repo_name stringlengths 9 75 | topic stringclasses 30
values | issue_number int64 1 203k | title stringlengths 1 976 | body stringlengths 0 254k | state stringclasses 2
values | created_at stringlengths 20 20 | updated_at stringlengths 20 20 | url stringlengths 38 105 | labels listlengths 0 9 | user_login stringlengths 1 39 | comments_count int64 0 452 |
|---|---|---|---|---|---|---|---|---|---|---|---|
kizniche/Mycodo | automation | 432 | "Execute commands" output doesn't turn off by itself | ## Mycodo Issue Report:
Mycodo Version: 5.6.6
#### Problem Description
When working with a "Execute commands" output, Mycodo turns this output on just fine but doesn't turn it off when appropriate.
### Steps to reproduce the issue:
This is reproducible directly on the "Output" page:
- Add a "Execute commands" output with its "On" and "Off" commands (can be configured as "echo" commands to a file in case nothing better is available for testing).
- Type in a "Duration On" and click the test "Turn On" button.
- Observe that the output turns on, but fails to turn off after the requested duration.
### Root cause and proposed fix:
Mycodo checks whether the output should be turned off with the following code snippet in controller_output.py:
```
if (self.output_on_until[output_id] < current_time and
self.output_on_duration[output_id] and
self.output_pin[output_id] is not None):
```
The issue is that "Execute commands" outputs aren't associated to a pin. So the above condition never becomes true.
I get the expected behavior with the following fix:
```
if (self.output_on_until[output_id] < current_time and
self.output_on_duration[output_id] and
(self.output_type[output_id] not in [
'pwm', 'wired', 'wireless_433MHz_pi_switch'] or
self.output_pin[output_id] is not None)):
``` | closed | 2018-03-23T20:47:46Z | 2018-03-27T17:49:02Z | https://github.com/kizniche/Mycodo/issues/432 | [] | antoinechauveau | 3 |
noirbizarre/flask-restplus | flask | 7 | Ui url is fixed and cannot be changed | In api.py:136:
```
self.blueprint.add_url_rule('/', 'root', self.render_root)
```
I think this url should be customizable. We not always want to expose ui on known uri.
| closed | 2014-12-18T13:49:17Z | 2015-11-04T15:46:51Z | https://github.com/noirbizarre/flask-restplus/issues/7 | [
"enhancement",
"question"
] | m-zajac | 10 |
saulpw/visidata | pandas | 2,052 | Display column as bar /histogram [] | At the moment it is possible to display an histogram of values (with '*****").
It would be nice to be able to do the same but use the value of column instead. The easiest would probably to be able add a column so that
Apple, 3, ###
Orange, 10, #########
It probably can be done by using a python expression, but it would be easier if the scaling was done automatically (using min and max value as well as column width). | closed | 2023-10-11T13:03:13Z | 2023-10-20T00:13:33Z | https://github.com/saulpw/visidata/issues/2052 | [
"wishlist",
"wish granted"
] | maxigit | 1 |
joke2k/django-environ | django | 534 | idea: using pydantic-settings as base / .env parser | Dear **django-environ** developers,
since [pydantic-settings](https://docs.pydantic.dev/latest/concepts/pydantic_settings/#usage) made recently quite big advances and has a substantial overlap with django-environ's functionality - and there is a big community behind it, testing it -
I am wondering, if it makes sense to base *django-environ* on **pydantic-settings** and just add the django related additions in **django-environ**. This would follow the DRY principles ;)
What do you think ? | open | 2024-09-15T07:49:44Z | 2024-11-04T07:47:43Z | https://github.com/joke2k/django-environ/issues/534 | [
"discussion"
] | markdoerr | 3 |
waditu/tushare | pandas | 1,021 | get_realtime_quotes 这个没有涨幅这个字段吗？ | 如题，在注释里没有找到涨幅字段，请问这个怎么获取实时涨幅？感谢各位大佬~
获取实时交易数据 getting real time quotes data
用于跟踪交易情况（本次执行的结果-上一次执行的数据）
Parameters
------
symbols : string, array-like object (list, tuple, Series).
return
-------
DataFrame 实时交易数据
属性:0：name，股票名字
1：open，今日开盘价
2：pre_close，昨日收盘价
3：price，当前价格
4：high，今日最高价
5：low，今日最低价
6：bid，竞买价，即“买一”报价
7：ask，竞卖价，即“卖一”报价
8：volumn，成交量 maybe you need do volumn/100
9：amount，成交金额（元 CNY）
10：b1_v，委买一（笔数 bid volume）
11：b1_p，委买一（价格 bid price）
12：b2_v，“买二”
13：b2_p，“买二”
14：b3_v，“买三”
15：b3_p，“买三”
16：b4_v，“买四”
17：b4_p，“买四”
18：b5_v，“买五”
19：b5_p，“买五”
20：a1_v，委卖一（笔数 ask volume）
21：a1_p，委卖一（价格 ask price）
...
30：date，日期；
31：time，时间； | closed | 2019-04-26T06:41:17Z | 2019-04-27T02:16:24Z | https://github.com/waditu/tushare/issues/1021 | [] | Qiekr | 1 |
d2l-ai/d2l-en | tensorflow | 2,445 | Build PDF release in default style (non cambridge) | Hello,
Could the team also provide the pdf version in both cambridge and 'default'=previous style? The cambridge style is too small to read and actually 'ugly'.
Bests, | open | 2023-02-18T14:31:54Z | 2023-09-30T10:47:13Z | https://github.com/d2l-ai/d2l-en/issues/2445 | [
"feature request"
] | vinbrule | 2 |
FlareSolverr/FlareSolverr | api | 933 | Can't find 'verify you are human' loop. | ### Have you checked our README?
- [X] I have checked the README
### Have you followed our Troubleshooting?
- [X] I have followed your Troubleshooting
### Is there already an issue for your problem?
- [X] I have checked older issues, open and closed
### Have you checked the discussions?
- [X] I have read the Discussions
### Environment
```markdown
- FlareSolverr version: 3.3.6
- Last working FlareSolverr version: 3.3.6
- Operating system: Win10
- Are you using Docker: [yes/no] no
- FlareSolverr User-Agent (see log traces or / endpoint): Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/118.0.0.0 Safari/537.36
- Are you using a VPN: [yes/no] yes
- Are you using a Proxy: [yes/no] no
- Are you using Captcha Solver: [yes/no] no
- If using captcha solver, which one:
- URL to test this issue:
eztv.re
```
### Description
Try to use the software w/ eztv.re
### Logged Error Messages
```text
2023-10-27 15:06:52 DEBUG ReqId 12744 Navigating to... https://eztv.re/
2023-10-27 15:06:53 INFO ReqId 12744 Challenge detected. Title found: Just a moment...
2023-10-27 15:06:53 DEBUG ReqId 12744 Waiting for title (attempt 1): Just a moment...
2023-10-27 15:06:54 DEBUG ReqId 12744 Timeout waiting for selector
2023-10-27 15:06:54 DEBUG ReqId 12744 Try to find the Cloudflare verify checkbox...
2023-10-27 15:06:54 DEBUG ReqId 12744 Cloudflare verify checkbox not found on the page.
2023-10-27 15:06:54 DEBUG ReqId 12744 Try to find the Cloudflare 'Verify you are human' button...
2023-10-27 15:06:55 DEBUG ReqId 12744 The Cloudflare 'Verify you are human' button not found on the page.
2023-10-27 15:06:57 DEBUG ReqId 12744 Waiting for title (attempt 2): Just a moment...
2023-10-27 15:06:58 DEBUG ReqId 12744 Timeout waiting for selector
2023-10-27 15:06:58 DEBUG ReqId 12744 Try to find the Cloudflare verify checkbox...
2023-10-27 15:06:58 DEBUG ReqId 12744 Cloudflare verify checkbox not found on the page.
2023-10-27 15:06:58 DEBUG ReqId 12744 Try to find the Cloudflare 'Verify you are human' button...
2023-10-27 15:06:58 DEBUG ReqId 12744 The Cloudflare 'Verify you are human' button not found on the page.
2023-10-27 15:07:00 DEBUG ReqId 12744 Waiting for title (attempt 3): Just a moment...
2023-10-27 15:07:01 DEBUG ReqId 12744 Timeout waiting for selector
2023-10-27 15:07:01 DEBUG ReqId 12744 Try to find the Cloudflare verify checkbox...
2023-10-27 15:07:01 DEBUG ReqId 12744 Cloudflare verify checkbox not found on the page.
2023-10-27 15:07:01 DEBUG ReqId 12744 Try to find the Cloudflare 'Verify you are human' button...
2023-10-27 15:07:01 DEBUG ReqId 12744 The Cloudflare 'Verify you are human' button not found on the page.
2023-10-27 15:07:03 DEBUG ReqId 12744 Waiting for title (attempt 4): Just a moment...
2023-10-27 15:07:04 DEBUG ReqId 12744 Timeout waiting for selector
2023-10-27 15:07:04 DEBUG ReqId 12744 Try to find the Cloudflare verify checkbox...
```
### Screenshots
_No response_ | closed | 2023-10-27T19:08:31Z | 2023-10-27T23:58:03Z | https://github.com/FlareSolverr/FlareSolverr/issues/933 | [
"duplicate"
] | CaptainCambodia | 2 |
huggingface/transformers | pytorch | 36,739 | `UnboundLocalError: cannot access local variable 'images_list'` when using Gemma 3 AutoProcessor with use_fast=True | ### System Info
- `transformers` version: 4.50.0.dev0
- Platform: Linux-6.13.6-100.fc40.x86_64-x86_64-with-glibc2.39
- Python version: 3.12.9
- Huggingface_hub version: 0.29.1
- Safetensors version: 0.4.5
- Accelerate version: 0.34.2
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.4.0+cu124 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA GeForce RTX 4060 Laptop GPU
### Who can help?
@ArthurZucker
Terribly sorry if it's the wrong person! I hope this passes as Text Model.
### Information
- [ ] The official example scripts
- [x] 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
Used base code given on HF for Gemma 3(modified to local path): https://huggingface.co/google/gemma-3-4b-it#running-the-model-on-a-singlemulti-gpu
Added use_fast=True to the AutoProcessor arguments.
> Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.48, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
```from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from PIL import Image
import requests
import torch
model_id = "gemma-3-4b-it"
model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto"
).eval()
processor = AutoProcessor.from_pretrained(model_id,use_fast=True)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
{"type": "text", "text": "Describe this image in detail."}
]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
# **Overall Impression:** The image is a close-up shot of a vibrant garden scene,
# focusing on a cluster of pink cosmos flowers and a busy bumblebee.
# It has a slightly soft, natural feel, likely captured in daylight.
```
Reason (I think): `images_list` variable is declared under `if do_pan_and_scan`. If `do_pan_and_scan` is not enabled, `images_list` is not available and hence it will error out.
Same variable is used for `group_images_by_shape` in Line No: 294.
Lines: https://github.com/huggingface/transformers/blob/6f3e0b68e0030051d9181fb1f494e539adeb069c/src/transformers/models/gemma3/image_processing_gemma3_fast.py#L280-L294
Since images variable is being passed as `List[List["torch.Tensor"]]`, passing `images_list = image_list` in else case can fix the issue. There might be a better way for fixing this. Local fix which I'm using:
```
else:
# assign variable to bypass unbounded error
images_list = image_list
num_crops = [[0] for _ in images_list]
# Group images by size for batched processing
processed_image_patches_grouped = {}
grouped_image_patches, grouped_image_patches_index = group_images_by_shape(images_list)
```
Got the idea from got_ocr2: https://github.com/huggingface/transformers/blob/6f3e0b68e0030051d9181fb1f494e539adeb069c/src/transformers/models/got_ocr2/image_processing_got_ocr2_fast.py#L183
### Expected behavior
Returns output without failure.
Sample (clips out due to token limit):
```Okay, here's a detailed description of the image:
**Overall Impression:**
The image is a close-up shot of a vibrant garden scene, focusing on a cluster of pink cosmos flowers and a busy bumblebee. It has a slightly soft, natural feel, likely due to the lighting and the focus on the foreground.
**Foreground:**
* **Cosmos Flowers:** The main subject is a group of pink cosmos flowers. The flower in the center is the most prominent,``` | closed | 2025-03-15T08:55:11Z | 2025-03-20T18:25:02Z | https://github.com/huggingface/transformers/issues/36739 | [
"bug",
"VLM"
] | Zebz13 | 5 |
marimo-team/marimo | data-visualization | 3,954 | Documentation for how to serve a wasm-powered HTML? | ### Documentation is
- [x] Missing
- [ ] Outdated
- [ ] Confusing
- [x] Not sure?
### Explain in Detail
What are the options serving the output of: https://docs.marimo.io/guides/exporting/#export-to-wasm-powered-html
Other than github pages?
Would this be things like apache and nginx?
I don't have any experience with either, so if there was a clear example of how to serve the output_dir folder with either of these, it would be super handy.
### Your Suggestion for Changes
Some extra documentation on how one would ideally serve the output wasm-html from their own computer/device when they want to so that other users can access the notebook (dashboard). While still hiding/privatizing the original code.
(github pages does not allow private repositories, unless you get github enterprise)
Hoping to understand the pros and cons of self-hosting. | closed | 2025-03-02T00:54:42Z | 2025-03-04T21:00:11Z | https://github.com/marimo-team/marimo/issues/3954 | [
"documentation",
"good first issue"
] | Justyouraveragehomie | 1 |
donnemartin/system-design-primer | python | 445 | Fix the reference URLs of the solution READMEs in the zh-Hans version修改中文答案链接 | Should change links like from
solutions/system_design/pastebin/README.md
to
solutions/system_design/pastebin/README-zh-Hans.md | open | 2020-07-12T11:54:30Z | 2020-07-18T00:46:35Z | https://github.com/donnemartin/system-design-primer/issues/445 | [
"needs-review"
] | julianShi | 2 |
openapi-generators/openapi-python-client | rest-api | 119 | Issues using main branch with pip only | **Describe the bug**
I hit numerous bugs when installing this library and also the generated client library when using only pip, and not poetry.
**To Reproduce**
Trying to install:
1. `python -m pip venv venv`
2. `source venv/bin/activate`
3. `pip install -U git+https://github.com/triaxtec/openapi-python-client.git`
At this point, it doesn't seem to understand the project metadata, so I get:
```
Building wheels for collected packages: UNKNOWN
Building wheel for UNKNOWN (PEP 517) ... done
Created wheel for UNKNOWN: filename=UNKNOWN-0.0.0-py3-none-any.whl size=1796 sha256=92844cf4cacb74a84fbabc818a001c29fe7a6faff33c6489dfa821a73ac35332
Stored in directory: /tmp/pip-ephem-wheel-cache-kywezuda/wheels/dd/d2/00/2654ebf9b83eac4fd4dd88ce1bbd33bb59776258f92e53c164
Successfully built UNKNOWN
Installing collected packages: UNKNOWN
Attempting uninstall: UNKNOWN
Found existing installation: UNKNOWN 0.0.0
Uninstalling UNKNOWN-0.0.0:
Successfully uninstalled UNKNOWN-0.0.0
Successfully installed UNKNOWN-0.0.0
```
This also doesn't seem to install the `openapi-python-client` executable.
