://platform.openai.com/docs/plugins/authentication/oauth).
Once backend support is merged, a new issue can be opened for the frontend to support it | open | 2023-05-09T19:48:03Z | 2023-06-06T15:12:53Z | https://github.com/LAION-AI/Open-Assistant/issues/3109 | [
"inference"
] | olliestanley | 0 |
dynaconf/dynaconf | django | 1,055 | Multiple cast validators get discarded | Hi there, thanks for this great project,
**Describe the bug**
After defining a dynaconf `cast` validator, attempting to define subsequent dynaconf `cast` validators on the same variable get discarded while they should also be taken into account
**To Reproduce**
1. Having the following a.toml file:
**a.toml**
```toml
var = "1"
```
2. And the following sample code:
**a.py**
```python
from dynaconf import Dynaconf, Validator
settings = Dynaconf(settings_file="a.toml")
def a(v):
print("called a")
return v
def b(v):
print("called b")
return v
validators = [
Validator("var", cast=a),
Validator("var", cast=b),
]
settings.validators.register(*validators)
settings.validators.validate_all()
```
3. Executing `a.py` under a virtualenv with `dynaconf` `3.2.4` installed:
```bash
$ python3 a.py
called a
```
I would expect to see `called a` and then an additional line `called b` after that.
**Additional context**
This bug is because of `Validator("var", cast=a)` and `Validator("var", cast=b)` comparing equal with the current definition of [`__eq__`](https://github.com/dynaconf/dynaconf/blob/0390393c27a7ef27104bbda2426b3382dcc7fb9f/dynaconf/validator.py#L155-L169):
```python
def __eq__(self, other: object) -> bool:
if self is other:
return True
if type(self).__name__ != type(other).__name__:
return False
identical_attrs = (
getattr(self, attr) == getattr(other, attr)
for attr in EQUALITY_ATTRS
)
if all(identical_attrs):
return True
return False
```
and with
```python
EQUALITY_ATTRS = (
"names",
"must_exist",
"when",
"condition",
"operations",
"envs",
)
```
Since `cast` is not in the `EQUALITY_ATTRS`, validators on the same name with just a cast defined are considered equal and the second validator are skipped altogether (see [this part of the code](https://github.com/dynaconf/dynaconf/blob/0390393c27a7ef27104bbda2426b3382dcc7fb9f/dynaconf/validator.py#L463))
I think a one-line fix for this would be to add the `cast` attribute to the EQUALITY_ATTRS. | closed | 2024-02-20T12:57:19Z | 2024-03-25T17:08:52Z | https://github.com/dynaconf/dynaconf/issues/1055 | [
"bug"
] | nikoskoukis-slamcore | 0 |
hzwer/ECCV2022-RIFE | computer-vision | 203 | ไฝฟ็จColab่ฟ่กๆถ้ๅฐไบ้ฎ้ข | 
```
INFO: pip is looking at multiple versions of <Python from Requires-Python> to determine which version is compatible with other requirements. This could take a while.
INFO: pip is looking at multiple versions of torch to determine which version is compatible with other requirements. This could take a while.
ERROR: Cannot install -r requirements.txt (line 7) and torch==1.3.0 because these package versions have conflicting dependencies.
The conflict is caused by:
The user requested torch==1.3.0
torchvision 0.7.0 depends on torch==1.6.0
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies
``` | closed | 2021-10-07T12:16:57Z | 2021-10-08T05:13:11Z | https://github.com/hzwer/ECCV2022-RIFE/issues/203 | [] | Neycrol | 1 |
globaleaks/globaleaks-whistleblowing-software | sqlalchemy | 3,216 | Regression on Export/Download of Files introduced in 4.9.1 | **Describe the bug**
On selecting single/multiple reports and clicking "Export", I receive a popup message which says "Error!". When I look at the network tab, I can see that I'm getting an error "Method Not Implemented". Likewise, when I attempt to download an attachment on the report, it opens a new tab, which notifies me that I'm "Not Authenticated" with an error code of 10.
**To Reproduce**
Steps to reproduce the behavior:
1. Log in as recipient
2. Click on reports tab
3. Select one or multiple records
4. Click export
5. Error appears
6. Click on single report
7. Navigate to the attached files
8. Click Download
9. New tab opens with error.
**Log Details**
```
- - [15/Apr/2022:21:03:22 +0000] "POST /api/token HTTP/1.1" 201 127 0ms - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.79 Safari/537.36"
- -[15/Apr/2022:21:03:22 +0000] "GET /api/rfile/cdb5480d-2122-4ada-8eff-9ed2f0d6f303?token=9b56570fc8fa7bb929d862a75cc94fc3829cf8dd36671af083bcea9b0f962c58 HTTP/1.1" 412 82 0ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET / HTTP/1.1" 200 1804 1ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /css/styles.min.css HTTP/1.1" 200 53361 26ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /js/scripts.min.js HTTP/1.1" 200 259617 90ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /api/public HTTP/1.1" 200 2886 1ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /data/favicon.ico HTTP/1.1" 200 5703 6ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /lib/js/locale/angular-locale_en.js HTTP/1.1" 200 955 1ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /l10n/en HTTP/1.1" 200 9513 4ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /webfonts/metropolis-all-700-normal.woff2 HTTP/1.1" 200 26456 1ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /webfonts/metropolis-all-400-normal.woff2 HTTP/1.1" 200 24180 2ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /data/logo.png HTTP/1.1" 200 6869 2ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:05:51 +0000] "GET /webfonts/fa-solid-900.woff2 HTTP/1.1" 200 154296 26ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:06:00 +0000] "POST /api/token HTTP/1.1" 201 127 0ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:06:05 +0000] "POST /api/authentication HTTP/1.1" 201 196 4254ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:06:05 +0000] "GET /data/favicon.ico HTTP/1.1" 200 5703 4ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:06:05 +0000] "GET /api/preferences HTTP/1.1" 200 508 13ms - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.79 Safari/537.36"
]- - [15/Apr/2022:21:06:07 +0000] "GET /data/favicon.ico HTTP/1.1" 200 5703 11ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:06:07 +0000] "GET /api/preferences HTTP/1.1" 200 508 27ms - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.79 Safari/537.36"
- - [15/Apr/2022:21:06:07 +0000] "GET /api/rtips HTTP/1.1" 200 873 41ms - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.79 Safari/537.36"
- - [15/Apr/2022:21:06:10 +0000] "POST /api/token HTTP/1.1" 201 126 0ms - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.79 Safari/537.36"
- - [15/Apr/2022:21:06:10 +0000] "GET /api/rtips/133b72f7-b854-4db2-8dcc-4c7b966cc1f8/export?token=1f402e0a739e1065d61c98b640fb87a02c1ac3f81aea3f6e3d81d5d34c637779 HTTP/1.1" 412 82 0ms - "[REMOVED_USER_AGENT]"
- - [15/Apr/2022:21:06:15 +0000] "POST /api/rtips/133b72f7-b854-4db2-8dcc-4c7b966cc1f8/export HTTP/1.1" 501 85 -1ms - "[REMOVED_USER_AGENT]"
```
**Expected behavior**
Successful export of record or download of file.
**Desktop (please complete the following information):**
- Ubuntu 20.08, accessing the recipient dashboard from Macbook iOS 12.0.1
| closed | 2022-04-15T20:32:32Z | 2022-04-16T10:02:06Z | https://github.com/globaleaks/globaleaks-whistleblowing-software/issues/3216 | [
"T: Bug",
"C: Client",
"C: Backend"
] | jennycalendar | 3 |
Farama-Foundation/PettingZoo | api | 1,150 | Passing the Parallel API tests in PettingZoo for custom multi-agent environment? | ### Question
```
from pettingzoo.test import (
parallel_api_test,
parallel_seed_test,
max_cycles_test,
performance_benchmark,
)
```
I have a custom multiagent environment that extends **ParallelEnv**, and since I passed the *parallel_api_test*,
I plan to pass the other ones as well before starting training:
1. **parallel_seed_test**
```
...
File "D:\anaconda3\Lib\site-packages\pettingzoo\test\seed_test.py", line 139, in parallel_seed_test
check_environment_deterministic_parallel(env1, env2, num_cycles)
File "D:\anaconda3\Lib\site-packages\pettingzoo\test\seed_test.py", line 108, in check_environment_deterministic_parallel
assert data_equivalence(actions1, actions2), "Incorrect action seeding"
```
I have no idea how to pass this one. I tried adding `np.random.seed()` statements in my observation_space and action_space functions, but I don't know how to get deterministic actions. Please advise. Are there any steps I can follow to pass the seed test and make my environment results reproducible?
2. **max_cycles_test**
```
...
File "D:\anaconda3\Lib\site-packages\pettingzoo\test\max_cycles_test.py", line 6, in max_cycles_test
parallel_env = mod.parallel_env(max_cycles=max_cycles)
^^^^^^^^^^^^^^^^
AttributeError: 'MultiAgentHighway' object has no attribute 'parallel_env'
```
I'm not sure how to use this? I have `end_of_sim` as the maximum number of steps in the simulation before which the simulation is closed forcefully?
3. **performance_benchmark**: Had to convert my `ParallelEnv` to `AEC` with `parallel_to_aec()` to use this.
```
2466.7955100048803 turns per second
123.33977550024402 cycles per second
Finished performance benchmark
```
How do I evaluate these numbers? Please advise.
Thank you in advance :) | closed | 2023-12-25T01:31:37Z | 2024-01-09T15:11:35Z | https://github.com/Farama-Foundation/PettingZoo/issues/1150 | [
"question"
] | hridayns | 3 |
DistrictDataLabs/yellowbrick | scikit-learn | 1,088 | AttributeError: 'KMeans' object has no attribute 'show' | **Describe the bug**
I am getting this AttributeError: 'KMeans' object has no attribute 'show' when implementing Elbow Method for KMeans clustering. The elbow graph is plotted after showing the error.
**To Reproduce**
```python
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from yellowbrick.cluster import KElbowVisualizer
# Generate synthetic dataset with 8 random clusters
X, y = make_blobs(n_samples=1000, n_features=12, centers=8, random_state=42)
# Instantiate the clustering model and visualizer
model = KMeans()
visualizer = KElbowVisualizer(model, k=(4,12))
visualizer.fit(X) # Fit the data to the visualizer
visualizer.show() # Finalize and render the figure
```
**Traceback**
```
<ipython-input-69-f375a0067df6> in <module>()
14
15 visualizer.fit(X) # Fit the data to the visualizer
---> 16 visualizer.show() # Finalize and render the figure
/usr/local/lib/python3.6/dist-packages/yellowbrick/utils/wrapper.py in __getattr__(self, attr)
40 def __getattr__(self, attr):
41 # proxy to the wrapped object
---> 42 return getattr(self._wrapped, attr)
AttributeError: 'KMeans' object has no attribute 'show'
```
**Desktop (please complete the following information):**
- OS: Windows 10
- Python Version 3.6, google colab
- Yellowbrick Version 0.9.1
**Additional context**
Nope.
| closed | 2020-07-28T13:38:10Z | 2020-07-28T16:26:30Z | https://github.com/DistrictDataLabs/yellowbrick/issues/1088 | [] | Gdkmak | 1 |
ultralytics/ultralytics | deep-learning | 19,316 | Inquiry Regarding Licensing for Commercial Use of YOLO with Custom Training Tool | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
Hello,
I have developed a user-friendly tool (UI) that enables code-free training of YOLO models on custom datasets. I am planning to commercialize this tool and would like to clarify the licensing requirements.
Specifically, I would like to know:
Do I need to obtain a commercial license to use YOLO within my tool, which will be marketed to customers for training custom models?
If my customers use the tool to train models and implement them in their production systems, will they also require a separate license?
Your guidance on the licensing implications for both the tool provider (myself) and the end users (my customers) would be highly appreciated.
Thank you in advance for your assistance. I look forward to your response.
Dorra
### Additional
_No response_ | open | 2025-02-19T16:45:49Z | 2025-02-24T07:08:11Z | https://github.com/ultralytics/ultralytics/issues/19316 | [
"question",
"enterprise"
] | DoBacc | 2 |
xinntao/Real-ESRGAN | pytorch | 340 | raise ValueError("Number of processes must be at least 1") | ### Can anyone help me, why is this?
โโ[Michael@code-me] - [~/Real-ESRGAN] - [Thu May 26, 00:40]
โโ[$]> python3 inference_realesrgan_video.py -i /Users/Michael/Downloads/aa.mp4 -n realesr-animevideov3 -s 2 --suffix outx2
Traceback (most recent call last):
File "/Users/Michael/Real-ESRGAN/inference_realesrgan_video.py", line 362, in <module>
main()
File "/Users/Michael/Real-ESRGAN/inference_realesrgan_video.py", line 354, in main
run(args)
File "/Users/Michael/Real-ESRGAN/inference_realesrgan_video.py", line 272, in run
pool = ctx.Pool(num_process)
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 119, in Pool
return Pool(processes, initializer, initargs, maxtasksperchild,
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/pool.py", line 205, in __init__
raise ValueError("Number of processes must be at least 1")
ValueError: Number of processes must be at least 1` | open | 2022-05-25T16:55:29Z | 2022-09-07T14:42:14Z | https://github.com/xinntao/Real-ESRGAN/issues/340 | [] | smoosex | 3 |
lucidrains/vit-pytorch | computer-vision | 230 | Attention maps for PiT | @lucidrains maybe it's only a typo, but in the PiT example, it is said that the attention maps are also outputed, but in fact it is not we only get the predictions. Could help me get those attention maps ?
Thanks in advance | open | 2022-08-09T10:08:08Z | 2022-08-09T10:08:08Z | https://github.com/lucidrains/vit-pytorch/issues/230 | [] | Maxlanglet | 0 |
geex-arts/django-jet | django | 396 | persian calendar need | in Iran and many other cuntres | open | 2019-06-03T19:57:42Z | 2019-06-03T19:57:42Z | https://github.com/geex-arts/django-jet/issues/396 | [] | shahriardn | 0 |
Yorko/mlcourse.ai | scikit-learn | 22 | ะ ะตัะตะฝะธะต ะฒะพะฟัะพัะฐ 5.11 ะฝะต ััะฐะฑะธะปัะฝะพ | ะะฐะถะต ะฟัะธ ะฒัััะฐะฒะปะตะฝะฝัั
random_state ะฟะฐัะฐะผะตััะฐั
, best_score ะปัััะตะน ะผะพะดะตะปะธ ะพัะปะธัะฐะตััั ะพั ะฒะฐัะธะฐะฝัะพะฒ ะฒ ะพัะฒะตัะฐั
.
ะะพะดัะฒะตัะถะดะตะฝะพ ะทะฐะฟััะบะพะผ ะฝะตัะบะพะปัะบะธะผะธ ััะฐััะฝะธะบะฐะผะธ.
ะะพะทะผะพะถะฝะพ ะฒะปะธััั ะบะพะฝะบัะตัะฝัะต ะฒะตััะธะธ ะฟะฐะบะตัะพะฒ ะฝะฐ ัะฐััะตัั.
ะะพะณั ะฟัะธะปะพะถะธัั ipynb, ะฝะฐ ะบะพัะพัะพะผ ะฒะพัะฟัะพะธะทะฒะพะดะธััั. | closed | 2017-04-03T08:43:37Z | 2017-04-03T08:52:22Z | https://github.com/Yorko/mlcourse.ai/issues/22 | [] | coodix | 2 |
FlareSolverr/FlareSolverr | api | 988 | [yggtorrent] (testing) Exception (yggtorrent): FlareSolverr was unable to process the request, please check FlareSolverr logs | ### Have you checked our README?
- [X] I have checked the README
### Have you followed our Troubleshooting?
- [X] I have followed your Troubleshooting
### Is there already an issue for your problem?
- [X] I have checked older issues, open and closed
### Have you checked the discussions?
- [X] I have read the Discussions
### Environment
```markdown
- FlareSolverr version: 3.3.10
- Last working FlareSolverr version: 3.3.10
- Operating system: OMV
- Are you using Docker: [yes/no] yes
- FlareSolverr User-Agent (see log traces or / endpoint): Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36
- Are you using a VPN: [yes/no] no
- Are you using a Proxy: [yes/no] no
- Are you using Captcha Solver: [yes/no] no
- If using captcha solver, which one:
- URL to test this issue:
```
### Description
in jackett I run the test on yggtorrent and get this error
### Logged Error Messages
```text
Exception (yggtorrent): FlareSolverr was unable to process the request, please check FlareSolverr logs. Message: Error: Error solving the challenge. Message: unknown error: net::ERR_CONNECTION_REFUSED\n (Session info: chrome=119.0.6045.123)\nStacktrace:\n#0 0x555fc69c5d23 <unknown>\n#1 0x555fc6693a3e <unknown>\n#2 0x555fc668c5a6 <unknown>\n#3 0x555fc667e345 <unknown>\n#4 0x555fc667fd20 <unknown>\n#5 0x555fc667e8b4 <unknown>\n#6 0x555fc667d717 <unknown>\n#7 0x555fc667d5ac <unknown>\n#8 0x555fc667c097 <unknown>\n#9 0x555fc667c4e4 <unknown>\n#10 0x555fc6696ff2 <unknown>\n#11 0x555fc671e8a6 <unknown>\n#12 0x555fc6705c92 <unknown>\n#13 0x555fc671e288 <unknown>\n#14 0x555fc6705a43 <unknown>\n#15 0x555fc66cfbc6 <unknown>\n#16 0x555fc66d0fb2 <unknown>\n#17 0x555fc699ada7 <unknown>\n#18 0x555fc699de8d <unknown>\n#19 0x555fc699d938 <unknown>\n#20 0x555fc699e3a5 <unknown>\n#21 0x555fc698d1df <unknown>\n#22 0x555fc699e732 <unknown>\n#23 0x555fc6977666 <unknown>\n#24 0x555fc69b6e95 <unknown>\n#25 0x555fc69b707b <unknown>\n#26 0x555fc69c52af <unknown>\n#27 0x7f4b8c43dea7 start_thread\n: FlareSolverr was unable to process the request, please check FlareSolverr logs. Message: Error: Error solving the challenge. Message: unknown error: net::ERR_CONNECTION_REFUSED\n (Session info: chrome=119.0.6045.123)\nStacktrace:\n#0 0x555fc69c5d23 <unknown>\n#1 0x555fc6693a3e <unknown>\n#2 0x555fc668c5a6 <unknown>\n#3 0x555fc667e345 <unknown>\n#4 0x555fc667fd20 <unknown>\n#5 0x555fc667e8b4 <unknown>\n#6 0x555fc667d717 <unknown>\n#7 0x555fc667d5ac <unknown>\n#8 0x555fc667c097 <unknown>\n#9 0x555fc667c4e4 <unknown>\n#10 0x555fc6696ff2 <unknown>\n#11 0x555fc671e8a6 <unknown>\n#12 0x555fc6705c92 <unknown>\n#13 0x555fc671e288 <unknown>\n#14 0x555fc6705a43 <unknown>\n#15 0x555fc66cfbc6 <unknown>\n#16 0x555fc66d0fb2 <unknown>\n#17 0x555fc699ada7 <unknown>\n#18 0x555fc699de8d <unknown>\n#19 0x555
```
### Screenshots
| closed | 2023-11-28T00:42:44Z | 2023-11-28T15:46:21Z | https://github.com/FlareSolverr/FlareSolverr/issues/988 | [
"more information needed"
] | tifo71 | 8 |
tflearn/tflearn | data-science | 1,094 | KeyError: "The name 'Momentum' refers to an Operation not in the graph | When I load a pretrained tflean model, an keyError is rased.
