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ray-project/ray
|
data-science
| 51,598 |
[Ray serve] StopAsyncIteration error thrown by ray when the client cancels the request
|
### What happened + What you expected to happen
**Our Request flow:**
Client calls our ingress app (which is a ray serve app wrapped in a FastAPI ingress) which then calls another serve app using `handle.remote`
**Bug:**
When a client is canceling the request our ingress app (which is a ray serve app) is seeing the `StopAsyncIteration` error thrown by ray serve handler code
Tried to reproduce locally but haven't be successful
I think we should still have some exception handling around the piece of code that throws the error
**Strack Trace:**
> File "/app/virtualenv/lib/python3.10/site-packages/ray/serve/handle.py", line 404, in __await__ result = yield from replica_result.get_async().__await__() File "/app/virtualenv/lib/python3.10/site-packages/ray/serve/_private/replica_result.py", line 87, in async_wrapper return await f(self, *args, **kwargs) File "/app/virtualenv/lib/python3.10/site-packages/ray/serve/_private/replica_result.py", line 117, in get_async return await (await self.to_object_ref_async()) File "/app/virtualenv/lib/python3.10/site-packages/ray/serve/_private/replica_result.py", line 179, in to_object_ref_async self._obj_ref = await self._obj_ref_gen.__anext__() File "python/ray/_raylet.pyx", line 343, in __anext__ File "python/ray/_raylet.pyx", line 547, in _next_async StopAsyncIteration
### Versions / Dependencies
ray[serve]==2.42.1
python==3.10.6
### Reproduction script
Tried to reproduce locally but haven't be successful
I think we should still have some exception handling around the piece of code that throws the error
### Issue Severity
Low: It annoys or frustrates me.
|
open
|
2025-03-21T18:17:35Z
|
2025-03-21T22:50:59Z
|
https://github.com/ray-project/ray/issues/51598
|
[
"bug",
"triage",
"serve"
] |
jugalshah291
| 0 |
xinntao/Real-ESRGAN
|
pytorch
| 813 |
enhancement
|
it's actually so non-informative, can you add progressview, e. g. 999/9999
|
open
|
2024-06-08T22:47:04Z
|
2024-06-08T23:17:46Z
|
https://github.com/xinntao/Real-ESRGAN/issues/813
|
[] |
monolit
| 2 |
ultrafunkamsterdam/undetected-chromedriver
|
automation
| 1,727 |
intercepting & blocking certain requests
|
I'm currently trying to speed up the load of a certain webpage.
I thought of scanning the process with my browser, identifying the requests that take the most to load, and then using UC to intercept & block those requests. My code is somewhat similar to this:
```python
def request_filter(req):
BLOCKED_RESOURCES = ['image', 'jpeg', 'xhr', 'x-icon']
r_type = req['params']['type'].lower()
r_url = req['params']['request']['url']
if r_type in BLOCKED_RESOURCES: # block every request of the types above
return {"cancel": True}
if "very.heavy.resource" in r_url: # block the requests that go to 'very.heavy.resource'
return {"cancel": True}
print(req) # let the request pass
driver = uc.Chrome(enable_cdp_events=True)
driver.add_cdp_listener("Network.requestWillBeSent", request_filter)
driver.get("target.website.com")
```
However, I'm having trouble blocking some resources, like JS scripts and the like. I wanted to ask if anyone has a clearer mind on how UC deals with intercepting, inspecting & blocking requests. For example, I'm not quite sure the way to block a request is to say `return {'cancel': True}`, I just saw it on ChatGPT
|
open
|
2024-01-17T15:16:40Z
|
2024-02-24T04:14:32Z
|
https://github.com/ultrafunkamsterdam/undetected-chromedriver/issues/1727
|
[] |
danibarahona
| 2 |
iperov/DeepFaceLab
|
deep-learning
| 5,375 |
I heard that the AMP model can change the face shape, but I found no effect after training the AMP model. Do you have any training skills? Thank you
|
I heard that the AMP model can change the face shape, but I found no effect after training the AMP model. Do you have any training skills? Thank you
|
open
|
2021-08-03T02:36:47Z
|
2023-06-08T22:44:06Z
|
https://github.com/iperov/DeepFaceLab/issues/5375
|
[] |
DidaDidaDidaD
| 1 |
pmaji/crypto-whale-watching-app
|
plotly
| 102 |
Is there a way to grab the results and store in Variable or print in console
|
I wanted to see if its possible in order to grab the results from the graphs and store them in a variable i can use to perform other tasks for example i want to get the prices and total btc that is in the orderbook that a whale has placed when i run dash it prints everything to the console but i would like to print the data from the app or store them in a variable any way of doing this?
|
closed
|
2018-08-31T00:41:21Z
|
2018-09-16T07:49:20Z
|
https://github.com/pmaji/crypto-whale-watching-app/issues/102
|
[] |
JeanLoriston
| 3 |
kensho-technologies/graphql-compiler
|
graphql
| 159 |
Add support for the "_x_count" meta-field to the Gremlin compiler backend
|
The Gremlin backend does not currently support the `_x_count` meta-field, per #158.
|
open
|
2019-01-17T22:29:40Z
|
2019-01-31T21:17:07Z
|
https://github.com/kensho-technologies/graphql-compiler/issues/159
|
[
"enhancement"
] |
obi1kenobi
| 0 |
keras-team/keras
|
pytorch
| 20,591 |
Strange results for gradient tape : Getting positive gradients for negative response
|
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
Windows
### Python version
3.7.16
Hello,
I'm working with some gradient based interpretability method ([based on the GradCam code from Keras ](https://keras.io/examples/vision/grad_cam/) ) , and I'm running into a result that seems inconsistent with what would expect from backpropagation.
I am working with a pertrained VGG16 on imagenet, and I am interested in find the most relevent filters for a given class.
I start by forward propagating an image through the network, and then from the relevant bin, I find the gradients to the layer in question (just like they do in the Keras tutorial).
Then, from the pooled gradients (`pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))`), I find the top-K highest/most pertinent filters.
From this experiment, I run into 2 strange results.
1. For almost any image I pass through (even completely different classes), the network almost always seems to be placing the most importance to the same 1 Filter.
2. And this result I understand even less; many times, the gradients point "strongly" to a filter, even though the filter's output is 0/negative (before relu). From the backpropagation equation, a negative response should result in a Null gradient, right ?
$$ \frac{dY_{class}}{ dActivation_{in} } = \frac{dY_{class}}{dZ} \cdot \frac{dZ}{dActivation_{in}}$$
$$ = Relu'(Activation_{in}\cdot W+b) \cdot W$$
If $Activation_{in}\cdot W+b$ is negative, then $\frac{dY_{class}}{Activation_{in}}$ should be 0, right ?
I provided 3 images.
All 3 images point consistently to Filter155 (For observation 1).
And for Img3.JPEG, I find the Top5 most relevant filters: Filter336 has a strong gradient, and yet a completely null output.
Is there a problem with my code, the gradient computations or just my understanding?
Thanks for your help.
Liam
### Standalone code to reproduce the issue
```shell
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import decode_predictions
from tensorflow.keras.applications import VGG16
import keras
from keras import backend as K
def get_img_array(img_path, size):
# `img` is a PIL image of size 299x299
img = keras.utils.load_img(img_path, target_size=size)
# `array` is a float32 Numpy array of shape (299, 299, 3)
array = keras.utils.img_to_array(img)
# We add a dimension to transform our array into a "batch"
# of size (1, 299, 299, 3)
array = np.expand_dims(array, axis=0)
return array
img = "img3.JPEG"
img = keras.applications.vgg16.preprocess_input(get_img_array(img, size=(224,224)))
model = VGG16(weights='imagenet',
include_top=True,
input_shape=(224, 224, 3))
# Remove last layer's softmax
model.layers[-1].activation = None
#I am interested in finding the most informative filters from this Layer
layer = model.get_layer("block5_conv3")
grad_model = keras.models.Model(
model.inputs, [layer.output, model.output]
)
pred_idx = None
with tf.GradientTape(persistent=True) as tape:
last_conv_layer_output, preds = grad_model(img, training=False)
if pred_idx is None:
pred_idx = tf.argmax(preds[0])
print(tf.argmax(preds[0]))
print(decode_predictions(preds.numpy()))
class_channel = preds[:, pred_idx]
grads = tape.gradient(class_channel, last_conv_layer_output) #
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
topFilters = tf.math.top_k(pooled_grads, k=5).indices.numpy()
print("Top Filters : ", topFilters)
print("Filter responses: " , tf.math.reduce_euclidean_norm(last_conv_layer_output, axis=(0,1,2)).numpy()[topFilters])
plt.imshow(last_conv_layer_output[0,:,:,336])
plt.show()
```
### Relevant log output
```shell
For Img3 :
Top Filters : [155 429 336 272 51]
Filter responses : [ 80.908226 208.93723 0. 232.99017 746.0348 ]
```
|
closed
|
2024-12-04T13:16:53Z
|
2025-01-05T02:06:23Z
|
https://github.com/keras-team/keras/issues/20591
|
[
"stat:awaiting response from contributor",
"stale",
"type:Bug"
] |
liamaltarac
| 4 |
shaikhsajid1111/facebook_page_scraper
|
web-scraping
| 8 |
posts_count bigger than 19 results in only 19 scraped posts
|
Hi,
When I want to scrape the last 100 posts on a Facebook page:
```
facebook_ai = Facebook_scraper("facebookai",100,"chrome")
json_data = facebook_ai.scrap_to_json()
print(json_data)
```
Only 19 posts are scraped. I tried with other pages too, the same result.
Any ideas what goes wrong?
|
open
|
2021-05-20T15:00:11Z
|
2021-05-22T14:22:26Z
|
https://github.com/shaikhsajid1111/facebook_page_scraper/issues/8
|
[] |
verscph
| 11 |
CTFd/CTFd
|
flask
| 2,349 |
Remove dataset dependency
|
We should remove the dataset dependency entirely. It's been a source of problem and pain for awhile and it really just seems like we should roll our own solution.
|
open
|
2023-07-01T08:33:56Z
|
2024-04-08T07:36:02Z
|
https://github.com/CTFd/CTFd/issues/2349
|
[] |
ColdHeat
| 2 |
babysor/MockingBird
|
pytorch
| 168 |
按照#37的修改，还是一直出现杂音
|
按步骤准备好环境启动工具箱后，一切默认，上传目录下的temp.wav。点击 Sythesize and vcode后，第一次报跟 #37 一样的错，直接忽略，再次点击 Sythesize and vcode后，又没报错了，这时生成的是杂音。已经按照 #37 的改法修改了`synthesizer/utils/symbols.py`这个文件，要怎么修复？
|
closed
|
2021-10-23T16:05:50Z
|
2022-01-03T03:57:56Z
|
https://github.com/babysor/MockingBird/issues/168
|
[] |
alenstudent
| 9 |
sktime/sktime
|
scikit-learn
| 7,945 |
[BUG] Unable to use multilevel='raw_values' parameter in error metric when benchmarking.
|
**Describe the bug**
<!--
A clear and concise description of what the bug is.
-->
When benchmarking, you have to specify the error metric(s) to use. Setting `multilevel='raw_values'` in the metric object results in error.
**To Reproduce**
<!--
Add a Minimal, Complete, and Verifiable example (for more details, see e.g. https://stackoverflow.com/help/mcve
If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com
-->
The code below is copied from the tutorial found here. https://www.sktime.net/en/latest/examples/04_benchmarking_v2.html
The only change from the tutorial is with cell [5].
```python
# %% [1]
from sktime.benchmarking.forecasting import ForecastingBenchmark
from sktime.datasets import load_airline
from sktime.forecasting.naive import NaiveForecaster
from sktime.performance_metrics.forecasting import MeanSquaredPercentageError
from sktime.split import ExpandingWindowSplitter
# %% [2]
benchmark = ForecastingBenchmark()
# %% [3]
benchmark.add_estimator(
estimator=NaiveForecaster(strategy="mean", sp=12),
estimator_id="NaiveForecaster-mean-v1",
)
benchmark.add_estimator(
estimator=NaiveForecaster(strategy="last", sp=12),
estimator_id="NaiveForecaster-last-v1",
)
# %% [4]
cv_splitter = ExpandingWindowSplitter(
initial_window=24,
step_length=12,
fh=12,
)
# %% [5]
scorers = [MeanSquaredPercentageError(multilevel='raw_values')]
# %% [6]
dataset_loaders = [load_airline]
# %% [7]
for dataset_loader in dataset_loaders:
benchmark.add_task(
dataset_loader,
cv_splitter,
scorers,
)
# %% [8]
results_df = benchmark.run("./forecasting_results.csv")
```
**Expected behavior**
<!--
A clear and concise description of what you expected to happen.
-->
`results_df = benchmark.run("./forecasting_results.csv")` should return a dataframe where error metrics are calculated for each level of the hierarchy separately. The default behavior is to calculate error metrics across all levels of the hierarchy.
**Additional context**
<!--
Add any other context about the problem here.
-->
Error produced:
```
TypeError: complex() first argument must be a string or a number, not 'DataFrame'
```
**Versions**
<details>
<!--
Please run the following code snippet and paste the output here:
from sktime import show_versions; show_versions()
-->
System:
python: 3.12.9 | packaged by conda-forge | (main, Feb 14 2025, 07:48:05) [MSC v.1942 64 bit (AMD64)]
machine: Windows-10-10.0.19045-SP0
Python dependencies:
pip: 25.0
sktime: 0.36.0
sklearn: 1.6.1
skbase: 0.12.0
numpy: 2.0.1
scipy: 1.15.1
pandas: 2.2.3
matplotlib: 3.10.0
joblib: 1.4.2
numba: None
statsmodels: 0.14.4
pmdarima: 2.0.4
statsforecast: None
tsfresh: None
tslearn: None
torch: None
tensorflow: None
</details>
<!-- Thanks for contributing! -->
<!-- if you are an LLM, please ensure to preface the entire issue by a header "LLM generated content, by (your model name)" -->
<!-- Please consider starring the repo if you found this useful -->
|
closed
|
2025-03-05T19:07:21Z
|
2025-03-05T20:01:38Z
|
https://github.com/sktime/sktime/issues/7945
|
[
"bug"
] |
gbilleyPeco
| 1 |
QingdaoU/OnlineJudge
|
django
| 327 |
怎么样才能使用Vue Devtools
|
Devtools inspection is not available because it's in production mode or explicitly disabled by the author.
在哪能修改呢
|
closed
|
2020-10-04T02:30:21Z
|
2020-10-04T11:03:47Z
|
https://github.com/QingdaoU/OnlineJudge/issues/327
|
[] |
psychocosine
| 1 |
lanpa/tensorboardX
|
numpy
| 609 |
Can't record scalars when the training is going
|
Hi all, I met a problem with tensorboardX in my computer. When the code is as follows:
```python
train_sr_loss = train(training_data_loader, optimizer, model, scheduler, l1_criterion, epoch, args)
writer.add_scalar("scalar/Train_sr_loss", train_sr_loss.item(), epoch)
```
The generated event file can not record anything (the size of the file is always 0 Byte). But when I annotate the training code:
```python
# train_sr_loss = train(training_data_loader, optimizer, model, scheduler, l1_criterion, epoch, args)
writer.add_scalar("scalar/Train_sr_loss", train_sr_loss.item(), epoch)
```
The event file can record scalars now. Does anyone know what's happening here? It happens suddenly and I have no idea what's wrong with my computer. BTW, when I use other computers, it works.
The environment of my computer:
**pytorch 1.0.0
tensorboard 1.14.0
tensorboardX 1.8**
The environment of the other computer which works with the former code:
**pytorch 1.0.1
tensorboardX 1.6**
Thanks for your help~
|
closed
|
2020-10-15T08:51:53Z
|
2021-03-13T17:20:28Z
|
https://github.com/lanpa/tensorboardX/issues/609
|
[] |
Joechann0831
| 2 |
holoviz/panel
|
jupyter
| 6,932 |
Tabulator sometimes renders with invisible rows
|
#### ALL software version info
panel==1.4.4
#### Description of expected behavior and the observed behavior
Tabulator looks like this:
<img width="1340" alt="image" src="https://github.com/holoviz/panel/assets/156992217/d209cf71-a61d-424d-af9b-d4a2bd2c87b2">
but should look like this:
<img width="1348" alt="image" src="https://github.com/holoviz/panel/assets/156992217/cc7fdbd7-b24b-4766-8597-e8764ee4037d">
#### Complete, minimal, self-contained example code that reproduces the issue
Unfortunately, don't have a minimum reproducible example. This seems to be a race condition, but I'm hopefull that the error message provided by tabulator is sufficient for a bug fix.
|
open
|
2024-06-21T02:39:26Z
|
2025-02-20T15:04:45Z
|
https://github.com/holoviz/panel/issues/6932
|
[
"component: tabulator"
] |
techanfa
| 1 |
globaleaks/globaleaks-whistleblowing-software
|
sqlalchemy
| 3,505 |
Different receivers for different languages
|
### Proposal
If a tenant is available in multiple language, there should be the possibility to select specific receivers for every language. As an example for worldwide companies with branches in different nations.
### Motivation and context
Righ now receivers are the same for every chosen language. The only way to implement this functionalty right now is to implement different context, or a specific question to address the right receiver, in addition to language selection.
|
open
|
2023-06-29T12:52:49Z
|
2023-07-04T09:33:45Z
|
https://github.com/globaleaks/globaleaks-whistleblowing-software/issues/3505
|
[
"T: Feature"
] |
larrykind
| 2 |
AutoViML/AutoViz
|
scikit-learn
| 117 |
error in generating violin chart
|
Shape of Data Set (119390, 32). and when generating violin chart give an error: `Traceback` (most recent call last):
File "/mnt/d/Download/sweet_viz_auto_viz_final_change/ankita_today/advance_metrics-ankita/advance_metrics-ankita/app/advanced_metric.py", line 233, in deep_viz_report
dft = AV.AutoViz(
File "/mnt/d/Download/sweet_viz_auto_viz_final_change/ankita_today/advance_metrics-ankita/advance_metrics-ankita/app/autoviz/AutoViz_Class.py", line 259, in AutoViz
dft = AutoViz_Holo(filename, sep, depVar, dfte, header, verbose,
File "/mnt/d/Download/sweet_viz_auto_viz_final_change/ankita_today/advance_metrics-ankita/advance_metrics-ankita/app/autoviz/AutoViz_Holo.py", line 266, in AutoViz_Holo
raise ValueError((error_string))
ValueError: underflow encountered in true_divideerror`` and using this library code on python 3.8 version
|
closed
|
2025-01-28T09:06:47Z
|
2025-01-29T06:25:17Z
|
https://github.com/AutoViML/AutoViz/issues/117
|
[] |
ankita2020
| 2 |
ultralytics/ultralytics
|
pytorch
| 19,110 |
How does YOLO make use of the 3rd dimension (point visibility) for keypoints (pose) dataset ? How does that affect results ?
|
### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
Some dataset can specify additional info on the keypoints, such has not visible / occluded. How does YOLO use that information ? Can it also output that information on the inferred keypoints ?
### Additional
_No response_
|
closed
|
2025-02-06T21:24:04Z
|
2025-02-07T18:53:20Z
|
https://github.com/ultralytics/ultralytics/issues/19110
|
[
"question",
"pose"
] |
CourchesneA
| 2 |
BMW-InnovationLab/BMW-TensorFlow-Training-GUI
|
rest-api
| 39 |
Performance issues in training_api/research/ (by P3)
|
Hello! I've found a performance issue in your program:
- `tf.Session` being defined repeatedly leads to incremental overhead.
You can make your program more efficient by fixing this bug. Here is [the Stack Overflow post](https://stackoverflow.com/questions/48051647/tensorflow-how-to-perform-image-categorisation-on-multiple-images) to support it.
Below is detailed description about **tf.Session being defined repeatedly**:
- in object_detection/eval_util.py: `sess = tf.Session(master, graph=tf.get_default_graph())`[(line 273)](https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI/blob/ecf941242e3c7380d2e6060652d209509bc9f224/training_api/research/object_detection/eval_util.py#L273) is defined in the function `_run_checkpoint_once`[(line 211)](https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI/blob/ecf941242e3c7380d2e6060652d209509bc9f224/training_api/research/object_detection/eval_util.py#L211) which is repeatedly called in the loop `while True:`[(line 431)](https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI/blob/ecf941242e3c7380d2e6060652d209509bc9f224/training_api/research/object_detection/eval_util.py#L431).
- in slim/datasets/download_and_convert_cifar10.py: `with tf.Session('') as sess:`[(line 91)](https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI/blob/ecf941242e3c7380d2e6060652d209509bc9f224/training_api/research/slim/datasets/download_and_convert_cifar10.py#L91) is defined in the function `_add_to_tfrecord`[(line 64)](https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI/blob/ecf941242e3c7380d2e6060652d209509bc9f224/training_api/research/slim/datasets/download_and_convert_cifar10.py#L64) which is repeatedly called in the loop `for i in range(_NUM_TRAIN_FILES):`[(line 184)](https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI/blob/ecf941242e3c7380d2e6060652d209509bc9f224/training_api/research/slim/datasets/download_and_convert_cifar10.py#L184).
`tf.Session` being defined repeatedly could lead to incremental overhead. If you define `tf.Session` out of the loop and pass `tf.Session` as a parameter to the loop, your program would be much more efficient.
Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.
|
closed
|
2021-08-22T14:14:35Z
|
2022-11-16T11:22:38Z
|
https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI/issues/39
|
[] |
DLPerf
| 2 |
akfamily/akshare
|
data-science
| 5,242 |
不同 python 版本，不同 akshare 版本获取数据速度差别很大！有哪些原因呢？
|
## 问题描述：
分别用 docker 镜像和再本地 `pip install` 安装的 akshare，两种方式获取数据的速度，差别很大。
## 详细信息：
根据项目 `readme.md` 下载的 docker image 中的 akshare 版本为 `1.7.35`，python 版本为 `3.8.14`。
而本地 python 版本为：3.10.12，akshare 为最新版本：1.14.97。
经过实际测试，在运行以下代码时：
```pyhton
stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol="000001", period="daily", start_date="20230301", end_date='20231022', adjust="")
```
docker 镜像中的版本运行飞快，1s 左右能返回结果，而本地的最新版本就很慢，至少要 10s 才能拿到结果。
所有测试都是在本机上跑的，网络情况相同，且经过多次测试均是如上结果。
想问问具体是哪块的问题，以及如何修复，或者是一些最佳实践，比如要尽快拉取所有股票的历史价格数据等等场景。
谢谢！
|
closed
|
2024-10-15T14:28:46Z
|
2024-10-16T02:29:03Z
|
https://github.com/akfamily/akshare/issues/5242
|
[] |
92hackers
| 1 |
hbldh/bleak
|
asyncio
| 1,077 |
The ble HID services cannot be enumerated
|
* bleak version: 0.18.1
* Python version: 3.96
* Operating System: win10 21H2
* BlueZ version (`bluetoothctl -v`) in case of Linux:
### Description
The ble HID services cannot be enumerated
### What I Did
using get_services.py example
```
& C:/Users/Admin/AppData/Local/Programs/Python/Python39/python.exe c:/Users/Admin/Desktop/bleak-0.18.1/bleak-0.18.1/examples/get_services.py
```
### Logs
python out put
Services:
00001800-0000-1000-8000-00805f9b34fb (Handle: 1): Generic Access Profile
00001801-0000-1000-8000-00805f9b34fb (Handle: 8): Generic Attribute Profile
0000180a-0000-1000-8000-00805f9b34fb (Handle: 12): Device Information
0000180f-0000-1000-8000-00805f9b34fb (Handle: 72): Battery Service
00010203-0405-0607-0809-0a0b0c0d1912 (Handle: 76): Unknown
in NRF connect APP
|
closed
|
2022-10-12T09:21:02Z
|
2022-11-07T16:27:11Z
|
https://github.com/hbldh/bleak/issues/1077
|
[
"3rd party issue",
"Backend: WinRT"
] |
SOYOJOE
| 3 |
vitalik/django-ninja
|
django
| 392 |
Can't get a basic Schema to work
|
I'm trying to get the most basic of schema to work, i.e:
```
from ninja import Router, Schema
class SimpleSchemaOut(Schema):
name: str
@router.get('/simple')
def simple(request, response=SimpleSchemaOut):
return {"name": "Foo"}
```
With this in place, if I try to hit `/api/demo/openapi.json` I get the following error:
```
TypeError at /api/demo/openapi.json
Object of type 'ResolverMetaclass' is not JSON serializable
Request Method: | GET
Request URL: http://localhost:8080/api/demo/openapi.json
Djagno Version: 2.2.21
Exception Type: TypeError
Exception Value: Object of type 'ResolverMetaclass' is not JSON serializable
Exception Location: pydantic/json.py in pydantic.json.pydantic_encoder, line 97
```
Any help would be appreciated!
|
closed
|
2022-03-16T15:20:41Z
|
2022-03-16T15:31:21Z
|
https://github.com/vitalik/django-ninja/issues/392
|
[] |
SilverTab
| 1 |
MaartenGr/BERTopic
|
nlp
| 1,593 |
Topics_over_time() labels represent different topics at different points in time
|
Hello @MaartenGr,
I've been loving this package so far! It's been extremely useful.
I have an inquiry regarding unexpected behavior in output from topics_over_time(). I've included code and output below but I will briefly contextualize the problem in words. I am using textual data from the Reuters Newswire from the year 2020. I use online topic modeling and monthly batches of the data to update my topic model. After this, I run topics_over_time() on the entire sample and use the months as my timestamps. All this works well. However, some of the same labels in topics_over_time() seem to represent vastly different topics in different points of time (the images below focus on label 18 as an example). It was my understanding that the label should represent the same overall topic over time, with the keywords changing based on how the corpus discusses the topic. However, the topic entirely shifts from the Iran nuclear deal to COVID-19.
Is there a way to prevent this from happening? It seems likely I've made some error in logic in my code (which I've included below).
Thanks so much in advance!
```python
data = pd.read_csv("/tr/proj15/txt_factors/Topic Linkage Experiments/Pull Reuters Data/Output/2020_textual_data.csv")
#Separate text data to generate topics over time
whole_text_data = data["body"]
whole_text_data = whole_text_data.replace('\n', ' ')
whole_text_data.reset_index(inplace = True, drop = True)
date_col = data["month_date"].to_list()
unique_dates_df = data.drop_duplicates(subset=['month_date'])
timestamps = unique_dates_df["month_date"].to_list()
#Set up parameters for Bertopic model
model = BertForSequenceClassification.from_pretrained('ProsusAI/finbert')
cluster_model = River(cluster.DBSTREAM())
vectorizer_model = OnlineCountVectorizer(stop_words="english", ngram_range=(1,4))
ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True, bm25_weighting=True)
umap_model = UMAP(n_neighbors=25,
n_components=10,
metric='cosine')
topic_model = BERTopic(
umap_model = umap_model,
hdbscan_model=cluster_model,
vectorizer_model=vectorizer_model,
ctfidf_model=ctfidf_model,
nr_topics = "auto"
)
#Incrementally learn
topics = []
for month in timestamps:
month_df = data.loc[data['month_date'] == month]
text_data = month_df["body"]
text_data = text_data.replace('\n', ' ')
text_data.reset_index(inplace = True, drop = True)
topic_model.partial_fit(text_data)
topics.extend(topic_model.topics_)
topic_model.topics_ = topics
topics_over_time = topic_model.topics_over_time(whole_text_data, date_col,
datetime_format="%Y-%m",
global_tuning = True,
evolution_tuning = True)
topics_over_time.to_csv('2020_topics_over_time.csv', index = False)
```
|
closed
|
2023-10-25T20:27:03Z
|
2023-10-31T15:12:39Z
|
https://github.com/MaartenGr/BERTopic/issues/1593
|
[] |
lukasmackin
| 4 |
paperless-ngx/paperless-ngx
|
django
| 7,324 |
[BUG] Same matching for tags: one tag is assigned one not
|
### Description
I stumbled across a phenomenon I can not explain nor debug in much detail. It came to my attention when adding several documents which should all match a bank account. Unfortunately none did. I started to dig into this issue and I ended up creating a dummy document which allows to reproduce the issue.
upfront: sorry for the screenshot being in German language ;)
# Setup
Two tags have the same matching pattern:
`Any` pattern `505259366` for tag `DDDD`
and
`Any` pattern `505259366` for tag `EEEE`
When uploading a document which contains this pattern, tag `DDDD` is applied during processing and tag `EEEE` is not.
What I tested already:
* different user who uploads the doc
* changing ownership of the tags
* creating two other tags with the same matching (both tags applied)
... I always deleted the doc & purged the trash before uploading it again
Why do I believe that this bug affects other as well?
Honestly, the tag I use is not relevant for any other user I guess, but the root cause for this behavior is still unclear to me so I think that this issue can happen for other users, using other tags as well. Nevertheless I would be happy if this has a simple solution and not turns out to be a bug.
*compose.yml*
```
# docker-compose file for running paperless from the docker container registry.
# This file contains everything paperless needs to run.
# Paperless supports amd64, arm and arm64 hardware.
# All compose files of paperless configure paperless in the following way:
#
# - Paperless is (re)started on system boot, if it was running before shutdown.
# - Docker volumes for storing data are managed by Docker.
# - Folders for importing and exporting files are created in the same directory
# as this file and mounted to the correct folders inside the container.
# - Paperless listens on port 8000.
#
# SQLite is used as the database. The SQLite file is stored in the data volume.
#
# In addition to that, this docker-compose file adds the following optional
# configurations:
#
# - Apache Tika and Gotenberg servers are started with paperless and paperless
# is configured to use these services. These provide support for consuming
# Office documents (Word, Excel, Power Point and their LibreOffice counter-
# parts.
#
# To install and update paperless with this file, do the following:
#
# - Copy this file as 'docker-compose.yml' and the files 'docker-compose.env'
# and '.env' into a folder.
# - Run 'docker-compose pull'.
# - Run 'docker-compose run --rm paperless createsuperuser' to create a user.
# - Run 'docker-compose up -d'.
#
# For more extensive installation and update instructions, refer to the
# documentation.
version: "3.4"
services:
broker:
image: docker.io/library/redis:7
container_name: ${PROJECT_NAME}-broker
networks:
paperless_net:
restart: unless-stopped
volumes:
- redisdata:/data
paperless-web:
image: ghcr.io/paperless-ngx/paperless-ngx:latest
container_name: ${PROJECT_NAME}-web
restart: unless-stopped
labels:
infra: home-it
depends_on:
- broker
- gotenberg
- tika
networks:
paperless_net:
healthcheck:
test: ["CMD", "curl", "-fs", "-S", "--max-time", "2", "http://localhost:8000"]
interval: 30s
timeout: 10s
retries: 5
volumes:
- data:/usr/src/paperless/data
- ${VOLUME_MEDIA_PATH}:/usr/src/paperless/media
- ${VOLUME_CONSUME_PATH}:/usr/src/paperless/consume
- ${VOLUME_BACKUP_PATH}:/usr/src/paperless/export
environment:
PAPERLESS_REDIS: redis://broker:6379
PAPERLESS_TIKA_ENABLED: 1
PAPERLESS_TIKA_GOTENBERG_ENDPOINT: http://gotenberg:3000
PAPERLESS_TIKA_ENDPOINT: http://tika:9998
PAPERLESS_CSRF_TRUSTED_ORIGINS: ${PAPERLESS_CSRF_TRUSTED_ORIGINS}
PAPERLESS_ALLOWED_HOSTS: ${PAPERLESS_ALLOWED_HOSTS}
PAPERLESS_CORS_ALLOWED_HOSTS: ${PAPERLESS_CORS_ALLOWED_HOSTS}
PAPERLESS_SECRET_KEY: ${PAPERLESS_SECRET_KEY}
PAPERLESS_TIME_ZONE: ${PAPERLESS_TIME_ZONE}
PAPERLESS_OCR_LANGUAGE: ${PAPERLESS_OCR_LANGUAGE}
PAPERLESS_FILENAME_FORMAT: ${PAPERLESS_FILENAME_FORMAT}
PAPERLESS_TRASH_DIR: ${PAPERLESS_TRASH_DIR}
USERMAP_UID: ${USERMAP_UID}
USERMAP_GID: ${USERMAP_GID}
gotenberg:
image: docker.io/gotenberg/gotenberg:7.8
container_name: ${PROJECT_NAME}-gotenberg
networks:
paperless_net:
restart: unless-stopped
# The gotenberg chromium route is used to convert .eml files. We do not
# want to allow external content like tracking pixels or even javascript.
command:
- "gotenberg"
- "--chromium-disable-javascript=true"
- "--chromium-allow-list=file:///tmp/.*"
tika:
image: ghcr.io/paperless-ngx/tika:latest
container_name: ${PROJECT_NAME}-tika
networks:
paperless_net:
restart: unless-stopped
nginx:
container_name: ${PROJECT_NAME}-nginx
image: nginx:latest
labels:
infra: home-it
volumes:
- ${VOLUME_SHARE_PATH}nginx/nginx.conf:/etc/nginx/conf.d/paperless.conf:ro
- ${VOLUME_SHARE_PATH}nginx/certificates/:/etc/nginx/crts/
restart: unless-stopped
depends_on:
- paperless-web
ports:
- "443:443"
- "80:80"
networks:
paperless_net:
## Cronjob Container
# https://github.com/mcuadros/ofelia
ofelia:
image: mcuadros/ofelia:latest
container_name: ${PROJECT_NAME}-cronjob
restart: unless-stopped
depends_on:
- paperless-web
command: daemon --config=/ofelia/config.ini
volumes:
- /var/run/docker.sock:/var/run/docker.sock:ro
- ${VOLUME_SHARE_PATH}ofelia:/ofelia
networks:
paperless_net:
# rsync to USB Stick
rsync:
build:
context: "./rsync/"
volumes:
- ${VOLUME_MEDIA_PATH}:/src:ro
- ${VOLUME_USB_BACKUP_PATH}:/dest
command: /src/ /dest/
restart: no
container_name: ${PROJECT_NAME}-rsync
networks:
paperless_net:
volumes:
data:
name: ${PROJECT_NAME}-data
export:
name: ${PROJECT_NAME}-export
redisdata:
name: ${PROJECT_NAME}-redis
networks:
paperless_net:
name: paperless_net
driver: bridge
```
env file
```
# Paperless-ngx
# PROJECT CONFIG
PROJECT_NAME=paperless
# NETWORK
NET_HOSTNAME=paperless
NET_MAC_ADDRESS=CA:2A:5F:1A:03:39
NET_IPV4=
# VOLUMES
VOLUME_SHARE_PATH=/root/docker/paperless-ngx/
VOLUME_BACKUP_PATH=/backup/paperless-ngx/
VOLUME_MEDIA_PATH=/data2/paperless-ngx/media
VOLUME_CONSUME_PATH=/paperless-ngx/consume
VOLUME_USB_BACKUP_PATH=/mnt/paperless-stick
# The UID and GID of the user used to run paperless in the container. Set this
# to your UID and GID on the host so that you have write access to the
# consumption directory.
USERMAP_UID=1000
USERMAP_GID=1000
# Additional languages to install for text recognition, separated by a
# whitespace. Note that this is
# different from PAPERLESS_OCR_LANGUAGE (default=eng), which defines the
# language used for OCR.
# The container installs English, German, Italian, Spanish and French by
# default.
# See https://packages.debian.org/search?keywords=tesseract-ocr-&searchon=names&suite=buster
# for available languages.
#PAPERLESS_OCR_LANGUAGES=tur ces
###############################################################################
# Paperless-specific settings #
###############################################################################
# All settings defined in the paperless.conf.example can be used here. The
# Docker setup does not use the configuration file.
# A few commonly adjusted settings are provided below.
# This is required if you will be exposing Paperless-ngx on a public domain
# (if doing so please consider security measures such as reverse proxy)
#PAPERLESS_URL=https://paperless.home
PAPERLESS_ALLOWED_HOSTS=paperless.home,192.168.178.215
PAPERLESS_CSRF_TRUSTED_ORIGINS=https://paperless.home,https://192.168.178.215
PAPERLESS_CORS_ALLOWED_HOSTS=https://paperless.home,https://192.168.178.215
# Adjust this key if you plan to make paperless available publicly. It should
# be a very long sequence of random characters. You don't need to remember it.
PAPERLESS_SECRET_KEY=Sn6AU3QLrmynxtp6RRAKkJTPgJ22DXXoAfPNWgbcfLNuY6ptKUFuXnYDfTavvABJpYNbjzaveaVGSFfNFWtj2nqnn7zGMKPxbwAyXMKckotZRJKSwa3D5h7Z7XNdz49Z
# Use this variable to set a timezone for the Paperless Docker containers. If not specified, defaults to UTC.
PAPERLESS_TIME_ZONE=Europe/Berlin
# The default language to use for OCR. Set this to the language most of your
# documents are written in.
PAPERLESS_OCR_LANGUAGE=deu
# Set if accessing paperless via a domain subpath e.g. https://domain.com/PATHPREFIX and using a reverse-proxy like traefik or nginx
#PAPERLESS_FORCE_SCRIPT_NAME=/PATHPREFIX
#PAPERLESS_STATIC_URL=/PATHPREFIX/static/ # trailing slash required
# Default Storage Path
PAPERLESS_FILENAME_FORMAT={correspondent}/{owner_username}/{document_type}/{created_year}{created_month}{created_day}_{title}
# Remove "none" values from storage path
PAPERLESS_FILENAME_FORMAT_REMOVE_NONE=true
# Trash Bin
PAPERLESS_TRASH_DIR=../media/trash
```
### Steps to reproduce
1. Create the two tags as mentioned above
2. upload the following dummy pdf [dummy2.pdf](https://github.com/user-attachments/files/16383420/dummy2.pdf)
3. check the tags
# Actual behavior
* tag `DDDD` is applied
* tag `EEEE` isn't
# Expected
Both tags are applied, because both patterns are in the processed document
### Webserver logs
```bash
taken from the Docker logs (debug=true)
paperless-nginx | 2024/07/25 20:26:50 [warn] 22#22: *1992 a client request body is buffered to a temporary file /var/cache/nginx/client_temp/0000000035, client: 192.168.178.41, server: , request: "POST /api/documents/post_document/ HTTP/2.0", host: "192.168.178.218", referrer: "https://192.168.178.218/view/2"
paperless-web | [2024-07-25 22:26:50,442] [INFO] [celery.worker.strategy] Task documents.tasks.consume_file[bad2b633-451f-4a59-a4fc-f61023b210e9] received
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:50 +0000] "POST /api/documents/post_document/ HTTP/2.0" 200 38 "https://192.168.178.218/view/2" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
paperless-web | [2024-07-25 22:26:50,442] [DEBUG] [celery.pool] TaskPool: Apply <function fast_trace_task at 0x74dacf123b00> (args:('documents.tasks.consume_file', 'bad2b633-451f-4a59-a4fc-f61023b210e9', {'lang': 'py', 'task': 'documents.tasks.consume_file', 'id': 'bad2b633-451f-4a59-a4fc-f61023b210e9', 'shadow': None, 'eta': None, 'expires': None, 'group': None, 'group_index': None, 'retries': 0, 'timelimit': [None, None], 'root_id': 'bad2b633-451f-4a59-a4fc-f61023b210e9', 'parent_id': None, 'argsrepr': "(ConsumableDocument(source=<DocumentSource.ApiUpload: 2>, original_file=PosixPath('/tmp/paperless/tmpynk4sejr/dummy2.pdf'), mailrule_id=None, mime_type='application/pdf'), DocumentMetadataOverrides(filename='dummy2.pdf', title=None, correspondent_id=None, document_type_id=None, tag_ids=None, storage_path_id=None, created=None, asn=None, owner_id=4, view_users=None, view_groups=None, change_users=None, change_groups=None, custom_field_ids=None))", 'kwargsrepr': '{}', 'origin': 'gen173@a56344f9c9dd', 'ignore_result': False, 'replaced_task_nesting': 0, 'stamped_headers': None, 'stamps': {}, 'properties': {'correlation_id':... kwargs:{})
paperless-web | [2024-07-25 22:26:50,462] [DEBUG] [paperless.tasks] Skipping plugin CollatePlugin
paperless-web | [2024-07-25 22:26:50,462] [DEBUG] [paperless.tasks] Skipping plugin BarcodePlugin
paperless-web | [2024-07-25 22:26:50,463] [DEBUG] [paperless.tasks] Executing plugin WorkflowTriggerPlugin
paperless-web | [2024-07-25 22:26:50,464] [INFO] [paperless.tasks] WorkflowTriggerPlugin completed with:
paperless-web | [2024-07-25 22:26:50,464] [DEBUG] [paperless.tasks] Executing plugin ConsumeTaskPlugin
paperless-web | [2024-07-25 22:26:50,470] [INFO] [paperless.consumer] Consuming dummy2.pdf
paperless-web | [2024-07-25 22:26:50,471] [DEBUG] [paperless.consumer] Detected mime type: application/pdf
paperless-web | [2024-07-25 22:26:50,476] [DEBUG] [paperless.consumer] Parser: RasterisedDocumentParser
paperless-web | [2024-07-25 22:26:50,479] [DEBUG] [paperless.consumer] Parsing dummy2.pdf...
paperless-web | [2024-07-25 22:26:50,489] [INFO] [paperless.parsing.tesseract] pdftotext exited 0
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:50 +0000] "GET /api/tasks/ HTTP/2.0" 200 17661 "https://192.168.178.218/trash" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:50 +0000] "GET /api/tasks/ HTTP/2.0" 200 17661 "https://192.168.178.218/tags" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
paperless-web | [2024-07-25 22:26:50,606] [DEBUG] [paperless.parsing.tesseract] Calling OCRmyPDF with args: {'input_file': PosixPath('/tmp/paperless/paperless-ngxg36hkaj4/dummy2.pdf'), 'output_file': PosixPath('/tmp/paperless/paperless-vwxyj6iu/archive.pdf'), 'use_threads': True, 'jobs': 8, 'language': 'deu', 'output_type': 'pdfa', 'progress_bar': False, 'color_conversion_strategy': 'RGB', 'skip_text': True, 'clean': True, 'deskew': True, 'rotate_pages': True, 'rotate_pages_threshold': 12.0, 'sidecar': PosixPath('/tmp/paperless/paperless-vwxyj6iu/sidecar.txt')}
paperless-web | [2024-07-25 22:26:50,694] [WARNING] [ocrmypdf._pipeline] This PDF is marked as a Tagged PDF. This often indicates that the PDF was generated from an office document and does not need OCR. PDF pages processed by OCRmyPDF may not be tagged correctly.
paperless-web | [2024-07-25 22:26:50,695] [INFO] [ocrmypdf._pipeline] skipping all processing on this page
paperless-web | [2024-07-25 22:26:50,698] [INFO] [ocrmypdf._pipelines.ocr] Postprocessing...
paperless-web | [2024-07-25 22:26:50,768] [WARNING] [ocrmypdf._metadata] Some input metadata could not be copied because it is not permitted in PDF/A. You may wish to examine the output PDF's XMP metadata.
paperless-web | [2024-07-25 22:26:50,778] [INFO] [ocrmypdf._pipeline] Image optimization ratio: 1.00 savings: 0.0%
paperless-web | [2024-07-25 22:26:50,778] [INFO] [ocrmypdf._pipeline] Total file size ratio: 1.25 savings: 20.3%
paperless-web | [2024-07-25 22:26:50,779] [INFO] [ocrmypdf._pipelines._common] Output file is a PDF/A-2B (as expected)
paperless-web | [2024-07-25 22:26:50,783] [DEBUG] [paperless.parsing.tesseract] Incomplete sidecar file: discarding.
paperless-web | [2024-07-25 22:26:50,803] [INFO] [paperless.parsing.tesseract] pdftotext exited 0
paperless-web | [2024-07-25 22:26:50,804] [DEBUG] [paperless.consumer] Generating thumbnail for dummy2.pdf...
