repo_name
stringlengths 9
75
| topic
stringclasses 30
values | issue_number
int64 1
203k
| title
stringlengths 1
976
| body
stringlengths 0
254k
| state
stringclasses 2
values | created_at
stringlengths 20
20
| updated_at
stringlengths 20
20
| url
stringlengths 38
105
| labels
listlengths 0
9
| user_login
stringlengths 1
39
| comments_count
int64 0
452
|
|---|---|---|---|---|---|---|---|---|---|---|---|
graphdeco-inria/gaussian-splatting
|
computer-vision
| 473
|
ValueError: too many values to unpack
|
I got an error when run train.py. It seems to occur in the GaussianRasterizer.
Optimizing
Output folder: ./output/92694154-4 [16/11 10:11:53]
Tensorboard not available: not logging progress [16/11 10:11:53]
Reading camera 2/2 [16/11 10:11:53]
Loading Training Cameras [16/11 10:11:53]
Loading Test Cameras [16/11 10:11:54]
Number of points at initialisation : 823 [16/11 10:11:54]
Training progress: 0%| | 0/30000 [00:00<?, ?it/s] Traceback (most recent call last):
File "train.py", line 219, in <module>
training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args .start_checkpoint, args.debug_from)
File "train.py", line 86, in training
render_pkg = render(viewpoint_cam, gaussians, pipe, bg)
File "/Desktop/CodeFolder/gaussian-splatting/gaussian_renderer/__init__.py", line 85, in render
rendered_image, radii = rasterizer(
ValueError: too many values to unpack (expected 2)
Training progress: 0%| | 0/30000 [00:00<?, ?it/s]
How can I fix this issue?
|
closed
|
2023-11-16T10:21:12Z
|
2024-10-19T19:34:57Z
|
https://github.com/graphdeco-inria/gaussian-splatting/issues/473
|
[] |
cfchi
| 5
|
tensorflow/tensor2tensor
|
machine-learning
| 962
|
Timeseries problems on TPU throw exception "unexpected keyword argument 'drop_remainder'"
|
Hello,
When training on both timeseries problems, timeseries_toy_problem and timeseries_synthetic_data_series10_samples100k, I get an exception, unexpected keyword argument 'drop_remainder'. Any tips?
Thanks!
update: the following change in problem.py seems to solve the problem in this case:
dataset.batch(batch_size, drop_remainder=True) -> dataset = dataset.batch(batch_size). However, I am not sure what's the functionality of drop_remainder and whether omitting it is ok here.
...
### Environment information
```
OS: Debian GNU/Linux 9.4 (stretch) on Google Cloud
$ pip freeze | grep tensor
-e git+https://github.com/tensorflow/tensor2tensor.git@814c4a13e1b03e3db444b31be2e6337c02c11bc7#egg=tensor2tensor
tensorboard==1.8.0
tensorflow==1.8.0
$ python -V
Python 2.7.13
```
# Steps to reproduce:
t2t-datagen --problem=timeseries_synthetic_data_series10_samples100k --data_dir=$DATA_DIR --tmp_dir=$TMP_DIR
t2t-trainer \
--model=transformer \
--hparams_set=transformer_tpu \
--data_dir=$DATA_DIR \
--problem=timeseries_synthetic_data_series10_samples100k \
--train_steps=1 \
--eval_steps=1 \
--output_dir=$OUT_DIR \
--use_tpu=True \
--master=$TPU_MASTER
```
```
# Error logs:
INFO:tensorflow:Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:CPU:0, CPU, -1)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:0, TPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:1, TPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:2, TPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:3, TPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:4, TPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:5, TPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:6, TPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:7, TPU, 17179869184)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:num_partitions = 1 partition_id = 0
INFO:tensorflow:Reading data files from gs://elec_transformer_bucket/TS/data/timeseries_synthetic_data_series10_samples100k-train*
INFO:tensorflow:partition: 0 num_data_files: 9
Traceback (most recent call last):
File "/usr/local/bin/t2t-trainer", line 6, in <module>
exec(compile(open(__file__).read(), __file__, 'exec'))
File "/home/meshx/tfrc_scripts/tensor2tensor/tensor2tensor/bin/t2t-trainer", line 32, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "/home/meshx/tfrc_scripts/tensor2tensor/tensor2tensor/bin/t2t-trainer", line 28, in main
t2t_trainer.main(argv)
File "/home/meshx/tfrc_scripts/tensor2tensor/tensor2tensor/bin/t2t_trainer.py", line 368, in main
execute_schedule(exp)
File "/home/meshx/tfrc_scripts/tensor2tensor/tensor2tensor/bin/t2t_trainer.py", line 315, in execute_schedule
getattr(exp, FLAGS.schedule)()
File "/home/meshx/tfrc_scripts/tensor2tensor/tensor2tensor/utils/trainer_lib.py", line 305, in continuous_train_and_eval
self._eval_spec)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 439, in train_and_evaluate
executor.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 518, in run
self.run_local()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 650, in run_local
hooks=train_hooks)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 363, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 843, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 856, in _train_model_default
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 831, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2025, in _model_fn
input_holders.generate_infeed_enqueue_ops_and_dequeue_fn())
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 992, in generate_infeed_enqueue_ops_and_dequeue_fn
self._invoke_input_fn_and_record_structure())
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 1055, in _invoke_input_fn_and_record_structure
host_device, host_id))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 687, in generate_per_host_enqueue_ops_fn_for_host
inputs = _Inputs.from_input_fn(input_fn())
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 1976, in _input_fn
return input_fn(**kwargs)
File "/home/meshx/tfrc_scripts/tensor2tensor/tensor2tensor/data_generators/problem.py", line 733, in estimator_input_fn
dataset_kwargs=dataset_kwargs)
File "/home/meshx/tfrc_scripts/tensor2tensor/tensor2tensor/data_generators/problem.py", line 865, in input_fn
dataset = dataset.batch(batch_size, drop_remainder=True)
TypeError: batch() got an unexpected keyword argument 'drop_remainder'
```
|
closed
|
2018-07-29T20:53:51Z
|
2019-07-24T18:31:52Z
|
https://github.com/tensorflow/tensor2tensor/issues/962
|
[] |
me-sh
| 1
|
kymatio/kymatio
|
numpy
| 632
|
Indexing 2D (Request)
|
Hi,
When using for instance
```
J, L= 3,4
S = Scattering2D(J=J,shape=(64,64),L=L,backend='torch', max_order=2)
```
I have the coefficient in a array with typically such dimension [Cin,25,16,16]. Now, if I use `out_type='list'`, then I get a list of dictionary
```
Slist = Scattering2D(J=J,shape=(64,64),L=L,backend='torch', max_order=2, out_type='list')
Lcoeff = Slist(t_img)
for i in range(len(Lcoeff)):
print(i,Lcoeff[i]['j'],Lcoeff[i]['theta'],Lcoeff[i]['coef'].shape)
```
which looks like
```
0 () () torch.Size([5, 8, 8])
1 (0,) (0,) torch.Size([5, 8, 8])
2 (0,) (1,) torch.Size([5, 8, 8])
3 (0,) (2,) torch.Size([5, 8, 8])
4 (0,) (3,) torch.Size([5, 8, 8])
5 (1,) (0,) torch.Size([5, 8, 8])
6 (1,) (1,) torch.Size([5, 8, 8])
7 (1,) (2,) torch.Size([5, 8, 8])
8 (1,) (3,) torch.Size([5, 8, 8])
9 (2,) (0,) torch.Size([5, 8, 8])
10 (2,) (1,) torch.Size([5, 8, 8])
11 (2,) (2,) torch.Size([5, 8, 8])
12 (2,) (3,) torch.Size([5, 8, 8])
13 (0, 1) (0, 0) torch.Size([5, 8, 8])
14 (0, 1) (0, 1) torch.Size([5, 8, 8])
...
```
Well, in some sense I get a link between the index i of `coeff_i` (i:0,24) and the corresponding (j,theta) or (j1,j2,theta_1,theta_2). Is there some indexing utility routines that can do it
```
idx -> (level, j1,theta1,j2,theta2)
with level=0 j1=theta1=j2=theta2=default value as we do not care
with level=1 j1,theta1 and j2=theta2=default valeue
with level=2 then all js and thetas matter
```
and vice versa
```
(level, j1,theta1,j2,theta2) -> idx
```
This could help to make combinaison of the coefficients....
Thanks
|
open
|
2020-10-07T07:32:53Z
|
2021-03-18T09:57:56Z
|
https://github.com/kymatio/kymatio/issues/632
|
[] |
jecampagne
| 9
|
pydantic/logfire
|
pydantic
| 542
|
LLM tool call not shown for streamed response
|
### Description
The Logfire UI nicely shows the tool call by an LLM for non-streamed responses
<img width="669" alt="image" src="https://github.com/user-attachments/assets/d363085f-ca73-4af9-a31f-9d74c0294836">
But for streamed responses the Assistant box is empty.
<img width="663" alt="image" src="https://github.com/user-attachments/assets/6550e674-5c68-4127-8dac-2cff1dd6d080">
Code to reproduce
```python
# Test logfire streamed responsea
from openai import Client
import logfire
logfire.configure()
logfire.instrument_openai()
client = Client()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Create a Superhero named Monkey Boy."}],
stream=True,
stream_options={"include_usage": True},
tool_choice={"type": "function", "function": {"name": "return_superhero"}},
tools=[
{
"type": "function",
"function": {
"name": "return_superhero",
"parameters": {
"properties": {
"name": {"title": "Name", "type": "string"},
"age": {"title": "Age", "type": "integer"},
"power": {"title": "Power", "type": "string"},
"enemies": {
"items": {"type": "string"},
"title": "Enemies",
"type": "array",
},
},
"required": ["name", "age", "power", "enemies"],
"type": "object",
},
},
},
],
)
for chunk in response:
print(chunk)
```
Related (closed) issue: https://github.com/pydantic/logfire/issues/54
### Python, Logfire & OS Versions, related packages (not required)
```TOML
logfire="0.50.1"
platform="macOS-15.0.1-arm64-arm-64bit"
python="3.10.12 (main, Jul 15 2023, 09:54:16) [Clang 14.0.3
(clang-1403.0.22.14.1)]"
[related_packages]
requests="2.32.3"
pydantic="2.8.2"
openai="1.52.0"
protobuf="4.25.3"
rich="13.7.1"
tomli="2.0.1"
executing="2.0.1"
opentelemetry-api="1.25.0"
opentelemetry-exporter-otlp-proto-common="1.25.0"
opentelemetry-exporter-otlp-proto-http="1.25.0"
opentelemetry-instrumentation="0.46b0"
opentelemetry-proto="1.25.0"
opentelemetry-sdk="1.25.0"
opentelemetry-semantic-conventions="0.46b0"
```
|
closed
|
2024-10-25T05:52:57Z
|
2024-11-13T11:57:51Z
|
https://github.com/pydantic/logfire/issues/542
|
[
"bug"
] |
jackmpcollins
| 2
|
matplotlib/matplotlib
|
matplotlib
| 29,646
|
[Bug]: Markers with string-type integer values are converted to integers in cycler and matplotlibrc
|
### Bug summary
When creating a cycler with a `"marker"` property value contains strings that _could_ be converted to integers, they are always converted to integers, even if the string value is valid and the intended value. This is most obvious with the list `["1", "2"]`; which is distinctly different from `[1, 2]`. However, the latter is always used. **Edit (20.02.2025):** This is also the case for `"lines.markers"`, `"boxplot.flierprops.marker"`, `"boxplot.meanprops.marker"`, and `"scatter.marker"`.
Relevant functions are `_validate_markers` ([here](https://github.com/matplotlib/matplotlib/blob/7ede0929dc39858a2801f8b6b2711db0d4fa5bfe/lib/matplotlib/rcsetup.py#L231)) and `_listify_validator` ([here](https://github.com/matplotlib/matplotlib/blob/7ede0929dc39858a2801f8b6b2711db0d4fa5bfe/lib/matplotlib/rcsetup.py#L97)). It seems like this issue was introduced by #27613, which solved a similar issue reported in #27596.
### Code for reproduction
**Edited 20.02.2025**
```Python
from matplotlib import cycler
import matplotlib.pyplot as plt
markers = ["1", "2", "o", 4]
print("Cycler")
test_cycler = cycler("marker", markers)
for prop_dict in test_cycler:
print(
"Marker {} with type {}.".format(prop_dict["marker"], type(prop_dict["marker"]))
)
print("lines.marker")
for marker in markers:
plt.rcParams["lines.marker"] = marker
print(
"Marker {} with type {}.".format(
plt.rcParams["lines.marker"], type(plt.rcParams["lines.marker"])
)
)
```
### Actual outcome
```bash
$ python main.py
Cycler
Marker 1 with type <class 'int'>.
Marker 2 with type <class 'int'>.
Marker o with type <class 'str'>.
Marker 4 with type <class 'int'>.
lines.marker
Marker 1 with type <class 'int'>.
Marker 2 with type <class 'int'>.
Marker o with type <class 'str'>.
Marker 4 with type <class 'int'>.
```
which indicates that the string entries `"1"` and `"2"` are converted into integers. As the string `"o"` cannot be converted into an integer with `int("o")`, it is used explicitly.
### Expected outcome
```bash
$ python main.py
Cycler
Marker 1 with type <class 'str'>.
Marker 2 with type <class 'str'>.
Marker o with type <class 'str'>.
Marker 4 with type <class 'int'>.
lines.marker
Marker 1 with type <class 'str'>.
Marker 2 with type <class 'str'>.
Marker o with type <class 'str'>.
Marker 4 with type <class 'int'>.
```
This output would indicate that strings containing integers are parsed correctly.
### Additional information
This bug was not present prior to #27613, but it did solve a similar bug.
Looking at the code which causes this bug, `_validate_markers` ([here](https://github.com/matplotlib/matplotlib/blob/7ede0929dc39858a2801f8b6b2711db0d4fa5bfe/lib/matplotlib/rcsetup.py#L231)) and `_listify_validator` ([here](https://github.com/matplotlib/matplotlib/blob/7ede0929dc39858a2801f8b6b2711db0d4fa5bfe/lib/matplotlib/rcsetup.py#L97)), it's not clear to me why the validation is done this way. I.e., to validate types, values are explicitly converted to see if a `ValueError` is raised, instead of testing the type explicitly. Is this the intended method for validation?
~~I have a draft solution, but I would like clarification on the intended validation mechanisms before I submit the code for review.~~ **Edit (20.02.2025):** Since finding that other RcParams entries are affected by this bug, my solution is no longer applicable.
### Operating system
Fedora 41
### Matplotlib Version
3.10.0
### Matplotlib Backend
_No response_
### Python version
Python 3.12.9
### Jupyter version
_No response_
### Installation
pip
|
open
|
2025-02-19T12:48:32Z
|
2025-02-20T15:21:29Z
|
https://github.com/matplotlib/matplotlib/issues/29646
|
[] |
conradhaupt
| 6
|
netbox-community/netbox
|
django
| 18,023
|
Extend `register_model_view()` to handle list views
|
### Proposed Changes
We currently use `register_model_view()` to register detail views (edit, delete, etc.) for models, which obviates the need to declare explicit URL paths for each. This could be extended to support list views as well. For example, a common set of views might look like this:
```python
# List view
@register_model_view(MyModel, list=True)
class FooListView(generic.ObjectListView):
# Detail view
@register_model_view(MyModel)
class MyModelView(generic.ObjectView):
# Add/edit views
@register_model_view(MyModel, 'edit')
@register_model_view(MyModel, 'add', list=True)
class MyModelEditView(generic.ObjectEditView):
# Delete view
@register_model_view(MyModel, 'delete')
class MyModelDeleteView(generic.ObjectDeleteView):
# Bulk import view
@register_model_view(MyModel, 'import', list=True)
class MyModelBulkImportView(generic.BulkImportView):
# Bulk delete view
@register_model_view(MyModel, 'delete', list=True)
class MyModelBulkDeleteView(generic.BulkDeleteView):
```
The corresponding set of URL paths could then be reduced to:
```python
path('my-models/', include(get_model_urls('myapp', 'mymodel', list=True))),
path('my-models/<int:pk>/', include(get_model_urls('myapp', 'mymodel'))),
```
(This could potentially be reduced even further, but I'm also wary of over-optimizing.)
`get_model_urls()` will likewise need to be extended to differentiate between list and detail views.
