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stanfordnlp/stanza | nlp | 894 | Garbage characters in printed result | **When I run the following python code:**
import stanza
from stanza.server import CoreNLPClient
text = "中国是一个伟大的国家。"
print(text)
with CoreNLPClient(
properties='chinese',
classpath=r'F:\StanfordCoreNLP\stanford-corenlp-4.2.2\*',
strict=False,
start_server=stanza.server.StartServer.TRY_START ,
annotators=['tokenize','ssplit','pos','lemma','ner', 'parse', 'depparse'],
timeout=30000,
memory='16G') as client:
pattern = 'NP'
matches = client.tregex(text, pattern)
# You can access matches similarly
print(matches['sentences'][0]['0']['match'])
**I got:**
中国是一个伟大的国家。
2021-12-08 19:35:10 INFO: Using CoreNLP default properties for: chinese. Make sure to have chinese models jar (available for download here: https://stanfordnlp.github.io/CoreNLP/) in CLASSPATH
2021-12-08 19:35:10 INFO: Connecting to existing CoreNLP server at localhost:9000
2021-12-08 19:35:10 INFO: Connecting to existing CoreNLP server at localhost:9000
(NP (NNP �й���һ��ΰ��Ĺ���) (SYM ��))
Any idea about the garbage characters?
Process finished with exit code 0
| closed | 2021-12-08T11:37:27Z | 2022-04-23T07:02:00Z | https://github.com/stanfordnlp/stanza/issues/894 | [
"fixed on dev"
] | jiangweiatgithub | 9 |
pytorch/pytorch | machine-learning | 149,533 | UnsupportedOperatorException: aten._fft_r2c.default | ### 🐛 Describe the bug
We ran into this error when trying to convert the <a href="https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa">VoxLingua107 ECAPA-TDNN Spoken Language Identification Model</a> to ONNX. Replication and error outputs are below; a more verbose logs file is attached as well.
[onnx_export_logs.md](https://github.com/user-attachments/files/19347887/onnx_export_logs.md)
### Steps to replicate the error (using Linux machine):
We followed the README for Linux to download and build Pytorch on a Conda environment, but checked out the commit at <a href="https://github.com/pytorch/pytorch/commit/f89309fb732f93a21b5a3e49124623949b20c7dc">f89309f</a>. The next steps detail how to replicate the error we encountered when exporting the VoxLingua model.
1. Install speechbrain dependencies:
```
pip install git+https://github.com/speechbrain/speechbrain.git@develop
```
2. Set up VoxLingua project in new Python file:
```
import torch
import torchaudio
from speechbrain.inference.classifiers import EncoderClassifier
import torch.onnx
language_id = EncoderClassifier.from_hparams(source="speechbrain/lang-id-voxlingua107-ecapa", savedir="tmp")
# Create dummy audio signal data
signal = torch.zeros(48000)
prediction = language_id.classify_batch(signal)
print(prediction)
```
3. Add torch.onnx command to end of Python file:
```
torch.onnx.export(language_id, signal, "langid.onnx", export_params=True,
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes={'input' : {0 : 'batch_size'}}, dynamo=True, report=True)
```
4. Run in conda environment:
```
python3 <FILENAME>.py
```
### Error message:
```
torch._subclasses.fake_tensor.UnsupportedOperatorException: aten._fft_r2c.default
```
### Stack trace:
```
Traceback (most recent call last):
File "/home/convertonnx.py", line 14, in <module>
torch.onnx.export(language_id, signal, "langid.onnx", export_params=True,
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
do_constant_folding=True, input_names=['input'], output_names=['output'],
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
dynamic_axes={'input' : {0 : 'batch_size'}}, dynamo=True, report=True) #variable length axes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/anaconda3/envs/pytorch_conda/lib/python3.13/site-packages/torch/onnx/__init__.py", line 351, in export
return _compat.export_compat(
~~~~~~~~~~~~~~~~~~~~~^
model,
^^^^^^
...<19 lines>...
fallback=fallback,
^^^^^^^^^^^^^^^^^^
)
^
File "/home/anaconda3/envs/pytorch_conda/lib/python3.13/site-packages/torch/onnx/_internal/exporter/_compat.py", line 304, in export_compat
onnx_program = _core.export(
model,
...<11 lines>...
verbose=verbose,
)
File "/home//anaconda3/envs/pytorch_conda/lib/python3.13/site-packages/torch/onnx/_internal/exporter/_core.py", line 1292, in export
raise _errors.TorchExportError(
...<7 lines>...
) from first_error
```
### Versions
```
PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.31.6
Libc version: glibc-2.35
Python version: 3.13.2 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:56:02) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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): 8
On-line CPU(s) list: 0-7
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU E5-2697A v4 @ 2.60GHz
CPU family: 6
Model: 79
Thread(s) per core: 1
Core(s) per socket: 1
Socket(s): 8
Stepping: 1
BogoMIPS: 5199.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 invpcid rtm rdseed adx smap xsaveopt arat md_clear flush_l1d arch_capabilities
Hypervisor vendor: VMware
Virtualization type: full
L1d cache: 256 KiB (8 instances)
L1i cache: 256 KiB (8 instances)
L2 cache: 2 MiB (8 instances)
L3 cache: 320 MiB (8 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT Host state unknown
Versions of relevant libraries:
[pip3] numpy==2.2.3
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnx==1.17.0
[pip3] onnxscript==0.2.2
[pip3] optree==0.14.1
[pip3] torch==2.6.0
[pip3] torchaudio==2.6.0
[pip3] triton==3.2.0
[conda] mkl-include 2025.0.1 pypi_0 pypi
[conda] mkl-static 2025.0.1 pypi_0 pypi
[conda] numpy 2.2.3 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.2 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi
[conda] optree 0.14.1 pypi_0 pypi
[conda] torch 2.6.0 pypi_0 pypi
[conda] torchaudio 2.6.0 pypi_0 pypi
[conda] triton 3.2.0 pypi_0 pypi
```
cc @chauhang @penguinwu @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4 | closed | 2025-03-19T17:37:55Z | 2025-03-21T20:30:06Z | https://github.com/pytorch/pytorch/issues/149533 | [
"module: onnx",
"triaged",
"oncall: pt2",
"oncall: export"
] | ivyw-ts | 7 |
Lightning-AI/pytorch-lightning | pytorch | 19,740 | Optimizer step based validation trigger | ### Description & Motivation
As far as I can see there are currently 2 modes to trigger the validation during training: 1) based on number of epochs (`check_val_every_n_epoch`) and 2) based on number of batches (`val_check_interval`)
Unfortunately we experience the case where another type is needed, based on (optimizer) steps.
1. `check_val_every_n_epoch` is not useful when the epoch size is too big.
2. `val_check_interval` could result in the training which is stuck when the number of batches seen across ranks goes out of sync which happens when the dataloaders on each rank have different number of microbatches. When `accumulate_grad_batches>1` one rank can exhaust all microbatches and even do a step using partial accumulation (as described in https://lightning.ai/forums/t/global-step-increased-at-new-epoch-regardless-of-gradient-accumulation/2480) while another rank advanced up until the optimizer step. Now the latter rank will start validation with a metric reduction at the end, while the former rank will iterate using a new dataloader batches until next optimizer. Now they stuck waiting for each other.
### Pitch
We can introduce a new option, e.g. `val_check_steps` that will make sure validation is triggered at optimizer step boundaries, after all ranks synchronize and hence all enter validation at the same time, reduce metrics at the end, and so on.
### Alternatives
Alternatively it should be possible, but clunky, to override `on_before_optimizer_step` and keep the count of `trainer.global_step` then when the module reminder is 0 set `trainer.val_check_batch` - potentially different for different ranks - so that on next `advance` all ranks enter validation.
Then again there could be a hard requirement in Lightning contract that requires that the number of batches is the same across all ranks (not sure if it's already required).
### Additional context
_No response_
cc @borda | open | 2024-04-05T20:57:04Z | 2024-04-05T20:58:12Z | https://github.com/Lightning-AI/pytorch-lightning/issues/19740 | [
"feature",
"needs triage"
] | clumsy | 1 |
ScrapeGraphAI/Scrapegraph-ai | machine-learning | 19 | Implement Graph Visualization with Graphviz | ### Description:
To enhance our understanding and debugging capabilities of the scraping workflows, we aim to introduce a feature for visualizing the graph structures using Graphviz. This visualization will help in quickly identifying the flow, relationships between nodes, and potential bottlenecks in the graph.
### Tasks:
- Research Graphviz syntax and integration options with Python;
- Develop a utility function or class method that converts our current graph structure into a Graphviz-compatible format;
- Ensure the visualization includes node names, types, and directional edges. | closed | 2024-02-14T10:31:02Z | 2024-04-26T18:50:58Z | https://github.com/ScrapeGraphAI/Scrapegraph-ai/issues/19 | [
"enhancement"
] | PeriniM | 1 |
Skyvern-AI/skyvern | api | 1,128 | Error converting CSS shape | ```
2024-11-04T21:53:29.156602Z [error ] Failed to convert css shape to string shape by secondary llm. Will retry if haven't met the max try attempt after 3s. element_id=mVYj retry=2 step_id=stp_322440236151819198 task_id=tsk_322439587611757096
Traceback (most recent call last):
File "/home/bond/Desktop/agent-collector/agents/open-source/skyvern/skyvern/forge/sdk/api/llm/utils.py", line 57, in parse_api_response
raise EmptyLLMResponseError(str(response))
skyvern.forge.sdk.api.llm.exceptions.EmptyLLMResponseError: LLM response content is empty: ModelResponse(id='chatcmpl-APzIlAGfLLvT57BhmY8vJCewWGOFk', choices=[Choices(finish_reason='stop', index=0, message=Message(content="I'm sorry, I can't assist with that.", role='assistant', tool_calls=None, function_call=None))], created=1730757207, model='gpt-4o-2024-08-06', object='chat.completion', system_fingerprint='fp_45cf54deae', usage=Usage(completion_tokens=9, prompt_tokens=386, total_tokens=395))
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/bond/Desktop/agent-collector/agents/open-source/skyvern/skyvern/forge/agent_functions.py", line 224, in _convert_css_shape_to_string
json_response = await app.SECONDARY_LLM_API_HANDLER(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/bond/Desktop/agent-collector/agents/open-source/skyvern/skyvern/forge/sdk/api/llm/api_handler_factory.py", line 252, in llm_api_handler
parsed_response = parse_api_response(response, llm_config.add_assistant_prefix)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/bond/Desktop/agent-collector/agents/open-source/skyvern/skyvern/forge/sdk/api/llm/utils.py", line 68, in parse_api_response
raise InvalidLLMResponseFormat(str(response)) from e
skyvern.forge.sdk.api.llm.exceptions.InvalidLLMResponseFormat: LLM response content is not a valid JSON: ModelResponse(id='chatcmpl-APzIlAGfLLvT57BhmY8vJCewWGOFk', choices=[Choices(finish_reason='stop', index=0, message=Message(content="I'm sorry, I can't assist with that.", role='assistant', tool_calls=None, function_call=None))], created=1730757207, model='gpt-4o-2024-08-06', object='chat.completion', system_fingerprint='fp_45cf54deae', usage=Usage(completion_tokens=9, prompt_tokens=386, total_tokens=395))
```
This is on the contributor setup of Skyvern on Ubuntu 24
This is the payload I have used:
```
{
"title": "null",
"url": "https://airbnb.com",
"webhook_callback_url": "https://6642f909995b.ngrok.app/webhook",
"navigation_goal": "For airbnb.com, Navigate to the search page for short-term rentals, enter \"76002\" as the location, select the date range from \"August 27, 2024\" to \"September 7, 2024\", and apply the search. COMPLETE when the search results page displays listings for Arlington, Texas, within the given date range.",
"data_extraction_goal": "Extract all available listings including details such as title, price per night, and host ratings for the specified location and date range.",
"navigation_payload": {
"location": "76002",
"check_in": "2024-08-27",
"check_out": "2024-09-07"
},
"error_code_mapping": {},
"proxy_location": "NONE",
"extracted_information_schema": "null"
}
``` | open | 2024-11-04T21:54:12Z | 2024-11-05T09:04:50Z | https://github.com/Skyvern-AI/skyvern/issues/1128 | [] | devinat1 | 3 |
flairNLP/flair | nlp | 3,338 | [Question]: Controlling the CUDA OOM error for SequenceTagger via the use of tokens threshold | ### Question
While trying to deploy the NER model, often the length of the generated token sequence can not be controlled even though the characters can be limited.
- With that said, I tried to limit the amount of tokens per batch by making the every batch inference dynamic.
- Each batch can have a fixed limit of total tokens, but the batch size will vary. This was done to mitigate any memory issue.
Although, It didn't played nice, Even though the total tokens were fixed amount,
- I still got CUDA memory errors ever so often in Colab T4 with 16 GB instance.
- Plus, it was slow as compared to just iterating over batches of a fixed sentences size `16` & `mini_batch_size=16`, **as compared** to providing all sentences I want inference of at once which would normally fail with CUDA OOM error a lot of times.
I'll write down the piece of code that I tried to work with:
```python
class NERExtractionError(Exception):
def __init__(self, error, articles_tokens_count=0):
super().__init__(error)
self.name = "NERExtractionError"
self.error = error
self.batch_tokens_count = articles_tokens_count
class NERExtractionWithAutoBatching:
def __init__(self, expected_total_tokens_in_batch=35_000):
self.articles_tokens_count: list[int] = []
self.expected_total_tokens_in_batch = expected_total_tokens_in_batch
def generate_batches_of_n_token_lengths(
self, df: pd.DataFrame
):
batches = []
df["tokens_count_cumsum"] = df["token_count"].cumsum(axis="index")
while df.shape[0] != 0:
if df.iloc[0]["tokens_count_cumsum"] > self.expected_total_tokens_in_batch:
batch = df.iloc[[0]]
# print(f"first if condition {type(batch)}")
batches.append(batch)
df = df.drop(index=batch.index)
df["tokens_count_cumsum"] = df["token_count"].cumsum(axis="index")
else:
batch = df[df["tokens_count_cumsum"] <= self.expected_total_tokens_in_batch]
# print(f"second if condition {type(batch)}")
batches.append(batch)
df = df.drop(index=batch.index)
df["tokens_count_cumsum"] = df["token_count"].cumsum(axis="index")
return batches
def inference(self, articles: pd.DataFrame):
total_tokens_in_each_article: list[int] = articles["token_count"].tolist()
sentences = articles["sentence"].tolist()
flair_model = SequenceTagger.load("flair/ner-english-ontonotes-large")
all_articles_ner = []
try:
flair_model.predict(
sentences, mini_batch_size=len(sentences),
embedding_storage_mode=None
)
except Exception as e:
del sentences
torch.cuda.empty_cache()
raise NERExtractionError(
e,
articles_tokens_count=total_tokens_in_each_article
)
# each sentence is an article instance
for sentence in sentences:
article_ner = []
for entity in sentence.get_labels('ner'):
ner_text_and_label = {
"text": entity.data_point.text,
"labels": entity.to_dict()
}
article_ner.append(ner_text_and_label)
all_articles_ner.append(article_ner)
sentence.clear_embeddings()
torch.cuda.empty_cache()
return all_articles_ner
def flair_inference(self, df):
"""Main function, pass it a pd.DataFrame containing "inputs" col filled with hundreds and thousands of characters
CHARACTERS ARE CAPPED AT 20_000
"""
flair_output = []
df["sentence"] = df["inputs"].apply(lambda x: Sentence(x))
df["token_count"] = df["sentence"].apply(lambda x: len(x.tokens))
articles_batches = []
batches = self.generate_batches_of_n_token_lengths(df)
for i, batch in enumerate(batches):
print(f"[INFO] Batch #{i} token count:"
f"{humanize.intcomma(sum(batch['token_count'].tolist()))} "
f"of dynamic batch size of {len(batch)}")
output = self.inference(batch)
flair_output.extend(output)
return flair_output
```
Is this a valid path to take, or are there any better option out there to work with humongous amount of data to gets it's NER.
| open | 2023-10-16T05:37:35Z | 2023-10-17T12:46:14Z | https://github.com/flairNLP/flair/issues/3338 | [
"question"
] | adhadse | 4 |
serengil/deepface | machine-learning | 576 | Verification issue | Hi there ,
i would like to ask you a question in the def verification section , i am newbie in computer science and i want to print the input images into vectors and then a function that will take the 2 vectors and it will make the similarity .
Can you help me ??
Thanks | closed | 2022-10-19T11:32:11Z | 2022-10-20T08:10:22Z | https://github.com/serengil/deepface/issues/576 | [
"question"
] | thomaskopalidis | 3 |
healthchecks/healthchecks | django | 515 | [Question] Checking healthchecks status | Me and my friend is selfhosting healthchecks on our servers. Is there any URL that we could ping to check that each server is up? hc.example.com/status. | closed | 2021-05-16T14:36:47Z | 2021-05-16T15:11:44Z | https://github.com/healthchecks/healthchecks/issues/515 | [] | Synt3x | 2 |
coqui-ai/TTS | python | 2,619 | [Bug] Letter-by-letter pronunciation not possible | ### Describe the bug
I'd like to force the TTS model to pronounce a word letter by letter, e.g. "ARD" should be pronounced "A R D" (/ˌeɪˌɑːɹdˈiː/). In systems with SSML support (#752) you could use `<speak><say-as interpret-as="verbatim">ard</say-as></speak>`, but another way would be fine as well.
Espeak supports this even for words not in its dictionary by adding periods between the characters: `espeak-ng --ipa -v en-us "A.R.D."` is read /ˌeɪˌɑːɹdˈiː/.
This doesn't work in Coqui because the input for Espeak is split at punctuation characters and each chunk `["A", "R", "D"]` is phonemized separately: https://github.com/coqui-ai/TTS/blob/bc0a532c7a7e165338da6711ea362cdbe9761820/TTS/tts/utils/text/phonemizers/base.py#L129
This results in the word, not the letter pronunciation of "a" being chosen (ɐ instead of eɪ). I could change `_phonemize_preprocess()` to pass the input to Espeak with punctuation included, but I'm not sure about the side effects. Is there a specific reason to do it this way?
### To Reproduce
```python
from TTS.api import TTS
p = TTS(model_name="tts_models/en/ljspeech/vits", gpu=False).synthesizer.tts_model.tokenizer.phonemizer
p.phonemize("A.R.D.")
```
Output: `'ˈɐ.ˈɑːɹ.d|ˈiː.'`
### Expected behavior
Expected output: `ˌeɪˌɑːɹdˈiː`
### Logs
_No response_
### Environment
```shell
{
"CUDA": {
"GPU": [],
"available": false,
"version": "11.7"
},
"Packages": {
"PyTorch_debug": false,
"PyTorch_version": "2.0.0+cu117",
"TTS": "0.10.2",
"numpy": "1.22.4"
},
"System": {
"OS": "Linux",
"architecture": [
"64bit",
"ELF"
],
"processor": "x86_64",
"python": "3.10.8",
"version": "#42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2"
}
}
```
### Additional context
_No response_ | closed | 2023-05-16T15:27:10Z | 2023-05-16T20:37:18Z | https://github.com/coqui-ai/TTS/issues/2619 | [
"bug"
] | eginhard | 2 |
amisadmin/fastapi-amis-admin | sqlalchemy | 30 | 能增加一个InputFile吗 | 最好也弄一个InputVideo | open | 2022-06-28T18:25:49Z | 2022-07-18T14:42:09Z | https://github.com/amisadmin/fastapi-amis-admin/issues/30 | [] | wanghaisheng | 1 |
recommenders-team/recommenders | deep-learning | 1,609 | [ASK] Consider erasing ms-recommenders from pypi | ### Description
<!--- Describe your general ask in detail -->
Consider erasing https://pypi.org/project/ms-recommenders/ since we are using https://pypi.org/project/recommenders/
@anargyri @gramhagen what do you think?