If I instead setup poetry and:
1. `poetry add git+https://github.com/triaxtec/openapi-python-client.git@main`
2. `openapi-python-client generate --url https://api.biocontainers.pro/ga4gh/trs/v2/openapi.json`
Here, I get:
```
Traceback (most recent call last):
File "/tmp/tmp.pvDxeqwflB/venv/bin/openapi-python-client", line 6, in <module>
from pkg_resources import load_entry_point
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 3261, in <module>
@_call_aside
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 3245, in _call_aside
f(*args, **kwargs)
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 3274, in _initialize_master_working_set
working_set = WorkingSet._build_master()
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 584, in _build_master
ws.require(__requires__)
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 901, in require
needed = self.resolve(parse_requirements(requirements))
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 787, in resolve
raise DistributionNotFound(req, requirers)
pkg_resources.DistributionNotFound: The 'importlib_metadata<2.0.0,>=1.6.0' distribution was not found and is required by openapi-python-client
```
3. `pip install importlib_metadata -U`
4. `openapi-python-client generate --url https://api.biocontainers.pro/ga4gh/trs/v2/openapi.json` again
This gives me:
```
Traceback (most recent call last):
File "/tmp/tmp.pvDxeqwflB/venv/bin/openapi-python-client", line 11, in <module>
load_entry_point('openapi-python-client', 'console_scripts', 'openapi-python-client')()
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 490, in load_entry_point
return get_distribution(dist).load_entry_point(group, name)
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 2862, in load_entry_point
return ep.load()
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 2462, in load
return self.resolve()
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 2468, in resolve
module = __import__(self.module_name, fromlist=['__name__'], level=0)
File "/tmp/tmp.pvDxeqwflB/venv/src/openapi-python-client/openapi_python_client/__init__.py", line 17, in <module>
from .parser import GeneratorData, import_string_from_reference
File "/tmp/tmp.pvDxeqwflB/venv/src/openapi-python-client/openapi_python_client/parser/__init__.py", line 5, in <module>
from .openapi import GeneratorData, import_string_from_reference
File "/tmp/tmp.pvDxeqwflB/venv/src/openapi-python-client/openapi_python_client/parser/openapi.py", line 8, in <module>
from .. import schema as oai
File "/tmp/tmp.pvDxeqwflB/venv/src/openapi-python-client/openapi_python_client/schema/__init__.py", line 41, in <module>
from .components import Components
File "/tmp/tmp.pvDxeqwflB/venv/src/openapi-python-client/openapi_python_client/schema/components.py", line 3, in <module>
from pydantic import BaseModel
ModuleNotFoundError: No module named 'pydantic'
```
5. `poetry add pydantic`
6. `openapi-python-client generate --url https://api.biocontainers.pro/ga4gh/trs/v2/openapi.json` again, now gives me:
```
Traceback (most recent call last):
File "/tmp/tmp.pvDxeqwflB/venv/bin/openapi-python-client", line 6, in <module>
from pkg_resources import load_entry_point
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 3261, in <module>
@_call_aside
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 3245, in _call_aside
f(*args, **kwargs)
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 3274, in _initialize_master_working_set
working_set = WorkingSet._build_master()
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 584, in _build_master
ws.require(__requires__)
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 901, in require
needed = self.resolve(parse_requirements(requirements))
File "/tmp/tmp.pvDxeqwflB/venv/lib/python3.7/site-packages/pkg_resources/__init__.py", line 787, in resolve
raise DistributionNotFound(req, requirers)
pkg_resources.DistributionNotFound: The 'importlib_metadata<2.0.0,>=1.6.0' distribution was not found and is required by openapi-python-client
```
6. `pip install 'importlib_metadata<2.0.0,>=1.6.0'`
7. `openapi-python-client generate --url https://api.biocontainers.pro/ga4gh/trs/v2/openapi.json`. This time it works
8. Finally, I try to install the generated code using pip:
```
pip install ga4gh-tool-discovery-api-client/
Processing ./ga4gh-tool-discovery-api-client
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing wheel metadata ... done
Building wheels for collected packages: UNKNOWN
Building wheel for UNKNOWN (PEP 517) ... done
Created wheel for UNKNOWN: filename=UNKNOWN-0.0.0-py3-none-any.whl size=980 sha256=cd1f4deff053e280188e11954b12a08920fa7bcff7a3dd3e08525a6bf5fab62f
Stored in directory: /home/michael/.cache/pip/wheels/08/8a/62/8b06f89059e834612fc1f707dc3644ba65660421ddbf78c83a
Successfully built UNKNOWN
Installing collected packages: UNKNOWN
Attempting uninstall: UNKNOWN
Found existing installation: UNKNOWN 0.0.0
Uninstalling UNKNOWN-0.0.0:
Successfully uninstalled UNKNOWN-0.0.0
Successfully installed UNKNOWN-0.0.0
```
**Expected behavior**
I would hope that simply running these commands would generate and install the API client:
```
pip install git+https://github.com/triaxtec/openapi-python-client.git
openapi-python-client generate --url https://api.biocontainers.pro/ga4gh/trs/v2/openapi.json
pip install ga4gh-tool-discovery-api-client
```
**OpenAPI Spec File**
https://api.biocontainers.pro/ga4gh/trs/v2/openapi.json
**Desktop (please complete the following information):**
- OS: Ubuntu 18.04
- Python Version: 3.7.8
- openapi-python-client version: 0.4.2 3afa089
- pip: 20.1.1 | closed | 2020-08-04T15:27:09Z | 2020-08-05T13:55:18Z | https://github.com/openapi-generators/openapi-python-client/issues/119 | [
"🐞bug"
] | multimeric | 3 |
streamlit/streamlit | data-science | 10,603 | Rerun fragment from anywhere, not just from within itself | ### Checklist
- [x] I have searched the [existing issues](https://github.com/streamlit/streamlit/issues) for similar feature requests.
- [x] I added a descriptive title and summary to this issue.
### Summary
Related to #8511 and #10045.
Currently the only way to rerun a fragment is calling `st.rerun(scope="fragment")` from within itself. Allowing a fragment rerun to be triggered from anywhere (another fragment or the main app) would unlock new powerful use-cases.
### Why?
When a fragment is dependent on another fragment or on a change on the main app, the only current way to reflect that dependency is to rerun the full application whenever a dependency changes.
### How?
I think adding a key to each fragment would allow the user to build his custom logic to manage the dependency chain and take full control on when to run a given fragment:
```python
@st.fragment(key="depends_on_b")
def a():
st.write(st.session_state.input_for_a)
@st.fragment(key="depends_on_main")
def b():
st.session_state.input_for_a = ...
if st.button("Should rerun a"):
st.rerun(scope="fragment", key="depends_on_b")
if other_condition:
st.rerun(scope="fragment", key="depends_on_main")
```
This implementation doesn't go the full mile to describe the dependency chain in the fragment's definition and let streamlit handle the rerun logic as suggested in #10045, but provides more flexibility for the user to rerun a fragment from anywhere and under any conditions that fits his use-case
### Additional Context
_No response_ | open | 2025-03-03T14:55:41Z | 2025-03-13T04:44:33Z | https://github.com/streamlit/streamlit/issues/10603 | [
"type:enhancement",
"feature:st.fragment"
] | Abdelgha-4 | 1 |
hootnot/oanda-api-v20 | rest-api | 106 | Get Instrument Candles in Unix Time | Sorry, this is more a question than an Issue, but it's because I do not find the answer. According to the [documentation about instrument candles](http://developer.oanda.com/rest-live-v20/instrument-ep/), you can request the time to be in Unix time by setting the header `Accept-Datetime-Format` as `UNIX`. Howerver in the implementation of this nice package, I'm unable to find this.
In the documentation the following example is given:
oandapyV20.endpoints.instruments.InstrumentsCandles(instrument, params=None)
The parameters for the query are well accepted but nothing about the header. Could somebody guide me with this?
Note: I can totally convert the data to Unix time myself, but since it is one of the parameters given, I think it would be nice to get it directly from oanda. | closed | 2018-01-11T18:17:29Z | 2018-01-13T20:54:35Z | https://github.com/hootnot/oanda-api-v20/issues/106 | [
"question"
] | silgon | 7 |
cobrateam/splinter | automation | 861 | mouse actions long_click and right_long_click | How can I lock_click on an object? I can do **double_click** and **right_click** without any problem but I would like to **long_click** or **right_long_click**. Is there anything I can do to achieve this?
| closed | 2021-03-09T11:59:41Z | 2021-07-01T04:48:31Z | https://github.com/cobrateam/splinter/issues/861 | [
"question"
] | thlengane | 3 |
freqtrade/freqtrade | python | 11,334 | Precise Stop-Loss and Take-Profit Execution |
Hello, good time
* Operating system: Ubuntu 24.04
* Python Version: python 3.12.3
* CCXT version: ccxt==4.4.49
* Freqtrade Version: freqtrade 2025.1-dev-2b915a76d
I want my trades to close exactly at my stop-loss or take-profit levels.
For example, if I set a stop-loss or take-profit at 5%, I want the trade to close at exactly 5% or with minimal deviation.
However, when I use leverage, there are sometimes significant differences—up to tens of percentage points—between my set levels and the actual closing points.
What should I do in this case?
Ideally, I want the stop-loss and take-profit levels to be set immediately after opening the trade at the specified percentage, both in demo and live modes.
| closed | 2025-02-04T09:54:37Z | 2025-02-13T19:33:20Z | https://github.com/freqtrade/freqtrade/issues/11334 | [
"Question",
"Strategy assistance"
] | ahmad-develop | 2 |
andrew-hossack/dash-tools | plotly | 57 | [Feature Request] Add free Deploy method | Heroku is removing free tier option. https://render.com/ does not support upload from cli | closed | 2022-08-28T15:57:39Z | 2022-10-12T13:30:33Z | https://github.com/andrew-hossack/dash-tools/issues/57 | [] | andrew-hossack | 1 |
tensorpack/tensorpack | tensorflow | 980 | Where is the XNOR kernal in svhn-digit-dorefa.py | In the svhn-digit-dorefa.py code, where is the XNOR kernel? | closed | 2018-11-14T09:25:45Z | 2018-11-21T14:52:06Z | https://github.com/tensorpack/tensorpack/issues/980 | [
"examples"
] | mohendra | 7 |
microsoft/qlib | deep-learning | 1,305 | Alpha360 data preprocess does not work. | Hi, I have run the example workflow_by_code.ipynb. I changed handler to Alpha360.
And I checked the results of the following statements.
dataset.prepare('test', data_key='raw')
dataset.prepare('test', data_key='infer')
I found that the results are the same. It seems the test data isn't preprocessed by the handler Alpha360.
If I changed the hander to Alpha158, then the results are different. So, it seems that Alpha360's data preprocess doesn't work while Alpha158 does.
I wonder if any other finds this? | closed | 2022-09-28T12:35:40Z | 2023-02-27T18:02:40Z | https://github.com/microsoft/qlib/issues/1305 | [
"question",
"stale"
] | quantcn | 2 |
django-oscar/django-oscar | django | 3,825 | Default Language is not set | I have in my settings.py:
LANGUAGE_CODE = 'de'
gettext_noop = lambda s: s
LANGUAGES = (
('de', gettext_noop('German')),
('en-gb', gettext_noop('British English')),
)
os.environ["LANGUAGE"] = 'de'
If i open Url / then i get a redirect to english page /en-gb/catalogue/. I would expect /de/catalogue. If i open /de/catalogue/ manual it works but redirect from / don't work as expect. | closed | 2021-12-14T11:42:29Z | 2021-12-18T18:26:57Z | https://github.com/django-oscar/django-oscar/issues/3825 | [] | Bastilla123 | 1 |
Anjok07/ultimatevocalremovergui | pytorch | 947 | ValueError | Last Error Received:
Process: VR Architecture
If this error persists, please contact the developers with the error details.
Raw Error Details:
ValueError: "zero-size array to reduction operation maximum which has no identity"
Traceback Error: "
File "UVR.py", line 6565, in process_start
File "separate.py", line 1042, in seperate
File "separate.py", line 345, in final_process
File "separate.py", line 409, in write_audio
File "separate.py", line 382, in save_with_message
File "separate.py", line 352, in save_audio_file
File "lib_v5/spec_utils.py", line 84, in normalize
if is_normalize:
File "numpy/core/_methods.py", line 40, in _amax
"
Error Time Stamp [2023-11-05 14:26:22]
Full Application Settings:
vr_model: 1_HP-UVR
aggression_setting: 5
window_size: 512
mdx_segment_size: 256
batch_size: Default
crop_size: 256
is_tta: False
is_output_image: False
is_post_process: False
is_high_end_process: False
post_process_threshold: 0.2
vr_voc_inst_secondary_model: No Model Selected
vr_other_secondary_model: No Model Selected
vr_bass_secondary_model: No Model Selected
vr_drums_secondary_model: No Model Selected
vr_is_secondary_model_activate: False
vr_voc_inst_secondary_model_scale: 0.9
vr_other_secondary_model_scale: 0.7
vr_bass_secondary_model_scale: 0.5
vr_drums_secondary_model_scale: 0.5
demucs_model: v4 | htdemucs
segment: Default
overlap: 0.25
overlap_mdx: Default
overlap_mdx23: 8
shifts: 2
chunks_demucs: Auto
margin_demucs: 44100
is_chunk_demucs: False
is_chunk_mdxnet: False
is_primary_stem_only_Demucs: False
is_secondary_stem_only_Demucs: False
is_split_mode: True
is_demucs_combine_stems: True
is_mdx23_combine_stems: True
demucs_voc_inst_secondary_model: No Model Selected
demucs_other_secondary_model: No Model Selected
demucs_bass_secondary_model: No Model Selected
demucs_drums_secondary_model: No Model Selected
demucs_is_secondary_model_activate: False
demucs_voc_inst_secondary_model_scale: 0.9
demucs_other_secondary_model_scale: 0.7
demucs_bass_secondary_model_scale: 0.5
demucs_drums_secondary_model_scale: 0.5
demucs_pre_proc_model: No Model Selected
is_demucs_pre_proc_model_activate: False
is_demucs_pre_proc_model_inst_mix: False
mdx_net_model: Choose Model
chunks: Auto
margin: 44100
compensate: Auto
denoise_option: None
is_match_frequency_pitch: True
phase_option: Automatic
phase_shifts: None
is_save_align: False
is_match_silence: True
is_spec_match: False
is_mdx_c_seg_def: False
is_invert_spec: False
is_deverb_vocals: False
deverb_vocal_opt: Main Vocals Only
voc_split_save_opt: Lead Only
is_mixer_mode: False
mdx_batch_size: Default
mdx_voc_inst_secondary_model: No Model Selected
mdx_other_secondary_model: No Model Selected
mdx_bass_secondary_model: No Model Selected
mdx_drums_secondary_model: No Model Selected
mdx_is_secondary_model_activate: False
mdx_voc_inst_secondary_model_scale: 0.9
mdx_other_secondary_model_scale: 0.7
mdx_bass_secondary_model_scale: 0.5
mdx_drums_secondary_model_scale: 0.5
is_save_all_outputs_ensemble: True
is_append_ensemble_name: False
chosen_audio_tool: Manual Ensemble
choose_algorithm: Min Spec
time_stretch_rate: 2.0
pitch_rate: 2.0
is_time_correction: True
is_gpu_conversion: True
is_primary_stem_only: False
is_secondary_stem_only: False
is_testing_audio: False
is_auto_update_model_params: True
is_add_model_name: False
is_accept_any_input: False
is_task_complete: False
is_normalization: False
is_wav_ensemble: False
is_create_model_folder: False
mp3_bit_set: 320k
semitone_shift: 0
save_format: MP3
wav_type_set: PCM_16
help_hints_var: True
set_vocal_splitter: No Model Selected
is_set_vocal_splitter: False
is_save_inst_set_vocal_splitter: False
model_sample_mode: False
model_sample_mode_duration: 30
demucs_stems: All Stems
mdx_stems: All Stems | open | 2023-11-05T03:27:48Z | 2023-11-05T03:27:48Z | https://github.com/Anjok07/ultimatevocalremovergui/issues/947 | [] | securis3000 | 0 |
modelscope/modelscope | nlp | 1,050 | cannot import name 'OfflineModeIsEnabled' |
**Describe the bug**
```
from modelscope.pipelines import pipeline
```
leads to error :
```
cannot import name 'OfflineModeIsEnabled' from 'datasets.utils.file_utils'
```
**To Reproduce**
1. install latest version (1.19.2)
2. from modelscope.pipelines import pipeline
**Your Environments (__required__)**
* OS: `Ubuntu 20.04`
Pipeline related: @tastelikefeet @wangxingjun778 | closed | 2024-10-24T08:34:15Z | 2024-10-26T08:25:06Z | https://github.com/modelscope/modelscope/issues/1050 | [] | emmettng | 7 |
tortoise/tortoise-orm | asyncio | 1,169 | How to specify mysql column name?? | ```python
class Device(models.Model)
type = ForeignKeyField("models.Type", related_name="devices")
```
I want the column name in the database to be type instead of type_id. I have tried many methods but failed. What should I do?