File "C:\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 1810, in import_meta_graph
**kwargs)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\meta_graph.py", line 696, in import_scoped_meta_graph
ops.prepend_name_scope(value, scope_to_prepend_to_names))
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3035, in as_graph_element
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3095, in _as_graph_element_locked
"graph." % repr(name))
KeyError: "The name 'Momentum' refers to an Operation not in the graph." | open | 2018-10-20T05:46:43Z | 2019-03-21T17:26:20Z | https://github.com/tflearn/tflearn/issues/1094 | [] | hongminli | 2 |
public-apis/public-apis | api | 3,843 | NASA API website | I just looked at the NASA API website and there it asks me to register with an API key.
Maybe I am getting something wrong here, but if not I would love to fix this. I haven't really done much open source work, but I would love to change that.
Here is the link:ย https://api.nasa.gov/
Someone who wants to join and help can click it.
| closed | 2024-04-28T02:57:55Z | 2024-04-28T03:39:27Z | https://github.com/public-apis/public-apis/issues/3843 | [] | Fooooooooool | 3 |
svc-develop-team/so-vits-svc | pytorch | 89 | [Help]: 4.0 ไธๅทฅไฝใๅจ่ฝฌๆขๅ็้ณ้ขไธญๅผๅ
ฅไธ้่ฆ็ๅคฑ็ใๆบ้ณ้ซๆชๆญฃ็กฎ่ฝฌๆขใ4.0 Not working. introducing unwanted distortion in converted audio. source pitch not properly converted. | ### Please check the checkboxes below.
- [X] I have read *[README.md](https://github.com/svc-develop-team/so-vits-svc/blob/4.0/README.md)* and *[Quick solution in wiki](https://github.com/svc-develop-team/so-vits-svc/wiki/Quick-solution)* carefully.
- [X] I have been troubleshooting issues through various search engines. The questions I want to ask are not common.
- [X] I am NOT using one click package / environment package.
### OS version
Linux e2e-99-151 4.15.0-206-generic #217-Ubuntu SMP Fri Feb 3 19:10:13 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
### GPU
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.60.13 Driver Version: 525.60.13 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA A100 80G... Off | 00000000:01:01.0 Off | On |
| N/A 36C P0 93W / 300W | 10627MiB / 81920MiB | N/A Default |
| | | Enabled |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| MIG devices: |
+------------------+----------------------+-----------+-----------------------+
| GPU GI CI MIG | Memory-Usage | Vol| Shared |
| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG|
| | | ECC| |
|==================+======================+===========+=======================|
| 0 3 0 0 | 8324MiB / 19968MiB | 14 0 | 1 0 1 0 0 |
| | 2MiB / 32767MiB | | |
+------------------+----------------------+-----------+-----------------------+
| 0 4 0 1 | 6MiB / 19968MiB | 14 0 | 1 0 1 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------+-----------+-----------------------+
| 0 5 0 2 | 6MiB / 19968MiB | 14 0 | 1 0 1 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------+-----------+-----------------------+
| 0 6 0 3 | 2290MiB / 19968MiB | 14 0 | 1 0 1 0 0 |
| | 2MiB / 32767MiB | | |
+------------------+----------------------+-----------+-----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 3 0 16814 C /root/anaconda3/bin/python 8312MiB |
| 0 6 0 5768 C python3 2276MiB |
+-----------------------------------------------------------------------------+
### Python version
Python 3.8.16
### PyTorch version
Name: torch Version: 1.13.1 Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration Home-page: https://pytorch.org/ Author: PyTorch Team Author-email: packages@pytorch.org License: BSD-3 Location: /root/anaconda3/envs/SOVITS/lib/python3.8/site-packages Requires: nvidia-cublas-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-runtime-cu11, nvidia-cudnn-cu11, typing-extensions Required-by: fairseq, torchaudio, torchvision, triton
### Branch of sovits
4.0(Default)
### Dataset source (Used to judge the dataset quality)
Recorded in recording studio
### Where thr problem occurs or what command you executed
python inference_main.py -m "logs/44k/G_200000.pth" -c "configs/config.json" -n "source.wav" -t 0 -s "aki" -a -cr 0.5
### Problem description
introducing unwanted distortion in converted audio. source pitch not properly converted.
4.0 version not working properly. Please help me
4.0 ไธๅทฅไฝใๅจ่ฝฌๆขๅ็้ณ้ขไธญๅผๅ
ฅไธ้่ฆ็ๅคฑ็ใๆบ้ณ้ซๆชๆญฃ็กฎ่ฝฌๆขใ
่ฏทๅธฎๆ
### Log
```python
python inference_main.py -m "logs/44k/G_200000.pth" -c "configs/config.json" -n "source.wav" -t 0 -s "aki" -a -cr 0.5
load model(s) from hubert/checkpoint_best_legacy_500.pt
INFO:fairseq.tasks.text_to_speech:Please install tensorboardX: pip install tensorboardX
INFO:fairseq.tasks.hubert_pretraining:current directory is /root/Experiments/NewExperiments/so-vits-svc-4.0-mean-spk-emb
INFO:fairseq.tasks.hubert_pretraining:HubertPretrainingTask Config {'_name': 'hubert_pretraining', 'data': 'metadata', '
fine_tuning': False, 'labels': ['km'], 'label_dir': 'label', 'label_rate': 50.0, 'sample_rate': 16000, 'normalize': Fals
e, 'enable_padding': False, 'max_keep_size': None, 'max_sample_size': 250000, 'min_sample_size': 32000, 'single_target':
False, 'random_crop': True, 'pad_audio': False}
INFO:fairseq.models.hubert.hubert:HubertModel Config: {'_name': 'hubert', 'label_rate': 50.0, 'extractor_mode': default,
'encoder_layers': 12, 'encoder_embed_dim': 768, 'encoder_ffn_embed_dim': 3072, 'encoder_attention_heads': 12, 'activati
on_fn': gelu, 'layer_type': transformer, 'dropout': 0.1, 'attention_dropout': 0.1, 'activation_dropout': 0.0, 'encoder_l
ayerdrop': 0.05, 'dropout_input': 0.1, 'dropout_features': 0.1, 'final_dim': 256, 'untie_final_proj': True, 'layer_norm_
first': False, 'conv_feature_layers': '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', 'conv_bias': False, 'logit_tem
p': 0.1, 'target_glu': False, 'feature_grad_mult': 0.1, 'mask_length': 10, 'mask_prob': 0.8, 'mask_selection': static, '
mask_other': 0.0, 'no_mask_overlap': False, 'mask_min_space': 1, 'mask_channel_length': 10, 'mask_channel_prob': 0.0, 'm
ask_channel_selection': static, 'mask_channel_other': 0.0, 'no_mask_channel_overlap': False, 'mask_channel_min_space': 1
, 'conv_pos': 128, 'conv_pos_groups': 16, 'latent_temp': [2.0, 0.5, 0.999995], 'skip_masked': False, 'skip_nomask': Fals
e, 'checkpoint_activations': False, 'required_seq_len_multiple': 2, 'depthwise_conv_kernel_size': 31, 'attn_type': '', '
pos_enc_type': 'abs', 'fp16': False}
load
INFO:root:Loaded checkpoint 'logs/44k/G_200000.pth' (iteration 574)
spk_list ===> ['sai_dharam_tej']
#=====segment start, 5.82s======
vits use time:1.1171600818634033
#=====segment start, 3.4s======
vits use time:0.19401907920837402
#=====segment start, 0.025s======
jump empty segment
#=====segment start, 4.6s======
vits use time:0.269317626953125
#=====segment start, 5.0s======
vits use time:0.1455228328704834
#=====segment start, 4.28s======
vits use time:0.12343764305114746
#=====segment start, 0.012s======
jump empty segment
#=====segment start, 8.92s======
vits use time:0.17324042320251465
#=====segment start, 5.66s======
vits use time:0.14951491355895996
#=====segment start, 1.66s======
vits use time:0.1753239631652832
#=====segment start, 0.013s======
jump empty segment
#=====segment start, 7.78s======
vits use time:0.14623379707336426
#=====segment start, 6.74s======
vits use time:0.12516403198242188
#=====segment start, 0.009s======
jump empty segment
#=====segment start, 5.1s======
vits use time:0.18128347396850586
#=====segment start, 0.002s======
jump empty segment
#=====segment start, 6.12s======
vits use time:0.1354503631591797
#=====segment start, 6.46s======
vits use time:0.1435403823852539
#=====segment start, 7.08s======
vits use time:0.12348651885986328
#=====segment start, 4.66s======
vits use time:0.1376965045928955
#=====segment start, 5.56s======
vits use time:0.1471116542816162
#=====segment start, 0.013s======
jump empty segment
#=====segment start, 7.1s======
vits use time:0.18711566925048828
#=====segment start, 0.01s======
jump empty segment
#=====segment start, 5.484s======
vits use time:0.13010954856872559
#=====segment start, 6.54s======
vits use time:0.11691427230834961
#=====segment start, 8.96s======
vits use time:0.20655536651611328
#=====segment start, 6.52s======
vits use time:0.17476463317871094
#=====segment start, 5.14s======
vits use time:0.18201518058776855
#=====segment start, 5.56s======
vits use time:0.10942959785461426
#=====segment start, 13.42s======
vits use time:0.26901769638061523
#=====segment start, 0.009s======
jump empty segment
```
### Screenshot `so-vits-svc` and `logs/44k` folders and paste here
# SOURCE
# CONVERTED
### Supplementary description
Almost all the converted samples pitch not converted properly. it is a major issue. please check this immediately friends
ๅ ไนๆๆ่ฝฌๆขๅ็ๆ ทๆฌ้ณ้ซ้ฝๆฒกๆๆญฃ็กฎ่ฝฌๆขใ่ฟๆฏไธไธช้ๅคง้ฎ้ขใ่ฏทๆๅไปฌ็ซๅณๆฅ็ | open | 2023-03-26T09:57:16Z | 2023-04-10T07:46:36Z | https://github.com/svc-develop-team/so-vits-svc/issues/89 | [
"help wanted"
] | MuruganR96 | 6 |
proplot-dev/proplot | matplotlib | 288 | Manually specify `title` and `abc` coordinate positions | <!-- Thanks for helping us make proplot a better package! If this is a bug report, please use the template provided below. If this is a feature request, you can delete the template text (just try to be descriptive with your request). -->
### Description
Hi, is it possible to make the abc labels slightly offset to the left from the axis? This would probably be a negative position.
<img width="306" alt="image" src="https://user-images.githubusercontent.com/8291800/134991597-a6722340-8aab-4878-a345-928175343d40.png">
I was hoping to have the (b) moved slightly left so that I can center the title without the two texts crashing into each other.
I tried the following after having all of my "imshow" and other formatting code run:
```python
ax = axes[2]
aobj = ax._title_dict['abc']
print(aobj.get_position()) # prints (0, 1.0)
# no effect
aobj.set_x(-.25)
# no effect
abc = ax.get_children()[1]
abc.set_position((-.25, 1.0))
```
I couldn't figure out what was running to overwrite these positions, but I assume it's something internal to proplot to make the layout nice and orderly.
### Proplot version
```
>>> import matplotlib; print(matplotlib.__version__); import proplot; print(proplot.version)
3.3.0
0.9.1
``` | open | 2021-09-27T22:08:14Z | 2022-07-08T15:54:21Z | https://github.com/proplot-dev/proplot/issues/288 | [
"feature"
] | scottstanie | 6 |
aiortc/aioquic | asyncio | 148 | Datagrams only getting sent every 15s | Hello,
I'm playing around with forwarding audio/video using aioquic as a server. Since I want the data to be delivered as quickly as possibly and unreliably, I'm using datagrams. aioquic basically receives datagrams from a "sender", and then sends them to however many "subscribers."
While the sender -> server datagrams are being received synchronously and the server -> subscriber sends are being called synchronously, I'm seeing on the client / in Wireshark that the server -> subscriber datagrams are only getting bulk sent every 15s. It looks like this is due to the ack_at timer. Instead, I'd like for the datagrams to be sent immediately upon `send_datagram_frame`. Does anyone have any ideas of what I can do to fix this?
The code is below (it's based on [this example](https://github.com/GoogleChrome/samples/blob/gh-pages/quictransport/quic_transport_server.py)).
```
import argparse
from datetime import datetime
import random
import asyncio
import io
import os
import struct
import urllib.parse
from collections import defaultdict
from typing import Dict, Optional
from aioquic.asyncio import QuicConnectionProtocol, serve
from aioquic.quic.configuration import QuicConfiguration
from aioquic.quic.connection import QuicConnection, END_STATES
from aioquic.quic.events import StreamDataReceived, StreamReset, DatagramFrameReceived, QuicEvent, ConnectionTerminated
from aioquic.tls import SessionTicket
BIND_ADDRESS = '::1'
BIND_PORT = 4433
ALLOWED_ORIGINS = {'localhost'}
streams = defaultdict(dict)
class SendHandler:
def __init__(self, stream_id, connection) -> None:
self.stream_id = stream_id
print('New send', self.stream_id)
self.connection = connection
def quic_event_received(self, event: QuicEvent) -> None:
if isinstance(event, DatagramFrameReceived):
print(' from', self.stream_id)
subs = streams[self.stream_id]
for sub in subs.values():
sub.handle(event)
if isinstance(event, ConnectionTerminated):
print('ConnectionTerminated send', self.stream_id)
self.close()
def close(self) -> None:
print('Send closed', self.stream_id)
class SubHandler:
def __init__(self, stream_id, connection) -> None:
self.stream_id = stream_id
self.sub_id = random.random()
print('New sub', self.stream_id, self.sub_id)
self.connection = connection
streams[self.stream_id][self.sub_id] = self
def handle(self, event: QuicEvent) -> None:
print(' f to', self.stream_id, self.sub_id)
self.connection.send_datagram_frame(event.data)
print(self.connection.get_timer())
def close(self) -> None:
print('Sub closed', self.stream_id, self.sub_id)
del streams[self.stream_id][self.sub_id]
def quic_event_received(self, event: QuicEvent) -> None:
if isinstance(event, ConnectionTerminated):
print('ConnectionTerminated sub', self.stream_id, self.sub_id)
self.close()
# QuicTransportProtocol handles the beginning of a QuicTransport connection: it
# parses the incoming URL, and routes the transport events to a relevant
# handler. It does that by waiting for a
# client indication (a special stream with protocol headers), and buffering all
# unrelated events until the client indication can be fully processed.
class QuicTransportProtocol(QuicConnectionProtocol):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.pending_events = []
self.handler = None
self.client_indication_data = b''
def quic_event_received(self, event: QuicEvent) -> None:
try:
if self.is_closing_or_closed():
if self.handler:
print('closing?')
self.handler.close()
self.handler = None
return
if self.handler is not None:
self.handler.quic_event_received(event)
return
if isinstance(event, StreamDataReceived) and event.stream_id == 2:
self.client_indication_data += event.data
if event.end_stream:
self.process_client_indication()
if self.is_closing_or_closed():
return
# Pass all buffered events into the handler now that it's
# available.
for e in self.pending_events:
self.handler.quic_event_received(e)
self.pending_events.clear()
else:
self.pending_events.append(event)
except Exception as e:
if self.handler:
print('closing bc exception:', e)
self.handler.close()
self.handler = None
self.close()
# Client indication follows a "key-length-value" format, where key and
# length are 16-bit integers. See
# https://tools.ietf.org/html/draft-vvv-webtransport-quic-01#section-3.2
def parse_client_indication(self, bs):
while True:
prefix = bs.read(4)
if len(prefix) == 0:
return # End-of-stream reached.
if len(prefix) != 4:
raise Exception('Truncated key-length tag')
key, length = struct.unpack('!HH', prefix)
value = bs.read(length)
if len(value) != length:
raise Exception('Truncated value')
yield (key, value)
def process_client_indication(self) -> None:
KEY_ORIGIN = 0
KEY_PATH = 1
indication = dict(
self.parse_client_indication(io.BytesIO(
self.client_indication_data)))
origin = urllib.parse.urlparse(indication[KEY_ORIGIN].decode())
path = urllib.parse.urlparse(indication[KEY_PATH]).decode()
# Verify that the origin host is allowed to talk to this server. This
# is similar to the CORS (Cross-Origin Resource Sharing) mechanism in
# HTTP. See <https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS>.
if origin.hostname not in ALLOWED_ORIGINS:
raise Exception('Wrong origin specified')
# Dispatch the incoming connection based on the path specified in the
# URL.
pieces = path.path.split("/")
print('got a client indication', path.path, pieces)
stream_id = pieces[2]
if pieces[1] == 'send':
self.handler = SendHandler(stream_id, self._quic)
elif pieces[1] == 'sub':
self.handler = SubHandler(stream_id, self._quic)
else:
raise Exception('Unknown path')
def is_closing_or_closed(self) -> bool:
return self._quic._close_pending or self._quic._state in END_STATES
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('certificate')
parser.add_argument('key')
args = parser.parse_args()
configuration = QuicConfiguration(
alpn_protocols=['wq-vvv-01'],
is_client=False,
max_datagram_frame_size=1500,
)
configuration.load_cert_chain(args.certificate, args.key)
print('Running loop')
loop = asyncio.get_event_loop()
loop.run_until_complete(
serve(
BIND_ADDRESS,
BIND_PORT,
configuration=configuration,
create_protocol=QuicTransportProtocol,
))
loop.run_forever()
``` | closed | 2020-11-03T18:45:10Z | 2021-11-21T17:55:48Z | https://github.com/aiortc/aioquic/issues/148 | [] | dsafreno | 7 |
ITCoders/Human-detection-and-Tracking | numpy | 11 | Port to C++ | **Port this repo to C++**
- code should be written in C++.
- make a separate branch for this.
- functionality should be same and performance should be better.
- useful scripts in scripts folder should also be ported.