### Justification
This will allow us to reduce a substantial amount of boilerplate URL patterns without any breaking changes (AFAICT).
|
closed
|
2024-11-15T15:55:37Z
|
2025-02-21T03:05:51Z
|
https://github.com/netbox-community/netbox/issues/18023
|
[
"status: accepted",
"type: feature",
"topic: plugins",
"netbox"
] |
jeremystretch
| 1
|
PokemonGoF/PokemonGo-Bot
|
automation
| 5,740
|
[issue] [dev] Bot incubates shorter eggs first
|
### Expected Behavior
`Bot Incubates longer eggs first, on both breakable and unbreakable`
### Actual Behavior
`Bot Incubates shorter eggs first.`
### Your FULL config.json (remove your username, password, gmapkey and any other private info)
```
{
"websocket_server": false,
"heartbeat_threshold": 10,
"enable_social": true,
"live_config_update": {
"enabled": false,
"tasks_only": false
},
"tasks": [
{
"type": "TelegramTask",
"config": {
"enabled": false,
"master": null,
"password": null,
"alert_catch": {
"all": {"operator": "and", "cp": 1300, "iv": 0.95},
"Snorlax": {"operator": "or", "cp": 900, "iv": 0.9}
}
}
},
{
"//NOTE: This task MUST be placed on the top of task list": {},
"type": "RandomAlivePause",
"config": {
"enabled": false,
"min_duration": "00:00:10",
"max_duration": "00:10:00",
"min_interval": "00:05:00",
"max_interval": "01:30:00"
}
},
{
"type": "HandleSoftBan"
},
{
"type": "RandomPause",
"config": {
"enabled": false,
"min_duration": "00:00:10",
"max_duration": "00:10:00",
"min_interval": "00:10:00",
"max_interval": "02:00:00"
}
},
{
"type": "CompleteTutorial",
"config": {
"enabled": true,
"// set a name": "",
"nickname": "",
"// 0 = No Team, 1 = Blue, 2 = Red, 3 = Yellow": "",
"team": 2
}
},
{
"type": "CollectLevelUpReward",
"config": {
"collect_reward": true,
"level_limit": -1
}
},
{
"type": "BuddyPokemon",
"config": {
"enabled": true,
"buddy_list": "dratini, magikarp",
"best_in_family": true,
"// candy_limit = 0 means no limit, so it will never change current buddy": {},
"candy_limit": 0,
"// force_first_change = true will always change buddy at start removing current one": {},
"force_first_change": false,
"buddy_change_wait_min": 3,
"buddy_change_wait_max": 5,
"min_interval": 120
}
},
{
"type": "IncubateEggs",
"config": {
"longer_eggs_first": true,
"breakable_longer_eggs_first": true,
"min_interval": 120,
"infinite": [2,5,10],
"breakable": [2,5,10]
}
},
{
"type": "UpdateLiveStats",
"config": {
"enabled": true,
"min_interval": 25,
"stats": ["uptime", "stardust_earned", "xp_earned", "xp_per_hour", "stops_visited", "level_stats"],
"terminal_log": true,
"terminal_title": true
}
},
{
"type": "UpdateLiveInventory",
"config": {
"enabled": true,
"min_interval": 240,
"show_all_multiple_lines": true,
"items": ["pokemon_bag", "space_info", "pokeballs", "greatballs", "ultraballs", "razzberries", "luckyegg"]
}
},
{
"type": "TransferPokemon",
"config": {
"enabled": true,
"min_free_slot": 5,
"transfer_wait_min": 3,
"transfer_wait_max": 54
}
},
{
"type": "NicknamePokemon",
"config": {
"enabled": false,
"nickname_above_iv": 0.9,
"nickname_template": "{iv_pct}-{iv_ads}",
"nickname_wait_min": 3,
"nickname_wait_max": 5
}
},
{
"type": "EvolvePokemon",
"config": {
"enabled": true,
"log_interval": 120,
"// evolve only pidgey and drowzee": "",
"// evolve_list": "pidgey, drowzee",
"// donot_evolve_list": "none",
"// evolve all but pidgey and drowzee": "",
"// evolve_list": "all",
"// donot_evolve_list": "pidgey, drowzee",
"evolve_list": "all",
"donot_evolve_list": "Bulbasaur, Charmander, Squirtle, Caterpie, Pidgey, Weedle, Oddish, Polywag, Abra, Machop, Bellsprout, Geodude, Gastly, Dratini, Nidoran F, Nidoran M",
"first_evolve_by": "cp",
"evolve_above_cp": 200,
"evolve_above_iv": 0.7,
"logic": "or",
"min_evolve_speed": 25,
"max_evolve_speed": 30,
"min_pokemon_to_be_evolved": 1,
"use_lucky_egg": false
}
},
{
"type": "UseIncense",
"config": {
"use_incense": false,
"use_order": [
"ordinary",
"spicy",
"cool",
"floral"
]
}
},
{
"type": "RecycleItems",
"config": {
"enabled": true,
"min_empty_space": 15,
"max_balls_keep": 200,
"max_potions_keep": 120,
"max_berries_keep": 90,
"max_revives_keep": 80,
"item_filter": {
"Pokeball": { "keep" : 75 },
"Greatball": { "keep" : 80 },
"Ultraball": { "keep" : 80 },
"Potion": { "keep" : 30 },
"Super Potion": { "keep" : 40 },
"Hyper Potion": { "keep" : 50 },
"Max Potion": { "keep" : 50 },
"Revive": { "keep" : 40 },
"Max Revive": { "keep" : 50 },
"Razz Berry": { "keep" : 50 }
},
"recycle_wait_min": 3,
"recycle_wait_max": 5,
"recycle_wait_max": 5,
"recycle_force": true,
"recycle_force_min": "00:06:00",
"recycle_force_max": "00:12:00"
}
},
{
"type": "CatchPokemon",
"config": {
"enabled": true,
"catch_visible_pokemon": true,
"catch_lured_pokemon": true,
"catch_incensed_pokemon": true,
"min_ultraball_to_keep": 5,
"berry_threshold": 0.35,
"vip_berry_threshold": 0.9,
"treat_unseen_as_vip": true,
"daily_catch_limit": 800,
"vanish_settings": {
"consecutive_vanish_limit": 10,
"rest_duration_min": "02:00:00",
"rest_duration_max": "04:00:00"
},
"catch_throw_parameters": {
"excellent_rate": 0.1,
"great_rate": 0.5,
"nice_rate": 0.3,
"normal_rate": 0.1,
"spin_success_rate" : 0.6,
"hit_rate": 0.75
},
"catch_simulation": {
"flee_count": 3,
"flee_duration": 2,
"catch_wait_min": 2,
"catch_wait_max": 6,
"berry_wait_min": 2,
"berry_wait_max": 3,
"changeball_wait_min": 2,
"changeball_wait_max": 3,
"newtodex_wait_min": 20,
"newtodex_wait_max": 30
}
}
},
{
"type": "SpinFort",
"config": {
"enabled": true,
"spin_wait_min": 3,
"spin_wait_max": 5,
"daily_spin_limit": 1900
}
},
{
"type": "UpdateWebInventory",
"config": {
"enabled": true
}
},
{
"type": "MoveToFort",
"config": {
"enabled": true,
"lure_attraction": true,
"lure_max_distance": 2000,
"walker": "StepWalker",
"log_interval": 5
}
},
{
"type": "FollowSpiral",
"config": {
"enabled": true,
"diameter": 4,
"step_size": 70
}
}
],
"map_object_cache_time": 5,
"forts": {
"avoid_circles": true,
"max_circle_size": 50
},
"pokemon_bag": {
"// if 'show_at_start' is true, it will log all the pokemons in the bag (not eggs) at bot start": {},
"show_at_start": false,
"// if 'show_count' is true, it will show the amount of each pokemon (minimum 1)": {},
"show_count": false,
"// if 'show_candies' is true, it will show the amount of candies for each pokemon": {},
"show_candies": false,
"// 'pokemon_info' parameter define which info to show for each pokemon": {},
"// the available options are": {},
"// ['cp', 'iv_ads', 'iv_pct', 'ivcp', 'ncp', 'level', 'hp', 'moveset', 'dps']": {},
"pokemon_info": ["cp", "iv_pct"]
},
"websocket_server": false,
"walk_max": 6.16,
"walk_min": 3.16,
"alt_min": 500,
"alt_max": 1000,
"sleep_schedule": {
"enabled": true,
"enable_reminder": false,
"reminder_interval": 600,
"entries": [
{
"enabled": true,
"time": "23:00",
"duration": "5:30",
"time_random_offset": "00:30",
"duration_random_offset": "00:30",
"wake_up_at_location": ""
},
{
"enabled": true,
"time": "17:45",
"duration": "3:00",
"time_random_offset": "01:00",
"duration_random_offset": "00:30",
"wake_up_at_location": ""
}
]
},
"gps_default_altitude": 8.0,
"replicate_gps_xy_noise": true,
"replicate_gps_z_noise": false,
"gps_xy_noise_range": 0.00125,
"gps_z_noise_range": 12.5,
"debug": false,
"test": false,
"walker_limit_output": false,
"health_record": false,
"location_cache": true,
"distance_unit": "km",
"reconnecting_timeout": 15,
"catch_randomize_reticle_factor": 1.0,
"catch_randomize_spin_factor": 1.0,
"logging": {
"color": true,
"show_datetime": true,
"show_process_name": false,
"show_log_level": true,
"show_thread_name": false
},
"catch": {
"any": {"candy_threshold" : 1900, "catch_above_cp": 0, "catch_above_iv": 0, "logic": "or"},
"// Example of always catching Rattata:": {},
"// Rattata": { "always_catch" : true },
"// Example of catching only Diglett and Horsea needed for Bubblestrat; you might want to also configure sniping": {},
"// (MoveToMap - helps you get hold of neede pokemons faster) and disable SpinFort": {},
"// (or you will advance past level 2 which will make it impossible to catch level-1 pokemons)": {},
"// Example of catching Vaporeon only with Water Gun and Hydro Pump": {},
"Vaporeon": { "catch_above_iv": 0.99, "charged_attack": ["Hydro Pump"], "fast_attack": ["Water Gun"]}
},
"release": {
"any": {"keep_best_cp": 2},
"//any": {"release_below_cp": 500, "release_below_iv": 0.6, "release_below_ivcp": 0, "logic": "or"},
"// Example of always releasing Rattata:": {},
"// Rattata": {"always_release": true},
"// Example of keeping 3 stronger (based on CP) Pidgey:": {},
"// Pidgey": {"keep_best_cp": 3},
"// Example of keeping 2 stronger (based on IV) Zubat:": {},
"// Zubat": {"keep_best_iv": 2},
"// Also, it is working with any": {},
"// any": {"keep_best_iv": 3},
"// Keep no more than 3 best IVCP pokemon for every pokemon type": {},
"// any": {"keep_best_ivcp": 3},
"// Example of keeping the 2 strongest (based on CP) and 3 best (based on IV) Zubat:": {},
"// Zubat": {"keep_best_cp": 2, "keep_best_iv": 3}
},
"vips" : {
"Any pokemon put here directly force to use Berry & Best Ball to capture, to secure the capture rate!": {},
"any": {"catch_above_cp": 1200, "catch_above_iv": 0.9, "logic": "or" },
"Lapras": {},
"Moltres": {},
"Zapdos": {},
"Articuno": {},
"// S-Tier pokemons (if pokemon can be evolved into tier, list the representative)": {},
"Mewtwo": {},
"Dragonite": {},
"Snorlax": {},
"// Mew evolves to Mewtwo": {},
"Mew": {},
"Arcanine": {},
"Vaporeon": {},
"Gyarados": {},
"Exeggutor": {},
"Muk": {},
"Weezing": {},
"Flareon": {}
}
}
```
### Output when issue occurred
`[2016-09-30 11:36:11] [INFO] Eggs incubating: 1.71/5.0 km (Eggs left: (10.0, 8), Incubating: 1)`
|
closed
|
2016-09-30T09:43:34Z
|
2016-10-03T07:12:09Z
|
https://github.com/PokemonGoF/PokemonGo-Bot/issues/5740
|
[] |
scarfield
| 4
|
mckinsey/vizro
|
pydantic
| 630
|
[Docs] Py.Cafe code snippets to-do list
|
Here's an issue (public, so we can ask for contributions to it from our readers) to record the bits and pieces left to do on following the [introduction of py.cafe to our docs' examples](https://github.com/mckinsey/vizro/pull/569).
- [ ] Change the requirement in `hatch.toml` when py.cafe release their mkdocs plugin (separate issue, stored as 1204 internally)
- [ ] Update the code examples in the `data.md` how-to guide to use py.cafe once we can solve how to illustrate data handling without creating multiple projects in a profile to store the `iris.csv` dataset.
- We considered using the function to create the dataframe directly for this example but disregarded it because it doesn't illustrate data loading,
- We could code the data download from storage in this GitHub repo
- We could make a bunch of projects like I did for other examples using custom css, for example, but there are a lot and it results in code example duplication, so it's not the optimum solution for any of the examples.
- Best longer-term solution is to either change the example to call an API to get a dataset (which varies over time, as that's what the example is about) or to wait to see if we can work something out with py.cafe to add files to projects dynamically via the plugin (a bit like we specify requirements files as `extra-requirements`)
- [ ] If a solution arises whereby we no longer need to create examples and link to them (rather than use the plugin) we can delete the examples I've created so far. But long-term, if we have these examples as part of our documentation, we should store them under a new profile in py.cafe that uses a shared email account so the team can update those projects as needed, rather than use my login.
|
open
|
2024-08-15T08:46:02Z
|
2025-01-14T09:40:02Z
|
https://github.com/mckinsey/vizro/issues/630
|
[
"Docs :spiral_notepad:"
] |
stichbury
| 1
|
AirtestProject/Airtest
|
automation
| 818
|
airtest.core.android.ADB.devices不返回ipv6远程连接的设备
|
**描述问题bug**
airtest.core.android.ADB.devices不返回ipv6远程连接的设备
原因在于对应源码219行中的正则并未匹配这种情况
[ipv6ip%interface]:port
adb devices
源码
其中`[fe80::105c:1fff:fe05:1c7b%19]:5555`不能被原先的`[\w\d.:-]+`匹配
其实这里用不上正则,直接第二行开始一行一个就行,然后按制表符分割
**相关截图**
见上
**复现步骤**
无
**预期效果**
无
**python 版本:** `python3.8`
**airtest 版本:** `1.1.6`
**设备:**
无
**其他相关环境信息**
无
|
open
|
2020-10-26T03:01:02Z
|
2022-05-30T09:58:18Z
|
https://github.com/AirtestProject/Airtest/issues/818
|
[] |
hgjazhgj
| 1
|
cvat-ai/cvat
|
pytorch
| 8,457
|
Labels added after project creation are not returned by https://app.cvat.ai/api/tasks?project_id=
|
### 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
1. Create a new project with some initial labels
2. After the project is created, add some more labels
3. Query https://app.cvat.ai/api/labels?project_id= for the the project labels
4. The project labels returned by the API should be only the original, and none of the newly added labels
4a. While this appears to be an API endpoint (based on the URL), the correct data is returned by the [labels api](https://docs.cvat.ai/docs/api_sdk/sdk/reference/apis/labels-api/#list)
### Expected Behavior
The project labels across any api match the current constructor
### Possible Solution
Have the source of https://app.cvat.ai/api/labels?project_id= updated and/or have it serve the labels list api.
### Context
I was trying to write a script to give me basic statistics on my project (labels per class, etc.). The following script is rather longer than it needs to be with all the debugging, but it will return the results:
```
from cvat_sdk import Client
from prettytable import PrettyTable
from collections import defaultdict
import requests
import logging
project_id = 123456 # Update with your project ID
# Set up logging to a file
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
#logging.basicConfig(filename='cvat_log.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') # Or log to file
# CVAT SDK connection (update these variables accordingly)
cvat_host = 'https://app.cvat.ai'
# CVAT credentials (replace with your username and password)
username = 'USERNAME'
#password = getpass.getpass(prompt='Enter your CVAT password: ')
password = r'PASSWORD'
# Manually login using requests to get the token
session = requests.Session()
login_url = f'{cvat_host}/api/auth/login'
login_data = {'username': username, 'password': password}
login_response = session.post(login_url, json=login_data)
if login_response.status_code == 200:
token = login_response.json().get('key')
logging.debug(f"Successfully logged in, received token: {token}")
else:
logging.error(f"Failed to log in. Status code: {login_response.status_code}")
raise Exception("Login failed. Please check your credentials.")
# Function to fetch project-level labels by making an API request to the provided URL
def fetch_project_labels(project_id):
client = Client(cvat_host)
client.login((username, password))
# Retrieve project data
project_data = client.projects.retrieve(project_id)
# Debugging to inspect the structure of labels
logging.debug(f"Project data for project {project_id}: {project_data}")
# Get the labels URL
label_url = project_data.labels['url']
# Fetch labels from the label URL
headers = {"Authorization": f"Token {token}"}
response = requests.get(label_url, headers=headers)
if response.status_code == 200:
label_data = response.json()
if 'results' in label_data:
project_labels = {label['id']: label['name'] for label in label_data['results']}
logging.debug(f"Fetched project-level labels from: {label_url}")
logging.debug(f"Project-level labels are: {project_labels}")
return project_labels
else:
logging.error(f"No labels found in the response from {label_url}")
return {}
else:
logging.error(f"Failed to fetch labels from {label_url}, Status Code: {response.status_code}")
return {}
fetch_project_labels(project_id)
```
The following is a sanitized version of the logs that are written:
```
2024-09-18 23:53:05,987 - DEBUG - Starting new HTTPS connection (1): app.cvat.ai:443
2024-09-18 23:53:06,991 - DEBUG - https://app.cvat.ai:443 "POST /api/auth/login HTTP/1.1" 200 50
2024-09-18 23:53:06,992 - DEBUG - Successfully logged in, received token: 8c742c94ef756723e1b2bc0f7344829b20316781
2024-09-18 23:53:08,339 - DEBUG - Project data for project 123456: {'assignee': None,
'assignee_updated_date': None,
'bug_tracker': '',
'created_date': datetime.datetime(2024, 6, 11, 22, 28, 35, 840052, tzinfo=tzutc()),
'dimension': '2d',
'guide_id': 12345,
'id': 123456,
'labels': {'url': 'https://app.cvat.ai/api/labels?project_id=123456'},
'name': 'ProjName',
'organization': None,
'owner': {'first_name': 'FIRST',
'id': 111111,
'last_name': 'LAST',
'url': 'https://app.cvat.ai/api/users/111111',
'username': 'USERNAME'},
'source_storage': {'cloud_storage_id': None,
'id': 2222222,
'location': 'local'},
'status': 'annotation',
'target_storage': {'cloud_storage_id': None,
'id': 3333333,
'location': 'local'},
'task_subsets': ['train'],
'tasks': {'count': 7,
'url': 'https://app.cvat.ai/api/tasks?project_id=123456'},
'updated_date': datetime.datetime(2024, 9, 19, 3, 7, 14, 522143, tzinfo=tzutc()),
'url': 'https://app.cvat.ai/api/projects/123456'}
2024-09-18 23:53:08,862 - DEBUG - Fetched project-level labels from: https://app.cvat.ai/api/labels?project_id=123456
2024-09-18 23:53:08,862 - DEBUG - Project-level labels are: {1: 'Raccoon', 2: 'Possum', 3: 'Armadillo', 4: 'Dog', 5: 'Cat'}
```
Missing from the Project-level labels on the last line are 6: Car, 7: Truck, and 8: Van
Again, sanitized, but the missing labels are in a similar numerical order. That is what leads me to believe it is the ones added after I started the project that are missing, though I can't be positive.