### Other Comments
| closed | 2022-01-13T22:24:04Z | 2022-01-14T05:15:36Z | https://github.com/recommenders-team/recommenders/issues/1609 | [
"help wanted"
] | miguelgfierro | 2 |
matplotlib/matplotlib | matplotlib | 28,990 | [Bug]: no longer able to set multiple hatch colors | ### Bug summary
Possible bug or maybe a usage issue following some recent updates...
In short, it used to be possible to set the hatch color for each filled contour area to a different color by iterating over the collections and then setting the edgecolor for each, but that's no longer the case with Matplotlib 3.10 and the removal of collections.
I've been trying to find a workaround after the recent updates, but am struggling a bit. Using `set_edgecolors` only sets a single hatch color for all filled contours (which seems unexpected based upon the docs and like a potential bug) and rcParams['hatch.color'] also just sets a single color (less suprising).
It's entirely possible I'm missing something here, but didn't see much in the docs to help either.
### Code for reproduction
```Python
# The new approach I was hoping would work, but got unexpected results from
import matplotlib.pyplot as plt
import numpy as np
# invent some numbers, turning the x and y arrays into simple
# 2d arrays, which make combining them together easier.
x = np.linspace(-3, 5, 150).reshape(1, -1)
y = np.linspace(-3, 5, 120).reshape(-1, 1)
z = np.cos(x) + np.sin(y)
# we no longer need x and y to be 2 dimensional, so flatten them.
x, y = x.flatten(), y.flatten()
fig1, ax1 = plt.subplots()
cs = ax1.contourf(x, y, z, hatches=['-', '/', '\\', '//'],
cmap='gray', extend='both', alpha=0.5)
cs.set_edgecolors(["blue","grey","yellow","red"])
fig1.colorbar(cs)
# The older approach that now fails with Matplotlib 3.10
import matplotlib.pyplot as plt
import numpy as np
# invent some numbers, turning the x and y arrays into simple
# 2d arrays, which make combining them together easier.
x = np.linspace(-3, 5, 150).reshape(1, -1)
y = np.linspace(-3, 5, 120).reshape(-1, 1)
z = np.cos(x) + np.sin(y)
# we no longer need x and y to be 2 dimensional, so flatten them.
x, y = x.flatten(), y.flatten()
fig1, ax1 = plt.subplots()
cs = ax1.contourf(x, y, z, hatches=['-', '/', '\\', '//'],
cmap='gray', extend='both', alpha=0.5)
colors = ["blue","grey","yellow","red"]
for i, collection in enumerate(cs.collections):
collection.set_edgecolor(colors[i % len(colors)])
fig1.colorbar(cs)
```
### Actual outcome
<img width="581" alt="Screen Shot 2024-10-17 at 4 19 18 PM" src="https://github.com/user-attachments/assets/6c2306b5-196b-4488-b02d-2175df0f3238">
### Expected outcome
<img width="589" alt="Screen Shot 2024-10-17 at 4 18 22 PM" src="https://github.com/user-attachments/assets/705fc1c6-7e35-46c8-a815-5e3f57458b0a">
### Additional information
_No response_
### Operating system
OS/X
### Matplotlib Version
3.9.2
### Matplotlib Backend
'module://matplotlib_inline.backend_inline'
### Python version
3.11.7
### Jupyter version
4.0.9
### Installation
conda | closed | 2024-10-17T22:21:16Z | 2024-10-22T07:31:11Z | https://github.com/matplotlib/matplotlib/issues/28990 | [
"status: confirmed bug"
] | kafitzgerald | 3 |
ranaroussi/yfinance | pandas | 1,934 | Volume = 0 | ### Describe bug
Hello,
in the 1 or 5 minute history, for example of 'BTC-USD' often the volume is 0. There seems to be no negative volume at all. Could it be that it is failing volume and whenever it has to be negative a 0 is stored?
I have tried both with 'repair' to True or False.
Thanks.
### Simple code that reproduces your problem
import yfinance as yf
stock = yf.Ticker('BTC-USD')
historical_prices = stock.history(start='2024-05-09', end='2024-05-10', interval='5m', repair=True)
### Debug log
DEBUG Entering history()
DEBUG Entering history()
DEBUG BTC-USD: Yahoo GET parameters: {'period1': '2024-05-09 00:00:00+00:00', 'period2': '2024-05-10 00:00:00+00:00', 'interval': '5m', 'includePrePost': False, 'events': 'div,splits,capitalGains'}
DEBUG BTC-USD: yfinance received OHLC data: 2024-05-09 00:00:00 -> 2024-05-09 23:55:00
DEBUG BTC-USD: OHLC after cleaning: 2024-05-09 00:00:00+00:00 -> 2024-05-09 23:55:00+00:00
DEBUG BTC-USD: OHLC after combining events: 2024-05-09 00:00:00+00:00 -> 2024-05-09 23:55:00+00:00
DEBUG BTC-USD: checking OHLC for repairs ...
DEBUG Entering _fix_unit_mixups()
DEBUG Entering _fix_unit_switch()
DEBUG Entering _fix_prices_sudden_change()
DEBUG price-repair-split: appears_suspended=False, idx_latest_active=0 (2024-05-09)
INFO price-repair-split: No 100x errors detected
DEBUG Exiting _fix_prices_sudden_change()
DEBUG Exiting _fix_unit_switch()
DEBUG Entering _fix_unit_random_mixups()
INFO price-repair-100x: No sporadic 100x errors
DEBUG Exiting _fix_unit_random_mixups()
DEBUG Exiting _fix_unit_mixups()
DEBUG Entering _fix_bad_stock_split()
DEBUG Exiting _fix_bad_stock_split()
DEBUG Entering _fix_zeroes()
DEBUG Entering _reconstruct_intervals_batch()
DEBUG min_dt=2024-03-18 00:00:00+00:00 interval=5m sub_interval=2m
DEBUG grp_max_size = 30 days, 0:00:00
DEBUG Repair groups:
DEBUG - 2024-05-09 00:00:00+00:00 -> 2024-05-09 23:45:00+00:00
DEBUG df_block:
Open High Low \
2024-05-09 00:00:00+00:00 61162.906250 61204.425781 61147.816406
2024-05-09 00:05:00+00:00 61224.207031 61224.207031 61186.906250
2024-05-09 00:10:00+00:00 61194.218750 61308.046875 61182.968750
2024-05-09 00:15:00+00:00 61247.027344 61266.984375 61221.957031
2024-05-09 00:20:00+00:00 61208.257812 61288.429688 61208.257812
... ... ... ...
2024-05-09 23:30:00+00:00 62953.804688 62975.207031 62940.707031
2024-05-09 23:35:00+00:00 62979.972656 62979.972656 62883.500000
2024-05-09 23:40:00+00:00 62895.757812 62899.800781 62886.890625
2024-05-09 23:45:00+00:00 62927.777344 62965.730469 62927.777344
2024-05-09 23:50:00+00:00 63046.648438 63147.433594 63046.648438
Close Adj Close Volume Dividends \
2024-05-09 00:00:00+00:00 61204.425781 61204.425781 -1.0 0.0
2024-05-09 00:05:00+00:00 61186.906250 61186.906250 -1.0 0.0
2024-05-09 00:10:00+00:00 61260.464844 61260.464844 2453504.0 0.0
2024-05-09 00:15:00+00:00 61221.957031 61221.957031 2572288.0 0.0
2024-05-09 00:20:00+00:00 61288.429688 61288.429688 -1.0 0.0
... ... ... ... ...
2024-05-09 23:30:00+00:00 62975.207031 62975.207031 -1.0 0.0
2024-05-09 23:35:00+00:00 62883.500000 62883.500000 -1.0 0.0
2024-05-09 23:40:00+00:00 62892.656250 62892.656250 -1.0 0.0
2024-05-09 23:45:00+00:00 62965.730469 62965.730469 -1.0 0.0
2024-05-09 23:50:00+00:00 63147.433594 63147.433594 23889920.0 0.0
Stock Splits Repaired?
2024-05-09 00:00:00+00:00 0.0 False
2024-05-09 00:05:00+00:00 0.0 False
2024-05-09 00:10:00+00:00 0.0 False
2024-05-09 00:15:00+00:00 0.0 False
2024-05-09 00:20:00+00:00 0.0 False
... ... ...
2024-05-09 23:30:00+00:00 0.0 False
2024-05-09 23:35:00+00:00 0.0 False
2024-05-09 23:40:00+00:00 0.0 False
2024-05-09 23:45:00+00:00 0.0 False
2024-05-09 23:50:00+00:00 0.0 False
[287 rows x 9 columns]
DEBUG Fetching 2m prepost=False 2024-05-08->2024-05-11
DEBUG Entering history()
DEBUG BTC-USD: Yahoo GET parameters: {'period1': '2024-05-08 00:00:00+00:00', 'period2': '2024-05-11 00:00:00+00:00', 'interval': '2m', 'includePrePost': False, 'events': 'div,splits,capitalGains'}
DEBUG BTC-USD: yfinance received OHLC data: 2024-05-08 00:00:00 -> 2024-05-10 23:58:00
DEBUG BTC-USD: OHLC after cleaning: 2024-05-08 00:00:00+00:00 -> 2024-05-10 23:58:00+00:00
DEBUG BTC-USD: OHLC after combining events: 2024-05-08 00:00:00+00:00 -> 2024-05-10 23:58:00+00:00
DEBUG BTC-USD: checking OHLC for repairs ...
DEBUG Entering _fix_unit_mixups()
DEBUG Entering _fix_unit_switch()
DEBUG Entering _fix_prices_sudden_change()
DEBUG price-repair-split: appears_suspended=True, idx_latest_active=4 (2024-05-10)
INFO price-repair-split: No 100x errors detected
DEBUG Exiting _fix_prices_sudden_change()
DEBUG Exiting _fix_unit_switch()
DEBUG Entering _fix_unit_random_mixups()
INFO price-repair-100x: No sporadic 100x errors
DEBUG Exiting _fix_unit_random_mixups()
DEBUG Exiting _fix_unit_mixups()
DEBUG Entering _fix_bad_stock_split()
DEBUG Exiting _fix_bad_stock_split()
DEBUG Entering _fix_zeroes()
DEBUG Entering _reconstruct_intervals_batch()
DEBUG min_dt=2024-04-17 00:00:00+00:00 interval=2m sub_interval=1m
DEBUG grp_max_size = 5 days, 0:00:00
DEBUG Repair groups:
DEBUG - 2024-05-08 00:02:00+00:00 -> 2024-05-10 23:58:00+00:00
DEBUG df_block:
Open High Low \
2024-05-08 00:00:00+00:00 62322.082031 62343.535156 62322.082031
2024-05-08 00:02:00+00:00 62367.093750 62412.796875 62367.093750
2024-05-08 00:04:00+00:00 62380.914062 62380.914062 62380.472656
2024-05-08 00:06:00+00:00 62374.738281 62374.738281 62374.738281
2024-05-08 00:08:00+00:00 62438.960938 62457.699219 62438.960938
... ... ... ...
2024-05-10 23:50:00+00:00 60899.343750 60899.343750 60898.917969
2024-05-10 23:52:00+00:00 60903.011719 60903.011719 60898.910156
2024-05-10 23:54:00+00:00 60881.300781 60881.300781 60881.300781
2024-05-10 23:56:00+00:00 60785.386719 60806.562500 60785.386719
2024-05-10 23:58:00+00:00 -1.000000 -1.000000 -1.000000
Close Adj Close Volume Dividends \
2024-05-08 00:00:00+00:00 62343.535156 62343.535156 5607424.0 0.0
2024-05-08 00:02:00+00:00 62412.796875 62412.796875 -1.0 0.0
2024-05-08 00:04:00+00:00 62380.472656 62380.472656 15757312.0 0.0
2024-05-08 00:06:00+00:00 62374.738281 62374.738281 0.0 0.0
2024-05-08 00:08:00+00:00 62457.699219 62457.699219 16173056.0 0.0
... ... ... ... ...
2024-05-10 23:50:00+00:00 60898.917969 60898.917969 -1.0 0.0
2024-05-10 23:52:00+00:00 60898.910156 60898.910156 -1.0 0.0
2024-05-10 23:54:00+00:00 60881.300781 60881.300781 0.0 0.0
2024-05-10 23:56:00+00:00 60806.562500 60806.562500 673792.0 0.0
2024-05-10 23:58:00+00:00 -1.000000 -1.000000 -1.0 0.0
Stock Splits Repaired?
2024-05-08 00:00:00+00:00 0.0 False
2024-05-08 00:02:00+00:00 0.0 False
2024-05-08 00:04:00+00:00 0.0 False
2024-05-08 00:06:00+00:00 0.0 False
2024-05-08 00:08:00+00:00 0.0 False
... ... ...
2024-05-10 23:50:00+00:00 0.0 False
2024-05-10 23:52:00+00:00 0.0 False
2024-05-10 23:54:00+00:00 0.0 False
2024-05-10 23:56:00+00:00 0.0 False
2024-05-10 23:58:00+00:00 0.0 False
[2160 rows x 9 columns]
DEBUG Fetching 1m prepost=False 2024-05-07->2024-05-13
DEBUG Entering history()
DEBUG BTC-USD: Yahoo GET parameters: {'period1': '2024-05-07 00:00:00+00:00', 'period2': '2024-05-13 00:00:00+00:00', 'interval': '1m', 'includePrePost': False, 'events': 'div,splits,capitalGains'}
DEBUG BTC-USD: yfinance received OHLC data: 2024-05-07 00:00:00 -> 2024-05-12 23:58:00
DEBUG BTC-USD: OHLC after cleaning: 2024-05-07 00:00:00+00:00 -> 2024-05-12 23:58:00+00:00
DEBUG BTC-USD: OHLC after combining events: 2024-05-07 00:00:00+00:00 -> 2024-05-12 23:58:00+00:00
DEBUG BTC-USD: checking OHLC for repairs ...
DEBUG Entering _fix_unit_mixups()
DEBUG Entering _fix_unit_switch()
DEBUG Entering _fix_prices_sudden_change()
DEBUG price-repair-split: appears_suspended=False, idx_latest_active=1 (2024-05-12)
INFO price-repair-split: No 100x errors detected
DEBUG Exiting _fix_prices_sudden_change()
DEBUG Exiting _fix_unit_switch()
DEBUG Entering _fix_unit_random_mixups()
INFO price-repair-100x: No sporadic 100x errors
DEBUG Exiting _fix_unit_random_mixups()
DEBUG Exiting _fix_unit_mixups()
DEBUG Entering _fix_bad_stock_split()
DEBUG Exiting _fix_bad_stock_split()
DEBUG Entering _fix_zeroes()
DEBUG Entering _reconstruct_intervals_batch()
DEBUG Exiting _reconstruct_intervals_batch()
DEBUG Exiting _fix_zeroes()
DEBUG Entering _fix_missing_div_adjust()
DEBUG Exiting _fix_missing_div_adjust()
DEBUG BTC-USD: yfinance returning OHLC: 2024-05-07 00:00:00+00:00 -> 2024-05-12 23:58:00+00:00
DEBUG Exiting history()
DEBUG df_new:
Open Close Adj Close \
2024-05-08 00:00:00+00:00 62322.082031 62343.535156 62343.535156
2024-05-08 00:02:00+00:00 62367.093750 62412.796875 62412.796875
2024-05-08 00:04:00+00:00 62380.914062 62380.472656 62380.472656
2024-05-08 00:06:00+00:00 62374.738281 62374.738281 62374.738281
2024-05-08 00:08:00+00:00 62438.960938 62457.699219 62457.699219
... ... ... ...
2024-05-10 23:50:00+00:00 60899.343750 60898.917969 60898.917969
2024-05-10 23:52:00+00:00 60903.011719 60898.910156 60898.910156
2024-05-10 23:54:00+00:00 60881.300781 60881.300781 60881.300781
2024-05-10 23:56:00+00:00 60785.386719 60806.562500 60806.562500
2024-05-10 23:58:00+00:00 NaN NaN NaN
Low High Dividends Volume
2024-05-08 00:00:00+00:00 62322.082031 62343.535156 0.0 6049792
2024-05-08 00:02:00+00:00 62367.093750 62412.796875 0.0 0
2024-05-08 00:04:00+00:00 62380.472656 62380.914062 0.0 15757312
2024-05-08 00:06:00+00:00 62374.738281 62374.738281 0.0 0
2024-05-08 00:08:00+00:00 62438.960938 62457.699219 0.0 16173056
... ... ... ... ...
2024-05-10 23:50:00+00:00 60898.917969 60899.343750 0.0 0
2024-05-10 23:52:00+00:00 60898.910156 60903.011719 0.0 0
2024-05-10 23:54:00+00:00 60881.300781 60881.300781 0.0 0
2024-05-10 23:56:00+00:00 60785.386719 60806.562500 0.0 673792
2024-05-10 23:58:00+00:00 NaN NaN 0.0 0
[2160 rows x 7 columns]
DEBUG Price calibration ratio (raw) = 1.000000
DEBUG Exiting _reconstruct_intervals_batch()
INFO price-repair-missing: BTC-USD: fixed 616/616 value=0 errors in 2m price data
DEBUG Exiting _fix_zeroes()
DEBUG Entering _fix_missing_div_adjust()
DEBUG Exiting _fix_missing_div_adjust()
DEBUG BTC-USD: yfinance returning OHLC: 2024-05-08 00:00:00+00:00 -> 2024-05-10 23:58:00+00:00
DEBUG Exiting history()
DEBUG df_new:
Open Close Adj Close \
2024-05-09 00:00:00+00:00 61162.906250 61186.906250 61186.906250
2024-05-09 00:10:00+00:00 61194.218750 61221.957031 61221.957031
2024-05-09 00:20:00+00:00 61208.257812 61354.125000 61354.125000
2024-05-09 00:30:00+00:00 61343.410156 61353.480469 61353.480469
2024-05-09 00:40:00+00:00 61357.382812 61403.101562 61403.101562
... ... ... ...
2024-05-09 23:10:00+00:00 63128.957031 62976.238281 62976.238281
2024-05-09 23:20:00+00:00 62987.125000 62955.250000 62955.250000
2024-05-09 23:30:00+00:00 62953.804688 62883.500000 62883.500000
2024-05-09 23:40:00+00:00 62895.757812 62965.730469 62965.730469
2024-05-09 23:50:00+00:00 63046.648438 63098.777344 63098.777344
Low High Dividends Volume
2024-05-09 00:00:00+00:00 61147.816406 61224.207031 0.0 4972544
2024-05-09 00:10:00+00:00 61182.968750 61308.046875 0.0 17688576
2024-05-09 00:20:00+00:00 61208.257812 61354.125000 0.0 4491264
2024-05-09 00:30:00+00:00 61304.511719 61361.765625 0.0 4648960
2024-05-09 00:40:00+00:00 61357.382812 61435.660156 0.0 0
... ... ... ... ...