- multiple pull requests are allowed for this but make sure that you are on correct path
:smile: :smile:
| closed | 2016-10-21T18:41:38Z | 2017-05-18T20:44:20Z | https://github.com/ITCoders/Human-detection-and-Tracking/issues/11 | [] | arpit1997 | 6 |
OFA-Sys/Chinese-CLIP | computer-vision | 162 | ๆขฏๅบฆ็ดฏ็งฏไธญ็้ฎ้ข | ๅ
ณไบๆขฏๅบฆ็ดฏ็งฏ็ไปฃ็ ๏ผๆๅ ็นไธๅคชๆ็ฝ๏ผๆณ่ฏทๆไธไธใ
1. accum_image_features ๅaccum_text_featuresๅทฒ็ปๅพๅฐไบ๏ผไธบไปไนๅจget_lossไธญๅ่ฆ้ๆฐ่ฎก็ฎไธไธชbatch็็นๅพใ่ฟๆ ท็่ฏ๏ผๆๆ็็นๅพ้ฝ่ขซ่ฎก็ฎไบไธคๆฌก๏ผ้ ๆ่ตๆบ็ๆตช่ดน
2. get_loss่ฟ่กไบaccum_freqๆฌก๏ผไฝๆฏๆฏๆฌก็accum_image_featuresๅaccum_text_featuresๅนถๆฒกๆๅ็ๅๅใloss็่ฎก็ฎๆฏๅฆๆฏ้ๅคไบ๏ผ
็ธๅ
ณไปฃ็ ๅฆไธ๏ผ
# First, cache the features without any gradient tracking.
with torch.no_grad():
with autocast(enabled=(args.precision == "amp")):
chunk_image_features, chunk_text_features, _ = model(images, texts)
accum_image_features.append(chunk_image_features)
accum_text_features.append(chunk_text_features)
accum_images.append(images)
accum_texts.append(texts)
# If (i + 1) % accum_freq is not zero, move on to the next batch.
if ((i + 1) % args.accum_freq) > 0:
# FIXME this makes data time logging unreliable when accumulating
continue
# Now, ready to take gradients for the last accum_freq batches.
# Re-do the forward pass for those batches, and use the cached features from the other batches as negatives.
# Call backwards each time, but only step optimizer at the end.
optimizer.zero_grad()
for j in range(args.accum_freq):
images = accum_images[j]
texts = accum_texts[j]
with autocast(enabled=(args.precision == "amp")):
# `total_loss` and `acc` are coarsely sampled, taking only the last result in the loop.
# Although each result should be the same in theory, it will be slightly different in practice
total_loss, acc = get_loss(model, images, texts, loss_img, loss_txt, args, accum_image_features, accum_text_features, j)
if args.precision == "amp":
scaler.scale(total_loss).backward()
else:
total_loss.backward()
if args.precision == "amp":
scaler.step(optimizer)
scaler.update()
else:
optimizer.step()
` | open | 2023-07-14T08:59:21Z | 2023-07-29T08:07:41Z | https://github.com/OFA-Sys/Chinese-CLIP/issues/162 | [] | ChaoLi977 | 1 |
feder-cr/Jobs_Applier_AI_Agent_AIHawk | automation | 240 | Not want to add a section | If I don't have a title blacklist, do I delete "word1" and "word2" and leave that area empty? Do I put "N/A"? Let me know. I also don't want to put in my GPA, can I leave that slot blank as well? | closed | 2024-09-02T18:18:45Z | 2024-09-03T16:04:40Z | https://github.com/feder-cr/Jobs_Applier_AI_Agent_AIHawk/issues/240 | [] | pacman20011 | 1 |
thtrieu/darkflow | tensorflow | 996 | bash: flow: command not found | [root@localhost darkflow]# flow -h
bash: flow: command not found
[root@localhost darkflow]# sudo flow -h
sudo: flow: command not found
[root@localhost darkflow]# python3 setup.py build_ext
running build_ext
[root@localhost darkflow]# sudo pip3 install -e .
Obtaining file:///var/tmp/darkflow
Installing collected packages: darkflow
Found existing installation: darkflow 1.0.0
Uninstalling darkflow-1.0.0:
Successfully uninstalled darkflow-1.0.0
Running setup.py develop for darkflow
Successfully installed darkflow
You are using pip version 9.0.3, however version 19.0.3 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
[root@localhost darkflow]# flow -h
bash: flow: command not found
what's wrong ? | open | 2019-03-09T09:37:56Z | 2020-05-13T07:52:46Z | https://github.com/thtrieu/darkflow/issues/996 | [] | bewithme | 4 |
BeanieODM/beanie | pydantic | 769 | [BUG] Pylance strict mode: type of "delete_all" is partially unknown | When using pylance in strict mode a lot of static document methods have a typing issue
```
Type of "delete_all" is partially unknown
Type of "delete_all" is "(session: ClientSession | None = None, bulk_writer: BulkWriter | None = None, **pymongo_kwargs: Unknown) -> Coroutine[Any, Any, DeleteResult | None]"Pylance[reportUnknownMemberType](https://github.com/microsoft/pyright/blob/main/docs/configuration.md#reportUnknownMemberType)
```
This is not only for `delete_all` but also for:
- `delete`
- `insert`
- `insert_many`
- ...
And probably more
**To Reproduce**
In VSCode, set pylance type checking mode to `strict`
```python
class Sample(Document):
name: str
age: int
is_active: bool
_id: Optional[PydanticObjectId] = None
Sample.delete_all() # pylance issue
```
**Expected behavior**
I would like to be able to use beanie document functions while using pylance strict type checking mode.
Is there a way to fix this apart from ignoring typing on the whole line with `# type: ignore`?
| closed | 2023-11-08T15:32:00Z | 2024-10-07T18:29:45Z | https://github.com/BeanieODM/beanie/issues/769 | [
"Stale"
] | dotKokott | 4 |
lukas-blecher/LaTeX-OCR | pytorch | 323 | incompatible error | Cannot mix incompatible Qt library (6.5.3) with this library (6.5.2) | open | 2023-10-11T12:59:12Z | 2023-10-11T14:27:24Z | https://github.com/lukas-blecher/LaTeX-OCR/issues/323 | [] | KarnanBala | 1 |
modin-project/modin | pandas | 7,191 | Fix ASV after changing default branch: "master" -> "main" | closed | 2024-04-16T18:28:16Z | 2024-04-16T20:40:16Z | https://github.com/modin-project/modin/issues/7191 | [
"Benchmarking ๐",
"Testing ๐",
"P0"
] | anmyachev | 0 | |
scanapi/scanapi | rest-api | 435 | Remove generic exception and raise a more dedicated exception that isn't very common for Class EndpointNode::run() | The [run method ](https://github.com/scanapi/scanapi/blob/main/scanapi/tree/endpoint_node.py#L94-L94) in the Class EndpointNode's catches very generic Exception.
We would need to be precise in catching a particular Exception so it is likely to include many unrelated errors too.
```
def run(self):
for request in self._get_requests():
try:
yield request.run()
except Exception as e:
error_message = f"\nError to make request `{request.full_url_path}`. \n{str(e)}\n"
logger.error(error_message)
session.exit_code = ExitCode.REQUEST_ERROR
continue
``` | closed | 2021-07-29T12:43:27Z | 2022-04-10T13:01:17Z | https://github.com/scanapi/scanapi/issues/435 | [
"Refactor",
"Code Quality"
] | Pradhvan | 1 |
tradingstrategy-ai/web3-ethereum-defi | pytest | 5 | Get Aave lending and borrow rates directly from on-chain | [Aave has deployed its v3 version on Ethereum mainnet and Polygon](https://docs.aave.com/developers/getting-started/readme).
[See Ethereum markets - MetaMask required](https://app.aave.com/?marketName=proto_mainnet)
<img width="1310" alt="image" src="https://user-images.githubusercontent.com/49922/177447328-c157fa9e-7a6e-4b0e-9264-c52535f4b101.png">
The market has two sides
- What lenders receive as a payment, called supply APR
- What borrowers must pay for having a loan, called borrow APR
## Business use case
- Aave lending markets can be used to build a short position of tokens
- By knowing how much borrowing costs, we know what is the cost of maintaining a short position
We need to be able to answer the questions
- How much having a loan for a token e.g. [ETH](https://app.aave.com/reserve-overview/?underlyingAsset=0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2&marketName=proto_mainnet) would have cost us historically
- Opening amoun
- Opening date
- Closing date
- Interest payments accumulated (how to calculate?)
- How much having a loan for a token would cost us right now (current interest rate)
Note that the interest rate is paid in the pool native token. E.g. ETH will accrue ETH interest and needs to be converted to US Dollar in some point.
## Task
- Create a proof of concept Aave lending and borrow rate fetcher in Python
- It must be a self-contained notebook similar to [Uniswap v3 price example](https://web3-ethereum-defi.readthedocs.io/tutorials/uniswap-v3-price-analysis.html)
- Study how Aave v3 lending and borrow rates are reported. The rates are variable over time.
- Make short README documentation of available events
- Use a [event reader](https://web3-ethereum-defi.readthedocs.io/api/_autosummary_block_reader/eth_defi.event_reader.reader.html) Python module to read this data to a CSV file
- Plot a graph using Plotly for the lending and borrow rate of one market, e.g. [ETH](https://app.aave.com/reserve-overview/?underlyingAsset=0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2&marketName=proto_mainnet)
- The graph matches what we have on the Aave website
## References
- See https://github.com/PatrickAlphaC/aave_web3_py
- See [Aave v2 lending rate query using Subgraph](https://github.com/tradingstrategy-ai/ethlisbon/blob/master/aave-lending-rate-query.py)
- See [Aave v2 Python tests](https://github.com/aave/tests-protocol-v2-sigmaprime/blob/3eabcdb79ae7a90d4bd3f1a0cf91e4c6d8f9b2ed/tests/test_lending_pool.py)
- [Third party Aave Python client](https://github.com/PathX-Projects/Aave-DeFi-Client)
Aave displays borrow rate on [its main UI](https://app.aave.com/reserve-overview/?underlyingAsset=0x0bc529c00c6401aef6d220be8c6ea1667f6ad93e&marketName=proto_mainnet):
<img width="853" alt="image" src="https://user-images.githubusercontent.com/49922/177447759-f468749f-577a-4647-a385-2090be9f1e23.png">
Aave is internally displaying this information on its website using [RatesHistory API](https://github.com/aave/aave-api/blob/master/src/services/RatesHistory.ts).
[Aave internal MongoDB schema is here](https://github.com/aave/aave-api/blob/70dde8a8119dfbdf33fd0708af18776a794a2b40/src/repositories/mongodb/models/Rate.ts#L4). It is using a [cron job to update rates](https://github.com/aave/aave-api/blob/master/src/cron-jobs/reserveHistory-updates.ts) by reading them from [Subgraph](https://github.com/aave/aave-api/blob/master/src/services/RatesHistory.ts#L158).
[Aave Subgraph code is an undocumented mess](https://github.com/aave/aave-api/blob/master/src/repositories/subgraph/v2Client.ts).
[The rate is probably determined by this event](https://github.com/aave/aave-v3-core/blob/master/contracts/interfaces/IPool.sol#L197). See [GithubSearch](https://github.com/search?q=ReserveDataUpdated&type=code) for ReserveDataUpdated.
| closed | 2022-03-06T13:13:40Z | 2022-09-25T03:41:26Z | https://github.com/tradingstrategy-ai/web3-ethereum-defi/issues/5 | [] | miohtama | 0 |
pallets-eco/flask-sqlalchemy | sqlalchemy | 567 | Add "first" and "last" to Pagination class? | I'm new to Flask, and was wondering about a feature that I think would be useful to add to the Pagination class. Unless I'm missing it, there doesn't seem to be a built-in way to get the numbers of the items you're viewing on the page itself. That is, if you want to display "387 records found; displaying 26โ50", you'd have to do calculations to get the "26" and "50".
I think something along these lines would work:
```
@property
def first(self):
"""The number of the first item on the current page"""
if self.total == 0:
first = 0
else:
first = ((self.page - 1) * self.per_page) + 1
return first
@property
def last(self):
"""The number of the last item on the current page"""
if self.page == self.pages:
last = self.total
else:
last = self.page * self.per_page
return last
```
Then you could do something like (in jinja2):
```{{ pagination.total }} records found; displaying {{ pagination.first }} โ {{pagination.last }}```
I'm new to Python programming, and I don't know enough about testing, running changed code, etc., to feel comfortable submitting this as a pull request, so I hope it's OK that I just posted this as an Issue.
Thanks.
| closed | 2017-11-17T16:39:16Z | 2022-10-03T00:21:38Z | https://github.com/pallets-eco/flask-sqlalchemy/issues/567 | [
"pagination"
] | jessesheidlower | 1 |
deepinsight/insightface | pytorch | 2,249 | The warning from `onnxruntime::VerifyEachNodeIsAssignedToAnEp` | I got this when I try to run insightface on Windows10 with CUDA/cuDNN:
```
2023-02-18 00:41:44.1541586 [W:onnxruntime:, session_state.cc:1136 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.
2023-02-18 00:41:44.1608231 [W:onnxruntime:, session_state.cc:1138 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Rerunning with verbose output on a non-minimal build will show node assignments.
```
The warning msg came from [here](https://github.com/microsoft/onnxruntime/blob/4bb95d7690bac6b25622dbef5b711c15ffb00eee/onnxruntime/core/framework/session_state.cc#L1136). I know it seems not like the business of insightface, but according to the comments above the codes about generating warning msg:
> If the user explicitly included the CPU provider anyway, then remain silent, but if it was implicitly added, and unexpected fallback happened to a non-preferred provider, warn the user.
Seems that if I set `['CUDAExecutionProvider', 'CPUExecutionProvider']` as `provider` arg of `insightface.app.FaceAnalysis()`, this warning msg should not be raised, but it would be raised whether I remove `'CPUExecutionProvider'` from `providers` or not.
Any checking tips about this issue? | closed | 2023-02-17T17:10:32Z | 2024-07-12T16:12:45Z | https://github.com/deepinsight/insightface/issues/2249 | [] | Chris-fullerton | 7 |
lexiforest/curl_cffi | web-scraping | 121 | ERROR: Failed building wheel for curl_cffi | When trying to install **curl_cffi** in Termux, via the `pip install curl_cffi` command, I get the error:
```
Collecting curl_cffi Using cached curl_cffi-0.5.7.tar.gz (27 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Requirement already satisfied: cffi>=1.12.0 in /data/data/com.termux/files/usr/lib/python3.11/site-packages (from curl_cffi) (1.15.1)
Requirement already satisfied: pycparser in /data/data/com.termux/files/usr/lib/python3.11/site-packages (from cffi>=1.12.0->curl_cffi) (2.21)
Building wheels for collected packages: curl_cffi
Building wheel for curl_cffi (pyproject.toml) ... error
error: subprocess-exited-with-error
ร Building wheel for curl_cffi (pyproject.toml) did not run successfully.
โ exit code: 1
โฐโ> [91 lines of output]
running bdist_wheel
running build
running build_py
creating build
creating build/lib.linux-aarch64-cpython-311
creating build/lib.linux-aarch64-cpython-311/curl_cffi
copying curl_cffi/[const.py](https://const.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi
copying curl_cffi/[build.py](https://build.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi
copying curl_cffi/[curl.py](https://curl.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi
copying curl_cffi/[aio.py](https://aio.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi
copying curl_cffi/[init.py](https://init.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi
creating build/lib.linux-aarch64-cpython-311/curl_cffi/requests
copying curl_cffi/requests/[cookies.py](https://cookies.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi/requests
copying curl_cffi/requests/[errors.py](https://errors.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi/requests
copying curl_cffi/requests/[headers.py](https://headers.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi/requests
copying curl_cffi/requests/[init.py](https://init.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi/requests
copying curl_cffi/requests/[session.py](https://session.py/) -> build/lib.linux-aarch64-cpython-311/curl_cffi/requests
running egg_info
writing curl_cffi.egg-info/PKG-INFO
writing dependency_links to curl_cffi.egg-info/dependency_links.txt
writing requirements to curl_cffi.egg-info/requires.txt
writing top-level names to curl_cffi.egg-info/top_level.txt
reading manifest file 'curl_cffi.egg-info/SOURCES.txt'
reading manifest template '[MANIFEST.in](https://manifest.in/)'
warning: no files found matching 'curl_cffi/cacert.pem'
warning: no files found matching 'curl_cffi/_wrapper.*'
warning: no files found matching 'curl_cffi/include/curl/*'
adding license file 'LICENSE'
writing manifest file 'curl_cffi.egg-info/SOURCES.txt'
/data/data/com.termux/files/usr/tmp/pip-build-env-wydui0is/overlay/lib/python3.11/site-packages/setuptools/command/build_py.py:204: _Warning: Package 'curl_cffi.ffi' is absent from the packages configuration.
!!
****************
############################
# Package would be ignored #
############################
Python recognizes 'curl_cffi.ffi' as an importable package[^1],
but it is absent from setuptools' packages configuration.
This leads to an ambiguous overall configuration. If you want to distribute this
package, please make sure that 'curl_cffi.ffi' is explicitly added
to the packages configuration field.
Alternatively, you can also rely on setuptools' discovery methods
(for example by using find_namespace_packages(...)/find_namespace:
instead of find_packages(...)/find:).
You can read more about "package discovery" on setuptools documentation page:
- https://setuptools.pypa.io/en/latest/userguide/package_discovery.html
If you don't want 'curl_cffi.ffi' to be distributed and are
already explicitly excluding 'curl_cffi.ffi' via
find_namespace_packages(...)/find_namespace or find_packages(...)/find,
you can try to use exclude_package_data, or include-package-data=False in
combination with a more fine grained package-data configuration.
You can read more about "package data files" on setuptools documentation page:
- https://setuptools.pypa.io/en/latest/userguide/datafiles.html
[^1]: For Python, any directory (with suitable naming) can be imported,
even if it does not contain any .py files.
On the other hand, currently there is no concept of package data
directory, all directories are treated like packages.