However, taking a page directly from the API documentation and creating the following script:
```
from pprint import pprint
from cvat_sdk.api_client import Configuration, ApiClient, exceptions
from cvat_sdk.api_client.models import *
configuration = Configuration(
host = 'https://app.cvat.ai',
username = 'USERNAME',
password = r'PASSWORD'
)
with ApiClient(configuration) as api_client:
project_id = 149228
try:
(data, response) = api_client.labels_api.list(
project_id=project_id,
)
pprint(data)
except exceptions.ApiException as e:
print("Exception when calling LabelsApi.list(): %s\n" % e)
```
Gets me the literal output of the raw constructor from the project page, with all 8 labels.
### Environment
```Markdown
- Windows 11 23H2
- PowerShell 7 shell
- Python 3.12.6
- CVAT SDK updated as of today
```
|
closed
|
2024-09-19T04:31:02Z
|
2024-10-11T09:58:07Z
|
https://github.com/cvat-ai/cvat/issues/8457
|
[
"bug"
] |
BeanBagKing
| 3
|
TracecatHQ/tracecat
|
fastapi
| 467
|
Sign in issue
|
I cant sign in to the default admin account using `admin@domain.com` and password `password` and sign up not working too
|
closed
|
2024-10-28T05:55:30Z
|
2024-10-28T09:37:40Z
|
https://github.com/TracecatHQ/tracecat/issues/467
|
[] |
Harasisco
| 16
|
modoboa/modoboa
|
django
| 2,429
|
Creating a disabled account imply an active (internal) alias
|
Testing against behavior similarly to #2120 it appears that creating a disabled account imply creation of an active (internal) alias.
# Impacted versions
* OS Type: Debian
* OS Version: Buster
* Database Type: PostgreSQL
* Database version: 11
* Modoboa: 1.17.3 (also on master branch)
# Steps to reproduce
* Create an account with Simple user role and disable it at the creation
* Send an email to this account
Here is a (failling) test case: https://github.com/fpoulain/modoboa/commit/05dda3cdcd27f6b4d651cf9622fa62ba03244a74
# Current behavior
```ipython
In [1]: from modoboa.core.models import User
In [2]: from modoboa.admin.models import Alias
In [3]: u = User.objects.get(email='tester@test.com')
In [4]: u.enabled
Out[4]: False
In [5]: a = Alias.objects.get(address=u.email)
In [6]: a.enabled
Out[6]: True
In [7]: a.internal
Out[7]: True
```
# Expected behavior
```ipython
In [1]: from modoboa.core.models import User
In [2]: from modoboa.admin.models import Alias
In [3]: u = User.objects.get(email='tester@test.com')
In [4]: u.enabled
Out[4]: False
In [5]: a = Alias.objects.get(address=u.email)
In [6]: a.enabled
Out[6]: False
In [7]: a.internal
Out[7]: True
```
|
closed
|
2021-12-29T17:03:07Z
|
2022-05-13T12:53:35Z
|
https://github.com/modoboa/modoboa/issues/2429
|
[
"bug"
] |
fpoulain
| 6
|
comfyanonymous/ComfyUI
|
pytorch
| 6,447
|
MAC STUDIO M1MAX Torch not compiled with CUDA enabled
|
### Your question
MAC STUDIO M1MAX run CogVideoX-5b-1.5 with ComfyUI,
Use python3 main.py --force-fp16 --use-split-cross-attentio --cpu to start ComfyUI.
But always throw exception -- AssertionError: Torch not compiled with CUDA enabled.
I was use CPU to run is model ,
and check pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu to reinstall torch with Mac . it is not work.
if you has this bug , and you fixed it ,please tell me how to fix this bug.
### Logs
```powershell
!!! Exception during processing !!! Torch not compiled with CUDA enabled
Traceback (most recent call last):
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/execution.py", line 328, in execute
output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/execution.py", line 203, in get_output_data
return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/execution.py", line 174, in _map_node_over_list
process_inputs(input_dict, i)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/execution.py", line 163, in process_inputs
results.append(getattr(obj, func)(**inputs))
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/custom_nodes/ComfyUI-CogVideoXWrapper/nodes.py", line 722, in process
latents = model["pipe"](
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/custom_nodes/ComfyUI-CogVideoXWrapper/pipeline_cogvideox.py", line 778, in __call__
noise_pred = self.transformer(
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/accelerate/hooks.py", line 170, in new_forward
output = module._old_forward(*args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/custom_nodes/ComfyUI-CogVideoXWrapper/custom_cogvideox_transformer_3d.py", line 569, in forward
emb = self.time_embedding(t_emb, timestep_cond)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/diffusers/models/embeddings.py", line 1304, in forward
sample = self.linear_1(sample)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/accelerate/hooks.py", line 165, in new_forward
args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/accelerate/hooks.py", line 355, in pre_forward
set_module_tensor_to_device(
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/accelerate/utils/modeling.py", line 329, in set_module_tensor_to_device
new_value = value.to(device)
File "/Users/qinyi/Downloads/ComfyUI-0.3.10/.venv/lib/python3.9/site-packages/torch/cuda/__init__.py", line 310, in _lazy_init
raise AssertionError("Torch not compiled with CUDA enabled")
AssertionError: Torch not compiled with CUDA enabled
```
### Other
_No response_
|
open
|
2025-01-13T03:59:25Z
|
2025-02-13T17:04:50Z
|
https://github.com/comfyanonymous/ComfyUI/issues/6447
|
[
"User Support"
] |
qinxiaoyi-stack
| 6
|
albumentations-team/albumentations
|
machine-learning
| 1,634
|
[Tech debt] Improve interface for GridDropOut
|
* `unit_size_min`, `unit_size_max` => `unit_size_range`
* `shift_x`, `shift_y` => `shift`
=>
We can update transform to use new signature, keep old as working, but mark as deprecated.
----
PR could be similar to https://github.com/albumentations-team/albumentations/pull/1704
|
closed
|
2024-04-05T19:33:52Z
|
2024-06-22T01:37:49Z
|
https://github.com/albumentations-team/albumentations/issues/1634
|
[
"good first issue",
"Tech debt"
] |
ternaus
| 0
|
jadore801120/attention-is-all-you-need-pytorch
|
nlp
| 89
|
Missing $ on loop variable at preprocess.perl line 1.
|
Thanks for making this available. Trying to run this code on windows and getting the following error:
Missing $ on loop variable at preprocess.perl line 1.
When I run
for l in en de; do for f in data/multi30k/*.$l; do if [[ "$f" != *"test"* ]]; then sed -i "$ d" $f; fi; done; done
for l in en de; do for f in data/multi30k/*.$l; do perl tokenizer.perl -a -no-escape -l $l -q < $f > $f.atok; done; done
Can someone just make the data files that are input to the training code available? That way I won't have to run the pre processing steps.
Thanks!
|
closed
|
2019-02-09T20:11:42Z
|
2019-12-08T11:48:44Z
|
https://github.com/jadore801120/attention-is-all-you-need-pytorch/issues/89
|
[] |
ankur6ue
| 4
|
huggingface/transformers
|
nlp
| 36,292
|
[Version: 4.49.0] Qwen2.5-VL is not supported in vLLM because of transformers
|
### System Info
error: Model architectures Qwen2 5 VLForConditionalGeneration' failed to be inspected. Please check the logs for more details.
right now people say that they are using methods like:
1. pip install --upgrade git+https://github.com/huggingface/transformers.git@336dc69d63d56f232a183a3e7f52790429b871ef (https://github.com/vllm-project/vllm/issues/13285)
2. pip install --force-reinstall git+https://github.com/huggingface/transformers.git@9985d06add07a4cc691dc54a7e34f54205c04d40 (https://github.com/vllm-project/vllm/issues/12932)
3. There is a breaking change in transformers dev. You need to update vLLM to latest dev and also redownload the HF model repo. (https://github.com/vllm-project/vllm/issues/12932)
I mean. This is not supposed to be like this. I can't connect to your github without a vpn, and with a vpn, I can't connect to my workspace. Could transformers team just fix the problem instead of letting people solve it by some weird method. Thanks!
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
use Xinference, download the newest vLLM so that you get transformers 4.49.0. You download Qwen2.5 VL and deploy in vLLM and get the error:
### Expected behavior
NO MORE ERROR DURING DEPLOYMENT OF QWEN2.5 VL 7B IN VLLM
|
closed
|
2025-02-20T01:44:22Z
|
2025-03-12T12:06:17Z
|
https://github.com/huggingface/transformers/issues/36292
|
[
"bug"
] |
usun1997
| 29
|
scikit-tda/kepler-mapper
|
data-visualization
| 120
|
Error in visualize (possibly) due to color_function scaling and histogram creation
|
Hi everyone,
I've been using kepler-mapper for analyzing some data, and found that certain choices of the color_function in mapper.visualize causes an error or incorrect output.
**To Reproduce**
The following is a minimal working example of the bug.
```
import kmapper as km
import numpy as np
import sklearn
data = np.array([[0],[1]])
lens = data
colorf = np.array([[1],[6]])
mapper = km.KeplerMapper()
graph = mapper.map(lens, data, clusterer=sklearn.cluster.DBSCAN(eps=0.1, min_samples=0),
coverer=km.Cover(nr_cubes=2, overlap_perc=0.2))
mapper.visualize(graph, color_function=colorf,path_html="test.html")
```
which produces the following message (directories truncated)
```
kepler-mapper/kmapper/visuals.py:93: RuntimeWarning: invalid value encountered in long_scalars
height = int(((bar / max_bucket_value) * 100) + 1)
Traceback (most recent call last):
File "bug.py", line 12, in <module>
mapper.visualize(graph, color_function=colorf,path_html="test.html")
File "kepler-mapper/kmapper/kmapper.py", line 514, in visualize
lens_names, custom_tooltips, env)
File "kepler-mapper/kmapper/visuals.py", line 62, in format_mapper_data
tt = _format_tooltip(env, member_ids, custom_tooltips, X, X_names, lens, lens_names, color_function, node_id)
File "kepler-mapper/kmapper/visuals.py", line 207, in _format_tooltip
histogram = build_histogram(color_function[member_ids])
File "kepler-mapper/kmapper/visuals.py", line 93, in build_histogram
height = int(((bar / max_bucket_value) * 100) + 1)
ValueError: cannot convert float NaN to integer
```
**System details**
- OS: macOS Mojave 10.14
- Python 3.7.0 via Homebrew
- scikit-learn 0.20.0 from PyPI
- numpy 1.15.3 from PyPI
- kepler-mapper installed from source (master)
**My guess**
I did some digging, and found that the error probably originates from here:
https://github.com/MLWave/kepler-mapper/blob/d640181bac67269b884186e038b3dec09705ef2c/kmapper/visuals.py#L84-L85
where the returned value `hist` is an array containing only 0's. This causes the "cannot convert float NaN to integer" ValueError as we divided by max_bucket_value, which is 0.
As to why `hist` can sometimes be a zero array:
First of all, color_function is scaled by MinMaxScaler to be in the range (0,1) by
https://github.com/MLWave/kepler-mapper/blob/d640181bac67269b884186e038b3dec09705ef2c/kmapper/visuals.py#L29-L30
However, it seems that due to floating point computation, the largest value is not scaled to exactly 1 (and may sometimes exceed it). The largest value of 6 in the MWE color_function is scaled to something like 1.0000000000000002, which falls outside [0,1].
Note that even if hist were not the zero array, the largest value of the color_function being excluded is a possibility due to this scaling and the assumption that it falls neatly within h_min=0 and h_max=1. When this happens, the resulting histogram will be incorrect but no error will be reported.
|
closed
|
2018-10-31T07:56:03Z
|
2018-11-02T15:27:55Z
|
https://github.com/scikit-tda/kepler-mapper/issues/120
|
[] |
emerson-escolar
| 3
|
scikit-learn/scikit-learn
|
python
| 30,950
|
Potential Problem in the Computation of Adjusted Mutual Info Score
|
### Describe the bug
It seems to me that for clusters of size 2 and 4, the AMI yields unexpected results of 0 instead of 1, if all items belong to different clusters.
### Steps/Code to Reproduce
Sample Code to Reproduce:
```python
>>> from sklearn.metrics.cluster import adjusted_mutual_info_score
>>> print(adjusted_mutual_info_score([1, 2], [3, 4])
0.0
>>> print(adjusted_mutual_info_score([1, 2, 3, 4], [5, 6, 7, 8])
0.0
>>> print(adjusted_mutual_info_score([1, 2, 3, 4, 5], [6, 7, 8, 9, 10])
1.0
```
### Expected Results
As the clusters are identical in all cases, I'd expect the result to be 1.0 in all cases. This happens with version 1.6.1.
### Actual Results
So we have the strange behavior that the code outputs for lists containing different labels with 2 items and with 4 items the value 0, while we deal with identical partitions. I tested until 1000 items, it only occurs with 2 and 4.
### Versions
```shell
System:
python: 3.13.1 (main, Dec 4 2024, 18:05:56) [GCC 14.2.1 20240910]
executable: /home/mattis/.envs/lexi/bin/python
machine: Linux-6.12.10-arch1-1-x86_64-with-glibc2.41
Python dependencies:
sklearn: 1.6.1
pip: 24.3.1
setuptools: 75.8.0
numpy: 2.2.2
scipy: 1.15.2
Cython: None
pandas: None
matplotlib: None
joblib: 1.4.2
threadpoolctl: 3.5.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 20
prefix: libscipy_openblas
filepath: /home/mattis/.envs/lexi/lib/python3.13/site-packages/numpy.libs/libscipy_openblas64_-6bb31eeb.so
version: 0.3.28
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 20
prefix: libscipy_openblas
filepath: /home/mattis/.envs/lexi/lib/python3.13/site-packages/scipy.libs/libscipy_openblas-68440149.so
version: 0.3.28
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 20
prefix: libgomp
filepath: /home/mattis/.envs/lexi/lib/python3.13/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
version: None
```
|
open
|
2025-03-06T09:01:58Z
|
2025-03-24T10:57:51Z
|
https://github.com/scikit-learn/scikit-learn/issues/30950
|
[
"Bug",
"Needs Investigation"
] |
LinguList
| 4
|
pandas-dev/pandas
|
pandas
| 61,031
|
BUG: `ValueError: ndarray is not C-contiguous` for `cummax` on nullable dtypes
|
### Pandas version checks
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [x] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
In [5]: df = pd.DataFrame({'a': [1,1,2], 'b': [4,5,6], 'i': [0,1,2]}, dtype='Int64')[::-1]
In [6]: df.groupby('a')['b'].cummax()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[6], line 1
----> 1 df.groupby('a')['b'].cummax()
File ~/pandas-dev/pandas/core/groupby/groupby.py:4945, in GroupBy.cummax(self, numeric_only, **kwargs)
4886 """
4887 Cumulative max for each group.
4888
(...)
4942 bull 6 9
4943 """
4944 skipna = kwargs.get("skipna", True)
-> 4945 return self._cython_transform(
4946 "cummax", numeric_only=numeric_only, skipna=skipna
4947 )
File ~/pandas-dev/pandas/core/groupby/generic.py:684, in SeriesGroupBy._cython_transform(self, how, numeric_only, **kwargs)
681 obj = self._obj_with_exclusions
683 try:
--> 684 result = self._grouper._cython_operation(
685 "transform", obj._values, how, 0, **kwargs
686 )
687 except NotImplementedError as err:
688 # e.g. test_groupby_raises_string
689 raise TypeError(f"{how} is not supported for {obj.dtype} dtype") from err
File ~/pandas-dev/pandas/core/groupby/ops.py:932, in BaseGrouper._cython_operation(self, kind, values, how, axis, min_count, **kwargs)
928 assert kind in ["transform", "aggregate"]
930 cy_op = WrappedCythonOp(kind=kind, how=how, has_dropped_na=self.has_dropped_na)
--> 932 return cy_op.cython_operation(
933 values=values,
934 axis=axis,
935 min_count=min_count,
936 comp_ids=self.ids,
937 ngroups=self.ngroups,
938 **kwargs,
939 )
File ~/pandas-dev/pandas/core/groupby/ops.py:546, in WrappedCythonOp.cython_operation(self, values, axis, min_count, comp_ids, ngroups, **kwargs)
542 self._validate_axis(axis, values)
544 if not isinstance(values, np.ndarray):
545 # i.e. ExtensionArray
--> 546 return values._groupby_op(
547 how=self.how,
548 has_dropped_na=self.has_dropped_na,
549 min_count=min_count,
550 ngroups=ngroups,
551 ids=comp_ids,
552 **kwargs,
553 )
555 return self._cython_op_ndim_compat(
556 values,
557 min_count=min_count,
(...)
561 **kwargs,
562 )
File ~/pandas-dev/pandas/core/arrays/masked.py:1602, in BaseMaskedArray._groupby_op(self, how, has_dropped_na, min_count, ngroups, ids, **kwargs)
1599 if how == "rank" and kwargs.get("na_option") in ["top", "bottom"]:
1600 result_mask[:] = False
-> 1602 res_values = op._cython_op_ndim_compat(
1603 self._data,
1604 min_count=min_count,
1605 ngroups=ngroups,
1606 comp_ids=ids,
1607 mask=mask,
1608 result_mask=result_mask,
1609 **kwargs,
1610 )
1612 if op.how == "ohlc":
1613 arity = op._cython_arity.get(op.how, 1)
File ~/pandas-dev/pandas/core/groupby/ops.py:331, in WrappedCythonOp._cython_op_ndim_compat(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)
329 if result_mask is not None:
330 result_mask = result_mask[None, :]
--> 331 res = self._call_cython_op(
332 values2d,
333 min_count=min_count,
334 ngroups=ngroups,
335 comp_ids=comp_ids,
336 mask=mask,
337 result_mask=result_mask,
338 **kwargs,
339 )
340 if res.shape[0] == 1:
341 return res[0]
File ~/pandas-dev/pandas/core/groupby/ops.py:477, in WrappedCythonOp._call_cython_op(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)
474 if self.how != "rank":
475 # TODO: should rank take result_mask?