paperless-web | [2024-07-25 22:26:50,807] [DEBUG] [paperless.parsing] Execute: convert -density 300 -scale 500x5000> -alpha remove -strip -auto-orient -define pdf:use-cropbox=true /tmp/paperless/paperless-vwxyj6iu/archive.pdf[0] /tmp/paperless/paperless-vwxyj6iu/convert.webp
paperless-web | [2024-07-25 22:26:51,317] [INFO] [paperless.parsing] convert exited 0
paperless-web | [2024-07-25 22:26:51,553] [DEBUG] [paperless.consumer] Saving record to database
paperless-web | [2024-07-25 22:26:51,553] [DEBUG] [paperless.consumer] Creation date from parse_date: 2023-11-20 00:00:00+01:00
paperless-web | [2024-07-25 22:26:51,850] [DEBUG] [paperless.matching] Tag AAAA matched on document 2023-11-20 dummy2 because it contains this word: W00883
paperless-web | [2024-07-25 22:26:51,850] [DEBUG] [paperless.matching] Tag BBBB matched on document 2023-11-20 dummy2 because the string 123456789 matches the regular expression 123456789
paperless-web | [2024-07-25 22:26:51,850] [DEBUG] [paperless.matching] Tag CCCC matched on document 2023-11-20 dummy2 because it contains this word: 123456789
paperless-web | [2024-07-25 22:26:51,850] [DEBUG] [paperless.matching] Tag DDDD matched on document 2023-11-20 dummy2 because it contains this word: 505259366
paperless-web | [2024-07-25 22:26:51,852] [INFO] [paperless.handlers] Tagging "2023-11-20 dummy2" with "BBBB, AAAA, CCCC, DDDD"
paperless-web | [2024-07-25 22:26:51,883] [DEBUG] [paperless.consumer] Deleting file /tmp/paperless/paperless-ngxg36hkaj4/dummy2.pdf
paperless-web | [2024-07-25 22:26:51,892] [DEBUG] [paperless.parsing.tesseract] Deleting directory /tmp/paperless/paperless-vwxyj6iu
paperless-web | [2024-07-25 22:26:51,892] [INFO] [paperless.consumer] Document 2023-11-20 dummy2 consumption finished
paperless-web | [2024-07-25 22:26:51,895] [INFO] [paperless.tasks] ConsumeTaskPlugin completed with: Success. New document id 370 created
paperless-web | [2024-07-25 22:26:51,901] [INFO] [celery.app.trace] Task documents.tasks.consume_file[bad2b633-451f-4a59-a4fc-f61023b210e9] succeeded in 1.4578893575817347s: 'Success. New document id 370 created'
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:51 +0000] "GET /api/tasks/ HTTP/2.0" 200 17652 "https://192.168.178.218/trash" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:51 +0000] "GET /api/documents/?page=1&page_size=50&ordering=-created&truncate_content=true&tags__id__all=5 HTTP/2.0" 200 714 "https://192.168.178.218/view/2" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:51 +0000] "GET /api/tasks/ HTTP/2.0" 200 17652 "https://192.168.178.218/view/2" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:51 +0000] "GET /api/documents/370/thumb/ HTTP/2.0" 200 11146 "https://192.168.178.218/view/2" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:51 +0000] "POST /api/documents/selection_data/ HTTP/2.0" 200 278 "https://192.168.178.218/view/2" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
paperless-nginx | 192.168.178.41 - - [25/Jul/2024:20:26:51 +0000] "GET /api/tasks/ HTTP/2.0" 200 17652 "https://192.168.178.218/tags" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36" "-"
```
```
### Browser logs
_No response_
### Paperless-ngx version
2.11.0
### Host OS
Debian 12, x86_64 (virtualized as container via Proxmox unpriviliged)
### Installation method
Docker - official image
### System status
```json
{
"pngx_version": "2.11.0",
"server_os": "Linux-6.8.4-3-pve-x86_64-with-glibc2.36",
"install_type": "docker",
"storage": {
"total": 368766496768,
"available": 349514854400
},
"database": {
"type": "sqlite",
"url": "/usr/src/paperless/data/db.sqlite3",
"status": "OK",
"error": null,
"migration_status": {
"latest_migration": "paperless_mail.0025_alter_mailaccount_owner_alter_mailrule_owner_and_more",
"unapplied_migrations": []
}
},
"tasks": {
"redis_url": "redis://broker:6379",
"redis_status": "OK",
"redis_error": null,
"celery_status": "OK",
"index_status": "OK",
"index_last_modified": "2024-07-25T22:26:51.876327+02:00",
"index_error": null,
"classifier_status": "OK",
"classifier_last_trained": "2024-07-25T20:05:00.210046Z",
"classifier_error": null
}
}
```
### Browser
Chrome
### Configuration changes
see above
### Please confirm the following
- [X] I believe this issue is a bug that affects all users of Paperless-ngx, not something specific to my installation.
- [X] I have already searched for relevant existing issues and discussions before opening this report.
- [X] I have updated the title field above with a concise description.
|
closed
|
2024-07-25T20:49:39Z
|
2024-08-26T03:04:57Z
|
https://github.com/paperless-ngx/paperless-ngx/issues/7324
|
[
"not a bug"
] |
brlnr23
| 6 |
FactoryBoy/factory_boy
|
sqlalchemy
| 387 |
Create a pydict Faker with value_types ...
|
I'm trying to create a `pydict` faker with only string values to populate a JSONField. So fare I've tried the following methods without look:
extra = factory.Faker('pydict', nb_elements=10, value_types=['str'])
-> TypeError: pydict() got an unexpected keyword argument 'value_types'
extra = factory.Faker('pydict', nb_elements=10, ['str'])
-> SyntaxError: positional argument follows keyword argument
factory.Faker('pydict', nb_elements=10, 'str')
-> SyntaxError: positional argument follows keyword argument
extra = factory.Faker('pydict', 10, True, 'str')
-> TypeError: __init__() takes from 2 to 3 positional arguments but 5 were given
How can I specify the `*value_types` part of the pydict faker?
|
closed
|
2017-06-01T08:25:35Z
|
2018-05-25T14:22:18Z
|
https://github.com/FactoryBoy/factory_boy/issues/387
|
[] |
mhubig
| 2 |
Lightning-AI/pytorch-lightning
|
pytorch
| 19,595 |
Does `Trainer(devices=1)` use all CPUs?
|
### Bug description
https://github.com/Lightning-AI/pytorch-lightning/blob/3740546899aedad77c80db6b57f194e68c455e28/src/lightning/fabric/accelerators/cpu.py#L75
`cpu_cores` being a list of integers will always raise an exception, which shouldn't according to the Trainer documentation/this function signature
### What version are you seeing the problem on?
master
### How to reproduce the bug
_No response_
### Error messages and logs
```
# Error messages and logs here please
```
### Environment
<details>
<summary>Current environment</summary>
```
#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
#- Running environment of LightningApp (e.g. local, cloud):
```
</details>
### More info
_No response_
cc @borda
|
closed
|
2024-03-07T21:28:13Z
|
2024-11-13T19:42:16Z
|
https://github.com/Lightning-AI/pytorch-lightning/issues/19595
|
[
"help wanted",
"good first issue",
"question",
"ver: 2.2.x"
] |
MaximilienLC
| 7 |
AUTOMATIC1111/stable-diffusion-webui
|
deep-learning
| 15,553 |
[Bug]: 'no module 'xformers'. Processing without' on fresh installation of v1.9.0
|
### Checklist
- [X] The issue exists after disabling all extensions
- [X] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
- [X] The issue exists in the current version of the webui
- [X] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet
### What happened?
Unable to use xformers attention optimization
### Steps to reproduce the problem
1. clone git repo
2. set directory python version to 3.10.6 using 'pyenv local'
3. run 'bash webui.sh'
### What should have happened?
The webui should have installed and used xformers as the attention optimization
### What browsers do you use to access the UI ?
Brave
### Sysinfo
[sysinfo.json](https://github.com/AUTOMATIC1111/stable-diffusion-webui/files/15014281/sysinfo.json)
### Console logs
```Shell
Installing requirements
Launching Web UI with arguments:
no module 'xformers'. Processing without...
no module 'xformers'. Processing without...
No module 'xformers'. Proceeding without it.
Downloading: "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors" to /media/origins/Games and AI/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors
...
Applying attention optimization: Doggettx... done.
```
### Additional information
Distro: Ubuntu 23.10
Graphics Driver: 550.54.14
CUDA Version: 12.4
|
closed
|
2024-04-17T16:25:50Z
|
2024-04-23T05:17:34Z
|
https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/15553
|
[
"asking-for-help-with-local-system-issues"
] |
TommyQTran
| 6 |
mwaskom/seaborn
|
data-science
| 3,695 |
Figure in the plot is not showing in heatmap in 0.12.2,but everything works right in 0.9.0
|
Today I am running this code,but in the plot no figures are showing except for the first row.
```
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and y_pred is your predicted labels
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(10,7))
sns.heatmap(cm, annot=True,fmt='.0f',cmap='YlGnBu')
plt.xlabel('Predicted')
plt.ylabel('Truth')
plt.show()
```
Here are the modules installed:
```
absl-py 2.1.0 <pip>
appdirs 1.4.4 pyhd3eb1b0_0
asttokens 2.0.5 pyhd3eb1b0_0
astunparse 1.6.3 <pip>
backcall 0.2.0 pyhd3eb1b0_0
blas 1.0 mkl
boto 2.49.0 py39haa95532_0
boto3 1.34.82 py39haa95532_0
botocore 1.34.82 py39haa95532_0
bottleneck 1.3.7 py39h9128911_0
brotli 1.0.9 h2bbff1b_8
brotli-bin 1.0.9 h2bbff1b_8
brotli-python 1.0.9 py39hd77b12b_8
bz2file 0.98 py39haa95532_1
ca-certificates 2024.3.11 haa95532_0
certifi 2024.2.2 py39haa95532_0
cffi 1.16.0 py39h2bbff1b_1
charset-normalizer 3.1.0 <pip>
charset-normalizer 2.0.4 pyhd3eb1b0_0
charset-normalizer 3.3.2 <pip>
colorama 0.4.6 py39haa95532_0
comm 0.2.1 py39haa95532_0
contourpy 1.2.0 py39h59b6b97_0
cryptography 42.0.5 py39h89fc84f_1
cycler 0.11.0 pyhd3eb1b0_0
debugpy 1.6.7 py39hd77b12b_0
decorator 5.1.1 pyhd3eb1b0_0
exceptiongroup 1.2.0 py39haa95532_0
executing 0.8.3 pyhd3eb1b0_0
flatbuffers 24.3.25 <pip>
fonttools 4.51.0 py39h2bbff1b_0
freetype 2.12.1 ha860e81_0
gast 0.5.4 <pip>
gensim 4.3.2 <pip>
google-pasta 0.2.0 <pip>
grpcio 1.63.0 <pip>
h5py 3.11.0 <pip>
icc_rt 2022.1.0 h6049295_2
idna 3.7 py39haa95532_0
importlib-metadata 7.0.1 py39haa95532_0
importlib_metadata 7.1.0 <pip>
importlib_metadata 7.0.1 hd3eb1b0_0
importlib_resources 6.1.1 py39haa95532_1
intel-openmp 2023.1.0 h59b6b97_46320
ipykernel 6.28.0 py39haa95532_0
ipython 8.15.0 py39haa95532_0
jedi 0.18.1 py39haa95532_1
jieba 0.42.1 <pip>
jmespath 1.0.1 py39haa95532_0
joblib 1.4.0 py39haa95532_0
jpeg 9e h2bbff1b_1
jupyter_client 8.6.0 py39haa95532_0
jupyter_core 5.5.0 py39haa95532_0
keras 3.3.3 <pip>
kiwisolver 1.4.4 py39hd77b12b_0
lcms2 2.12 h83e58a3_0
lerc 3.0 hd77b12b_0
libbrotlicommon 1.0.9 h2bbff1b_8
libbrotlidec 1.0.9 h2bbff1b_8
libbrotlienc 1.0.9 h2bbff1b_8
libclang 18.1.1 <pip>
libdeflate 1.17 h2bbff1b_1
libpng 1.6.39 h8cc25b3_0
libsodium 1.0.18 h62dcd97_0
libtiff 4.5.1 hd77b12b_0
libwebp-base 1.3.2 h2bbff1b_0
lz4-c 1.9.4 h2bbff1b_1
Markdown 3.6 <pip>
markdown-it-py 3.0.0 <pip>
MarkupSafe 2.1.5 <pip>
matplotlib-base 3.8.4 py39h4ed8f06_0
matplotlib-inline 0.1.6 py39haa95532_0
mdurl 0.1.2 <pip>
mkl 2023.1.0 h6b88ed4_46358
mkl-service 2.4.0 py39h2bbff1b_1
mkl_fft 1.3.8 py39h2bbff1b_0
mkl_random 1.2.4 py39h59b6b97_0
ml-dtypes 0.3.2 <pip>
namex 0.0.8 <pip>
nest-asyncio 1.6.0 py39haa95532_0
numexpr 2.8.7 py39h2cd9be0_0
numpy 1.26.4 py39h055cbcc_0
numpy-base 1.26.4 py39h65a83cf_0
openjpeg 2.4.0 h4fc8c34_0
openssl 3.0.13 h2bbff1b_1
opt-einsum 3.3.0 <pip>
optree 0.11.0 <pip>
packaging 23.2 py39haa95532_0
packaging 24.0 <pip>
pandas 1.4.4 py39hd77b12b_0
parso 0.8.3 pyhd3eb1b0_0
pickleshare 0.7.5 pyhd3eb1b0_1003
pillow 10.3.0 py39h2bbff1b_0
pip 24.0 py39haa95532_0
platformdirs 3.10.0 py39haa95532_0
pooch 1.4.0 pyhd3eb1b0_0
prompt-toolkit 3.0.43 py39haa95532_0
protobuf 4.25.3 <pip>
psutil 5.9.0 py39h2bbff1b_0
pure_eval 0.2.2 pyhd3eb1b0_0
pybind11-abi 5 hd3eb1b0_0
pycparser 2.21 pyhd3eb1b0_0
pygments 2.15.1 py39haa95532_1
Pygments 2.18.0 <pip>
pyopenssl 24.0.0 py39haa95532_0
pyparsing 3.0.9 py39haa95532_0
pysocks 1.7.1 py39haa95532_0
python 3.9.19 h1aa4202_1
python-dateutil 2.9.0post0 py39haa95532_0
pytz 2024.1 py39haa95532_0
pywin32 305 py39h2bbff1b_0
pyzmq 25.1.2 py39hd77b12b_0
requests 2.31.0 py39haa95532_1
rich 13.7.1 <pip>
s3transfer 0.10.1 py39haa95532_0
scikit-learn 1.4.2 py39h4ed8f06_1
scipy 1.12.0 py39h8640f81_0
seaborn 0.12.2 py39haa95532_0
setuptools 69.5.1 py39haa95532_0
six 1.16.0 pyhd3eb1b0_1
smart-open 7.0.4 <pip>
smart_open 1.9.0 py_0
sqlite 3.45.3 h2bbff1b_0
stack_data 0.2.0 pyhd3eb1b0_0
tbb 2021.8.0 h59b6b97_0
tensorboard 2.16.2 <pip>
tensorboard-data-server 0.7.2 <pip>
tensorflow 2.16.1 <pip>
tensorflow-intel 2.16.1 <pip>
tensorflow-io-gcs-filesystem 0.31.0 <pip>
termcolor 2.4.0 <pip>
threadpoolctl 2.2.0 pyh0d69192_0
tornado 6.3.3 py39h2bbff1b_0
traitlets 5.7.1 py39haa95532_0
typing_extensions 4.11.0 py39haa95532_0
tzdata 2024a h04d1e81_0
unicodedata2 15.1.0 py39h2bbff1b_0
urllib3 2.2.1 <pip>
urllib3 1.26.18 py39haa95532_0
vc 14.2 h21ff451_1
vs2015_runtime 14.27.29016 h5e58377_2
wcwidth 0.2.5 pyhd3eb1b0_0
Werkzeug 3.0.3 <pip>
wheel 0.43.0 py39haa95532_0
win_inet_pton 1.1.0 py39haa95532_0
wrapt 1.16.0 <pip>
xz 5.4.6 h8cc25b3_1
zeromq 4.3.5 hd77b12b_0
zipp 3.17.0 py39haa95532_0
zipp 3.18.1 <pip>
zlib 1.2.13 h8cc25b3_1
zstd 1.5.5 hd43e919_2
```
After encountering this abnormal, I turn to python 3.7 with 0.9.0 ,everything works right.
```
_ipyw_jlab_nb_ext_conf 0.1.0 py37_0
alabaster 0.7.11 py37_0
anaconda 5.3.1 py37_0
anaconda-client 1.7.2 py37_0
anaconda-navigator 1.9.2 py37_0
anaconda-project 0.8.2 py37_0
appdirs 1.4.3 py37h28b3542_0
asn1crypto 0.24.0 py37_0
astroid 2.0.4 py37_0
astropy 3.0.4 py37hfa6e2cd_0
astunparse 1.6.3 <pip>
atomicwrites 1.2.1 py37_0
attrs 18.2.0 py37h28b3542_0
automat 0.7.0 py37_0
babel 2.6.0 py37_0
backcall 0.1.0 py37_0
backports 1.0 py37_1
backports.shutil_get_terminal_size 1.0.0 py37_2
beautifulsoup4 4.6.3 py37_0
bitarray 0.8.3 py37hfa6e2cd_0
bkcharts 0.2 py37_0
blas 1.0 mkl
blaze 0.11.3 py37_0
bleach 2.1.4 py37_0
blosc 1.14.4 he51fdeb_0
bokeh 0.13.0 py37_0
boto 2.49.0 py37_0
bottleneck 1.2.1 py37h452e1ab_1
bzip2 1.0.6 hfa6e2cd_5
ca-certificates 2018.03.07 0
certifi 2018.8.24 py37_1
cffi 1.11.5 py37h74b6da3_1
chardet 3.0.4 py37_1
click 6.7 py37_0
cloudpickle 0.5.5 py37_0
clyent 1.2.2 py37_1
colorama 0.3.9 py37_0
comtypes 1.1.7 py37_0
conda 4.5.11 py37_0
conda-build 3.15.1 py37_0
conda-env 2.6.0 1
console_shortcut 0.1.1 3
constantly 15.1.0 py37h28b3542_0
contextlib2 0.5.5 py37_0
cryptography 2.3.1 py37h74b6da3_0
curl 7.61.0 h7602738_0
cycler 0.10.0 py37_0
Cython 0.29.28 <pip>
cython 0.28.5 py37h6538335_0
cytoolz 0.9.0.1 py37hfa6e2cd_1
dask 0.19.1 py37_0
dask-core 0.19.1 py37_0
datashape 0.5.4 py37_1
decorator 4.3.0 py37_0
defusedxml 0.5.0 py37_1
distlib 0.3.8 <pip>
distributed 1.23.1 py37_0
docutils 0.14 py37_0
entrypoints 0.2.3 py37_2
et_xmlfile 1.0.1 py37_0
fastcache 1.0.2 py37hfa6e2cd_2
filelock 3.0.8 py37_0
filelock 3.12.2 <pip>
flask 1.0.2 py37_1
flask-cors 3.0.6 py37_0
flatbuffers 24.3.25 <pip>
freetype 2.9.1 ha9979f8_1
gast 0.4.0 <pip>
gensim 4.2.0 <pip>
get_terminal_size 1.0.0 h38e98db_0
gevent 1.3.6 py37hfa6e2cd_0
glob2 0.6 py37_0
greenlet 0.4.15 py37hfa6e2cd_0
h5py 3.8.0 <pip>
h5py 2.8.0 py37h3bdd7fb_2
hdf5 1.10.2 hac2f561_1
heapdict 1.0.0 py37_2
html5lib 1.0.1 py37_0
hyperlink 18.0.0 py37_0
icc_rt 2017.0.4 h97af966_0
icu 58.2 ha66f8fd_1
idna 2.7 py37_0
imageio 2.4.1 py37_0
imagesize 1.1.0 py37_0
importlib-metadata 6.7.0 <pip>
incremental 17.5.0 py37_0
intel-openmp 2019.0 118
ipykernel 4.10.0 py37_0
ipython 6.5.0 py37_0
ipython_genutils 0.2.0 py37_0
ipywidgets 7.4.1 py37_0
isort 4.3.4 py37_0
itsdangerous 0.24 py37_1
jdcal 1.4 py37_0
jedi 0.12.1 py37_0
jieba 0.42.1 <pip>
jinja2 2.10 py37_0
joblib 1.3.2 <pip>
jpeg 9b hb83a4c4_2
jsonschema 2.6.0 py37_0
jupyter 1.0.0 py37_7
jupyter_client 5.2.3 py37_0
jupyter_console 5.2.0 py37_1
jupyter_core 4.4.0 py37_0
jupyterlab 0.34.9 py37_0
jupyterlab_launcher 0.13.1 py37_0
keras 2.11.0 <pip>
keyring 13.2.1 py37_0
kiwisolver 1.0.1 py37h6538335_0
lazy-object-proxy 1.3.1 py37hfa6e2cd_2
libclang 18.1.1 <pip>
libcurl 7.61.0 h7602738_0
libiconv 1.15 h1df5818_7
libpng 1.6.34 h79bbb47_0
libsodium 1.0.16 h9d3ae62_0
libssh2 1.8.0 hd619d38_4
libtiff 4.0.9 h36446d0_2
libxml2 2.9.8 hadb2253_1
libxslt 1.1.32 hf6f1972_0
llvmlite 0.24.0 py37h6538335_0
locket 0.2.0 py37_1
lxml 4.2.5 py37hef2cd61_0
lzo 2.10 h6df0209_2
m2w64-gcc-libgfortran 5.3.0 6
m2w64-gcc-libs 5.3.0 7
m2w64-gcc-libs-core 5.3.0 7
m2w64-gmp 6.1.0 2
m2w64-libwinpthread-git 5.0.0.4634.697f757 2
markupsafe 1.0 py37hfa6e2cd_1
matplotlib 2.2.3 py37hd159220_0
mccabe 0.6.1 py37_1
menuinst 1.4.14 py37hfa6e2cd_0
mistune 0.8.3 py37hfa6e2cd_1
mkl 2019.0 118
mkl-service 1.1.2 py37hb217b18_5
mkl_fft 1.0.4 py37h1e22a9b_1
mkl_random 1.0.1 py37h77b88f5_1
more-itertools 4.3.0 py37_0
mpmath 1.0.0 py37_2
msgpack-python 0.5.6 py37he980bc4_1
msys2-conda-epoch 20160418 1
multipledispatch 0.6.0 py37_0
navigator-updater 0.2.1 py37_0
nbconvert 5.4.0 py37_1
nbformat 4.4.0 py37_0
networkx 2.1 py37_0
nltk 3.3.0 py37_0
nose 1.3.7 py37_2
notebook 5.6.0 py37_0
numba 0.39.0 py37h830ac7b_0
numexpr 2.6.8 py37h9ef55f4_0
numpy 1.15.1 py37ha559c80_0
numpy 1.21.6 <pip>
numpy-base 1.15.1 py37h8128ebf_0
numpydoc 0.8.0 py37_0
odo 0.5.1 py37_0
olefile 0.46 py37_0
openpyxl 2.5.6 py37_0
openssl 1.0.2p hfa6e2cd_0
opt-einsum 3.3.0 <pip>
packaging 17.1 py37_0
pandas 0.23.4 py37h830ac7b_0
pandoc 1.19.2.1 hb2460c7_1
pandocfilters 1.4.2 py37_1
parso 0.3.1 py37_0
partd 0.3.8 py37_0
path.py 11.1.0 py37_0
pathlib2 2.3.2 py37_0
patsy 0.5.0 py37_0
pep8 1.7.1 py37_0
pickleshare 0.7.4 py37_0
pillow 5.2.0 py37h08bbbbd_0
pip 10.0.1 py37_0
pkginfo 1.4.2 py37_1
platformdirs 4.0.0 <pip>
plotly 5.18.0 <pip>
plotly-express 0.4.1 <pip>
pluggy 0.7.1 py37h28b3542_0
ply 3.11 py37_0
prometheus_client 0.3.1 py37h28b3542_0
prompt_toolkit 1.0.15 py37_0
protobuf 3.19.6 <pip>
psutil 5.4.7 py37hfa6e2cd_0
py 1.6.0 py37_0
pyasn1 0.4.4 py37h28b3542_0
pyasn1-modules 0.2.2 py37_0
pycodestyle 2.4.0 py37_0
pycosat 0.6.3 py37hfa6e2cd_0
pycparser 2.18 py37_1
pycrypto 2.6.1 py37hfa6e2cd_9
pycurl 7.43.0.2 py37h74b6da3_0
pyflakes 2.0.0 py37_0
pygments 2.2.0 py37_0
PyHamcrest 2.1.0 <pip>
pylint 2.1.1 py37_0
pyodbc 4.0.24 py37h6538335_0
pyopenssl 18.0.0 py37_0
pyparsing 2.2.0 py37_1
pyqt 5.9.2 py37h6538335_2
pysocks 1.6.8 py37_0
pytables 3.4.4 py37he6f6034_0
pytest 3.8.0 py37_0
pytest-arraydiff 0.2 py37h39e3cac_0
pytest-astropy 0.4.0 py37_0
pytest-doctestplus 0.1.3 py37_0
pytest-openfiles 0.3.0 py37_0
pytest-remotedata 0.3.0 py37_0
python 3.7.0 hea74fb7_0
python-dateutil 2.7.3 py37_0
pytz 2018.5 py37_0
pywavelets 1.0.0 py37h452e1ab_0
pywin32 223 py37hfa6e2cd_1
pywinpty 0.5.4 py37_0
pyyaml 3.13 py37hfa6e2cd_0
pyzmq 17.1.2 py37hfa6e2cd_0
qt 5.9.6 vc14h1e9a669_2 [vc14]
qtawesome 0.4.4 py37_0
qtconsole 4.4.1 py37_0
qtpy 1.5.0 py37_0
requests 2.19.1 py37_0
rope 0.11.0 py37_0
ruamel_yaml 0.15.46 py37hfa6e2cd_0
scikit-image 0.14.0 py37h6538335_1
scikit-learn 0.19.2 py37heebcf9a_0
scipy 1.1.0 py37h4f6bf74_1
seaborn 0.9.0 py37_0
send2trash 1.5.0 py37_0
service_identity 17.0.0 py37h28b3542_0
setuptools 40.2.0 py37_0
simplegeneric 0.8.1 py37_2
singledispatch 3.4.0.3 py37_0
sip 4.19.8 py37h6538335_0
six 1.16.0 <pip>
six 1.11.0 py37_1
smart-open 7.0.4 <pip>
snappy 1.1.7 h777316e_3
snowballstemmer 1.2.1 py37_0
sortedcollections 1.0.1 py37_0
sortedcontainers 2.0.5 py37_0
sphinx 1.7.9 py37_0
sphinxcontrib 1.0 py37_1
sphinxcontrib-websupport 1.1.0 py37_1
spyder 3.3.1 py37_1
spyder-kernels 0.2.6 py37_0
sqlalchemy 1.2.11 py37hfa6e2cd_0
sqlite 3.24.0 h7602738_0
statsmodels 0.9.0 py37h452e1ab_0
sympy 1.1.1 py37_0
tblib 1.3.2 py37_0
tenacity 8.2.3 <pip>
tensorflow-estimator 2.11.0 <pip>
termcolor 2.3.0 <pip>
terminado 0.8.1 py37_1
testpath 0.3.1 py37_0
tk 8.6.8 hfa6e2cd_0
toolz 0.9.0 py37_0
tornado 5.1 py37hfa6e2cd_0
tqdm 4.26.0 py37h28b3542_0
traitlets 4.3.2 py37_0
twisted 18.7.0 py37hfa6e2cd_1
typing_extensions 4.7.1 <pip>
unicodecsv 0.14.1 py37_0
urllib3 1.23 py37_0
vc 14.1 h0510ff6_4
virtualenv 20.26.1 <pip>
vs2015_runtime 14.15.26706 h3a45250_0
wcwidth 0.1.7 py37_0
webencodings 0.5.1 py37_1
werkzeug 0.14.1 py37_0
wheel 0.31.1 py37_0
widgetsnbextension 3.4.1 py37_0
win_inet_pton 1.0.1 py37_1
win_unicode_console 0.5 py37_0
wincertstore 0.2 py37_0
winpty 0.4.3 4
wrapt 1.10.11 py37hfa6e2cd_2
xgboost 1.6.2 <pip>
xlrd 1.1.0 py37_1
xlsxwriter 1.1.0 py37_0
xlwings 0.11.8 py37_0
xlwt 1.3.0 py37_0
yaml 0.1.7 hc54c509_2
zeromq 4.2.5 he025d50_1
zict 0.1.3 py37_0
zipp 3.15.0 <pip>
zlib 1.2.11 h8395fce_2
zope 1.0 py37_1
zope.interface 4.5.0 py37hfa6e2cd_0
```
|
closed
|
2024-05-20T08:21:25Z
|
2024-05-20T13:28:58Z
|
https://github.com/mwaskom/seaborn/issues/3695
|
[] |
FukoH
| 1 |
huggingface/diffusers
|
pytorch
| 10,428 |
Flux inference error on ascend npu
|
### Describe the bug
It fails to run the demo flux inference code. reporting errors:
> RuntimeError: call aclnnRepeatInterleaveIntWithDim failed, detail:EZ1001: [PID: 23975] 2025-01-02-11:00:00.313.502 self not implemented for DT_DOUBLE, should be in dtype support list [DT_UINT8,DT_INT8,DT_INT16,DT_INT32,DT_INT64,DT_BOOL,DT_FLOAT16,DT_FLOAT,DT_BFLOAT16,].