****************
!!
check.warn(importable)
creating build/lib.linux-aarch64-cpython-311/curl_cffi/ffi
copying curl_cffi/ffi/cdef.c -> build/lib.linux-aarch64-cpython-311/curl_cffi/ffi
copying curl_cffi/ffi/shim.c -> build/lib.linux-aarch64-cpython-311/curl_cffi/ffi
copying curl_cffi/ffi/shim.h -> build/lib.linux-aarch64-cpython-311/curl_cffi/ffi
running build_ext
generating cffi module 'build/temp.linux-aarch64-cpython-311/curl_cffi._wrapper.c'
creating build/temp.linux-aarch64-cpython-311
building 'curl_cffi._wrapper' extension
creating build/temp.linux-aarch64-cpython-311/build
creating build/temp.linux-aarch64-cpython-311/build/temp.linux-aarch64-cpython-311
creating build/temp.linux-aarch64-cpython-311/curl_cffi
creating build/temp.linux-aarch64-cpython-311/curl_cffi/ffi
aarch64-linux-android-clang -DNDEBUG -g -fwrapv -O3 -Wall -fstack-protector-strong -O3 -fstack-protector-strong -O3 -fPIC -Icurl_cffi/include -Icurl_cffi/ffi -I/data/data/com.termux/files/usr/include/python3.11 -c build/temp.linux-aarch64-cpython-311/curl_cffi._wrapper.c -o build/temp.linux-aarch64-cpython-311/build/temp.linux-aarch64-cpython-311/curl_cffi._wrapper.o
build/temp.linux-aarch64-cpython-311/curl_cffi._wrapper.c:884:10: error: call to undeclared function 'curl_easy_impersonate'; ISO C99 and later do not support implicit function declarations [-Wimplicit-function-declaration]
return curl_easy_impersonate(x0, x1, x2);
^
build/temp.linux-aarch64-cpython-311/curl_cffi._wrapper.c:928:14: error: call to undeclared function 'curl_easy_impersonate'; ISO C99 and later do not support implicit function declarations [-Wimplicit-function-declaration]
{ result = curl_easy_impersonate(x0, x1, x2); }
^
2 errors generated.
error: command '/data/data/com.termux/files/usr/bin/aarch64-linux-android-clang' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for curl_cffi
Failed to build curl_cffi
ERROR: Could not build wheels for curl_cffi, which is required to install pyproject.toml-based projects
``` | closed | 2023-09-08T09:14:38Z | 2023-09-08T09:21:02Z | https://github.com/lexiforest/curl_cffi/issues/121 | [
"duplicate"
] | Gertasan | 1 |
AntonOsika/gpt-engineer | python | 893 | The term 'gpt-engineer' is not recognized | hey guys i'm on windows 11 and I installed via python 'pip install gpt-engineer'
everything seems to install fine
but now I get this in PowerShell
```
gpt-engineer : The term 'gpt-engineer' is not recognized as the name of a cmdlet, function, script file, or operable
program. Check the spelling of the name, or if a path was included, verify that the path is correct and try again.
```
how do I add gpt-engineer in the path? | closed | 2023-12-08T21:35:41Z | 2023-12-09T01:32:09Z | https://github.com/AntonOsika/gpt-engineer/issues/893 | [
"documentation",
"triage"
] | wp-coin | 2 |
feder-cr/Jobs_Applier_AI_Agent_AIHawk | automation | 486 | [FEATURE]: Need an option to hide GPA crap at top of resume | ### Feature summary
Need an option to hide GPA crap at top of resume
### Feature description
Need an option to hide GPA crap at top of resume
### Motivation
I graduated from college 30 years ago, GPA is completely irrelevant for me.
### Alternatives considered
_No response_
### Additional context
_No response_ | closed | 2024-10-06T22:58:45Z | 2024-10-25T05:29:18Z | https://github.com/feder-cr/Jobs_Applier_AI_Agent_AIHawk/issues/486 | [
"enhancement"
] | ralyodio | 0 |
plotly/dash | dash | 3,148 | Dash 3.0 feedback | Thanks for making the dash 3.0 pre-release available. ๐
Just a couple questions and comments:
- Are you still planning on removing the `_timestamp` props? https://github.com/plotly/dash/issues/3055
- The `dcc.Dropdown` is still using `defaultProps` which is causing a warning in the console. There are other console warning with `dcc.Dropdown`, but those aren't new, just wondering if there are plans to fix those too.
- In `dcc.Loading`, the `custom_component` shows a typehints warning if you use components other than `html` components.
Feature request:
- In DMC, we are relying on `renderDashComponents` from the dash-extensions.js library to render components as props defined in `children`. For more details see: https://github.com/plotly/dash/pull/3066#issuecomment-2544729487. Philippe mentioned he could add a `render(component, path)` to `dash_component_api` quite easily, but wanted to wait for another release. | closed | 2025-02-03T15:37:26Z | 2025-03-06T21:34:49Z | https://github.com/plotly/dash/issues/3148 | [
"P1"
] | AnnMarieW | 31 |
killiansheriff/LovelyPlots | matplotlib | 3 | imshow | Well done for creating this library and getting it compatible with Adobe Illustrator (spent too many hours of my life fixing figures!). Are you planning to expand on other plots like [imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html)? It does export correctly, but color scheme doesn't seem to change. Also, you may need color schemes which are appropriate for showing linear gradient changes, like heat maps, Matlab's jet, etc. | closed | 2022-07-27T22:09:11Z | 2022-07-28T19:29:51Z | https://github.com/killiansheriff/LovelyPlots/issues/3 | [] | danchitnis | 2 |
ymcui/Chinese-BERT-wwm | nlp | 70 | ๅจ้ข่ฎญ็ปRoberta็ๆถๅ้่ฆๅๅๆฅ่ฎญ็ปBertไธๆ ทๅ ไธCLS SEP SEP, ่ฟๆฏ็ดๆฅCLS SEP | @ymcui | closed | 2019-11-07T10:18:28Z | 2019-11-08T04:03:21Z | https://github.com/ymcui/Chinese-BERT-wwm/issues/70 | [] | xiongma | 2 |
keras-rl/keras-rl | tensorflow | 343 | Where is the environment specified in DQNAgent | I am trying to understand where exactly in the DQNAgent is the environment specified. I see that there is mentions of self.step, self.recent_observation, self.recent_action, reward and terminal but I don't see where are these being generated. I am trying to develop my own environment and am trying to understand how it will pass through the system. Also, in the statement
dqn.fit(env, nb_steps=3000, visualize=False, verbose=1)
I don't understand how is the environment passed into the function.
Thank you! | closed | 2019-10-18T19:12:50Z | 2020-01-24T03:01:03Z | https://github.com/keras-rl/keras-rl/issues/343 | [
"wontfix"
] | kdawar1 | 1 |
ray-project/ray | tensorflow | 50,850 | [Dashboard] Serve Grafana panels shows metrics from multiple clusters instead of filtering on SessionName or ray_io_cluster | ### What happened + What you expected to happen
**Context**
We are running multiple Ray clusters on version 2.41.0 and sending metrics to a single c**ommon Thanos instance**.
Each Ray cluster is launched by the Kuberay operator
We are launching multiple Ray clusters by creating RayCluster and RayService custom resources
**Observed**
The Ray Dashboard Overview tab, "Cluster Utilization card" shows CPU, memory and Disk metrics for the ray cluster on which the Dashboard process is running.
We noticed that the Dashboard frontend code queries Thanos and includes a filter on SessionName
However, in the Serve tab, the "QPS per application", "Error QPS per application" panels include applications from multiple clusters
We noticed that the frontend Thanos queries do not filter on SessionName or ray_io_cluster
**Expected**
_The Grafana panels in the Serve tab of the Ray Dashboard should only show applications for the Ray Cluster on which the Dashboard hosted instead of showing all applications running on every Ray cluster_
**Relevant code**
I dug into the code base and believe that these code lines are relevant.
The Cluster Utilization card in the Overview tab has a SessionName filter
https://github.com/ray-project/ray/blob/f100fe8da7875f982ce7487b86984debaca04ee4/python/ray/dashboard/client/src/pages/overview/cards/ClusterUtilizationCard.tsx#L55
The queries in the Serve tab do not
https://github.com/ray-project/ray/blob/f100fe8da7875f982ce7487b86984debaca04ee4/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.tsx#L186
### Versions / Dependencies
Kuberay operator 1.2.2
Ray 2.41.0
Python 3.11.11
Thanos 0.37
Grafana 10.4.4
OpenShift 4.13
### Reproduction script
These env are set in the RayCluster and RayService Kubernetes custom resources
- name: RAY_GRAFANA_IFRAME_HOST
value: "https://grafana.apps.uat/.<redacted_root_domain>"
- name: RAY_GRAFANA_HOST
value: "http://grafana.monitoring.svc"
- name: RAY_PROMETHEUS_NAME
value: "Thanos"
- name: RAY_PROMETHEUS_HOST
value: "http://thanos-query.monitoring.svc.cluster.local:9090"
### Issue Severity
Medium: It is a significant difficulty but I can work around it. | open | 2025-02-24T07:26:58Z | 2025-02-24T17:24:20Z | https://github.com/ray-project/ray/issues/50850 | [
"bug",
"dashboard",
"triage"
] | frenoid | 0 |
explosion/spaCy | deep-learning | 13,039 | spacy.cli.download is no longer available | In the last release (3.7.0), the ability to call `spacy.cli.download(MODEL)` is no longer available.
Is there another way to download models through a Python script, or can the spacy.cli package be reintroduced?
[In our use case](https://github.com/microsoft/presidio/blob/7400dc4b357595406954e13c0ecbdee4b27e5cd8/presidio-analyzer/install_nlp_models.py#L54), we propose the use of downloaded spacy models by default. If the user inputted a spacy model name which isn't installed, we wish to install it lazily, therefore the CLI command isn't a good fit for us.
Potentially an outcome of #12962
## How to reproduce the behaviour
```python
import spacy
spacy.cli.download("en_core_web_lg")
# AttributeError: module 'spacy' has no attribute 'cli'
```
## Your Environment
- **spaCy version:** 3.7.0
- **Platform:** macOS-14.0-arm64-arm-64bit
- **Python version:** 3.9.16
- **Pipelines:** el_core_news_lg (3.5.0), en_core_web_lg (3.6.0), sv_core_news_sm (3.6.0), en_core_web_sm (3.6.0), en_core_web_trf (3.5.0), es_core_news_sm (3.5.0), es_core_news_md (3.5.0), en_core_web_md (3.5.0)
| closed | 2023-10-04T10:17:42Z | 2023-11-04T00:02:14Z | https://github.com/explosion/spaCy/issues/13039 | [
"bug",
"feat / cli"
] | omri374 | 5 |
pydantic/pydantic-core | pydantic | 913 | Maximum value of `int` field does not reflect bytes used when subclassing | Noticed this issue when working with documents from MongoDB (using `motor`).
It returns integers as [BSON types](https://pymongo.readthedocs.io/en/stable/api/bson/int64.html), which are subclasses of the `int` type.
Minimal reproduction examples: (pydantic 2.2.1)
```py
from pydantic import BaseModel
class MyInt(int):
...
class Test(BaseModel):
id: int
x = 1046862536621953054
a = Test(id=MyInt(x))
print(a.id)
assert x == a.id # Fails, a.id is equal to 1046862536621953024
```
```py
from pydantic import BaseModel, ConfigDict
class MyInt(int):
...
class Test(BaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
id: MyInt
x = 1046862536621953054
a = Test(id=MyInt(x))
print(a.id)
assert x == a.id # Passes
```
```py
from pydantic import BaseModel
class MyInt(int):
...
class Test(BaseModel):
id: int
x = 1046862536621953054
a = Test(id=int(MyInt(x)))
print(a.id)
assert x == a.id # Passes
```
The first example should either not allow passing the type in, or it should get the value correctly (same behaviour as in v1).
Selected Assignee: @samuelcolvin | closed | 2023-08-21T21:59:15Z | 2023-08-23T12:50:32Z | https://github.com/pydantic/pydantic-core/issues/913 | [
"unconfirmed"
] | NiceAesth | 0 |
OpenInterpreter/open-interpreter | python | 1,063 | When refreshing screen it stores each snapshopt to scroll history on the terminal | ### Describe the bug
when running anything in the open-interpreter terminal installed from pip in linux, we get a history of EVERY "screen refersh" happened so far, this is annoying for scrolling, let me give you a screenshot
### Reproduce
instal open interpreter on linux
ask to code something
scroll up
### Expected behavior
when refreshing screen it should overwrite what happened before and not clear screen by scrolling down, if that is not possible, then you can have it work ala vim.
### Screenshots
### Open Interpreter version
0.2.0
### Python version
3.10.12
### Operating System name and version
Mint 21.3
### Additional context
_No response_ | closed | 2024-03-09T12:24:27Z | 2024-03-20T01:22:17Z | https://github.com/OpenInterpreter/open-interpreter/issues/1063 | [
"Bug"
] | Kreijstal | 1 |
biolab/orange3 | scikit-learn | 6,100 | Basic information about how data objects in Orange3 are handled in memory / tips for profiling add-on memory performance | <!--
Thanks for taking the time to submit a feature request!
For the best chance at our team considering your request, please answer the following questions to the best of your ability.
-->
## **What's your use case?**
<!-- In other words, what's your pain point? -->
<!-- Is your request related to a problem, or perhaps a frustration? -->
<!-- Tell us the story that led you to write this request. -->
I'm developing add-on for Orange, at now mostly to add features for data preparation (e.g. before the data becomes a Data or DataFrame). The ideal scenario would be have a subset of boring, low level file operations (such as KNIME or pentaho-kettle have) to make data cleaning before it become some sort of a tabular format good enough to be imported traditionally with Orange.
> **Boring internals, not really need for this issue**
>> The strategy I'm doing to be able to allow raw data preparation before converting to orange is a two tyoes, `FileRAW` and `FileRAWCollection` which mostly only have identifiers which explain how to find the real files (or directory with real files) on disk. In other words, I'm already somewhat using a way to pass the information between widgets, but it still following the philosophy of _"In Linux and UNIX, everything is a file"_ in a literal sense. In this sense, even if eventually we here add features such as using pandas to convert a FileRAW to another FileRAW the end result will release memory as soon as it stops. To advantage of this development approach, the optimizations are mostly generic to how to handle memory with python (or pandas)
>>
>> For now, the add-on is able to use abstract low level `pandas.read_table`, `pandas.read_csv`, `pandas.read_excel`, `pandas.read_feather`, `pandas.read_fwf`, `pandas.read_html`, `pandas.read_json`, `pandas.json_normalize`, `pandas.read_orc`, `pandas.read_parquet`, `pandas.read_sas`, ` pandas.read_spss`, `pandas.read_stata`, `pandas.read_xml` to a dataframe, and I discovered some function in your code that convert data frames to Orange Table format.
>>
>> Note: is explicitly out of my plans "reinvent the wheel" of what Orange3 do. For example, maybe one "smart default" if users are reusing workflows from someone else, but the data they have now is way much bigger, would be slice like 25% or 10% of the data and warn the user to optimize the types before passing for orange. So the user could know by next steps what could be optimized on previous ones until it fits the memory.
However, my **challenge becomes know how Orange deal with memory** before releasing the add-on for general use. For sake of this issue, while I still need to deal with interface "freezing" for long downloads (this article in on my todo list https://orange3.readthedocs.io/projects/orange-development/en/latest/tutorial-responsive-gui.html). **However, as long as the user have disk space, the "importer" to generate the allow user add gigabyte size files on disk**. And even for data which would on a 1:1 fits on memory, by using only pandas without proper optimized data types, it is easily to also use way too many memory.
## **What's your proposed solution?**
With all this context said, I think two questions could solve it
1. **Did exist some way like** `import logging; log = logging.getLogger(__name__); log.exception(get_memory_size_of(self.data))**` **in which the get_memory_size_of is something I can use to know the Orange3 internals?** If this alone is not sufficient, maybe there's something you here already use, which would list all data objects sizes and which widgets created then or sort of?*
2. **There's some general summary like how Orange manage memory?** I assume it will reuse as much as possible one Data from a previous widget. I know low level way computer works, and I'm conformable with python, but not with GUIs or QT and I'm aware long-running scripts for like nodejs can memory leak.
1. I think my main question here is... what happens if I generate different objects (such as Data and DataFrame) as output from an Widget, but the will never attach the DataFrame input of my widget to another widget, the way Orange works will free memory of the outputs which are not used by anything else? Does the `self.Outputs.data_frame.send(self.data_frame)` is smart to discard the memory no widgets wants?
1. This question is relevant, because if is the case, I will avoid creating too much outputs for all potential widgets that would make use of it. So, for me would be easier to workaround (even if take 10's of hours) than wait for something be implemented/tested on Orange3
## **Are there any alternative solutions?**
Orange3 is actually quite fantastic to protect errors in specific widgets to blow up entire interface, but this don't work for memory-related issues. So I think is better to make the widgets on this add-on that prepare data for use with the Orange be aware of it to protect orange. But for now, think that most of what the data preparation steps are doing is... a visual frontend for what would be possible doing with one-time operation with python (not just pandas).
Also, maybe point for another topic, but since `FileRAW` and `FileRAWCollection` just have codes to represent physical files on disk, this strategy could be used as lazy loading for other widgets. I just started with extension development around 2 weeks, so by now I'm mostly dealing with QT and deal with the basics, but I think would be feasible to export `FileRAW` to some Dask object or something you have.
| closed | 2022-08-19T00:15:11Z | 2022-09-10T06:07:48Z | https://github.com/biolab/orange3/issues/6100 | [] | fititnt | 3 |
thp/urlwatch | automation | 611 | How to install on MacOS in 2021? | Last year I had this working perfectly, but as soon as 2021 started something went wrong with my installation (MacOS Mojave) and I couldn't run urlwatch anymore.
I uninstalled it with `python3 -m pip uninstall urlwatch` to see if reinstalling a newer version would help, but after running `python3 -m pip install --upgrade urlwatch` it still did not link properly to my /usr/local/bin and I was unable to run it (not found)
(adding the `--user` flag did not help, it just put it in my /library/ for some reason)
I attempted to link it myself with `ln -s /usr/local/lib/python3.9/site-packages/urlwatch /usr/local/bin/urlwatch` but I can't get it to work, it keeps saying `zsh: permission denied: urlwatch` even if I use chmod -R to change the permissions on both items.
I think the install documentation might need a bit of a touch up. | closed | 2021-01-09T17:40:42Z | 2021-01-11T19:44:04Z | https://github.com/thp/urlwatch/issues/611 | [] | Kezzsim | 1 |
junyanz/pytorch-CycleGAN-and-pix2pix | pytorch | 948 | Newer versions of CycleGAN? | Hi. I have worked with CycleGAN the last year and I really like it!. But since cyclegan is from 2017 it is almost 3 years old now. Do you know if there are some "better" versions of CycleGAN in 2020? Or some other extensions of GANs that does the image to image translation just as CycleGAN but just more optimal? | closed | 2020-03-07T12:54:57Z | 2020-03-31T06:41:12Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/948 | [] | kpagels | 4 |
polakowo/vectorbt | data-visualization | 585 | Exit after N days? | Using `from_signals_ method, is it possible to have a boolean signal for entries with a timed exit N days after entry? Simply shifting the entries dataframe forward by N rows doesn't work because the dataframe may have superfluous entry signals that are not used to actually enter a position because an open position already exists. In this situation, shifting the entries dataframe forward creates false exit signals that lead to premature exit. | closed | 2023-04-17T08:48:20Z | 2024-03-16T10:44:15Z | https://github.com/polakowo/vectorbt/issues/585 | [] | posidonius | 1 |
JaidedAI/EasyOCR | deep-learning | 936 | AttributeError: 'numpy.float64' object has no attribute 'lower' | Hi.