476 kwargs["result_mask"] = result_mask
--> 477 func(
478 out=result,
479 values=values,
480 labels=comp_ids,
481 ngroups=ngroups,
482 is_datetimelike=is_datetimelike,
483 mask=mask,
484 **kwargs,
485 )
487 if self.kind == "aggregate" and self.how not in ["idxmin", "idxmax"]:
488 # i.e. counts is defined. Locations where count<min_count
489 # need to have the result set to np.nan, which may require casting,
490 # see GH#40767. For idxmin/idxmax is handled specially via post-processing
491 if result.dtype.kind in "iu" and not is_datetimelike:
492 # if the op keeps the int dtypes, we have to use 0
File groupby.pyx:2287, in pandas._libs.groupby.group_cummax()
File <stringsource>:663, in View.MemoryView.memoryview_cwrapper()
File <stringsource>:353, in View.MemoryView.memoryview.__cinit__()
ValueError: ndarray is not C-contiguous
```
### Issue Description
groupby-cummax raises for nullable integers in this case
Note that for pyarrow-backed integers it works fine
### Expected Behavior
```
2 6
1 5
0 5
Name: b, dtype: Int64
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : b552dc95c9fa50e9ca2a0c9f9cdb8757f794fedb
python : 3.10.12
python-bits : 64
OS : Linux
OS-release : 5.15.167.4-microsoft-standard-WSL2
Version : #1 SMP Tue Nov 5 00:21:55 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 3.0.0.dev0+618.gb552dc95c9
numpy : 1.26.4
dateutil : 2.9.0.post0
pip : 25.0
Cython : 3.0.11
sphinx : 8.1.3
IPython : 8.32.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.3
blosc : None
bottleneck : 1.4.2
fastparquet : 2024.11.0
fsspec : 2025.2.0
html5lib : 1.1
hypothesis : 6.125.2
gcsfs : 2025.2.0
jinja2 : 3.1.5
lxml.etree : 5.3.0
matplotlib : 3.10.0
numba : 0.61.0
numexpr : 2.10.2
odfpy : None
openpyxl : 3.1.5
psycopg2 : 2.9.10
pymysql : 1.4.6
pyarrow : 19.0.0
pyreadstat : 1.2.8
pytest : 8.3.4
python-calamine : None
pytz : 2025.1
pyxlsb : 1.0.10
s3fs : 2025.2.0
scipy : 1.15.1
sqlalchemy : 2.0.37
tables : 3.10.1
tabulate : 0.9.0
xarray : 2024.9.0
xlrd : 2.0.1
xlsxwriter : 3.2.2
zstandard : 0.23.0
tzdata : 2025.1
qtpy : None
pyqt5 : None
</details>
Spotted in [Narwhals](https://github.com/narwhals-dev/narwhals)
|
open
|
2025-03-02T16:21:03Z
|
2025-03-05T01:44:09Z
|
https://github.com/pandas-dev/pandas/issues/61031
|
[
"Bug",
"Groupby",
"NA - MaskedArrays",
"Transformations"
] |
MarcoGorelli
| 3
|
PokeAPI/pokeapi
|
api
| 1,104
|
Server is down
|
Schema no aviable in graphQL
|
closed
|
2024-06-02T19:26:10Z
|
2024-06-03T13:19:47Z
|
https://github.com/PokeAPI/pokeapi/issues/1104
|
[] |
Waanma
| 1
|
yzhao062/pyod
|
data-science
| 241
|
COF and SOD huge bugs?
|
Hi all. Something is happening that I cannot understand, I'm doing hyperparameter tuning to all proximity based algorithms with RandomSearchCV form sklearn and only COF and SOD have problems. What is happening is the following: the constructor parameters are all None even though I'm passing values to it. I tried to change the code of SOD and hardcoded the values of the constructor and I'm still getting NoneType. Please help.
|
closed
|
2020-10-24T19:28:15Z
|
2020-10-31T16:36:27Z
|
https://github.com/yzhao062/pyod/issues/241
|
[] |
dcabanas
| 11
|
falconry/falcon
|
api
| 2,157
|
When following raw URL recipe based on RAW_URI, it breaks routing in TestClient
|
I'm building REST API with many %-encoded slashes in URI (i.e., as data, not path separator). For example, request URI `/book/baking-book/document/sweet%2Fcheescake.md` will never reach the route declared as `/book/{book}/document/{path}`.
I understand that's not Falcon's fault, but the WSGI server, which "eats" %-encoding before providing it to Falcon in the `PATH_INFO` CGI variable. Thank you for the well-explained reason in FAQ and [decoding raw URL recipe](https://falcon.readthedocs.io/en/stable/user/recipes/raw-url-path.html) based on Gunicorn's `RAW_URI` non-standard variable.
However, if I follow this recipe, it breaks all tests because [TestClient hard-code `RAW_URI` to `/`](https://github.com/falconry/falcon/blob/master/falcon/testing/helpers.py#L1197), so no URI will route properly anymore.
|
closed
|
2023-07-14T17:00:44Z
|
2023-11-30T20:26:40Z
|
https://github.com/falconry/falcon/issues/2157
|
[
"bug",
"good first issue"
] |
liborjelinek
| 5
|
django-oscar/django-oscar
|
django
| 3,797
|
Custom group prices
|
In a lot of ecommerce frameworks, customer group prices exists. Or even specific prices for a single user.
I think this can and should be a default feature in oscar.
Django groups already exists, so I guess this can be used for it.
Would this be something that can and may be implemented in oscar?
|
closed
|
2021-10-30T20:48:01Z
|
2023-06-23T13:45:06Z
|
https://github.com/django-oscar/django-oscar/issues/3797
|
[] |
thecodeiot
| 2
|
geex-arts/django-jet
|
django
| 313
|
Wrong spelling of template variable
|
Since I've got the template options settings set to `'string_if_invalid': "<Couldn't render variable '%s'>"` I'm seeing an error that `'module.pre_contenta'` isn't found. It seems like it's a simple typo in the template:
https://github.com/geex-arts/django-jet/blob/552191c5be551741b3349aa104c92435702f62e5/jet/dashboard/templates/jet.dashboard/module.html#L31
|
open
|
2018-04-03T14:54:54Z
|
2018-04-03T14:54:54Z
|
https://github.com/geex-arts/django-jet/issues/313
|
[] |
lauritzen
| 0
|
JaidedAI/EasyOCR
|
deep-learning
| 581
|
Pypy support?
|
Has anyone tried using PyPy with this project? My attempts so far on windows with anaconda have been failures, wondering if I'm wasting time
|
closed
|
2021-11-01T07:23:29Z
|
2022-08-07T05:00:33Z
|
https://github.com/JaidedAI/EasyOCR/issues/581
|
[] |
ckcollab
| 0
|
sherlock-project/sherlock
|
python
| 1,601
|
False Negative for twitter search
|
False negative when trying to scan usernames on that have attached twitters. This could lead to false reports and would be greatly appreciated if it's fixed. This is for every username I've tried that have valid accounts but show up as false negatives for example holiday8993
|
closed
|
2022-10-31T11:17:26Z
|
2023-02-04T17:17:35Z
|
https://github.com/sherlock-project/sherlock/issues/1601
|
[
"false negative"
] |
Cr7pt0nic
| 4
|
koaning/scikit-lego
|
scikit-learn
| 218
|
[BUG] fix gaussian methos
|
The gaussian methods are a bit nasty now.
- they use **kwargs instead of strict params
- they don't support the bayesian variant
it'd be great if these could be added.
|
closed
|
2019-10-17T21:13:19Z
|
2019-10-20T15:49:37Z
|
https://github.com/koaning/scikit-lego/issues/218
|
[
"bug"
] |
koaning
| 0
|
pydantic/logfire
|
pydantic
| 855
|
LLM structured output support (client.beta.chat.completions.parse)
|
### Description
Hi,
Structured output in https://platform.openai.com/docs/guides/structured-outputs has a different response format and is using a different method, which https://logfire.pydantic.dev/docs/integrations/llms/openai/#methods-covered does not seem to be supporting currently.
```python
completion = client.beta.chat.completions.parse(
model="gpt-4o-2024-08-06",
messages=[
{"role": "system", "content": "Extract the event information."},
{"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
],
response_format=CalendarEvent,
)
```
|
open
|
2025-02-11T12:18:05Z
|
2025-02-11T12:18:05Z
|
https://github.com/pydantic/logfire/issues/855
|
[
"Feature Request"
] |
thedotedge
| 0
|
pydata/bottleneck
|
numpy
| 183
|
bn.nansum on array with np.newaxis added dimension
|
Bottleneck's nansum (and nanmean, etc) doesn't seem to give the right answer when dimensions are added with np.newaxis. An example is:
In [27]: a = np.ones((2,2))
In [28]: a[1, :] = np.nan
In [29]: bn.nansum(a) # Correct
Out[29]: 2.0
In [30]: bn.nansum(a[..., np.newaxis]) # **Wrong**
Out[30]: 4.0
In [31]: np.nansum(a[..., np.newaxis]) # numpy does the right thing.
Out[31]: 2.0
In [32]: np.__version__
Out[32]: '1.12.1'
In [33]: bn.__version__
Out[33]: '1.2.1'
|
closed
|
2018-01-02T17:28:09Z
|
2018-01-03T16:47:45Z
|
https://github.com/pydata/bottleneck/issues/183
|
[] |
ng0
| 4
|
ymcui/Chinese-BERT-wwm
|
nlp
| 80
|
能否获得其他维数的向量?
|
例如50维或100维?
|
closed
|
2019-12-31T08:01:10Z
|
2019-12-31T09:04:57Z
|
https://github.com/ymcui/Chinese-BERT-wwm/issues/80
|
[] |
lhy2749
| 2
|
netbox-community/netbox
|
django
| 18,110
|
Allow multiple config templates attached to a device
|
### NetBox version
v4.1.7
### Feature type
Data model extension
### Triage priority
N/A
### Proposed functionality
Extend the config template field on a device/virtual machine to allow for attaching multiple config templates.
### Use case
We have multiple stages of a device lifespan, and one of the stages is the provisioning phase. In the first phase we create a file enabling the provisioning and enrollment to the management system. In the second phase we apply the actual configuration. Currently we use one config template for both. This is cumbersome as users then need to download the one file and manually split it into two. It would be useful to allow multiple config templates to be attached to a device in this case.
### Database changes
I assume you would need to change the relationship of the RenderConfigMixin from many-to-one to many-to-many.
### External dependencies
_No response_
|
open
|
2024-11-26T21:31:16Z
|
2025-03-17T15:04:14Z
|
https://github.com/netbox-community/netbox/issues/18110
|
[
"type: feature",
"status: under review"
] |
rodvand
| 4
|
abhiTronix/vidgear
|
dash
| 28
|
Bug: sys.stderr.close() throws ValueError bug in WriteGear class
|
### Bug Description:
The use of `sys.stderr.close()` at line: https://github.com/abhiTronix/vidgear/blob/8d1fd2afec1e42b5583c37d411a29a9f25e0e2fb/vidgear/gears/writegear.py#L230 breaks stderr and throws `ValueError: I/O operation on closed file` at the output. This also results in hidden tracebacks, since tracebacks are written to `stder` thereby making it difficult to identify this bug.
### Code to reproduce:
```python
from vidgear.gears import WriteGear
import numpy as np
try:
np.random.seed(0)
# generate random data for 10 frames
random_data = np.random.random(size=(10, 1080, 1920, 3)) * 255
input_data = random_data.astype(np.uint8)
writer = WriteGear("garbage.garbage")
writer.write(input_data)
writer.close()
except Exception as e:
print(e)
finally:
einfo = sys.exc_info()
import traceback; print(''.join(traceback.format_exception(*einfo)))
print('finally?')
```
This code will result in traceback with ultimately leads to `ValueError: I/O operation on closed file` at the output.
|
closed
|
2019-07-07T17:47:01Z
|
2019-07-08T02:51:04Z
|
https://github.com/abhiTronix/vidgear/issues/28
|
[
"BUG :bug:",
"SOLVED :checkered_flag:"
] |
abhiTronix
| 1
|
InstaPy/InstaPy
|
automation
| 6,310
|
unfollow_users method isn't working. Activity is being restricted.
|
## Expected Behavior
Unfollow users.
## Current Behavior
Showing an alert from instagram
The unfollow action is not blocked on the account only with InstaPy, selenium and geckodriver.
I figured out that they are recognizing some patterns to identify this kind of automation.
## Possible Solution (optional)
- Change the user agent, because unfollow action on instagram is not blocked, only with InstaPy, selenium and geckodriver.
- Change the behaviour of the action, maybe introduce some random activity to avoid repeating patterns.
## InstaPy configuration
```
session.unfollow_users(amount=random.randint(75, 100),
nonFollowers=True,
style="FIFO",
unfollow_after=24 * 60 * 60, sleep_delay=600)
```
|
closed
|
2021-09-11T07:51:33Z
|
2021-09-19T17:19:31Z
|
https://github.com/InstaPy/InstaPy/issues/6310
|
[] |
Dieg0x17
| 1
|
amidaware/tacticalrmm
|
django
| 1,342
|
Default Option for "Take Control"
|
**Is your feature request related to a problem? Please describe.**
We generally do not want to take over a workstation unnoticed by the user. It's prone to error to remember to right click on the connect button and choose the appropriate option.
**Describe the solution you'd like**
An Option to change the default behavior of the connect button. Just like there is the option on "Agent table default tab" in each user accounts preference, I would like to have a setting "Default Take Control Mode". If possible it might even make sense to have a "Default Server Take Control Mode" and "Default Workstation Take Control Mode".
**Describe alternatives you've considered**
Don't think there are any other than trying really hard to always remember to not click on "connect" right away.
|
closed
|
2022-11-04T15:52:52Z
|
2022-11-04T16:09:43Z
|
https://github.com/amidaware/tacticalrmm/issues/1342
|
[] |
Byolock
| 1
|
Yorko/mlcourse.ai
|
data-science
| 354
|
Topic 9 Kaggle template broken
|
https://www.kaggle.com/kashnitsky/topic-9-part-2-time-series-with-facebook-prophet
|
closed
|
2018-09-24T12:50:54Z
|
2018-10-04T14:12:09Z
|
https://github.com/Yorko/mlcourse.ai/issues/354
|
[
"minor_fix"
] |
Vozf
| 1
|
scikit-learn/scikit-learn
|
python
| 30,181
|
DOC grammar issue in the governance page
|
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other references, such as at
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L174
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L177
It is correctly written as `GitHub`.
### Suggest a potential alternative/fix
To maintain consistency throughout the document, we should change `github` to `GitHub` at line no. 70 on governance page.
|
closed
|
2024-10-30T20:01:44Z
|
2024-11-05T07:31:27Z
|
https://github.com/scikit-learn/scikit-learn/issues/30181
|
[
"Documentation"
] |
AdityaInnovates
| 5
|
vastsa/FileCodeBox
|
fastapi
| 292
|
文本复制失败
|
点击按钮复制，会弹出失败。
我不确定是不是因为版本问题，因为我看官网demo是没有复制按钮的。
使用的是昨天装的 lanol/filecodebox:beta
|
closed
|
2025-03-13T09:03:23Z
|
2025-03-15T14:56:47Z
|
https://github.com/vastsa/FileCodeBox/issues/292
|
[] |
BlackWhite2000
| 0
|
rthalley/dnspython
|
asyncio
| 574
|
dnspython fails to see SRV record from headless Kubernetes service
|
NOTE: Also posted to [StackOverflow](https://stackoverflow.com/questions/63430708/dnspython-fails-to-see-srv-record-from-headless-kubernetes-service) in case the behavior described below is not a bug in `dnspython`.
I am able to retrieve the SRV record for a headless Kubernetes service from the command line, but I'm unable to retrieve the same record using dnspython. Is this a bug?
The service's FQDN is `mongodb-headless.ahcj.svc.cluster.local`.
I can verify that my service is running:
```
> kubectl get services -n ahcj
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
mongodb-arbiter-headless ClusterIP None <none> 27017/TCP 25h
mongodb-headless ClusterIP None <none> 27017/TCP 25h
```
Following the example of Marko Luksa in _Kubernetes in Action_, 1st ed (p. 300), I look up the SRV record using `dig`, which works fine.
```
> kubectl run -it srvlookup --image=tutum/dnsutils --rm --restart=Never -n ahcj -- dig SRV mongodb-headless.ahcj.svc.cluster.local
; <<>> DiG 9.9.5-3ubuntu0.2-Ubuntu <<>> SRV mongodb-headless.ahcj.svc.cluster.local
;; global options: +cmd
;; Got answer:
;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 46367
;; flags: qr aa rd; QUERY: 1, ANSWER: 2, AUTHORITY: 0, ADDITIONAL: 3
;; WARNING: recursion requested but not available
;; OPT PSEUDOSECTION:
; EDNS: version: 0, flags:; udp: 4096
;; QUESTION SECTION:
;mongodb-headless.ahcj.svc.cluster.local. IN SRV
;; ANSWER SECTION:
mongodb-headless.ahcj.svc.cluster.local. 5 IN SRV 0 50 27017 mongodb-1.mongodb-headless.ahcj.svc.cluster.local.
mongodb-headless.ahcj.svc.cluster.local. 5 IN SRV 0 50 27017 mongodb-0.mongodb-headless.ahcj.svc.cluster.local.