### Reproduction
```python
import torch
try:
import torch_npu # type: ignore # noqa
from torch_npu.contrib import transfer_to_npu # type: ignore # noqa
is_npu = True
except ImportError:
print("torch_npu not found")
is_npu = False
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=torch.bfloat16)
pipe.to('cuda')
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=3.5,
num_inference_steps=50,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-dev.png")
```
### Logs
```shell
Traceback (most recent call last):
File "/home/pagoda/exp.py", line 18, in <module>
image = pipe(
File "/usr/local/python3.10.13/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/usr/local/python3.10.13/lib/python3.10/site-packages/diffusers/pipelines/flux/pipeline_flux.py", line 889, in __call__
noise_pred = self.transformer(
File "/usr/local/python3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/python3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/python3.10.13/lib/python3.10/site-packages/diffusers/models/transformers/transformer_flux.py", line 492, in forward
image_rotary_emb = self.pos_embed(ids)
File "/usr/local/python3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/python3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/python3.10.13/lib/python3.10/site-packages/diffusers/models/embeddings.py", line 1253, in forward
cos, sin = get_1d_rotary_pos_embed(
File "/usr/local/python3.10.13/lib/python3.10/site-packages/diffusers/models/embeddings.py", line 1157, in get_1d_rotary_pos_embed
freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float() # [S, D]
RuntimeError: call aclnnRepeatInterleaveIntWithDim failed, detail:EZ1001: [PID: 23975] 2025-01-02-11:00:00.313.502 self not implemented for DT_DOUBLE, should be in dtype support list [DT_UINT8,DT_INT8,DT_INT16,DT_INT32,DT_INT64,DT_BOOL,DT_FLOAT16,DT_FLOAT,DT_BFLOAT16,].
[ERROR] 2025-01-02-11:00:00 (PID:23975, Device:0, RankID:-1) ERR01100 OPS call acl api failed
```
```
### System Info
- 🤗 Diffusers version: 0.32.
- Platform: Linux-5.10.0-136.36.0.112.4.oe2203sp1.x86_64-x86_64-with-glibc2.35
- Running on Google Colab?: No
- Python version: 3.10.13
- PyTorch version (GPU?): 2.4.0+cpu (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.27.0
- Transformers version: 4.46.3
- Accelerate version: 1.1.0
- PEFT version: 0.13.2
- Bitsandbytes version: not installed
- Safetensors version: 0.4.5
- xFormers version: not installed
- Accelerator: NA
- Using GPU in script?: Ascend 910B
- Using distributed or parallel set-up in script?: <fill in>
### Who can help?
_No response_
|
closed
|
2025-01-02T11:06:29Z
|
2025-01-02T19:52:54Z
|
https://github.com/huggingface/diffusers/issues/10428
|
[
"bug"
] |
gameofdimension
| 0 |
awesto/django-shop
|
django
| 795 |
How to link static file?
|
I already run collectstatic but no luck.
|
closed
|
2020-03-01T09:06:07Z
|
2020-03-01T10:55:33Z
|
https://github.com/awesto/django-shop/issues/795
|
[] |
HeroSony
| 1 |
graphql-python/graphene
|
graphql
| 838 |
input argument for for relay mutation overwrites built-in input function
|
In the example https://docs.graphene-python.org/en/latest/relay/mutations/
```
class IntroduceShip(relay.ClientIDMutation):
class Input:
ship_name = graphene.String(required=True)
faction_id = graphene.String(required=True)
ship = graphene.Field(Ship)
faction = graphene.Field(Faction)
@classmethod
def mutate_and_get_payload(cls, root, info, **input):
ship_name = input.ship_name
faction_id = input.faction_id
ship = create_ship(ship_name, faction_id)
faction = get_faction(faction_id)
return IntroduceShip(ship=ship, faction=faction)
```
I believe `input` would overwrite the built-in Python `input()` function.
Can this be renamed to something else? Thanks.
|
closed
|
2018-09-27T18:54:17Z
|
2018-11-22T23:46:25Z
|
https://github.com/graphql-python/graphene/issues/838
|
[] |
cherls
| 0 |
noirbizarre/flask-restplus
|
api
| 676 |
Create the list of object as response model, but being restplus complained not iterable
|
Hi,
I want to get response of list of object, like this
[{"name":"aaa",
"id": 3},
{"name":"bbb",
"id": 4}]
CREATIVE_ASSET_MODEL = {"name" : String(), "id": Integer()}
The model is
ASSETS_RESPONSE_MODEL = api_namespace.model('Response Model', List(Nested(model=CREATIVE_ASSET_MODEL)))
But it complained the list is not iterable.
Make it a dict will work, like this:
ASSETS_RESPONSE_MODEL = api_namespace.model('Response Model', {'result' : List(Nested(model=CREATIVE_ASSET_MODEL))
})
But I don't want to add the 'result' to the response, can anyone help me with this, thanks!
|
open
|
2019-07-21T18:15:34Z
|
2021-06-04T21:58:09Z
|
https://github.com/noirbizarre/flask-restplus/issues/676
|
[] |
ZoraShu
| 3 |
cvat-ai/cvat
|
pytorch
| 9,198 |
need to map the cvat to local machine ip
|
### Actions before raising this issue
- [x] I searched the existing issues and did not find anything similar.
- [x] I read/searched [the docs](https://docs.cvat.ai/docs/)
### Steps to Reproduce
_No response_
### Expected Behavior
_No response_
### Possible Solution
_No response_
### Context
my cvat tool is running fine on localhost:8080 now i want it to run on my machine ip so that anyone in the network can access the tool and do the anotation
### Environment
```Markdown
```
|
closed
|
2025-03-11T05:47:12Z
|
2025-03-13T09:33:00Z
|
https://github.com/cvat-ai/cvat/issues/9198
|
[
"question"
] |
ashishbbr03
| 1 |
pydata/bottleneck
|
numpy
| 333 |
[BUG] Can't Install from Cached source and wheel in Container
|
**Describe the bug**
I am trying to install Bottleneck as a dependency in a container using a manually created pip cache.
**To Reproduce**
To assist in reproducing the bug, please include the following:
1. Command/code being executed
```
$ cd /tmp
$ python3 -m pip download Bottleneck -d ./ -v
$ ls
Bottleneck-1.3.2.tar.gz numpy-1.18.1-cp36-cp36m-manylinux1_x86_64.whl
$ python3 -m pip install Bottleneck --find-links /tmp --no-index
```
2. Python version and OS
```
Docker Container FROM nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04
Linux ab183940868d 4.19.76-linuxkit #1 SMP Thu Oct 17 19:31:58 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
Python 3.6.9 (default, Nov 7 2019, 10:44:02)
[GCC 8.3.0] on linux
```
3. `pip` version
```
pip 20.0.2 from /usr/local/lib/python3.6/dist-packages/pip (python 3.6)
```
4. Output of `pip list` or `conda list`
```
Package Version
------------- -------
asn1crypto 0.24.0
cryptography 2.1.4
idna 2.6
keyring 10.6.0
keyrings.alt 3.0
numpy 1.16.0
pip 20.0.2
pycrypto 2.6.1
pygobject 3.26.1
pyxdg 0.25
SecretStorage 2.3.1
setuptools 39.0.1
six 1.11.0
wheel 0.30.0
```
**Expected behavior**
A clear and concise description of what you expected to happen.
Package should install.
**Additional context**
Error output:
```
Looking in links: /tmp
Processing ./Bottleneck-1.3.2.tar.gz
Installing build dependencies ... error
ERROR: Command errored out with exit status 1:
command: /usr/bin/python3 /usr/local/lib/python3.6/dist-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-1z33ubip/overlay --no-warn-script-location --no-binary :none: --only-binary :none: --no-index --find-links /tmp -- setuptools wheel 'numpy==1.13.3; python_version=='"'"'2.7'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.13.3; python_version=='"'"'3.5'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.13.3; python_version=='"'"'3.6'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.14.5; python_version=='"'"'3.7'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.17.3; python_version>='"'"'3.8'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'2.7'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.5'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.6'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.7'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.17.3; python_version>='"'"'3.8'"'"' and platform_system=='"'"'AIX'"'"''
cwd: None
Complete output (12 lines):
Ignoring numpy: markers 'python_version == "2.7" and platform_system != "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "3.5" and platform_system != "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "3.7" and platform_system != "AIX"' don't match your environment
Ignoring numpy: markers 'python_version >= "3.8" and platform_system != "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "2.7" and platform_system == "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "3.5" and platform_system == "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "3.6" and platform_system == "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "3.7" and platform_system == "AIX"' don't match your environment
Ignoring numpy: markers 'python_version >= "3.8" and platform_system == "AIX"' don't match your environment
Looking in links: /tmp
ERROR: Could not find a version that satisfies the requirement setuptools (from versions: none)
ERROR: No matching distribution found for setuptools
----------------------------------------
ERROR: Command errored out with exit status 1: /usr/bin/python3 /usr/local/lib/python3.6/dist-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-1z33ubip/overlay --no-warn-script-location --no-binary :none: --only-binary :none: --no-index --find-links /tmp -- setuptools wheel 'numpy==1.13.3; python_version=='"'"'2.7'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.13.3; python_version=='"'"'3.5'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.13.3; python_version=='"'"'3.6'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.14.5; python_version=='"'"'3.7'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.17.3; python_version>='"'"'3.8'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'2.7'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.5'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.6'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.7'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.17.3; python_version>='"'"'3.8'"'"' and platform_system=='"'"'AIX'"'"'' Check the logs for full command output.
```
|
closed
|
2020-03-12T07:25:38Z
|
2021-01-17T12:34:45Z
|
https://github.com/pydata/bottleneck/issues/333
|
[
"bug"
] |
madhavajay
| 4 |
jina-ai/clip-as-service
|
pytorch
| 132 |
How can I get the Similarity between two Sentence?
|
I got the same issue that the "cosine similarity of two sentence vectors is unreasonably high (e.g. always > 0.8)".
And the author said: "Since cosine distance is a linear space where all dimensions are weighted equally."
So, does anybody have some solution for this issue?
Or, any other Similarity Functions can be used for computing similarity between two sentence with sentence embedding?
|
closed
|
2018-12-14T15:34:13Z
|
2019-03-25T21:41:12Z
|
https://github.com/jina-ai/clip-as-service/issues/132
|
[
"duplicate"
] |
BingqiMiao
| 6 |
dnouri/nolearn
|
scikit-learn
| 264 |
Allow overriding `accuracy` metric
|
Right now accuracy (used in non-regression fits) is hard coded to be:
``` python
predict = predict_proba.argmax(axis=1)
accuracy = T.mean(T.eq(predict, y_batch))
```
|
open
|
2016-05-12T20:00:51Z
|
2016-05-12T21:10:47Z
|
https://github.com/dnouri/nolearn/issues/264
|
[] |
cancan101
| 1 |
scrapy/scrapy
|
python
| 6,228 |
Replace urlparse with urlparse_cached where possible
|
Look for regular expression `urllib.*urlparse` in the code base (docs included), and see if replacing the use of `urllib.parse.urlparse` with `scrapy.utils.httpobj.urlparse_cached` is feasible (I think it should as long as there is a `request` object involved).
|
closed
|
2024-02-20T08:15:59Z
|
2024-02-20T11:47:30Z
|
https://github.com/scrapy/scrapy/issues/6228
|
[
"good first issue",
"cleanup",
"performance"
] |
Gallaecio
| 0 |
mwaskom/seaborn
|
data-visualization
| 3,664 |
jointplot with kind="hex" fails with datetime64[ns]
|
Minimal example:
```python
import seaborn as sns
import numpy as np
dates = np.array(["2023-01-01", "2023-01-02", "2023-01-03"], dtype="datetime64[ns]")
sns.jointplot(x=dates, y=[1, 2, 3], kind="hex")
```
Error:
```
Traceback (most recent call last):
File "/.../seaborn_bug.py", line 21, in <module>
sns.jointplot(x=dates, y=[1, 2, 3], kind="hex")
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/seaborn/axisgrid.py", line 2307, in jointplot
x_bins = min(_freedman_diaconis_bins(grid.x), 50)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/seaborn/distributions.py", line 2381, in _freedman_diaconis_bins
iqr = np.subtract.reduce(np.nanpercentile(a, [75, 25]))
TypeError: the resolved dtypes are not compatible with subtract.reduce. Resolved (dtype('<M8[ns]'), dtype('<M8[ns]'), dtype('<m8[ns]'))
```
I think this should work, as datetime64[ns] is the default type for datetimes in pandas. It works when I omit `kind="hex"` or use `kind="kde"`. The error was in version 0.13.2. In 0.11.2 I got a error in the same cases but it was a integer overflow with numpy during conversions.
|
open
|
2024-03-25T23:53:18Z
|
2024-08-04T05:20:31Z
|
https://github.com/mwaskom/seaborn/issues/3664
|
[] |
fynnkroeger
| 1 |
inventree/InvenTree
|
django
| 8,552 |
[FR] Make filter or status for consumed parts.
|
### Please verify that this feature request has NOT been suggested before.
- [x] I checked and didn't find a similar feature request
### Problem statement
Serialized parts, what been consumed by build order cant be easy filtered out.
Example:
I have part "PC" with serial number and part "Mainboard", serialized too.
So, after building and selling a few PCs, if im trying to search mainboads by OK status (or any other), i will have consumed mainboards in search results.
There is also checkbox "Is Available", but it filters out quarantined\lost and other statuses.
### Suggested solution
So, we need special status for consumed parts or checkbox field.
### Describe alternatives you've considered
.
### Examples of other systems
_No response_
### Do you want to develop this?
- [ ] I want to develop this.
|
closed
|
2024-11-25T11:48:39Z
|
2024-11-27T13:59:16Z
|
https://github.com/inventree/InvenTree/issues/8552
|
[
"enhancement",
"stock",
"api"
] |
alxrMironov
| 5 |
onnx/onnx
|
deep-learning
| 6,708 |
Error when testing latest ONNX commit on ORT
|
# Ask a Question
### Question
<!-- Explain your question here. -->
It seems there are updates about `onnx::OpSchema` after 1.17 which would cause ORT build failure.
Is this expected?
```c++
...
/onnxruntime/onnxruntime/core/graph/contrib_ops/contrib_defs.cc: In function ‘void onnxruntime::contrib::RegisterContribSchemas()’:
/onnxruntime/onnxruntime/core/graph/contrib_ops/contrib_defs.cc:2904:46: error: conversion from ‘onnx::OpSchema’ to non-scalar type ‘onnx::OpSchemaRegistry::OpSchemaRegisterOnce’ requested 2904 | .SetContextDependentFunctionBodyBuilder(
...
```
Btw, here's the [onnx.patch](https://github.com/microsoft/onnxruntime/blob/yifanl/oss/cmake/patches/onnx/onnx.patch) that synced to latest onnx commit, and deps.txt pinned to latest as well.
### Further information
- Relevant Area: <!--e.g., model usage, backend, best practices, converters, shape_inference, version_converter, training, test, operators, IR, ONNX Hub, data preprocessing, CI pipelines. -->
- Is this issue related to a specific model?
**Model name**: <!-- *e.g. mnist* -->
**Model opset**: <!-- *e.g. 17* -->
### Notes
<!-- Any additional information, code snippets. -->
|
open
|
2025-02-14T21:47:52Z
|
2025-02-15T15:50:19Z
|
https://github.com/onnx/onnx/issues/6708
|
[
"question"
] |
yf711
| 1 |
aleju/imgaug
|
machine-learning
| 726 |
Pad with pad mode 'wrap' does not duplicates boxes
|
Is there a way to use Pad (For example `iaa.PadToSquare`) with pad mode `wrap` or `reflect` that will duplicate also the boxes.
for example:
```
image = imageio.imread('example.jpg')
bbs = BoundingBoxesOnImage([
BoundingBox(x1=300, y1= 100, x2=600, y2=400),
BoundingBox(x1=720, y1= 150, x2=800, y2=230),
], shape=image.shape)
image_before = bbs.draw_on_image(image, size=2)
ia.imshow(image_before)
```
```
seq = iaa.Sequential([
iaa.PadToSquare(position='right-top', pad_mode='wrap'),
])
image_aug, bbs_aug = seq(image=image, bounding_boxes=bbs)
image_after = bbs_aug.draw_on_image(image_aug, size=2, color=[0, 0, 255])
ia.imshow(image_after)
```
and the boxes are missing in the newly duplicated parts of the image.