I am trying to train a model. I have created a dataset as required and I tried to run the training script.
This is the result:
```
File "Downloads/EasyOCR-master/trainer/start_train.py", line 30, in <module>
train(opt, amp=False)
File "Downloads/EasyOCR-master/trainer/train.py", line 40, in train
train_dataset = Batch_Balanced_Dataset(opt)
File "Downloads/EasyOCR-master/trainer/dataset.py", line 56, in __init__
_dataset, _dataset_log = hierarchical_dataset(root=opt.train_data, opt=opt, select_data=[selected_d])
File "Downloads/EasyOCR-master/trainer/dataset.py", line 132, in hierarchical_dataset
dataset = OCRDataset(dirpath, opt)
File "Downloads/EasyOCR-master/trainer/dataset.py", line 164, in __init__
if re.search(out_of_char, label.lower()):
AttributeError: 'numpy.float64' object has no attribute 'lower'
```
I found that this is correlated with my dataset containing only numbers (so the labels.csv are only numbers). In fact, If I try to modify my dataset and add some letters, the error is gone.
But I don't need to add letters, my dataset are composed by numbers only.
I think I need to modify dataset.py in some way.
Furthermore, for some reason, when I trained another model some weeks ago with a similar dataset containing only numbers, I hadn't this error. I don't know what happened in meantime. I only modified some model parameters in opt and nothing more.
Thanks. | closed | 2023-01-25T09:14:06Z | 2024-02-13T11:07:46Z | https://github.com/JaidedAI/EasyOCR/issues/936 | [] | proclaim5584 | 1 |
modin-project/modin | pandas | 6,718 | Reimplement the `_axes_lengths` property to avoid materializing both axes at the same time | Source: https://github.com/modin-project/modin/pull/6700#pullrequestreview-1714797952 | closed | 2023-11-07T12:12:00Z | 2023-11-07T14:42:39Z | https://github.com/modin-project/modin/issues/6718 | [
"Performance ๐"
] | anmyachev | 0 |
jmcnamara/XlsxWriter | pandas | 640 | Feature request: String formatting in chart title | While adding text to chart title, natively in Excel, some string elements could be formatted differently, despite 'general' format of the chart title, eg. while having regular font, some letters can be italic/bold, etc. Is this feature somewhere hidden in existing module or taken into consideration for development? | closed | 2019-07-01T14:10:56Z | 2020-09-19T19:59:59Z | https://github.com/jmcnamara/XlsxWriter/issues/640 | [
"feature request",
"long term"
] | oskaruchanski | 2 |
ultralytics/yolov5 | deep-learning | 13,341 | Significant Variations in Training Results with Same Dataset and Parameters | ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
Hi everyone,
Weโve encountered a noticeable discrepancy in the performance metrics when training the same model (yolov8n.pt) on the same dataset but with different hardware and similar training parameters. The results, specifically the mAP (50-95), vary significantly across different setups.
#### Base Model: yolov8n.pt
#### Training Parameters:
| No. | Hardware | Epochs | Batch Size | mAP (50-95) |
|---|---|---|---|---|
| 1 | A6000(48GB vram) | 100 | 16 | 0.961 |
| 2 | 4090(24GB vram) | 100 | 12 | 0.93 |
| 3 | 4090(24GB vram) | 150 | 12 | 0.92 |
| 4 | L20(48GB vram) | 100 | 16 | 0.976 |
Weโve also tried enabling or disabling `coslr`, but it seems to have little to no effect on the outcome.
Could anyone shed light on what might be causing this inconsistency? Additionally, what strategies could we adopt to achieve better performance on more limited hardware setups?
Thank you in advance for your help!
### Additional
_No response_ | open | 2024-10-03T03:47:05Z | 2024-11-09T06:29:48Z | https://github.com/ultralytics/yolov5/issues/13341 | [
"question"
] | timiil | 2 |
PokemonGoF/PokemonGo-Bot | automation | 5,622 | virtualenv does not exist (may be exits) | $ ./run.sh
Virtualenv does not exits
Run: ./setup.sh -i
| closed | 2016-09-22T21:33:30Z | 2016-09-24T17:24:39Z | https://github.com/PokemonGoF/PokemonGo-Bot/issues/5622 | [] | PeshBG | 8 |
indico/indico | sqlalchemy | 6,416 | Custom menu pages title leaked when access is deined | **Describe the bug**
The custom pages you can add to event menus all have an id. You can cycle through them by visiting e.g. https://events.example.com/event/12/page/345 which will redirect to https://events.example.com/event/12/page/345-sekrit-page . If the user doesn't have permission for that page, they can still read the title via the URL slug.
**To Reproduce**
Steps to reproduce the behavior:
1. Create custom pages that have access restrictions
2. Visit the URL in a private window
3. See redirected URL title
**Expected behavior**
Access restrictions should be applied before the redirect occurs. If the user has no access, the URL should not redirect. | closed | 2024-06-24T19:31:19Z | 2024-06-25T16:08:51Z | https://github.com/indico/indico/issues/6416 | [
"bug"
] | kewisch | 3 |
whitphx/streamlit-webrtc | streamlit | 1,362 | Server to client media playback with frame-based processing | Many of the examples in this repo show client to server media sinks (mic / video capture), which have frame based callback processing. I am looking to do server to client media playback, with frame based callback processing. This would be useful for real-time audio playback with real-time processing.
After searching through this discussion https://discuss.streamlit.io/t/new-component-streamlit-webrtc-a-new-way-to-deal-with-real-time-media-streams/8669, and the example pages in streamlit-webrtc, I have not been able to find an example of this.
To be specific, I am looking to do the following:
1. Load an audio file (server)
2. Start playback (from server to client), frame by frame
3. Process each frame (before it is sent to the client) via a callback (processing should occur on the server, for example ML inference)
4. Playback processed audio frame to client
5. Continue in real-time
This example uses the MediaPlayer class from aiortc: https://github.com/whitphx/streamlit-webrtc/blob/ff697dc0fb85df58dec2307251e57105ebb737bb/pages/8_media_files_streaming.py#L9. However it does not seem that this provides any sort of callback on the stream (at the audio frame level).
Digging deeper, the MediaPlayer class has a MediaStreamTrack instance (https://aiortc.readthedocs.io/en/latest/api.html#aiortc.MediaStreamTrack) which has a `recv` callback method for each frame.
Would the correct approach be to create a new subclass of MediaStreamTrack and write a custom `recv` for the required processing? I found this related thread: https://github.com/aiortc/aiortc/issues/571
Is this functionality supported currently? I would appreciate any guidance here.
Thanks heaps! | open | 2023-08-25T00:19:15Z | 2024-10-30T10:31:19Z | https://github.com/whitphx/streamlit-webrtc/issues/1362 | [] | jamjambles | 3 |
jupyter/nbviewer | jupyter | 141 | Intelligently handle dropbox link | One of my favorite things to do is share notebooks that I have in my Dropbox using nbviewer. The only mildly annoying thing is that I have to manually change the url that dropbox gives me (`www.dropbox.com/...`) to (`dl.dropbox.com/...`) to force dropbox to cough up the file itself instead of serving their web interface.
Since I imagine this is a fairly common usecase, would it be hard to handle this substitution on the nbviewer side?
| closed | 2013-12-10T23:40:37Z | 2014-01-14T17:51:54Z | https://github.com/jupyter/nbviewer/issues/141 | [
"type:Enhancement"
] | mwaskom | 5 |
google/trax | numpy | 976 | TPU deadlock | ### Description
Hello,
I am trying to train reformer model using Trax and JAX. The training seems to be fine on Google Colab, but when I run it on google cloud server + TPU, it hangs on the "trax.supervised.Trainer".
The warning is as follows:
`2020-08-26 17:46:37.421334: W external/org_tensorflow/tensorflow/compiler/xla/python/tpu_driver/client/tpu_client.cc:601] TPU Execute is taking a long time. This might be due to a deadlock between multiple TPU cores or a very slow program.`
### Environment information
```
Ubuntu
$ pip freeze | grep trax
trax==1.3.4
$ pip freeze | grep tensor
mesh-tensorflow==0.1.16
tensor2tensor==1.15.7
tensorboard==2.3.0
tensorboard-plugin-wit==1.7.0
tensorflow==2.3.0
tensorflow-addons==0.11.1
tensorflow-datasets==3.2.1
tensorflow-estimator==2.3.0
tensorflow-gan==2.0.0
tensorflow-hub==0.9.0
tensorflow-metadata==0.23.0
tensorflow-probability==0.7.0
tensorflow-text==2.3.0
$ pip freeze | grep jax
jax==0.1.75
jaxlib==0.1.52
$ python -V
Python 3.6.10 :: Anaconda, Inc.
```
### For bugs: reproduction and error logs
# Steps to reproduce:
...
```
import requests
import os
from jax.config import config
config.FLAGS.jax_xla_backend = "tpu_driver"
config.FLAGS.jax_backend_target = "grpc://" + "10.206.164.18"
print(config.FLAGS.jax_backend_target)
from tensorflow.compat.v1.io.gfile import GFile
import gin
import os
import jax
import trax
from trax.data import inputs
import numpy as np
import jax.numpy as jnp
from scipy.special import softmax
import sentencepiece as spm
from sentencepiece import SentencePieceProcessor
import random, glob, os
def fake_data():
with open("vocab.txt",'w') as f:
f.write("[MASK]\nL\nA\nG\nV\nE\nS\nI\nK\nR\nD\nT\nP\nN\nQ\nF\nY\nM\nH\nC\nW\nX\nU\nB\nZ\nO")
if not os.path.exists('dataset'):
os.makedirs('dataset')
with open("dataset/train_0.txt",'w') as f:
for i in range(50):
f.write("M A F S A E D V L K E Y D R R R R M E A L L L S L Y Y P N D R K L L D Y K E W S P P R V Q V E C P K A P V E W N N P P S E K G L I V G H F S G I K Y K G E K A Q A S E V D V N K M C C W V S K F K D A M R R Y Q G I Q T C K I P G K V L S D L D A K I K A Y N L T V E G V E G F V R Y S R V T K Q H V A A F L K E L R H S K Q Y E N V N L I H Y I L T D K R V D I Q H L E K D L V K D F K A L V E S A H R M R Q G H M I N V K Y I L Y Q L L K K H G H G P D G P D I L T V K T G S K G V L Y D D S F R K I Y T D L G W K F T P L\n")
f.write("M S I I G A T R L Q N D K S D T Y S A G P C Y A G G C S A F T P R G T C G K D W D L G E Q T C A S G F C T S Q P L C A R I K K T Q V C G L R Y S S K G K D P L V S A E W D S R G A P Y V R C T Y D A D L I D T Q A Q V D Q F V S M F G E S P S L A E R Y C M R G V K N T A G E L V S R V S S D A D P A G G W C R K W Y S A H R G P D Q D A A L G S F C I K N P G A A D C K C I N R A S D P V Y Q K V K T L H A Y P D Q C W Y V P C A A D V G E L K M G T Q R D T P T N C P T Q V C Q I V F N M L D D G S V T M D D V K N T I N C D F S K Y V P P P P P P K P T P P T P P T P P T P P T P P T P P T P P T P R P V H N R K V M F F V A G A V L V A I L I S T V R W\n")
f.write("M A S N T V S A Q G G S N R P V R D F S N I Q D V A Q F L L F D P I W N E Q P G S I V P W K M N R E Q A L A E R Y P E L Q T S E P S E D Y S G P V E S L E L L P L E I K L D I M Q Y L S W E Q I S W C K H P W L W T R W Y K D N V V R V S A I T F E D F Q R E Y A F P E K I Q E I H F T D T R A E E I K A I L E T T P N V T R L V I R R I D D M N Y N T H G D L G L D D L E F L T H L M V E D A C G F T D F W A P S L T H L T I K N L D M H P R W F G P V M D G I K S M Q S T L K Y L Y I F E T Y G V N K P F V Q W C T D N I E T F Y C T N S Y R Y E N V P R P I Y V W V L F Q E D E W H G Y R V E D N K F H R R Y M Y S T I L H K R D T D W V E N N P L K T P A Q V E M Y K F L L R I S Q L N R D G T G Y E S D S D P E N E H F D D E S F S S G E E D S S D E D D P T W A P D S D D S D W E T E T E E E P S V A A R I L E K G K L T I T N L M K S L G F K P K P K K I Q S I D R Y F C S L D S N Y N S E D E D F E Y D S D S E D D D S D S E D D C\n")
f.write("M Y Q A I N P C P Q S W Y G S P Q L E R E I V C K M S G A P H Y P N Y Y P V H P N A L G G A W F D T S L N A R S L T T T P S L T T C T P P S L A A C T P P T S L G M V D S P P H I N P P R R I G T L C F D F G S A K S P Q R C E C V A S D R P S T T S N T A P D T Y R L L I T N S K T R K N N Y G T C R L E P L T Y G I\n")
f.write("M A R P L L G K T S S V R R R L E S L S A C S I F F F L R K F C Q K M A S L V F L N S P V Y Q M S N I L L T E R R Q V D R A M G G S D D D G V M V V A L S P S D F K T V L G S A L L A V E R D M V H V V P K Y L Q T P G I L H D M L V L L T P I F G E A L S V D M S G A T D V M V Q Q I A T A G F V D V D P L H S S V S W K D N V S C P V A L L A V S N A V R T M M G Q P C Q V T L I I D V G T Q N I L R D L V N L P V E M S G D L Q V M A Y T K D P L G K V P A V G V S V F D S G S V Q K G D A H S V G A P D G L V S F H T H P V S S A V E L N Y H A G W P S N V D M S S L L T M K N L M H V V V A E E G L W T M A R T L S M Q R L T K V L T D A E K D V M R A A A F N L F L P L N E L R V M G T K D S N N K S L K T Y F E V F E T F T I G A L M K H S G V T P T A F V D R R W L D N T I Y H M G F I P W G R D M R F V V E Y D L D G T N P F L N T V P T L M S V K R K A K I Q E M F D N M V S R M V T S\n")
f.write("M N A K Y D T D Q G V G R M L F L G T I G L A V V V G G L M A Y G Y Y Y D G K T P S S G T S F H T A S P S F S S R Y R Y\n")
f.write("M R Y T V L I A L Q G A L L L L L L I D D G Q G Q S P Y P Y P G M P C N S S R Q C G L G T C V H S R C A H C S S D G T L C S P E D P T M V W P C C P E S S C Q L V V G L P S L V N H Y N C L P N Q C T D S S Q C P G G F G C M T R R S K C E L C K A D G E A C N S P Y L D W R K D K E C C S G Y C H T E A R G L E G V C I D P K K I F C T P K N P W Q L A P Y P P S Y H Q P T T L R P P T S L Y D S W L M S G F L V K S T T A P S T Q E E E D D Y\n")
f.write("M Q N P L P E V M S P E H D K R T T T P M S K E A N K F I R E L D K K P G D L A V V S D F V K R N T G K R L P I G K R S N L Y V R I C D L S G T I Y M G E T F I L E S W E E L Y L P E P T K M E V L G T L E S C C G I P P F P E W I V M V G E D Q C V Y A Y G D E E I L L F A Y S V K Q L V E E G I Q E T G I S Y K Y P D D I S D V D E E V L Q Q D E E I Q K I R K K T R E F V D K D A Q E F Q D F L N S L D A S L L S\n")
f.write("M D S L N E V C Y E Q I K G T F Y K G L F G D F P L I V D K K T G C F N A T K L C V L G G K R F V D W N K T L R S K K L I Q Y Y E T R C D I K T E S L L Y E I K G D N N D E I T K Q I T G T Y L P K E F I L D I A S W I S V E F Y D K C N N I I I N Y F V N E Y K T M D K K T L Q S K I N E V E E K M Q K L L N E K E E E L Q E K N D K I D E L I L F S K R M E E D R K K D R E M M I K Q E K M L R E L G I H L E D V S S Q N N E L I E K V D E Q V E Q N A V L N F K I D N I Q N K L E I A V E D R A P Q P K Q N L K R E R F I L L K R N D D Y Y P Y Y T I R A Q D I N A R S A L K R Q K N L Y N E V S V L L D L T C H P N S K T L Y V R V K D E L K Q K G V V F N L C K V S I S N S K I N E E E L I K A M E T I N D E K R D V\n")
with open("dataset/train_1.txt",'w') as f:
for i in range(50):
f.write("M A F S A E D V L K E Y D R R R R M E A L L L S L Y Y P N D R K L L D Y K E W S P P R V Q V E C P K A P V E W N N P P S E K G L I V G H F S G I K Y K G E K A Q A S E V D V N K M C C W V S K F K D A M R R Y Q G I Q T C K I P G K V L S D L D A K I K A Y N L T V E G V E G F V R Y S R V T K Q H V A A F L K E L R H S K Q Y E N V N L I H Y I L T D K R V D I Q H L E K D L V K D F K A L V E S A H R M R Q G H M I N V K Y I L Y Q L L K K H G H G P D G P D I L T V K T G S K G V L Y D D S F R K I Y T D L G W K F T P L\n")
f.write("M S I I G A T R L Q N D K S D T Y S A G P C Y A G G C S A F T P R G T C G K D W D L G E Q T C A S G F C T S Q P L C A R I K K T Q V C G L R Y S S K G K D P L V S A E W D S R G A P Y V R C T Y D A D L I D T Q A Q V D Q F V S M F G E S P S L A E R Y C M R G V K N T A G E L V S R V S S D A D P A G G W C R K W Y S A H R G P D Q D A A L G S F C I K N P G A A D C K C I N R A S D P V Y Q K V K T L H A Y P D Q C W Y V P C A A D V G E L K M G T Q R D T P T N C P T Q V C Q I V F N M L D D G S V T M D D V K N T I N C D F S K Y V P P P P P P K P T P P T P P T P P T P P T P P T P P T P P T P R P V H N R K V M F F V A G A V L V A I L I S T V R W\n")
f.write("M A S N T V S A Q G G S N R P V R D F S N I Q D V A Q F L L F D P I W N E Q P G S I V P W K M N R E Q A L A E R Y P E L Q T S E P S E D Y S G P V E S L E L L P L E I K L D I M Q Y L S W E Q I S W C K H P W L W T R W Y K D N V V R V S A I T F E D F Q R E Y A F P E K I Q E I H F T D T R A E E I K A I L E T T P N V T R L V I R R I D D M N Y N T H G D L G L D D L E F L T H L M V E D A C G F T D F W A P S L T H L T I K N L D M H P R W F G P V M D G I K S M Q S T L K Y L Y I F E T Y G V N K P F V Q W C T D N I E T F Y C T N S Y R Y E N V P R P I Y V W V L F Q E D E W H G Y R V E D N K F H R R Y M Y S T I L H K R D T D W V E N N P L K T P A Q V E M Y K F L L R I S Q L N R D G T G Y E S D S D P E N E H F D D E S F S S G E E D S S D E D D P T W A P D S D D S D W E T E T E E E P S V A A R I L E K G K L T I T N L M K S L G F K P K P K K I Q S I D R Y F C S L D S N Y N S E D E D F E Y D S D S E D D D S D S E D D C\n")