;; ADDITIONAL SECTION:
mongodb-0.mongodb-headless.ahcj.svc.cluster.local. 5 IN A 10.244.1.57
mongodb-1.mongodb-headless.ahcj.svc.cluster.local. 5 IN A 10.244.0.192
;; Query time: 6 msec
;; SERVER: 10.245.0.10#53(10.245.0.10)
;; WHEN: Sat Aug 15 20:11:47 UTC 2020
;; MSG SIZE rcvd: 414
pod "srvlookup" deleted
```
I then create a pod running an Ubuntu container (in the same namespace, FWIW), install Python 3 and dnspython on it, pseudo-ssh into it, and run the following script, following the example of the [dnspython documentation](https://www.dnspython.org/examples/) and [an SO question on getting SRV records in Python](https://stackoverflow.com/questions/1189253/how-do-i-resolve-an-srv-record-in-python):
```
import dns.resolver
srv_records = dns.resolver.resolve("mongodb-headless.ahcj.srv.cluster.local", "SRV")
print(srv_records)
```
When I run it, I get:
```
Traceback (most recent call last):
File "srv.py", line 2, in <module>
srv_records = dns.resolver.resolve("mongodb-headless.ahcj.srv.cluster.local", "SRV")
File "/usr/local/lib/python3.8/dist-packages/dns/resolver.py", line 1205, in resolve
return get_default_resolver().resolve(qname, rdtype, rdclass, tcp, source,
File "/usr/local/lib/python3.8/dist-packages/dns/resolver.py", line 1030, in resolve
(request, answer) = resolution.next_request()
File "/usr/local/lib/python3.8/dist-packages/dns/resolver.py", line 584, in next_request
raise NXDOMAIN(qnames=self.qnames_to_try,
dns.resolver.NXDOMAIN: The DNS query name does not exist: mongodb-headless.ahcj.srv.cluster.local.
```
For good measure, I also try supplying just `mongodb-headless` as the hostname, as well as `_xmpp-client._tcp.mongodb-headless.ahcj.srv.cluster.local`. I also tried the deprecated `dns.resolver.query(...)` method from `dnspython`, to no avail. (I don't know why the `_xmpp-client._tcp` prefix would help, but it was supplied in an upvoted answer in the SO question.)
I readily admit that SRV records are a new one for me, and clearly, they are by nature more complex than a DNS entry that resolves to a single value. But I do not understand why one DNS utility would be able to resolve the hostname, and another DNS utility would be unable to resolve an identical hostname when running in the same network.
|
closed
|
2020-08-15T20:58:05Z
|
2020-08-15T22:00:37Z
|
https://github.com/rthalley/dnspython/issues/574
|
[] |
borenstein
| 2
|
AUTOMATIC1111/stable-diffusion-webui
|
deep-learning
| 15,986
|
[Bug]: Why does the quality on pics look bad?
|
### 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
- [ ] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet
### What happened?
I freshly installed everything and my pics still look bad. I'm not sure what to do.
### Steps to reproduce the problem
I'm not sure why it's happening.
### What should have happened?
It should make a good looking and clean picture and I have no clue as to why it doesn't.
### What browsers do you use to access the UI ?
Google Chrome
### Sysinfo
[sysinfo-2024-06-10-07-08.json](https://github.com/user-attachments/files/15757928/sysinfo-2024-06-10-07-08.json)
### Console logs
```Shell
venv "C:\Users\sydne\Desktop\bewbies\stable-diffusion-webui\venv\Scripts\Python.exe"
Python 3.10.11 (tags/v3.10.11:7d4cc5a, Apr 5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)]
Version: v1.9.4
Commit hash: feee37d75f1b168768014e4634dcb156ee649c05
Installing sd-webui-controlnet requirement: changing opencv-python version from 4.10.0.82 to 4.8.0
Launching Web UI with arguments:
no module 'xformers'. Processing without...
no module 'xformers'. Processing without...
No module 'xformers'. Proceeding without it.
Tag Autocomplete: Could not locate model-keyword extension, Lora trigger word completion will be limited to those added through the extra networks menu.
[-] ADetailer initialized. version: 24.5.1, num models: 10
ControlNet preprocessor location: C:\Users\sydne\Desktop\bewbies\stable-diffusion-webui\extensions\sd-webui-controlnet\annotator\downloads
2024-06-10 02:48:54,074 - ControlNet - INFO - ControlNet v1.1.449
Loading weights [67ab2fd8ec] from C:\Users\sydne\Desktop\bewbies\stable-diffusion-webui\models\Stable-diffusion\ponyDiffusionV6XL_v6StartWithThisOne.safetensors
2024-06-10 02:48:54,587 - ControlNet - INFO - ControlNet UI callback registered.
Running on local URL: http://127.0.0.1:7860
To create a public link, set `share=True` in `launch()`.
Creating model from config: C:\Users\sydne\Desktop\bewbies\stable-diffusion-webui\repositories\generative-models\configs\inference\sd_xl_base.yaml
C:\Users\sydne\Desktop\bewbies\stable-diffusion-webui\venv\lib\site-packages\huggingface_hub\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
IIB Database file has been successfully backed up to the backup folder.
Startup time: 16.7s (prepare environment: 5.8s, import torch: 4.4s, import gradio: 1.0s, setup paths: 1.3s, initialize shared: 0.3s, other imports: 0.8s, load scripts: 1.9s, create ui: 0.7s, gradio launch: 0.4s).
Loading VAE weights specified in settings: C:\Users\sydne\Desktop\bewbies\stable-diffusion-webui\models\VAE\sdxl_vae.safetensors
Applying attention optimization: Doggettx... done.
```
### Additional information
My GPU driver is up to date.
|
open
|
2024-06-10T07:10:49Z
|
2024-06-12T01:56:32Z
|
https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/15986
|
[] |
blehh666
| 3
|
vitalik/django-ninja
|
pydantic
| 552
|
[BUG]
|
**Describe the bug**
test_api.py turns off auth checking in the test for patient coverage.
we should restore the previous value at the end of the test, in case if affects other tests.
|
closed
|
2022-09-05T17:02:21Z
|
2022-09-06T15:42:53Z
|
https://github.com/vitalik/django-ninja/issues/552
|
[] |
ntwyman
| 1
|
CorentinJ/Real-Time-Voice-Cloning
|
pytorch
| 1,185
|
Can run demo_cli.py or demo_toolbox.py
|
I have followed the steps and I'm stuck here, couldn't find any similar issue either.
Traceback (most recent call last):
File "demo_toolbox.py", line 5, in <module>
from toolbox import Toolbox
File "C:\Users\SwedishMusic\Desktop\test\voice\Real-Time-Voice-Cloning\toolbox\__init__.py", line 9, in <module>
from encoder import inference as encoder
File "C:\Users\SwedishMusic\Desktop\test\voice\Real-Time-Voice-Cloning\encoder\inference.py", line 2, in <module>
from encoder.model import SpeakerEncoder
File "C:\Users\SwedishMusic\Desktop\test\voice\Real-Time-Voice-Cloning\encoder\model.py", line 3, in <module>
from scipy.interpolate import interp1d
File "C:\Users\SwedishMusic\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\LocalCache\local-packages\Python37\site-packages\scipy\__init__.py", line 136, in <module>
from . import _distributor_init
File "C:\Users\SwedishMusic\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\LocalCache\local-packages\Python37\site-packages\scipy\_distributor_init.py", line 64, in <module>
WinDLL(os.path.abspath(filename))
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.7_3.7.2544.0_x64__qbz5n2kfra8p0\lib\ctypes\__init__.py", line 364, in __init__
self._handle = _dlopen(self._name, mode)
OSError: [WinError 126] The specified module could not be found
|
open
|
2023-04-05T17:55:52Z
|
2023-04-05T17:55:52Z
|
https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1185
|
[] |
SwedishMusic
| 0
|
taverntesting/tavern
|
pytest
| 89
|
py2.7 unicode problem
|
In the begin project, i set sys.defalutcoding to utf-8.
The expected body is {'body': {'message': u'\u624b\u673a\u53f7\u683c\u5f0f\u4e0d\u6b63\u786e'}}. they are unicode type.
however, actual body is Response: '<Response [400]>' ({
"message": "\u624b\u673a\u53f7\u683c\u5f0f\u4e0d\u6b63\u786e"
} . they are str type.
In the error log :
KeyMismatchError: Type of returned data was different than expected (expected = '手机号格式不正确', actual = '手机号格式不正确')
actually, they are the same.
how to do this?
i write
` # Normal matching
expected_matches = isinstance(expected_val, actual_type)
if type(expected_val) == type(u''):
expected_matches = True`
in the util/dict_util.py.
|
closed
|
2018-04-13T02:23:58Z
|
2018-05-29T09:38:18Z
|
https://github.com/taverntesting/tavern/issues/89
|
[] |
Fosity
| 2
|
matplotlib/matplotlib
|
data-visualization
| 29,141
|
[MNT]: Colormap refactoring
|
### Summary
This is a collection of topics on colormaps to improve the API and also to eventually make colormaps immutable.
### Tasks
- [x] Deprecate parameter `ListedColormap(..., N=...)`
It's beyond the scope of `ListedColormap` to post-process the given list of colors.
PR: #29135
- [x] Narrow the type of the colors parameter to `ListedColormap` to a sequence, i.e. no single colors https://github.com/matplotlib/matplotlib/issues/28763#issuecomment-2319169328
PR: Implicitly via #29135, because a single color has only worked when also passing `N`.
- [x] Fix `ListedColormap.monochrome` attribute #28763
PR: #29125
- [x] Add add `over`, `under`, `bad` kwargs to constructor, so that one doesn't have to write `ListedColormap(...).with_extremes(...)`
PR: #29460
- [ ] Deprecate `set_over()`, `set_under()`, `set_bad()`
- [ ] Change default name of `ListedColormap` "from_list" to ""
- [ ] Separate the APIs for mapping [0, 1] -> color (currently `__call__(float)`) from lut-element access (currently `__call__(int)`), because `cmap(1)` and `cmap(1.0)` giving different results is surprising #28198
- [ ] Decide whether we can better handle lookup tables resolution for `LinearSegmentedColormap`
and possibly deprecated `rcParams['image.lut']` - globally tuning that parameter seems unreasonable
and error-prone
|
open
|
2024-11-14T14:53:03Z
|
2025-01-31T18:31:51Z
|
https://github.com/matplotlib/matplotlib/issues/29141
|
[
"topic: color/color & colormaps",
"Maintenance"
] |
timhoffm
| 0
|
torchbox/wagtail-grapple
|
graphql
| 169
|
Object type and/or field level authentication?
|
Hello. Any pointers regarding applying (custom) authentication logic to object types or fields? Not quite sure where in the chain, if at all, this could be injected.
E.g. similarly to:
- https://docs.graphene-python.org/projects/django/en/latest/authorization/#user-based-queryset-filtering
- https://docs.graphene-python.org/projects/django/en/latest/authorization/#global-filtering
|
closed
|
2021-04-17T20:06:05Z
|
2021-08-21T21:20:48Z
|
https://github.com/torchbox/wagtail-grapple/issues/169
|
[
"type: Feature"
] |
PeterDekkers
| 9
|
flasgger/flasgger
|
api
| 74
|
`import: external_yaml` should work for multiple paths
|
Ẁhen a function is decorated multiple times with `@swag_from` the `import` should take care of the original root_path.
reference: https://github.com/rochacbruno/flasgger/blob/master/flasgger/base.py#L65-L79
|
open
|
2017-03-30T01:38:12Z
|
2018-09-10T05:31:54Z
|
https://github.com/flasgger/flasgger/issues/74
|
[
"hacktoberfest"
] |
rochacbruno
| 0
|
microsoft/nni
|
data-science
| 5,730
|
mint
|
{
"p": "GRC20",
"op": "mint",
"tick": "GitHub",
"amt": "2000"
}
|
open
|
2023-12-30T19:22:09Z
|
2023-12-30T19:22:09Z
|
https://github.com/microsoft/nni/issues/5730
|
[] |
monirulnaim897
| 0
|
harry0703/MoneyPrinterTurbo
|
automation
| 291
|
docker安装
|
$docker-compose up
ERROR: In file './docker-compose.yml', service 'x-common-volumes' must be a mapping not an array
|
closed
|
2024-04-19T08:09:53Z
|
2024-04-19T08:33:57Z
|
https://github.com/harry0703/MoneyPrinterTurbo/issues/291
|
[] |
a631069724
| 1
|
flasgger/flasgger
|
rest-api
| 271
|
Validate breaks when a property is referenced
|
Hello,
I am using a nested reference, which is documented successfully by Flasgger. However, Flasgger breaks when I try to use validation for it.
For example, this code doesn't allow the ```requestBody``` endpoint to be called:
```
# BOILER PLATE CODE:
from flask import Flask, jsonify, request
from flasgger import Swagger, swag_from
app = Flask(__name__)
swag = Swagger(app )
# 1. CREATE A DEFINITION:
@swag.definition('Pet')
class Pet(object):
"""
Pet Object
---
properties:
name:
type: string
"""
# 2. USE THE DEFINITION FOR VALIDATION:
@app.route('/requestBody', methods=['POST'])
@swag.validate("whatever_name") ## 3. IF I COMMENT THIS, THE ENDPOINT WORKS.
def request_body_endpoint():
"""
An endpoint for testing requestBody documentation.
---
description: Post a request body
parameters:
- name: body
in: body
required: true
schema:
id: whatever_name
properties:
ha:
type: integer
something:
$ref: '#/definitions/Pet'
required: true
responses:
200:
description: The posted request body
"""
return jsonify(request.json)
```
|
closed
|
2018-12-07T02:46:38Z
|
2020-04-16T02:48:58Z
|
https://github.com/flasgger/flasgger/issues/271
|
[] |
josepablog
| 4
|
autogluon/autogluon
|
computer-vision
| 4,521
|
[github] Add issue templates for bug reports per module
|
Add issue templates for bug reports per module so it is easier to track for developers and lowers ambiguity.
|
open
|
2024-10-04T03:15:18Z
|
2024-11-25T22:47:16Z
|
https://github.com/autogluon/autogluon/issues/4521
|
[
"API & Doc"
] |
Innixma
| 0
|
slackapi/bolt-python
|
fastapi
| 335
|
Getting operation timeout after immediate ack() call
|
Hey!
In the api doc you says that i should return immediately wiht ack() method call to avoid operation timeout message.
I have an AWS lambda function with SlackRequestHandler, and I immediately call ack() after i receive a slash command, but i always get operation timeout message.
After ack() i have some business logic implementation. (Db reads and so on...)
Thank you
### Reproducible in:
```bash
pip freeze | grep slack
python --version
sw_vers && uname -v # or `ver`
```
#### The `slack_bolt` version
slack-bolt==1.5.0
slack-sdk==3.5.0
#### Python runtime version
3.8.5
(Paste the output of `python --version`)
#### OS info
Darwin Kernel Version 20.4.0:
(Paste the output of `sw_vers && uname -v` on macOS/Linux or `ver` on Windows OS)
#### Steps to reproduce:
(Share the commands to run, source code, and project settings (e.g., setup.py))
```
@app.command("/help")
def handle_help_command(
ack: Ack, client: WebClient, body: dict, respond: Respond, logger: Logger
) -> None:
ack()
```
3 business logic goes here....
### Expected result:
Theres no any error message due to timeouts...
### Actual result:
operation timeout
(Tell what actually happened with logs, screenshots)
## Requirements
Please read the [Contributing guidelines](https://github.com/slackapi/bolt-python/blob/main/.github/contributing.md) and [Code of Conduct](https://slackhq.github.io/code-of-conduct) before creating this issue or pull request. By submitting, you are agreeing to those rules.
|
closed
|
2021-05-10T15:46:10Z
|
2021-05-12T07:44:25Z
|
https://github.com/slackapi/bolt-python/issues/335
|
[
"question"
] |
tomeszmh
| 2
|
iperov/DeepFaceLab
|
deep-learning
| 840
|
Image turns black after GAN is turned on
|
Things have been going well with the new version but I just turned again on with my Titan video card and I got the image you see below. I did it on my RTX 8000 and it doesn’t do this.
|
open
|
2020-07-22T19:02:44Z
|
2023-06-08T23:11:45Z
|
https://github.com/iperov/DeepFaceLab/issues/840
|
[] |
kilerb
| 2
|
saulpw/visidata
|
pandas
| 2,312
|
[eml loader] The email loader doesn't import email.parser
|
**Small description**
The eml loader uses the Python built email.parser module, but does not import it.
**Expected result**
Load and read MIME messages.
**Actual result with screenshot**
If you get an unexpected error, please include the full stack trace that you get with `Ctrl-E`.
```
Traceback (most recent call last):
File "/usr/lib/python3.9/site-packages/visidata/threads.py", line 220, in _toplevelTryFunc
t.status = func(*args, **kwargs)
File "/usr/lib/python3.9/site-packages/visidata/sheets.py", line 260, in reload
self.loader()
File "/usr/lib/python3.9/site-packages/visidata/sheets.py", line 285, in loader
for r in self.iterload():
File "/usr/lib/python3.9/site-packages/visidata/loaders/eml.py", line 18, in iterload
parser = email.parser.Parser()
AttributeError: module 'email' has no attribute 'parser'
```
**Steps to reproduce with sample data and a .vd**
```
vd -f eml foo.mhtml
```
EDIT: add example command:
```
vd -z -f eml https://raw.githubusercontent.com/testimio/mhtml-parser/master/demos/wikipedia.mhtml
```
For this error you do not even need a valid email file or MIME file, since there is a missing `import email.parser` which fails when the code tries to parse the input
**Additional context**
Please include the version of VisiData and Python. Latest develop branch. Python 3.9.2
I was able to save a webpage as an MHTML file via my web browser. Since this is an MIME format, I though VisiData could read it. There is a missing import.
Even with the proper import in place, there is another bug that causes the code to not properly open or save individual file content from within the file. I only tried using an mhtml file.
|
closed
|
2024-02-13T21:55:14Z
|
2024-04-09T18:06:33Z
|
https://github.com/saulpw/visidata/issues/2312
|
[
"bug"
] |
frosencrantz
| 8
|
kubeflow/katib
|
scikit-learn
| 1,700
|
Multiple trials with same set of parameters using bayesianoptimization and parallelTrialCount > 1
|
/kind bug
**What steps did you take and what happened:**
[A clear and concise description of what the bug is.]