2024-05-09 23:10:00+00:00 62976.238281 63128.957031 0.0 8067072
2024-05-09 23:20:00+00:00 62902.449219 62987.125000 0.0 33947648
2024-05-09 23:30:00+00:00 62883.500000 62979.972656 0.0 833536
2024-05-09 23:40:00+00:00 62886.890625 62965.730469 0.0 301056
2024-05-09 23:50:00+00:00 63046.648438 63147.433594 0.0 38248448
[144 rows x 7 columns]
DEBUG Price calibration ratio (raw) = 0.999958
DEBUG Yahoo didn't return finer-grain data for these intervals: [Timestamp('2024-05-09 00:05:00+0000', tz='UTC'), Timestamp('2024-05-09 00:25:00+0000', tz='UTC'), Timestamp('2024-05-09 00:35:00+0000', tz='UTC'), Timestamp('2024-05-09 00:45:00+0000', tz='UTC'), Timestamp('2024-05-09 00:55:00+0000', tz='UTC'), Timestamp('2024-05-09 01:05:00+0000', tz='UTC'), Timestamp('2024-05-09 01:15:00+0000', tz='UTC'), Timestamp('2024-05-09 01:25:00+0000', tz='UTC'), Timestamp('2024-05-09 01:35:00+0000', tz='UTC'), Timestamp('2024-05-09 01:45:00+0000', tz='UTC'), Timestamp('2024-05-09 02:05:00+0000', tz='UTC'), Timestamp('2024-05-09 02:15:00+0000', tz='UTC'), Timestamp('2024-05-09 02:25:00+0000', tz='UTC'), Timestamp('2024-05-09 02:55:00+0000', tz='UTC'), Timestamp('2024-05-09 03:15:00+0000', tz='UTC'), Timestamp('2024-05-09 03:45:00+0000', tz='UTC'), Timestamp('2024-05-09 04:05:00+0000', tz='UTC'), Timestamp('2024-05-09 04:25:00+0000', tz='UTC'), Timestamp('2024-05-09 04:35:00+0000', tz='UTC'), Timestamp('2024-05-09 04:45:00+0000', tz='UTC'), Timestamp('2024-05-09 04:55:00+0000', tz='UTC'), Timestamp('2024-05-09 05:05:00+0000', tz='UTC'), Timestamp('2024-05-09 05:15:00+0000', tz='UTC'), Timestamp('2024-05-09 05:55:00+0000', tz='UTC'), Timestamp('2024-05-09 06:05:00+0000', tz='UTC'), Timestamp('2024-05-09 06:25:00+0000', tz='UTC'), Timestamp('2024-05-09 06:35:00+0000', tz='UTC'), Timestamp('2024-05-09 06:45:00+0000', tz='UTC'), Timestamp('2024-05-09 06:55:00+0000', tz='UTC'), Timestamp('2024-05-09 07:05:00+0000', tz='UTC'), Timestamp('2024-05-09 07:15:00+0000', tz='UTC'), Timestamp('2024-05-09 07:25:00+0000', tz='UTC'), Timestamp('2024-05-09 07:35:00+0000', tz='UTC'), Timestamp('2024-05-09 07:55:00+0000', tz='UTC'), Timestamp('2024-05-09 08:05:00+0000', tz='UTC'), Timestamp('2024-05-09 08:15:00+0000', tz='UTC'), Timestamp('2024-05-09 08:35:00+0000', tz='UTC'), Timestamp('2024-05-09 08:45:00+0000', tz='UTC'), Timestamp('2024-05-09 08:55:00+0000', tz='UTC'), Timestamp('2024-05-09 09:15:00+0000', tz='UTC'), Timestamp('2024-05-09 09:25:00+0000', tz='UTC'), Timestamp('2024-05-09 09:35:00+0000', tz='UTC'), Timestamp('2024-05-09 09:55:00+0000', tz='UTC'), Timestamp('2024-05-09 10:15:00+0000', tz='UTC'), Timestamp('2024-05-09 10:25:00+0000', tz='UTC'), Timestamp('2024-05-09 10:45:00+0000', tz='UTC'), Timestamp('2024-05-09 12:25:00+0000', tz='UTC'), Timestamp('2024-05-09 13:05:00+0000', tz='UTC'), Timestamp('2024-05-09 13:15:00+0000', tz='UTC'), Timestamp('2024-05-09 13:25:00+0000', tz='UTC'), Timestamp('2024-05-09 13:35:00+0000', tz='UTC'), Timestamp('2024-05-09 13:55:00+0000', tz='UTC'), Timestamp('2024-05-09 14:25:00+0000', tz='UTC'), Timestamp('2024-05-09 14:45:00+0000', tz='UTC'), Timestamp('2024-05-09 14:55:00+0000', tz='UTC'), Timestamp('2024-05-09 15:15:00+0000', tz='UTC'), Timestamp('2024-05-09 17:45:00+0000', tz='UTC'), Timestamp('2024-05-09 18:15:00+0000', tz='UTC'), Timestamp('2024-05-09 18:25:00+0000', tz='UTC'), Timestamp('2024-05-09 18:35:00+0000', tz='UTC'), Timestamp('2024-05-09 18:45:00+0000', tz='UTC'), Timestamp('2024-05-09 19:05:00+0000', tz='UTC'), Timestamp('2024-05-09 19:15:00+0000', tz='UTC'), Timestamp('2024-05-09 19:25:00+0000', tz='UTC'), Timestamp('2024-05-09 19:45:00+0000', tz='UTC'), Timestamp('2024-05-09 20:15:00+0000', tz='UTC'), Timestamp('2024-05-09 20:25:00+0000', tz='UTC'), Timestamp('2024-05-09 20:35:00+0000', tz='UTC'), Timestamp('2024-05-09 20:45:00+0000', tz='UTC'), Timestamp('2024-05-09 20:55:00+0000', tz='UTC'), Timestamp('2024-05-09 21:25:00+0000', tz='UTC'), Timestamp('2024-05-09 21:35:00+0000', tz='UTC'), Timestamp('2024-05-09 22:15:00+0000', tz='UTC'), Timestamp('2024-05-09 22:25:00+0000', tz='UTC'), Timestamp('2024-05-09 23:05:00+0000', tz='UTC'), Timestamp('2024-05-09 23:15:00+0000', tz='UTC'), Timestamp('2024-05-09 23:25:00+0000', tz='UTC'), Timestamp('2024-05-09 23:35:00+0000', tz='UTC'), Timestamp('2024-05-09 23:45:00+0000', tz='UTC')]
DEBUG Exiting _reconstruct_intervals_batch()
INFO price-repair-missing: BTC-USD: fixed 86/165 value=0 errors in 5m price data
DEBUG Exiting _fix_zeroes()
DEBUG Entering _fix_missing_div_adjust()
DEBUG Exiting _fix_missing_div_adjust()
DEBUG BTC-USD: yfinance returning OHLC: 2024-05-09 00:00:00+00:00 -> 2024-05-09 23:55:00+00:00
DEBUG Exiting history()
DEBUG Exiting history()
### Bad data proof
_No response_
### `yfinance` version
0.2.38
### Python version
3.12.3
### Operating system
Windows 11 | closed | 2024-05-15T22:05:44Z | 2024-05-15T22:12:47Z | https://github.com/ranaroussi/yfinance/issues/1934 | [] | jvallsgm | 1 |
viewflow/viewflow | django | 41 | Admin interface for Process and Tasks | closed | 2014-05-07T05:31:12Z | 2014-05-12T06:58:38Z | https://github.com/viewflow/viewflow/issues/41 | [
"request/enhancement"
] | kmmbvnr | 1 |
|
DistrictDataLabs/yellowbrick | scikit-learn | 663 | print important features | It visualizes the number of important features but I can't find how to print/show those selected important features.
| closed | 2018-12-03T02:45:28Z | 2018-12-03T03:52:38Z | https://github.com/DistrictDataLabs/yellowbrick/issues/663 | [] | mubashirtuf | 3 |
fastapi/sqlmodel | pydantic | 136 | Doesn't create all tables when starting up the app | ### First Check
- [X] I added a very descriptive title to this issue.
- [X] I used the GitHub search to find a similar issue and didn't find it.
- [X] I searched the SQLModel documentation, with the integrated search.
- [X] I already searched in Google "How to X in SQLModel" and didn't find any information.
- [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 SQLModel but to [Pydantic](https://github.com/samuelcolvin/pydantic).
- [X] I already checked if it is not related to SQLModel but to [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy).
### Commit to Help
- [X] I commit to help with one of those options 👆
### Example Code
```python
from typing import Optional
from pydantic.types import UUID4
from sqlmodel import Field, Session, SQLModel, create_engine, select
import envconfig
class User(SQLModel, table=True):
id: str = Field(default=UUID4, primary_key=True)
username: str
user_pfp: str
class Templates(SQLModel, table=True):
id: str = Field(default=UUID4, primary_key=True)
name: str
badge: Optional[str]
user_id: Optional[str] = Field(default=None, foreign_key="user.id")
class Pages(SQLModel, tabel=True):
id: str = Field(default=UUID4, primary_key=True)
name: str
badge: str
content: str
engine = create_engine(envconfig.config("DATABASE_URL"), echo=True)
def create_db_and_tables():
SQLModel.metadata.create_all(engine)
```
### Description
When the app starts
```
@app.on_event("startup")
def on_startup():
create_db_and_tables()
```
it doesn't seem to create all tables [output](https://sourceb.in/EorEneC3hw)
### Operating System
Linux, Windows
### Operating System Details
Tested on both windows and linux (wsl)
### SQLModel Version
0.0.4
### Python Version
Python 3.8.10
### Additional Context
_No response_ | closed | 2021-10-17T16:11:27Z | 2021-10-19T06:20:19Z | https://github.com/fastapi/sqlmodel/issues/136 | [
"question"
] | mooyg | 1 |
tensorly/tensorly | numpy | 452 | Multiple implementations of functions with the same backend | Continuing of the discussion in https://github.com/tensorly/tensorly/pull/434#issuecomment-1204339420. We may want to allow multiple implementations of the same function. For example, we may want to make it possible to use opt-einsum to cache contraction paths without this being the default behaviour.
Here is an example with a possible API (without any accompanying implementation).
````python
@allow_alternative_implementations
def absolute_value(x):
return abs(x)
@absolute_value.alternative_implementation(name="sqrt_based")
def _sqrt_based_abs(x):
return sqrt(x*x)
absolute_value(x) # use original implementation
absolute_value.use_alternative("sqrt_based")
absolute_value(x) # use sqrt_based
absolute_value.use_alternative(None)
absolute_value(x) # use original implementation again
```` | open | 2022-10-30T17:04:21Z | 2022-10-30T19:50:58Z | https://github.com/tensorly/tensorly/issues/452 | [
"enhancement",
"API design"
] | yngvem | 1 |
plotly/dash-table | dash | 522 | Validate React version of standalone table against Dash React version | First mentioned in https://github.com/plotly/dash-table/pull/508#discussion_r308946435.
Making sure the versions are aligned would ensure the table is always truly tested against the expected version.
Since the table generation needs to happen against the latest version of Dash and is part of the normal development process, it should be possible to retrieve the version in dash-renderer and compare it with the version referenced in the standalone `index.htm`. | open | 2019-07-30T23:42:53Z | 2020-01-30T20:47:32Z | https://github.com/plotly/dash-table/issues/522 | [
"dash-type-maintenance",
"dash-stage-revision_needed"
] | Marc-Andre-Rivet | 0 |
Esri/arcgis-python-api | jupyter | 1,298 | Standalone ArcPy Anaconda Distribution for ArcGIS Pro 3.0 crashes Jupyter Notebook Kernel in VSCode | **Describe the bug**
After creating a new ArcPy environment within a standalone installation of Anaconda, any attempt to import ArcPy within a VSCode Jupyter Notebook crashes the kernel. This is the new distribution release on Anaconda from the ArcGIS Pro 3.0 release.
Works appropriately in a regular Jupyter Notebook. Also the distribution that comes with ArcGIS Pro 3.0 does not break the VSCode Jupyter Notebook.
**To Reproduce**
Steps to reproduce the behavior:
```shell
conda create -n esri
conda activate esri
conda install -c esri arcpy
```
Within a VSCode Jupyter Notebook, import ArcPy
```python
import ArcPy
```
error:
```python
Canceled future for execute_request message before replies were done.
The Kernel crashed while executing code in the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for more details.
```
**Screenshots**
**Expected behavior**
ArcPy should be usable within a VSCode Jupyter Notebook (when downloaded from Anaconda) as has been the behavior since ArcGIS Pro 2.7.
**Platform (please complete the following information):**
- OS: Windows 10
- Browser [e.g. chrome, safari]
- Python API Version 2.0.1 py39_2826 esri
**Additional context**
Add any other context about the problem here, attachments etc.
| closed | 2022-07-12T19:08:00Z | 2022-09-22T18:05:08Z | https://github.com/Esri/arcgis-python-api/issues/1298 | [
"bug"
] | FeralCatColonist | 3 |
miguelgrinberg/python-socketio | asyncio | 308 | Clarify reconnection details in documentation | I just had to read code because I wanted to know what happens to the session data when a client reconnects. It turns out that a client gets a new `sid` on reconnect and the session data is lost.
> The server can maintain application-specific information in a user session dedicated to each connected client. Applications can use the user session to write any details about the user that need to be preserved throughout the life of the connection, such as usernames or user ids.
Maybe add a sentence like "Please note that in the event of a disconnect the session is deleted and lost even if a reconnect occurs, because the client will receive a new `sid`."
Additional quesiton:
So if I would like, for example, save the 'logged-in' state of a client in my server I would have to save that "out of band" and manage my own client IDs? So adding a whole protocol layer on top of Socket.IO? Or is there a more idiomatic way of doing it?
| closed | 2019-06-21T19:46:38Z | 2019-06-29T09:10:22Z | https://github.com/miguelgrinberg/python-socketio/issues/308 | [
"documentation"
] | ccoors | 1 |
junyanz/pytorch-CycleGAN-and-pix2pix | deep-learning | 977 | Unet generator for large images | Hello, thanks for providing your code.
I applied cycleGAN to my own dataset whose size is 900x900.
First, I used the code with default setting and it worked well. (of course train with option `--preprocess scale_width_and_crop --load_size 900 --crop_size 256 ` and test with `--preprocess scale_width --load_size 900`)
After then, I tried to train and test the dataset with unet generator with same options.
However, it didn't work, and I think because the images size should be 256x256 for unet.
Is there any way to use large images with unet generator except cropping the dataset?
| open | 2020-04-03T06:21:32Z | 2023-12-04T12:00:50Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/977 | [] | myfrannie | 6 |
flasgger/flasgger | api | 18 | customize apidocs/index.html | this is awesome! It is working and wonder can we overwrite the apidocs/index.html.
Is that possible you add some doc on how / where to put the swaggerui to customize the css template etc?
thanks in advance
| closed | 2016-04-08T01:08:14Z | 2017-03-24T20:09:36Z | https://github.com/flasgger/flasgger/issues/18 | [] | jeffpeiyt | 1 |
google-research/bert | tensorflow | 1,067 | Pooled output is different from first vector of sequence output | There is one detail about BERT's output I don't quite understand. Accordin the the documentation (https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1), pooled output is the of the entire sequence. Based on the original paper, it seems like this is the output for the token "CLS" at the beginning of the setence.
````
pooled_output[0]
````
However, when I look at the output corresponding to the first token in the sentence
````
setence_output[0,0,:]
````
which I believe corresponds to the token "CLS" (the first token in the sentence), the 2 results are different.
Thanks in advance for your help | open | 2020-04-21T15:00:08Z | 2020-04-21T15:00:08Z | https://github.com/google-research/bert/issues/1067 | [] | tqdo | 0 |
mlfoundations/open_clip | computer-vision | 7 | Usage of title and/or description column in YFCC100M | Hello,
In your training of CLIP, did you use only the `description` column as text input, or both the `title` and `description` columns?
The reason I am asking is because in the github folder where OpenAI provide info on their [YFCC100M subset](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md), there is a sentence that I find quite ambiguous:
> [...] which have been filtered to only keep those with natural languag titles and/or descriptions in English
This seems to imply that it sufficed that only one of `title` and `description` was considered natural language for an observation (image) to be kept as part of the subset. However, they do not clarify whether they also proceeded to use the results of this natural language filter to choose whether to use only the `title` or only the `description` in the case that one of them was not deemed to be natural language. Alternatively, they may have concatenated the columns and used both of them in training.
Anyway, what I'm interested in knowing here is what you guys decided to do in your training. Did you use both columns or just the `description`?
Also, did you clean the text in any manner (e.g. remove html tags present in the text)? | closed | 2021-08-20T19:50:53Z | 2021-08-24T10:48:36Z | https://github.com/mlfoundations/open_clip/issues/7 | [] | Lauler | 4 |
huggingface/datasets | nlp | 6,997 | CI is broken for tests using hf-internal-testing/librispeech_asr_dummy | CI is broken: https://github.com/huggingface/datasets/actions/runs/9657882317/job/26637998686?pr=6996
```
FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other']
Right contains one more item: 'other'
Full diff:
[
'clean',
- 'other',
]
FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None
```
Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a | closed | 2024-06-25T07:55:44Z | 2024-06-25T08:13:43Z | https://github.com/huggingface/datasets/issues/6997 | [
"maintenance"
] | albertvillanova | 0 |
Johnserf-Seed/TikTokDownload | api | 143 | [BUG]出现IndexError: list index out of range 下载单个视频时出现 | **描述出现的错误**
确定是单个视频链接，但仍然出现了IndexError: list index out of range的错误
**bug复现**
使用pip安装的，在venv环境下运行pip安装好的脚本
```
D:\github_room\testtiktokdownload\venv\Scripts\python.exe D:/github_room/testtiktokdownload/venv/Lib/site-packages/TikTokDownload/TikTokDownload.py
请输入抖音链接:https://v.douyin.com/FdCpfch/
```
将链接更改为该脚本中的样例 https://v.douyin.com/JtcjTwo/ 同样报错
**截图**
**桌面（请填写以下信息）：**
-操作系统：windows10 64bit
-vpn代理:[关闭]
-版本:1.2.3
**附文**
在此处添加有关此问题的文字。
| closed | 2022-04-23T14:31:39Z | 2022-04-27T06:46:29Z | https://github.com/Johnserf-Seed/TikTokDownload/issues/143 | [
"故障(bug)",
"额外求助(help wanted)",
"无效(invalid)"
] | lingr7 | 5 |
sammchardy/python-binance | api | 906 | Execute asyncio coroutines concurrently | I have this coroutine that receives messages, from the documentation:
```
import asyncio
from binance import AsyncClient, BinanceSocketManager
async def main():
client = await AsyncClient.create()
bm = BinanceSocketManager(client)
# start any sockets here, i.e a trade socket
ts = bm.trade_socket('BNBBTC')
# then start receiving messages
async with ts as tscm:
while True:
res = await tscm.recv()
print(res)
await client.close_connection()
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
```
I want to do other tasks while receiving and treating the messages, so I want another coroutine to be executed concurrently with ```main()```. How could I do this? I tried with ```asyncio.gather()```, but it does not work. It seems that ```main()``` any other coroutine from being executed.
Thanks. | closed | 2021-06-05T17:29:39Z | 2021-06-14T08:44:09Z | https://github.com/sammchardy/python-binance/issues/906 | [] | LeonhardOiler | 0 |
mwaskom/seaborn | data-science | 3,346 | sns.pairplot() fails with ValueError: output array is read-only | Dataset:
[train.csv](https://github.com/mwaskom/seaborn/files/11337012/train.csv)
Code:
```python
import numpy as np
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
train_df = pd.read_csv("train.csv")
train_df["CryoSleep"] = train_df["CryoSleep"].astype(bool)
train_df["VIP"] = train_df["VIP"].astype(bool)
train_df["CryoSleep"] = train_df["CryoSleep"].astype(int)
train_df["VIP"] = train_df["VIP"].astype(int)
train_df["Transported"] = train_df["Transported"].astype(int)
train_df["HomePlanet"] = train_df["HomePlanet"].astype("category")
train_df["Destination"] = train_df["Destination"].astype("category")
_ = train_df.drop(columns=["PassengerId", "Cabin", "Name"]).copy(deep=True)
sns.pairplot(_.dropna())
```
No matter what, I cannot make it work on this dataset. Even if you skip the type conversion block and apply it to the original `train_df` it still fails. Even without `dropna()` it still fails.
Complete error message:
https://gist.github.com/FlorinAndrei/f84c68a3efdab40968ffd84687e5e343
Python 3.11.3
Pandas 2.0.0
Numpy 1.24.2
Seaborn 0.12.2
Jupyter Notebook | closed | 2023-04-26T20:40:22Z | 2023-04-26T22:30:28Z | https://github.com/mwaskom/seaborn/issues/3346 | [] | FlorinAndrei | 3 |
httpie/cli | python | 1,469 | Support for WebAppBoundary Request | ## Checklist
- [x] I've searched for similar feature requests.