| closed | 2022-06-30T09:12:51Z | 2022-07-07T03:08:47Z | https://github.com/tortoise/tortoise-orm/issues/1169 | [] | tufbel | 1 |
numpy/numpy | numpy | 27,811 | TYP: Wrong type hint for `interp` | ### Describe the issue:
The type hints for `interp` assert that the return dtype is a numpy array, but when the function is called with a scaler it gives a float. This raises some mypy errors.
https://github.com/numpy/numpy/blob/b85a149fea2c005d395772fcc6078ce370a6bcc4/numpy/lib/_function_base_impl.pyi#L306-L323
### Reproduce the code example:
```python
>>> np.interp(0, [1, 2], [3, 4])
np.float64(3.0)
```
### Error message:
```shell
my_code.py::123 error: Incompatible return value type (got "ndarray[Any, Any]", expected "float") [return-value]
```
### Python and NumPy Versions:
python: 3.12.2
numpy: 2.1.3
### Runtime Environment:
_No response_
### Context for the issue:
_No response_ | closed | 2024-11-21T17:52:44Z | 2025-01-09T17:53:22Z | https://github.com/numpy/numpy/issues/27811 | [
"00 - Bug",
"41 - Static typing"
] | NoureldinYosri | 0 |
strawberry-graphql/strawberry | django | 3,777 | Memory leak in subscriptions | Release 0.240.0 introduced a regression in GQL subscriptions. When WS connection is closed abruptly, subscription coroutine isn't properly finished causing memory leaks. The problem does NOT occur when the subscription ends on its own or when the connection is closed by the client-side.
## Reproduction steps
Please find the attached snippets to reproduce the issue.
```python
# test_server.py
import asyncio
import logging
import uuid
from typing import AsyncGenerator
import strawberry
from fastapi import FastAPI
from strawberry import Schema
from strawberry.fastapi import GraphQLRouter
logging.basicConfig(level=logging.INFO, format="[%(asctime)s] %(message)s")
logger = logging.getLogger()
@strawberry.type
class Query:
"""
Just a stub because strawberry requires non-empty Query type.
"""
@strawberry.field()
def active_subscriptions(self) -> int:
return len(active_subscriptions)
# some globally available set to count active subscriptions
active_subscriptions = set()
@strawberry.type
class Subscription:
@strawberry.subscription()
async def test(self) -> AsyncGenerator[str, None]:
# add subscription to the set to track active connections
subscription_id = uuid.uuid4()
active_subscriptions.add(subscription_id)
logger.info(f"[{subscription_id}] Started new subscription (active = {len(active_subscriptions)})")
try:
# return a single value after 5 seconds and finish the subscription
await asyncio.sleep(5)
yield "finished"
logger.info(f"[{subscription_id}] Returning value after 5 seconds")
finally:
# remove subscription from the set
active_subscriptions.remove(subscription_id)
logger.info(f"[{subscription_id}] Subscription finished (active = {len(active_subscriptions)})")
schema = Schema(
query=Query,
subscription=Subscription,
)
app = FastAPI()
app.include_router(
GraphQLRouter(
schema,
),
prefix="/graphql",
)
```
```python
# test_client.py
import asyncio
import sys
import time
from multiprocessing import Process
from gql import gql, Client
from gql.transport.websockets import WebsocketsTransport
transport = WebsocketsTransport(
url="ws://localhost:8000/graphql",
)
async def start_listening():
async with Client(
transport=transport,
fetch_schema_from_transport=False,
) as session:
query = gql("""
subscription onTest {
test
}
""")
print("Connecting")
async for msg in session.subscribe(query):
print("Received:", msg)
print("Disconnected")
def main():
asyncio.run(start_listening())
if __name__ == '__main__':
wait_for = int(sys.argv[1]) # read timeout value from argv
p = Process(target=main)
p.start()
time.sleep(wait_for)
if p.is_alive():
print("Killing subprocess")
p.kill()
```
```
# requirements.txt
annotated-types==0.7.0
anyio==4.8.0
backoff==2.2.1
click==8.1.8
fastapi==0.115.8
gql==3.5.0
graphql-core==3.2.6
h11==0.14.0
idna==3.10
multidict==6.1.0
propcache==0.2.1
pydantic==2.10.6
pydantic_core==2.27.2
python-dateutil==2.9.0.post0
six==1.17.0
sniffio==1.3.1
starlette==0.45.3
strawberry-graphql==0.259.0
typing_extensions==4.12.2
uvicorn==0.34.0
websockets==14.2
yarl==1.18.3
```
Start the server using:
```shell
uvicorn test_server:app
```
Run the `test_client.py` script a couple of times using:
```shell
python test_client.py 2
```
When tested using `python==3.11.11` and the requirements posted above, it generates the following logs proving that the connections aren't properly closed server-side (see the growing number of active connection and the lack of matching "Subscription finished" messages):
```plain
INFO: Started server process [55734]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO: ('127.0.0.1', 65042) - "WebSocket /graphql" [accepted]
INFO: connection open
[2025-02-11 14:57:24,900] [3cfcf4eb-e119-4c4e-8b23-9d2fb5ddeb92] Started new subscription (active = 1)
INFO: connection closed
INFO: ('127.0.0.1', 65044) - "WebSocket /graphql" [accepted]
INFO: connection open
[2025-02-11 14:58:08,408] Task exception was never retrieved
future: <Task finished name='Task-10' coro=<BaseGraphQLWSHandler.handle_async_results() done, defined at /Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/strawberry/subscriptions/protocols/graphql_ws/handlers.py:160> exception=RuntimeError("Unexpected ASGI message 'websocket.send', after sending 'websocket.close' or response already completed.")>
Traceback (most recent call last):
File "/Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/strawberry/subscriptions/protocols/graphql_ws/handlers.py", line 188, in handle_async_results
await self.send_data_message(result, operation_id)
File "/Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/strawberry/subscriptions/protocols/graphql_ws/handlers.py", line 222, in send_data_message
await self.send_message(data_message)
File "/Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/strawberry/subscriptions/protocols/graphql_ws/handlers.py", line 225, in send_message
await self.websocket.send_json(message)
File "/Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/strawberry/asgi/__init__.py", line 110, in send_json
await self.ws.send_text(self.view.encode_json(message))
File "/Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/starlette/websockets.py", line 165, in send_text
await self.send({"type": "websocket.send", "text": data})
File "/Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/starlette/websockets.py", line 85, in send
await self._send(message)
File "/Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 39, in sender
await send(message)
File "/Users/jbacic/Desktop/strawberry-bug/.venv/lib/python3.11/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 359, in asgi_send
raise RuntimeError(msg % message_type)
RuntimeError: Unexpected ASGI message 'websocket.send', after sending 'websocket.close' or response already completed.
[2025-02-11 14:58:08,410] [cd32c121-064b-4ab7-9ff7-e9034bb5a56c] Started new subscription (active = 2)
[2025-02-11 14:58:08,410] [3cfcf4eb-e119-4c4e-8b23-9d2fb5ddeb92] Subscription finished (active = 1)
INFO: connection closed
INFO: ('127.0.0.1', 65046) - "WebSocket /graphql" [accepted]
INFO: connection open
[2025-02-11 14:58:15,408] [630da7f5-56fd-4301-9345-855eb6242c9c] Started new subscription (active = 2)
INFO: connection closed
INFO: ('127.0.0.1', 65047) - "WebSocket /graphql" [accepted]
INFO: connection open
[2025-02-11 14:58:19,129] [d361025b-2878-49ea-89c0-b2f3c58ab20e] Started new subscription (active = 3)
INFO: connection closed
```
Same scenario executed against `strawberry-graphql==0.239.2` yields the following results:
```plain
INFO: Started server process [55873]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO: ('127.0.0.1', 65061) - "WebSocket /graphql" [accepted]
INFO: connection open
[2025-02-11 15:01:40,706] [e0a3b742-533e-4bbd-8dd6-6e068ff70b9e] Started new subscription (active = 1)
INFO: connection closed
[2025-02-11 15:01:42,505] [e0a3b742-533e-4bbd-8dd6-6e068ff70b9e] Subscription finished (active = 0)
INFO: ('127.0.0.1', 65062) - "WebSocket /graphql" [accepted]
INFO: connection open
[2025-02-11 15:01:44,672] [5745715e-ff41-49af-9551-e2ac0ffefdd2] Started new subscription (active = 1)
INFO: connection closed
[2025-02-11 15:01:46,516] [5745715e-ff41-49af-9551-e2ac0ffefdd2] Subscription finished (active = 0)
INFO: ('127.0.0.1', 65063) - "WebSocket /graphql" [accepted]
INFO: connection open
[2025-02-11 15:01:48,253] [14240b03-b406-41ef-86b6-b803a59dd5e8] Started new subscription (active = 1)
INFO: connection closed
[2025-02-11 15:01:50,100] [14240b03-b406-41ef-86b6-b803a59dd5e8] Subscription finished (active = 0)
```
This regression was most likely introduced in this (https://github.com/strawberry-graphql/strawberry/pull/3554) and because of that, all versions `>= 0.240.0` are affected by this. This bug prevents us from upgrading `strawberry-graphql` in our project. Luckily, we noticed the memory leak in our staging environment first and it didn't reach production.
## System Information
- Operating system: Mac OS / Linux (docker container) and python 3.11 (but it's most probably reproducible on other python versions as well)
- Strawberry version: >= 0.240.0 (0.239.2 works as expected)
## Additional notes
As mentioned in the beginning, the problem doesn't occur when connection is closed more gently:
- test is executed with `python test_client.py 7` - strawberry ends the subscription before the client is killed
- client disconnects (e.g. by applying `asyncio.timeout` context manager) instead of being killed (that's why I used `multiprocessing` module in `test_client.py` to reproduce the issue) | closed | 2025-02-11T14:14:16Z | 2025-02-27T11:59:53Z | https://github.com/strawberry-graphql/strawberry/issues/3777 | [
"bug"
] | jakub-bacic | 2 |
explosion/spaCy | data-science | 13,351 | Incorrect detection of sentence boundaries, if last sentence missing eos symbol for trf model | ## How to reproduce the behaviour
```
In [69]: len(list(spacy.load("en_core_web_trf")("The first sentence. The second sentence. The last one").sents))
Out[69]: 1 <<<<<<<<<<<<<<<<<<<<<< WRONG
In [70]: len(list(spacy.load("en_core_web_trf")("The first sentence. The second sentence. The last one.").sents))
Out[70]: 3
In [71]: len(list(spacy.load("en_core_web_sm")("The first sentence. The second sentence. The last one").sents))
Out[71]: 3
In [72]: len(list(spacy.load("en_core_web_sm")("The first sentence. The second sentence. The last one.").sents))
Out[72]: 3
```
## Your Environment
* Operating System: max os x 10.3
* Python Version Used: 3.11
* spaCy Version Used: 3.7.4
* Environment Information:
```
en_core_web_trf.__version__ >> '3.7.3'
en_core_web_sm.__version__ >> '3.7.1'
``` | closed | 2024-02-25T20:50:50Z | 2024-02-27T12:58:36Z | https://github.com/explosion/spaCy/issues/13351 | [
"feat / parser",
"perf / accuracy"
] | koder-ua | 0 |
quokkaproject/quokka | flask | 646 | archives (date grouped content) | /archives view with aggregation by year/month
https://github.com/rochacbruno/quokka_ng/issues/21 | open | 2018-02-07T01:59:45Z | 2018-02-07T01:59:45Z | https://github.com/quokkaproject/quokka/issues/646 | [
"1.0.0",
"hacktoberfest"
] | rochacbruno | 0 |
nschloe/tikzplotlib | matplotlib | 244 | Not working with Seaborn | Dear friends,
I am using seaborn. See the example bellow:
```
criterion_raw_dataframe = pd.DataFrame(data=criterion_raw_dict)
print(criterion_raw_dataframe)
ax = sns.lineplot(x="Epoch", y="Value", hue="Type", data=criterion_raw_dataframe)
ax.set_title("Criteria")
plt.show()
plt.savefig(os.path.join(path, 'criterion_raw'))
tikz_save(os.path.join(path, 'criterion_raw.tex'), figurewidth='\\0.5textwidth')
criterion_raw_dataframe.to_csv(os.path.join(path, 'criterion_raw.csv'), encoding='utf-8', index=False)
plt.close()
```
The resulting tex file is the following:
```
% This file was created by matplotlib2tikz v0.6.17.
\begin{tikzpicture}
\end{tikzpicture}
```
My question is: should have worked with Seaborn?
| closed | 2018-08-02T05:35:44Z | 2018-08-02T05:49:13Z | https://github.com/nschloe/tikzplotlib/issues/244 | [] | dlmacedo | 0 |
jumpserver/jumpserver | django | 14,547 | [Bug] IPv6 clients unable to login starting from 4.4.1 | ### Product Version
v4.4.1
### Product Edition
- [X] Community Edition
- [ ] Enterprise Edition
- [ ] Enterprise Trial Edition
### Installation Method
- [X] Online Installation (One-click command installation)
- [ ] Offline Package Installation
- [ ] All-in-One
- [ ] 1Panel
- [ ] Kubernetes
- [ ] Source Code
### Environment Information
Jumpserver is installed on a IPv6-only machine and load-balanced by nginx.
### 🐛 Bug Description
Unable to login when client ip is IPv6. Error when writing audit log.
### Recurrence Steps
1. Login with an IPv6 address
### Expected Behavior
```
jms_postgresql | 2024-11-28 18:21:07.735 CST [335] ERROR: invalid input syntax for type inet: "2400" at character 221
jms_postgresql | 2024-11-28 18:21:07.735 CST [335] STATEMENT: INSERT INTO "audits_userloginlog" ("id", "username", "type", "ip", "city", "user_agent", "mfa", "reason", "status", "datetime", "backend") VALUES ('bbfe7a8d-c711-4bab-b218-99bf951491a2'::uuid, '*', 'W', '2400'::inet, 'Unknown', '***', 1, '', true, '2024-11-28T10:21:07.734824+00:00'::timestamptz, 'Password')
```
### Additional Information
_No response_
### Attempted Solutions
_No response_ | closed | 2024-11-28T10:25:08Z | 2024-12-19T10:47:46Z | https://github.com/jumpserver/jumpserver/issues/14547 | [
"🐛 Bug",
"✅ Done",
"⏳ Pending feedback",
"📦 z~release:v4.5.0"
] | wfjsw | 6 |
CorentinJ/Real-Time-Voice-Cloning | deep-learning | 1,100 | Numpy Unavailable/what versions of programs do I use to get this working? | Trying to run the program and have installed using:
Python 3.10.5
CUDA 11.6
Pytorch 1.12.0
I can open the window fine, but numpy will not install. I've tried older versions and other things will break. What specific versions of these programs do I need to install? | open | 2022-08-01T21:58:02Z | 2022-08-06T17:07:07Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1100 | [] | Meepmeep189 | 1 |
sunscrapers/djoser | rest-api | 101 | RemovedInDjango110Warning: django.conf.urls.patterns() is deprecated and will be removed in Django 1.10 | ```
RemovedInDjango110Warning: django.conf.urls.patterns() is deprecated and will be removed in Django 1.10. Update your urlpatterns to be a list of django.conf.urls.url() instances instead.
urlpatterns = base_urlpatterns + patterns('', url(r'^$', views.RootView.as_view(), name='root'))
```
| closed | 2015-12-02T13:20:12Z | 2016-02-24T10:58:15Z | https://github.com/sunscrapers/djoser/issues/101 | [] | ochronus | 1 |
aleju/imgaug | deep-learning | 780 | imgaug converts grayscale image to RGB! | I am trying to add some augmentations to some of my grayscale images using
```
def augment_it(img):
seq = iaa.OneOf( [
iaa.ScaleX((0.7,1.3), cval=255),
iaa.CoarseDropout(0.02, size_percent=(0.02,0.2)) #I want the new pixels to be white in colour.