Using bayesianoptimization and parallelTrialCount > 1 will result in multiple trials having suggested the same set of parameters. This is due to the way https://github.com/kubeflow/katib/blob/2db65b2c6b349cfd4901bfe9ee1f308f7ea6444e/pkg/suggestion/v1beta1/skopt/base_service.py#L121-L125 are structured
|
closed
|
2021-10-07T07:25:11Z
|
2021-11-02T19:42:07Z
|
https://github.com/kubeflow/katib/issues/1700
|
[
"kind/bug"
] |
fabianvdW
| 1
|
mljar/mercury
|
jupyter
| 59
|
create rich demo
|
The rich package works in the notebook - it can be a nice demo with it.
https://github.com/Textualize/rich
|
closed
|
2022-03-10T13:33:52Z
|
2023-02-15T10:06:06Z
|
https://github.com/mljar/mercury/issues/59
|
[] |
pplonski
| 0
|
dynaconf/dynaconf
|
django
| 872
|
[RFC] Specify settings file from environment variable
|
**Is your feature request related to a problem? Please describe.**
I have an application that uses Dynaconf for its configuration. I want to deploy it into K8s using Kustomize. Since I consider the configuration to be part of the deployment specification, I'd like to have the configuration in the Kustomize repository, as a ConfigMap.
As far as I understand I must mount the settings file exactly at the right location for Dynaconf to pick it up. This is a rather unfortunate coupling of the image and the configuration, because it forces me to adapt the deployment files if in the application's image the Python code is moved to a different folder.
**Describe the solution you'd like**
I'd like to mount the settings to somewhere (e.g. /config/) and create an environment variable that tells dynaconf where to look for the configuration file (e.g. `DYNACONF_SETTINGS_FILE=/config/settings.toml`). If present, this environment variable should take precedence over a local config folder.
**Describe alternatives you've considered**
I am currently rewriting the application to do the thing described above manually. I import Dynaconf and assign it to `settings`, manually setting the `settings_file` argument. It is cumbersome. I'd rather not rewrite the application.
**Additional context**
I carefully read the documentation on [environment variables](https://www.dynaconf.com/envvars/) and [settings files](https://www.dynaconf.com/settings_files/). I couldn't find the feature in there.
|
closed
|
2023-03-02T09:26:54Z
|
2023-07-13T19:11:07Z
|
https://github.com/dynaconf/dynaconf/issues/872
|
[
"Not a Bug",
"Docs"
] |
MaxG87
| 4
|
SciTools/cartopy
|
matplotlib
| 1,760
|
Few tests failing with numpy-1.20 because of warnings
|
### Description
Hi,
We are having few failing tests when packaging `cartopy` in [`nixpkgs`](https://github.com/NixOS/nixpkgs) since we bumped to `numpy-1.20` (see .https://github.com/NixOS/nixpkgs/issues/118449). The problems seems to come to the fact that `numpy` adds new warnings, and some tests in `cartopy` rely on counting the number of warnings generated during some invocations.
This seems straight forward to fix, I’ll provide a PR ASAP.
#### Code to reproduce
With numpy-1.20+, from cartopy source
```bash
$ python -m pytest
```
#### Traceback
```bash
=================================== FAILURES ===================================
______________________________ test_ellipse_globe ______________________________
def test_ellipse_globe(): globe = ccrs.Globe(ellipse='WGS84')
with pytest.warns(UserWarning,
match='does not handle elliptical globes.') as w:
mill = ccrs.Miller(globe=globe)
> assert len(w) == 1
E assert 2 == 1
E + where 2 = len(WarningsChecker(record=True))
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/tests/crs/test_miller.py:58: AssertionError _____________________________ test_eccentric_globe _____________________________
def test_eccentric_globe():
globe = ccrs.Globe(semimajor_axis=1000, semiminor_axis=500,
ellipse=None)
with pytest.warns(UserWarning,
match='does not handle elliptical globes.') as w:
mill = ccrs.Miller(globe=globe)
> assert len(w) == 1
E assert 2 == 1
E + where 2 = len(WarningsChecker(record=True))
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/tests/crs/test_miller.py:78: AssertionError
______________________________ test_ellipse_globe ______________________________
def test_ellipse_globe():
globe = ccrs.Globe(ellipse='WGS84')
with pytest.warns(UserWarning,
match='does not handle elliptical globes.') as w:
ortho = ccrs.Orthographic(globe=globe)
> assert len(w) == (2
if (5, 0, 0) <= ccrs.PROJ4_VERSION < (5, 1, 0) else 1)
E assert 2 == 1
E + where 2 = len(WarningsChecker(record=True))
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/tests/crs/test_orthographic.py:59: AssertionError
_____________________________ test_eccentric_globe _____________________________
def test_eccentric_globe(): globe = ccrs.Globe(semimajor_axis=1000, semiminor_axis=500,
ellipse=None)
with pytest.warns(UserWarning,
match='does not handle elliptical globes.') as w:
ortho = ccrs.Orthographic(globe=globe)
> assert len(w) == (2
if (5, 0, 0) <= ccrs.PROJ4_VERSION < (5, 1, 0)
else 1) E assert 2 == 1
E + where 2 = len(WarningsChecker(record=True))
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/tests/crs/test_orthographic.py:77: AssertionError
```
The captured output of the tests shows the following warnings:
```
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:1476: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:1477: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
b = np.float(self.globe.semiminor_axis or WGS84_SEMIMINOR_AXIS)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:1553: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:2320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:2321: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
b = np.float(self.globe.semiminor_axis or a)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:2110: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:2111: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
h = np.float(satellite_height)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:1299: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:1342: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
a = np.float(globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS)
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/crs.py:2177: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify a
ny behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
[…]
/nix/store/7pj22jb61nprjah9f19pv0mk1sal88ig-python3.8-cartopy-0.18.0/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py:1699: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not mod
ify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
mask = np.zeros(C.shape, dtype=np.bool)
[…]
```
Here is a confirmation:
```bash
$ python -m pytest --pdb
============================================================================================================================== test session starts ===============================================================================================================================
platform linux -- Python 3.8.8, pytest-6.2.2, py-1.9.0, pluggy-0.13.1
rootdir: /home/lsix/nixpkgs/Cartopy-0.18.0
collected 616 items
lib/cartopy/tests/test_coastline.py . [ 0%]
lib/cartopy/tests/test_coding_standards.py s. [ 0%]
lib/cartopy/tests/test_crs.py ...................... [ 4%]
lib/cartopy/tests/test_crs_transform_vectors.py ........ [ 5%]
lib/cartopy/tests/test_features.py ...... [ 6%]
lib/cartopy/tests/test_geodesic.py ............. [ 8%]
lib/cartopy/tests/test_img_nest.py ......X.X [ 9%]
lib/cartopy/tests/test_img_tiles.py .........s [ 11%]
lib/cartopy/tests/test_img_transform.py .................................................................. [ 22%]
lib/cartopy/tests/test_line_string.py ..................x [ 25%]
lib/cartopy/tests/test_linear_ring.py ...... [ 26%]
lib/cartopy/tests/test_polygon.py ............................ [ 30%]
lib/cartopy/tests/test_shapereader.py ..s.. [ 31%]
lib/cartopy/tests/test_util.py ........ [ 32%]
lib/cartopy/tests/test_vector_transform.py ...... [ 33%]
lib/cartopy/tests/crs/test_albers_equal_area.py ........ [ 35%] lib/cartopy/tests/crs/test_azimuthal_equidistant.py ........ [ 36%]
lib/cartopy/tests/crs/test_eckert.py .............................................. [ 43%]
lib/cartopy/tests/crs/test_equal_earth.py ..... [ 44%]
lib/cartopy/tests/crs/test_equidistant_conic.py ........ [ 46%]
lib/cartopy/tests/crs/test_geostationary.py .... [ 46%]
lib/cartopy/tests/crs/test_gnomonic.py ............... [ 49%]
lib/cartopy/tests/crs/test_interrupted_goode_homolosine.py .... [ 49%]
lib/cartopy/tests/crs/test_lambert_azimuthal_equal_area.py ..... [ 50%]
lib/cartopy/tests/crs/test_lambert_conformal.py ........ [ 51%]
lib/cartopy/tests/crs/test_mercator.py ........ [ 53%]
lib/cartopy/tests/crs/test_miller.py ..F
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
def test_ellipse_globe():
globe = ccrs.Globe(ellipse='WGS84')
with pytest.warns(UserWarning,
match='does not handle elliptical globes.') as w:
mill = ccrs.Miller(globe=globe)
> assert len(w) == 1
E assert 2 == 1
E + where 2 = len(WarningsChecker(record=True))
lib/cartopy/tests/crs/test_miller.py:58: AssertionError
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB post_mortem (IO-capturing turned off) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/lsix/nixpkgs/Cartopy-0.18.0/lib/cartopy/tests/crs/test_miller.py(58)test_ellipse_globe()
-> assert len(w) == 1
(Pdb) p w[0].message
DeprecationWarning('`np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nDeprecated in NumPy 1
.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations')
(Pdb) p w[1].message
UserWarning('The "Miller" projection does not handle elliptical globes.')
Pdb) c [12/1853]
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB continue (IO-capturing resumed) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
.... [ 54%]
lib/cartopy/tests/crs/test_mollweide.py ......... [ 56%]
lib/cartopy/tests/crs/test_nearside_perspective.py ... [ 56%]
lib/cartopy/tests/crs/test_orthographic.py ..F
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
def test_ellipse_globe():
globe = ccrs.Globe(ellipse='WGS84')
with pytest.warns(UserWarning,
match='does not handle elliptical globes.') as w:
ortho = ccrs.Orthographic(globe=globe)
> assert len(w) == (2
if (5, 0, 0) <= ccrs.PROJ4_VERSION < (5, 1, 0)
else 1)
E assert 2 == 1
E + where 2 = len(WarningsChecker(record=True))
lib/cartopy/tests/crs/test_orthographic.py:59: AssertionError
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB post_mortem (IO-capturing turned off) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /home/lsix/nixpkgs/Cartopy-0.18.0/lib/cartopy/tests/crs/test_orthographic.py(59)test_ellipse_globe()
-> assert len(w) == (2
(Pdb) p w[0].message
UserWarning('The "Orthographic" projection does not handle elliptical globes.')
(Pdb) p w[1].message
DeprecationWarning('`np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nDeprecated in NumPy 1
.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations')
(Pdb)
DeprecationWarning('`np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nDeprecated in NumPy 1
.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations')
(Pdb) c
[…]
```
<details>
<summary>Full environment definition</summary>
### Operating system
[Nixos](https://nixos.org)
### Cartopy version
0.18.0
</details>
|
closed
|
2021-04-11T19:35:09Z
|
2021-04-11T23:37:12Z
|
https://github.com/SciTools/cartopy/issues/1760
|
[] |
lsix
| 2
|
coqui-ai/TTS
|
pytorch
| 3,347
|
[Bug] Num. instances discarded samples: 252. VITS finetuning
|
### Describe the bug
Hello, I'm finetuning the VITS model on my 979 audio dataset.
I have followed this tutorial (https://tts.readthedocs.io/en/latest/formatting_your_dataset.html) on how to format my dataset and it looks just fine.
I am able to start training but right before the first epoch I see the log:
> DataLoader initialization
| > Tokenizer:
| > add_blank: True
| > use_eos_bos: False
| > use_phonemes: False
| > Number of instances : 979
| > Preprocessing samples
| > Max text length: 94
| > Min text length: 15
| > Avg text length: 50.786795048143055
|
| > Max audio length: 223959.0
| > Min audio length: 46143.0
| > Avg audio length: 144398.36176066025
| > **Num. instances discarded samples: 252**
| > Batch group size: 0.
My first thought was to check the min/mix permited length of audio samples on my config.json file.
My minimal desired length is of 16.000 (about 1 second) but after editing it in the config.json file it does not seem to have any effect in the code since the log still prints **Min audio length: 46143.0**.
What is even funnier is that 46143.0 is about 2 seconds and I only have 77 audio samples smaller than 2 seconds so I'm not really sure what happens for the network to discart 252 samples.
Any insights are very welcomed!
### To Reproduce
To reproduce the error you need to install coqui-ai TTS and set up a custom dataset for finetuning (ljspeech format).
After that run: python train_tts.py --config_path tts_models--pt--cv--vits/config.json --restore_path tts_models--pt--cv--vits/model_file.pth.tar
### Expected behavior
_No response_
### Logs
_No response_
### Environment
```shell
TTS 0.20.6
torch gpu 2.1.0
cuda 12.0
nvidia driver 527.41
```
### Additional context
_No response_
|
closed
|
2023-12-01T01:37:52Z
|
2023-12-06T20:32:43Z
|
https://github.com/coqui-ai/TTS/issues/3347
|
[
"bug"
] |
nicholasguimaraes
| 1
|
dynaconf/dynaconf
|
flask
| 747
|
[bug] Cannot merge using env var with dict that has empty string as its key
|
**SOLUTION IN** https://github.com/pulp/pulpcore/pull/3146/files
**Describe the bug**
I tried to use an env_var to merge a dictionary, but it has the key '' which is not able to work, although I expected it to work.
**To Reproduce**
I tried to set this variable:
` PULP_LOGGING={'dynaconf_merge': True, 'loggers': {'': {'handlers': ['console'], 'level': 'DEBUG'}}}`
**Expected behavior**
I expected it to merge, but it does not. When showing the effective config with `dynaconf list` I see it is not merged as expected.
|
closed
|
2022-05-03T14:55:40Z
|
2022-08-29T17:08:13Z
|
https://github.com/dynaconf/dynaconf/issues/747
|
[
"bug",
"Not a Bug"
] |
bmbouter
| 2
|
junyanz/pytorch-CycleGAN-and-pix2pix
|
pytorch
| 891
|
Not getting saved checkpoints on Google Colab
|
When I am trying to train on my dataset, I'm not getting checkpoints saved after every 5 epochs. I know the problem with google colab, that it has issues with visdom. I'm okay with visdom not working, but even the checkpoints aren't saved in the checkpoints directory. Please help.
|
closed
|
2020-01-04T07:59:52Z
|
2020-01-05T06:53:25Z
|
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/891
|
[] |
himansh1314
| 3
|
pytorch/pytorch
|
deep-learning
| 148,938
|
[triton 3.3] `AOTInductorTestABICompatibleGpu.test_triton_kernel_tma_descriptor_1d_dynamic_False_cuda`
|
### 🐛 Describe the bug
1. Update triton to `release/3.3.x` https://github.com/triton-lang/triton/tree/release/3.3.x
2. run `python test/inductor/test_aot_inductor.py -vvv -k test_triton_kernel_tma_descriptor_1d_dynamic_False_cuda`
Possibly an easier repro is
```
TORCHINDUCTOR_CPP_WRAPPER=1 python test/inductor/test_triton_kernels.py -k test_tma_descriptor_1d_dynamic_False_backend_inductor
```
errors:
<details>
```
/home/dberard/local/triton-env2/pytorch/torch/backends/cudnn/__init__.py:108: UserWarning: PyTorch was compiled without cuDNN/MIOpen support. To use cuDNN/MIOpen, rebuild PyTorch making sure the library is visible to the build system.
warnings.warn(
/home/dberard/local/triton-env2/pytorch/torch/backends/mkldnn/__init__.py:78: UserWarning: TF32 acceleration on top of oneDNN is available for Intel GPUs. The current Torch version does not have Intel GPU Support. (Triggered internally at /home/dberard/local/triton-env2/pytorch/aten/src/ATen/Context.cpp:148.)
torch._C._set_onednn_allow_tf32(_allow_tf32)
W0310 18:58:17.091000 2102274 torch/_export/__init__.py:67] +============================+
W0310 18:58:17.091000 2102274 torch/_export/__init__.py:68] | !!! WARNING !!! |
W0310 18:58:17.092000 2102274 torch/_export/__init__.py:69] +============================+
W0310 18:58:17.092000 2102274 torch/_export/__init__.py:70] torch._export.aot_compile()/torch._export.aot_load() is being deprecated, please switch to directly calling torch._inductor.aoti_compile_and_package(torch.export.export())/torch._inductor.aoti_load_package() instead.