---
## Enhancement request
For file sending with Form Data, sending with WebAppBoundary syntax is very common and with Httpie, i am not able to send any request to with files in which backend expects WebAppBounday
---
## Problem it solves
Could not use Httpie with files and formdata. Postman handles this scenario well. And it is the common behaviour for JetBrains based IDE HTTP files too for file sending
---
## Additional information, screenshots, or code examples
```
POST {{host}}/test
Authorization: {{token}}
Content-Type: multipart/form-data; boundary=WebAppBoundary
--WebAppBoundary
Content-Disposition: form-data; name="prospect"; filename="file.csv"
< files/test/file.csv
--WebAppBoundary
Content-Disposition: form-data; name="data"
{"name":"ASDF"}
--WebAppBoundary--
```
| open | 2023-01-10T08:34:20Z | 2023-01-15T18:41:42Z | https://github.com/httpie/cli/issues/1469 | [
"enhancement",
"new"
] | AbinashSankaran | 2 |
deezer/spleeter | deep-learning | 547 | [Discussion] Using spleeter for dialog separation | What would be the best way to go about using spleeter for dialog separation? Specifically, isolation of dialog in noisy or unfriendly contexts (i.e. lots of background noise).
Have located training info in manual - hopefully this will shed some light. | closed | 2021-01-03T02:23:38Z | 2021-01-08T13:24:47Z | https://github.com/deezer/spleeter/issues/547 | [
"question"
] | edwar64896 | 1 |
kizniche/Mycodo | automation | 486 | MH-Z19 issue | ## Mycodo Issue Report:
- Specific Mycodo Version:6.1.2
#### Problem Description
Please list:
Installed MH-Z19.
Wired
Vc = 5V
GND = GND
RX = TX
TX = RX
With standard input settings in Mycodo.
### Errors
The raspberry reads no data.
Measurement | Timestamp
0 ppm | No Data Last 16 sec
and it stays like this forever. Sensor is doing something, it's blinking red.
Maybe i wired something wrong....?
Edit: I found the issue, i changed the ttyS0 to serial0 and now it's working. You can close this. | closed | 2018-05-30T19:34:05Z | 2018-06-04T19:42:01Z | https://github.com/kizniche/Mycodo/issues/486 | [] | MrPsyco | 0 |
sunscrapers/djoser | rest-api | 558 | setting USER_ID_FIELD with custom user | I would like to hide the database primary key of the User model. This is described in:
[https://djoser.readthedocs.io/en/latest/settings.html#user-id-field](https://djoser.readthedocs.io/en/latest/settings.html#user-id-field)
The documentation is not very clear on how to do this.
The CustomUser model is set via AUTH_USER_MODEL in the Django settings. The Djoser setting `USER_ID_FIELD` needs the full model (as it requires access to the models `._meta.pk`). However, in the Django settings, that model is not yet available (error: `django.core.exceptions.AppRegistryNotReady: Models aren't loaded yet.`).
I do not see another method of defining the `USER_ID_FIELD`.
- How do I get this done?
- Where do I set the `USER_ID_FIELD` setting, and if it is the Django settings then the question is:
- How do I set the `USER_ID_FIELD` setting.
| closed | 2020-11-14T23:35:46Z | 2020-11-15T14:50:38Z | https://github.com/sunscrapers/djoser/issues/558 | [] | hanckmann | 1 |
statsmodels/statsmodels | data-science | 9,034 | ENH: TOST for quantiles, tolerance interval | mainly parking a reference
(found looking for Bland-Altman agreement comparisons)
Gwowen Shieh (2020) Assessing Agreement Between Two Methods of
Quantitative Measurements: Exact Test Procedure and Sample Size Calculation, Statistics in
Biopharmaceutical Research, 12:3, 352-359, DOI: 10.1080/19466315.2019.1677495
I didn't try to understand the details.
looks at TOST test for the equivalence range of a normal quantile, one-sample interpreted as the diff of paired 2 samples.
small quantiles indicates agreement between 2 paired measuring methods.
related to tolerance intervals, i.e. get confidence interval for quantiles or for region between 2 quantiles with required coverage/content.
(AFAIR, Shieh's articles were useful for other stats) | open | 2023-10-19T22:15:51Z | 2023-10-20T16:58:45Z | https://github.com/statsmodels/statsmodels/issues/9034 | [
"type-enh",
"comp-stats"
] | josef-pkt | 0 |
microsoft/unilm | nlp | 883 | The value of F in VLMo | Dear Sir,
I recently read your VLMO: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts paper and found it very intriguing.
I didn't find the description of the F layers in Figure 1. Could you clarify the value of F? thanks!
Looking forward to your reply! | closed | 2022-09-28T16:10:10Z | 2022-09-28T23:50:52Z | https://github.com/microsoft/unilm/issues/883 | [] | chaochen99 | 2 |
datadvance/DjangoChannelsGraphqlWs | graphql | 19 | Breaking changes in rx dependency | Project [currently](https://github.com/datadvance/DjangoChannelsGraphqlWs/blob/master/pyproject.toml#L57) depends on `rx = '*'`. However, the `rx` package has recently been updated to `>3`, and this is breaking this project.
I would recommend locking down the `rx` dependency with `rx = '1.6.1'` as well as specifying other `*` dependencies within `pyproject.toml`. | closed | 2019-07-18T21:54:17Z | 2019-08-13T16:39:23Z | https://github.com/datadvance/DjangoChannelsGraphqlWs/issues/19 | [] | clarkmoody | 1 |
plotly/dash | data-visualization | 3,107 | unrecognized capability: loggingPrefs | **Context**
```
dash 2.18.2
dash-bootstrap-components 1.6.0
dash-core-components 2.0.0
dash-html-components 2.0.0
dash-table 5.0.0
dash-testing-stub 0.0.2
```
- Front-end
- OS: MacOS
- Browser: chrome
- Version: 131
**Describe the bug**
Trying to execute dash test through pytest I get the following error:
```bash
========================================================================= ERRORS ==========================================================================
_________________________________________________________ ERROR at setup of test_001_child_with_0 _________________________________________________________
request = <SubRequest 'dash_duo' for <Function test_001_child_with_0>>
dash_thread_server = <dash.testing.application_runners.ThreadedRunner object at 0x141313da0>
tmpdir = local('/private/var/folders/3k/220tdhsn33709gq9ylr8c6kh0000gp/T/pytest-of-christian/pytest-8/test_001_child_with_00')
@pytest.fixture
def dash_duo(request, dash_thread_server, tmpdir) -> DashComposite:
> with DashComposite(
dash_thread_server,
browser=request.config.getoption("webdriver"),
remote=request.config.getoption("remote"),
remote_url=request.config.getoption("remote_url"),
headless=request.config.getoption("headless"),
options=request.config.hook.pytest_setup_options(),
download_path=tmpdir.mkdir("download").strpath,
percy_assets_root=request.config.getoption("percy_assets"),
percy_finalize=request.config.getoption("nopercyfinalize"),
pause=request.config.getoption("pause"),
) as dc:
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/dash/testing/plugin.py:179:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/dash/testing/composite.py:6: in __init__
super().__init__(**kwargs)
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/dash/testing/browser.py:68: in __init__
self._driver = until(self.get_webdriver, timeout=1)
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/dash/testing/wait.py:17: in until
res = wait_cond()
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/dash/testing/browser.py:461: in get_webdriver
return getattr(self, f"_get_{self._browser}")()
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/dash/testing/browser.py:502: in _get_chrome
else webdriver.Chrome(options=options)
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/selenium/webdriver/chrome/webdriver.py:45: in __init__
super().__init__(
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/selenium/webdriver/chromium/webdriver.py:66: in __init__
super().__init__(command_executor=executor, options=options)
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/selenium/webdriver/remote/webdriver.py:241: in __init__
self.start_session(capabilities)
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/selenium/webdriver/remote/webdriver.py:329: in start_session
response = self.execute(Command.NEW_SESSION, caps)["value"]
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/selenium/webdriver/remote/webdriver.py:384: in execute
self.error_handler.check_response(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <selenium.webdriver.remote.errorhandler.ErrorHandler object at 0x1413be450>
response = {'status': 400, 'value': '{"value":{"error":"invalid argument","message":"invalid argument: unrecognized capability: l...ff8192241d3 _pthread_start + 125\\n22 libsystem_pthread.dylib 0x00007ff81921fbd3 thread_start + 15\\n"}}'}
def check_response(self, response: Dict[str, Any]) -> None:
"""Checks that a JSON response from the WebDriver does not have an
error.
:Args:
- response - The JSON response from the WebDriver server as a dictionary
object.
:Raises: If the response contains an error message.
"""
status = response.get("status", None)
if not status or status == ErrorCode.SUCCESS:
return
value = None
message = response.get("message", "")
screen: str = response.get("screen", "")
stacktrace = None
if isinstance(status, int):
value_json = response.get("value", None)
if value_json and isinstance(value_json, str):
import json
try:
value = json.loads(value_json)
if len(value) == 1:
value = value["value"]
status = value.get("error", None)
if not status:
status = value.get("status", ErrorCode.UNKNOWN_ERROR)
message = value.get("value") or value.get("message")
if not isinstance(message, str):
value = message
message = message.get("message")
else:
message = value.get("message", None)
except ValueError:
pass
exception_class: Type[WebDriverException]
e = ErrorCode()
error_codes = [item for item in dir(e) if not item.startswith("__")]
for error_code in error_codes:
error_info = getattr(ErrorCode, error_code)
if isinstance(error_info, list) and status in error_info:
exception_class = getattr(ExceptionMapping, error_code, WebDriverException)
break
else:
exception_class = WebDriverException
if not value:
value = response["value"]
if isinstance(value, str):
raise exception_class(value)
if message == "" and "message" in value:
message = value["message"]
screen = None # type: ignore[assignment]
if "screen" in value:
screen = value["screen"]
stacktrace = None
st_value = value.get("stackTrace") or value.get("stacktrace")
if st_value:
if isinstance(st_value, str):
stacktrace = st_value.split("\n")
else:
stacktrace = []
try:
for frame in st_value:
line = frame.get("lineNumber", "")
file = frame.get("fileName", "<anonymous>")
if line:
file = f"{file}:{line}"
meth = frame.get("methodName", "<anonymous>")
if "className" in frame:
meth = f"{frame['className']}.{meth}"
msg = " at %s (%s)"
msg = msg % (meth, file)
stacktrace.append(msg)
except TypeError:
pass
if exception_class == UnexpectedAlertPresentException:
alert_text = None
if "data" in value:
alert_text = value["data"].get("text")
elif "alert" in value:
alert_text = value["alert"].get("text")
raise exception_class(message, screen, stacktrace, alert_text) # type: ignore[call-arg] # mypy is not smart enough here
> raise exception_class(message, screen, stacktrace)
E selenium.common.exceptions.InvalidArgumentException: Message: invalid argument: unrecognized capability: loggingPrefs
E Stacktrace:
E 0 chromedriver 0x00000001086f6e12 chromedriver + 6696466
E 1 chromedriver 0x00000001086eec2a chromedriver + 6663210
E 2 chromedriver 0x00000001080f6e3e chromedriver + 405054
E 3 chromedriver 0x000000010811f18b chromedriver + 569739
E 4 chromedriver 0x00000001081771bf chromedriver + 930239
E 5 chromedriver 0x00000001081769e8 chromedriver + 928232
E 6 chromedriver 0x000000010817870f chromedriver + 935695
E 7 chromedriver 0x00000001081785e6 chromedriver + 935398
E 8 chromedriver 0x000000010816c613 chromedriver + 886291
E 9 chromedriver 0x0000000108139950 chromedriver + 678224
E 10 chromedriver 0x000000010813a34e chromedriver + 680782
E 11 chromedriver 0x00000001086c4700 chromedriver + 6489856
E 12 chromedriver 0x00000001086c71a9 chromedriver + 6500777
E 13 chromedriver 0x00000001086c6ccb chromedriver + 6499531
E 14 chromedriver 0x00000001086c7635 chromedriver + 6501941
E 15 chromedriver 0x00000001086af4b4 chromedriver + 6403252
E 16 chromedriver 0x00000001086c791f chromedriver + 6502687
E 17 chromedriver 0x00000001086a08e4 chromedriver + 6342884
E 18 chromedriver 0x00000001086df308 chromedriver + 6599432
E 19 chromedriver 0x00000001086df4c5 chromedriver + 6599877
E 20 chromedriver 0x00000001086ee7f8 chromedriver + 6662136
E 21 libsystem_pthread.dylib 0x00007ff8192241d3 _pthread_start + 125
E 22 libsystem_pthread.dylib 0x00007ff81921fbd3 thread_start + 15
../../opt/anaconda3/envs/base12/lib/python3.12/site-packages/selenium/webdriver/remote/errorhandler.py:232: InvalidArgumentException
================================================================= short test summary info =================================================================
ERROR pylossless/dash/tests/test_mne_visualizer.py::test_001_child_with_0 - selenium.common.exceptions.InvalidArgumentException: Message: invalid argument: unrecognized capability: loggingPrefs
```
Removing the following lines solves the issues: https://github.com/plotly/dash/blob/7ba267bf9e1c956816f76900bbdbcf85dbf3ff6d/dash/testing/browser.py#L478-L479
as proposed in #2590 | open | 2024-12-13T03:02:49Z | 2024-12-16T18:53:23Z | https://github.com/plotly/dash/issues/3107 | [
"bug",
"P2"
] | christian-oreilly | 0 |
mljar/mljar-supervised | scikit-learn | 729 | Optuna Mode fails. Optuna has migrated to optuna-integration. | Exception in LightgbmObjective Optuna's integration modules for third-party libraries have started migrating from Optuna itself to a package called `optuna-integration`. The module you are trying to use has already been migrated to `optuna-integration`. Please install the package by running `pip install optuna-integration`.
Is it possible to upgrade mljar-supervised so that Optuna functionality is still available after the implementation of optuna-integration? | open | 2024-06-24T15:01:36Z | 2024-07-10T07:26:44Z | https://github.com/mljar/mljar-supervised/issues/729 | [] | decmca | 3 |
AirtestProject/Airtest | automation | 621 | pip安装airtest速度非常慢 | **(重要！问题分类)**
* 图像识别、设备控制相关问题 -> 按下面的步骤
**描述问题bug**
我使用台式机（外接无线网卡）+pip+无翻墙下载airtest库，但下载速度非常慢，经常报超时错误
已经尝试将源更改为阿里云，速度无提高
将pip install命令的timeout系数设置为1000，也会慢到超时。。。
这是一个问题吗？能不能将它下载的速度尽量加快呢？我试过下载selenium、easygui等库也可以正常下载
下面是cmd控制台的错误信息
```
ERROR: Exception:
Traceback (most recent call last):
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_vendor\urllib3\response.py", line 397, in _error_catcher
yield
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_vendor\urllib3\response.py", line 479, in read
data = self._fp.read(amt)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_vendor\cachecontrol\filewrapper.py", line 62, in read
data = self.__fp.read(amt)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\http\client.py", line 454, in read
n = self.readinto(b)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\http\client.py", line 498, in readinto
n = self.fp.readinto(b)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\socket.py", line 669, in readinto
return self._sock.recv_into(b)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\ssl.py", line 1241, in recv_into
return self.read(nbytes, buffer)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\ssl.py", line 1099, in read
return self._sslobj.read(len, buffer)
socket.timeout: The read operation timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\cli\base_command.py", line 188, in main
status = self.run(options, args)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\commands\install.py", line 345, in run
resolver.resolve(requirement_set)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\legacy_resolve.py", line 196, in resolve
self._resolve_one(requirement_set, req)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\legacy_resolve.py", line 359, in _resolve_one
abstract_dist = self._get_abstract_dist_for(req_to_install)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\legacy_resolve.py", line 305, in _get_abstract_dist_for
abstract_dist = self.preparer.prepare_linked_requirement(
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\operations\prepare.py", line 195, in prepare_linked_requirement
unpack_url(
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\download.py", line 1058, in unpack_url
unpack_http_url(
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\download.py", line 920, in unpack_http_url
from_path, content_type = _download_http_url(link,
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\download.py", line 1152, in _download_http_url
_download_url(resp, link, content_file, hashes, progress_bar)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\download.py", line 861, in _download_url
hashes.check_against_chunks(downloaded_chunks)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\utils\hashes.py", line 75, in check_against_chunks
for chunk in chunks:
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\download.py", line 829, in written_chunks
for chunk in chunks:
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\utils\ui.py", line 156, in iter
for x in it:
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_internal\download.py", line 794, in resp_read
for chunk in resp.raw.stream(
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_vendor\urllib3\response.py", line 531, in stream
data = self.read(amt=amt, decode_content=decode_content)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_vendor\urllib3\response.py", line 496, in read
raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\contextlib.py", line 131, in __exit__
self.gen.throw(type, value, traceback)
File "c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\pip\_vendor\urllib3\response.py", line 402, in _error_catcher
raise ReadTimeoutError(self._pool, None, 'Read timed out.')
pip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out.
```
**python 版本:** `python3.7.2、python3.8.0均出现此问题
**airtest 版本:** `1.1.1
**设备:**
- 系统: Windows 10 x64 1909
| closed | 2019-11-25T14:17:06Z | 2019-11-27T02:18:59Z | https://github.com/AirtestProject/Airtest/issues/621 | [] | niuniuprice | 4 |
ahmedfgad/GeneticAlgorithmPython | numpy | 133 | Drawing different plot in one graph | HI ! I am trying to solve my problem with pygad :)
I want to see the difference of fitness values by increasing population in one graph.
For example, like the figure below.
Please let me know if there is any fuctions in pygad.
Thank a lot 👍 | closed | 2022-09-20T14:16:06Z | 2023-02-01T01:56:01Z | https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/133 | [
"question"
] | xoyeon | 3 |
ultralytics/ultralytics | computer-vision | 19,288 | Tracking results differ depending on batch size despite persist | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
### Ultralytics YOLO Component
Predict
### Bug
Hello,
To reduce memory constraints, I am trying to call `model.track(..., persist=True)` with batches of frames rather than a single call with all the frames of a video. But while testing, I've noticed that the tracking results differ when using the batching call.
See example below. The results when passing batches of 15 frames (`results1`) is visibly worse than when passing all frames at once (`results2`), with detection dropping out every 15 frames, and the tracks getting sometimes split.
`results2` (single batch of 120 frames), the person with id 1009 gets consistently tracked throughout:
https://github.com/user-attachments/assets/cf095968-7e7d-473b-8cac-42aec0888d04
`results1` (multiple batches of 15 frames), the same person gets 3 different ids throughout the video, the bounding box is jumping around and sometimes dropped entirely:
https://github.com/user-attachments/assets/28a43df5-872e-435f-b47c-98a0279e1fc2
My understanding from the [documentation](https://docs.ultralytics.com/modes/track/#persisting-tracks-loop) was that by using `persist=True` I could split inference into multiple calls while still getting the same results, am I reading this wrong? Is there no way to get the same quality while splitting processing in batches?
Thanks,
A.