])
# aug_img = iaa.Grayscale(1.0)
aug_img = seq(image=img)
return aug_img
```
I use this as the preprocessing function with imagedatagenerator in keras.
But this function returns me an RGB image with 3 channels (the coarse dropout being in any of the colours)
What should I do to keep my images grayscale? | closed | 2021-07-19T14:34:56Z | 2021-07-20T05:18:32Z | https://github.com/aleju/imgaug/issues/780 | [] | jayanthante | 0 |
keras-team/autokeras | tensorflow | 1,569 | [BUG] Deffault hyper opts produce wrong models | ### Bug Description
Found thad deafult hyper-opt tuning mechanism producec invalid options combinations
```
Hyperparameter |Value |Best Value So Far
rnn_block_1/layer_type |gru |gru
rnn_block_1/num_layers |1 |1
rnn_block_2/layer_type |gru |gru
rnn_block_2/num_layers |3 |3
regression_head_1/dropout |0.5 |0
dense_block_1/use_batchnorm |False |False
dense_block_1/num_layers |2 |2
dense_block_1/units_0 |32 |32
dense_block_1/dropout |0.5 |0.5
dense_block_1/units_1 |32 |32
classification_head_1/dropout |0 |0
optimizer |adam |adam
learning_rate |0.001 |0.001
dense_block_1/units_2 |1024 |None
```
As you could see it tries to build model with
```
dense_block_1/num_layers |2
dense_block_1/units_0 |32
dense_block_1/units_1 |32
dense_block_1/units_2 |1024
```
### Bug Reproduction
Code for reproducing the bug:
```py
layer = ak.Input()
layer = ak.DenseBlock()(layer)
class = ak.ClassificationHead()(layer)
```
Data used by the code: `Any data`
### Setup Details
Include the details about the versions of:
- OS type and version: Win10
- Python: 3.5, 3.8
- autokeras: 1.0.12
- keras-tuner: 1.0.2
- scikit-learn: 0.24.0
- numpy: 1.19.5
- pandas: 1.2.0
- tensorflow: 2.4.0
| closed | 2021-05-21T12:08:26Z | 2021-07-28T04:36:54Z | https://github.com/keras-team/autokeras/issues/1569 | [
"wontfix"
] | holinov | 1 |
fastapi/fastapi | asyncio | 13,023 | StreamingResponse was block my program??? | ### Privileged issue
- [X] I'm @tiangolo or he asked me directly to create an issue here.
### Issue Content
async def generate_predictions(model_name):
global stop_flag
mean, std, labels, model, pca_model = get_init(model_name)
async for prediction in collect_predict(mean, std, labels, model, pca_model):
if stop_flag == True:
stop_flag = False
break
yield json.dumps({"prediction": prediction}) + "\n"
@router.get("/end_predict")
def end_predict():
global stop_flag
stop_flag = True
return {'message' : 'End predict'}
@router.post("/predict")
def predict_func(data: predict):
model_name = data.name_predict
return StreamingResponse(
generate_predictions(model_name),
media_type="application/json"
)
| closed | 2024-12-02T15:37:55Z | 2024-12-02T16:22:27Z | https://github.com/fastapi/fastapi/issues/13023 | [] | noaft | 0 |
microsoft/hummingbird | scikit-learn | 344 | Weirdness with sphinx docs | It seems that sphinx doc generation in HB works with torch==1.5 but not 1.6.
Several of the files (ex: `onnx_converter.py`, `iforest.py` get an error such as:
```
134Handler <function doctree_read at 0x7f6aa2c89d40> for event 'doctree-read' threw an exception (exception: type object 'Cast' has no attribute 'training')
```
There are mentions around the `torch` forums on `has no attribute 'training`. Ex: [this torch sample](https://github.com/blue-season/pywarm/issues/3). In general, this error message seems nonsensical, as many of the errors occur in python files that do not involve torch.
Further investigation needed.
For now, using torch==1.5 in the pipeline run for doc gen. | closed | 2020-10-23T20:15:03Z | 2020-10-30T20:30:57Z | https://github.com/microsoft/hummingbird/issues/344 | [] | ksaur | 2 |
jowilf/starlette-admin | sqlalchemy | 535 | Bug: Cannot resolve field "id" | **Describe the bug**
A clear and concise description of what the bug is.
i want to use 'user_id' as the primary key for mongoengine ORM, but when i click the [edit] button from the author admin view, it shows:
mongoengine.errors.InvalidQueryError: Cannot resolve field "id"
so, is the 'id' name the only one ? could i use annother name, such as 'user_id' as the primary key?
**To Reproduce**
Explain how to reproduce the bug.
class Author(db.Document):
meta = {
'collection': 'author_profile'
}
user_id = db.StringField(min_length=3)
name = db.StringField(min_length=3)
description= db.StringField(max_length=20)
class AuthorView(ModelView):
pk_attr = "user_id"
fields = [
"name",
StringField('description')
]
**Environment (please complete the following information):**
- Starlette-Admin version: [0.4.0]
- ORM/ODMs: [MongoEngine]
**Additional context**
Add any other context about the problem here.
| open | 2024-04-16T15:57:20Z | 2024-04-16T15:57:20Z | https://github.com/jowilf/starlette-admin/issues/535 | [
"bug"
] | binhetech | 0 |
lundberg/respx | pytest | 155 | Enhance the docs | - User Guide
- Mock HTTPX
- [x] Add ` Using the pytest Fixture` section
- Routing Requests
- [x] Mention that the order of added routes matters as discussed in
#129
- Mocking Responses
- [x] Mention identical API to the python built-in `Mock`
e.g. side effect has precedence over return value, and iterable stop iteration vs return value.
- [ ] ~Add `Streaming Responses` section with some examples, relates to~
Moved to #156
- [x] Add `Mock using an app`
- [x] Document new optional `route` argument in side effects | closed | 2021-07-08T13:46:07Z | 2021-09-17T14:52:30Z | https://github.com/lundberg/respx/issues/155 | [] | lundberg | 0 |
bmoscon/cryptofeed | asyncio | 335 | duplicate | closed | 2020-11-27T07:45:13Z | 2020-11-27T07:54:31Z | https://github.com/bmoscon/cryptofeed/issues/335 | [
"bug"
] | DavidOtherAcc | 0 | |
iam-abbas/FastAPI-Production-Boilerplate | rest-api | 3 | Pydantic BaseSetting is not reading .env file | I copied .env.example to .env in the root directory. Pydantic Config is not reading and not setting variables and neither overriding existing variables. | closed | 2023-04-21T21:40:53Z | 2023-04-24T22:27:01Z | https://github.com/iam-abbas/FastAPI-Production-Boilerplate/issues/3 | [] | vnktsh | 1 |
jina-ai/serve | machine-learning | 5,480 | bug: indexing flow breaks on network problems | The user code for the jina client has to look like this:
```python
for batch in create_batches(da):
try:
batch.index(batch)
except:
pass
```
Please improve the usability. Something like this should be the functionality of the jina client | closed | 2022-12-02T10:32:39Z | 2022-12-22T20:08:54Z | https://github.com/jina-ai/serve/issues/5480 | [] | florian-hoenicke | 3 |
Johnserf-Seed/TikTokDownload | api | 42 | 链接输进去就是闪退，闪退怎么查看报错信息？ | closed | 2021-08-08T01:35:01Z | 2021-08-08T01:52:54Z | https://github.com/Johnserf-Seed/TikTokDownload/issues/42 | [] | gexiaoqing2009 | 1 | |
stanford-oval/storm | nlp | 6 | gender diversity impacts the writing style | closed | 2024-04-13T06:52:34Z | 2024-04-13T18:33:35Z | https://github.com/stanford-oval/storm/issues/6 | [] | creativehuivy | 0 | |
ageitgey/face_recognition | machine-learning | 1,057 | Text Extraction from Images | * currency_recognition:
* Python version:
*Windows
Description
I did extraction of text from image. I got the following error. I have to convert text from images, but I'm not able to do the so.
What I Did
error Traceback (most recent call last)
<ipython-input-21-b43ba1abd8e9> in <module>
27
28 return result
---> 29 print(get_string(src_path + "aa.png"))
<ipython-input-21-b43ba1abd8e9> in get_string(img_path)
12
13 # Convert to gray
---> 14 img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
15 # Apply dilation and erosion to remove some noise
16 kernel = np.ones((1, 1), np.uint8)
error: OpenCV(4.0.1) C:\ci\opencv-suite_1573470242804\work\modules\imgproc\src\color.cpp:181: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'
| open | 2020-02-19T16:38:54Z | 2020-02-19T16:38:54Z | https://github.com/ageitgey/face_recognition/issues/1057 | [] | kaviyasri105 | 0 |
yunjey/pytorch-tutorial | deep-learning | 158 | Add some things to gitignore | Add some general Python things as well as repo-specific stuff to the `gitignore`. Some examples include the following:
```
__pycache__/
logs/
venv/
env.sh
``` | open | 2019-02-17T02:20:58Z | 2019-02-17T02:20:58Z | https://github.com/yunjey/pytorch-tutorial/issues/158 | [] | sordonia120446 | 0 |
JaidedAI/EasyOCR | deep-learning | 794 | Adjusting Custom Model's Hyperparameter's is allowed, but not functional. | The codebase allows for custom models to have hyperparameters input programmatically, when that doesn't work. I'm sure this is intentional, but there's no documentation on the issue. There should be some sort of warning/exception for adjusting hyperparameters programmatically of a custom model, as a user may waste time on ineffective hyperparameter tuning. | open | 2022-07-21T17:40:01Z | 2022-07-21T17:40:01Z | https://github.com/JaidedAI/EasyOCR/issues/794 | [] | macksjeremy | 0 |
Yorko/mlcourse.ai | data-science | 351 | Topic 6 | In kaggle kernel In [38] it is said that the performance got worse when it actually got better.
https://www.kaggle.com/kashnitsky/topic-6-feature-engineering-and-feature-selection
In habr article however the numbers have really gotten worse.
Also in assignment 6 "Train a LASSO model " it is not said whether I should train on scaled X or original X. As I understand it should be scaled X to perform next task on finding least important features | closed | 2018-09-21T18:57:18Z | 2018-11-09T16:37:06Z | https://github.com/Yorko/mlcourse.ai/issues/351 | [] | Vozf | 2 |
ultralytics/yolov5 | pytorch | 13,313 | Report errors while continuing training | ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
When I run train.py for target detection, my resume is set to True and I want to continue my previous training. However, when I have last.pt in my tarin-seg folder, I will read the tarin-seg file directly, instead of reading the weight under the tarin file that my target detects. I don't quite understand this question, please answer it for me. Is that my problem.
### Additional
_No response_ | open | 2024-09-14T09:16:38Z | 2024-11-09T02:25:11Z | https://github.com/ultralytics/yolov5/issues/13313 | [
"question"
] | pjh11214 | 2 |
jina-ai/clip-as-service | pytorch | 337 | Server Fails to Start | @hanxiao I am getting the same error on Ubuntu 16.04 with version 1.8.9
I am starting the server from python code (below). The machine has two GPUs. But I get this even if I try to force run on cpus.
```
def startBERTServer(self):
if self.device == -1:
args = get_args_parser().parse_args(['-model_dir', self.bertModelPath,
'-port', '5555',
'-port_out', '5556',
'-max_seq_len', 'NONE',
'-pooling_strategy', 'NONE',
'-cpu'])
else:
args = get_args_parser().parse_args(['-model_dir', self.bertModelPath,
'-port', '5555',
'-port_out', '5556',
'-max_seq_len', 'NONE',
'-pooling_strategy', 'NONE'])
self.server = BertServer(args)
self.server.start()
```
<img width="1675" alt="Screen Shot 2019-05-04 at 2 25 31 PM" src="https://user-images.githubusercontent.com/44900081/57184999-0249cd00-6e79-11e9-9f21-6d7330ce5b1c.png">
| open | 2019-05-05T16:43:47Z | 2019-05-07T17:21:48Z | https://github.com/jina-ai/clip-as-service/issues/337 | [] | ashutosh-modi | 0 |
adbar/trafilatura | web-scraping | 537 | Downloads: Add ZStandard as optional Accept-Encoding header | - If the corresponding Python package is installed, add [Accept-Encoding: zstd](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Accept-Encoding) to HTTP headers and request processing
- Review the Accept-Encoding headers in general (e.g. quality value syntax) | closed | 2024-04-03T11:15:10Z | 2024-05-15T16:12:16Z | https://github.com/adbar/trafilatura/issues/537 | [
"enhancement"
] | adbar | 0 |
NVIDIA/pix2pixHD | computer-vision | 252 | how does the program match the input photo with its corresponding real_image | I have gotten a dataset which contains pairs of photos, labels and ground truth. Is it really enough to place the labels in one directory called train_A and the ground_truth in another directory called train_B? I did my experiment with setting like this but found that the program would find false real_images for some input label? An example is shown below:
As what I know in pix2pix, the program takes in pairs of photos, and one pair of photo should be placed in one file.
| closed | 2021-03-19T07:36:47Z | 2021-03-31T08:10:10Z | https://github.com/NVIDIA/pix2pixHD/issues/252 | [] | 1999kevin | 6 |
django-import-export/django-import-export | django | 1,073 | Foreign Key architecture issue | Hello,
Thank you for your library it is really awesome.
I am facing an issue because I am building a database, its purpose is to translate json to db and db to json.
So I have a json looking like this ( I simplify a lot)
There are the corresponding models:
and I aim to reproduce the entire json so I have an other model on wich I have foreign key pointing to these models:
I created a resource like this, actually I want to get 'field1' and no uuid but it doesn't matter right now
So as you can see the result is a new field 'agricole__uuid' with the right value. But I loose all my architecture. What I want as explained on the images is to get my value nested as it should.
Is the import/export library made for this or should I code it my self ?
Thank you :)
| closed | 2020-01-28T12:08:51Z | 2023-04-12T13:56:41Z | https://github.com/django-import-export/django-import-export/issues/1073 | [
"question"
] | Kimor-hello | 4 |
feder-cr/Jobs_Applier_AI_Agent_AIHawk | automation | 358 | Add user checkpoints | This is more of a design request. But it would be good to allow the user a little more control over a few things in this automated process:
- Approving / denying which jobs to apply for
- Approving / editing the customized CVs
This might require some thinking around how to re-design the flow of this application. | closed | 2024-09-12T06:24:32Z | 2024-10-23T02:01:34Z | https://github.com/feder-cr/Jobs_Applier_AI_Agent_AIHawk/issues/358 | [] | youngchingjui | 6 |
nschloe/tikzplotlib | matplotlib | 327 | Ticks Issue | Hi,
I executed the exact same example code given in the manual and rendered the picture in Latex.
However, the outcome differs.
The axis ticks are shown outside the plot and on all 4 edges, which looks silly.