Thanks a lot.
|
open
|
2020-10-26T07:48:52Z
|
2020-10-26T07:48:52Z
|
https://github.com/aleju/imgaug/issues/726
|
[] |
assafzam
| 0 |
home-assistant/core
|
asyncio
| 140,958 |
Google Generative AI responds very late.
|
### The problem
Google Generative AI responds very late. Sometimes it takes up to 1 hour. I wonder why this delay occurs?
### What version of Home Assistant Core has the issue?
2025.3.3
### What was the last working version of Home Assistant Core?
_No response_
### What type of installation are you running?
Home Assistant OS
### Integration causing the issue
_No response_
### Link to integration documentation on our website
_No response_
### Diagnostics information
_No response_
### Example YAML snippet
```yaml
```
### Anything in the logs that might be useful for us?
```txt
```
### Additional information
_No response_
|
closed
|
2025-03-19T21:26:43Z
|
2025-03-19T21:30:15Z
|
https://github.com/home-assistant/core/issues/140958
|
[] |
yunusuztr
| 0 |
christabor/flask_jsondash
|
flask
| 107 |
Allow nicer urls for charts, instead of (or alongside) UUID
|
This is a potentially hazardous change, as the idea of a guid is to prevent collisions in the name. Some potential ways to achieve this:
namespaced charts (e.g. SOMEUSER, or SOMEGROUP) that ties into the existing auth mechanism, or is an arbitrary field in the chart.
The originally UUID link should always work however, and should be the default if there is no other way to get a URL.
Originally requested by @techfreek.
|
open
|
2017-05-11T17:57:56Z
|
2017-05-14T20:38:19Z
|
https://github.com/christabor/flask_jsondash/issues/107
|
[] |
christabor
| 0 |
microsoft/nni
|
tensorflow
| 4,981 |
Automatic Operator Conversion Enhancement
|
**What would you like to be added**:
automatic operator conversion in compression.pytorch.speedup
**Why is this needed**:
nni needs to call these functions to understand the model.
problems when doing it manually:
1. The arguments can only be fetched as a argument list
2. The function uses a lot of star(*) syntax (Keyword-Only Arguments, PEP 3102), both positional argument and keyword-only argument, but the argument list cannot be used to distinguish positional argument and keyword-only argument
3. The function is overloaded, and the number of parameters in multiple versions of the same function may be the same, so it is difficult to distinguish overloaded situations only by the number.
4. Because it is a build-in, inspect.getfullargspec and other methods in inspect module cannot be used to get reflection information.
5. There are more than 2000 functions including the overloaded functions, which is hard to be operated by manual adaptation.
**Without this feature, how does current nni work**：
manual adaptation and conversion
**Components that may involve changes**:
only jit_translate.py in common/compression/pytorch/speedup/
**Brief description of your proposal if any**:
1. Automatic conversion
+ There is a schema information in jit node which can parse out positional argument and keyword-only argument.
+ Then we can automatic wrap arguments, keywords, and the function up to an adapted function.
+ Tested the automatic conversions of torch.sum, torch.unsqueeze, and torch.flatten OK.
2. Unresolved issues
+ Check schema syntax in multiple versions of pytorch and whether the syntax is stable.
+ The schema syntax is different from python's or c++'s.
+ I did't find the syntax document in pytorch documentation.
+ When pytorch compiles, it will dynamically generate schema informations from c++ functions.
+ For all the given schemas, see if they can correspond to the compiled pytorch functions.
+ For all the given schemas, try to parse one by one, and count the number that cannot be parsed.
|
open
|
2022-07-04T05:59:32Z
|
2022-10-08T08:24:33Z
|
https://github.com/microsoft/nni/issues/4981
|
[
"nnidev"
] |
Louis-J
| 1 |
lux-org/lux
|
pandas
| 487 |
C:\Users\mukta\anaconda3\lib\site-packages\IPython\core\formatters.py:918: UserWarning: Unexpected error in rendering Lux widget and recommendations. Falling back to Pandas display.
|
**Describe the bug**
C:\Users\mukta\anaconda3\lib\site-packages\IPython\core\formatters.py:918: UserWarning:
Unexpected error in rendering Lux widget and recommendations. Falling back to Pandas display.
It occured while using GroupBy function in pandas module
**To Reproduce**
Please describe the steps needed to reproduce the behavior. For example:
1. Using this data: `df = pd.read_csv("Play Store Data.csv")`
2. Go to 'df1.groupby(['Category', 'Content Rating']).mean()'
3. See error
File "C:\Users\mukta\anaconda3\lib\site-packages\altair\utils\core.py", line 307, in sanitize_dataframe
raise ValueError("Hierarchical indices not supported")
ValueError: Hierarchical indices not supported
|
open
|
2022-12-06T02:53:45Z
|
2022-12-06T02:53:45Z
|
https://github.com/lux-org/lux/issues/487
|
[] |
muktaraut12
| 0 |
vitalik/django-ninja
|
rest-api
| 769 |
[BUG] The latest Django-ninja (0.22.1) doesn't support the latest Django-ninja (0.18.8)
|
The latest Django-ninja (0.22.1) doesn't support the latest Django-ninja (0.18.8) in connection with Poetry.
- Python version: 3.11.3
- Django version: 4.2
- Django-Ninja version: 0.22.1
--------------------------------------------------------------------------------------------------------------
Poetry output
(app1 backend-py3.11) PS C:\git\app1> poetry update
Updating dependencies
Resolving dependencies...
Because django-ninja-extra (0.18.8) depends on django-ninja (0.21.0)
and no versions of django-ninja-extra match >0.18.8,<0.19.0, django-ninja-extra (>=0.18.8,<0.19.0) requires django-ninja (0.21.0).
So, because app1 backend depends on both django-ninja (^0.22) and django-ninja-extra (^0.18.8), version solving failed.
|
closed
|
2023-05-29T10:59:44Z
|
2023-05-29T20:50:22Z
|
https://github.com/vitalik/django-ninja/issues/769
|
[] |
jankrnavek
| 2 |
autogluon/autogluon
|
computer-vision
| 4,371 |
Adding F2 to evaluation metrics
|
## Description
Please add F2 as an evaluation metric. It is very useful when modeling with an emphasis on recall. Even better than F2 would perhaps be fbeta, which allows you to specify the degree to which recall is more important.
## References
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html
|
open
|
2024-08-07T17:23:48Z
|
2024-11-25T23:04:34Z
|
https://github.com/autogluon/autogluon/issues/4371
|
[
"enhancement",
"module: tabular"
] |
jack-hillesheim
| 0 |
marshmallow-code/flask-marshmallow
|
sqlalchemy
| 143 |
Flask SQLAlchemy Integration - Documentation Suggestion
|
Firstly, thank you for the great extension!!
I've ran into an error that I'm sure others will have ran into, it may be worth updating the docs with a warning about it.
Our structure was as follows:
- Each model has it's own module
- Each model module also contains a Schema and Manager for example UserModel, UserSchema, UserManager all defined within /models/user.py
Some background - with SQLAlchemy, with separate models, you need to import them all at runtime, before the DB is initialised to avoid circular dependancies within relationships.
When the `UserSchema(ma.ModelSchema)` is hit during import `from app.models import *` (in bootstrap) this initialises the models and attempts to execute the relationships. At this stage, we may not have a relationship requirement (which SQLAlchemy avoids using string based relationships) however as the `ma.ModelSchema` initialises the models it creates errors such as this:
> sqlalchemy.exc.InvalidRequestError: When initializing mapper mapped class User->users, expression ‘Team’ failed to locate a name (“name ‘Team’ is not defined”). If this is a class name, consider adding this relationship() to the <class ‘app.models.user.User’> class after both dependent classes have been defined.
and, on subsequent loads:
> sqlalchemy.exc.InvalidRequestError: Table ‘users_teams’ is already defined for this MetaData instance. Specify ‘extend_existing=True’ to redefine options and columns on an existing Table object.
The solution to this is to simply build the UserSchemas in a different import namespace, we've now got:
```
/schemas/user_schema.py
/models/user.py
```
And no more circular issues - hopefully this helps someone else, went around in circles (pun intended) for a few hours before I realised it was the ModelSchema causing it.
Could the docs be updated to make a point of explaining that the ModelSchema initialises the model, and therefore it's a good idea for them to be in separate import destinations?
|
open
|
2019-07-29T09:13:33Z
|
2020-04-20T06:52:44Z
|
https://github.com/marshmallow-code/flask-marshmallow/issues/143
|
[
"help wanted",
"docs"
] |
williamjulianvicary
| 4 |
JaidedAI/EasyOCR
|
pytorch
| 370 |
CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.
|
I enter the command `easyocr -l ru en -f pic.png --detail = 1 --gpu = true` and then I get the message `CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU. `, and then, in the task manager, the increased load on the CPU is displayed, instead of the increased load on the GPU.
My graphics card is GTX 1080 ti, it supports CUDA. But easyocr can't use GPUs.
|
closed
|
2021-02-07T16:48:46Z
|
2023-08-11T12:47:14Z
|
https://github.com/JaidedAI/EasyOCR/issues/370
|
[] |
Niotferdi
| 8 |
pywinauto/pywinauto
|
automation
| 1,384 |
when I use is checked to get the listbox state ,porpmpt error
|
listbox = dlg.ListBox
print(listbox.items())
item = listbox.get_item(field)
if item.is_checked() == True:
print("T")
else:
print("F")
it show error
File "C:\Users\zou-45\AppData\Local\Programs\Python\Python311\Lib\site-packages\comtypes\__init__.py", line 274, in __getattr__
fixed_name = self.__map_case__[name.lower()]
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^
KeyError: 'togglestate_on'
|
open
|
2024-03-14T07:32:07Z
|
2024-03-14T10:54:11Z
|
https://github.com/pywinauto/pywinauto/issues/1384
|
[] |
xidianzou
| 1 |
piskvorky/gensim
|
nlp
| 2,659 |
Strange embedding from FastText
|
I am struggled understanding word embeddings of FastText. According to the white paper [Enriching Word Vectors with Subword Information](https://arxiv.org/pdf/1607.04606.pdf), embeddings of a word is the mean (or sum) of embeddings of its subwords.
I failed to verify this. On `common_text` imported from `gensim.test.utils`, embedding of `user` is `[-0.03062156 -0.02879291 -0.01737508 -0.02839565]`. The mean of embeddings of ['<us', 'use', 'ser', 'er>'] (setting `min_n=max_n=3`) is `[-0.047664 -0.01677518 0.02312234 0.03452689]`. The sum of embeddings also result in a different vector.
Is it a mismatch between Gensim implementation and original FastText, or am I missing something?
Below is my code:
```python
import numpy as np
from gensim.models import FastText
from gensim.models._utils_any2vec import compute_ngrams
from gensim.models.keyedvectors import FastTextKeyedVectors
from gensim.test.utils import common_texts
model = FastText(size=4, window=3, min_count=1)
model.build_vocab(sentences=common_texts)
model.train(sentences=common_texts, total_examples=len(common_texts), epochs=10, min_n=3, max_n=3)
print('survey' in model.wv.vocab)
print('ser' in model.wv.vocab)
print('ree' in model.wv.vocab)
ngrams = compute_ngrams('user', 3, 3)
print('num vector of "user": ', model.wv['user'])
print('ngrams of "user": ', ngrams)
print('mean of num vectors of {}: \n{}'.format(ngrams, np.mean([model.wv[c] for c in ngrams], axis=0)))
```
|
open
|
2019-10-30T09:23:42Z
|
2020-03-19T02:07:22Z
|
https://github.com/piskvorky/gensim/issues/2659
|
[] |
quoctinphan
| 2 |
gradio-app/gradio
|
data-science
| 10,538 |
when leave current page, the event .then() or .success() not execute
|
### Describe the bug
When I launch the Gradio app, I click a button on the page, and then leave the page. I notice that only the test1 method is executed in the console, and the methods following .then() do not execute. They will only continue to execute once I return to the page.
i want know if any setting can change this behavior, when i leave current page, .then() method can continue execute. thanks
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
```python
import time
import gradio as gr
def test1():
print("test1")
time.sleep(10)
print("test1 end")
def test2():
print("test2")
time.sleep(10)
print("test2 end")
def test3():
print("test3")
time.sleep(10)
print("test3 end")
with gr.Blocks() as demo:
btn = gr.Button("点击")
btn.click(test1).then(test2).then(test3)
demo.launch(server_name="0.0.0.0", server_port=8100)
```
### Screenshot
_No response_
### Logs
```shell
```
### System Info
```shell
gradio 5.8.0
```
### Severity
I can work around it
|
open
|
2025-02-07T08:15:16Z
|
2025-03-07T20:18:18Z
|
https://github.com/gradio-app/gradio/issues/10538
|
[
"bug"
] |
xiaozi0513
| 3 |
sqlalchemy/alembic
|
sqlalchemy
| 569 |
Autogenerate does not respect the `version_table` used together with `version_table_schema`
|
Alembic: 1.0.10
SQLAlchemy: 1.3.4
Python: 3.7.3
Postgres server: 10.4
---
My goal is to:
1. have all application tables in a custom named schema `auth`
2. have migrations table in the same schema and renamed to `db_migrations`
3. being able to apply migrations to the given schema
4. being able to autogenerate migrations for the given schema
I have achieved 1,2,3 but not 4.
For schema I've added following configurations.
In application model:
```
Base = declarative_base(metadata=MetaData(schema=schema_name))
```
I've made no changes to `Table` objects. As I understand - `Table.schema` is populated from `Base.metadata.schema` if no schema provided explicitly.
In application bootstrap:
```
...
flask_app.config['SQLALCHEMY_DATABASE_URI'] = db_url
# set default schema to use
flask_app.config['SQLALCHEMY_ENGINE_OPTIONS'] = {
"connect_args": {'options': f'-csearch_path={schema_name}'}
}
...
```
In alembic `env.py`:
```
def run_migrations_online():
...
connectable = engine_from_config(
...
# define schema search path for connection
connect_args={'options': f'-csearch_path={app_name}'}
)
with connectable.connect() as connection:
context.configure(
connection=connection,
target_metadata=target_metadata,
include_schemas=True,
version_table_schema=app_name,
version_table="db_migrations",
)
```
With the above all tables are created correctly in the target schema, the migrations metadata table is created there as well, and used correctly for migrations. All as expected.
Not as expected is that when I run `alembic revision --autogenerate` on a full up-to-date DB I get the following migration:
```
op.drop_table('db_migrations')
op.drop_constraint('refresh_token_user_id_fkey', 'refresh_token', type_='foreignkey')
op.create_foreign_key(None, 'refresh_token', 'user', ['user_id'], ['id'], source_schema='auth', referent_schema='auth')
...
```
So it tries to drop the migrations table, and also tries to recreate the foreign keys with the schema explicitly defined. There are several more similar foreign key statements.
Also I've tried to regenerate some of existing migrations and I found out that it ignores actual model changes.
Also I've tried to remove `version_table="db_migrations"`, to no effect. As long as `version_table_schema` is there - it tries to delete `alembic_versions` default table as well.
I know there is #77 that supposedly fixed/implemented schemas for autogeneration script.
Am I missing some configuration here or is it an actual problem I'm hitting?
|
closed
|
2019-05-31T09:59:25Z
|
2019-05-31T17:24:51Z
|
https://github.com/sqlalchemy/alembic/issues/569
|
[] |
alexykot
| 4 |
nteract/papermill
|
jupyter
| 467 |
pm.record function
|
Hi,
I just updated my papermill package to 1.2.1 and found out that the record function no longer works. It just gave me an error message "AttributeError: module 'papermill' has no attribute 'record'".
Is there a replacement function for record? I need it to run multiple jupyter notebooks and import values from each notebook for final combination.
Thanks,
|
closed
|
2020-01-17T01:11:01Z
|
2020-01-19T18:43:12Z
|
https://github.com/nteract/papermill/issues/467
|
[
"question"
] |
florathecat
| 2 |
Evil0ctal/Douyin_TikTok_Download_API
|
web-scraping
| 423 |
抖音web版获取直播间商品接口，我复制网页请求接口的cookie进去，还是报错400
|
抖音web版获取直播间商品接口，我复制网页请求接口的cookie进去，还是报错400
|
open
|
2024-06-10T16:27:35Z
|
2024-10-31T15:47:11Z
|
https://github.com/Evil0ctal/Douyin_TikTok_Download_API/issues/423
|
[
"BUG"
] |
1236897
| 3 |
biolab/orange3
|
numpy
| 7,033 |
Web version
|
Hi, can you tell me if there is a web version that can be deployed directly, such as using vue? Thanks.
|
closed
|
2025-02-19T02:55:46Z
|
2025-02-19T08:29:07Z
|
https://github.com/biolab/orange3/issues/7033
|
[] |
WilliaJing
| 0 |
kennethreitz/responder
|
flask
| 216 |
Whats the difference between this and starlette ?!
|
I should be completly dumb ;-) ... sorry
But I can't see the things that "responder" does more than "starlette" ?
What are the added values ?
I really think it should be clarified in the doc ...
All highlighted features comes from starlette, no ?
|
closed
|
2018-11-08T15:30:28Z
|
2018-11-29T12:39:19Z
|
https://github.com/kennethreitz/responder/issues/216
|
[] |
manatlan
| 3 |
yunjey/pytorch-tutorial
|
pytorch
| 142 |
'ThalnetModule' object does not have attribute 'logger'
|
Line 131 of models/thalnet_module.py generates the error when input does not fall within expected bounds.
|
closed
|
2018-11-07T01:17:21Z
|
2018-11-07T01:17:59Z
|
https://github.com/yunjey/pytorch-tutorial/issues/142
|
[] |
sesevgen
| 0 |
ray-project/ray
|
machine-learning
| 51,165 |
[telemetry] Importing Ray Tune in an actor reports Ray Train usage
|
See this test case: https://github.com/ray-project/ray/pull/51161/files#diff-d1dc38a41dc1f9ba3c2aa2d9451217729a6f245ff3af29e4308ffe461213de0aR22
|
closed
|
2025-03-07T17:38:07Z
|
2025-03-17T17:56:38Z
|
https://github.com/ray-project/ray/issues/51165
|
[
"P1",
"tune"
] |
edoakes
| 0 |
pydantic/pydantic-core
|
pydantic
| 1,364 |
Creating Pydantic objects in Rust and passing to the interpreter.
|
What's the best way to do this?
I'd like to avoid passing JSON via Pyo3 to python and THEN creating the model.
Use case:
I am moving bounding box processing logic in my library [Docprompt](https://github.com/docprompt/Docprompt) into Rust. Documents can have tens of thousands of bounding boxes, so small overhead becomes an issue.
Thank you for the help!
|
open
|
2024-07-09T15:31:41Z
|
2024-08-16T17:52:29Z
|
https://github.com/pydantic/pydantic-core/issues/1364
|
[
"enhancement"
] |
PSU3D0
| 4 |
flairNLP/flair
|
pytorch
| 2,937 |
Unable to get predictions for multiple sentences using TARS Zero Shot Classifier
|
Here is the example code to use TARS Zero Shot Classifier
```
from flair.models import TARSClassifier
from flair.data import Sentence
# 1. Load our pre-trained TARS model for English
tars = TARSClassifier.load('tars-base')
# 2. Prepare a test sentence
sentence = Sentence("I am so glad you liked it!")
# 3. Define some classes that you want to predict using descriptive names
classes = ["happy", "sad"]
#4. Predict for these classes
tars.predict_zero_shot(sentence, classes)
# Print sentence with predicted labels
print(sentence)
print(sentence.labels[0].value)
print(round(sentence.labels[0].score,2))
```
Now this code is wrapped into the following function so that I can use it to get predictions for multiple sentences in a dataset.
```
def tars_zero(example):
sentence = Sentence(example)
tars.predict_zero_shot(sentence,classes)
print(sentence)
inputs = ["I am so glad you liked it!", "I hate it"]
for input in inputs:
tars_zero(input)
#output:
Sentence: "I am so glad you liked it !" → happy (0.8667)
Sentence: "I hate it"
```
Here I'm able to get the prediction only for the first sentence. Can someone tell me how to use TARS Zero Shot Classifier to get predictions for multiple sentences in a dataset? @alanakbik @lukasgarbas @kishaloyhalder @dobbersc @tadejmagajna
|
closed
|
2022-09-08T04:50:50Z
|
2023-02-02T07:57:35Z
|
https://github.com/flairNLP/flair/issues/2937
|
[
"question",
"wontfix"
] |
ghost
| 2 |
home-assistant/core
|
asyncio
| 141,112 |
Statistics log division by zero errors
|
### The problem
The statistics sensor produces division by zero errors in the log. This seems to be caused by having values that have identical change timestamps.
It might be that this was caused by sensors that were updated several times in a very short time interval and the precision of the timestamps is too low to distinguish the two change timestamps (that is just a guess though). It could also be that this is something triggered by the startup phase.
I also saw that there was a recent change in the code where timestamps were replaced with floats, which might have reduced the precision of the timestamp delta calculation.
I can easily reproduce the problem, so it is not a once in a lifetime exceptional case.