f.write("M Y Q A I N P C P Q S W Y G S P Q L E R E I V C K M S G A P H Y P N Y Y P V H P N A L G G A W F D T S L N A R S L T T T P S L T T C T P P S L A A C T P P T S L G M V D S P P H I N P P R R I G T L C F D F G S A K S P Q R C E C V A S D R P S T T S N T A P D T Y R L L I T N S K T R K N N Y G T C R L E P L T Y G I\n")
f.write("M A R P L L G K T S S V R R R L E S L S A C S I F F F L R K F C Q K M A S L V F L N S P V Y Q M S N I L L T E R R Q V D R A M G G S D D D G V M V V A L S P S D F K T V L G S A L L A V E R D M V H V V P K Y L Q T P G I L H D M L V L L T P I F G E A L S V D M S G A T D V M V Q Q I A T A G F V D V D P L H S S V S W K D N V S C P V A L L A V S N A V R T M M G Q P C Q V T L I I D V G T Q N I L R D L V N L P V E M S G D L Q V M A Y T K D P L G K V P A V G V S V F D S G S V Q K G D A H S V G A P D G L V S F H T H P V S S A V E L N Y H A G W P S N V D M S S L L T M K N L M H V V V A E E G L W T M A R T L S M Q R L T K V L T D A E K D V M R A A A F N L F L P L N E L R V M G T K D S N N K S L K T Y F E V F E T F T I G A L M K H S G V T P T A F V D R R W L D N T I Y H M G F I P W G R D M R F V V E Y D L D G T N P F L N T V P T L M S V K R K A K I Q E M F D N M V S R M V T S\n")
f.write("M N A K Y D T D Q G V G R M L F L G T I G L A V V V G G L M A Y G Y Y Y D G K T P S S G T S F H T A S P S F S S R Y R Y\n")
f.write("M R Y T V L I A L Q G A L L L L L L I D D G Q G Q S P Y P Y P G M P C N S S R Q C G L G T C V H S R C A H C S S D G T L C S P E D P T M V W P C C P E S S C Q L V V G L P S L V N H Y N C L P N Q C T D S S Q C P G G F G C M T R R S K C E L C K A D G E A C N S P Y L D W R K D K E C C S G Y C H T E A R G L E G V C I D P K K I F C T P K N P W Q L A P Y P P S Y H Q P T T L R P P T S L Y D S W L M S G F L V K S T T A P S T Q E E E D D Y\n")
f.write("M Q N P L P E V M S P E H D K R T T T P M S K E A N K F I R E L D K K P G D L A V V S D F V K R N T G K R L P I G K R S N L Y V R I C D L S G T I Y M G E T F I L E S W E E L Y L P E P T K M E V L G T L E S C C G I P P F P E W I V M V G E D Q C V Y A Y G D E E I L L F A Y S V K Q L V E E G I Q E T G I S Y K Y P D D I S D V D E E V L Q Q D E E I Q K I R K K T R E F V D K D A Q E F Q D F L N S L D A S L L S\n")
f.write("M D S L N E V C Y E Q I K G T F Y K G L F G D F P L I V D K K T G C F N A T K L C V L G G K R F V D W N K T L R S K K L I Q Y Y E T R C D I K T E S L L Y E I K G D N N D E I T K Q I T G T Y L P K E F I L D I A S W I S V E F Y D K C N N I I I N Y F V N E Y K T M D K K T L Q S K I N E V E E K M Q K L L N E K E E E L Q E K N D K I D E L I L F S K R M E E D R K K D R E M M I K Q E K M L R E L G I H L E D V S S Q N N E L I E K V D E Q V E Q N A V L N F K I D N I Q N K L E I A V E D R A P Q P K Q N L K R E R F I L L K R N D D Y Y P Y Y T I R A Q D I N A R S A L K R Q K N L Y N E V S V L L D L T C H P N S K T L Y V R V K D E L K Q K G V V F N L C K V S I S N S K I N E E E L I K A M E T I N D E K R D V\n")
fake_data()
spm.SentencePieceTrainer.train(input='vocab.txt',
model_prefix='protein',
vocab_size=30,
model_type="word",
#user_defined_symbols="<MASK>",
pad_id=0,
unk_id=1,
bos_id=2,
eos_id=3,
pad_piece="[PAD]",
unk_piece="[UNK]",
bos_piece="[BOS]",
eos_piece="[EOS]")
tokenizer = spm.SentencePieceProcessor(model_file='protein.model')
train_files = glob.glob("dataset/train*",recursive=True)
random.shuffle(train_files)
def mask_seq(seq,mask_prob=0.15):
seq = np.array(seq)
minValue = 1
maxValue = len(seq) - 2
max_mask_tokens = int(maxValue * 0.15 + 0.5)
randomlist = random.sample(range(minValue, maxValue), max_mask_tokens)
seq_masked = seq
seq_masked[randomlist] = tokenizer.encode("[MASK]")[0]
return seq_masked
def get_seq(train_files):
while True:
for file in train_files:
with open(file) as fp:
for line in fp:
yield line
def get_batch(seq_gen, batch_length):
batch = []
while True:
seq = next(seq_gen)
seq_ids = tokenizer.encode(seq,add_bos=True,add_eos=True)
new_batch_len = len(batch) + len(seq_ids)
if new_batch_len <= batch_length :
batch = batch + seq_ids
continue
next_batch = batch
batch = seq_ids
yield next_batch
# Set up the data pipeline.
def my_inputs(n_devices):
MAX_BATCH_LENGTH = 1024*4
seq_gen = get_seq(train_files)
batch_gen = get_batch(seq_gen,MAX_BATCH_LENGTH)
while True:
inputs = []
targets = []
mask = []
for i in range(n_devices):
batch_ids = next(batch_gen)
masked_seq_ids = mask_seq(batch_ids)
pad_amount = MAX_BATCH_LENGTH - len(batch_ids)
inputs.append(np.pad(masked_seq_ids, (0,pad_amount)))
targets.append(np.pad(batch_ids, (0,pad_amount)))
mask.append(np.pad(np.ones_like(batch_ids, dtype=np.float32),
(0,pad_amount),
mode='constant'))
inputs = np.stack(inputs)
targets = np.stack(targets)
mask = np.stack(mask)
yield (inputs, targets, mask)
inp_gen_test = my_inputs(trax.fastmath.device_count())
res = next(inp_gen_test)
print(tokenizer.decode(res[0][0].tolist()))
print(tokenizer.decode(res[1][0].tolist()))
# Configure hyperparameters.
gin.parse_config("""
import trax.layers
import trax.models
import trax.optimizers
import trax.data.inputs
import trax.supervised.trainer_lib
# Parameters that will vary between experiments:
# ==============================================================================
train.model = @trax.models.Reformer
n_layers = 15
n_heads = 16
dropout = 0.1
n_tokens = 40000 # They have used very small n_tokens = 2048
vocab_size= 30
d_model = 1024
d_ff = 4096
# Done
# Parameters for MultifactorSchedule:
# ==============================================================================
multifactor.constant = 0.088
multifactor.decay_factor = 0.5
multifactor.factors = 'constant * linear_warmup * rsqrt_decay'
multifactor.steps_per_cycle = 100000
multifactor.steps_per_decay = 20000
multifactor.warmup_steps = 8000
# Done
# Parameters for Adam:
# ==============================================================================
Adam.b1 = 0.9
Adam.b2 = 0.98
Adam.eps = 1e-09
Adam.weight_decay_rate = 1e-05
# Done
# Parameters for SelfAttention:
# ==============================================================================
#trax.layers.SelfAttention.attention_dropout = 0.05
#trax.layers.SelfAttention.chunk_len = 64
#trax.layers.SelfAttention.n_chunks_before = 1
#trax.layers.SelfAttention.n_parallel_heads = 1
trax.layers.SelfAttention.causal = False
trax.layers.SelfAttention.chunk_len = None
trax.layers.SelfAttention.masked = False
trax.layers.SelfAttention.n_chunks_after = 0
trax.layers.SelfAttention.n_chunks_before = 0
trax.layers.SelfAttention.n_parallel_heads = None
trax.layers.SelfAttention.predict_drop_len = 64
trax.layers.SelfAttention.predict_mem_len = 192
trax.layers.SelfAttention.share_qk = False
trax.layers.SelfAttention.use_python_loop = False
trax.layers.SelfAttention.use_reference_code = False
# Done
# Parameters for EncDecAttention:
# ==============================================================================
trax.layers.EncDecAttention.masked = True
trax.layers.EncDecAttention.n_parallel_heads = None
trax.layers.EncDecAttention.use_python_loop = False
trax.layers.EncDecAttention.use_reference_code = False
# Done
# Parameters for LSHSelfAttention:
# ==============================================================================
#LSHSelfAttention.attention_dropout = 0.0
#LSHSelfAttention.chunk_len = 64
#LSHSelfAttention.n_buckets = [64, 128]
#LSHSelfAttention.n_chunks_after = 0
#LSHSelfAttention.n_chunks_before = 1
#LSHSelfAttention.n_hashes = 1
#LSHSelfAttention.n_parallel_heads = 1
#LSHSelfAttention.predict_drop_len = 128
#LSHSelfAttention.predict_mem_len = 1024
# Done
# Parameters for Reformer:
# ==============================================================================
Reformer.d_model = %d_model
Reformer.d_ff = %d_ff
Reformer.dropout = %dropout
Reformer.ff_activation = @trax.layers.Relu
Reformer.max_len = %n_tokens
Reformer.mode = 'train'
Reformer.n_heads = %n_heads
Reformer.n_encoder_layers = %n_layers
Reformer.n_decoder_layers = %n_layers
Reformer.input_vocab_size = %vocab_size
""")
# Set up a Trainer.
output_dir = os.path.expanduser('train_dir/')
trainer = trax.supervised.Trainer(
model=trax.models.Reformer,
loss_fn=trax.layers.CrossEntropyLoss(),
optimizer=trax.optimizers.Adam,
lr_schedule=trax.lr.multifactor(),
inputs=trax.data.inputs.Inputs(my_inputs),
output_dir=output_dir)
# Run one training step, to make sure the model fits in memory.
# The first time trainer.train_epoch is called, it will JIT the entire network
# architecture, which takes around 2 minutes. The JIT-compiled model is saved
# so subsequent runs will be much faster than the first.
trainer.train_epoch(n_steps=1, n_eval_steps=1)
```
# Error logs:
...
```
2020-08-26 17:46:37.421334: W external/org_tensorflow/tensorflow/compiler/xla/python/tpu_driver/client/tpu_client.cc:601] TPU Execute is taking a long time. This might be due to a deadlock between multiple TPU cores or a very slow program.
2020-08-26 17:51:46.613101: W external/org_tensorflow/tensorflow/compiler/xla/python/tpu_driver/client/tpu_client.cc:601] TPU Execute is taking a long time. This might be due to a deadlock between multiple TPU cores or a very slow program.
```
Any idea what could be the problem ? | open | 2020-08-26T15:58:20Z | 2020-08-26T15:58:20Z | https://github.com/google/trax/issues/976 | [] | agemagician | 0 |
ultrafunkamsterdam/undetected-chromedriver | automation | 1,389 | Rotating proxies im receiving selenium.common.exceptions.SessionNotCreatedException: Message: session not created | Im having this in order to rotate proxies, everytime a new_chrome starts it use a new proxy
```python
def navigate_selenium(self, doc_id):
while True:
try:
self.chrome = self.new_chrome()
self.chrome.get(self.origin + "/document/" + doc_id)
self.chrome.wait_for_element_display(60, "#head")
except Exception as e:
logcolor_warning("EXCEPCION, RECARGANDO")
if hasattr(self, "chrome"):
detail = Soup(self.chrome.driver.page_source, "html.parser")
if "captcha" in detail:
logcolor_warning("CAPTCHA EN DETALLE SELENIUM, RECARGANDO")
del self.chrome
self.navigate_selenium(doc_id)
else:
detail = Soup(self.chrome.driver.page_source, "html.parser")
self._obtain_cookies()
return detail
finally:
logcolor_debug(f"CERRANDO VENTANA Y NAVEGADOR")
if hasattr(self, "chrome"):
del self.chrome
```
, but sometimes I receive the following error:
```
File "/u02/user/git/modulos/lib/modulos/utils/js_render.py", line 199, in __init__
self._driver = uc.Chrome(
File "/home/user/.local/lib/python3.8/site-packages/undetected_chromedriver/__init__.py", line 441, in __init__
super(Chrome, self).__init__(
File "/home/user/.local/lib/python3.8/site-packages/selenium/webdriver/chrome/webdriver.py", line 84, in __init__
super().__init__(
File "/home/user/.local/lib/python3.8/site-packages/selenium/webdriver/chromium/webdriver.py", line 104, in __init__
super().__init__(
File "/home/user/.local/lib/python3.8/site-packages/selenium/webdriver/remote/webdriver.py", line 286, in __init__
self.start_session(capabilities, browser_profile)
File "/home/user/.local/lib/python3.8/site-packages/undetected_chromedriver/__init__.py", line 704, in start_session
super(selenium.webdriver.chrome.webdriver.WebDriver, self).start_session(
File "/home/user/.local/lib/python3.8/site-packages/selenium/webdriver/remote/webdriver.py", line 378, in start_session
response = self.execute(Command.NEW_SESSION, parameters)
File "/home/user/.local/lib/python3.8/site-packages/selenium/webdriver/remote/webdriver.py", line 440, in execute
self.error_handler.check_response(response)
File "/home/user/.local/lib/python3.8/site-packages/selenium/webdriver/remote/errorhandler.py", line 245, in check_response
raise exception_class(message, screen, stacktrace)
selenium.common.exceptions.SessionNotCreatedException: Message: session not created
from no such execution context: uniqueContextId not found
(Session info: chrome=114.0.5735.106)
Stacktrace:
#0 0x562bbbd104e3 <unknown>
#1 0x562bbba3fc76 <unknown>
#2 0x562bbba298c3 <unknown>
#3 0x562bbba27bac <unknown>
#4 0x562bbba28162 <unknown>
#5 0x562bbba43af1 <unknown>
#6 0x562bbba4597e <unknown>
#7 0x562bbba45a4c <unknown>
#8 0x562bbbaa4f77 <unknown>
#9 0x562bbbaa347f <unknown>
#10 0x562bbba9ade3 <unknown>
#11 0x562bbba702dd <unknown>
#12 0x562bbba7134e <unknown>
#13 0x562bbbcd03e4 <unknown>
#14 0x562bbbcd43d7 <unknown>
#15 0x562bbbcdeb20 <unknown>
#16 0x562bbbcd5023 <unknown>
#17 0x562bbbca31aa <unknown>
#18 0x562bbbcf96b8 <unknown>
#19 0x562bbbcf9847 <unknown>
#20 0x562bbbd09243 <unknown>
#21 0x7f69f745dea5 start_thread
``` | closed | 2023-07-12T07:28:02Z | 2023-07-19T10:54:21Z | https://github.com/ultrafunkamsterdam/undetected-chromedriver/issues/1389 | [] | juanfrilla | 2 |
vitalik/django-ninja | django | 585 | I need to comment the parameters in the URL address | Here's my view definition:
This is openapi. Json:
I need to change the title in the schema to Chinese | closed | 2022-10-10T06:56:10Z | 2022-10-27T06:32:41Z | https://github.com/vitalik/django-ninja/issues/585 | [] | hanbinloop | 2 |
MaartenGr/BERTopic | nlp | 1,697 | ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part. | Hi, I encountered the following error while performing semi-supervised training:
embeddings[indices] = np.average([embeddings[indices], seed_topic_embeddings[seed_topic]], weights=[3, 1])
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
The clustering corpus is formatted as a one-dimensional list with a length of 50779, and the vector dimensions are (50779, 1024). Importing the seed_topic_list as either a one-dimensional or two-dimensional list will result in this error. Here is a snippet of the code๏ผ
reduction_model = BaseDimensionalityReduction()
cluster_model = KMeans(n_clusters=num_topics)
topic_model = BERTopic(nr_topics=num_topics,
top_n_words=10,
seed_topic_list=seed_topic_list,
embedding_model=sentence_model,
umap_model=reduction_model,
# min_topic_size=50,
calculate_probabilities=False,
hdbscan_model=cluster_model,
# vectorizer_model=vectorizer,
verbose=True)
topics, probs = topic_model.fit_transform(documents=recall, embeddings=embeddings)
I look forward to your reply. | closed | 2023-12-15T10:23:51Z | 2024-12-20T17:03:24Z | https://github.com/MaartenGr/BERTopic/issues/1697 | [] | shyzzz521 | 4 |
google-research/bert | nlp | 981 | "intermediate" hidden layer | Hi,
what is the purpose of this "intermediate" hidden layer with activation + output layer w/o activation?
https://github.com/google-research/bert/blob/cc7051dc592802f501e8a6f71f8fb3cf9de95dc9/modeling.py#L866
| open | 2020-01-03T09:34:38Z | 2020-01-03T09:34:38Z | https://github.com/google-research/bert/issues/981 | [] | congchan | 0 |
sigmavirus24/github3.py | rest-api | 1,160 | Add support for Deployment Environments | It appears that Deployment Environments ( https://docs.github.com/en/rest/deployments/environments?apiVersion=2022-11-28 ) are not currently supported. Unfortunately I don't have time to implement this myself, but wanted to open an issue to track the feature request. | open | 2023-09-15T12:02:13Z | 2023-12-15T12:57:55Z | https://github.com/sigmavirus24/github3.py/issues/1160 | [] | jantman | 1 |
pyg-team/pytorch_geometric | pytorch | 9,792 | Install a full PyG environment using only a single pip command | ### ๐ต Describe the installation problem
The current installation process requires installing PyTorch first then running a second pip install for all the PyG components. This is because torch is required in the setup of the PyG components:
- https://github.com/pyg-team/pytorch_geometric/issues/861
- https://github.com/pyg-team/pytorch_geometric/issues/1440
There are cases where only a single pip command using requirements.txt can be used to set up the full env, for example the following frameworks for queuing jobs:
- https://clear.ml/docs/latest/docs/apps/clearml_task
- https://docs.wandb.ai/guides/launch/create-launch-job#requirements-file
Is there a way of setting up a full Python environment for PyG using a _single_ pip command?