ETEST SUITE EARLY TERMINATION due to torch.cuda.synchronize() failure
CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
======================================================================
ERROR: test_triton_kernel_tma_descriptor_1d_dynamic_False_cuda (__main__.AOTInductorTestABICompatibleGpu)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_comparison.py", line 1221, in not_close_error_metas
pair.compare()
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_comparison.py", line 700, in compare
self._compare_values(actual, expected)
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_comparison.py", line 830, in _compare_values
compare_fn(
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_comparison.py", line 1009, in _compare_regular_values_close
matches = torch.isclose(
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_internal/common_utils.py", line 3150, in wrapper
method(*args, **kwargs)
File "/home/dberard/local/triton-env2/pytorch/test/inductor/test_torchinductor.py", line 12836, in new_test
return value(self)
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_internal/common_utils.py", line 552, in instantiated_test
test(self, **param_kwargs)
File "/home/dberard/local/triton-env2/pytorch/test/inductor/test_aot_inductor.py", line 2568, in test_triton_kernel_tma_descriptor_1d
self.check_model(
File "/home/dberard/local/triton-env2/pytorch/test/inductor/test_aot_inductor_utils.py", line 207, in check_model
self.assertEqual(actual, expected, atol=atol, rtol=rtol)
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_internal/common_utils.py", line 4052, in assertEqual
error_metas = not_close_error_metas(
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_comparison.py", line 1228, in not_close_error_metas
f"Comparing\n\n"
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_comparison.py", line 367, in __repr__
body = [
File "/home/dberard/local/triton-env2/pytorch/torch/testing/_comparison.py", line 368, in <listcomp>
f" {name}={value!s},"
File "/home/dberard/local/triton-env2/pytorch/torch/_tensor.py", line 590, in __repr__
return torch._tensor_str._str(self, tensor_contents=tensor_contents)
File "/home/dberard/local/triton-env2/pytorch/torch/_tensor_str.py", line 710, in _str
return _str_intern(self, tensor_contents=tensor_contents)
File "/home/dberard/local/triton-env2/pytorch/torch/_tensor_str.py", line 631, in _str_intern
tensor_str = _tensor_str(self, indent)
File "/home/dberard/local/triton-env2/pytorch/torch/_tensor_str.py", line 363, in _tensor_str
formatter = _Formatter(get_summarized_data(self) if summarize else self)
File "/home/dberard/local/triton-env2/pytorch/torch/_tensor_str.py", line 146, in __init__
tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0)
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
To execute this test, run the following from the base repo dir:
python test/inductor/test_aot_inductor.py AOTInductorTestABICompatibleGpu.test_triton_kernel_tma_descriptor_1d_dynamic_False_cuda
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
----------------------------------------------------------------------
Ran 1 test in 5.612s
FAILED (errors=1)
inline_call []
unimplemented []
stats [('calls_captured', 2), ('unique_graphs', 1)]
inductor [('extern_calls', 4), ('async_compile_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark_gpu', 2), ('pattern_matcher_count', 1), ('pattern_matcher_nodes', 1), ('async_compile_cache_hit', 1)]
graph_break []
aten_mm_info []
```
</details>
errors w/ compute-sanitizer:
https://gist.github.com/davidberard98/ecd9fefff91393b3a3fa0725dea96e22
### Versions
triton: release/3.3.x
pytorch: viable/strict from mar 10
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @aakhundov @bertmaher @int3 @nmacchioni @embg @peterbell10 @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4 @desertfire @yushangdi @oulgen
|
open
|
2025-03-11T02:00:45Z
|
2025-03-13T16:58:07Z
|
https://github.com/pytorch/pytorch/issues/148938
|
[
"oncall: pt2",
"module: inductor",
"upstream triton",
"oncall: export",
"module: aotinductor",
"module: user triton"
] |
davidberard98
| 1
|
guohongze/adminset
|
django
| 78
|
返回数据对象而不是数据
|
改成python3,mysql之后
所有外键返回的都是对象，而不是实际的数据
请介绍下这个bug的原理及修复方法（目前在静态页面上修改了几个明显的，其他的都没修改）
|
open
|
2018-11-07T14:06:20Z
|
2018-12-04T04:42:42Z
|
https://github.com/guohongze/adminset/issues/78
|
[] |
xiang5
| 4
|
hankcs/HanLP
|
nlp
| 1,406
|
怎么查看GPU是否成功使用
|
<!--
Please carefully fill out this form to bypass our spam filter. Please make sure that this is a bug. We only address bugs and feature requests issues on GitHub. Other questions should be posted on stackoverflow or https://bbs.hankcs.com/
以下必填，否则直接关闭。
-->
**Describe the bug**
pip安装之后，可以成功运行NER，但是增加batch后GPU的显存始终停留在100多MB，怎么查看GPU是否成功使用？
* [x] I've completed this form and searched the web for solutions.
|
closed
|
2020-01-15T10:16:19Z
|
2020-01-15T20:11:53Z
|
https://github.com/hankcs/HanLP/issues/1406
|
[
"question"
] |
PromptExpert
| 1
|
chaos-genius/chaos_genius
|
data-visualization
| 359
|
Selecting a custom schema when adding KPIs
|
## Tell us about the problem you're trying to solve
For postgres, there is data in custom schemas which is not being read. We cannot add KPIs directly using those tables via the TABLE functionality. In our case information from different sources lives in different schemas in the data warehouse.
## Describe the solution you'd like
The process of adding a KPI would become easier if we can read data in custom schemas as well using the TABLE functionality. A drop-down to select the schema from which we need to read the data might solve this.
## Describe alternatives you've considered
Use the QUERY functionality to write a custom query for defining the KPIs in order to use data outside public schema.
## Additional context
In postgres, a DB can have many schemas and each schema has tables - Databases -> Schemas -> Tables. The default schema is named public. Currently, only the tables under the default public schema are being read for direct use.
|
closed
|
2021-11-01T20:30:17Z
|
2022-01-04T07:27:59Z
|
https://github.com/chaos-genius/chaos_genius/issues/359
|
[
"🔗 connectors",
"P1"
] |
mvaerle
| 3
|
nerfstudio-project/nerfstudio
|
computer-vision
| 3,224
|
can we just load any .ply file in the viewer
|
let's say if i just want to visualize the results can i load it direclty in the viewer the .ply file and also if there is other way to load it with other supported files
|
open
|
2024-06-14T03:07:25Z
|
2024-10-11T12:33:48Z
|
https://github.com/nerfstudio-project/nerfstudio/issues/3224
|
[] |
sumanttyagi
| 2
|
roboflow/supervision
|
deep-learning
| 1,392
|
How do I get the current frame count
|
### Search before asking
- [X] I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar feature requests.
### Question
video.py ： how to obtain the current video frame number，thanks
def get_video_frames_generator(
source_path: str, stride: int = 1, start: int = 0, end: Optional[int] = None
) -> Generator[np.ndarray, None, None]:
"""
Get a generator that yields the frames of the video.
Args:
source_path (str): The path of the video file.
stride (int): Indicates the interval at which frames are returned,
skipping stride - 1 frames between each.
start (int): Indicates the starting position from which
video should generate frames
end (Optional[int]): Indicates the ending position at which video
should stop generating frames. If None, video will be read to the end.
Returns:
(Generator[np.ndarray, None, None]): A generator that yields the
frames of the video.
Examples:
```python
import supervision as sv
for frame in sv.get_video_frames_generator(source_path=<SOURCE_VIDEO_PATH>):
...
```
"""
video, start, end = _validate_and_setup_video(source_path, start, end)
frame_position = start
while True:
success, frame = video.read()
if not success or frame_position >= end:
break
yield frame
for _ in range(stride - 1):
success = video.grab()
if not success:
break
frame_position += stride
video.release()
### Additional
_No response_
|
closed
|
2024-07-22T02:19:16Z
|
2024-07-22T10:58:45Z
|
https://github.com/roboflow/supervision/issues/1392
|
[
"question"
] |
dearMOMO
| 5
|
zappa/Zappa
|
flask
| 1,240
|
exclude_glob feature attempts to delete files outside the tmp_file_path
|
## Context
When using the exclude_glob parameter to exclude files from the lambda file, if you set
exclude_glob = [ "/app/static/**"] (notice the leading forward slasj -- that is the problem )
then Zappa will delete the folder "/app/static" and everything in it.
## Expected Behavior
Setting exclude_glob should never result in deleting files off of the users machine. It should only delete files that are inside the tmp_file_path it creates.
## Actual Behavior
Zappa deletes all files of the machine that match the glob pattern, including ones not inside the tmp_file_path directory zappa created.
## Possible Fix
Currently the process is:
1. Copy the entire app tree to a temporary path
2. Iterate through the glob expressions deleting all files in the tmp directory that match.
The cause of the bug lies in the nuances of the "os.path.join" function. In particular, the issue is with the leading "/" forward slash in the glob expression. Basically:
path = os.path.join("/tmp/file/path", "/home/user/app")
print(path)
path = "/home/user/app"
Possible fix is to add a check to see if file is inside
## Steps to Reproduce
1. Set "exclude_glob" to include a valid absolute glob expression.
2. Run zappa
3. Notice how all the files matching that absolute url are gone
Note: A similar issue occurs if you set exclude_glob to be a "string" instead of a "list of strings". In that case zappa actually tries to delete your entire filesystem which throws a permission denied error (ask me how I know)
## Your Environment
* Zappa version used: 0.56.1
* Operating System and Python version: Ubuntu with python 3.8.
|
closed
|
2023-05-01T17:09:50Z
|
2024-03-13T01:20:07Z
|
https://github.com/zappa/Zappa/issues/1240
|
[
"bug",
"next-release-candidate"
] |
beon9273
| 1
|
modin-project/modin
|
data-science
| 6,778
|
BUG: Unable to read parquet files using fastparquet
|
### Modin version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest released version of Modin.
- [x] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)
### Reproducible Example
```python
import ray
import modin.pandas as pd
ray.init("localhost:6379")
manifest_files = ['s3://ari-public-test-data/test.parquet'] # 774 MB
df = pd.read_parquet(manifest_files, engine='fastparquet') # 774 MB
```
### Issue Description
Ray + Modin are unable to read a parquet file (774 MB) using `fastparquet`. While reading the file, the submission crashes. Looking at the error, it appears that fastparquet is unable to correctly identify how many rows are in the dataframe. I believe this is happening when it's assessing the index, but I'm unable to verify that — I'm not familiar enough with the modin codebase.
### Expected Behavior
It should exit without error after successfully reading the file and distributing it to the cluster.
### Error Logs
<details>
```python-traceback
2023-11-28 16:30:57,042 INFO util.py:375 -- setting max workers for head node type to 0
2023-11-28 16:30:57,042 INFO util.py:379 -- setting max workers for ray.worker.default to 2
Loaded cached provider configuration
If you experience issues with the cloud provider, try re-running the command with --no-config-cache.
Fetched IP: 44.201.213.145
Shared connection to 44.201.213.145 closed.
Shared connection to 44.201.213.145 closed.
2023-11-28 16:31:02,532 INFO util.py:375 -- setting max workers for head node type to 0
2023-11-28 16:31:02,532 INFO util.py:379 -- setting max workers for ray.worker.default to 2
Fetched IP: 44.201.213.145
Shared connection to 44.201.213.145 closed.
2023-11-28 13:31:05,197 INFO worker.py:1489 -- Connecting to existing Ray cluster at address: 172.31.93.255:6379...
2023-11-28 13:31:05,208 INFO worker.py:1664 -- Connected to Ray cluster. View the dashboard at http://172.31.93.255:8265
Traceback (most recent call last):
File "/home/ray/mre.py", line 8, in <module>
df = pd.read_parquet(manifest_files, engine='fastparquet')
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/logging/logger_decorator.py", line 128, in run_and_log
return obj(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/pandas/io.py", line 309, in read_parquet
query_compiler=FactoryDispatcher.read_parquet(
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/execution/dispatching/factories/dispatcher.py", line 183, in read_parquet
return cls.get_factory()._read_parquet(**kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/execution/dispatching/factories/factories.py", line 213, in _read_parquet
return cls.io_cls.read_parquet(**kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/logging/logger_decorator.py", line 128, in run_and_log
return obj(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/io/file_dispatcher.py", line 157, in read
query_compiler = cls._read(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/logging/logger_decorator.py", line 128, in run_and_log
return obj(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/io/column_stores/parquet_dispatcher.py", line 632, in _read
return compilers[0].concat(axis=0, other=compilers[1:], ignore_index=True)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/logging/logger_decorator.py", line 128, in run_and_log
return obj(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/storage_formats/pandas/query_compiler.py", line 357, in concat
return result.reset_index(drop=True)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/logging/logger_decorator.py", line 128, in run_and_log
return obj(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/storage_formats/pandas/query_compiler.py", line 784, in reset_index
new_self.index = (
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/storage_formats/pandas/query_compiler.py", line 108, in set_axis
self._modin_frame.index = idx
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/dataframe/pandas/dataframe/dataframe.py", line 556, in _set_index
new_index = self._validate_set_axis(new_index, self._index_cache)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/logging/logger_decorator.py", line 128, in run_and_log
return obj(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.8/site-packages/modin/core/dataframe/pandas/dataframe/dataframe.py", line 504, in _validate_set_axis
raise ValueError(
ValueError: Length mismatch: Expected axis has 29522988 elements, new values have 0 elements
Shared connection to 44.201.213.145 closed.
Error: Command failed:
ssh -tt -i /Users/ari/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_fc292bd7df/c21f969b5f/%C -o ControlPersist=10s -o ConnectTimeout=120s ubuntu@44.201.213.145 bash --login -c -i 'source ~/.bashrc; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && (docker exec -it ray_container /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'source ~/.bashrc; export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && (python /home/ray/mre.py)'"'"'"'"'"'"'"'"''"'"' )'
```
</details>
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : b99cf06a0d3bf27cb5d2d03d52a285cc91cfb6f6
python : 3.10.4.final.0
python-bits : 64
OS : Darwin
OS-release : 21.6.0
Version : Darwin Kernel Version 21.6.0: Thu Mar 9 20:10:19 PST 2023; root:xnu-8020.240.18.700.8~1/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
Modin dependencies
------------------
modin : 0.25.1
ray : 2.8.0
dask : 2023.11.0
distributed : 2023.11.0
hdk : None
pandas dependencies
-------------------
pandas : 2.1.3
numpy : 1.26.0
pytz : 2023.3
dateutil : 2.8.2
setuptools : 67.8.0
pip : 23.3.1
Cython : 0.29.32
pytest : None
hypothesis : None
sphinx : 3.3.1
blosc : 1.11.1
feather : None
xlsxwriter : 3.0.3
lxml.etree : 4.9.1
html5lib : 1.1
pymysql : None
psycopg2 : 2.9.6
jinja2 : 3.1.2
IPython : 8.4.0
pandas_datareader : None
bs4 : 4.11.1
bottleneck : None
dataframe-api-compat: None
fastparquet : 2023.10.1
fsspec : 2023.6.0
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : 2.8.0
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 14.0.1
pyreadstat : None
pyxlsb : None
s3fs : 2023.6.0
scipy : 1.10.1
sqlalchemy : 2.0.9
tables : 3.7.0
tabulate : 0.8.9
xarray : 2022.10.0
xlrd : None
zstandard : 0.18.0
tzdata : 2023.3
qtpy : None
pyqt5 : None
</details>
|
closed
|
2023-11-28T21:36:18Z
|
2023-12-01T16:50:22Z
|
https://github.com/modin-project/modin/issues/6778
|
[
"bug 🦗",
"pandas concordance 🐼",
"External"
] |
seydar
| 12
|
ufoym/deepo
|
tensorflow
| 145
|
tensorflow 2.5.0 CUDA compatibility
|
The latest versions use `tensorflow==2.5.0`, `CUDA==10.2`, `cudnn7`.
Apparently, `tensorflow==2.5.0` seems to be not compatible with `CUDA==10.2`.
full log:
```
2021-06-22 06:10:01.725884: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
2021-06-22 06:10:01.725969: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
2021-06-22 06:10:01.725984: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-06-22 06:10:01.864938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-06-22 06:10:01.864983: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2021-06-22 06:10:01.864993: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2021-06-22 06:10:02.428699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:3e:00.0 name: Tesla V100-SXM2-32GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-06-22 06:10:02.428764: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-06-22 06:10:02.429027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-06-22 06:10:02.429043: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]
```
GPU test:
```
import tensorflow as tf
print(tf.__version__)
print(tf.test.is_gpu_available())
>>> 2.5.0
>>> False
```
A simple workaround was downgrading to `tensorflow-gpu==2.3.0`, which was still OK in my project.
|
closed
|
2021-06-22T06:18:06Z
|
2021-12-25T09:22:47Z
|
https://github.com/ufoym/deepo/issues/145
|
[] |
syncdoth
| 6
|
jacobgil/pytorch-grad-cam
|
computer-vision
| 13
|
License
|
Hi,
Thanks for the great work.
Would it be possible for you to add an appropriate license to the work?
I noticed that your corresponding keras implementation is licensed under MIT: https://github.com/jacobgil/keras-grad-cam
Thanks,
Raghav
|
closed
|
2018-10-11T16:15:00Z
|
2020-01-27T06:47:44Z
|
https://github.com/jacobgil/pytorch-grad-cam/issues/13
|
[] |
raghavgoyal14
| 1
|
Teemu/pytest-sugar
|
pytest
| 262
|
Broken support for Python 3.6
|
This commit breaks python 3.6 compatibility due to termcolor 2 requiring >= python 3.7.
- https://github.com/Teemu/pytest-sugar/pull/259
- https://github.com/Teemu/pytest-sugar/commit/6b82c48c4cadefc6f26eed3662863292e1c77101
And, seems you forgot to update tox.ini:
- https://github.com/Teemu/pytest-sugar/blob/b42404819db5c09b9337dd1e3a7a95c5744c7e60/tox.ini#L12
|
closed
|
2022-12-03T19:39:37Z
|
2022-12-07T01:19:55Z
|
https://github.com/Teemu/pytest-sugar/issues/262
|
[] |
xkszltl
| 15
|
google-research/bert
|
tensorflow
| 616
|
650M corpus was splited to 40 files pass to create_pretraining_data, but it takes too long time
|
after 24 hours, it still runing.
python create_pretraining_data.py --input_file=BERT_BASE_DIR/all_file_merge.copyblock_notitle_linesplit.txt-6400* --output_file=BERT_BASE_DIR/tf_examples-64.tfrecord --vocab_file=chn_vocab --do_lower_case=True --max_seq_length=64 --max_predictions_per_seq=20 --masked_lm_prob=0.15 --random_seed=12345 --dupe_factor=5
all_file_merge.copyblock_notitle_linesplit.txt-6400* is all_file_merge.copyblock_notitle_linesplit.txt-640001 to all_file_merge.copyblock_notitle_linesplit.txt-640039
each file less than 20M
|
closed
|
2019-05-03T06:55:00Z
|
2019-05-04T01:02:28Z
|
https://github.com/google-research/bert/issues/616
|
[] |
SeekPoint
| 3
|
sherlock-project/sherlock
|
python
| 1,427
|
# clone the repo
$ git clone https://github.com/sherlock-project/sherlock.git
# change the working directory to sherlock
$ cd sherlock
# install the requirements
$ python3 -m pip install -r requirements.txt
|
<!--
######################################################################
WARNING!
IGNORING THE FOLLOWING TEMPLATE WILL RESULT IN ISSUE CLOSED AS INCOMPLETE
######################################################################
-->
## Checklist
<!--
Put x into all boxes (like this [x]) once you have completed what they say.
Make sure complete everything in the checklist.