### Environment
```
Ultralytics 8.3.75 🚀 Python-3.12.0 torch-2.6.0+cu124 CUDA:0 (NVIDIA A100-SXM4-40GB, 40507MiB) │ │ MEM 1│
Setup complete ✅ (24 CPUs, 176.9 GB RAM, 772.1/983.7 GB disk) │ │ │
│75%│ │
OS Linux-5.15.0-131-generic-x86_64-with-glibc2.31 │ │ │
Environment Linux │ │ │
Python 3.12.0 │ │ │
Install pip │ │ │
RAM 176.88 GB │50%│ │
Disk 772.1/983.7 GB │ │ │
CPU Intel Xeon Gold 6342 2.80GHz │ │ │
CPU count 24 │ │ │
GPU NVIDIA A100-SXM4-40GB, 40507MiB │ │ │
GPU count 2 │25%│ │
CUDA 12.4 │ │ │
│ │ │
numpy ✅ 2.1.1<=2.1.1,>=1.23.0 │ │────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────│
matplotlib ✅ 3.10.0>=3.3.0 │ │ │
opencv-python ✅ 4.11.0.86>=4.6.0 │ 0%│────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────│
pillow ✅ 11.1.0>=7.1.2 │ └────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
pyyaml ✅ 6.0.2>=5.3.1 │ PID USER GPU TYPE GPU MEM CPU HOST MEM Command
requests ✅ 2.32.3>=2.23.0 │ 2093 paperspace 0 Graphic 89MB 0% 0% 357MB /usr/bin/gnome-shell
scipy ✅ 1.15.2>=1.4.1 │ 1872 root 0 Graphic 72MB 0% 0% 91MB /usr/lib/xorg/Xorg vt2 -displayfd 3 -auth /run/user/1000/gdm/Xauthority -background none -noreset -keeptty -verbose 3
torch ✅ 2.6.0>=1.8.0 │ 1872 root 1 Graphic 4MB 0% 0% 91MB /usr/lib/xorg/Xorg vt2 -displayfd 3 -auth /run/user/1000/gdm/Xauthority -background none -noreset -keeptty -verbose 3
torch ✅ 2.6.0!=2.4.0,>=1.8.0; sys_platform == "win32" │43054 paperspace 0 Compute 2760MB 7% 0% 1996MB python -m ipykernel_launcher -f /home/paperspace/.local/share/jupyter/runtime/kernel-7411ba0f-b09d-4f03-997c-ff50f31cc279.json
torchvision ✅ 0.21.0>=0.9.0 │
tqdm ✅ 4.67.1>=4.64.0 │
psutil ✅ 7.0.0 │
py-cpuinfo ✅ 9.0.0 │
pandas ✅ 2.2.3>=1.1.4 │
seaborn ✅ 0.13.2>=0.11.0 │
ultralytics-thop ✅ 2.0.14>=2.0.0
```
### Minimal Reproducible Example
```python
import os
import itertools
import tempfile
import numpy as np
import torch
import torchvision
from torchvision.datasets.utils import download_url
download_url("https://github.com/pytorch/vision/blob/main/test/assets/videos/SOX5yA1l24A.mp4?raw=true", ".", "SOX5yA1l24A.mp4")
reader = torchvision.io.VideoReader("./SOX5yA1l24A.mp4", "video")
fps = float(reader.get_metadata()["video"]["fps"][0])
reader.set_current_stream("video")
frames = [frame['data'] for frame in reader]
video = torch.stack(frames)[0:120, ..., :320]/255.
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
def track(video, split):
return list(itertools.chain(*(model.track(chunk, persist=True, verbose=False, stream=True, stream_buffer=True, tracker="bytetrack.yaml") for chunk in video.split(split))))
results1 = track(video, 15)
results2 = track(video, 120)
```
### Additional
_No response_
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | open | 2025-02-18T01:55:11Z | 2025-02-21T07:22:58Z | https://github.com/ultralytics/ultralytics/issues/19288 | [
"question",
"track"
] | antoine-metaphysic | 17 |
developmentseed/lonboard | jupyter | 245 | maps are blank | Thanks for the awesome project. I am just trying it out on my mac
```
➜ uname -a
Darwin terrier.fritz.box 23.0.0 Darwin Kernel Version 23.0.0: Fri Sep 15 14:41:34 PDT 2023; root:xnu-10002.1.13~1/RELEASE_ARM64_T8103 arm64
```
```
➜ pip show lonboard
Name: lonboard
Version: 0.4.1
Summary: Python library for fast, interactive geospatial vector data visualization in Jupyter.
Home-page:
Author: Kyle Barron
Author-email: kyle@developmentseed.org
License: MIT
Location: /Users/shoaib/.pyenv/versions/3.8.12/envs/venv3_8_12/lib/python3.8/site-packages
Requires: anywidget, geopandas, matplotlib, palettable, pandas, pyarrow, shapely
Required-by:
```
I don't have geoparquet installed I assume that's expected coz lonboard has its own version of it.
```
➜ pip show geoparquet
WARNING: Package(s) not found: geoparquet
```
I tried 3 of the examples none show the map layers.
I'm gonna check for debug options also on a different architecture next but any pointers would be great! | closed | 2023-11-13T18:43:11Z | 2023-11-14T19:59:08Z | https://github.com/developmentseed/lonboard/issues/245 | [] | sabman | 5 |
arogozhnikov/einops | tensorflow | 115 | PyTorch jit.script interoperability | I'm not sure if this is the expected behavior, but not being able to script modules that use einops would be very unfortunate.
**Describe the bug**
When you try and `script` a class that uses an `einops` operation in its forward path, `torch.jit.script` throws an error:
```
NotSupportedError: Compiled functions can't take variable number of arguments or use keyword-only arguments with defaults:
File "~/.local/lib/python3.8/site-packages/einops/einops.py", line 379
def rearrange(tensor, pattern, **axes_lengths):
~~~~~~~~~~~~~ <--- HERE
```
**Reproduction steps**
```
In [1]: from torch import nn
In [2]: import torch
In [3]: from einops import rearrange
In [4]: class Foo(nn.Module):
...: def __init__(self):
...: super(Foo, self).__init__()
...: def forward(self, x):
...: return rearrange(x, 'a b -> b a')
...:
In [5]: torch.jit.script(Foo())
```
**Expected behavior**
The scripting should work.
**Your platform**
python @ 3.8.5
einops @ 0.3.0
pytorch @ 1.8.1
| closed | 2021-05-11T13:00:57Z | 2022-02-10T00:40:53Z | https://github.com/arogozhnikov/einops/issues/115 | [
"enhancement"
] | boris-kuz | 3 |
hyperspy/hyperspy | data-visualization | 2,812 | Character encoding in JEOL EDS file (jeol.py in the developper version) | In JEOL EDS data file, character encoding of "ShiftJIS" is used instead of "utf-8".
(In my case, Analysis Station 3. 8. 0. 53)
For example , in Line 376,
kwrd == b"\xce\xdf\xb0\xc4"
This is the Japanese word of "port" in ShiftJIS encoding, "ﾎﾟｰﾄ"
Line 206, reading "memo" often generate this encoding error, so, please just skip this without "utf-8" decode.
_ = fd.read(132).rstrip(b"\x00").decode("utf-8")
should be
_ = fd.read(132)
I don't know the case of new JEOL software, but,
If the file reading with "utf-8" encoding is failed, retry with "ShiftJIS" encoding will get better result.
| closed | 2021-08-25T03:30:38Z | 2021-10-04T08:03:56Z | https://github.com/hyperspy/hyperspy/issues/2812 | [
"type: bug",
"status: fix-submitted",
"release: next patch"
] | nem1234 | 7 |
jazzband/django-oauth-toolkit | django | 1,064 | [maint] Resolve failing djmain tests | Tox tests are currently failing with djmain. Figure out and resolve before the next Django 4.x release is published and those tests fail too. | closed | 2021-12-30T13:57:06Z | 2022-01-04T19:03:46Z | https://github.com/jazzband/django-oauth-toolkit/issues/1064 | [] | n2ygk | 5 |
healthchecks/healthchecks | django | 920 | Disabling emails causes 500 error when adding new project members | Healthchecks should not raise 500 error when email configuration is not provided. It should not send any emails and fail gracefully.
```
Internal Server Error: /projects/142d4567-6d58-4484-ac27-31b447996fbc/settings/
Traceback (most recent call last):
File "/lsiopy/lib/python3.11/site-packages/django/core/handlers/exception.py", line 55, in inner
response = get_response(request)
^^^^^^^^^^^^^^^^^^^^^
File "/lsiopy/lib/python3.11/site-packages/django/core/handlers/base.py", line 197, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/lsiopy/lib/python3.11/site-packages/django/contrib/auth/decorators.py", line 23, in _wrapper_view
return view_func(request, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/healthchecks/hc/accounts/views.py", line 428, in project
if project.invite(user, role=invite_form.cleaned_data["role"]):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/healthchecks/hc/accounts/models.py", line 415, in invite
profile.send_instant_login_link(membership=m, redirect_url=checks_url)
File "/app/healthchecks/hc/accounts/models.py", line 146, in send_instant_login_link
emails.login(self.user.email, ctx)
File "/app/healthchecks/hc/lib/emails.py", line 84, in login
send(make_message("login", to, ctx))
File "/app/healthchecks/hc/lib/emails.py", line 67, in send
assert settings.EMAIL_HOST, (
AssertionError: No SMTP configuration, see https://github.com/healthchecks/healthchecks#sending-emails
```
happens on any version >=v2.10 | closed | 2023-11-30T01:57:34Z | 2023-12-18T13:53:37Z | https://github.com/healthchecks/healthchecks/issues/920 | [] | marlenekoh | 3 |
tensorflow/tensor2tensor | deep-learning | 1,117 | Has anyone tried using float16 to compute or training? | ### Description
use transformer to training a translate model.
### Environment information
```
OS: <your answer here>
$ pip freeze | grep tensor
# your output here
$ python -V
# your output here
```
### For bugs: reproduction and error logs
```
# Steps to reproduce:
...
```
```
# Error logs:
...
```
| open | 2018-10-08T06:56:31Z | 2018-10-08T06:56:31Z | https://github.com/tensorflow/tensor2tensor/issues/1117 | [] | kudou1994 | 0 |
seleniumbase/SeleniumBase | pytest | 3,120 | Add an auto-retry for `uc_gui_click_captcha()` and `uc_gui_handle_captcha()` | ### Add an auto-retry for `uc_gui_click_captcha()` and `uc_gui_handle_captcha()`
----
If you click the CAPTCHA with one of the special methods and you're still on the Turnstile page, then let's have UC Mode do one automatic retry, even if the `retry` or `blind` args are not set. This gives you another shot at getting through the CAPTCHA, even if you didn't on your first try.
There was some discussion on Discord (https://discord.com/invite/HDk5wYvzEZ) that calling `driver` methods between the `uc_open_with_reconnect()` and `uc_gui_click_captcha()` methods could cause CF to detect you, and then fail the CAPTCHA click after that. During the retry, it will call `uc_open_with_reconnect()` and `uc_gui_click_captcha()` together so that the user isn't given a chance to add a bad call in between there. (Or in the case of `uc_gui_handle_captcha()`, it will call `uc_open_with_reconnect()` and `uc_gui_handle_captcha()` together.)
This feature is needed because CF recently made some changes, which may prevent some existing scripts from keeping backwards compatibility.
Ideally, it would be better to make `driver` calls not be detectable at all. (I'm working on that too.) This ticket buys me some time so that I can keep the gears turning until I release a more stealthy version. | closed | 2024-09-11T17:51:50Z | 2024-09-12T13:42:12Z | https://github.com/seleniumbase/SeleniumBase/issues/3120 | [
"enhancement",
"UC Mode / CDP Mode"
] | mdmintz | 3 |
AntonOsika/gpt-engineer | python | 876 | KeyError: "File 'some/dir/models.py' could not be found in 'C:\\Users\\Asus\\gptengineer\\crm\\projects\\example'" | ## Expected Behvior
To work normally as it was working normally before.
## Current Behavior
it always gives this or similiar error I dont understand these paths are not exist , ıdk where it came u with these.... KeyError: "File 'some/dir/models.py' could not be found in 'C:\\Users\\Asus\\gptengineer\\crm\\projects\\example'"
## Failure Information
KeyError: "File 'some/dir/models.py' could not be found in 'C:\\Users\\Asus\\gptengineer\\crm\\projects\\example'" - used conda , used normal terminal
### Steps to Reproduce
If possible, provide detailed steps for reproducing the issue.
well , I was just running normally on my project folder , for some time later it started to give me these errors thats all,
### Failure Logs
>>>>>>> updated
```Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "C:\ProgramData\miniconda3\envs\myenv\Scripts\gpt-engineer.exe\__main__.py", line 7, in <module>
sys.exit(app())
^^^^^
File "C:\ProgramData\miniconda3\envs\myenv\Lib\site-packages\gpt_engineer\cli\main.py", line 169, in main
messages = step(ai, dbs)
^^^^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\myenv\Lib\site-packages\gpt_engineer\core\steps.py", line 588, in improve_existing_code
overwrite_files_with_edits(messages[-1].content.strip(), dbs)
File "C:\ProgramData\miniconda3\envs\myenv\Lib\site-packages\gpt_engineer\core\chat_to_files.py", line 220, in overwrite_files_with_edits
apply_edits(edits, dbs.workspace)
File "C:\ProgramData\miniconda3\envs\myenv\Lib\site-packages\gpt_engineer\core\chat_to_files.py", line 281, in apply_edits
if workspace[filename].count(edit.before) > 1:
~~~~~~~~~^^^^^^^^^^
File "C:\ProgramData\miniconda3\envs\myenv\Lib\site-packages\gpt_engineer\core\db.py", line 126, in __getitem__
raise KeyError(f"File '{key}' could not be found in '{self.path}'")
KeyError: "File 'some/dir/models.py' could not be found in 'C:\\Users\\Asus\\gptengineer\\crm\\projects\\example'"
(myenv) C:\Users\Asus\gptengineer\gpt-engineer> | closed | 2023-11-29T22:24:18Z | 2023-12-21T19:37:41Z | https://github.com/AntonOsika/gpt-engineer/issues/876 | [
"bug",
"triage"
] | nusretocakturk | 2 |
flavors/django-graphql-jwt | graphql | 215 | Add 'code' field to PermissionDenied exception | Hi,
Thanks for all of your work on this project. It works really nice! I have a quick question on the PermissionDenied exception when using the `login_required` decorator.
I receive this response when the token is invalid:
```
{
"errors": [
{
"message": "You do not have permission to perform this action",
"locations": [
{
"line": 12,
"column": 5
}
],
"path": [
"me"
]
}
],
"data": {
"me": null
}
}
```
Is there any way we could add a "code" field to the error object? It would make it much easier to parse the error on the client.
Cheers!
| open | 2020-08-03T18:59:39Z | 2021-08-16T09:18:44Z | https://github.com/flavors/django-graphql-jwt/issues/215 | [] | franzwarning | 2 |
HumanSignal/labelImg | deep-learning | 323 | App crash when drag a corner of rectbox to border of image | Below is output when app crash.
```
Traceback (most recent call last):
File "/Users/admin/Desktop/ML/Tools/labelImg/libs/canvas.py", line 152, in mouseMoveEvent
self.boundedMoveVertex(pos)
File "/Users/admin/Desktop/ML/Tools/labelImg/libs/canvas.py", line 321, in boundedMoveVertex
pos = self.intersectionPoint(point, pos)
File "/Users/admin/Desktop/ML/Tools/labelImg/libs/canvas.py", line 499, in intersectionPoint
d, i, (x, y) = min(self.intersectingEdges((x1, y1), (x2, y2), points))
ValueError: min() arg is an empty sequence
Abort trap: 6
``` | open | 2018-07-03T08:46:12Z | 2019-01-17T21:23:44Z | https://github.com/HumanSignal/labelImg/issues/323 | [] | zeuspnt | 7 |
thp/urlwatch | automation | 807 | Consider releasing version 2.29 | It seems that version 5 of python-lxml doesn't support using the old "CSSSelector evaluate" style. As this version is now slowly landing in the repos of distributions, urlwatch 2.28 needs patches to continue working. However, the new call style is already implemented in the urlwatch master branch, so releasing a new version would keep urlwatch working without patches in distros upgrading python-lxml. Thank you for your great work! | closed | 2024-03-15T10:01:56Z | 2024-10-28T09:10:28Z | https://github.com/thp/urlwatch/issues/807 | [] | maclomhair | 11 |
521xueweihan/HelloGitHub | python | 1,988 | 自荐项目 | Context Note - 带上下文的网页笔记插件 | ## 项目推荐
- 项目地址：https://github.com/betterRunner/context-note
- 类别：JS
- 项目后续更新计划：持续更新维护
- 项目描述：
- 生产力工具，基于vue3&vite2开发的一款网页笔记插件
- 记录笔记的上下文信息，一键跳转，方便回顾与溯源
- 便捷标签系统和完善的搜索，快速找到任一条笔记
- 推荐理由：
- 较为创新地实现了一种记录上下文的网页笔记记录方式，**提高网页笔记的管理和回顾质量**
- 在producthunt投票活动获得当日第六名，https://www.producthunt.com/posts/context-note
- 截图：
https://user-images.githubusercontent.com/7123136/138256405-f86d1e4e-3990-4a22-bac2-4e22e33b0245.mov
| closed | 2021-11-29T03:28:23Z | 2021-12-28T04:46:42Z | https://github.com/521xueweihan/HelloGitHub/issues/1988 | [
"已发布",
"JavaScript 项目"
] | betterRunner | 2 |
ray-project/ray | tensorflow | 50,882 | [core] Split object manager into smaller C++ targets | ### Description
part of #50586
### Use case
_No response_ | closed | 2025-02-25T03:17:46Z | 2025-02-27T02:35:38Z | https://github.com/ray-project/ray/issues/50882 | [
"enhancement",
"core"
] | kevin85421 | 0 |
521xueweihan/HelloGitHub | python | 2,542 | 【开源自荐】API Testing 追求极致开发者体验的接口测试工具套件 | ## 推荐项目
<!-- 这里是 HelloGitHub 月刊推荐项目的入口，欢迎自荐和推荐开源项目，唯一要求：请按照下面的提示介绍项目。-->
<!-- 点击上方 “Preview” 立刻查看提交的内容 -->
<!--仅收录 GitHub 上的开源项目，请填写 GitHub 的项目地址-->
- 项目地址：https://github.com/LinuxSuRen/api-testing
<!--请从中选择（C、C#、C++、CSS、Go、Java、JS、Kotlin、Objective-C、PHP、Python、Ruby、Rust、Swift、其它、书籍、机器学习）-->
- 类别：Go
<!--请用 20 个左右的字描述它是做什么的，类似文章标题让人一目了然 -->
- 项目标题：API Testing 接口测试工具套件
<!--这是个什么项目、能用来干什么、有什么特点或解决了什么痛点，适用于什么场景、能够让初学者学到什么。长度 32-256 字符-->
- 项目描述：
API Testing 采用 MIT 开源协议，用 Golang 编写并提供了可执行二进制文件以及 Docker 镜像等发行格式。用户可以分别以命令行、VS Code YAML 编辑器、守护进程服务（用于第三方以 gRPC 的形式集成）。API Testing 提供了非常强大的接口（例如 HTTP）请求的模板化功能，以及丰富的结果断言。
把 YAML 格式的测试用例提交到 Git 仓库后，就会天然地支持团队内部协作、共享，无需注册、登录任何额外的账号，也可以借助 Git 仓库的功能实现诸如：回滚、review 等诉求。
截至编写本文时，API Testing 有 4 位 [contributors](https://github.com/LinuxSuRen/api-testing/graphs/contributors)，期待您的加入！
<!--令人眼前一亮的点是什么？类比同类型项目有什么特点！-->
- 亮点：
项目本身的单元测试覆盖率高达 93%
支持 Markdown、HTML 等测试报告的输出
支持通过 gRPC 协议进行第三方对接
- 示例代码：（可选）
- 截图：（可选）gif/png/jpg
- 后续更新计划：
持续提高项目代码质量，持续追求极致的开发者体验，支持 gRPC 等接口协议。
| closed | 2023-05-06T01:25:41Z | 2024-01-23T08:10:05Z | https://github.com/521xueweihan/HelloGitHub/issues/2542 | [
"Go 项目"
] | LinuxSuRen | 1 |
marimo-team/marimo | data-science | 3,532 | stdio in mo.Thread goes to Linux console after sleep() | ### Describe the bug
When running the following code, the first two print statements are shown in the marimo cell, and all the `print` statements that follow the first `sleep` end up bring printed in the stdout of the Linux shell where the marimo command was run.