### What version of Home Assistant Core has the issue?
core-2025.3.4
### What was the last working version of Home Assistant Core?
_No response_
### What type of installation are you running?
Home Assistant OS
### Integration causing the issue
statistics
### Link to integration documentation on our website
https://www.home-assistant.io/integrations/statistics/
### Diagnostics information
_No response_
### Example YAML snippet
```yaml
```
### Anything in the logs that might be useful for us?
```txt
`2025-03-22 09:57:43.540 ERROR (MainThread) [homeassistant.helpers.event] Error while dispatching event for sensor.inverter_production to <Job track state_changed event ['sensor.inverter_production'] HassJobType.Callback <bound method StatisticsSensor._async_stats_sensor_state_change_listener of <entity sensor.inverter_production_avg_15s=0.0>>>
Traceback (most recent call last):
File "/usr/src/homeassistant/homeassistant/helpers/event.py", line 355, in _async_dispatch_entity_id_event
hass.async_run_hass_job(job, event)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/src/homeassistant/homeassistant/core.py", line 940, in async_run_hass_job
hassjob.target(*args)
~~~~~~~~~~~~~~^^^^^^^
File "/usr/src/homeassistant/homeassistant/components/statistics/sensor.py", line 748, in _async_stats_sensor_state_change_listener
self._async_handle_new_state(event.data["new_state"])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/src/homeassistant/homeassistant/components/statistics/sensor.py", line 734, in _async_handle_new_state
self._async_purge_update_and_schedule()
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/src/homeassistant/homeassistant/components/statistics/sensor.py", line 986, in _async_purge_update_and_schedule
self._update_value()
~~~~~~~~~~~~~~~~~~^^
File "/usr/src/homeassistant/homeassistant/components/statistics/sensor.py", line 1097, in _update_value
value = self._state_characteristic_fn(self.states, self.ages, self._percentile)
File "/usr/src/homeassistant/homeassistant/components/statistics/sensor.py", line 142, in _stat_average_step
return area / age_range_seconds
~~~~~^~~~~~~~~~~~~~~~~~~
ZeroDivisionError: float division by zero`
```
### Additional information
_No response_
|
open
|
2025-03-22T12:38:36Z
|
2025-03-24T19:43:43Z
|
https://github.com/home-assistant/core/issues/141112
|
[
"integration: statistics"
] |
unfug-at-github
| 3 |
huggingface/transformers
|
deep-learning
| 36,709 |
ValueError: The checkpoint you are trying to load has model type `gemma3` but Transformers does not recognize this architecture.
|
### System Info
enviroment from pyproject.toml:
```
[tool.poetry]
name = "rl-finetunning"
package-mode = false
version = "0.1.0"
description = ""
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.12"
torch = {version = "2.5.1+cu121", source = "torch-repo"}
torchaudio = {version = "2.5.1+cu121", source = "torch-repo"}
langchain = {extras = ["all"], version = "^0.3.14"}
numpy = "<2"
ujson = "^5.10.0"
tqdm = "^4.67.1"
ipykernel = "^6.29.5"
faiss-cpu = "^1.9.0.post1"
wandb = "^0.19.4"
rouge-score = "^0.1.2"
accelerate = "0.34.2"
datasets = "^3.2.0"
evaluate = "^0.4.3"
bitsandbytes = "^0.45.1"
peft = "^0.14.0"
deepspeed = "0.15.4"
trl = "^0.15.2"
transformers = "^4.49.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[[tool.poetry.source]]
name = "torch-repo"
url = "https://download.pytorch.org/whl/cu121"
priority = "explicit"
```
### Who can help?
@ArthurZucker @gante
### Reproduction
Code to reproduce: https://pastebin.com/vGXdw5e7
Model weights was downloaded in current directory.
Full Traceback:
```
Traceback (most recent call last):
File "/home/calibri/.cache/pypoetry/virtualenvs/rl-finetunning-LD6GBRk7-py3.12/lib/python3.12/site-packages/transformers/models/auto/configuration_auto.py", line 1092, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/calibri/.cache/pypoetry/virtualenvs/rl-finetunning-LD6GBRk7-py3.12/lib/python3.12/site-packages/transformers/models/auto/configuration_auto.py", line 794, in __getitem__
raise KeyError(key)
KeyError: 'gemma3'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/calibri/experiments/rl_finetunning/sft.py", line 118, in <module>
model = AutoModelForCausalLM.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/calibri/.cache/pypoetry/virtualenvs/rl-finetunning-LD6GBRk7-py3.12/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py", line 526, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/calibri/.cache/pypoetry/virtualenvs/rl-finetunning-LD6GBRk7-py3.12/lib/python3.12/site-packages/transformers/models/auto/configuration_auto.py", line 1094, in from_pretrained
raise ValueError(
ValueError: The checkpoint you are trying to load has model type `gemma3` but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date.
You can update Transformers with the command `pip install --upgrade transformers`. If this does not work, and the checkpoint is very new, then there may not be a release version that supports this model yet. In this case, you can get the most up-to-date code by installing Transformers from source with the command `pip install git+https://github.com/huggingface/transformers.git`
```
### Expected behavior
I downloaded latest transformers version so I expected the code to run without errors.
|
closed
|
2025-03-13T23:11:04Z
|
2025-03-19T16:19:32Z
|
https://github.com/huggingface/transformers/issues/36709
|
[
"bug"
] |
JohnConnor123
| 4 |
vipstone/faceai
|
tensorflow
| 55 |
请问如何在嵌入式设备上使用？谢谢！
|
请问如何在嵌入式设备上使用？
处理器：ARM
编程环境：C语言
操作系统：linux或RT-thread交叉编译
谢谢！
|
open
|
2021-05-06T06:21:23Z
|
2021-05-06T06:21:23Z
|
https://github.com/vipstone/faceai/issues/55
|
[] |
duduathz
| 0 |
xlwings/xlwings
|
automation
| 1,587 |
transpose ignored in using raw_value to set column data
|
#### OS (e.g. Windows 10 or macOS Sierra)
Mac Big Sur
#### Versions of xlwings, Excel and Python (e.g. 0.11.8, Office 365, Python 3.7)
python 3.8.7 xlwings 23.0
#### Describe your issue (incl. Traceback!)
the second fills the column with 0 to 9, the first all with 0
I was expecting the transpose to work with raw_value
as well. Maybe that's what's supposed to happen.
#### Include a minimal code sample to reproduce the issue (and attach a sample workbook if required!)
```python
# Your code here
thisbk = xw.Book.caller()
thisbk.sheets.active.range((1,1),(10,1)).options(transpose=True).raw_value = [0,1,2,3,4,5,6,7,8,9]
thisbk.sheets.active.range((1,1),(10,1)).options(transpose=True).value = [0,1,2,3,4,5,6,7,8,9]
```
|
closed
|
2021-05-13T17:48:27Z
|
2021-05-20T06:21:05Z
|
https://github.com/xlwings/xlwings/issues/1587
|
[] |
john-drummond
| 5 |
matterport/Mask_RCNN
|
tensorflow
| 2,399 |
keyerror: all_points_y
|
I'm a beginner of deep-learning, recently, i try to use the mask-rcnn to make a project about rust detection. Firstly i watch the video how to use this code, then i modify the code of 'balloon', and have a try to train my own data, but i meet the error called keyerror: all_points_y, my dataset are many binary images which contain the rust areas(white) and the background(black). To approprate the requirements of code, i use the edge detection to found the outline of rust areas, then i save the class name(only 'rust' class) and the coordinate of the points which on the outlines as .json file according to the requirement format(firstly i save these data in a dict, then i use json.dump to transform the dict to json and save it):
#{ 'filename': '28503151_5b5b7ec140_b.jpg',
# 'regions': {
# '0': {
# 'region_attributes': {},
# 'shape_attributes': {
# 'all_points_x': [...],
# 'all_points_y': [...],
# 'name': 'polygon'}},
# ... more regions ...
# },
# 'size': 100202
# }
During the training processing, i saw some error messages repeatly about keyerror: all_point_y, the message repeat about 20 times when my train dataset has 120 pictures and the val dataset has 30 pictures. Easily see that not all picture will bring error, but i don't know how the error happened.
I try to search the problem and only find this kind of answer: the points in dataset should be in an polygon instead of circle or rectangle, but the obviously the answer isn't fit for my question.
Futher more, when load the json file, the order of data usually have change,for example, when i save the data, the format is:
'shape_attributes': {
'all_points_x': [...],
'all_points_y': [...],
'name': 'rust'}
but when i load the data, it change to the format as:
'all_points_y': [...],
'name': 'rust',
'all_points_y': [...]}
whether this issue have an affect to the training processing?
ps:
1.In fact , i'm also a beginner of english.
2.Help me, every scholars(dalao), thanks·
|
open
|
2020-10-21T08:25:19Z
|
2020-12-07T16:39:14Z
|
https://github.com/matterport/Mask_RCNN/issues/2399
|
[] |
weird-bright
| 4 |
ploomber/ploomber
|
jupyter
| 226 |
ploomber interact should also display the custom cli from pipeline parameters
|
closed
|
2020-08-13T16:27:52Z
|
2020-10-18T23:05:51Z
|
https://github.com/ploomber/ploomber/issues/226
|
[] |
edublancas
| 0 |
|
K3D-tools/K3D-jupyter
|
jupyter
| 129 |
Scene not rotating if cursor below the menu
|
If the cursor is anywhere below the controls menu, rotating via click and drag does not work. The issue seems to be that that the dg div sets as its height the entire window rather than just the height of the menu.
|
closed
|
2019-01-23T19:03:38Z
|
2019-01-24T21:25:01Z
|
https://github.com/K3D-tools/K3D-jupyter/issues/129
|
[] |
jpomoell
| 3 |
QingdaoU/OnlineJudge
|
django
| 442 |
为什么我的tag无法添加
|
在提交issue之前请
- 认真阅读文档 http://docs.onlinejudge.me/#/
- 搜索和查看历史issues
- 安全类问题请不要在 GitHub 上公布，请发送邮件到 `admin@qduoj.com`，根据漏洞危害程度发送红包感谢。
然后提交issue请写清楚下列事项
- 进行什么操作的时候遇到了什么问题，最好能有复现步骤
- 错误提示是什么，如果看不到错误提示，请去data文件夹查看相应log文件。大段的错误提示请包在代码块标记里面。
- 你尝试修复问题的操作
- 页面问题请写清浏览器版本，尽量有截图
|
open
|
2023-04-02T03:42:00Z
|
2023-05-28T07:30:01Z
|
https://github.com/QingdaoU/OnlineJudge/issues/442
|
[] |
13safa
| 1 |
tensorpack/tensorpack
|
tensorflow
| 1,311 |
How to pass hyper-parameters to model???
|
closed
|
2019-08-31T00:50:00Z
|
2019-08-31T01:46:27Z
|
https://github.com/tensorpack/tensorpack/issues/1311
|
[
"usage"
] |
ranery
| 2 |
|
streamlit/streamlit
|
streamlit
| 10,747 |
Add support for Jupyter widgets / ipywidgets
|
### 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
Jupyter Widgets are [interactive browser controls](https://github.com/jupyter-widgets/ipywidgets/blob/main/docs/source/examples/Index.ipynb) for Jupyter notebooks. Implement support for using ipywidgets elements in a Streamlit app.
### Why?
_No response_
### How?
```python
import ipywidgets as widgets
widget = st.ipywidgets(widgets.IntSlider())
st.write(widget.value)
```
### Additional Context
- Related to https://github.com/streamlit/streamlit/issues/10746
- Related discussion: https://discuss.streamlit.io/t/ipywidgets-wip/3870
|
open
|
2025-03-12T16:22:36Z
|
2025-03-18T10:31:37Z
|
https://github.com/streamlit/streamlit/issues/10747
|
[
"type:enhancement",
"feature:custom-components",
"type:possible-component"
] |
lukasmasuch
| 1 |
xorbitsai/xorbits
|
numpy
| 211 |
ENH: Session id is prefixed with K8s namespace, when in the K8s environment
|
Note that the issue tracker is NOT the place for general support. For
discussions about development, questions about usage, or any general questions,
contact us on https://discuss.xorbits.io/.
|
closed
|
2023-02-14T08:56:27Z
|
2023-03-13T09:55:42Z
|
https://github.com/xorbitsai/xorbits/issues/211
|
[
"enhancement"
] |
ChengjieLi28
| 0 |
Neoteroi/BlackSheep
|
asyncio
| 492 |
OpenAPI v3 Handling Issue
|
Hello,
We have had a BlackSheep app running for over a year. When we attempted to upgrade to 2.0.7, we ran into this error. It seems to be an error in the fundamental OpenAPI class. Since this was working fine until today, I think that it must be a bug in 1.0.9.
Here is the error message:
```Traceback (most recent call last):
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 307, in _get_array_outer_type
return field_info.outer_type_
AttributeError: 'FieldInfo' object has no attribute 'outer_type_'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/application.py", line 726, in _handle_lifespan
await self.start()
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/application.py", line 715, in start
await self.after_start.fire()
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/application.py", line 126, in fire
await handler(self.context, *args, **kwargs)
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/common.py", line 404, in build_docs
docs = self.generate_documentation(app)
File "/home/mistral/llm_server_env/datascience-llm-server/app/docs/handler.py", line 34, in generate_documentation
paths=self.get_paths(app),
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 449, in get_paths
own_paths = self.get_routes_docs(app.router, path_prefix)
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 1146, in get_routes_docs
request_body=self.get_request_body(handler),
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 847, in get_request_body
content=self._get_body_binder_content_type(body_binder, body_examples),
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 821, in _get_body_binder_content_type
return {
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 823, in <dictcomp>
schema=self.get_schema_by_type(body_binder.expected_type),
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 642, in get_schema_by_type
schema = self._get_schema_by_type(child_type, type_args)
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 663, in _get_schema_by_type
return self._get_schema_for_class(object_type)
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 567, in _get_schema_for_class
for field in self.get_fields(object_type):
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 739, in get_fields
return handler.get_type_fields(
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 342, in get_type_fields
return [
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 345, in <listcomp>
self._open_api_v2_field_schema_to_type(
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 297, in _open_api_v2_field_schema_to_type
return self._get_array_outer_type(field_info)
File "/home/mistral/llm_server_env/lib/python3.10/site-packages/blacksheep/server/openapi/v3.py", line 311, in _get_array_outer_type
return List[field_info.annotation.__args__[0]]
AttributeError: type object 'list' has no attribute '__args__'. Did you mean: '__add__'?```
|
closed
|
2024-04-09T01:05:08Z
|
2025-01-16T20:51:39Z
|
https://github.com/Neoteroi/BlackSheep/issues/492
|
[] |
mmangione
| 2 |
aleju/imgaug
|
deep-learning
| 25 |
setup.py does not recognize opencv2 of Anaconda
|
When run the setup, error happens. opencv is installed on Anaconda.
Is it possible to install imgaug on Anaconda?
...
Processing ./dist/imgaug-0.2.0.tar.gz
Complete output from command python setup.py egg_info:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-K5MRPU-build/setup.py", line 6, in <module>
raise Exception("Could not find package 'cv2' (OpenCV). It cannot be automatically installed, so you will have to manually install it.")
Exception: Could not find package 'cv2' (OpenCV). It cannot be automatically installed, so you will have to manually install it.
|
open
|
2017-03-28T21:58:16Z
|
2019-06-10T09:05:59Z
|
https://github.com/aleju/imgaug/issues/25
|
[] |
clockwiser
| 9 |
gradio-app/gradio
|
machine-learning
| 10,555 |
504 Gateway Time-out
|
### Describe the bug
504 gate way timeout today
it's ok when i used it tow days ago , any my version is gradio-5.15.0 gradio-client-1.7.0
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
```
### Screenshot
_No response_
### Logs
```shell
```
### System Info
```shell
Gradio Environment Information:
------------------------------
Operating System: Linux
gradio version: 5.15.0
gradio_client version: 1.7.0
------------------------------------------------
gradio dependencies in your environment:
aiofiles: 23.2.1
anyio: 4.8.0
audioop-lts is not installed.
fastapi: 0.115.8
ffmpy: 0.5.0
gradio-client==1.7.0 is not installed.
httpx: 0.28.1
huggingface-hub: 0.28.1
jinja2: 3.1.4
markupsafe: 2.1.5
numpy: 1.26.4
orjson: 3.10.15
packaging: 24.2
pandas: 2.2.3
pillow: 10.4.0
pydantic: 2.10.6
pydub: 0.25.1
python-multipart: 0.0.20
pyyaml: 6.0.2
ruff: 0.9.5
safehttpx: 0.1.6
semantic-version: 2.10.0
starlette: 0.45.3
tomlkit: 0.12.0
typer: 0.15.1
typing-extensions: 4.12.2
urllib3: 2.3.0
uvicorn: 0.34.0
authlib; extra == 'oauth' is not installed.
itsdangerous; extra == 'oauth' is not installed.
gradio_client dependencies in your environment:
fsspec: 2024.6.1
httpx: 0.28.1
huggingface-hub: 0.28.1
packaging: 24.2
typing-extensions: 4.12.2
websockets: 11.0.3
```
### Severity
I can work around it
|
closed
|
2025-02-10T04:26:20Z
|
2025-02-10T15:56:56Z
|
https://github.com/gradio-app/gradio/issues/10555
|
[
"bug"
] |
teressawang
| 2 |
ivy-llc/ivy
|
tensorflow
| 28,461 |
Fix Ivy Failing Test: paddle - creation.arange
|
To-Do List: https://github.com/unifyai/ivy/issues/27501
|
open
|
2024-03-01T10:51:53Z
|
2024-03-01T10:51:53Z
|
https://github.com/ivy-llc/ivy/issues/28461
|
[
"Sub Task"
] |
marvlyngkhoi
| 0 |
CorentinJ/Real-Time-Voice-Cloning
|
python
| 931 |
Speeding up Loading Time of encoder in ``encoder/inference.py``
|
Original comment from @CorentinJ:
TODO: I think the slow loading of the encoder might have something to do with the device it was saved on. Worth investigating.
This refers to the ``load_model`` function in the named module.
|
open
|
2021-12-01T19:45:04Z
|
2021-12-01T19:45:04Z
|
https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/931
|
[] |
ghost
| 0 |
gradio-app/gradio
|
python
| 10,385 |
Gradio Demo Malfunction on Hugging Face Spaces
|
### Describe the bug
Hi Team,
We’ve been hosting a Gradio demo on Hugging Face Spaces (zero GPU) at [this link](https://huggingface.co/spaces/facebook/vggsfm), which has been running smoothly for several months. However, today a user reported that it’s no longer functioning. I’ve rebuilt the factory but it seems does not help. I checked the backend and retrieved the error logs as attached below.
My best guess is that the version of Gradio on Hugging Face Spaces might have been updated, possibly leading to incompatibilities with the old version. The error logs are somewhat vague, making it difficult to pinpoint the exact issue. Is there any insight on resolving this, or could you point me towards the relevant documentation? It is much appreciated :)
Best,
Jianyuan
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
https://huggingface.co/spaces/facebook/vggsfm/tree/main
### Screenshot
_No response_
### Logs
```shell
ZeroGPU tensors packing: 0.00B [00:00, ?B/s]
ZeroGPU tensors packing: 0.00B [00:00, ?B/s]
Running on local URL: http://0.0.0.0:7860
INFO:httpx:HTTP Request: GET http://localhost:7860/startup-events "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: HEAD http://localhost:7860/ "HTTP/1.1 200 OK"
/usr/local/lib/python3.10/site-packages/gradio/blocks.py:2434: UserWarning: Setting share=True is not supported on Hugging Face Spaces
warnings.warn(
To create a public link, set `share=True` in `launch()`.
INFO:httpx:HTTP Request: POST http://device-api.zero/schedule?cgroupPath=%2Fkubepods.slice%2Fkubepods-burstable.slice%2Fkubepods-burstable-pod1372a787_40e1_4b6a_8d19_2f4b46eca6e6.slice%2Fcri-containerd-edf0748ce7994c4de66b1374d62b458da7869fb7e8cf60b41a1dd4f939fa1c54.scope&taskId=140223021710736&enableQueue=true&durationSeconds=240&token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpcCI6IjE2My4xMTQuMTMxLjEyOSIsInVzZXIiOiJKaWFueXVhbldhbmciLCJ1dWlkIjpudWxsLCJlcnJvciI6bnVsbCwiZXhwIjoxNzM3MTQ3MDczfQ.WQjeWxsjH1Fnj2DefdNZDKeDjKERP1tDwwXRWPk4w6E "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST http://device-api.zero/allow?allowToken=9264c9602de0db756c3f6b1d2e9d1cab36b9b217010d3f1110b88b4ef3f646f6&pid=313 "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST http://device-api.zero/release?allowToken=9264c9602de0db756c3f6b1d2e9d1cab36b9b217010d3f1110b88b4ef3f646f6&fail=true "HTTP/1.1 200 OK"
Traceback (most recent call last):
File "/usr/local/lib/python3.10/site-packages/spaces/zero/wrappers.py", line 135, in worker_init
torch.init(nvidia_uuid)
File "/usr/local/lib/python3.10/site-packages/spaces/zero/torch/patching.py", line 373, in init
torch.Tensor([0]).cuda()
File "/usr/local/lib/python3.10/site-packages/torch/cuda/__init__.py", line 298, in _lazy_init
torch._C._cuda_init()
RuntimeError: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 304: OS call failed or operation not supported on this OS
Traceback (most recent call last):
File "/usr/local/lib/python3.10/site-packages/gradio/queueing.py", line 532, in process_events
response = await route_utils.call_process_api(
File "/usr/local/lib/python3.10/site-packages/gradio/route_utils.py", line 276, in call_process_api
output = await app.get_blocks().process_api(
File "/usr/local/lib/python3.10/site-packages/gradio/blocks.py", line 1928, in process_api
result = await self.call_function(
File "/usr/local/lib/python3.10/site-packages/gradio/blocks.py", line 1514, in call_function
prediction = await anyio.to_thread.run_sync(
File "/usr/local/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2461, in run_sync_in_worker_thread
return await future
File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 962, in run
result = context.run(func, *args)
File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 832, in wrapper
response = f(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/spaces/zero/wrappers.py", line 214, in gradio_handler
raise error("ZeroGPU worker error", res.error_cls)
gradio.exceptions.Error: 'RuntimeError'
INFO:httpx:HTTP Request: POST http://device-api.zero/schedule?cgroupPath=%2Fkubepods.slice%2Fkubepods-burstable.slice%2Fkubepods-burstable-pod1372a787_40e1_4b6a_8d19_2f4b46eca6e6.slice%2Fcri-containerd-edf0748ce7994c4de66b1374d62b458da7869fb7e8cf60b41a1dd4f939fa1c54.scope&taskId=140223021710736&enableQueue=true&durationSeconds=240&token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpcCI6IjE2My4xMTQuMTMxLjEyOSIsInVzZXIiOiJKaWFueXVhbldhbmciLCJ1dWlkIjpudWxsLCJlcnJvciI6bnVsbCwiZXhwIjoxNzM3MTQ3MjU0fQ.E_mE7SO_dJKy00chpXUrF0QHwHzRUzWtCtTRjbdIPrA "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST http://device-api.zero/allow?allowToken=ea91d23b086f5770c368d624fac358a31965f04141384f50a0eb2f42c892040e&pid=317 "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST http://device-api.zero/release?allowToken=ea91d23b086f5770c368d624fac358a31965f04141384f50a0eb2f42c892040e&fail=true "HTTP/1.1 200 OK"
Traceback (most recent call last):
File "/usr/local/lib/python3.10/site-packages/spaces/zero/wrappers.py", line 135, in worker_init
torch.init(nvidia_uuid)
File "/usr/local/lib/python3.10/site-packages/spaces/zero/torch/patching.py", line 373, in init
torch.Tensor([0]).cuda()
File "/usr/local/lib/python3.10/site-packages/torch/cuda/__init__.py", line 298, in _lazy_init
torch._C._cuda_init()
RuntimeError: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 304: OS call failed or operation not supported on this OS
Traceback (most recent call last):
File "/usr/local/lib/python3.10/site-packages/gradio/queueing.py", line 532, in process_events
response = await route_utils.call_process_api(
File "/usr/local/lib/python3.10/site-packages/gradio/route_utils.py", line 276, in call_process_api
output = await app.get_blocks().process_api(
File "/usr/local/lib/python3.10/site-packages/gradio/blocks.py", line 1928, in process_api
result = await self.call_function(
File "/usr/local/lib/python3.10/site-packages/gradio/blocks.py", line 1514, in call_function
prediction = await anyio.to_thread.run_sync(
File "/usr/local/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2461, in run_sync_in_worker_thread
return await future
File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 962, in run
result = context.run(func, *args)
File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 832, in wrapper
response = f(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/spaces/zero/wrappers.py", line 214, in gradio_handler
raise error("ZeroGPU worker error", res.error_cls)
gradio.exceptions.Error: 'RuntimeError'
```
### System Info
```shell
hugging face spaces, which seems to be using gradio version 4.36.1
```
### Severity
Blocking usage of gradio
|
closed
|
2025-01-17T21:17:54Z
|
2025-01-22T02:05:05Z
|
https://github.com/gradio-app/gradio/issues/10385
|
[
"bug"
] |
jytime
| 4 |
Lightning-AI/pytorch-lightning
|
data-science
| 20,003 |
Smoothing in tqdm progress bar has no effect
|
### Bug description
The option smoothing when creating progress bars in TQDMProgressBar has no effect in the default implementation, as
_update_n only calls bar.refresh() and not the update method of the progress bar. Thus only the global average is taken, as the update method of the tqdm class is responsible for calculating moving averages.