I run the Python plot on python 3.7 and use TexMaker for editing the Latex file.
Any leads?
| open | 2019-08-17T11:33:42Z | 2020-01-16T17:03:19Z | https://github.com/nschloe/tikzplotlib/issues/327 | [] | johanneskrost | 2 |
computationalmodelling/nbval | pytest | 28 | Check links (URLs) in notebook | A possible extension of the notebook testing: can we check URLs (in the markdown presumably) to see if those are accessible (i.e. URL linting)?
This could be activated by an additional switch (maybe `--url-lint` or so) and report failures if a URL is not accessible. (To run the test, Internet access will be required.)
This was proposed at the Jupyter Workshop January 2017 in Edinburgh (http://opendreamkit.org/meetings/2017-01-16-ICMS/programme/). | closed | 2017-01-17T12:31:53Z | 2017-02-20T15:50:10Z | https://github.com/computationalmodelling/nbval/issues/28 | [
"enhancement"
] | fangohr | 7 |
youfou/wxpy | api | 372 | new-core emoji 3120e3 解码错误 | 3120e3 应该是 1⃣️
| open | 2019-03-16T06:27:48Z | 2019-03-16T06:27:48Z | https://github.com/youfou/wxpy/issues/372 | [] | xuefer | 0 |
davidteather/TikTok-Api | api | 634 | Do I need chromedriver inside Docker? Or should I pull playwright's image | ### Discussed in https://github.com/davidteather/TikTok-Api/discussions/633
<div type='discussions-op-text'>
<sup>Originally posted by **alvaroserrrano** June 25, 2021</sup>
I would like to use a Docker container for an application. I am currently installing chromedriver inside the docker container because the api said "chromedriver needs to be in path". Is this step really necessary? Could that be the reason why I am having a conflict with playwright to which I get the log ```"please run python -m install playwright"``` (which does not make sense). I have also seen people say that there is a conflict between playwright and pyppeteer, but I do not think my problem is related to that.
As of right now I am installing requirements as usual and the running python -m playwright install, which I believe is redundant and not really necessary.
Also, I can see that Dockerfile of the api pulls from the microfts/playwright image and then installs python3-pip. Do I have to use this in my Dockerfile? My base image is FROM python3.8. Perhaps I should create a virtual environment inside the container?
Thanks a lot for your help</div> | closed | 2021-06-25T11:35:21Z | 2021-08-07T00:13:29Z | https://github.com/davidteather/TikTok-Api/issues/634 | [] | alvaroserrrano | 1 |
Lightning-AI/pytorch-lightning | deep-learning | 20,032 | Lightning vulnerability CVE-2024-5980 | ### Bug description
According to https://github.com/advisories/GHSA-mr7h-w2qc-ffc2, all latest lightning version are vulnerable. Could you please detail how you want to fix this or whether support from the community would be helpful?
### What version are you seeing the problem on?
v1.8, v1.9, v2.0, v2.1, v2.2
### How to reproduce the bug
_No response_
### Error messages and logs
_No response_
### Environment
_No response_
### More info
_No response_ | closed | 2024-07-01T06:38:06Z | 2024-07-08T21:00:00Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20032 | [
"bug",
"ver: 2.0.x",
"ver: 1.9.x",
"ver: 1.8.x",
"ver: 2.1.x"
] | lukas-folle-snkeos | 4 |
pallets-eco/flask-sqlalchemy | sqlalchemy | 753 | raise_from_cause should try to use exception.__traceback__ | nevermind, its my own confusion :) | closed | 2019-06-19T14:17:00Z | 2020-12-05T20:21:48Z | https://github.com/pallets-eco/flask-sqlalchemy/issues/753 | [] | ReallyLiri | 0 |
influxdata/influxdb-client-python | jupyter | 55 | I cannot connect to the cloud db | **InfluxDB version:** e.g. 1.7.9
**InfluxDB-python version:** e.g. 5.2.3
**Python version:** 3.7.4
**Operating system version:** macOS 10.14.5
I cannot seem to connect to my cloud database. It works ok for a local db.
Code is:
import codecs
from datetime import datetime
from influxdb_client import WritePrecision, InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS
url = "https://eu-central-1-1.aws.cloud2.influxdata.com"
token = "eJfb567aSoJnoDZJRaTI48lgzafILzOEaW70CEQqStin4xm9mboyYQO7dLJWVwRBXyLbrje9nvI6aXMJN1HlCg=="
bucket = "Keithley2410"
org = "paul.dervan@liverpool.ac.uk"
user= "dervan"
password = "something"
client = InfluxDBClient(url=url, user=user, token=token, password=password)
Error is:
TypeError: __init__() got an unexpected keyword argument 'user'
If I use:
client = InfluxDBClient(url=url, token=token)
I get this error:
HTTP response body: {"code":"forbidden","message":"insufficient permissions for write"}
That bucket does have read/write permissions.
Any help would be great.
Paul
| closed | 2020-02-03T12:17:31Z | 2020-02-03T13:48:06Z | https://github.com/influxdata/influxdb-client-python/issues/55 | [
"question"
] | pjdervan | 6 |
vllm-project/vllm | pytorch | 15,196 | [Usage]: Does vLLM support MoE model use --cpu-offload-gb | ### Your current environment
When run LLM with vLLM and --cpu-offload-gb. LLM can startup normally, but runing with error 'Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!'
Steps to reproduce
run deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with vLLM
with parameters listed on Environment.
Environment
GPU: L40s
vLLM: 0.7.2
--max-model-len=100000
--trust-remote-code
--max-num-batched-tokens=40000
--enforce-eager
--gpu-memory-utilization=0.45
--cpu-offload-gb=30
Additional context
INFO 03-13 01:31:55 model_runner.py:1115] Loading model weights took 0.8008 GB
WARNING 03-13 01:31:56 fused_moe.py:806] Using default MoE config. Performance might be sub-optimal! Config file not found at /usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/fused_moe/configs/E=64,N=1408,device_name=NVIDIA_L40S.json
INFO 03-13 01:31:59 worker.py:267] Memory profiling takes 4.09 seconds
INFO 03-13 01:31:59 worker.py:267] the current vLLM instance can use total_gpu_memory (44.31GiB) x gpu_memory_utilization (0.45) = 19.94GiB
INFO 03-13 01:31:59 worker.py:267] model weights take 0.80GiB; non_torch_memory takes 0.16GiB; PyTorch activation peak memory takes 4.01GiB; the rest of the memory reserved for KV Cache is 14.96GiB.
INFO 03-13 01:31:59 executor_base.py:110] # CUDA blocks: 29055, # CPU blocks: 7767
INFO 03-13 01:31:59 executor_base.py:115] Maximum concurrency for 100000 tokens per request: 4.65x
INFO 03-13 01:32:01 llm_engine.py:431] init engine (profile, create kv cache, warmup model) took 6.58 seconds
INFO 03-13 01:32:02 api_server.py:756] Using supplied chat template:
INFO 03-13 01:44:14 chat_utils.py:332] Detected the chat template content format to be 'string'. You can set --chat-template-content-format to override this.
INFO 03-13 01:44:14 logger.py:39] Received request chatcmpl-3022247fdb184950a3d5eb818d6818f1: prompt: '<｜begin▁of▁sentence｜>User: Your task is to find potential bugs in the given code snippet.\nCarefully examine the code for potential bugs, logical errors, or common programming mistakes. Consider issues such as:\nSyntax errors\nOff-by-one errors\nNull pointer exceptions\nMemory leaks\nInfinite loops\nIncorrect logic\nUnhandled exceptions\nProvide a concise list of potential bugs you've identified. If you don't find any bugs, state that the code appears to be bug-free based on your analysis. 请用中文回答。\nHere's the code to analyze:\n public class WarehouseManagerImpl implements WarehouseManager {\n needLimitWarehouseCountryStoreMapping = JSONObject.parseObject(limitMapping);\n }\n }\n+ String warehouseTenant = warehouseEntity.getTenant();\n for(StoreNodeDto storeNodeDto:storeNodeDtos){\n WarehouseStoreEntity iws=new WarehouseStoreEntity();\n iws.setWarehouseId(warehouseEntity.getId());\n public class WarehouseManagerImpl implements WarehouseManager {\n iws.setStoreNodeName(storeNodeDto.getName());\n iws.setPath(storeNodeDto.getPath());\n \n+ if (StringUtils.isNotBlank(warehouseTenant)) {\n+ String storeTenant = storeManager.getChannelByStoreId(storeNodeDto.getCode());\n+ if (!warehouseTenant.equals(storeTenant)) {\n+ throw new InventoryException("can't mapping other tenant records!", InventoryMessageEnum.ERROR.getMsgCode());\n+ }\n+ }\n+\n String countryCode=null;\n if(StringUtils.isNotBlank(storeNodeDto.getType()) && Integer.parseInt(storeNodeDto.getType()) >=StoreNodeTypeEnum.COUNTRY.getKey()){\n countryCode=storeManager.getContryCodeByChildNodeId(storeNodeDto.getCode());\n\n\n\nAssistant:', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=1.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=99581, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None.
INFO 03-13 01:44:14 engine.py:275] Added request chatcmpl-3022247fdb184950a3d5eb818d6818f1.
CRITICAL 03-13 01:44:15 launcher.py:101] MQLLMEngine is already dead, terminating server process
ERROR 03-13 01:44:15 engine.py:139] RuntimeError('Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat2 in method wrapper_CUDA_mm)')
ERROR 03-13 01:44:15 engine.py:139] Traceback (most recent call last):
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/multiprocessing/engine.py", line 137, in start
ERROR 03-13 01:44:15 engine.py:139] self.run_engine_loop()
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/multiprocessing/engine.py", line 200, in run_engine_loop
ERROR 03-13 01:44:15 engine.py:139] request_outputs = self.engine_step()
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/multiprocessing/engine.py", line 218, in engine_step
ERROR 03-13 01:44:15 engine.py:139] raise e
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/multiprocessing/engine.py", line 209, in engine_step
ERROR 03-13 01:44:15 engine.py:139] return self.engine.step()
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 1386, in step
ERROR 03-13 01:44:15 engine.py:139] outputs = self.model_executor.execute_model(
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 138, in execute_model
ERROR 03-13 01:44:15 engine.py:139] output = self.collective_rpc("execute_model",
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/executor/uniproc_executor.py", line 51, in collective_rpc
ERROR 03-13 01:44:15 engine.py:139] answer = run_method(self.driver_worker, method, args, kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/utils.py", line 2220, in run_method
ERROR 03-13 01:44:15 engine.py:139] return func(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 413, in execute_model
ERROR 03-13 01:44:15 engine.py:139] output = self.model_runner.execute_model(
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
ERROR 03-13 01:44:15 engine.py:139] return func(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1719, in execute_model
ERROR 03-13 01:44:15 engine.py:139] hidden_or_intermediate_states = model_executable(
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
ERROR 03-13 01:44:15 engine.py:139] return self._call_impl(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
ERROR 03-13 01:44:15 engine.py:139] return forward_call(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/deepseek_v2.py", line 687, in forward
ERROR 03-13 01:44:15 engine.py:139] hidden_states = self.model(input_ids, positions, kv_caches,
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/compilation/decorators.py", line 172, in call
ERROR 03-13 01:44:15 engine.py:139] return self.forward(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/deepseek_v2.py", line 643, in forward
ERROR 03-13 01:44:15 engine.py:139] hidden_states, residual = layer(positions, hidden_states,
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
ERROR 03-13 01:44:15 engine.py:139] return self._call_impl(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
ERROR 03-13 01:44:15 engine.py:139] return forward_call(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/utils.py", line 531, in forward
ERROR 03-13 01:44:15 engine.py:139] output = functional_call(module,
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/functional_call.py", line 148, in functional_call
ERROR 03-13 01:44:15 engine.py:139] return nn.utils.stateless._functional_call(
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/utils/stateless.py", line 298, in _functional_call
ERROR 03-13 01:44:15 engine.py:139] return module(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
ERROR 03-13 01:44:15 engine.py:139] return self._call_impl(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
ERROR 03-13 01:44:15 engine.py:139] return forward_call(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/deepseek_v2.py", line 561, in forward
ERROR 03-13 01:44:15 engine.py:139] hidden_states = self.self_attn(
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
ERROR 03-13 01:44:15 engine.py:139] return self._call_impl(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
ERROR 03-13 01:44:15 engine.py:139] return forward_call(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/deepseek_v2.py", line 480, in forward
ERROR 03-13 01:44:15 engine.py:139] return self.mla_attn(hidden_states_or_q_c, kv_c_normed, k_pe, kv_cache,
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
ERROR 03-13 01:44:15 engine.py:139] return self._call_impl(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
ERROR 03-13 01:44:15 engine.py:139] return forward_call(*args, **kwargs)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/attention/layer.py", line 201, in forward
ERROR 03-13 01:44:15 engine.py:139] return torch.ops.vllm.unified_attention(
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/torch/_ops.py", line 1116, in call
ERROR 03-13 01:44:15 engine.py:139] return self._op(*args, **(kwargs or {}))
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/attention/layer.py", line 307, in unified_attention
ERROR 03-13 01:44:15 engine.py:139] return self.impl.forward(self, query, key, value, kv_cache, attn_metadata)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/attention/backends/mla/utils.py", line 472, in forward
ERROR 03-13 01:44:15 engine.py:139] q_nope = self._q_proj_and_k_up_proj(hidden_states_or_q_c)
ERROR 03-13 01:44:15 engine.py:139] File "/usr/local/lib/python3.10/dist-packages/vllm/attention/backends/mla/utils.py", line 212, in _q_proj_and_k_up_proj
ERROR 03-13 01:44:15 engine.py:139] return torch.matmul(x, self.W_Q_UK)
ERROR 03-13 01:44:15 engine.py:139] RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat2 in method wrapper_CUDA_mm)
INFO: 10.62.217.48:23512 - "POST /v1/chat/completions HTTP/1.1" 500 Internal Server Error
INFO: Shutting down
INFO: Waiting for application shutdown.
INFO: Application shutdown complete.
INFO: Finished server process [222787]
### How would you like to use vllm
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
### Before submitting a new issue...
- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions. | open | 2025-03-20T08:08:21Z | 2025-03-20T08:12:49Z | https://github.com/vllm-project/vllm/issues/15196 | [
"usage"
] | artemus717 | 0 |
TencentARC/GFPGAN | deep-learning | 139 | Not working on middle eastern - Muslim faces | Hi
I used gfp-gan on many different old photos and it works just fine. Except when women have scarf, and the photo is a bit low quality (e.g. eyes are in shadow), for these typically brown eyed people, the eye are blue-ish, or the relatively skin tone becomes mustaches on women faces. Really weird!
So I am wondering how I can work this out. I can send you a few photos if needed.
BTW, I was using colab-demo files and their default settings.
Bests
Mohammad | open | 2022-01-04T07:27:13Z | 2022-01-11T02:27:25Z | https://github.com/TencentARC/GFPGAN/issues/139 | [] | mmdrahmani | 2 |
graphdeco-inria/gaussian-splatting | computer-vision | 354 | After executing" Python convert. py - s data \mp4", only half of the images folder in the input folder is obtained? How to solve the missing part of PLY after training | closed | 2023-10-21T06:55:43Z | 2023-10-21T14:01:39Z | https://github.com/graphdeco-inria/gaussian-splatting/issues/354 | [] | liuzhao6322 | 2 | |
plotly/dash-bio | dash | 706 | callback of AlignmentChart is unable to change page hight | I have a callback function that generates an alignment file and creates AlignmentChart based on it and it is working perfectly well. The issue is that the height variable seems to persist within the session and to actually change the height parameter I have to refresh the flask app, even with the default height settings. In other words, when I create an AlignmentChart with X genes, the callback creates AlignmentChart with nicely spaced rows but if I try to update the AlignmentChart with 10X genes it squeezes it into the same height as if it was X genes. On the other hand, if I refresh the page and create an AlignmentChart with 10X genes it spreads out the rows nicely but if, within the same session, I use X genes it spreads them out over an area equivalent to the 10X genes.