If the `sleep` statement is commented out, then all `print` statements end up in the marimo cell.
Issue #2642 seems to imply that all I/O should go to the notebook if using `mo.Thread`
### Environment
<details>
``` json
{
"marimo": "0.10.15",
"OS": "Linux",
"OS Version": "5.15.0-125-generic",
"Processor": "x86_64",
"Python Version": "3.13.1",
"Binaries": {
"Browser": "--",
"Node": "v18.20.4"
},
"Dependencies": {
"click": "8.1.8",
"docutils": "0.21.2",
"itsdangerous": "2.2.0",
"jedi": "0.19.2",
"markdown": "3.7",
"narwhals": "1.23.0",
"packaging": "24.2",
"psutil": "6.1.1",
"pygments": "2.19.1",
"pymdown-extensions": "10.14",
"pyyaml": "6.0.2",
"ruff": "0.9.2",
"starlette": "0.45.2",
"tomlkit": "0.13.2",
"typing-extensions": "4.12.2",
"uvicorn": "0.34.0",
"websockets": "14.2"
},
"Optional Dependencies": {
"pandas": "2.2.3"
}
}
```
</details>
### Code to reproduce
``` python
import marimo as mo
import time
def long_running():
print("Thread starting:")
i = 0
while i < 20:
print(f"Loop {i}...")
i += 1
time.sleep(1)
print("Thread exiting.")
mo.Thread(target=long_running).start()
``` | closed | 2025-01-22T05:22:11Z | 2025-01-30T22:27:03Z | https://github.com/marimo-team/marimo/issues/3532 | [
"bug"
] | apowers313 | 8 |
tableau/server-client-python | rest-api | 1,308 | Error when downloading tableau server view to csv | I would like to export a view of a Tableau Server dashboard to a csv file.
Using the sample [export.py](https://github.com/tableau/server-client-python/blob/master/samples/export.py), I get an error
`export.py: error: one of the arguments --csv is required`
I have written arguments such as --server and --token-name in the launch.json of the .vscode, but I do not know what to set for --csv.
If you know, please let me know.
**Versions**
Details of your environment, including:
- Tableau Server version: 2022.3.1
- Python version: Python 3.10.8
**To Reproduce**
samples/export.py
```
import argparse
import logging
import tableauserverclient as TSC
def main():
parser = argparse.ArgumentParser(description="Export a view as an image, PDF, or CSV")
# Common options; please keep those in sync across all samples
parser.add_argument("--server", "-s", help="server address")
# parser.add_argument("--site", "-S", help="site name")
parser.add_argument("--token-name", "-p", help="name of the personal access token used to sign into the server")
parser.add_argument("--token-value", "-v", help="value of the personal access token used to sign into the server")
parser.add_argument(
"--logging-level",
"-l",
choices=["debug", "info", "error"],
default="error",
help="desired logging level (set to error by default)",
)
# Options specific to this sample
group = parser.add_mutually_exclusive_group(required=True)
# group.add_argument(
# "--pdf", dest="type", action="store_const", const=("populate_pdf", "PDFRequestOptions", "pdf", "pdf")
# )
# group.add_argument(
# "--png", dest="type", action="store_const", const=("populate_image", "ImageRequestOptions", "image", "png")
# )
group.add_argument(
"--csv", dest="type", action="store_const", const=("populate_csv", "CSVRequestOptions", "csv", "csv")
)
# other options shown in explore_workbooks: workbook.download, workbook.preview_image
parser.add_argument("--workbook", action="store_true")
parser.add_argument("--file", "-f", help="filename to store the exported data")
parser.add_argument("--filter", "-vf", metavar="COLUMN:VALUE", help="View filter to apply to the view")
parser.add_argument("--resource_id", help="LUID for the view or workbook")
args = parser.parse_args()
# Set logging level based on user input, or error by default
logging_level = getattr(logging, args.logging_level.upper())
logging.basicConfig(level=logging_level)
tableau_auth = TSC.PersonalAccessTokenAuth(args.token_name, args.token_value) # site_id=args.site削除
server = TSC.Server(args.server, use_server_version=True, http_options={"verify": False})
with server.auth.sign_in(tableau_auth):
print("Connected")
if args.workbook:
item = server.workbooks.get_by_id(args.resource_id)
else:
item = server.views.get_by_id(args.resource_id)
if not item:
print("No item found for id {}".format(args.resource_id))
exit(1)
print("Item found: {}".format(item.name))
# We have a number of different types and functions for each different export type.
# We encode that information above in the const=(...) parameter to the add_argument function to make
# the code automatically adapt for the type of export the user is doing.
# We unroll that information into methods we can call, or objects we can create by using getattr()
(populate_func_name, option_factory_name, member_name, extension) = args.type
populate = getattr(server.views, populate_func_name)
if args.workbook:
populate = getattr(server.workbooks, populate_func_name)
option_factory = getattr(TSC, option_factory_name)
if args.filter:
options = option_factory().vf(*args.filter.split(":"))
else:
options = None
if args.file:
filename = args.file
else:
filename = "out.{}".format(extension)
populate(item, options)
with open(filename, "wb") as f:
if member_name == "csv":
f.writelines(getattr(item, member_name))
else:
f.write(getattr(item, member_name))
print("saved to " + filename)
if __name__ == "__main__":
main()
```
**Results**
`export.py: error: one of the arguments --csv is required`
| closed | 2023-10-27T10:11:06Z | 2023-10-30T08:30:30Z | https://github.com/tableau/server-client-python/issues/1308 | [] | ishizuka-mihoko | 1 |
explosion/spaCy | data-science | 12,488 | German Entity Recognition incorrect star_char/end_char | ## How to reproduce the behaviour
```
model = spacy.load("de_core_news_lg")
# "Freundliche Grüße Nadia" (start_char: 0, end_char: 22, label_: "PER")
tokens = model("Freundliche Grüße Nadia")
# "Freundliche Grüße" (start_char: 0, end_char: 16, label_: "PER")
# "Nadia" (start_char: 32, end_char: 36, label_: MISC)
tokens = model("Freundliche Grüße meine liebste Nadia")
# "Hallo Herr Müller" (start_char: 0, end_char: 16, label_: "PER")
tokens = model("Hallo Herr Müller.")
# no entities recognized
tokens = model("Hallo Herr Müller")
```
In the comments above we are examining `tokens.ents`.
Note for English we were in a position to reproduce the [documented](https://spacy.io/usage/spacy-101#annotations-ner) behavior, where it would accurately identify the entity.
That being said, in English we noticed some issues with typos, however using `en_core_web_trf` solved those.
Unfortunately `de_dep_news_trf` does not support entities.
## Your Environment
<!-- Include details of your environment. You can also type `python -m spacy info --markdown` and copy-paste the result here.-->
* Operating System: OSX
* Python Version Used: 3.11
* spaCy Version Used: 3.5.1 (de-core-news-lg)
* Environment Information:
| closed | 2023-03-30T12:45:24Z | 2023-04-19T13:27:30Z | https://github.com/explosion/spaCy/issues/12488 | [
"lang / de",
"feat / ner",
"perf / accuracy"
] | lsmith77 | 3 |
keras-team/keras | data-science | 20,946 | validation_split | Hi, if validation_split (of model.fit) is assigned a percentage, does keras shuffle the entire training data _before_ splitting it? Sorry if this is a repeated question since i saw some discussion from years ago saying that it does not shuffle and the keras' doc pages does not explicitly mention shuffling in validation_split section. But chatgpt says that in more recent versions of keras (later than 2.2.3) validation_split shuffles the entire data before splitting. Thanks for the help. | closed | 2025-02-23T08:49:35Z | 2025-02-24T15:19:21Z | https://github.com/keras-team/keras/issues/20946 | [
"type:support"
] | cuneyt76 | 6 |
mirumee/ariadne-codegen | graphql | 153 | Unwrapping fields with custom scalars does not add the import | It seems like I missed a case when using the `ShorterResultsPlugin` and the unwrapped field contains a custom scalar type. In this situation the type does not get imported.
```graphql
# schema.graphql
scalar MyScalar
type Query {
getScalar: MyScalar!
}
# queries.graphql
query TestQuery {
getScalar
}
```
```toml
# pyproject.toml
[tool.ariadne-codegen]
plugins = ["ariadne_codegen.contrib.shorter_results.ShorterResultsPlugin"]
[tool.ariadne-codegen.scalars.MyScalar]
type = "scalars.MyScalar"
```
```python
# scalars.py
MyScalar = dict
```
Will generate `client.py`:
```python
async def test_query(self) -> MyScalar
```
But `MyScalar` will not be imported to `client.py`.
It does work for input types so doing something like this will import the scalar:
```graphql
# schema.graphql
type Query {
getScalar(input: MyScalar!): MyScalar!
}
# queries.graphql
query TestQuery($input: MyScalar!) {
getScalar(input: $input)
}
```
I'll debug this and file a PR as soon as I find the root cause. | closed | 2023-05-22T12:08:34Z | 2023-05-23T09:49:37Z | https://github.com/mirumee/ariadne-codegen/issues/153 | [] | bombsimon | 0 |
custom-components/pyscript | jupyter | 581 | "async with" statement fails without target variable assignment | This code causes an error: `RuntimeError: Lock is not acquired.`
```python
lock = asyncio.Lock()
async with lock:
pass
```
Workaround:
```python
lock = asyncio.Lock()
async with lock as lock_test:
pass
``` | closed | 2024-02-01T01:17:35Z | 2025-01-21T21:57:00Z | https://github.com/custom-components/pyscript/issues/581 | [] | dmamelin | 0 |
awesto/django-shop | django | 698 | Tutorial demo needs djangoshop-stripe | When following the tutorial demo, I found a missing dependency on `djangoshop-stripe`. Being a newcomer, I installed `django-shop-stripe`, which led to further problems, until sombody was kind and guided me to the correct `djangoshop-stripe`. The tutorial `requirements.txt` should depend on `djangoshop-stripe` or at least `$ pip install djangoshop-stripe` should be mentioned in the tutorial. | open | 2018-01-02T19:23:53Z | 2018-04-12T07:56:45Z | https://github.com/awesto/django-shop/issues/698 | [] | mortlind | 6 |
BeanieODM/beanie | asyncio | 869 | [DOCUMENTATION] Update & Deleting give wrong example command | https://beanie-odm.dev/tutorial/updating-%26-deleting/
```
bar = await Product.find_all(Product.price > .5).inc({Product.price: 1})
```
`find_all` can not be used with a query, this command will update all documents. | closed | 2024-02-20T02:24:28Z | 2024-10-25T19:23:32Z | https://github.com/BeanieODM/beanie/issues/869 | [
"bug",
"documentation",
"good first issue"
] | wangerone | 1 |
piccolo-orm/piccolo | fastapi | 662 | Unexpected changes to ID values during object creation in sqlite | I am observing a situation where a BigInt primary key that is not defined to auto-increment results in a different value than what I am passing in to be saved.
The issue may be sqlite specific. I have not tried this with postgres yet.
## Schema
The trends field should not matter here, just including it for completeness in how I am current setup:
```
class Tweet(Table, tablename="tweets"):
tweet_id: int = BigInt(primary_key=True)
data: dict = JSON()
trends = M2M(LazyTableReference("TrendTweet", module_path=__name__))
```
## Migration
```
async def forwards():
manager = MigrationManager(
migration_id=ID,
app_name="myapp",
description=DESCRIPTION,
)
manager.add_table("Tweet", tablename="tweets")
manager.add_column(
table_class_name="Tweet",
tablename="tweets",
column_name="tweet_id",
db_column_name="tweet_id",
column_class_name="BigInt",
column_class=BigInt,
params={
"default": None,
"null": False,
"primary_key": True,
"unique": True,
"index": True,
"index_method": IndexMethod.btree,
"choices": None,
"db_column_name": None,
"secret": False,
},
)
manager.add_column(
table_class_name="Tweet",
tablename="tweets",
column_name="data",
db_column_name="data",
column_class_name="JSON",
column_class=JSON,
params={
"default": "{}",
"null": False,
"primary_key": False,
"unique": False,
"index": False,
"index_method": IndexMethod.btree,
"choices": None,
"db_column_name": None,
"secret": False,
},
)
```
## The resulting sqlite schema
which looks correct to me:
```
CREATE TABLE tweets ("tweet_id" INTEGER PRIMARY KEY UNIQUE NOT NULL DEFAULT null, "data" JSON NOT NULL DEFAULT '{}');
CREATE INDEX tweets_tweet_id ON tweets ("tweet_id");
```
## Example code snippet
```
for _tweet in tweets: # tweets is a list of response objects from the twitter API
print("GET OR CREATE:", _tweet.id)
tweet = Tweet.objects().get_or_create( Tweet.tweet_id == _tweet.id,
defaults={ "data": _tweet }).run_sync()
print("TWEET OBJ ID ", tweet.tweet_id)
print("ORIG TWEET ID", _tweet["id"])
```
## Output showing the problem
```
GET OR CREATE: 1587206613131317250
TWEET OBJ ID 1587206613131317248
ORIG TWEET ID 1587206613131317250
```
The issue does not seem to be specific to get_or_create. I have also used create:
```
tweet = Tweet.objects().where( Tweet.tweet_id == _tweet.id).first().run_sync()
if tweet is None:
tweet = Tweet.objects().create (tweet_id=_tweet.id, data=_tweet).run_sync()
```
and with save ...
```
tweet = Tweet.objects().where( Tweet.tweet_id == _tweet.id).first().run_sync()
if tweet is None:
tweet = Tweet(tweet_id=_tweet.id, data=_tweet)
tweet.save().run_sync()
```
I see the same problem with these other approaches.
A particularly odd thing is that it is not consistent. Sometimes my loop runs for as many as 3 or 4 tweets, meaning the ID that results is correct in those cases. The resulting tweet_id on the created object seems to be anywhere from n+0 to n+3 where n is the original ID.
I am very confused by this. I know the above does not really isolate the problem in a replicable way, but maybe you have a theory about what might be happening? | open | 2022-10-31T22:34:37Z | 2022-11-27T18:25:04Z | https://github.com/piccolo-orm/piccolo/issues/662 | [] | scott2b | 19 |
modin-project/modin | pandas | 6,594 | BUG: Using Modin objects within an `apply` fails, with unclear error message | ### Modin version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest released version of Modin.
- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)
### Reproducible Example
```python
import modin.pandas as pd
abbreviations = pd.Series(['Major League Baseball', 'National Basketball Association'], index=['MLB', 'NBA'])
teams = pd.DataFrame({'name': ['Mariners', 'Lakers'] * 500, 'league_abbreviation': ['MLB', 'NBA'] * 500})
print(teams.set_index('name').league_abbreviation.apply(lambda abbr: abbreviations.loc[abbr]).rename('league'))
```
### Issue Description
This code fails because it tries to use the Modin Series `abbreviations` within a `.apply` function.
Running on either Ray or Dask, the error you get is pretty cryptic -- it's not easy to tell that this is the problem.
### Expected Behavior
Ideally, it would behave like Pandas, allowing the use of a Modin object within an `apply`. If this is not possible, at least a nicer error message would help.