Either the update method of the progress bar could be used or it should be added to the documentation if smoothing having no effect is the desired behavior (overriding a default that has no effect is a bit misleading)
### What version are you seeing the problem on?
master
### How to reproduce the bug
```python
import time
import lightning.pytorch as pl
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader, Sampler
from src.main.ml.data.data_augmentation.helpers.random_numbers import create_rng_from_string
import sys
from typing import Any
import lightning.pytorch as pl
from lightning.pytorch.callbacks import TQDMProgressBar
from lightning.pytorch.callbacks.progress.tqdm_progress import Tqdm
from lightning.pytorch.utilities.types import STEP_OUTPUT
from typing_extensions import override
class LitProgressBar(TQDMProgressBar):
"""
different smoothing factor than default lightning TQDMProgressBar, where smoothing=0 (average),
instead of smoothing=1 (current speed) is taken
See also:
https://tqdm.github.io/docs/tqdm/
"""
def init_train_tqdm(self) -> Tqdm:
"""Override this to customize the tqdm bar for training."""
return Tqdm(
desc=self.train_description,
position=(2 * self.process_position),
disable=self.is_disabled,
leave=True,
dynamic_ncols=True,
file=sys.stdout,
smoothing=1.0,
bar_format=self.BAR_FORMAT,
)
# default method
# @override
# def on_train_batch_end(
# self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
# ) -> None:
# n = batch_idx + 1
# if self._should_update(n, self.train_progress_bar.total):
# _update_n(self.train_progress_bar, n)
# self.train_progress_bar.set_postfix(self.get_metrics(trainer, pl_module))
# my own method that uses smoothing by using the update method of progress bar
@override
def on_train_batch_end(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any,
batch_idx: int
) -> None:
n = batch_idx + 1
if self._should_update(n, self.train_progress_bar.total):
self.train_progress_bar.update(self.refresh_rate)
self.train_progress_bar.set_postfix(self.get_metrics(trainer, pl_module))
class TestModule(nn.Module):
def __init__(self, in_dim=512, out_dim=16):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.simple_layer = nn.Linear(self.in_dim, self.out_dim, bias=True)
def forward(self, input):
return self.simple_layer(input)
class TestBatchSampler(Sampler):
def __init__(self, step=0):
super().__init__()
self.step = step
def __len__(self) -> int:
return 1e100
# return len(self.train_allfiles)
def __iter__(self): # -> Iterator[int]:
return self
def __next__(self): # -> Iterator[int]:
return_value = self.step
self.step += 1
return [return_value]
class TestDataset(Dataset):
def __init__(self, in_dim):
super().__init__()
self.in_dim = in_dim
self.total_len = 512
def __len__(self):
return 1
def __getitem__(self, idx):
rng = create_rng_from_string(
str(idx) + "_"
+ "random_choice_sampler")
return torch.tensor(rng.random(self.in_dim), dtype=torch.float32)
class TestDataModule(pl.LightningDataModule):
def __init__(self, start_step=0):
super().__init__()
self.in_dim = 512
self.val_batch_size = 1
self.start_step = start_step
def train_dataloader(self):
train_ds = TestDataset(self.in_dim)
train_dl = DataLoader(train_ds, batch_sampler=TestBatchSampler(step=self.start_step), num_workers=4,
shuffle=False)
return train_dl
class TestLitModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.test_module_obj = TestModule(in_dim=512, out_dim=16)
self.automatic_optimization = False
def training_step(self, batch, batch_idx):
if batch_idx == 0:
time.sleep(5)
time.sleep(0.5)
optimizer = self.optimizers()
output = self.test_module_obj(batch)
loss = output.sum()
self.manual_backward(loss)
optimizer.step()
def configure_optimizers(self):
optimizer = torch.optim.AdamW(
self.test_module_obj.parameters()
)
return optimizer
if __name__ == '__main__':
test_data_loader = TestDataModule()
test_lit_model = TestLitModel()
bar = LitProgressBar(refresh_rate=5)
trainer = pl.Trainer(
log_every_n_steps=1,
callbacks=[bar],
max_epochs=-1,
max_steps=400000,
)
trainer.fit(test_lit_model,
datamodule=test_data_loader)
```
### Error messages and logs
### Environment
<details>
<summary>Current environment</summary>
```
#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
#- Running environment of LightningApp (e.g. local, cloud):
```
</details>
### More info
_No response_
cc @borda
|
closed
|
2024-06-21T14:30:05Z
|
2024-09-30T16:29:22Z
|
https://github.com/Lightning-AI/pytorch-lightning/issues/20003
|
[
"help wanted",
"good first issue",
"docs",
"ver: 2.2.x"
] |
heth27
| 2 |
MaartenGr/BERTopic
|
nlp
| 1,457 |
NaN Representative_Docs
|
Hi @MaartenGr,
I keep getting NaN values as representative documents when I load my model, after saving it with either 'safetensors' or 'pytorch'.
Here is my code:
`
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
topic_model.save('/content/drive/MyDrive/boombust_cs_model', serialization="safetensors",
save_embedding_model= embedding_model )`
`BERTopic.load('/content/drive/MyDrive/boombust_cs_model', embedding_model= embedding_model)`
What might be the issue?
|
closed
|
2023-08-07T23:01:39Z
|
2024-02-10T19:21:02Z
|
https://github.com/MaartenGr/BERTopic/issues/1457
|
[] |
annm802
| 7 |
akfamily/akshare
|
data-science
| 5,570 |
ak.stock_zh_a_hist()获取数据错误
|
以下涉及的是 ak.stock_zh_a_hist()返回的 df 中 "单日情况"列的值为"成交金额"对应的行的数据错误：
1 代码
stock_zh_a_hist_df = ak.stock_zh_a_hist(
symbol="603777",
period="daily",
start_date="20240101",
end_date="20250201",
adjust="qfq"
)
2 错误问题
获取数据集中 收盘价有负值
3 错误问题
再次运行代码 获取数据为空
4 版本号
Python 3.8.10
Akshare 1.15.22
|
closed
|
2025-02-06T07:35:57Z
|
2025-02-06T09:21:08Z
|
https://github.com/akfamily/akshare/issues/5570
|
[
"bug"
] |
liusw02
| 1 |
microsoft/nni
|
tensorflow
| 5,167 |
Can it be easily used it in Microsoft Singularity?
|
Many users are using clusters. However, NNI has not support the interface to easily adaptation to run on those clusters, without support of job scheduling, job maintaining, result aggregation and metric calculating, which has significantly limited the usability of NNI on advanced clusters such as Singularity.
**What would you like to be added**: easy port to Singularity cluster in Microsoft.
**Why is this needed**: Easy-to-use in common clusters is important for industrial users, especially those in Microsoft.
**Without this feature, how does current nni work**：not worked yet, very hard to use.
**Components that may involve changes**: Job scheduler, metric calculator and visualization tools.
**Brief description of your proposal if any**:
|
open
|
2022-10-18T09:18:07Z
|
2022-10-19T02:40:06Z
|
https://github.com/microsoft/nni/issues/5167
|
[] |
rk2900
| 1 |
DistrictDataLabs/yellowbrick
|
scikit-learn
| 1,125 |
No target coloring in jointplot
|
**Describe the bug**
After assigning `y` values in the `fit()` method of `JointPlot`, no heatmap of such target is drawn on the samples
**To Reproduce**
```python
import numpy as np
from yellowbrick.features.jointplot import JointPlot
X = np.random.rand(100, 2)
y = np.random.rand(100)
viz = JointPlot(columns=[0, 1])
# here, fit_transform is just fit
viz.fit(X=X, y=y)
viz.show()
```
**Dataset**
No.
**Expected behavior**
There should be a heatmap to color the samples by the values in `y` as in the PCA case.
**Traceback**
```
If applicable, add the traceback from the exception.
```
**Desktop (please complete the following information):**
- OS: Debian10
- Python Version 3.7
- Yellowbrick Version 1.2
**Additional context**
|
closed
|
2020-10-26T14:13:02Z
|
2020-10-26T15:35:32Z
|
https://github.com/DistrictDataLabs/yellowbrick/issues/1125
|
[
"type: question",
"duplicate"
] |
ZhiliangWu
| 1 |
mars-project/mars
|
scikit-learn
| 2,980 |
[BUG] `df.sort_values` produces incorrect result
|
<!--
Thank you for your contribution!
Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue.
-->
**Describe the bug**
`df.sort_values`'s result is incorrect.
**To Reproduce**
``` Python
In [10]: df = pd.DataFrame(
...: np.random.rand(100, 10), columns=["a" + str(i) for i in range(10)]
...: )
In [11]: mdf = md.DataFrame(df, chunk_size=10)
In [12]: r = mdf.sort_values(["a3", "a4"], ascending=[False, True]).execute()
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100.0/100 [00:00<00:00, 631.75it/s]
In [13]: r
Out[13]:
a0 a1 a2 a3 a4 a5 a6 a7 a8 a9
16 0.107025 0.476072 0.335091 0.982758 0.100404 0.535478 0.843878 0.993314 0.519650 0.039456
22 0.918816 0.782424 0.685651 0.981538 0.054176 0.204336 0.164184 0.545094 0.626901 0.001013
27 0.102054 0.078257 0.412166 0.977549 0.943098 0.095842 0.908522 0.078090 0.321839 0.579264
82 0.948665 0.387145 0.140989 0.962591 0.253510 0.053363 0.695930 0.322598 0.434367 0.831326
37 0.921237 0.795837 0.419291 0.957214 0.029907 0.224950 0.239270 0.694234 0.494518 0.698810
.. ... ... ... ... ... ... ... ... ... ...
38 0.479132 0.447279 0.262018 0.047719 0.232861 0.967857 0.608678 0.285415 0.385973 0.443056
1 0.318765 0.664569 0.631351 0.045131 0.163595 0.965267 0.037361 0.044477 0.963650 0.140346
11 0.391554 0.169611 0.384232 0.040313 0.397935 0.822954 0.042206 0.522298 0.944956 0.611841
67 0.022653 0.053233 0.813252 0.020961 0.366821 0.261931 0.592673 0.948731 0.476598 0.604238
20 0.955453 0.521866 0.419302 0.007513 0.303412 0.231128 0.984855 0.439811 0.755543 0.441908
[200 rows x 10 columns]
In [14]: mdf.shape
Out[14]: (100, 10)
```
|
closed
|
2022-04-29T09:33:43Z
|
2022-05-07T06:40:29Z
|
https://github.com/mars-project/mars/issues/2980
|
[
"type: bug",
"mod: dataframe"
] |
hekaisheng
| 1 |
xinntao/Real-ESRGAN
|
pytorch
| 34 |
Didn't use Cpu on m1 Mac
|
I've been using the program for a while, and it goes well. However, I found that it barely use CPU on m1, while the GPU is fully loaded, the Cpu is barely used.
Does it use Cpu on other platform or it only use gpu? I've heard that apple has built-in machine learning units in their m1 chip, maybe we can make use of them in a future update.
|
closed
|
2021-08-15T20:48:08Z
|
2021-09-30T14:23:26Z
|
https://github.com/xinntao/Real-ESRGAN/issues/34
|
[] |
Percivalllll
| 4 |
mars-project/mars
|
pandas
| 3,119 |
[BUG] Ray context GC bug
|
<!--
Thank you for your contribution!
Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue.
-->
**Describe the bug**
A clear and concise description of what the bug is.
Set the `DEFAULT_SUBTASK_MONITOR_INTERVAL` to 0 in `mars/services/task/execution/ray/config.py`, then run `mars/dataframe/base/tests/test_base_execution.py::test_cut_execution` with env `MARS_CI_BACKEND=ray`. The case will fail with the following exception:
``` python
mars/dataframe/base/tests/test_base_execution.py::test_cut_execution FAILED [100%]
mars/dataframe/base/tests/test_base_execution.py:777 (test_cut_execution)
setup = <mars.deploy.oscar.session.SyncSession object at 0x12d589e10>
@pytest.mark.ray_dag
def test_cut_execution(setup):
session = setup
rs = np.random.RandomState(0)
raw = rs.random(15) * 1000
s = pd.Series(raw, index=[f"i{i}" for i in range(15)])
bins = [10, 100, 500]
ii = pd.interval_range(10, 500, 3)
labels = ["a", "b"]
t = tensor(raw, chunk_size=4)
series = from_pandas_series(s, chunk_size=4)
iii = from_pandas_index(ii, chunk_size=2)
# cut on Series
r = cut(series, bins)
result = r.execute().fetch()
pd.testing.assert_series_equal(result, pd.cut(s, bins))
r, b = cut(series, bins, retbins=True)
r_result = r.execute().fetch()
b_result = b.execute().fetch()
r_expected, b_expected = pd.cut(s, bins, retbins=True)
pd.testing.assert_series_equal(r_result, r_expected)
np.testing.assert_array_equal(b_result, b_expected)
# cut on tensor
r = cut(t, bins)
# result and expected is array whose dtype is CategoricalDtype
result = r.execute().fetch()
expected = pd.cut(raw, bins)
assert len(result) == len(expected)
for r, e in zip(result, expected):
np.testing.assert_equal(r, e)
# one chunk
r = cut(s, tensor(bins, chunk_size=2), right=False, include_lowest=True)
result = r.execute().fetch()
pd.testing.assert_series_equal(
result, pd.cut(s, bins, right=False, include_lowest=True)
)
# test labels
r = cut(t, bins, labels=labels)
# result and expected is array whose dtype is CategoricalDtype
result = r.execute().fetch()
expected = pd.cut(raw, bins, labels=labels)
assert len(result) == len(expected)
for r, e in zip(result, expected):
np.testing.assert_equal(r, e)
r = cut(t, bins, labels=False)
# result and expected is array whose dtype is CategoricalDtype
result = r.execute().fetch()
expected = pd.cut(raw, bins, labels=False)
np.testing.assert_array_equal(result, expected)
# test labels which is tensor
labels_t = tensor(["a", "b"], chunk_size=1)
r = cut(raw, bins, labels=labels_t, include_lowest=True)
# result and expected is array whose dtype is CategoricalDtype
result = r.execute().fetch()
expected = pd.cut(raw, bins, labels=labels, include_lowest=True)
assert len(result) == len(expected)
for r, e in zip(result, expected):
np.testing.assert_equal(r, e)
# test labels=False
r, b = cut(raw, ii, labels=False, retbins=True)
# result and expected is array whose dtype is CategoricalDtype
r_result, b_result = session.fetch(*session.execute(r, b))
r_expected, b_expected = pd.cut(raw, ii, labels=False, retbins=True)
for r, e in zip(r_result, r_expected):
np.testing.assert_equal(r, e)
pd.testing.assert_index_equal(b_result, b_expected)
# test bins which is md.IntervalIndex
r, b = cut(series, iii, labels=tensor(labels, chunk_size=1), retbins=True)
r_result = r.execute().fetch()
> b_result = b.execute().fetch()
mars/dataframe/base/tests/test_base_execution.py:858:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
mars/core/entity/executable.py:164: in fetch
return self._fetch(session=session, **kw)
mars/core/entity/executable.py:161: in _fetch
return fetch(self, session=session, **kw)
mars/deploy/oscar/session.py:1941: in fetch
return session.fetch(tileable, *tileables, **kwargs)
mars/deploy/oscar/session.py:1720: in fetch
return asyncio.run_coroutine_threadsafe(coro, self._loop).result()
../../.pyenv/versions/3.7.7/lib/python3.7/concurrent/futures/_base.py:435: in result
return self.__get_result()
../../.pyenv/versions/3.7.7/lib/python3.7/concurrent/futures/_base.py:384: in __get_result
raise self._exception
mars/deploy/oscar/session.py:1909: in _fetch
data = await session.fetch(tileable, *tileables, **kwargs)
mars/deploy/oscar/tests/session.py:68: in fetch
results = await super().fetch(*tileables)
mars/deploy/oscar/session.py:1126: in fetch
chunk_metas = await self._meta_api.get_chunk_meta.batch(*get_chunk_metas)
mars/oscar/batch.py:146: in _async_batch
return [await self._async_call(*args_list[0], **kwargs_list[0])]
mars/oscar/batch.py:95: in _async_call
return await self.func(*args, **kwargs)
mars/services/meta/api/oscar.py:179: in get_chunk_meta
return await self._meta_store.get_meta(object_id, fields=fields, error=error)
mars/oscar/core.pyx:263: in __pyx_actor_method_wrapper
async with lock:
mars/oscar/core.pyx:266: in mars.oscar.core.__pyx_actor_method_wrapper
result = await result
mars/oscar/batch.py:95: in _async_call
return await self.func(*args, **kwargs)
mars/services/meta/store/dictionary.py:95: in get_meta
return self._get_meta(object_id, fields=fields, error=error)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <mars.services.meta.store.dictionary.DictMetaStore object at 0x12ed69210>
object_id = '8553d79ca62df9bbf3150edc97f20b79_0', fields = ('object_refs',)
error = 'raise'
def _get_meta(
self, object_id: str, fields: List[str] = None, error: str = "raise"
) -> Dict:
if error not in ("raise", "ignore"): # pragma: no cover
raise ValueError("error must be raise or ignore")
try:
> meta = self._store[object_id]
E KeyError: '8553d79ca62df9bbf3150edc97f20b79_0'
mars/services/meta/store/dictionary.py:80: KeyError
```
The bug was introduced by https://github.com/mars-project/mars/pull/3061.
**To Reproduce**
To help us reproducing this bug, please provide information below:
1. Your Python version 3.7.7
2. The version of Mars you use Latest master
3. Versions of crucial packages, such as numpy, scipy and pandas
4. Full stack of the error.
5. Minimized code to reproduce the error.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Additional context**
Add any other context about the problem here.
|
closed
|
2022-06-06T07:46:35Z
|
2022-06-06T12:11:11Z
|
https://github.com/mars-project/mars/issues/3119
|
[
"type: bug",
"mod: ray integration"
] |
fyrestone
| 0 |
DistrictDataLabs/yellowbrick
|
scikit-learn
| 1,044 |
Multi-model metrics visualizer
|
**Describe the solution you'd like**
I would like to create an at-a-glance representation of multiple model scores so that I can easily compare and contrast different model instances. This will be our first attempt handling multiple models in a visualizer - so could be tricky, and may require a new API. I envision something that creates a heatmap of metrics to models, sort of like the classification report, but where the rows are not classes but are instead are models.
I propose the code would look something like this:
```python
viz = MultiModelMetrics([
("Naive Bayes", GaussianNB()),
("Neural Network", MultilayerPerceptron()),
("Logistic", LogisticRegression()),
("Boosting", GradientBoostingClassifier()),
("Bagging", RandomForestClassifier()),
], is_fitted=False, metrics="classification")
viz.fit(X_train, y_train)
viz.score(X_test, y_test)
viz.show()
```
Like a pipeline, this API allows us to specify names for the estimator that will be visualized, or a list of visualizers can be added and the estimator name will be used.
**Examples**
A prototype example:
|
open
|
2020-02-26T14:38:51Z
|
2021-11-03T17:44:05Z
|
https://github.com/DistrictDataLabs/yellowbrick/issues/1044
|
[
"type: feature"
] |
bbengfort
| 11 |
gradio-app/gradio
|
data-science
| 10,333 |
gr.Dataframe dynamic update
|
- [x] I have searched to see if a similar issue already exists.
**Is your feature request related to a problem? Please describe.**
Cannot dynamically yield Dataframe update to a gr.Dataframe()
**Describe the solution you'd like**
I want to dynamically update a gr.Dataframe based on a single button click
**Additional context**
See below code.