I hope that there's an easy fix to it, let me know if I should provide code. It didn't seem like something I can workout on my side.
Thank you so much! | open | 2022-08-18T08:05:10Z | 2022-08-18T08:05:10Z | https://github.com/plotly/dash-bio/issues/706 | [] | eporetsky | 0 |
unionai-oss/pandera | pandas | 1,912 | BUG: Polars DataFrameModel.validate crashes with `sample` specified | **Describe the bug**
A clear and concise description of what the bug is.
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandera.
- [x] (optional) I have confirmed this bug exists on the main branch of pandera.
#### Code Sample, a copy-pastable example
```python
import pandera.polars as pa
import pandas as pd
import polars as pl
from pandera.typing import Series
class SchemaPolars(pa.DataFrameModel):
col1: Series[int]
col2: Series[int]
pandas_df = pd.DataFrame({"col1": [1, 2, 3], "col2": [1, 2, 3]})
polars_df = pl.from_pandas(pandas_df)
result2 = SchemaPolars.validate(polars_df, sample=10)
Traceback (most recent call last):
File "C:\Data\myDocuments\Code\python_other\pandera\fork\tester_sample.py", line 24, in <module>
result2 = SchemaPolars.validate(lazyframe, sample=10)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Data\myDocuments\Code\python_other\pandera\fork\pandera\api\dataframe\model.py", line 289, in validate
cls.to_schema().validate(
File "C:\Data\myDocuments\Code\python_other\pandera\fork\pandera\api\polars\container.py", line 64, in validate
output = self.get_backend(check_obj).validate(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Data\myDocuments\Code\python_other\pandera\fork\pandera\backends\polars\container.py", line 80, in validate
sample = self.subsample(check_obj, head, tail, sample, random_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Data\myDocuments\Code\python_other\pandera\fork\pandera\backends\polars\base.py", line 50, in subsample
check_obj.sample(sample, random_state=random_state)
^^^^^^^^^^^^^^^^
AttributeError: 'LazyFrame' object has no attribute 'sample'
```
#### Expected behavior
Sample should work, or should raise a NotImplementedError if not supported.
#### Desktop (please complete the following information):
- OS: Windows 11, using python 3.12.8
#### Additional context
I came across this looking at running mypy over pandera.
The implementation calls .sample which is a pl.DataFrame method, but there is no lazy equivalent.
https://github.com/pola-rs/polars/issues/3933 discusses this with some potential workarounds listed, e.g.
`lazy_df.with_row_index().filter(col("index").hash(seed)%10 == 1).drop("index")`
| open | 2025-02-15T02:29:32Z | 2025-02-18T16:03:56Z | https://github.com/unionai-oss/pandera/issues/1912 | [
"bug"
] | m-richards | 1 |
piskvorky/gensim | data-science | 3,500 | Vocabulary size is much smaller than requested | <!--
**IMPORTANT**:
- Use the [Gensim mailing list](https://groups.google.com/g/gensim) to ask general or usage questions. Github issues are only for bug reports.
- Check [Recipes&FAQ](https://github.com/RaRe-Technologies/gensim/wiki/Recipes-&-FAQ) first for common answers.
Github bug reports that do not include relevant information and context will be closed without an answer. Thanks!
-->
#### Problem description
I was training a w2v model on a rather large corpus (about 35B tokens). I set the `min_count` to 50 and `max_vocab_size` to 250,000. I expected at the end of the training to have a vocabulary of 250k words. Instead, I got one at around 70k.
The logs are telling:
```
PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
PROGRESS: at sentence #10000, processed 1149505 words, keeping 149469 word types
PROGRESS: at sentence #20000, processed 2287292 words, keeping 232917 word types
pruned out 0 tokens with count <=1 (before 250001, after 250001)
pruned out 140618 tokens with count <=2 (before 250007, after 109389)
PROGRESS: at sentence #30000, processed 3442707 words, keeping 179514 word types
pruned out 148589 tokens with count <=3 (before 250005, after 101416)
...
pruned out 179627 tokens with count <=16330 (before 250006, after 70379)
PROGRESS: at sentence #301310000, processed 35302183879 words, keeping 92987 word types
collected 112874 word types from a corpus of 35302368099 raw words and 301311561 sentences
Creating a fresh vocabulary
Word2Vec lifecycle event {'msg': 'effective_min_count=50 retains 70380 unique words (62.35% of original 112874, drops 42494)', 'datetime': '2023-09-26T15:19:19.866236', 'gensim': '4.3.2', 'python': '3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0]', 'platform': 'Linux-5.4.0-150-generic-x86_64-with-glibc2.31', 'event' : 'prepare_vocab'}
Word2Vec lifecycle event {'msg': 'effective_min_count=50 leaves 30437195857 word corpus (100.00% of original 30437248987, drops 53130)', ' datetime': '2023-09-26T15:19:19.870161', 'gensim': '4.3.2', 'python': '3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0]', 'platform': 'Linux-5.4.0-150-generic-x86_64-with-glibc2.31 ', 'event': 'prepare_vocab'}
deleting the raw counts dictionary of 112874 items
sample=0.001 downsamples 21 most-common words
Word2Vec lifecycle event {'msg': 'downsampling leaves estimated 24482242512.167667 word corpus (80.4%% of prior 30437195857)', 'datetime': '2023-09-26T15:19:20.211104', 'gensim': '4.3.2', 'python': '3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0]', 'platform': 'Linux-5.4.0-150-generic-x86_64-with-glibc2.31', 'event' : 'prepare_vocab'}
estimated required memory for 70380 words and 300 dimensions: 204102000 bytes
```
So it seems as if `min_count` is only taken into consideration **after** the vocabulary has been pruned with a continuously increasing threshold. However, the threshold throws away a lot of words that otherwise should be in the vocabulary.
A few observations about this:
1. I am not sure thresholding works really well, especially in the latter stages: how could a word amass 16,000 occurrences if its previous (say, 15,998) occurrences have been pruned previously? Even if it occurs 100 times in the new batch, it will just be pruned again.
1. The log mentions `effective_min_count=50`, which then manages to prune 20k words at the end. I mean, if the final threshold was over 16,000, how could a threshold of 50 result in any more pruning?
1. [The documentation](https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec) only says this about `min_count`: _Ignores all words with total frequency lower than this._ Which is clearly not what it does.
So the questions that naturally follow:
1. **Can we switch of the increasing thresholding?**
2. What does `min_count` **actually** do?
#### Steps/code/corpus to reproduce
```python
model = models.Word2Vec(
sentences=corpus, vector_size=300, min_count=50,
max_vocab_size=250000, workers=processes, epochs=1,
compute_loss=True, sg=int(args.sg)
)
```
#### Versions
```
Linux-5.4.0-150-generic-x86_64-with-glibc2.31
Python 3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0]
Bits 64
NumPy 1.25.2
SciPy 1.11.2
gensim 4.3.2
FAST_VERSION 0
``` | closed | 2023-10-09T07:30:21Z | 2023-10-17T08:40:52Z | https://github.com/piskvorky/gensim/issues/3500 | [] | DavidNemeskey | 2 |
junyanz/pytorch-CycleGAN-and-pix2pix | pytorch | 921 | Save custom metric in "--save_epoch_freq". | I read the resource about metric of image segmentation.
I use the `pix2pix` model to do my task.
The input is an input and target like blow.
<img src="https://user-images.githubusercontent.com/42731603/74712281-e628e800-5260-11ea-8e2c-ca1bf097dfe8.png" alt="drawing" width="120"/>
<img src="https://user-images.githubusercontent.com/42731603/74712297-ee812300-5260-11ea-802d-5dc68d77f373.png" alt="drawing" width="120"/>
And I already get the prediction result following `!python test.py` and get the result.
The prediction is look like blow.
<img src="https://user-images.githubusercontent.com/42731603/74712328-ffca2f80-5260-11ea-9553-1d8597552118.png" alt="drawing" width="120"/>
I define the `IoU_mean` for evaluation my test data.
```
import numpy as np
from sklearn.metrics import jaccard_similarity_score
def IoU_mean(real, pred):
real_x = (real>0).astype('int')
fake_x = (pred>0).astype('int')
real_r, real_g, real_b = real_x[:,:,0], real_x[:,:,1], real_x[:,:,2]
fake_r, fake_g, fake_b = fake_x[:,:,0], fake_x[:,:,1], fake_x[:,:,2]
score_r = jaccard_similarity_score(real_r, fake_r)
score_g = jaccard_similarity_score(real_g, fake_g)
score_b = jaccard_similarity_score(real_b, fake_b)
score_mean = np.mean([score_r, score_g, score_b])
return(score_mean)
```
How do I save the history of my custom metric to the "IoU.txt" when run the `!python test.py` command?
Thanks.
| closed | 2020-02-18T07:21:33Z | 2020-02-18T09:23:39Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/921 | [] | houzeyu2683 | 0 |
Anjok07/ultimatevocalremovergui | pytorch | 768 | commandline support? | Is it possible to use files from the v5-beta-cml branch to add commandline support to uvr5-gui? I'd just like to start an inference by delivering input file, output dir and a profile name, while not showing the window and printing a % to the console instead of the progress bar.
I tried to dig into the files, but as a complete Python noob, this is kind of hard to understand. I'm a c# guy ^^' | open | 2023-08-30T15:32:34Z | 2023-11-29T10:13:15Z | https://github.com/Anjok07/ultimatevocalremovergui/issues/768 | [] | LittleCyberCortex | 3 |
deezer/spleeter | deep-learning | 71 | [Feature] Progress of process | ## Description
I'd love to get some way to get the progress of what's going on with the song.
Say, percentage or ETA.
## Additional information
Having lot's of fun.
| closed | 2019-11-09T19:25:06Z | 2019-11-14T06:40:46Z | https://github.com/deezer/spleeter/issues/71 | [
"enhancement",
"feature"
] | aidv | 1 |
babysor/MockingBird | deep-learning | 1,018 | 【建议】参考Fish Speech、MoneyPrinterTurbo，易用性、效果和速度都很好 | **Summary[问题简述（一句话）]**
A clear and concise description of what the issue is.
https://github.com/harry0703/MoneyPrinterTurbo
https://github.com/fishaudio/fish-speech
**Env & To Reproduce[复现与环境]**
描述你用的环境、代码版本、模型
**Screenshots[截图（如有）]**
If applicable, add screenshots to help
| open | 2024-12-20T03:17:44Z | 2024-12-20T03:17:44Z | https://github.com/babysor/MockingBird/issues/1018 | [] | bigsinger | 0 |
LibrePhotos/librephotos | django | 1,312 | Cannot install dependency matplotlib | # 🐛 Bug Report
* [N/A - didn't get that far ] 📁 I've Included a ZIP file containing my librephotos `log` files
* [x] ❌ I have looked for similar issues (including closed ones)
* [ ] 🎬 (If applicable) I've provided pictures or links to videos that clearly demonstrate the issue
## 📝 Description of issue:
Installation fails with missing dependencies:
```
ERROR: Could not find a version that satisfies the requirement matplotlib==3.9.0 (from -r requirements.txt (line 23)) (from versions: 0.86, 0.86.1, 0.86.2, 0.91.0, 0.91.1, 1.0.1, 1.1.0, 1.1.1, 1.2.0, 1.2.1, 1.3.0, 1.3.1, 1.4.0, 1.4.1rc1, 1.4.1, 1.4.2, 1.4.3, 1.5.0, 1.5.1, 1.5.2, 1.5.3, 2.0.0b1, 2.0.0b2, 2.0.0b3, 2.0.0b4, 2.0.0rc1, 2.0.0rc2, 2.0.0, 2.0.1, 2.0.2, 2.1.0rc1, 2.1.0, 2.1.1, 2.1.2, 2.2.0rc1, 2.2.0, 2.2.2, 2.2.3, 2.2.4, 2.2.5, 3.0.0rc2, 3.0.0, 3.0.1, 3.0.2, 3.0.3, 3.1.0rc1, 3.1.0rc2, 3.1.0, 3.1.1, 3.1.2, 3.1.3, 3.2.0rc1, 3.2.0rc3, 3.2.0, 3.2.1, 3.2.2, 3.3.0rc1, 3.3.0, 3.3.1, 3.3.2, 3.3.3, 3.3.4, 3.4.0rc1, 3.4.0rc2, 3.4.0rc3, 3.4.0, 3.4.1, 3.4.2, 3.4.3, 3.5.0b1, 3.5.0rc1, 3.5.0, 3.5.1, 3.5.2, 3.5.3, 3.6.0rc1, 3.6.0rc2, 3.6.0, 3.6.1, 3.6.2, 3.6.3, 3.7.0rc1, 3.7.0, 3.7.1, 3.7.2, 3.7.3, 3.7.4, 3.7.5)
ERROR: No matching distribution found for matplotlib==3.9.0 (from -r requirements.txt
```
It seems that only v3.1.2 is available in the ubuntu repository.
I tried using the instructions here: https://matplotlib.org/stable/install/index.html for a nightly build, but that only installed v3.7.5
## 🔁 How can we reproduce it:
```
git clone https://github.com/LibrePhotos/librephotos-linux.git
cd librephotos-linux
./install-librephotos.sh
```
## Please provide additional information:
- 💻 Operating system: Ubuntu 20.04.6 LTS
- ⚙ Architecture (x86 or ARM): x86
- 🔢 Librephotos version: Downloaded using git 12 hours ago
- 📸 Librephotos installation method (Docker, Kubernetes, .deb, etc.): "./install-librephotos.sh"
How can I get the right version for my OS? | closed | 2024-07-09T12:56:11Z | 2024-07-27T19:02:46Z | https://github.com/LibrePhotos/librephotos/issues/1312 | [
"bug"
] | Chewie9999 | 1 |
mitmproxy/pdoc | api | 776 | Apply Python syntax highlighting to all code blocks by default | #### Problem Description
Code blocks are not syntax-highlighted in Python by default. For example, compare:
```
```
def test():
pass
```
```py
def test():
pass
```
```
This generates:
<img width="490" alt="Image" src="https://github.com/user-attachments/assets/81055f15-5f90-4246-bb77-b6d1ecac603b" />
It can also be seen that the Python-highlighted code block has different margins.
Further, when using indentation to indicate code, according to the Google format, there is no way to specify syntax highlighting at all. It is off. E.g.:
```py
def test():
"""
A docstring in Google format.
Here is a code block:
# This example code can't be syntax-highlighted
foo = bar
"""
```
#### Proposal
Since this project is intended for Python, it would make sense for all code blocks to be syntax highlighted by default.
It would also make sense for margins to be consistent.
But most importantly, when using indentation to indicate code in the Google format, it seems most important to syntax highlight by default, since there isn't any way of enabling it manually, the way it can be done with Markdown.
Also, if there are markdown code blocks that shouldn't have highlighting (e.g. terminal output), one solution could be to specify them as `text`, a language code [supported on GitHub](https://github.com/jincheng9/markdown_supported_languages)
#### Alternatives
If syntax highlighting isn't enabled by default for Markdown, to do it at least for Google-style indented code. | open | 2025-02-12T16:44:53Z | 2025-02-12T20:47:11Z | https://github.com/mitmproxy/pdoc/issues/776 | [
"enhancement"
] | mjbaldwin | 1 |
onnx/onnx | tensorflow | 6,649 | [Feature request] Can SpaceToDepth also add mode attribute? | ### System information
ONNX 1.17
### What is the problem that this feature solves?
Current SpaceToDepth Op https://github.com/onnx/onnx/blob/main/docs/Operators.md#spacetodepth doesn't have attributes to assign the DCR/CRD, and can only supports CRD in computation.