### Error Logs
With Ray:
<details>
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/series.py", line 393, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/base.py", line 261, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/storage_formats/pandas/query_compiler.py", line 282, in to_pandas
return self._modin_frame.to_pandas()
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/utils.py", line 501, in run_f_on_minimally_updated_metadata
result = f(self, *args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/dataframe.py", line 4015, in to_pandas
df = self._partition_mgr_cls.to_pandas(self._partitions)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition_manager.py", line 694, in to_pandas
retrieved_objects = cls.get_objects_from_partitions(partitions.flatten())
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition_manager.py", line 903, in get_objects_from_partitions
return cls._execution_wrapper.materialize(
File "~/src/modin/modin/core/execution/ray/common/engine_wrapper.py", line 92, in materialize
return ray.get(obj_id)
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/ray/_private/auto_init_hook.py", line 24, in auto_init_wrapper
return fn(*args, **kwargs)
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/ray/_private/client_mode_hook.py", line 103, in wrapper
return func(*args, **kwargs)
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/ray/_private/worker.py", line 2493, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(TypeError): ray::_apply_list_of_funcs() (pid=781021, ip=10.158.106.10)
At least one of the input arguments for this task could not be computed:
ray.exceptions.RayTaskError: ray::_apply_list_of_funcs() (pid=781021, ip=10.158.106.10)
File "~/src/modin/modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py", line 421, in _apply_list_of_funcs
partition = func(partition, *args, **kwargs)
File "~/src/modin/modin/core/dataframe/algebra/map.py", line 51, in <lambda>
lambda x: function(x, *args, **kwargs), *call_args, **call_kwds
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/pandas/core/frame.py", line 10123, in map
return self.apply(infer).__finalize__(self, "map")
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/pandas/core/frame.py", line 10037, in apply
return op.apply().__finalize__(self, method="apply")
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/pandas/core/apply.py", line 837, in apply
return self.apply_standard()
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/pandas/core/apply.py", line 963, in apply_standard
results, res_index = self.apply_series_generator()
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/pandas/core/apply.py", line 979, in apply_series_generator
results[i] = self.func(v, *self.args, **self.kwargs)
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/pandas/core/frame.py", line 10121, in infer
return x._map_values(func, na_action=na_action)
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/pandas/core/base.py", line 921, in _map_values
return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/pandas/core/algorithms.py", line 1814, in map_array
return lib.map_infer(values, mapper, convert=convert)
File "lib.pyx", line 2917, in pandas._libs.lib.map_infer
File "~/src/modin/modin/pandas/series.py", line 1222, in <lambda>
lambda s: arg(s)
File "<stdin>", line 1, in <lambda>
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/indexing.py", line 656, in __getitem__
return self._helper_for__getitem__(
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/indexing.py", line 703, in _helper_for__getitem__
result = self._get_pandas_object_from_qc_view(
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/indexing.py", line 399, in _get_pandas_object_from_qc_view
return res_df.squeeze(axis=axis)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/series.py", line 1808, in squeeze
return self._reduce_dimension(self._query_compiler)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/series.py", line 2265, in _reduce_dimension
return query_compiler.to_pandas().squeeze()
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/storage_formats/pandas/query_compiler.py", line 282, in to_pandas
return self._modin_frame.to_pandas()
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/utils.py", line 501, in run_f_on_minimally_updated_metadata
result = f(self, *args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/dataframe.py", line 4015, in to_pandas
df = self._partition_mgr_cls.to_pandas(self._partitions)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition_manager.py", line 694, in to_pandas
retrieved_objects = cls.get_objects_from_partitions(partitions.flatten())
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition_manager.py", line 903, in get_objects_from_partitions
return cls._execution_wrapper.materialize(
File "~/src/modin/modin/core/execution/ray/common/engine_wrapper.py", line 92, in materialize
return ray.get(obj_id)
ray.exceptions.RayTaskError(TypeError): ray::_apply_func() (pid=781713, ip=10.158.106.10)
File "~/src/modin/modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py", line 379, in _apply_func
result = func(partition, *args, **kwargs)
TypeError: 'NoneType' object is not callable
```
</details>
With dask:
<details>
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/series.py", line 393, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/base.py", line 261, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/storage_formats/pandas/query_compiler.py", line 282, in to_pandas
return self._modin_frame.to_pandas()
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/utils.py", line 501, in run_f_on_minimally_updated_metadata
result = f(self, *args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/dataframe.py", line 4015, in to_pandas
df = self._partition_mgr_cls.to_pandas(self._partitions)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition_manager.py", line 694, in to_pandas
retrieved_objects = cls.get_objects_from_partitions(partitions.flatten())
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition_manager.py", line 903, in get_objects_from_partitions
return cls._execution_wrapper.materialize(
File "~/src/modin/modin/core/execution/dask/common/engine_wrapper.py", line 109, in materialize
return client.gather(future)
File "~/src/modin/modin/core/execution/dask/implementations/pandas_on_dask/partitioning/partition.py", line 358, in apply_list_of_funcs
partition = func(partition, *f_args, **f_kwargs)
File "~/src/modin/modin/core/dataframe/algebra/map.py", line 51, in <lambda>
lambda x: function(x, *args, **kwargs), *call_args, **call_kwds
File "lib.pyx", line 2917, in pandas._libs.lib.map_infer
File "~/src/modin/modin/pandas/series.py", line 1222, in <lambda>
lambda s: arg(s)
File "<stdin>", line 1, in <lambda>
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/indexing.py", line 656, in __getitem__
return self._helper_for__getitem__(
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/pandas/indexing.py", line 702, in _helper_for__getitem__
qc_view = self.qc.take_2d_labels(row_loc, col_loc)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/storage_formats/base/query_compiler.py", line 4135, in take_2d_labels
return self.take_2d_positional(row_lookup, col_lookup)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/storage_formats/pandas/query_compiler.py", line 4277, in take_2d_positional
self._modin_frame.take_2d_labels_or_positional(
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/utils.py", line 501, in run_f_on_minimally_updated_metadata
result = f(self, *args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/dataframe.py", line 954, in take_2d_labels_or_positional
return self._take_2d_positional(row_positions, col_positions)
File "~/src/modin/modin/logging/logger_decorator.py", line 129, in run_and_log
return obj(*args, **kwargs)
File "~/src/modin/modin/core/dataframe/pandas/dataframe/dataframe.py", line 1149, in _take_2d_positional
[
File "~/src/modin/modin/core/dataframe/pandas/dataframe/dataframe.py", line 1150, in <listcomp>
[
File "~/src/modin/modin/core/dataframe/pandas/dataframe/dataframe.py", line 1151, in <listcomp>
self._partitions[row_idx][col_idx].mask(
File "~/src/modin/modin/core/execution/dask/implementations/pandas_on_dask/partitioning/partition.py", line 177, in mask
new_obj = super().mask(row_labels, col_labels)
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition.py", line 257, in mask
new_obj = self.add_to_apply_calls(self._iloc_func, row_labels, col_labels)
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition.py", line 162, in add_to_apply_calls
return self.__constructor__(
File "~/src/modin/modin/core/execution/dask/implementations/pandas_on_dask/partitioning/partition.py", line 46, in __init__
super().__init__()
File "~/src/modin/modin/core/dataframe/pandas/partitioning/partition.py", line 55, in __init__
self.execution_wrapper.put(self._iloc)
File "~/src/modin/modin/core/execution/dask/common/engine_wrapper.py", line 135, in put
client = default_client()
File "~/mambaforge/envs/modin/lib/python3.10/site-packages/distributed/client.py", line 5550, in default_client
raise ValueError(
ValueError: No clients found
Start a client and point it to the scheduler address
from distributed import Client
client = Client('ip-addr-of-scheduler:8786')
```
</details>
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 513166faea5926101ad14ecc149b6c72d3376866
python : 3.10.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-135-generic
Version : #152-Ubuntu SMP Wed Nov 23 20:19:22 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
Modin dependencies
------------------
modin : 0.23.0+107.g513166fa
ray : 2.6.1
dask : 2023.7.1
distributed : 2023.7.1
hdk : None
pandas dependencies
-------------------
pandas : 2.1.1
numpy : 1.25.1
pytz : 2023.3
dateutil : 2.8.2
setuptools : 68.0.0
pip : 23.2.1
Cython : None
pytest : 7.4.0
hypothesis : None
sphinx : 7.1.0
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.9.3
html5lib : None
pymysql : None
psycopg2 : 2.9.6
jinja2 : 3.1.2
IPython : 8.14.0
pandas_datareader : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : 2022.12.0
fsspec : 2023.6.0
gcsfs : None
matplotlib : 3.7.2
numba : None
numexpr : 2.8.4
odfpy : None
openpyxl : 3.1.2
pandas_gbq : 0.15.0
pyarrow : 12.0.1
pyreadstat : None
pyxlsb : None
s3fs : 2023.6.0
scipy : 1.11.1
sqlalchemy : 1.4.45
tables : 3.8.0
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2023.3
qtpy : 2.3.1
pyqt5 : None
</details>
| closed | 2023-09-21T17:03:01Z | 2023-11-14T19:52:00Z | https://github.com/modin-project/modin/issues/6594 | [
"bug 🦗",
"Dask ⚡",
"Ray ⚡",
"P1",
"External"
] | zmbc | 4 |
arogozhnikov/einops | numpy | 344 | Combine rearrange and repeat? | I asked ChatGPT about it and it said:
I think it would be cool if it worked. :)
| closed | 2024-10-02T17:25:07Z | 2024-10-02T18:23:41Z | https://github.com/arogozhnikov/einops/issues/344 | [
"feature suggestion"
] | bjourne | 1 |
huggingface/transformers | python | 36,399 | Siglip2VisionModel weights mismatch | ### System Info
```
RuntimeError: Error(s) in loading state_dict for Siglip2VisionModel:
size mismatch for vision_model.embeddings.patch_embedding.weight: copying a param with shape torch.Size([768, 3, 16, 16]) from checkpoint, the shape in current model is torch.Size([768, 768]).
size mismatch for vision_model.embeddings.position_embedding.weight: copying a param with shape torch.Size([196, 768]) from checkpoint, the shape in current model is torch.Size([256, 768]).
You may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.
```
**How to reproduce:**
```
pip install git+https://github.com/huggingface/transformers@v4.49.0-SigLIP-2
from PIL import Image
import requests
from transformers import AutoProcessor, Siglip2VisionModel
model = Siglip2VisionModel.from_pretrained("google/siglip2-base-patch16-224")
processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
pooled_output = outputs.pooler_output # pooled features
```
**Same for google/siglip-so400m-patch14-384**
### Who can help?
```
pip install git+https://github.com/huggingface/transformers@v4.49.0-SigLIP-2
from PIL import Image
import requests
from transformers import AutoProcessor, Siglip2VisionModel
model = Siglip2VisionModel.from_pretrained("google/siglip2-base-patch16-224")
processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
pooled_output = outputs.pooler_output # pooled features
```
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```
pip install git+https://github.com/huggingface/transformers@v4.49.0-SigLIP-2
from PIL import Image
import requests
from transformers import AutoProcessor, Siglip2VisionModel
model = Siglip2VisionModel.from_pretrained("google/siglip2-base-patch16-224")
processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
pooled_output = outputs.pooler_output # pooled features
```
### Expected behavior
It loads the weights | closed | 2025-02-25T14:35:47Z | 2025-02-25T15:24:45Z | https://github.com/huggingface/transformers/issues/36399 | [
"bug",
"Vision"
] | deanflaviano | 3 |
tiangolo/uvicorn-gunicorn-fastapi-docker | pydantic | 4 | Could not import (fastapi.)HTTPException but starlette OK | Hi tiangolo.
Thank you for your docker images
However, with this one "FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7-alpine3.8"
I had to import HTTPException from starlette because it wasn't known in fastapi, in my import.
fixed for me but I thought it would be interesting for you to know | closed | 2019-02-20T16:27:38Z | 2019-03-17T17:58:35Z | https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker/issues/4 | [] | JSchoreels | 6 |
modelscope/data-juicer | data-visualization | 127 | 计算中英文PPL使用的语言模型是什么？ | ### Before Asking 在提问之前
- [X] I have read the [README](https://github.com/alibaba/data-juicer/blob/main/README.md) carefully. 我已经仔细阅读了 [README](https://github.com/alibaba/data-juicer/blob/main/README_ZH.md) 上的操作指引。
- [X] I have pulled the latest code of main branch to run again and the problem still existed. 我已经拉取了主分支上最新的代码，重新运行之后，问题仍不能解决。
### Search before asking 先搜索，再提问
- [X] I have searched the Data-Juicer [issues](https://github.com/alibaba/data-juicer/issues) and found no similar questions. 我已经在 [issue列表](https://github.com/alibaba/data-juicer/issues) 中搜索但是没有发现类似的问题。
### Question
算子:perplexity_filter
看源码,这个算子用的语言模型是在
https://huggingface.co/edugp/kenlm
下载的,但这个模型的介绍页里只说用wiki之类的数据训练的,没说具体用了哪个模型进行训练,只是说一个用例用了西班牙语的bert模型
所以想问一下,用来计算中文和英文的ppl的模型是什么模型？
### Additional 额外信息
_No response_ | closed | 2023-12-11T03:29:45Z | 2024-01-06T09:32:00Z | https://github.com/modelscope/data-juicer/issues/127 | [
"question",
"stale-issue"
] | pkugyf | 3 |
pytest-dev/pytest-html | pytest | 797 | pytest html file in a Chinese path, there ate some error | pytest html file in a Chinese path, there ate some error
in pure english path:
path has some Chinese
| open | 2024-01-30T11:15:27Z | 2024-06-11T14:25:29Z | https://github.com/pytest-dev/pytest-html/issues/797 | [] | Mutiantian | 2 |
cvat-ai/cvat | pytorch | 8,521 | ECONNREFUSED Error. | ### Actions before raising this issue
- [X] I searched the existing issues and did not find anything similar.
- [X] I read/searched [the docs](https://docs.cvat.ai/docs/)
| closed | 2024-10-08T17:51:56Z | 2024-10-14T15:24:46Z | https://github.com/cvat-ai/cvat/issues/8521 | [
"need info"
] | Krishna-UGit | 3 |
tflearn/tflearn | data-science | 570 | Issue with time_distributed layer | Hi,
This is related to issue #559 [(https://github.com/tflearn/tflearn/issues/559)]
Did anyone manage to fix this problem? I think its the same error in my case too. Error comes from,
x = map(lambda t: tf.reshape(t, [-1, 1]+utils.get_incoming_shape(t)[1:]), x) . I updated both tensorflow and tflearn. But there is no effect. Can anyone please suggest me a solution for this? I am using python 3.5
Error Message:
File "LSTM_Train_many_to_many.py", line 121, in
net=network_initialization(no_classes,time_step)
File "LSTM_Train_many_to_many.py", line 81, in network_initialization
net = tflearn.time_distributed(lstm_net, tflearn.fully_connected,[14])
File "/usr/local/lib/python3.5/dist-packages/tflearn-0.2.2-py3.5.egg/tflearn/layers/core.py", line 663, in time_distributed
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py", line 1077, in concat
return identity(values[0], name=scope)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1424, in identity
result = _op_def_lib.apply_op("Identity", input=input, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 493, in apply_op
raise err
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 490, in apply_op
preferred_dtype=default_dtype)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 669, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 441, in make_tensor_proto
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 441, in
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/compat.py", line 65, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got <map object at 0x7fd54afbd828> | open | 2017-01-23T07:38:17Z | 2017-05-12T01:30:19Z | https://github.com/tflearn/tflearn/issues/570 | [] | Rithmax | 1 |
autogluon/autogluon | data-science | 4,530 | [BUG] [timeseries] Do not quietly ignore failed HPO configurations | **Bug Report Checklist**
<!-- Please ensure at least one of the following to help the developers troubleshoot the problem: -->
- [x] I provided code that demonstrates a minimal reproducible example. <!-- Ideal, especially via source install -->
- [x] I confirmed bug exists on the latest mainline of AutoGluon via source install. <!-- Preferred -->
- [ ] I confirmed bug exists on the latest stable version of AutoGluon. <!-- Unnecessary if prior items are checked -->
**Describe the bug**
If some model configurations sampled from the HPO search space fail with an exception (e.g., invalid parameter value provided), these errors are quietly ignored. The predictor simply returns a much smaller fraction of models (as a % of `num_trials`).
**Expected behavior**
<!-- A clear and concise description of what you expected to happen. -->
We should instead report something along the lines of
```
73/100 (73%) model configurations failed. The exception that caused the first model to fail is:
... # traceback
```
so that the user is aware that there might be a mistake in the searchspace definition.
**To Reproduce**
```python
from autogluon.common import space
from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor
data = TimeSeriesDataFrame('https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly/train.csv')
data = data.loc[data.item_ids[:5]]
predictor = TimeSeriesPredictor().fit(
data,
# If hidden_dim % n_heads != 0, the model will raise an exception and fail. These configurations are quietly ignored during HPO
hyperparameters={"TemporalFusionTransformer": {"hidden_dim": space.Int(1, 100), "epochs": 1, "num_batches_per_epoch": 1}},
hyperparameter_tune_kwargs={
"num_trials": 100,
"searcher": "random",
"scheduler": "local",
},
enable_ensemble=False,
)
print(f"Trained {len(predictor.model_names())} models") # Only 25% of the configurations are trained, the rest are ignored without any information provided to the user
```
**Screenshots / Logs**
<!-- If applicable, add screenshots or logs to help explain your problem. -->
| open | 2024-10-08T12:08:01Z | 2024-11-08T15:29:48Z | https://github.com/autogluon/autogluon/issues/4530 | [
"bug",
"module: timeseries",
"priority: 2"
] | shchur | 1 |
deepspeedai/DeepSpeed | deep-learning | 7,156 | [REQUEST] Support for Expert Optimizer State Partitioning with ZeRO Optimization in DeepSpeed MoE | **Is your feature request related to a problem? Please describe.**
I am currently training a Transformer model using DeepSpeed's MoE (Mixture of Experts) layer. After enabling Expert Parallelism (EP) and ZeRO-2 optimization, the total activation size of the experts is 2B, and the total parameter count is 6B. Since the expert parameters account for the majority of the parameters (5B), while the shared parameters are minimal, I observed that the memory usage reduction after enabling ZeRO-2 is not significant. From my understanding, as mentioned in the DeepSpeed MoE documentation, in the EP + ZeRO-2 setup, only the gradients and optimizer states of non-expert parameters are partitioned, while the gradients and optimizer states of the MoE layer are not optimized. This results in a substantial memory overhead. As a comparison, I tested the training of a non-MoE 2B model. With almost the same activation size, the non-MoE model can use a batch size 6-7 times larger than the MoE model, and it also trains faster. Are there any plans to optimize the memory consumption of DeepSpeed MoE in the future? Alternatively, do you have any suggestions to help optimize the training process for my model?
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
I would like DeepSpeed to extend ZeRO optimization to the MoE layer parameters, enabling more efficient memory usage and allowing the training of larger models with MoE architectures.
**Describe alternatives you've considered**
Enable zero optimization in Deepspeed moe layer parameters so that we can train larger model.
**Additional context**
Add any other context or screenshots about the feature request here.
| open | 2025-03-20T04:20:53Z | 2025-03-20T04:20:53Z | https://github.com/deepspeedai/DeepSpeed/issues/7156 | [
"enhancement"
] | leeruibin | 0 |
miguelgrinberg/Flask-SocketIO | flask | 1,304 | Transport error/close along with CORS and 502 errors | I have a React app with socket.io on HTTPS and Flask SocketIO with eventlet on a different server. | closed | 2020-06-15T22:12:43Z | 2021-04-27T12:33:36Z | https://github.com/miguelgrinberg/Flask-SocketIO/issues/1304 | [
"question"
] | nakamigo | 3 |
ShishirPatil/gorilla | api | 550 | Possible answer for "parallel_function_29" | https://github.com/ShishirPatil/gorilla/blob/f1417a8092540302257f7030aaa1d6ba163ac363/berkeley-function-call-leaderboard/data/possible_answer/gorilla_openfunctions_v1_test_parallel_function.json#L30
I think `number = 0` can be omitted, because it will not affect the calculation result of waste. Maybe this answer is also correct:
```
{"id": "parallel_function_29", "ground_truth": {"waste_calculation.calculate_1": {"population": [{"adults": [2], "children": [2]}], "location": ["Los Angeles", "Los Angeles, CA", "LA"]}, "waste_calculation.calculate_2": {"population": [{"singles": [1]}], "location": ["New York", "New York, NY", "NY", "New York City", "NYC"]}}}
``` | closed | 2024-07-25T06:37:55Z | 2024-08-05T02:06:03Z | https://github.com/ShishirPatil/gorilla/issues/550 | [
"BFCL-General"
] | lucenzhong | 2 |
kochlisGit/ProphitBet-Soccer-Bets-Predictor | seaborn | 11 | Predict Matches from different leagues | Or any way to add Champions League | open | 2023-02-21T17:52:24Z | 2023-02-21T17:52:24Z | https://github.com/kochlisGit/ProphitBet-Soccer-Bets-Predictor/issues/11 | [] | RonEskinder | 0 |
ets-labs/python-dependency-injector | flask | 78 | Replace pypin's with something else or remove them | closed | 2015-07-20T07:51:04Z | 2015-07-20T10:01:02Z | https://github.com/ets-labs/python-dependency-injector/issues/78 | [
"bug",
"docs"
] | rmk135 | 0 |
|
koxudaxi/datamodel-code-generator | pydantic | 1,944 | [pydantic_v2.BaseModel] Field specified as `date-time` is generated as `AwareDatetime` | **Is your feature request related to a problem? Please describe.**
A field, specified as a `date-time` is generated as `AwareDatetime` when the output model type is `pydantic_v2.BaseModel`, requiring a timezone to be specified.
While I understand it is best practice to work with timezones, not all systems have a timezone stored with their date-time data.
**Describe the solution you'd like**
I would like it to be possible to opt out of the AwareDatetime type, and use a type that doesn't require a timezone to be specified.
**Describe alternatives you've considered**
I have considered monkey-patching the generated code, but that doesn't feel right, and I have looked at the possibility to set a default timezone for AwareDatetime, but that doesn't seem to exist. | closed | 2024-05-02T11:40:28Z | 2024-05-02T13:15:35Z | https://github.com/koxudaxi/datamodel-code-generator/issues/1944 | [] | benverhees | 1 |
KaiyangZhou/deep-person-reid | computer-vision | 87 | [Tiny Bug] lower limit of 'train-batch-size' argument | Video based Re-ID is very memory cosuming (especially when sequence length is long).Due to limited GPU resource and large model size,some user may set train batch size to a very small value.
If 'hard mining triplet loss' is enabled, and `train-batch-size` is wrongly set to value `2` or `1` (lower than `3`),triplet loss may work incorrectly.
Fix:
I suggest give some warning info when the argument is wrong. | closed | 2018-12-11T12:35:11Z | 2019-01-01T03:28:30Z | https://github.com/KaiyangZhou/deep-person-reid/issues/87 | [] | ghost | 2 |
graphdeco-inria/gaussian-splatting | computer-vision | 907 | I have write a line-wise annotation to the realization of simple_knn in submodules folder. Hope it helps people in 3dgs realm. | The link is [simple_knn_illustration](https://github.com/shuyueW1991/simple_knn_illustration.git).
| open | 2024-07-25T06:11:40Z | 2024-07-25T06:11:40Z | https://github.com/graphdeco-inria/gaussian-splatting/issues/907 | [] | shuyueW1991 | 0 |
cvat-ai/cvat | tensorflow | 8,907 | Can CVAT 2.24.0 use Nuclio 1.14.1 | Hi,
in the composite file of CVAT 2.24.0 we find only Nuclio 1.13.0
````
image: quay.io/nuclio/dashboard:1.13.0-amd64
````
Can we change it to nuclio 1.14.1 ?
Best
Regards Rose | closed | 2025-01-08T09:04:19Z | 2025-01-15T12:45:56Z | https://github.com/cvat-ai/cvat/issues/8907 | [] | RoseDeSable | 2 |
modoboa/modoboa | django | 3,087 | Logging: ERROR modoboa.admin No AAAA record for mail.example.com | # Impacted versions
* OS Type: Debian/Ubuntu
* OS Version: 22.10
* Database Type: PostgreSQL
* Database version: 14.9
* Modoboa: 2.0.3
* installer used: Yes
* Webserver: Nginx
# Steps to reproduce
Clean install, no IPv6 addresses available
# Current behavior
<!--
Explain the behavior you're seeing that you think is a bug, and explain how you
think things should behave instead.