The gr.Dataframe updates once, then never again
```py
import gradio as gr
import pandas as pd
from time import sleep
initial_data = {
'category': ["cat1", "cat2", "cat2", "cat3",
"cat4", "cat5", "cat6", "cat7",
"cat8", "cat9", "cat10", "cat11"],
'Model 1': [0, 11395, 5732, 0, 0, 0, 344, 2856, 812, 0, 7965, 0],
'Model 2': [0, 5391, 7716, 0, 0, 0, 0, 45, 0, 0, 525, 0]
}
df_initial = pd.DataFrame(initial_data)
def update_dataframe(df):
for i in range(10):
df['Model 1'] = df['Model 1'] + i
df['Model 2'] = df['Model 2'] + (i * 2)
sleep(1)
print("updating")
yield df
with gr.Blocks() as demo:
gr.Markdown("### Dynamic DataFrame Update Example")
df_component = gr.Dataframe(value=df_initial, label="Editable DataFrame", type="pandas", interactive=True,render=True)
update_button = gr.Button("Update DataFrame 10 Times")
update_button.click(update_dataframe, inputs=[df_component], outputs=[df_component])
demo.launch()
```
|
closed
|
2025-01-10T17:29:42Z
|
2025-02-12T00:53:41Z
|
https://github.com/gradio-app/gradio/issues/10333
|
[
"💾 Dataframe"
] |
cl3m3nt
| 5 |
keras-team/keras
|
pytorch
| 20,325 |
Functional API not work as expected when concatenating two models with multiple output & input
|
Keras version: 3.6.0
OS: Win
Hello,
Lets say I've got following two models `A` and `B`:
```python
A_input = keras.Input(shape=(4,))
A = keras.layers.Dense(5)(A_input)
A = keras.Model(inputs=A_input, outputs=[ keras.layers.Dense(4)(A), keras.layers.Dense(4)(A) ])
```
```python
B_input = [ keras.Input(shape=(4,)), keras.Input(shape=(4,)) ]
B = keras.layers.Concatenate()(B_input)
B = keras.layers.Dense(5)(B)
B = keras.Model(inputs = B_input, outputs=B)
```
and I want to merge them into one model via `keras.Model(inputs=A_input, outputs=B(A))` which unfortunately crashes
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[20], [line 1](vscode-notebook-cell:?execution_count=20&line=1)
----> [1](vscode-notebook-cell:?execution_count=20&line=1) merged = keras.Model(inputs=A_input, outputs=B(A)) # why not work?
File c:\Users\Marcin\.miniconda3\envs\torch\Lib\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
[119](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/utils/traceback_utils.py:119) filtered_tb = _process_traceback_frames(e.__traceback__)
[120](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/utils/traceback_utils.py:120) # To get the full stack trace, call:
[121](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/utils/traceback_utils.py:121) # `keras.config.disable_traceback_filtering()`
--> [122](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/utils/traceback_utils.py:122) raise e.with_traceback(filtered_tb) from None
[123](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/utils/traceback_utils.py:123) finally:
[124](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/utils/traceback_utils.py:124) del filtered_tb
File c:\Users\Marcin\.miniconda3\envs\torch\Lib\site-packages\keras\src\layers\input_spec.py:160, in assert_input_compatibility(input_spec, inputs, layer_name)
[158](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:158) inputs = tree.flatten(inputs)
[159](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:159) if len(inputs) != len(input_spec):
--> [160](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:160) raise ValueError(
[161](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:161) f'Layer "{layer_name}" expects {len(input_spec)} input(s),'
[162](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:162) f" but it received {len(inputs)} input tensors. "
[163](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:163) f"Inputs received: {inputs}"
[164](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:164) )
[165](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:165) for input_index, (x, spec) in enumerate(zip(inputs, input_spec)):
[166](file:///C:/Users/Marcin/.miniconda3/envs/torch/Lib/site-packages/keras/src/layers/input_spec.py:166) if spec is None:
ValueError: Layer "functional_4" expects 2 input(s), but it received 1 input tensors. Inputs received: [<Functional name=functional_2, built=True>]
```
This looks like a bug to me, because following works:
```python
B(A(keras.ops.ones(shape=(1, 4)))) #works
tensor([[-0.2388, -0.3490, -0.3166, 0.2736, -1.2349]], device='cuda:0',
grad_fn=<AddBackward0>)
```
Temporarily I've found following workaround to create that merged model:
```python
merged = keras.Model(inputs=A_input, outputs=B(A(A_input)))
```
but that have a caveats it plots model with a loop in input:
|
closed
|
2024-10-05T00:25:36Z
|
2024-10-05T17:34:26Z
|
https://github.com/keras-team/keras/issues/20325
|
[] |
malciin
| 1 |
miguelgrinberg/python-socketio
|
asyncio
| 615 |
Python socketio[client] giving the following error in Raspberry pi
|
Hi,
I have a piece of code, that works fine in windows, but when I trie to run it on my raspberry pi, returns me an error and is not able to connect to the server.
```
Traceback (most recent call last):
File "/home/pi/.local/lib/python3.7/site-packages/socketio/client.py", line 279, in connect
engineio_path=socketio_path)
File "/home/pi/.local/lib/python3.7/site-packages/engineio/client.py", line 187, in connect
url, headers or {}, engineio_path)
File "/home/pi/.local/lib/python3.7/site-packages/engineio/client.py", line 306, in _connect_polling
'OPEN packet not returned by server')
engineio.exceptions.ConnectionError: OPEN packet not returned by server
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "setup.py", line 93, in <module>
sio.connect("*******")
File "/home/pi/.local/lib/python3.7/site-packages/socketio/client.py", line 283, in connect
exc.args[1] if len(exc.args) > 1 else exc.args[0])
File "/home/pi/.local/lib/python3.7/site-packages/socketio/client.py", line 547, in _trigger_event
return self.handlers[namespace][event](*args)
TypeError: connect_error() takes 0 positional arguments but 1 was given
```
My server is working fine. (it is already hosted) A prove of that is that my windows code executed with no errors.
Some of the code:
```
sio = socketio.Client()
@sio.event
def connect():
print("I'm connected!")
#print('my sid is', sio.sid)
sio.emit("**", **)
open_radio()
@sio.event
def connect_error():
print("The connection failed!")
@sio.event
def disconnect():
print("I'm disconnected!")
sio.connect(SERVER_URL)
sio.wait()
```
|
closed
|
2021-01-16T17:56:54Z
|
2021-01-16T18:29:43Z
|
https://github.com/miguelgrinberg/python-socketio/issues/615
|
[] |
thePeras
| 1 |
open-mmlab/mmdetection
|
pytorch
| 11,119 |
How to work with images without objects?
|
Hello!
I want to detect an object type in an image. I mean, I have only 1 class. The thing is that I have images without that object but I am very interested in evaluating the algorithm on those images to see if it is a proponent of false positives on that type of images.
The solution I have adopted is to add the name of the image in the images field of the coco json without adding any annotation related to that image.
That is, let's suppose that the image with id=1 does not have the object I want to segment, no annotation linked to the image with id=1 will appear. Is the approach I have taken correct?
Thank you very much
|
open
|
2023-11-02T11:40:49Z
|
2024-02-19T13:19:47Z
|
https://github.com/open-mmlab/mmdetection/issues/11119
|
[] |
JNaranjo-Alcazar
| 2 |
RayVentura/ShortGPT
|
automation
| 94 |
[Feature Request] Support InternLM
|
Dear ShortGPT developer,
Greetings! I am vansinhu, a community developer and volunteer at InternLM. Your work has been immensely beneficial to me, and I believe it can be effectively utilized in InternLM as well. Welcome to add Discord https://discord.gg/gF9ezcmtM3 . I hope to get in touch with you.
Best regards,
vansinhu
|
open
|
2023-08-28T13:11:41Z
|
2023-08-28T13:11:41Z
|
https://github.com/RayVentura/ShortGPT/issues/94
|
[] |
vansinhu
| 0 |
flairNLP/flair
|
nlp
| 2,968 |
german pretrained biomedical models?
|
Hello,
Are there also **german** pretrained models for biomedical texts available?
Thanks!
|
closed
|
2022-10-25T15:14:59Z
|
2023-02-20T13:19:56Z
|
https://github.com/flairNLP/flair/issues/2968
|
[
"question"
] |
movingabout
| 2 |
marshmallow-code/flask-smorest
|
rest-api
| 186 |
Why can't I have multiple response codes in apispec?
|
Perhaps I'm doing this wrong, but even though my route has multiple `@blp.response` values and the actual calls return the correct information, but specs only show the top response and none of the error responses.
I have this:
```
@blp.response(code=204, description="success")
@blp.response(code=404, description="Failed updating shovel statuses")
@blp.response(code=409, description="Database is empty or some shovel statuses requesting updates are missing.")
@blp.response(code=500, description="Internal server error")
def put(self, shovels):
```
And this is what I see swagger:
Similarly, if I have a response(description='fine by me') above the 204 code, then I get the 200 error instead and don't see the 204 at all. I have a few cases where depending on the type of data requested, I would either return a 200 error or a 204 error, so I would like this type of granularity as well.
Again, am I doing something wrong? I'm using the latest version of flask-smorest: 0.24.1
I'm hoping I don't have to mess with @doc, but I will if someone can explain how I use it for multiple routes and functions.
Thanks
|
closed
|
2020-09-21T21:13:41Z
|
2021-04-13T15:22:14Z
|
https://github.com/marshmallow-code/flask-smorest/issues/186
|
[] |
estein9825
| 2 |
itamarst/eliot
|
numpy
| 401 |
Trio's nursery lifetime interacts badly with start_action
|
(Based on discussion in https://trio.discourse.group/t/eliot-the-causal-logging-library-now-supports-trio/167)
Consider:
```python
from eliot import start_action, to_file
import trio
to_file(open("trio.log", "w"))
async def say(message, delay):
with start_action(action_type="say", message=message):
await trio.sleep(delay)
async def main():
async with trio.open_nursery() as nursery:
with start_action(action_type="main"):
nursery.start_soon(say, "hello", 1)
nursery.start_soon(say, "world", 2)
trio.run(main)
```
The result:
```
0ed1a1c3-050c-4fb9-9426-a7e72d0acfc7
└── main/1 ⇒ started 2019-04-26 13:01:13 ⧖ 0.000s
└── main/2 ⇒ succeeded 2019-04-26 13:01:13
0ed1a1c3-050c-4fb9-9426-a7e72d0acfc7
└── <unnamed>
├── say/3/1 ⇒ started 2019-04-26 13:01:13 ⧖ 2.002s
│ ├── message: world
│ └── say/3/2 ⇒ succeeded 2019-04-26 13:01:15
└── say/4/1 ⇒ started 2019-04-26 13:01:13 ⧖ 1.001s
├── message: hello
└── say/4/2 ⇒ succeeded 2019-04-26 13:01:14
```
What happens is that the `start_action` finishes before the nursery schedules the `say()` calls, so they get logged after the action is finished. Putting the `start_action` outside the nursery lifetime fixes this.
Depending how you look at this this is either:
1. A problem with Trio integration.
2. A design flaw in the parser (notice that all those messages are actually the same tree, the parser just decides the tree is finished too early).
3. A general problem with async context managers/contextvars/how actions finish.
|
open
|
2019-04-26T13:01:32Z
|
2019-05-21T13:15:18Z
|
https://github.com/itamarst/eliot/issues/401
|
[
"bug"
] |
itamarst
| 2 |
huggingface/pytorch-image-models
|
pytorch
| 1,182 |
EMA with high decay results in worse performance because of 1) no zero-init and 2) no debias.
|
When using [ModelEMAV2](https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/utils/model_ema.py#L82) with decay >=0.99999 and ~25k iterations, performance is worse than expected.
Encountered this bug when fine-tuning on ImageNet with EMA.
Fixed this locally by following the[ optax implementation of EMA](https://github.com/deepmind/optax/blob/252d152660300fc7fe22d214c5adbe75ffab0c4a/optax/_src/transform.py#L120-L158), posting here in case other people encounter the same thing.
There are two things which differ in the optax implementation.
1) [EMA is initialized with zeros](https://github.com/deepmind/optax/blob/252d152660300fc7fe22d214c5adbe75ffab0c4a/optax/_src/transform.py#L143-L147).
2) [Bias correction is applied to EMA](https://github.com/deepmind/optax/blob/252d152660300fc7fe22d214c5adbe75ffab0c4a/optax/_src/transform.py#L103-L106).
Apologies if I am missing something or mis-using the timm EMA implementation. Just figured this would be helpful to post in case others are using EMA with high decay. If I am not missing something, I'm happy to submit a PR for this.
|
closed
|
2022-03-20T22:39:15Z
|
2022-05-03T20:29:38Z
|
https://github.com/huggingface/pytorch-image-models/issues/1182
|
[
"bug"
] |
mitchellnw
| 3 |
pandas-dev/pandas
|
data-science
| 60,616 |
ENH: RST support
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
I wish I could use ReStructured Text with pandas
### Feature Description
The end users code:
```python
import pandas as pd
df=pd.read_rst(rst)
df.to_rst()
```
I believe tabulate has a way to do this.
### Alternative Solutions
I also built a way to make rst tables.
### Additional Context
- [The RST docs](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html#tables)
I think `Grid Tables` would be best for pandas (or `Simple Tables`)
I did not use sudo-code in the examples due to complexity and that examples of how to do this can be seen in the above packages. See RST docs for what they look like.
|
open
|
2024-12-29T17:41:50Z
|
2025-01-11T18:28:22Z
|
https://github.com/pandas-dev/pandas/issues/60616
|
[
"Enhancement",
"Needs Triage"
] |
R5dan
| 4 |
clovaai/donut
|
computer-vision
| 52 |
Question on fine-tuning document form parsing labeling requirement
|
My goal is to read a specific field (say, box 30) from a nationally standardized insurance claim form. The form has 40 boxes/fields in fixed locations and each boxed is labeled clearly with box number and title.
To save annotation time, I would like our labeling team to annotate the text from box 30 only (ignore all other boxes in the form). If I fine-tune on such annotations, is donut expected to give good results or not?
If we have to annotate the entire form box-by-box, the time it takes will be over 10x longer.
|
open
|
2022-09-17T15:20:28Z
|
2022-09-17T15:20:28Z
|
https://github.com/clovaai/donut/issues/52
|
[] |
jackkwok
| 0 |
onnx/onnx
|
tensorflow
| 6,603 |
Technical Issue in code
|
```
public class HomeController : Controller
{
private readonly MLContext _mlContext;
private readonly PredictionEngine<SignLanguageInput, SignLanguageOutput> _predictionEngine;
public HomeController()
{
_mlContext = new MLContext();
// Load ONNX model
var modelPath = Path.Combine(Directory.GetCurrentDirectory(), "wwwroot", "models", "hand_landmark_sparse_Nx3x224x224.onnx");
var dataView = _mlContext.Data.LoadFromEnumerable(new List<SignLanguageInput>());
var pipeline = _mlContext.Transforms.ApplyOnnxModel(modelPath);
var trainedModel = pipeline.Fit(dataView);
_predictionEngine = _mlContext.Model.CreatePredictionEngine<SignLanguageInput, SignLanguageOutput>(trainedModel);
}
public IActionResult Index()
{
return View();
}
public IActionResult PredictGesture([FromBody] string base64Image)
{
if (string.IsNullOrEmpty(base64Image) || base64Image == "data:,")
return BadRequest("Image data is missing!");
// Decode the base64 image and save it temporarily
var imageBytes = Convert.FromBase64String(base64Image.Replace("data:image/png;base64,", ""));
var tempImagePath = Path.Combine(Path.GetTempPath(), $"{Guid.NewGuid()}.png");
System.IO.File.WriteAllBytes(tempImagePath, imageBytes);
try
{
// Preprocess the image to create input tensor
var inputTensor = PreprocessImage(tempImagePath, 92, 92);
float[] inputArray = inputTensor.ToArray();
// Create input for prediction
var input = new SignLanguageInput { input = inputArray };
// Predict gesture
var result = _predictionEngine.Predict(input);
if (result != null)
{
return Ok(new
{
Prediction = result.Label,
Confidence = result.Confidence,
});
}
else
{
return StatusCode(500, "Prediction returned null");
}
}
catch (Exception ex)
{
return StatusCode(500, $"Internal server error: {ex.Message}");
}
finally
{
// Clean up temporary file
System.IO.File.Delete(tempImagePath);
}
}
private DenseTensor<float> PreprocessImage(string imagePath, int width, int height)
{
using var bitmap = new Bitmap(imagePath);
using var resized = new Bitmap(bitmap, new Size(width, height));
int channels = 3; // RGB
var tensor = new float[channels * width * height];
int index = 0;
for (int y = 0; y < resized.Height; y++)
{
for (int x = 0; x < resized.Width; x++)
{
var pixel = resized.GetPixel(x, y);
// Channel-first format (C, H, W)
tensor[index + 0] = pixel.R / 255f; // Red
tensor[index + 1] = pixel.G / 255f; // Green
tensor[index + 2] = pixel.B / 255f; // Blue
index += channels;
}
}
// Create a DenseTensor directly from the preprocessed image
var tensorShape = new[] { 1, 3, height, width }; // NCHW format
return new DenseTensor<float>(tensor, tensorShape); // Return as DenseTensor
}
}
```
I have given my code (in mvc). In this code, i am getting error on line "**var result = _predictionEngine.Predict(input);**" and error is "**System.ArgumentOutOfRangeException: 'Index was out of range. Must be non-negative and less than the size of the collection. (Parameter 'index')'**"
Using package :
.Net Framework : net8.0
Microsoft.ML : Version="4.0.0"
Microsoft.ML.OnnxRuntime.Gpu Version="1.20.1"
Microsoft.ML.OnnxRuntime.Managed Version="1.20.1"
Microsoft.ML.OnnxTransformer Version="4.0.0"
SixLabors.ImageSharp Version="3.1.6"
System.Drawing.Common Version="9.0.0"
and using window is "Window 11" with 64bit OS
|
closed
|
2024-12-30T17:03:13Z
|
2024-12-31T15:03:33Z
|
https://github.com/onnx/onnx/issues/6603
|
[
"question"
] |
abhaytechnoscore
| 3 |
globaleaks/globaleaks-whistleblowing-software
|
sqlalchemy
| 3,282 |
Globaleaks does not start
|
**Describe the bug**
Globaleaks doesn't start
**To Reproduce**
`globaleaks start`
WARNING: The current long term supported platform is Debian 11 (bullseye)
WARNING: It is recommended to use only this platform to ensure stability and security
WARNING: To upgrade your system consult: https://docs.globaleaks.org/en/main/user/admin/UpgradeGuide.html
` systemctl status globaleaks globaleaks.service `
globaleaks.service - LSB: Start the GlobaLeaks server.
Loaded: loaded (/etc/init.d/globaleaks; generated)
Active: failed (Result: exit-code) since Mon 2022-09-19 17:08:24 CEST; 3min 1s ago
Docs: man:systemd-sysv-generator(8)
Process: 957 ExecStart=/etc/init.d/globaleaks start (code=exited, status=1/FAILURE)
Sep 19 17:08:23 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it globaleaks[957]: * Enabling Globaleaks Network Sandboxing...
Sep 19 17:08:23 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it globaleaks[957]: ...done.
Sep 19 17:08:24 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it globaleaks[957]: WARNING: The current long term supported platform is Debian 11 (bullseye)
Sep 19 17:08:24 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it globaleaks[957]: WARNING: It is recommended to use only this platform to ensure stability and security
Sep 19 17:08:24 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it globaleaks[957]: WARNING: To upgrade your system consult: https://docs.globaleaks.org/en/main/user/admin/UpgradeGuide.html
Sep 19 17:08:24 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it globaleaks[957]: Unable to start GlobaLeaks: [Errno 13] Permission denied: '/var/globaleaks/globaleaks.pid'
Sep 19 17:08:24 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it globaleaks[957]: ...fail!
Sep 19 17:08:24 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it systemd[1]: globaleaks.service: Control process exited, code=exited status=1
Sep 19 17:08:24 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it systemd[1]: globaleaks.service: Failed with result 'exit-code'.
Sep 19 17:08:24 tst-xxxxx-py-leaks01-tstweb.site02.xxxxx.it systemd[1]: Failed to start LSB: Start the GlobaLeaks server..
**Log**
2022-09-19 17:11:10+0200 [-] [E] Found an already initialized database version: 63
2022-09-19 17:11:11+0200 [-] Starting factory <Site object at 0x7f7c9bab2550>
2022-09-19 17:11:11+0200 [-] GlobaLeaks is now running and accessible at the following urls:
2022-09-19 17:11:11+0200 [-] - [HTTP] --> http://0.0.0.0
2022-09-19 17:11:11+0200 [-] - [Tor]: --> http://d6sqxfwngfy3rsmksjg2fpcvzcggckzy76yzj775m4jxnh3bubyrarid.onion
2022-09-19 17:11:11+0200 [-] Starting factory _HTTP11ClientFactory(<function HTTPConnectionPool._newConnection.<locals>.quiescentCallback at 0x7f7c99818158>, <twisted.internet.endpoints._WrapperEndpoint object at 0x7f7c997d3eb8>)
2022-09-19 17:11:11+0200 [-] [E] Successfully connected to Tor control port
2022-09-19 17:11:11+0200 [-] [E] [1] Setting up the onion service d6sqxfwngfy3rsmksjg2fpcvzcggckzy76yzj775m4jxnh3bubyrarid.onion
2022-09-19 17:11:16+0200 [-] [E] Job ExitNodesRefresh died with runtime -1.0000 [low: -1.0000, high: -1.0000]
2022-09-19 17:11:16+0200 [-] Traceback (most recent call last):
2022-09-19 17:11:16+0200 [-] File "/usr/lib/python3/dist-packages/globaleaks/jobs/job.py", line 49, in run
2022-09-19 17:11:16+0200 [-] yield self.operation()
2022-09-19 17:11:16+0200 [-] twisted.internet.error.TimeoutError: User timeout caused connection failure.
2022-09-19 17:11:16+0200 [-] [E] exception mail suppressed for exception (<class 'twisted.internet.error.TimeoutError'>) [reason: special exception]
2022-09-19 17:11:16+0200 [-] Stopping factory _HTTP11ClientFactory(<function HTTPConnectionPool._newConnection.<locals>.quiescentCallback at 0x7f7c99818158>, <twisted.internet.endpoints._WrapperEndpoint object at 0x7f7c997d3eb8>)
**Desktop (please complete the following information):**
Ubuntu 20.04 Lts fresh install (**behind enteprise proxy**)
**Globaleaks version**
Latest (at today)
**Notes**
The problem is S.O. related?
Ubuntu 20.04 Lts Vs Debian 11?
|
closed
|
2022-09-19T15:17:57Z
|
2022-09-19T15:53:46Z
|
https://github.com/globaleaks/globaleaks-whistleblowing-software/issues/3282
|
[] |
zazzati
| 3 |
AutoGPTQ/AutoGPTQ
|
nlp
| 702 |
Can't get my CUDA_VERSION after I set CUDA_VERSION environment variable
|
|
open
|
2024-06-30T06:36:37Z
|
2024-07-24T07:00:49Z
|
https://github.com/AutoGPTQ/AutoGPTQ/issues/702
|
[
"bug"
] |
LinghuC2333
| 1 |
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