But the DepthToSpace Op https://github.com/onnx/onnx/blob/main/docs/Operators.md#depthtospace has such mode attributes, and is more flexible in supporting models conversion from tensorflow.
### Alternatives considered
_No response_
### Describe the feature
_No response_
### Will this influence the current api (Y/N)?
_No response_
### Feature Area
_No response_
### Are you willing to contribute it (Y/N)
None
### Notes
_No response_ | open | 2025-01-22T03:33:51Z | 2025-02-20T03:56:36Z | https://github.com/onnx/onnx/issues/6649 | [
"module: spec"
] | vera121 | 0 |
hzwer/ECCV2022-RIFE | computer-vision | 183 | Any C++/Cuda C version pretrained model is provided?Or how can i convert it | closed | 2021-07-19T15:59:17Z | 2021-07-20T09:15:36Z | https://github.com/hzwer/ECCV2022-RIFE/issues/183 | [] | Eric-chuan | 1 | |
deeppavlov/DeepPavlov | tensorflow | 792 | Error while training model with config "ner_conll2003_pos" | I'm trying to train a NER model using "train_model(configs.ner.ner_conll2003_pos)" on Colab. There are only three things I've changed in original ner_conll2003_pos.json file: number of epochs = 1, DOWNLOADS_PATH and MODELS_PATH. After I start, it terminates with this error:
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-5-683c58afa1f4> in <module>()
1 from deeppavlov import configs, train_model
----> 2 ner_model = train_model(configs.ner.ner_conll2003_pos)
/usr/local/lib/python3.6/dist-packages/deeppavlov/__init__.py in train_model(config, download, recursive)
29 # TODO: make better
30 def train_model(config: [str, Path, dict], download: bool = False, recursive: bool = False) -> Chainer:
---> 31 train_evaluate_model_from_config(config, download=download, recursive=recursive)
32 return build_model(config, load_trained=True)
33
/usr/local/lib/python3.6/dist-packages/deeppavlov/core/commands/train.py in train_evaluate_model_from_config(config, iterator, to_train, evaluation_targets, to_validate, download, start_epoch_num, recursive)
119
120 if to_train:
--> 121 trainer.train(iterator)
122
123 res = {}
/usr/local/lib/python3.6/dist-packages/deeppavlov/core/trainers/nn_trainer.py in train(self, iterator)
292 if callable(getattr(self._chainer, 'train_on_batch', None)):
293 try:
--> 294 self.train_on_batches(iterator)
295 except KeyboardInterrupt:
296 log.info('Stopped training')
/usr/local/lib/python3.6/dist-packages/deeppavlov/core/trainers/nn_trainer.py in train_on_batches(self, iterator)
232 self.start_time = time.time()
233 if self.validate_first:
--> 234 self._validate(iterator)
235
236 while True:
/usr/local/lib/python3.6/dist-packages/deeppavlov/core/trainers/nn_trainer.py in _validate(self, iterator, tensorboard_tag, tensorboard_index)
142 self._send_event(event_name='before_validation')
143 report = self.test(iterator.gen_batches(self.batch_size, data_type='valid', shuffle=False),
--> 144 start_time=self.start_time)
145
146 report['epochs_done'] = self.epoch
/usr/local/lib/python3.6/dist-packages/deeppavlov/core/trainers/fit_trainer.py in test(self, data, metrics, start_time, show_examples)
204 for x, y_true in data:
205 examples += len(x)
--> 206 y_predicted = list(self._chainer.compute(list(x), list(y_true), targets=expected_outputs))
207 if len(expected_outputs) == 1:
208 y_predicted = [y_predicted]
/usr/local/lib/python3.6/dist-packages/deeppavlov/core/common/chainer.py in compute(self, x, y, targets)
141 in_params += self.in_y
142
--> 143 return self._compute(*args, pipe=pipe, param_names=in_params, targets=targets)
144
145 def __call__(self, *args):
/usr/local/lib/python3.6/dist-packages/deeppavlov/core/common/chainer.py in _compute(***failed resolving arguments***)
167 res = component(**dict(zip(in_keys, x)))
168 else:
--> 169 res = component(*x)
170 if len(out_params) == 1:
171 mem[out_params[0]] = res
/usr/local/lib/python3.6/dist-packages/deeppavlov/models/preprocessors/one_hotter.py in __call__(self, batch, **kwargs)
68 one_hotted_utt = np.sum(one_hotted_utt, axis=0)
69
---> 70 one_hotted_batch.append(one_hotted_utt)
71
72 if self._pad_zeros:
UnboundLocalError: local variable 'one_hotted_utt' referenced before assignment
-----------------------------------------------------------------------------------------------
How can I fix this? | closed | 2019-04-09T09:51:48Z | 2019-04-12T08:59:52Z | https://github.com/deeppavlov/DeepPavlov/issues/792 | [] | BloodSource | 1 |
plotly/plotly.py | plotly | 4,456 | Slider button goes out of range |
As you can see in python plotly when I use a slider to filter a graph the graph size and the related slider resizes horizontally and this causes the slider button to go out of range of the slider body so I want tthe button to stay inside the slider range
Thanking you
Aditya Sharma A | closed | 2023-12-14T11:02:43Z | 2023-12-14T15:14:59Z | https://github.com/plotly/plotly.py/issues/4456 | [] | Ad-it-ya-27 | 1 |
dpgaspar/Flask-AppBuilder | rest-api | 1,485 | add ability for user to reset his own password | Currently there is no feature in the framework that allows user to reset his password in case he forget it and can't log in.
example for actual use case: https://github.com/apache/airflow/issues/11521 | closed | 2020-10-14T16:54:23Z | 2022-04-28T14:41:02Z | https://github.com/dpgaspar/Flask-AppBuilder/issues/1485 | [
"enhancement",
"question",
"stale"
] | RosterIn | 7 |
ranaroussi/yfinance | pandas | 1,444 | find in accurate data in KSL.BK ticker |
Hello i find inaccurate data in this KSL.BK ticker
yfinance: version 0.2.12
python: version 3.9.7
OS: window
```
ksl = yf.Ticker("KSL.BK")
hist = ksl.history(period="1mo",rounding=True)
```
rounding or not rounding give me the same result
https://finance.yahoo.com/quote/KSL.BK?p=KSL.BK&.tsrc=fin-srch
data prior before 2023-03-01 is not accurate as it show in yahoo finance link provide above
| closed | 2023-03-05T02:49:17Z | 2023-03-10T08:14:21Z | https://github.com/ranaroussi/yfinance/issues/1444 | [] | saetthakij | 3 |
521xueweihan/HelloGitHub | python | 2,410 | Hello | closed | 2022-10-31T22:07:23Z | 2022-10-31T22:08:11Z | https://github.com/521xueweihan/HelloGitHub/issues/2410 | [] | InnocentNdeke | 0 | |
Skyvern-AI/skyvern | automation | 1,942 | Browser refresh needed to see editable workflow first time. | Clicking on "edit" for a successfully completed task doesn't actually "edit" it just allows you to open a new fresh workflow. This is not ideal because this task ran successfully and in my opinion it's results were good so I would like to begin using that is the foundation for a workflow.
Example screen recording: https://zipline.kaiyerlab.com/u/WQY8xo.webm | open | 2025-03-16T07:23:31Z | 2025-03-17T06:42:26Z | https://github.com/Skyvern-AI/skyvern/issues/1942 | [] | tylerdurden4285 | 2 |
jupyter-book/jupyter-book | jupyter | 1,847 | Issue on page /intro.html | Your issue content here. | open | 2022-10-01T00:57:43Z | 2022-10-01T00:57:43Z | https://github.com/jupyter-book/jupyter-book/issues/1847 | [] | MuhammadFaisalAvicenna | 0 |
deepspeedai/DeepSpeed | machine-learning | 6,883 | nv-nightly CI test failure | The Nightly CI for https://github.com/microsoft/DeepSpeed/actions/runs/12403691190 failed.
| closed | 2024-12-17T01:34:30Z | 2024-12-19T23:18:43Z | https://github.com/deepspeedai/DeepSpeed/issues/6883 | [
"ci-failure"
] | github-actions[bot] | 0 |
yunjey/pytorch-tutorial | pytorch | 155 | 'models/encoder-2-1000.ckpt' file not found | No such file or directory: 'models/encoder-2-1000.ckpt'
I downloaded the pretrained model to use. But I don't have the models/encoder-2 1000.ckpt' file. What I have from the model.zip are just "decoder-5 3000.pkl" and "encoder-5 3000.pkl". | closed | 2019-01-27T19:37:36Z | 2019-01-29T07:07:37Z | https://github.com/yunjey/pytorch-tutorial/issues/155 | [] | anjalinagel12 | 2 |
coqui-ai/TTS | pytorch | 2,458 | [Bug] yourTTS Python API Portuguese (and French) not working | ### Describe the bug
Python API for yourTTS is not working for Portuguese (and also, it seems, French). I used the exactly same example as the given one on readme.md.
It gives me a "KeyError: 'pt'"
### To Reproduce
from TTS.api import TTS
tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts")
tts.tts_to_file("Olá, tudo bem?", speaker_wav="teste.wav", language="pt", file_path="output.wav")
### Expected behavior
Generate output.wav with the standard given by speaker_wav.
### Logs
```shell
KeyError: 'pt'
```
### Environment
```shell
{
"CUDA": {
"GPU": [
"NVIDIA GeForce RTX 3050 Laptop GPU"
],
"available": true,
"version": "11.8"
},
"Packages": {
"PyTorch_debug": false,
"PyTorch_version": "2.0.0+cu118",
"TTS": "0.12.0",
"numpy": "1.22.4"
},
"System": {
"OS": "Linux",
"architecture": [
"64bit",
""
],
"processor": "x86_64",
"python": "3.10.6",
"version": "#1 SMP Fri Apr 2 22:23:49 UTC 2021"
}
```
### Additional context
_No response_ | closed | 2023-03-26T15:31:32Z | 2023-03-27T13:50:41Z | https://github.com/coqui-ai/TTS/issues/2458 | [
"bug"
] | elhombrejd | 3 |
streamlit/streamlit | data-visualization | 10,758 | Unable to see tracebacks in console for app exceptions | ### Checklist
- [x] I have searched the [existing issues](https://github.com/streamlit/streamlit/issues) for similar issues.
- [x] I added a very descriptive title to this issue.
- [x] I have provided sufficient information below to help reproduce this issue.
### Summary
Hello, I have encountered a very frustrating issue. I am working on a somewhat complex app, and at some point during development, streamlit stopped giving me tracebacks when exceptions are raised. This includes the console; nothing is being logged except the exception message like "ZeroDivisionError: division by zero". I can't find any reason why this would occur, and I have scoured the forums / google / github issues but found nothing. I tried explicitly setting the .streamlit/config.toml to have showErrorDetails = "full" but this changes nothing.
I thought it might have something to do with fragments, or cached resources, but even after removing these and restarting the server, I'm still not getting tracebacks. As a sort of minimum repro, I added "1/0" as the first line in my app file, and it still only shows me "ZeroDivisionError: division by zero" with no traceback when I rerun the app. I have observed that this only happens later in the app's execution; if I let the app run and crash on its own, any subsequent page refresh will no longer show me tracebacks, even if it was showing before.
I can't reproduce the issue from scratch and I have no idea what is different about my production app. Any advice would be appreciated.
### Reproducible Code Example
```Python
# this code snippet does not work in isolation - there is some other root cause
# if this error is triggered before any other operation, there will be a traceback in the console
# triggering this after an app refresh only prints "ZeroDivisionError: division by zero" and no traceback - even if it's the first line of code
try:
1/0
except Exception:
import traceback
print(traceback.format_exc())
```
### Steps To Reproduce
1. Add the caught zero division error as first line of code
2. Observe proper traceback in console
3. App continues execution and encounters an error
4. Observe error message but no traceback shown in console
5. Refresh the page
6. Observe that now there's no traceback in console for the zero division error
### Expected Behavior
I expect to see tracebacks in the console so I can debug my code more easily.
### Current Behavior
_No response_
### Is this a regression?
- [ ] Yes, this used to work in a previous version.
### Debug info
- Streamlit version: 1.43.2
- Python version: 3.11.11
- Operating System: Windows WSL
- Browser: Chrome
### Additional Information
_No response_ | closed | 2025-03-12T21:52:06Z | 2025-03-13T16:10:11Z | https://github.com/streamlit/streamlit/issues/10758 | [
"type:bug",
"status:needs-triage"
] | cc-c4 | 2 |
horovod/horovod | machine-learning | 3,082 | Lightning Estimator: Runtime error from petastorm | **Environment:**
1. Framework: PyTorch
2. Framework version: pytorch lighting 1.2.9; pytorch 1.8.1
3. Horovod version: master branch
4. MPI version:
5. CUDA version:
6. NCCL version:
7. Python version: 3.7.10
8. Spark / PySpark version:
9. Ray version:
10. OS and version:OSX Big Sur
11. GCC version:
12. CMake version:
**Bug report:**
Reprdocer:
```
python pytorch_lightning_spark_mnist.py --num-proc 2 --epochs 1 --batch-size 1024
```
Error:
```
Thu Aug 5 14:38:47 2021[0]<stderr>: row_as_dict[k] = self.transform_fn(v)
Thu Aug 5 14:38:54 2021[0]<stderr>:Iteration on Petastorm DataLoader raise error: RuntimeError('Trying to read a sample after a reader created by make_reader/make_batch_reader has stopped. This may happen if the make_reader/make_batch_reader context manager has exited but you try to fetch a sample from it anyway')
```
Possible reason: async dataloader kills/deletes petastorm reader before joining worker thread, which is still loading data from reader. | closed | 2021-08-05T21:45:38Z | 2021-08-17T18:49:22Z | https://github.com/horovod/horovod/issues/3082 | [
"bug"
] | chongxiaoc | 1 |
deeppavlov/DeepPavlov | tensorflow | 1,193 | Can I train slot filter or introduce a RegEx? | Hi, I am trying to use the slot filter to make it recognise **megapixels** slot but in my database I do not have all kind of megapixels. Can I make it learn that the previous number from "megapixels" word or "Megapixels" word are actually being megapixels or is it the only way to put all kind of options in the **slot_values config** file?
PS: I am trying to modify [go_bot_extended tutorial ](https://github.com/deepmipt/DeepPavlov/blob/master/examples/gobot_extended_tutorial.ipynb)to my own domine.
Thank you a lot! | closed | 2020-04-30T10:24:52Z | 2020-05-18T08:52:23Z | https://github.com/deeppavlov/DeepPavlov/issues/1193 | [] | paulagd | 2 |
AUTOMATIC1111/stable-diffusion-webui | pytorch | 15,841 | [Feature Request]: dedicated dark mode needed | ### Is there an existing issue for this?
- [X] I have searched the existing issues and checked the recent builds/commits
### What would your feature do ?
For protecting my eyes
### Proposed workflow
1. Go to .... :-)
2. Press ....;-(
### Additional information
_No response_ | closed | 2024-05-19T17:21:48Z | 2024-06-01T15:20:16Z | https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/15841 | [
"enhancement"
] | Greatz08 | 2 |
gradio-app/gradio | machine-learning | 10,165 | Using gr.Markdown as output of gr.Button disables the loading spinner | ### Describe the bug
Using `gr.Markdown` as output of `gr.Button` disables the loading spinner.
The spinner works in other components such as `gr.Textbox` or `gr.HTML`.
The loading spinner worked in **Gradio 4.42.0** but doesn't work in **Gradio 5.8.0**.
The user would not know if the app is running as the loading spinner is not working for `gr.Markdown`.