### Environment
* PyG version:
* PyTorch version:
* OS:
* Python version:
* CUDA/cuDNN version:
* How you installed PyTorch and PyG (`conda`, `pip`, source):
* Any other relevant information (*e.g.*, version of `torch-scatter`):
| open | 2024-11-15T21:02:49Z | 2025-02-14T11:05:07Z | https://github.com/pyg-team/pytorch_geometric/issues/9792 | [
"installation"
] | Anjum48 | 5 |
pyeve/eve | flask | 1,472 | 422 UNPROCESSABLE ENTITY when using user_id and data_relation on PATCH | ### Expected Behavior: 200 OK
### Actual Behavior: 422 UNPROCESSABLE ENTITY
If using `set_request_auth_value` on a schema and having defined a `data_relation` to a schema that is not using any auth value,
a PATCH operation will fail with the follwoing error:
```
value 'XXX' must exist in resource 'YYY', field '_id'
```
POST works fine.
The only workaround is not to define any data_relation.
According to my research, the `_filter` variable contains the `user_id` in an `$and` condition when PATCH but not on POST.
https://github.com/pyeve/eve/blob/c62941992b2f66fa02b822581891bd7c18e76d9c/eve/io/mongo/mongo.py#L335
### Environment
* Python version: 3.8.10
* Eve version: 1.1.5
| open | 2022-03-29T09:33:39Z | 2023-08-17T04:41:23Z | https://github.com/pyeve/eve/issues/1472 | [] | xibriz | 1 |
comfyanonymous/ComfyUI | pytorch | 6,984 | How to composite noise per region in regional sampling? | ### Your question
I want to have the effect equivalent of a "different denoise value" for each regional prompt in a RegionalSampler workflow. Is there an easy way to accomplish this? I was considering manually setting sigma schedules for the sampler in each regional prompt, but it's not clear to me how I'd then prepare the latent noise so that this could work well - or if that even makes since (since RegionalSampler seems to add noise in various ways as it goes!)
Is anything like this possible? I basically just want to have sliders for each region that set the amount of noise/denoise to perform, just as I could with sequential regional prompts. If this were made easy to do it would be an absolutely killer feature - I think I could replace many of my workflows with Impact Regional Sampler....
### Logs
```powershell
```
### Other
_No response_ | open | 2025-02-26T20:06:06Z | 2025-03-10T06:28:49Z | https://github.com/comfyanonymous/ComfyUI/issues/6984 | [
"User Support"
] | dtromb | 2 |
AirtestProject/Airtest | automation | 1,264 | error: metadata-generation-failed | ```bash
python --version
Python 3.13.0
```
```log
(.venv) PS xxx.air> pip install -U airtest
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/
Collecting airtest
Using cached https://mirrors.aliyun.com/pypi/packages/b2/52/62391b32309ce0cbf5e2d2ba5751a6a4a4cf8aec470f3d94ec76f2d85099/airtest-1.3.5.tar.gz (49.5 MB)
Preparing metadata (setup.py) ... done
Collecting Jinja2>=2.8 (from airtest)
Using cached https://mirrors.aliyun.com/pypi/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl (133 kB)
Collecting Pillow>=3.4.0 (from airtest)
Using cached https://mirrors.aliyun.com/pypi/packages/fb/01/3755ba287dac715e6afdb333cb1f6d69740a7475220b4637b5ce3d78cec2/pillow-11.0.0-cp313-cp313-win_amd64.whl (2.6 MB)
Collecting requests>=2.11.1 (from airtest)
Using cached https://mirrors.aliyun.com/pypi/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl (64 kB)
Collecting six<=1.16.0,>=1.9.0 (from airtest)
Using cached https://mirrors.aliyun.com/pypi/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting mss==6.1.0 (from airtest)
Using cached https://mirrors.aliyun.com/pypi/packages/d7/5f/77dece686b8d08a17430e169e936722693712b8cf1ee638caa8b1cb6452b/mss-6.1.0-py3-none-any.whl (76 kB)
Collecting numpy<2.0 (from airtest)
Using cached https://mirrors.aliyun.com/pypi/packages/65/6e/09db70a523a96d25e115e71cc56a6f9031e7b8cd166c1ac8438307c14058/numpy-1.26.4.tar.gz (15.8 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Installing backend dependencies ... done
Preparing metadata (pyproject.toml) ... error
error: subprocess-exited-with-error
ร Preparing metadata (pyproject.toml) did not run successfully.
โ exit code: 2
โฐโ> [35 lines of output]
+ xxx.air\.venv\Scripts\python.exe C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\vendored-meson\meson\meson.py setup C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3 C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\.mesonpy-c7quwdt1 -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --native-file=C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\.mesonpy-c7quwdt1\meson-python-native-file.ini
Traceback (most recent call last):
File "C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\vendored-meson\meson\mesonbuild\mesonmain.py", line 194, in run
return options.run_func(options)
~~~~~~~~~~~~~~~~^^^^^^^^^
File "C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\vendored-meson\meson\mesonbuild\msetup.py", line 358, in run
app.generate()
~~~~~~~~~~~~^^
File "C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\vendored-meson\meson\mesonbuild\msetup.py", line 178, in generate
env = environment.Environment(self.source_dir, self.build_dir, self.options)
File "C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\vendored-meson\meson\mesonbuild\environment.py", line 571, in __init__
config = coredata.parse_machine_files(self.coredata.config_files)
File "C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\vendored-meson\meson\mesonbuild\coredata.py", line 1032, in parse_machine_files
parser = MachineFileParser(filenames)
File "C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\vendored-meson\meson\mesonbuild\coredata.py", line 973, in __init__
self.parser.read(filenames)
~~~~~~~~~~~~~~~~^^^^^^^^^^^
File "C:\Users\admin\AppData\Local\Temp\pip-install-zvzk__iw\numpy_dc9f6a4dc9314a7eb557e738b3d5f2d3\vendored-meson\meson\mesonbuild\coredata.py", line 960, in read
return super().read(filenames, encoding)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python313\Lib\configparser.py", line 735, in read
self._read(fp, filename)
~~~~~~~~~~^^^^^^^^^^^^^^
File "C:\Program Files\Python313\Lib\configparser.py", line 1050, in _read
ParsingError._raise_all(self._read_inner(fp, fpname))
~~~~~~~~~~~~~~~~^^^^^^^^^^^^
File "C:\Program Files\Python313\Lib\configparser.py", line 1058, in _read_inner
for st.lineno, line in enumerate(map(Line, fp), start=1):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen codecs>", line 325, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc9 in position 46: invalid continuation byte
ERROR: Unhandled python exception
This is a Meson bug and should be reported!
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
ร Encountered error while generating package metadata.
โฐโ> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
(.venv) PS xxx.air>
``` | open | 2024-11-23T20:09:06Z | 2024-11-23T20:09:06Z | https://github.com/AirtestProject/Airtest/issues/1264 | [] | Ran-Xing | 0 |
microsoft/JARVIS | deep-learning | 190 | {available task list} slot is missing | in the Table 5 of the paper, it designs an injectable slot called {{available task list}} in the prompt for LLM. plz release more details about this slot.
| open | 2023-04-30T10:51:40Z | 2023-04-30T10:51:40Z | https://github.com/microsoft/JARVIS/issues/190 | [] | henern | 0 |
yezz123/authx | pydantic | 224 | Failed to import RedisCacheBackend and JWTBackend class from authx. Version 0.4.0 | ### First Check
- [X] I added a very descriptive title to this issue.
- [X] I already read and followed all the tutorial in the docs and didn't find an answer.
- [X] I already checked if it is not related to AuthX but to [Pydantic](https://github.com/samuelcolvin/pydantic).
- [X] I already checked if it is not related to AuthX but to [FastAPI](https://github.com/tiangolo/fastapi).
### Example Code
```python
from authx import RedisCacheBackend, JWTBackend, RedisBackend
from fastapi import Depends, FastAPI
from fastapi.security import HTTPBearer
from starlette.config import Config
app = FastAPI(
title="AuthX",
description="AuthX authentication system for fastapi.",
version="0.1.0",
)
config = Config(".env")
SecurityConfig = JWTBackend(
cache_backend=RedisBackend,
private_key=config("PRIVATE_KEY", default="private_key"),
public_key=config("PUBLIC_KEY", default="public_key"),
access_expiration=3600,
refresh_expiration=3600
)
oauth_token_scheme = HTTPBearer()
# app.include_router(starlette_app.router) # pygeoapi
# Set Anonymous User
@app.get("/anonym")
def anonym_test():
pass
# Set Authenticated User
@app.get("/user")
async def user_test(token=Depends(oauth_token_scheme)):
is_verified = await SecurityConfig.decode_token(token)
print(is_verified)
pass
```
### Description
as specified in the documentation for jwt implementation [https://authx.yezz.codes/configuration/security/jwt/](https://github.com/yezz123/authx/discussions/url) , from authx import RedisCacheBackend, JWTBackend should not return an error. as shown below
- i want to use JWTBackend to verify token using FastAPI dependency injection. a token should be passed to the swaggerUI docs from fastapi. and should be accessible via the oauth_token_scheme.
- is_verified should return a boolean showing the verification state of the token, returns None instead of a boolean
Suspected problem
RedisCacheBackend is not an exported class from the module - not part of the module, but that is what was shown according to the documentation.
using RedisBackend(this is exported from the module) instead, returns TypeError `Expected Type 'RedisBackend', got Type[RedisBackend] instead. in the JWTBackend class
### Operating System
Linux
### Operating System Details
_No response_
### FastAPI Version
0.75.1
### Python Version
python 3.8.10
### Additional Context
_No response_ | closed | 2022-04-04T14:21:11Z | 2023-03-06T09:31:41Z | https://github.com/yezz123/authx/issues/224 | [
"bug",
"question"
] | r-scheele | 3 |
lorien/grab | web-scraping | 69 | ะัะตะฟััะบะฐ ะฒ PyquerySelector | ะคะฐะนะป selector/selector.py ัััะพะบะฐ 296 - ะฝะฐะฟะธัะฐะฝะพ pyquery ะฒะผะตััะพ query
``` python
class PyquerySelector(LxmlNodeBaseSelector):
__slots__ = ()
def pyquery_node(self):
return PyQuery(self.node)
def process_query(self, query):
return self.pyquery_node().find(pyquery)
```
ะดะพะปะถะฝะพ ะฑััั
``` python
def process_query(self, query):
return self.pyquery_node().find(query)
```
| closed | 2014-10-08T09:04:06Z | 2015-03-26T10:56:57Z | https://github.com/lorien/grab/issues/69 | [] | antipooh | 1 |
stanfordnlp/stanza | nlp | 1,057 | Problem when adding a new language |
Hi all!
First time posting a question, feel free to correct me if I'm not following conventions. I'm a Python newbie trying to start an NLP project, so all help is welcome!
I'm trying to add a new language (Old English) to Stanza, to train a model to automatically annotate OE texts. My data is converted into the corresponding format, I have word2vec word vectors, and I have a tokenized raw text file according to the documentation (https://stanfordnlp.github.io/stanza/new_language.html#data-format). My main issue is that the documentation is not clear for me. The case example used to explain how to add new languages to Stanza, and more concretely the section CharacterLM, assumes that the user is going to use data either from Wikipedia or from conll17 or OSCAR, so the terminal commands examples are fitting those scenarios. As my data is from other source, I'm using this command `python3 -m stanza.utils.charlm.make_lm_data extern_data/charlm_raw extern_data/charlm` from the third bulletpoint, giving my source directory and target directory.
This is where my problem starts, when I enter the command in the terminal, the following error appears:
<img width="942" alt="Captura de Pantalla 2022-06-23 a las 14 52 40" src="https://user-images.githubusercontent.com/65161098/175506650-ef389dac-e2c7-4715-a325-013911cafbe8.png">
The command automatically looks for a language, although the language parameter is optional in the command. It also appears that the command tries to create the target directory, although the target directory parameter is not optional for the command, and then the error NotADirectoryError appears.
Is there anything that I'm doing wrong that prevents me from progressing in this project? Any thoughts on how I can solve this problem? I have tried looking for info in the published issues in this repo and on the internet, but I haven't found any extra info about how to add new languages.
Thanks for your help, and sorry for the long post!
| closed | 2022-06-24T09:31:04Z | 2022-09-16T00:46:45Z | https://github.com/stanfordnlp/stanza/issues/1057 | [
"question",
"stale"
] | dmetola | 7 |
piskvorky/gensim | machine-learning | 3,108 | For sponsors | ## Thank you for your [Gensim sponsorship](https://github.com/sponsors/piskvorky) โค๏ธ
I don't know who you are, I can only see your Github handle. So please leave your desired Twitter handle here as a comment, for a mention in the monthly [@gensim_py](https://twitter.com/gensim_py) tweet.
If you chose one of the tiers where I send you stuff, **contact me [via email](mailto:me+sponsorship@radimrehurek.com) with your mailing address and T-shirt sizes / company logo**.
Thanks again! | open | 2021-04-08T07:18:03Z | 2021-04-22T13:42:32Z | https://github.com/piskvorky/gensim/issues/3108 | [] | piskvorky | 1 |
ivy-llc/ivy | numpy | 28,765 | Fix Frontend Failing Test: tensorflow - tensor.torch.Tensor.new_zeros | To-do List: https://github.com/Transpile-AI/ivy/issues/27499 | open | 2024-06-17T09:46:14Z | 2025-03-18T14:49:08Z | https://github.com/ivy-llc/ivy/issues/28765 | [
"Sub Task"
] | Mubashirshariq | 1 |
browser-use/browser-use | python | 858 | Mistook My Instructions and sent the browser to a bad website! | ### Bug Description
I was trying to get the web browser to help me write some blogs and it took me to a tranny escort service website... WTF MAN??? NOT COOL! Now all three chats that I had started are not loading and there is no way to delete them? I would like a refund for my subscription please...
### Reproduction Steps
gave instructions to write me a blog post on wordpress.com and it bugged out on me...
### Code Sample
```python
Updated Instructions for Your Tool
Navigate to WordPress
Go to your WordPress dashboard at yourdomain.com/wp-admin.
Log in with the provided credentials (username and password) if required.
Start a New Post
Click Posts in the left sidebar.
Click Add New (the blue button on the top right or left sidebar) to open a new post.
Enter the Title
Look for the big โAdd titleโ field at the very top of the page (itโs a gray box that says โAdd titleโ in light text).
Click inside that box to focus on it.
Type exactly: Darn Tough - Socks (including the space and hyphen).
Wait 2 seconds after typing to ensure the text is entered correctly.
Publish the Post
Look for the Publish button. Itโs usually on the right sidebar (under โStatus & Visibilityโ) or at the top-right corner of the page (a blue button labeled โPublishโ or โUpdateโ).
Click the Publish button to publish the post immediately.
Wait 3 seconds after clicking to ensure the post is published.
Confirm and Move On
Check if the post appears in the WordPress โPostsโ list with the title โDarn Tough - Socksโ and a โPublishedโ status.
If successful, show a message in the browser (e.g., a popup or text box) saying: โPublished โDarn Tough - Socksโ successfully.โ
If thereโs an error (e.g., the title isnโt entered or the post doesnโt publish), show an error message (e.g., โFailed to publish โDarn Tough - Socksโโcheck title or button locationโ) and pause for user instructions.
Repeat for Other Products (Optional)
If instructed, repeat steps 2โ5 for the next product title in the list (e.g., โVermont Flannel - Flannelโ), following the same process. Use the full list of 50 product titles I provided earlier, entering and publishing one at a time.
```
### Version
Brave
### LLM Model
Other (specify in description)
### Operating System
Windows 11
### Relevant Log Output
```shell
Navigate to WordPress dashboard at yourdomain.com/wp-admin.
Navigate back to WordPress dashboard at yourdomain.com/wp-admin.
go_to_url({"url":"http://yourdomain.com/wp-admin"})
``` | open | 2025-02-25T00:24:22Z | 2025-02-25T05:28:09Z | https://github.com/browser-use/browser-use/issues/858 | [
"bug"
] | SubliminalCoding | 1 |
wkentaro/labelme | computer-vision | 749 | Problem with python labelme2voc.py data_annotated data_dataset_voc --labels labels.txt in my data | dear all,
(tensorflow1x) D:\segmentation\labelme-master\examples\semantic_segmentation>python labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
I try them with my data below
[labels.txt](https://github.com/wkentaro/labelme/files/5060621/labels.txt)
[data_annotated.zip](https://github.com/wkentaro/labelme/files/5060624/data_annotated.zip)
Result: Can not show all labels. It only show 1 label is __ignore__. Why?
Help me!
Thank you so much!
| closed | 2020-08-12T04:38:31Z | 2022-06-25T04:58:21Z | https://github.com/wkentaro/labelme/issues/749 | [] | NguyenDangBinh | 0 |
aio-libs-abandoned/aioredis-py | asyncio | 1,003 | [2.0] Use loop.time() instead of time.time() for health checks | In the 2.0 alpha, health checks are based around `time.time`. Using `asyncio.get_event_loop().time()` has several advantages:
- The default implementation uses a monotonic clock, so it won't go wonky if the system time is changed.
- It sticks to a consistent source of time relative to things like `async_timeout`, which makes mocking out time easier (e.g. with [async-solipsism](https://github.com/bmerry/async-solipsism)).
I'm happy to submit a PR if there is agreement that this is a reasonable change. | closed | 2021-06-08T18:49:33Z | 2021-07-22T00:37:20Z | https://github.com/aio-libs-abandoned/aioredis-py/issues/1003 | [
"enhancement"
] | bmerry | 2 |
biolab/orange3 | data-visualization | 6,169 | Splash screen on Windows with scaling | Splash screen on Widows is pixelated when using scaling. The screenshot below was captured using 125% scaling. The image is nice without scaling.
| closed | 2022-10-14T11:15:24Z | 2022-11-25T08:35:04Z | https://github.com/biolab/orange3/issues/6169 | [
"bug report"
] | thocevar | 0 |
microsoft/unilm | nlp | 1,032 | Pre-training code of BEiT-3 | Great work! However, the code of [BEiT-3](https://github.com/microsoft/unilm/tree/master/beit3) only includes code for various downstream tasks. Is there any way I can reproduce the pre-training task?
| closed | 2023-03-14T12:17:58Z | 2023-03-15T15:27:28Z | https://github.com/microsoft/unilm/issues/1032 | [] | MonsterZhZh | 3 |
serengil/deepface | deep-learning | 1,193 | 'dfs' is not recognized as an internal or external command, operable program or batch file. | Okay great but now i have a new issue i did your command with my specific addins to it but this came back of command prompt
I dont know maybe im just stupid and didnt comprehend something
Command Prompt
C:\Windows\System32>dfs = DeepFace.find(img_path = "102-1000-2.jpg", db_path = "C:/Users/Manti/Documents")
'dfs' is not recognized as an internal or external command,
operable program or batch file.