-->
The admin interface in modoboa - logs shows an error:
```
09.10.2023 17:30 | ERROR | modoboa.admin | No AAAA record for mail.example.com
```
# Expected behavior
Do not show the error, when the machine does not support IPv6.
Can we have an option to exclude IPv6 checks, if it is not available?
# Video/Screenshot link (optional)
| closed | 2023-10-09T17:19:51Z | 2023-12-14T13:56:55Z | https://github.com/modoboa/modoboa/issues/3087 | [
"enhancement"
] | sando38 | 0 |
RayVentura/ShortGPT | automation | 25 | Adding edge_tts as a free voice alternative | Task description:
Adding edge_tts python module as a the `edgeVoiceModule` under the shortGPT/audio/
https://pypi.org/project/edge-tts/ | closed | 2023-07-20T06:22:37Z | 2023-07-27T03:06:59Z | https://github.com/RayVentura/ShortGPT/issues/25 | [
"enhancement"
] | RayVentura | 7 |
numba/numba | numpy | 9,383 | np.isnan false in nopython jit with fastmath | Hello!
release candidate version
`python 3.12`
numpy 1.26.3
llvmlite 0.42.0rc1
nptyping 2.5.0
`numba 0.59.0rc1`
Problem is in way of defining nan values. Any way shows False under jit nopython.
Here is a simple example
```
import numpy as np
from numba import njit
from nptyping import NDArray, Shape, Float
check = np.empty(4, dtype=np.float32)
check.fill(np.nan)
print("numpy")
print(np.isnan(check[0]))
print(np.isnan(check).any())
@njit(cache=True, parallel=False, fastmath=True)
def example(check: NDArray[Shape["4 point"], Float]):
print("njit")
print(np.isnan(check[0]))
print(np.isnan(check).any())
print(check[0] is np.nan)
print(check[0] == np.nan)
print(check[0])
example(check)
```
output:
```
numpy
True
True
njit
False
False
False
False
nan
```
Some similar has been described in https://github.com/numba/numba/issues/5086 but np.isnan was a working way. | closed | 2024-01-08T00:59:09Z | 2024-01-08T10:55:29Z | https://github.com/numba/numba/issues/9383 | [] | genaix | 3 |
tableau/server-client-python | rest-api | 827 | TSC.Pager(server.workbooks) unable to print all workbooks from tableau server | **Versions**
- Tableau Server version (or note if using Tableau Online) - tableau server 2020.2.7
- Python version - 3.7
- TSC library version
**To Reproduce**
**Results**
Below code is not printing all the workbooks on my tableau server 2020.2.7, one of the example is below - We have workbooks named like 2018-****-**** but it doesn't prints them. I even tried with TSC.RequestOptions(pagesize=1000). How can i iterate through all the workbook names (there are more than 3000+ workbooks)
for wb in TSC.Pager(server.workbooks):
if wb.name.startswith('2018'):
print("I am here:",wb.name)
| closed | 2021-03-30T02:25:14Z | 2021-11-08T06:16:28Z | https://github.com/tableau/server-client-python/issues/827 | [
"help wanted"
] | Bipin007 | 3 |
SYSTRAN/faster-whisper | deep-learning | 1,145 | nce | closed | 2024-11-15T15:51:25Z | 2024-11-15T15:52:27Z | https://github.com/SYSTRAN/faster-whisper/issues/1145 | [] | pierrepv8 | 1 |
|
supabase/supabase-py | fastapi | 783 | Connection Keep Alive | Hey !
It seems that the TLS Handshake happens every query.
<img width="717" alt="image" src="https://github.com/supabase-community/supabase-py/assets/19614572/1ebb1f74-4791-494e-98e9-9604135fd1bd">
Implementing a Keep-alive and keeping the connection alive would save around 60ms per query. | closed | 2024-04-28T20:39:14Z | 2024-09-28T19:07:56Z | https://github.com/supabase/supabase-py/issues/783 | [
"bug"
] | StanGirard | 7 |
scrapy/scrapy | web-scraping | 6,331 | Provide an addon for Broad Crawls | There are common practices for broad crawls, explained here: https://docs.scrapy.org/en/latest/topics/broad-crawls.html. It involves modifying many settings. It seems we can provide a Scrapy addon to simplify all of that.
It can start as a third-party package, but I think Scrapy itself may also benefit from such addon. | open | 2024-05-03T20:38:54Z | 2024-05-11T16:09:45Z | https://github.com/scrapy/scrapy/issues/6331 | [
"discuss"
] | kmike | 2 |
bmoscon/cryptofeed | asyncio | 296 | Custom Mongo Callback feature | Hello,
Thanks a lot for the great efforts you put on this amazing project. It's proving very helpful to us.
However, my requirement is to extend the feature to use private websocket streams as well, i.e. I need to call streams which requires authentication. Once we started fetching the data, we need to save it in MongoDB.
For existing websocket streams mongo callback is working fine. But for my case, let's say for userstream in Binance, I want to implement a new Mongo callback as UserStreamCallback, which seems to be failing for me.
I get no errors in the code, data from userstream is getting printed in console, but seems like mongo callback is not getting wired up somewhere in the project.
Can you please help?
Let me know if you need more information on this.
Thanks, | closed | 2020-09-23T08:21:15Z | 2020-11-28T14:30:10Z | https://github.com/bmoscon/cryptofeed/issues/296 | [
"question"
] | xnxxabcd | 4 |
RomelTorres/alpha_vantage | pandas | 366 | New release | Are you planning to release the latest changes? Specifically interested in the new parameters from commit https://github.com/RomelTorres/alpha_vantage/commit/2122903650d608c612bd9ddabd683f598956222f | closed | 2024-05-28T19:18:00Z | 2024-06-08T07:05:38Z | https://github.com/RomelTorres/alpha_vantage/issues/366 | [] | yalishanda42 | 1 |
Lightning-AI/pytorch-lightning | deep-learning | 20,495 | Error learning rate when load ckpt for cotinue training if check_val_every_n_epoch > 1 | ### Bug description
I run with LightningCLI. Set check_val_every_n_epoch > 1 (e.g. 2) to run an experiment with 20 max_epoches, the model ckpt is save by lightning.pytorch.callbacks.ModelCheckpoint. The learning rate schedular is torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=self.trainer.max_epochs, T_mult=1, eta_min=self.eta_min) and update each epoch. When I load a ckpt (e.g. saved at epoch 3) to continue training, the learning rate will update 1 epoch quicker than expected.
Here the red is original learning rate curve and the yellow is the continued. The lr is logged by lightning.pytorch.callbacks.LearningRateMonitor.
### What version are you seeing the problem on?
v2.4
### How to reproduce the bug
```yaml
the trainer setting
seed_everything: 0
trainer:
accelerator: gpu
strategy: auto
devices:
- 0
num_nodes: 1
precision: 32-true
logger:
class_path: lightning.pytorch.loggers.TensorBoardLogger
init_args:
save_dir: runs
name: name
version: null
log_graph: false
default_hp_metric: true
prefix: ''
sub_dir: test
callbacks:
- class_path: utils.log_manager.LogManager
- class_path: lightning.pytorch.callbacks.LearningRateMonitor
init_args:
logging_interval: epoch
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
dirpath: null
filename: 'epoch={epoch}-psnr={hp_metric:.4f}'
monitor: hp_metric
verbose: false
save_last: true
save_top_k: 5
save_weights_only: false
mode: max
auto_insert_metric_name: false
every_n_train_steps: null
train_time_interval: null
every_n_epochs: 1
save_on_train_epoch_end: null
fast_dev_run: false
max_epochs: 20
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: null
check_val_every_n_epoch: 2
num_sanity_val_steps: null
log_every_n_steps: 20
enable_checkpointing: null
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: null
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: null
```
```py
def configure_optimizers(self):
params = []
params.append({'params': self.network.parameters(), 'lr': self.lr, 'weight_decay': self.weight_decay, 'name': 'network'})
optimizer = torch.optim.AdamW(params)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=self.trainer.max_epochs, T_mult=1, eta_min=self.eta_min)
lr_scheduler_config = {
"scheduler": lr_scheduler,
"interval": 'epoch',
"name": 'AdamW'
}
return {"optimizer": optimizer, "lr_scheduler": lr_scheduler_config}
```
### Error messages and logs
```
# Error messages and logs here please
```
### Environment
<details>
<summary>Current environment</summary>
```
#- PyTorch Lightning Version (e.g., 2.4.0):
#- PyTorch Version (e.g., 2.4):
#- Python version (e.g., 3.12):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
```
</details>
### More info
_No response_ | open | 2024-12-13T11:14:58Z | 2025-03-17T10:33:31Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20495 | [
"bug",
"needs triage",
"ver: 2.4.x"
] | razgzy | 3 |
pytorch/vision | computer-vision | 8,837 | Code block in `Using models from Hub` failed to render correctly. | ### 📚 The doc issue
In https://pytorch.org/vision/main/models.html#using-models-from-hub, the resulting code block on my browser looks like
, which is different from the original documents.
```python
import torch
# Option 1: passing weights param as string
model = torch.hub.load("pytorch/vision", "resnet50", weights="IMAGENET1K_V2")
# Option 2: passing weights param as enum
weights = torch.hub.load("pytorch/vision", "get_weight", weights="ResNet50_Weights.IMAGENET1K_V2") # --> this is where it looks weird on Chrome.
model = torch.hub.load("pytorch/vision", "resnet50", weights=weights)
```
### Suggest a potential alternative/fix
Not sure if the following changes could solve the rendering problem.
```python
# Option 2: passing weights param as enum
weights = torch.hub.load(
"pytorch/vision",
"get_weight",
weights="ResNet50_Weights.IMAGENET1K_V2",
)
``` | closed | 2025-01-05T07:43:29Z | 2025-01-13T10:37:57Z | https://github.com/pytorch/vision/issues/8837 | [] | GdoongMathew | 1 |
lexiforest/curl_cffi | web-scraping | 138 | get raw response(without decompress) | Is there a way to receive a raw response compressed with gzip etc. without decompressing? | closed | 2023-10-07T12:00:57Z | 2023-10-07T13:55:56Z | https://github.com/lexiforest/curl_cffi/issues/138 | [] | tapdo | 2 |
opengeos/leafmap | jupyter | 278 | Integrating voilalibrary | - https://jeodpp.jrc.ec.europa.eu/services/shared/voilalibrary/
- https://github.com/widgetti/ipyvuetify
 | closed | 2022-07-30T01:40:17Z | 2023-03-16T23:08:27Z | https://github.com/opengeos/leafmap/issues/278 | [
"Feature Request"
] | giswqs | 0 |
bmoscon/cryptofeed | asyncio | 613 | 429: Too Many Requests error on Binance spot feed | **Describe the bug**
Ran OrderBook and Trade feeds for Binance for all pairs with top 20 symbols (excluding stables), a total of about 125 symbols in total). After running for more than 24 hours, cryptofeed received "429: Too Many Requests" exception from Binance for each symbol and could not recover from it (i.e. data collection eventually stopped).
Two issues concern me here and I hope someone can help:
1. What are some possible reasons for 429 error triggering only on the 2nd day?
2. How can I gracefully recover from a 429 error? Should I use the `exception_handler` to pause cryptofeed, wait for the 429 exception to pass, and then restart the feed?
**Operating System:**
Windows
**Cryptofeed Version**
1.9.1 from pypi | closed | 2021-08-30T04:28:08Z | 2023-06-14T06:08:31Z | https://github.com/bmoscon/cryptofeed/issues/613 | [
"bug"
] | mix-protocol | 8 |
Evil0ctal/Douyin_TikTok_Download_API | fastapi | 265 | [Feature request] 发现cookie失效的时候，没法下载，需要更新代码，能把cookie的获取也写成方法包含在代码里吗？ | 最近，一直部署的docker无法解析了。。。一看，原来是cookie需要更新啦。。
https://github.com/Evil0ctal/Douyin_TikTok_Download_API/commit/9addcca7b12d85ea891b69b03dbe3d867ee9fec2
不知道，作者方便把cookie的获取方法也自动化吗？ | closed | 2023-09-02T15:46:06Z | 2024-03-25T22:33:43Z | https://github.com/Evil0ctal/Douyin_TikTok_Download_API/issues/265 | [
"enhancement"
] | gillbates | 2 |
FactoryBoy/factory_boy | django | 487 | Add support for Django unique_together | `DjangoModelFactory._get_or_create()` doesn't appear to have any support for the `unique_together` attribute of `Model.Meta`. Perhaps it'd be a good idea to allow `unique_together` tuples in `DjangoModelFactory.Meta.django_get_or_create`?
I'll attach a PR to illustrate a potential approach. Can modify/add tests as necessary.
Thanks! | closed | 2018-07-13T16:43:23Z | 2018-07-16T19:04:56Z | https://github.com/FactoryBoy/factory_boy/issues/487 | [
"NeedInfo"
] | broxeph | 3 |
stanfordnlp/stanza | nlp | 1,339 | Coref model predicted a span that crossed two sentences | **Describe the bug**
Depending on whether `tokenize_pretokenized` and `tokenize_no_ssplit` are each `True` or `False`, the following sentence results in the coref processor yielding either the exception `ValueError: The coref model predicted a span that crossed two sentences!` or the exception `IndexError: list index out of range` error, on lines 120 and 119 of `stanza/pipeline/coref_processor.py`, respectively.
The sentence: `The son of Mr. and Mrs. X. He is four during the events of the first book . <eos>`
**To Reproduce**
Steps to reproduce the behavior:
Set up code:
```python
import stanza
s = "The son of Mr. and Mrs. X. He is four during the events of the first book . <eos>"
pipeline = stanza.Pipeline(lang="en", processors="tokenize,pos,lemma,depparse,coref")
pipeline_no_ssplit = stanza.Pipeline(lang="en", processors="tokenize,pos,lemma,depparse,coref", tokenize_no_ssplit=True)
pipeline_pretok = stanza.Pipeline(lang="en", processors="tokenize,pos,lemma,depparse,coref", tokenize_pretokenized=True)
pipeline_pretok_no_ssplit = stanza.Pipeline(lang="en", processors="tokenize,pos,lemma,depparse,coref", tokenize_pretokenized=True, tokenize_no_ssplit=True)
```
Then the following line of code:
```py
a = pipeline(s)
```
produces the exception:
```py
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/apatil/anaconda3/envs/base_nlp/lib/python3.9/site-packages/stanza/pipeline/core.py", line 476, in __call__
return self.process(doc, processors)
File "/Users/apatil/anaconda3/envs/base_nlp/lib/python3.9/site-packages/stanza/pipeline/core.py", line 427, in process
doc = process(doc)
File "/Users/apatil/anaconda3/envs/base_nlp/lib/python3.9/site-packages/stanza/pipeline/coref_processor.py", line 120, in process
raise ValueError("The coref model predicted a span that crossed two sentences! Please send this example to us on our github")
ValueError: The coref model predicted a span that crossed two sentences! Please send this example to us on our github
```
whereas any of the following lines of code:
```py
b = pipeline_nossplit(s)
c = pipeline_pretok(s)
d = pipeline_pretok_nossplit(s)
```
produce the exception:
```py
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/apatil/anaconda3/envs/base_nlp/lib/python3.9/site-packages/stanza/pipeline/core.py", line 476, in __call__
return self.process(doc, processors)
File "/Users/apatil/anaconda3/envs/base_nlp/lib/python3.9/site-packages/stanza/pipeline/core.py", line 427, in process
doc = process(doc)
File "/Users/apatil/anaconda3/envs/base_nlp/lib/python3.9/site-packages/stanza/pipeline/coref_processor.py", line 119, in process
if sent_ids[span[1]] != sent_id:
IndexError: list index out of range
```
**Expected behavior**
All of these should just work. They should not throw any of the issues above.
**Environment (please complete the following information):**
- OS: Reproduced on Mac (with CPU) and Oracle Linux (with GPU)
- Python version: `Python 3.9.16 | packaged by conda-forge`
- Stanza version: `1.7.0`
**Additional context**
I have also seen sporadic instances of the `coref model predicted a span that crossed two sentences!` error elsewhere, but previously only with a large group of sentences in a single doc, omitting any one of which, strangely, resulting in the error no longer surfacing. This is the first time I've been able to reproduce it with a single sentence, hence why I am reporting it. I can, however, provide other batches of sentences that result in the same issue, if it helps.
| closed | 2024-01-29T23:29:12Z | 2024-03-03T21:44:01Z | https://github.com/stanfordnlp/stanza/issues/1339 | [
"bug"
] | Ubadub | 3 |
hyperspy/hyperspy | data-visualization | 3,469 | Scalebar Widget set_length crash | #### Describe the bug
using set_length on a scalebar widget crashes
#### To Reproduce
Steps to reproduce the behavior:
attempt to call set_length on a ScaleBar widget object
Minimum working example of code
```
import hyperspy.api as hs
import matplotlib.pyplot as plt
sig = hs.data.atomic_resolution_image()
sig.calibrate(0,0,10,60, 4, "nm", False)
sig.plot(colorbar=False, axes_ticks=False, axes_off=True, gamma=0.3)
ax = plt.gca();
ax.scalebar.set_length(10)
```
#### Expected behavior
I was expecting this to let me set the length of the scalebar; I did find a workaround but a way to do this easily would be nice because the automatic scalebar length picker sometimes chooses bad numbers for the length (e.x. it gave me a scalebar of length 9nm).
```
import hyperspy.api as hs
import matplotlib.pyplot as plt
sig = hs.data.atomic_resolution_image()
sig.calibrate(0,0,10,60, 4, "nm", False)
sig.plot(colorbar=False, axes_ticks=False, axes_off=True, gamma=0.3)
ax = plt.gca()
ax.scalebar.length = 5
ax.scalebar.calculate_text_position()
ax.scalebar.plot_scale(line_width=ax.scalebar.line.get_linewidth())
ax.scalebar.set_color("white")
```
This works to set the scalebar and update the position correctly but it is required to set the color manually; if it is unspecified it will default to black text and an orange line instead of whatever the image originally had before modifying the scalebar manually.
#### Python environment:
- HyperSpy version: 2.1.1
- Python version: 3.11.9
#### Additional context
Image of the strange coloring resulting from not setting the color.
If the scalebar isn't manually edited, then it is white by default as expected.
| closed | 2024-12-02T22:21:51Z | 2025-01-22T01:03:06Z | https://github.com/hyperspy/hyperspy/issues/3469 | [
"type: bug"
] | alion8me | 1 |
pytorch/vision | machine-learning | 8,836 | Question: Modify Resnet File structure and how to import it | Hi, I would like to modify the structure of the model [Resnet50 ](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py). My goal is neither to add nor to remove layers, only to replace the convolutions that in the code are made by the pytorch nn.Conv function by convolutions made by the Nvidia CUTLASS library (https://github.com/NVIDIA/cutlass/blob/main/examples/python/02_pytorch_extension_grouped_gemm.ipynb).
I don't intend either to retrain or to modify weights, only to substitute the call to the convolutions with the call to a convolution of cutlass in a similar way to how I describe it in the pytorch forum: https://discuss.pytorch.org/t/using-diffetent-conv2d-ops-with-pre-trained-models/214367
My question is if it is possible, within the guidelines of the repository and then how can I import the file [Resnet50 ](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) from another file or pytorch as it would be done with https://pytorch.org/vision/main/models.html.
Thanks. | closed | 2025-01-03T12:43:50Z | 2025-02-19T15:45:16Z | https://github.com/pytorch/vision/issues/8836 | [] | IzanCatalan | 10 |
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