### Have you searched existing issues? 🔎
- [X] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
import time
def func(x):
time.sleep(5)
return x
with gr.Blocks() as demo:
text = gr.Textbox()
# output = gr.Textbox()
output = gr.Markdown()
run = gr.Button('Run')
run.click(
fn=func,
inputs=[text],
outputs=[output]
)
if __name__ == '__main__':
demo.launch()
```
### Screenshot
### Logs
```shell
No logs. It is a UI issue.
```
### System Info
```shell
Gradio Environment Information:
------------------------------
Operating System: Windows
gradio version: 5.8.0
gradio_client version: 1.5.1
------------------------------------------------
gradio dependencies in your environment:
aiofiles: 23.2.1
anyio: 4.7.0
audioop-lts is not installed.
fastapi: 0.115.6
ffmpy: 0.4.0
gradio-client==1.5.1 is not installed.
httpx: 0.28.1
huggingface-hub: 0.26.5
jinja2: 3.1.4
markupsafe: 2.1.5
numpy: 2.2.0
orjson: 3.10.12
packaging: 24.2
pandas: 2.2.3
pillow: 10.4.0
pydantic: 2.10.3
pydub: 0.25.1
python-multipart: 0.0.19
pyyaml: 6.0.2
ruff: 0.8.2
safehttpx: 0.1.6
semantic-version: 2.10.0
starlette: 0.41.3
tomlkit: 0.12.0
typer: 0.15.1
typing-extensions: 4.12.2
urllib3: 2.2.3
uvicorn: 0.32.1
authlib; extra == 'oauth' is not installed.
itsdangerous; extra == 'oauth' is not installed.
gradio_client dependencies in your environment:
fsspec: 2024.10.0
httpx: 0.28.1
huggingface-hub: 0.26.5
packaging: 24.2
typing-extensions: 4.12.2
websockets: 12.0
```
### Severity
I can work around it. | closed | 2024-12-10T12:11:03Z | 2024-12-10T17:27:07Z | https://github.com/gradio-app/gradio/issues/10165 | [
"bug"
] | maxkskhor | 2 |
miguelgrinberg/Flask-SocketIO | flask | 1,008 | WebSocket opening handshake timed out in https | **its timed out only when open UI in HTTPS, in HTTP its working...**
I have generated the certificate using OpenSSL in ubuntu
**my uwsgi configuration is**
```
socket = /tmp/uwsgi.sock
chmod-socket = 666
socket-timeout = 60
chdir = <django path>
wsgi-file = <django_path>/wsgi.py
virtualenv = <path_to_virtualenv>
vacuum = true
enable-threads = true
threads=500
startup-timeout = 15
graceful-timeout = 15
http-socket=<my_ip>:8008
http-websockets=true
```
**my nginx configuration is**
```
server {
listen <ip>:80 default;
listen <ip>:443 ssl http2 default_server;
ssl_certificate <path>/generate_crt.crt;
ssl_certificate_key <path>/generated_key.key;
client_body_buffer_size 500M;
client_body_timeout 300s;
keepalive_timeout 5000;
client_max_body_size 700M;
access_log syslog:server=unix:/dev/log;
root /tmp/MVM_APPS/angularjs/dist;
index index.html index.htm;
server_name localhost;
location /api {
uwsgi_pass unix:///tmp/uwsgi.sock;
include uwsgi_params;
uwsgi_read_timeout 120;
uwsgi_send_timeout 1000;
}
location /ws/ {
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection upgrade;
proxy_pass http://<ip>:8008;
proxy_read_timeout 86400;
}
location /static {
alias /<path>/static;
}
location / {
try_files $uri $uri/ /index.html;
}
}
```
| closed | 2019-06-21T09:21:26Z | 2019-06-21T11:12:08Z | https://github.com/miguelgrinberg/Flask-SocketIO/issues/1008 | [
"question"
] | shihabkaranchery | 1 |
Evil0ctal/Douyin_TikTok_Download_API | api | 237 | [BUG] 一键部署报错 | 
Run API or Web? [api/web/all/quit] all
Do you want to start the app and api service when system boot? [y/n] y
Created symlink from /etc/systemd/system/multi-user.target.wants/web_app.service to /etc/systemd/system/web_app.service.
Created symlink from /etc/systemd/system/multi-user.target.wants/web_api.service to /etc/systemd/system/web_api.service.
Starting WEB and API Services...
Failed to start web_app.service: Unit is not loaded properly: Invalid argument.
See system logs and 'systemctl status web_app.service' for details.
Failed to start web_api.service: Unit is not loaded properly: Invalid argument.
See system logs and 'systemctl status web_api.service' for details.
API and APP service are running!
You can stop the api service by running following command:
systemctl stop (web_app.service||web_api.service)
[root@glory ~]# systemctl status web_api.service
● web_api.service - www/wwwroot/Douyin_TikTok_Download_API/web_api.py deamon
Loaded: error (Reason: Invalid argument)
Active: inactive (dead)
Aug 11 09:32:44 glory systemd[1]: [/etc/systemd/system/web_api.service:10] Executable path is not absolute, ignoring: python3 web_api.py
Aug 11 09:32:44 glory systemd[1]: web_api.service lacks both ExecStart= and ExecStop= setting. Refusing.
Aug 11 09:32:44 glory systemd[1]: [/etc/systemd/system/web_api.service:10] Executable path is not absolute, ignoring: python3 web_api.py
Aug 11 09:32:44 glory systemd[1]: web_api.service lacks both ExecStart= and ExecStop= setting. Refusing. | closed | 2023-08-11T01:35:02Z | 2023-08-11T01:39:40Z | https://github.com/Evil0ctal/Douyin_TikTok_Download_API/issues/237 | [
"BUG"
] | cqLJH | 1 |
serengil/deepface | deep-learning | 726 | Facial Attribute Analysis Accuracy | Hello,
this looks very interesting!
what accuracy does deepface attain for facial expression analysis?
Could this information be added to the readme? | closed | 2023-04-20T10:26:46Z | 2023-04-20T10:28:28Z | https://github.com/serengil/deepface/issues/726 | [
"question"
] | raayu83 | 1 |
pallets-eco/flask-sqlalchemy | sqlalchemy | 562 | Queries aren't returning any records | I'm having a problem with my local version of my website. I'm using Flask, Flask-SQLAlchemy, and Python 3.5.2, and calls to my MySQL database that had worked in the past (although on a different laptop) are now not working, instead returning the error "MySQL Connection not available".
Somewhat bizarrely, the exact behavior I'm seeing is that when I try to log into my website the first time, the database call returns "None" for the user, even though the user is present in the database. When I then reload the login page and try again, I get sent to an error page that says "MySQL Connection not available".
I have tried running a debugger and running a query that returns all of the database records, and it returns an empty list, even though there should be a single record (my test account).
I have forum posts of others who have had this issue, but those fixes have not worked.
- I already had `SQLALCHEMY_POOL_RECYCLE` set to a lower value than the corresponding MySQL variables (as recommended [here](https://stackoverflow.com/a/26898700/4115031)).
- I had the `SQLALCHEMY_POOL_RECYCLE` value set to 299, as recommended [here](https://blog.pythonanywhere.com/121/).
- Changing my MySQL `wait_timeout` and `interactive_timeout` values to be much lower (300 from something like 28800), as described [here](https://stackoverflow.com/a/32211825/4115031), didn't seem to have any effect.
- Putting my queries within a `try` / `except` with a `db.session.rollback()` in the `except` clause (as described [here](https://stackoverflow.com/a/32211825/4115031) and rec'd [here](https://www.pythonanywhere.com/forums/topic/885/#id_post_6354)) does stop the app from crashing, but it still says it isn't finding the record that is clearly there.
It seems like there may be two problems here:
- The query isn't finding the record I know is there.
- When I try to run the query a second time, it returns the "MySQL Connection not available" error.
Other things tried:
- If I run the debugger and try inserting records into the database, they show up in my PyCharm database viewer.
- Adding the line `db.init_app(app)` to the bottom of my app.py initialization function stops the error messages from crashing the app, but still doesn't result in the ORM finding the user record. | closed | 2017-10-13T18:47:10Z | 2020-12-05T20:55:28Z | https://github.com/pallets-eco/flask-sqlalchemy/issues/562 | [] | NathanWailes | 2 |
trevorstephens/gplearn | scikit-learn | 48 | Wrong calculation from 'mean absolute error'? | Hi, I just try to fit a program to solve XOR problem, so here is my data:
```
X = [[1,1],
[1,0],
[0,1],
[0,0]]
y = [0,1,1,0]
```
This is settings:
```
est_gp = SymbolicRegressor(population_size=5000,
generations=20, stopping_criteria=0.01,
init_depth=(2,4),
p_crossover=0.7, p_subtree_mutation=0.1,
p_hoist_mutation=0.05, p_point_mutation=0.1,
max_samples=1, verbose=1,
parsimony_coefficient=0.01, random_state=0)
est_gp.fit(X, y)
print (est_gp._program)
```
Result is: sub(X1, X0), which is unexpected, and not right. Below are the running info:
```
| Population Average | Best Individual |
---- ------------------------- ------------------------------------------ ----------
Gen Length Fitness Length Fitness OOB Fitness Time Left
0 15.48 7.45612899871 3 0.0 2.0 1.23m
```
Thus, I checked the function evaluation:
```
est_gp.predict([1,0])
array([-1])
```
It seems right, but,
```
est_gp.score([1,0],[1])
0.0
```
| closed | 2017-10-13T00:10:32Z | 2017-10-15T21:38:16Z | https://github.com/trevorstephens/gplearn/issues/48 | [] | walkcoolboy | 2 |
ray-project/ray | pytorch | 50,711 | [nsys plugin] How about add an option `name` to nsys dumped file | ### Description
Currently the nsys file is `worker_process_<PID>.nsys-rep`, but it is difficult to check which file is related to the interested actors. How about change to `worker_process_<NAME>_<PID>.nsys-rep`.
with the `<NAME>` is a config option in
```
runtime_env={ "nsight": {
"t": "cuda,cudnn,cublas",
"cuda-memory-usage": "true",
"cuda-graph-trace": "graph",
"name": "what_ever_user_named"
}})
```
### Use case
_No response_ | open | 2025-02-19T03:11:08Z | 2025-03-22T00:55:33Z | https://github.com/ray-project/ray/issues/50711 | [
"enhancement",
"P1",
"core"
] | davidmlw | 0 |
AUTOMATIC1111/stable-diffusion-webui | pytorch | 16,038 | [Feature Request]: Update documentation for Unraid template | ### Is there an existing issue for this?
- [X] I have searched the existing issues and checked the recent builds/commits
### What would your feature do ?
I have added AUTOMATIC1111 as an "app" to the [Unraid Community Apps store](https://unraid.net/community/apps?q=automatic1111#r) (just submitted, will be live in ~2 hours), making it easier for Unraid users to run this app on their machines.
App template based on [this Docker image](https://hub.docker.com/r/goolashe/automatic1111-sd-webui), which is based on [this Dockerized port](https://github.com/AbdBarho/stable-diffusion-webui-docker) of the application.
Looking to update Docker section of Wiki with brief install instructions for Unraid users.
### Proposed workflow
1. Allow edit proposal to [this wiki section](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Containers)
### Additional information
_No response_ | open | 2024-06-17T21:34:11Z | 2024-07-08T01:05:21Z | https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/16038 | [
"enhancement"
] | nwithan8 | 2 |
clovaai/donut | computer-vision | 76 | .jsonl file format for training | Hi,
Thanks for this great project!!
I have been trying to create .jsonl files for training and validation and run into some issues here. I have checked with a json validator that the lines as such are in a valid json format, and they are. My file looks like this:
`{"file_name": "./abc.png", "ground_truth": "{\"gt_parse\": [{\"order_date\": \"11th May, 2010\", {\"Transfers\":[{\"product\":\"one\",\"transfer_date\":null}]}}]}"}`
which looks like this if reformatted for making it easier readable here:
```
{
"file_name": "./abc.png",
"ground_truth": "{\"gt_parse\": [{\"order_date\": \"11th May, 2010\", {\"Transfers\":[{\"product\":\"one\",\"transfer_date\":null}]}}]}"
}
```
Do I understand correctly that ground_truth contains a simple string which hence requires to escape all following _"s_ within the string?
Running this results in a json related error:
```
Traceback (most recent call last):
File "train.py", line 149, in <module>
train(config)
File "train.py", line 78, in train
DonutDataset(
File "util.py", line 69, in __init__
ground_truth = json.loads(sample["ground_truth"])
File "/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 47 (char 46)
```
Character 46 refers to to the first **"** of "ground_truth".
I might be missing something obvious here, so any hints would be very helpful.
Thanks!
Best,
Martin | closed | 2022-10-28T14:51:43Z | 2022-11-06T23:53:09Z | https://github.com/clovaai/donut/issues/76 | [] | MartinHaus1993 | 1 |
plotly/dash | jupyter | 3,003 | Temporary failure in name resolution | This is occurring in latest Dash 2.18.1.
Doing this
app.run_server(host='0.0.0.0') .... did nothing
[https://github.com/plotly/dash/issues/1480](url) Referring to old issue. It expects HOST environment variable to be set under conda. Passing this host parameter is not overriding. | open | 2024-09-14T22:49:33Z | 2024-10-01T14:31:18Z | https://github.com/plotly/dash/issues/3003 | [
"regression",
"bug",
"P1"
] | summa-code | 5 |
google-research/bert | nlp | 1,172 | No ckpt.meta file in the release of 24 smaller BERT models | I can only find:
1. bert_config.json
2. bert_model.ckpt.data-00000-of-00001
3. bert_model.ckpt.index
4. vocab.txt
seems bert_model.ckpt.meta is missing | closed | 2020-11-16T04:11:41Z | 2022-02-04T04:15:20Z | https://github.com/google-research/bert/issues/1172 | [] | qfdong | 4 |
junyanz/pytorch-CycleGAN-and-pix2pix | pytorch | 1,682 | Multi-card training | Hello, I want to use two cards (RTX 6000) for training, and the following are my training instructions. nvidia-smi also shows that both cards are working, but the first round of training takes a long time to complete. Why is that?
`python train.py --gpu_ids 0,1 --batch_size 4 --num_threads 8 --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan` | open | 2024-11-05T09:42:15Z | 2024-11-05T09:42:15Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1682 | [] | dailonggang | 0 |
davidsandberg/facenet | tensorflow | 889 | Improving accuracy | I used the following hyperparams to train on casia.
`python src/train_tripletloss.py --logs_base_dir ./logs --models_base_dir ./models --data_dir facenet/data/casia_maxpy_mtcnnpy_182 --image_size 160 --model_def models.inception_resnet_v1 --optimizer RMSPROP --learning_rate 0.01 --weight_decay 1e-4 --max_nrof_epochs 600 --gpu_memory_fraction 0.1
`
The Embedding is 512 dimensions. people_per_batch is 6 and batch_size is 9 and images_per_person is also 6.
However after training the model , i tried to validate on lfw.
This is the output on validation.
> Accuracy: 0.76133+-0.01545
> Validation rate: 0.07233+-0.01984 @ FAR=0.00100
> Area Under Curve (AUC): 0.845
> Equal Error Rate (EER): 0.238
why is my accuracy just 76 %? How do I improve it? | closed | 2018-10-04T02:36:31Z | 2019-04-15T13:52:07Z | https://github.com/davidsandberg/facenet/issues/889 | [] | Zumbalamambo | 2 |
CorentinJ/Real-Time-Voice-Cloning | deep-learning | 1,319 | Eu Teamo Douglas | Eu Teamo Douglas desculpa por tudo | open | 2024-11-24T08:33:16Z | 2024-11-24T08:33:16Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1319 | [] | Douglas128738 | 0 |
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