I thought i did it correctly i added the info of where it can find my exact photo by the photo name which is 102-1000-2 i didnt get a chance yet to actually customize it after its default naming by the computer when saving the photo and i added the complete path of where the photo is located
| closed | 2024-04-18T05:39:12Z | 2024-04-18T07:54:51Z | https://github.com/serengil/deepface/issues/1193 | [
"invalid"
] | olstice | 6 |
supabase/supabase-py | fastapi | 926 | Supabase Client Requires Explicit `sign_out()` to Terminate Properly | ## Summary
The Supabase client currently requires an explicit call to `client.auth.sign_out()` for processes to terminate correctly. Without this, background WebSocket connections and other resources may remain active, leading to incomplete shutdowns and potential resource leaks.
## Problem Explanation:
The current behavior of the Supabase client involves establishing WebSocket connections and listening for authentication events. These processes, especially those involving real-time functionality, do not automatically terminate upon the programโs end. Explicitly calling client.auth.sign_out() is necessary to clean up these resources and ensure proper process termination.
```
# From SyncClient class in SyncClient.py
class SyncClient:
def __init__(self, ...):
# ...
self.realtime = self._init_realtime_client(
realtime_url=self.realtime_url,
supabase_key=self.supabase_key,
options=options.realtime if options else None,
)
# ...
@staticmethod
def _init_realtime_client(
realtime_url: str, supabase_key: str, options: Optional[Dict[str, Any]]
) -> SyncRealtimeClient:
"""Private method for creating an instance of the realtime-py client."""
return SyncRealtimeClient(
realtime_url, token=supabase_key, params=options or {}
)
def _listen_to_auth_events(
self, event: AuthChangeEvent, session: Union[Session, None]
):
# ...
self.realtime.set_auth(access_token)
# From SyncRealtimeClient in realtime-py
class SyncRealtimeClient:
def __init__(self, ...):
# ...
self._endpointWebSocket = None
# ...
def connect(self):
# ...
self._endpointWebSocket = websocket.WebSocketApp(
# ...
)
# ...
def set_auth(self, token):
# ...
self.connect() # This might create a new WebSocket connection
# From GoTrueClient in gotrue-py
class SyncGoTrueClient:
def sign_out(self, options: SignOutOptions = {"scope": "global"}) -> None:
# ...
self._remove_session()
self._notify_all_subscribers("SIGNED_OUT", None)
```
## Key points:
1. Real-time Connections: The WebSocket connections created by SyncRealtimeClient continue running in the background and need to be manually terminated.
2. Authentication Events: Sign-out triggers an event that helps reset real-time client authentication, which won't occur unless sign_out() is called.
3. Resource Management: The sign_out() function ensures proper cleanup of sessions and network connections, preventing potential memory leaks or resource hogging.
4. Daemon Threads: Real-time connections might be running as daemon threads, which do not automatically terminate, leading to hanging processes unless explicitly stopped with sign_out().
Given this behavior, the necessity of an explicit `client.auth.sign_out()` call should be clearly documented and potentially re-evaluated for a more intuitive shutdown process. | open | 2024-09-16T07:17:13Z | 2025-01-23T16:28:07Z | https://github.com/supabase/supabase-py/issues/926 | [
"bug"
] | sigridjineth | 7 |
miguelgrinberg/Flask-SocketIO | flask | 1,257 | Recursive stack overflow | **Describe the bug**
Occasional stack overflow happens, looks similar to #230 but i'm unsure if it's happening when I'm emitting a message.
```
# pip show flask_socketio
Name: Flask-SocketIO
Version: 4.2.1
Summary: Socket.IO integration for Flask applications
Home-page: http://github.com/miguelgrinberg/Flask-SocketIO/
Author: Miguel Grinberg
Author-email: miguelgrinberg50@gmail.com
License: MIT
Location: /usr/local/lib/python3.6/site-packages
Requires: python-socketio, Flask
Required-by:
```
**Logs**
```(7) accepted ('172.18.0.4', 42154)
109.41.129.0,172.18.0.4 - - [17/Apr/2020 05:03:45] "GET /socket.io/?id=&type=undefined&uid=21169&EIO=3&transport=polling&t=N66YjHM HTTP/1.1" 200 1292 0.034741
(7) accepted ('172.18.0.4', 42172)
disconnect handler error
Traceback (most recent call last):
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 545, in _trigger_event
return self.handlers[event](*args)
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 725, in _handle_eio_disconnect
self._handle_disconnect(sid, '/')
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 632, in _handle_disconnect
self._trigger_event('disconnect', '/', sid)
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 680, in _trigger_event
return self.handlers[namespace][event](*args)
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 284, in _handler
*args)
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 675, in _handle_event
with app.request_context(self.server.environ[sid]):
File "/usr/local/lib/python3.6/site-packages/flask/app.py", line 2358, in request_context
return RequestContext(self, environ)
RecursionError: maximum recursion depth exceeded while calling a Python object
Fatal Python error: Cannot recover from stack overflow.
Current thread 0x00007fdd71352700 (most recent call first):
File "/usr/local/lib/python3.6/site-packages/flask/app.py", line 2358 in request_context
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 675 in _handle_event
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 284 in _handler
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 680 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 632 in _handle_disconnect
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 725 in _handle_eio_disconnect
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 545 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 131 in close
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 80 in check_ping_timeout
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 86 in send
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 217 in send
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 588 in _send_packet
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 577 in _emit_internal
File "/usr/local/lib/python3.6/site-packages/socketio/base_manager.py", line 141 in emit
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 286 in emit
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 417 in emit
File "/app/backend.py", line 235 in disconnect
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 698 in _handle_event
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 284 in _handler
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 680 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 632 in _handle_disconnect
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 725 in _handle_eio_disconnect
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 545 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 131 in close
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 80 in check_ping_timeout
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 86 in send
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 217 in send
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 588 in _send_packet
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 577 in _emit_internal
File "/usr/local/lib/python3.6/site-packages/socketio/base_manager.py", line 141 in emit
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 286 in emit
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 417 in emit
File "/app/backend.py", line 235 in disconnect
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 698 in _handle_event
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 284 in _handler
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 680 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 632 in _handle_disconnect
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 725 in _handle_eio_disconnect
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 545 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 131 in close
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 80 in check_ping_timeout
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 86 in send
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 217 in send
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 588 in _send_packet
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 577 in _emit_internal
File "/usr/local/lib/python3.6/site-packages/socketio/base_manager.py", line 141 in emit
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 286 in emit
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 417 in emit
File "/app/backend.py", line 235 in disconnect
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 698 in _handle_event
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 284 in _handler
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 680 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 632 in _handle_disconnect
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 725 in _handle_eio_disconnect
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 545 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 131 in close
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 80 in check_ping_timeout
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 86 in send
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 217 in send
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 588 in _send_packet
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 577 in _emit_internal
File "/usr/local/lib/python3.6/site-packages/socketio/base_manager.py", line 141 in emit
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 286 in emit
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 417 in emit
File "/app/backend.py", line 235 in disconnect
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 698 in _handle_event
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 284 in _handler
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 680 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 632 in _handle_disconnect
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 725 in _handle_eio_disconnect
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 545 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 131 in close
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 80 in check_ping_timeout
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 86 in send
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 217 in send
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 588 in _send_packet
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 577 in _emit_internal
File "/usr/local/lib/python3.6/site-packages/socketio/base_manager.py", line 141 in emit
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 286 in emit
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 417 in emit
File "/app/backend.py", line 235 in disconnect
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 698 in _handle_event
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 284 in _handler
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 680 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 632 in _handle_disconnect
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 725 in _handle_eio_disconnect
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 545 in _trigger_event
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 131 in close
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 80 in check_ping_timeout
File "/usr/local/lib/python3.6/site-packages/engineio/socket.py", line 86 in send
File "/usr/local/lib/python3.6/site-packages/engineio/server.py", line 217 in send
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 588 in _send_packet
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 577 in _emit_internal
File "/usr/local/lib/python3.6/site-packages/socketio/base_manager.py", line 141 in emit
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 286 in emit
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 417 in emit
File "/app/backend.py", line 235 in disconnect
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 698 in _handle_event
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 284 in _handler
File "/usr/local/lib/python3.6/site-packages/socketio/server.py", line 680 in _trigger_event
...
Thread 0x00007fdd7a730700 (most recent call first):
File "/usr/local/lib/python3.6/site-packages/werkzeug/_reloader.py", line 214 in run
File "/usr/local/lib/python3.6/site-packages/werkzeug/_reloader.py", line 337 in run_with_reloader
File "/usr/local/lib/python3.6/site-packages/werkzeug/serving.py", line 1060 in run_with_reloader
File "/usr/local/lib/python3.6/site-packages/flask_socketio/__init__.py", line 569 in run
File "/app/backend.py", line 256 in main
File "/app/backend.py", line 261 in <module>
```
I've enabled `logger=True` and `engineio_logger=True` and I'll try to catch any more information that I can
```
socketio = SocketIO(app, async_mode='eventlet', logger=True, engineio_logger=True)
``` | closed | 2020-04-19T18:23:17Z | 2020-06-30T22:53:17Z | https://github.com/miguelgrinberg/Flask-SocketIO/issues/1257 | [
"question"
] | dgtlmoon | 13 |
mljar/mercury | jupyter | 301 | Can you stream the output as the notebook is evaluating? | Currently, the output only displays after the entire notebook evaluates. However, you may have some computationally expensive cells. From a UI/UX perspective, it would be helpful to see the results get stream as they are available | open | 2023-05-29T19:02:54Z | 2023-05-30T10:07:09Z | https://github.com/mljar/mercury/issues/301 | [
"enhancement",
"help wanted"
] | kapily | 1 |
matplotlib/matplotlib | matplotlib | 29,534 | [Bug]: missing graph | ### Bug summary
Good day, I'm having issues with my graphs showing after running my command, I only get axis but no graph
### Code for reproduction
```Python
Gby_plt.plot()
```
### Actual outcome
### Expected outcome
### Additional information
_No response_
### Operating system
_No response_
### Matplotlib Version
3.9.2
### Matplotlib Backend
_No response_
### Python version
3.10.1
### Jupyter version
_No response_
### Installation
None | open | 2025-01-28T16:37:05Z | 2025-01-29T17:58:27Z | https://github.com/matplotlib/matplotlib/issues/29534 | [
"Community support"
] | Gidman21 | 2 |
taverntesting/tavern | pytest | 640 | How to understand and solve the error: "List item(s) not present in response" | There is below the output of my test. How can I understand which item(s) doesn't present in a response:
the strict options provided for the whole file:
```yaml
strict:
- headers:off
- json:off
```
Format variables:
tavern.env_vars.PA_HOST = 'localhost:8080'
Source test stage (line 681):
```yaml
- name: Average payment by country
request:
url: '{tavern.env_vars.PA_HOST}/v2/data/avg_payment'
method: POST
headers:
X-Auth-Key: faketoken
content-type: application/json
json:
filter: []
grouping:
- user_country_name
ordering:
- direction: desc
field: sum_revenue_usd
aggregation:
- aggr: avg
field: avgpayment_usd
- aggr: sum
field: revenue_usd
others:
count: 6
time_interval:
from: 1606813200
to: 1609405200
response:
status_code: 200
headers:
content-type: application/json
json:
data:
- data:
- avg_avgpayment_usd: 34.9
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 56608.99
sum_revenue_usd_symbol: $
user_country_name: USA
- avg_avgpayment_usd: 34.75
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 9183.92
sum_revenue_usd_symbol: $
user_country_name: United Kingdom
- avg_avgpayment_usd: 40.36
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 8213.64
sum_revenue_usd_symbol: $
user_country_name: Germany
- avg_avgpayment_usd: 34.11
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 7967.49
sum_revenue_usd_symbol: $
user_country_name: Canada
- avg_avgpayment_usd: 42.52
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 4023.08
sum_revenue_usd_symbol: $
user_country_name: France
- avg_avgpayment_usd: 26.75
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 3351.44
sum_revenue_usd_symbol: $
user_country_name: South Korea
- avg_avgpayment_usd: 34.4
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 29498.04
sum_revenue_usd_symbol: $
user_country_name: Others
name: avg_payment
error: {}
```
Formatted stage:
```yaml
name: Average payment by country
request:
headers:
X-Auth-Key: faketoken
content-type: application/json
json:
aggregation:
- aggr: avg
field: avgpayment_usd
- aggr: sum
field: revenue_usd
filter: []
grouping:
- user_country_name
ordering:
- direction: desc
field: sum_revenue_usd
others:
count: 6
time_interval:
from: 1606813200
to: 1609405200
method: POST
url: 'localhost:8080/v2/data/avg_payment'
response:
headers:
content-type: application/json
json:
data:
- data:
- avg_avgpayment_usd: 34.9
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 56608.99
sum_revenue_usd_symbol: $
user_country_name: USA
- avg_avgpayment_usd: 34.75
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 9183.92
sum_revenue_usd_symbol: $
user_country_name: United Kingdom
- avg_avgpayment_usd: 40.36
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 8213.64
sum_revenue_usd_symbol: $
user_country_name: Germany
- avg_avgpayment_usd: 34.11
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 7967.49
sum_revenue_usd_symbol: $
user_country_name: Canada
- avg_avgpayment_usd: 42.52
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 4023.08
sum_revenue_usd_symbol: $
user_country_name: France
- avg_avgpayment_usd: 26.75
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 3351.44
sum_revenue_usd_symbol: $
user_country_name: South Korea
- avg_avgpayment_usd: 34.4
avg_avgpayment_usd_symbol: $
sum_revenue_usd: 29498.04
sum_revenue_usd_symbol: $
user_country_name: Others
name: avg_payment
error: {}
status_code: 200
```
```
Errors:
E tavern.util.exceptions.TestFailError: Test 'Average payment by country' failed:
- List item(s) not present in response: [{'data': [{'avg_avgpayment_usd': 34.9, 'avg_avgpayment_usd_symbol': '$', 'sum_revenue_usd': 56608.99, 'sum_revenue_usd_symbol': '$', 'user_country_name': 'USA'}, {'avg_avgpayment_usd': 34.75, 'avg_avgpayment_usd_symbol': '$', 'sum_revenue_usd': 9183.92, 'sum_revenue_usd_symbol': '$', 'user_country_name': 'United Kingdom'}, {'avg_avgpayment_usd': 40.36, 'avg_avgpayment_usd_symbol': '$', 'sum_revenue_usd': 8213.64, 'sum_revenue_usd_symbol': '$', 'user_country_name': 'Germany'}, {'avg_avgpayment_usd': 34.11, 'avg_avgpayment_usd_symbol': '$', 'sum_revenue_usd': 7967.49, 'sum_revenue_usd_symbol': '$', 'user_country_name': 'Canada'}, {'avg_avgpayment_usd': 42.52, 'avg_avgpayment_usd_symbol': '$', 'sum_revenue_usd': 4023.08, 'sum_revenue_usd_symbol': '$', 'user_country_name': 'France'}, {'avg_avgpayment_usd': 26.75, 'avg_avgpayment_usd_symbol': '$', 'sum_revenue_usd': 3351.44, 'sum_revenue_usd_symbol': '$', 'user_country_name': 'South Korea'}, {'avg_avgpayment_usd': 34.4, 'avg_avgpayment_usd_symbol': '$', 'sum_revenue_usd': 29498.04, 'sum_revenue_usd_symbol': '$', 'user_country_name': 'Others'}], 'name': 'avg_payment'}]
```
Formated json I got from server:
```json
{
"error": {},
"data": [
{
"name": "avg_payment",
"data": [
{
"avg_avgpayment_usd": 34.9,
"avg_avgpayment_usd_symbol": "$",
"sum_revenue_usd": 56608.99,
"sum_revenue_usd_symbol": "$",
"user_country_name": "USA"
},
{
"avg_avgpayment_usd": 34.75,
"avg_avgpayment_usd_symbol": "$",
"sum_revenue_usd": 9183.92,
"sum_revenue_usd_symbol": "$",
"user_country_name": "United Kingdom"
},
{
"avg_avgpayment_usd": 40.36,
"avg_avgpayment_usd_symbol": "$",
"sum_revenue_usd": 8213.64,
"sum_revenue_usd_symbol": "$",
"user_country_name": "Germany"
},
{
"avg_avgpayment_usd": 34.11,
"avg_avgpayment_usd_symbol": "$",
"sum_revenue_usd": 7967.49,
"sum_revenue_usd_symbol": "$",
"user_country_name": "Canada"
},
{
"avg_avgpayment_usd": 42.52,
"avg_avgpayment_usd_symbol": "$",
"sum_revenue_usd": 4023.08,
"sum_revenue_usd_symbol": "$",
"user_country_name": "France"
},
{
"avg_avgpayment_usd": 26.75,
"avg_avgpayment_usd_symbol": "$",
"sum_revenue_usd": 3351.44,
"sum_revenue_usd_symbol": "$",
"user_country_name": "South Korea"
},
{
"avg_avgpayment_usd": 34.4,
"avg_avgpayment_usd_symbol": "$",
"sum_revenue_usd": 29498.04,
"sum_revenue_usd_symbol": "$",
"user_country_name": "Others"
}
]
}
]
}
``` | closed | 2021-02-07T21:17:37Z | 2023-01-09T15:48:24Z | https://github.com/taverntesting/tavern/issues/640 | [] | rpoletaev | 1 |
SALib/SALib | numpy | 127 | morris analysis method requires problem['group'] to exist | The sample method uses problem.get('groups') and the analysis method should, too, to avoid requiring call to set problem['group'] = None. | closed | 2017-01-05T18:21:53Z | 2017-01-07T23:39:59Z | https://github.com/SALib/SALib/issues/127 | [
"bug"
] | rjplevin | 2 |
yezz123/authx | pydantic | 462 | ๐๏ธ Add Security Tests including Examples | In that situation, it is crucial to address the security of the backend system for the package.
This involves ensuring that we thoroughly adhere to each step we take in the process of token creation.
```py
config = AuthXConfig(
JWT_ALGORITHM = "HS256",
JWT_SECRET_KEY = "SECRET_KEY",
# Here in the JWT Location
JWT_TOKEN_LOCATION = ["headers"],
)
``` | closed | 2023-06-01T20:32:33Z | 2025-03-21T10:01:02Z | https://github.com/yezz123/authx/issues/462 | [
"enhancement",
"python"
] | yezz123 | 0 |
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