-->
- [ ] I'm reporting a feature request
- [ ] I've checked for similar feature requests including closed ones
## Description
<!--
Provide a detailed description of the feature you would like Sherlock to have
-->
WRITE DESCRIPTION HERE
|
closed
|
2022-08-05T07:35:40Z
|
2022-08-05T07:36:14Z
|
https://github.com/sherlock-project/sherlock/issues/1427
|
[
"enhancement"
] |
Darknessss19
| 0
|
taverntesting/tavern
|
pytest
| 111
|
Tavern create results as xml
|
Hello tavern-ci Team,
I'm using tavern in an open-source project because of the simplicity in the test creation. I really would like to use this solution with Jenkins. To this end, I need to generate the result output as XML. Since you are using pytest, I think would be easy to add this feature in further versions. Do you think is it will be possible?
|
closed
|
2018-05-29T11:31:40Z
|
2018-06-13T16:53:38Z
|
https://github.com/taverntesting/tavern/issues/111
|
[] |
felipevicens
| 2
|
Layout-Parser/layout-parser
|
computer-vision
| 23
|
AttributeError: 'Layout' object has no attribute 'sort'
|
Dear team, I was trying to implement https://github.com/Layout-Parser/layout-parser/blob/master/docs/example/deep_layout_parsing/index.rst but get the error "AttributeError: 'Layout' object has no attribute 'sort'". I see that I am easily able to write a sort function using "left_blocks._blocks[0].block.x_1" but just wanted to let you know. Thanks!
PS: I am using Mac and python 3.9, and followed https://github.com/Layout-Parser/layout-parser/issues/7#issuecomment-817200888 for installation.
|
closed
|
2021-04-12T12:05:54Z
|
2021-04-12T16:40:07Z
|
https://github.com/Layout-Parser/layout-parser/issues/23
|
[] |
mlamlamla
| 0
|
sqlalchemy/alembic
|
sqlalchemy
| 1,299
|
`alembic merge` ignores `revision_environment` and cannot find the hooks defined in `env.py`
|
**Describe the bug**
As title says, `alembic merge` ignores `revision_environment` and cannot find the hooks defined in `env.py`.
**Expected behavior**
Custom hooks work the way they do when generating revisions
**To Reproduce**
```ini
# alembic.ini
revision_environment = true
hooks = ruff
ruff.type = ruff
ruff.options = --select I --select F401 --fix
```
```py
# env.py
from alembic.script import write_hooks
@write_hooks.register("ruff")
def run_ruff(filename, options):
ruff = os.path.join(sysconfig.get_path("scripts"), "ruff")
os.system(f"{ruff} {options['options']} {filename}") # noqa: S605
```
**Error**
```
Running post write hook 'ruff' ...
FAILED
FAILED: No formatter with name 'ruff' registered.
```
**Versions.**
- Python: 3.11.1
- Alembic: 1.11.3
**Additional context**
For the ruff example, this may become useless after #1276, but it is still needed for the rest of the custom hooks .
**Have a nice day!**
Thanks, you too!
|
closed
|
2023-08-22T18:58:03Z
|
2023-08-31T18:27:42Z
|
https://github.com/sqlalchemy/alembic/issues/1299
|
[
"bug",
"command interface"
] |
FeeeeK
| 2
|
PaddlePaddle/PaddleHub
|
nlp
| 1,642
|
使用paddlehub报错
|
使用如下代码
import paddlehub as hub
hub.version.hub_version
AttributeError: module 'paddlehub' has no attribute 'version'
i请问是我paddlehub版本不对吗
|
open
|
2021-10-03T10:30:42Z
|
2023-09-15T08:57:41Z
|
https://github.com/PaddlePaddle/PaddleHub/issues/1642
|
[
"installation"
] |
jjchen460287
| 2
|
mwouts/itables
|
jupyter
| 115
|
itables does not work under Python 2
|
closed
|
2022-11-10T23:38:11Z
|
2022-11-11T00:48:30Z
|
https://github.com/mwouts/itables/issues/115
|
[] |
mwouts
| 0
|
|
scrapy/scrapy
|
web-scraping
| 6,525
|
PIL Image Ignores Orientation in EXIF Data
|
<!--
Thanks for taking an interest in Scrapy!
If you have a question that starts with "How to...", please see the Scrapy Community page: https://scrapy.org/community/.
The GitHub issue tracker's purpose is to deal with bug reports and feature requests for the project itself.
Keep in mind that by filing an issue, you are expected to comply with Scrapy's Code of Conduct, including treating everyone with respect: https://github.com/scrapy/scrapy/blob/master/CODE_OF_CONDUCT.md
The following is a suggested template to structure your issue, you can find more guidelines at https://doc.scrapy.org/en/latest/contributing.html#reporting-bugs
-->
### Description
This problem seems to exist only for photos taken with a Samsung device but when the image is loaded in PIL it doesn't properly handle the orientation in the EXIF data.
### Steps to Reproduce
This section of code replicates (roughly) what happens in [Image Pipeline](https://github.com/scrapy/scrapy/blob/d2156696c45e023479ae1bdee8623bb6212e975c/scrapy/pipelines/images.py#L181)
```python
img_url = "https://auctioneersoftware.s3.amazonaws.com/mar/2024/10/ZIC8mPdtm32SMiJmkHcb5-jo.jpg"
response = requests.get(img_url)
img = Image.open(BytesIO(response.content))
img.show() # image will be upside down
```
```python
img_url = "https://auctioneersoftware.s3.amazonaws.com/mar/2024/10/ZIC8mPdtm32SMiJmkHcb5-jo.jpg"
response = requests.get(img_url)
img = Image.open(BytesIO(response.content))
exif = { ExifTags.TAGS[k]: v for k, v in img._getexif().items() if k in ExifTags.TAGS and ExifTags.TAGS[k] != 'MakerNote' }
if exif["Orientation"] == 3:
# Rotate 180 degrees
img = img.rotate(180, expand=True)
elif exif["Orientation"] == 6:
# Rotate 90 degrees
img = img.rotate(270, expand=True)
elif exif["Orientation"] == 8:
# Rotate 270 degrees
img = img.rotate(90, expand=True)
img.show() # image will show correct
```
**Expected behavior:** [What you expect to happen]
I would expect images saved through pipeline to match orientation of the site it was scraped from.
**Actual behavior:** [What actually happens]
Images are upside down
**Reproduces how often:** [What percentage of the time does it reproduce?]
Every time the orientation is given in this form it fails.
### Versions
```
Scrapy : 2.11.1
lxml : 5.2.1.0
libxml2 : 2.12.6
cssselect : 1.2.0
parsel : 1.9.1
w3lib : 2.1.2
Twisted : 24.3.0
Python : 3.12.2 (main, Apr 5 2024, 14:54:25) [Clang 14.0.0 (clang-1400.0.29.202)]
pyOpenSSL : 24.1.0 (OpenSSL 3.2.1 30 Jan 2024)
cryptography : 42.0.5
Platform : macOS-14.4.1-arm64-arm-64bit
```
### Additional context
I believe that the `if/else` above would be the correct fix. Adding this either to `get_images` or `convert_images` functions make sense to me but interested in feedback.
|
open
|
2024-11-04T16:45:45Z
|
2025-01-10T12:29:31Z
|
https://github.com/scrapy/scrapy/issues/6525
|
[
"needs more info",
"media pipelines"
] |
hmandsager
| 2
|
httpie/cli
|
python
| 548
|
Posting a form field string with spaces results in error
|
I'm currently trying to post to a form and the argument I'm trying to pass is a string with spaces in it.
```
$ http --form POST example.com name="John Smith"
```
But I keep getting this error back:
```
http: error: argument REQUEST_ITEM: "Smith" is not a valid value
```
I've seen this example on a couple of different blogs so it must of worked at some point in time. Am i doing something wrong?
Debug printout
```
HTTPie 0.9.9
Requests 2.12.4
Pygments 2.1.3
Python 2.7.12 (default, Jun 29 2016, 09:13:05)
[GCC 4.9.2]
/usr/bin/python
Linux 4.4.27-moby
<Environment {
"colors": 8,
"config": {
"__meta__": {
"about": "HTTPie configuration file",
"help": "https://httpie.org/docs#config",
"httpie": "0.9.9"
},
"default_options": "[]"
},
"config_dir": "/root/.httpie",
"is_windows": false,
"stderr": "<open file '<stderr>', mode 'w' at 0x7ff88d3df1e0>",
"stderr_isatty": true,
"stdin": "<open file '<stdin>', mode 'r' at 0x7ff88d3df0c0>",
"stdin_encoding": "UTF-8",
"stdin_isatty": true,
"stdout": "<open file '<stdout>', mode 'w' at 0x7ff88d3df150>",
"stdout_encoding": "UTF-8",
"stdout_isatty": true
}>
```
|
closed
|
2016-12-16T18:07:43Z
|
2016-12-16T22:44:21Z
|
https://github.com/httpie/cli/issues/548
|
[] |
thornycrackers
| 4
|
coqui-ai/TTS
|
pytorch
| 3,510
|
[Bug] impossible to resume train_gpt_xtts training
|
### Describe the bug
When resuming training with train_gpt_xtts.py the GPTArgs in the saved config,json is deserialized incorretly as its parent XttsArgs, therefore not containing all required files.
### To Reproduce
Try resuming from checkpoint via setting continue_path
### Expected behavior
It trains
### Logs
```shell
No time
```
### Environment
```shell
--
```
### Additional context
_No response_
|
closed
|
2024-01-10T19:06:06Z
|
2024-03-09T10:40:03Z
|
https://github.com/coqui-ai/TTS/issues/3510
|
[
"bug",
"wontfix"
] |
KukumavMozolo
| 3
|
kymatio/kymatio
|
numpy
| 68
|
1D classif example now fails
|
Specifically, we get
```
TEST, average loss = nan, accuracy = 0.100
```
which is not very impressive. This used to work before (as of 5b41a31), giving
```
TEST, average loss = 0.092, accuracy = 0.973
```
but no longer...
|
closed
|
2018-10-29T02:56:43Z
|
2018-10-30T01:31:29Z
|
https://github.com/kymatio/kymatio/issues/68
|
[
"bug"
] |
janden
| 7
|
kevlened/pytest-parallel
|
pytest
| 60
|
--exitfirst pytest CLI option is being ignored
|
Given the following pytest.ini configuration file:
```
[pytest]
addopts = --exitfirst --log-cli-level=debug --workers auto
```
I see the following when running my test suite containing an unhandled exception.
```
# ...
tests/unit/test_foo.py::test_foo /home/appuser/app
PASSED
tests/unit/test_bar.py::test_bar /home/appuser/app
FAILED
tests/unit/test_baz.py::test_baz /home/appuser/app
PASSED
# ...
1 failed, 114 passed, 2 skipped, 9 warnings in 122.04s (0:02:02)
```
I've only used Python's `Process` API in very trivial ways, so the following may be wildly off the mark: I took a brief look at how the parallelization provided by this library works and I _think_ you could achieve the standard pytest behavior by running a loop while the processes are in progress, checking their `exitcode`s and terminating all of the others (and closing the queue?) if any one returns a failure `exitcode`.
Is this something you'd consider doing? If so, I could _try_ to put a PR together.
Either way, this library has been a tremendous productivity booster and reduced the run time of my test suite from ~11 minutes to ~2 minutes. So, thanks!
|
open
|
2019-12-11T15:15:20Z
|
2019-12-11T15:15:20Z
|
https://github.com/kevlened/pytest-parallel/issues/60
|
[] |
ethagnawl
| 0
|
Teemu/pytest-sugar
|
pytest
| 192
|
lots of project metadata is missing from setup.cfg
|
Current project listing on pypa is missing a lot of metadata including at least source code link, docs link, issue tracker link, list of open PRs/Bugs/starts.
Adding these should not be hard, just compare the setup.cfg with another project that lists them , example https://pypi.org/project/pytest-molecule/
|
closed
|
2020-03-30T08:25:28Z
|
2020-08-25T18:10:32Z
|
https://github.com/Teemu/pytest-sugar/issues/192
|
[] |
ssbarnea
| 1
|
miguelgrinberg/Flask-SocketIO
|
flask
| 2,062
|
Namespace catch-all handling of connect/disconnect events
|
**Is your feature request related to a problem? Please describe.**
I'd like to use dynamic namespaces, i.e. my server should respond to a client that connects on any arbitrary namespace. For that, the connection handler needs to accept and handle all namespaces with the '*' catch-all.
**Describe the solution you'd like**
The namespace catch-all mechanism should apply to all event handlers.
**Additional context**
> The `connect` and `disconnect` handlers are not dispatched to catch-all handlers, only user-defined events are.
_Originally posted by @miguelgrinberg in https://github.com/miguelgrinberg/Flask-SocketIO/issues/2061#issuecomment-2094248770_
|
closed
|
2024-05-05T17:37:24Z
|
2024-05-10T06:50:58Z
|
https://github.com/miguelgrinberg/Flask-SocketIO/issues/2062
|
[] |
mooomooo
| 1
|
graphql-python/graphene-django
|
django
| 1,328
|
SerializerMutation have output_fields with defination of return_field_name similar to DjangoModelFormMutation
|
**Is your feature request related to a problem? Please describe.**
A SerializerMutation provided by the package returns a output_fields as the serializer fields provided int serializer class. But as graphql we should be able to provided a DjangoObjectType of the required models. As looking into DjangoModelFormMutation and SerializerMutation I have found that DjangoModelFormMutation contains return_field_name which describes the output_fields but in SerializerMuatation the output_fields are generated by serializerfields.
**Describe the solution you'd like**
We could implement this by adding a new filed return_field_name in SerializerMutation and overriding the perform_mutate of the SerializerMutation.
[original code](https://github.com/graphql-python/graphene-django/blob/main/graphene_django/rest_framework/mutation.py#L54)
update code below
```
class SerializerMutationOptions(MutationOptions):
lookup_field = None
model_class = None
model_operations = ["create", "update"]
serializer_class = None
# add new field here for return_field_name
return_field_name = None
class DupSerializerMutation(ClientIDMutation):
class Meta:
abstract = True
errors = graphene.List(
ErrorType, description="May contain more than one error for same field."
)
# add new argument return_field_name
@classmethod
def __init_subclass_with_meta__(
cls,
lookup_field=None,
serializer_class=None,
model_class=None,
return_field_name=None,
model_operations=("create", "update"),
only_fields=(),
exclude_fields=(),
convert_choices_to_enum=True,
_meta=None,
**options
):
if not serializer_class:
raise Exception("serializer_class is required for the SerializerMutation")
if "update" not in model_operations and "create" not in model_operations:
raise Exception('model_operations must contain "create" and/or "update"')
serializer = serializer_class()
if model_class is None:
serializer_meta = getattr(serializer_class, "Meta", None)
if serializer_meta:
model_class = getattr(serializer_meta, "model", None)
if lookup_field is None and model_class:
lookup_field = model_class._meta.pk.name
input_fields = fields_for_serializer(
serializer,
only_fields,
exclude_fields,
is_input=True,
convert_choices_to_enum=convert_choices_to_enum,
lookup_field=lookup_field,
)
#Code change start
registry = get_global_registry()
model_type = registry.get_type_for_model(model_class)
if not model_type:
raise Exception("No type registered for model: {}".format(model_class.__name__))
if not return_field_name:
model_name = model_class.__name__
return_field_name = model_name[:1].lower() + model_name[1:]
output_fields = OrderedDict()
output_fields[return_field_name] = graphene.Field(model_type)
# Code Change end
if not _meta:
_meta = SerializerMutationOptions(cls)
_meta.lookup_field = lookup_field
_meta.model_operations = model_operations
_meta.serializer_class = serializer_class
_meta.model_class = model_class
# code change start
_meta.return_field_name = return_field_name
# code change end
_meta.fields = yank_fields_from_attrs(output_fields, _as=graphene.Field)
input_fields = yank_fields_from_attrs(input_fields, _as=graphene.InputField)
super().__init_subclass_with_meta__(
_meta=_meta, input_fields=input_fields, **options
)
@classmethod
def get_serializer_kwargs(cls, root, info, **input):
lookup_field = cls._meta.lookup_field
model_class = cls._meta.model_class
if model_class:
if "update" in cls._meta.model_operations and lookup_field in input:
instance = get_object_or_404(
model_class, **{lookup_field: input[lookup_field]}
)
partial = True
elif "create" in cls._meta.model_operations:
instance = None
partial = False
else:
raise Exception(
'Invalid update operation. Input parameter "{}" required.'.format(
lookup_field
)
)
return {
"instance": instance,
"data": input,
"context": {"request": info.context},
"partial": partial,
}
return {"data": input, "context": {"request": info.context}}
@classmethod
def mutate_and_get_payload(cls, root, info, **input):
kwargs = cls.get_serializer_kwargs(root, info, **input)
serializer = cls._meta.serializer_class(**kwargs)
if serializer.is_valid():
return cls.perform_mutate(serializer, info)
else:
errors = ErrorType.from_errors(serializer.errors)
return cls(errors=errors)
@classmethod
def perform_mutate(cls, serializer, info):
obj = serializer.save()
# code change start
kwargs = {cls._meta.return_field_name: obj}
# code change end
return cls(errors=None, **kwargs)
```
|
open
|
2022-06-28T05:00:35Z
|
2022-06-28T05:00:35Z
|
https://github.com/graphql-python/graphene-django/issues/1328
|
[
"✨enhancement"
] |
sushilkhadka165
| 0
|
coqui-ai/TTS
|
deep-learning
| 2,386
|
[Feature request] Add a hint on how to solve "not enough sample"
|
<!-- Welcome to the 🐸TTS project!
We are excited to see your interest, and appreciate your support! --->
**🚀 Feature Description**
Hi,
In section "SPeaker Adaptation" of [YourTTS paper](https://arxiv.org/pdf/2112.02418.pdf) it is mentioned that the experiment is conducted with eg 15 samples. I am trying to reproduce this with the YourTTS recipe.
However trying to reproduce this experiment with so little amount of samples leads to `You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of` error message as it requires at least 100 samples to get a non-zero integer value for eval_split_size default value (0.01 or 1%).
But this error happens in "compute_embeddings" section of the recipe which cannot change the eval split size (correct me if I am wrong).
**Solution**
One way of solving this is to provide the meta_file_val parameter in the dataset config and manually set in this metadata_val.csv the sample(s) to use for validation (even though this won't be used in compute embeddings script).
Consequently for better user friendliness it could be added to the previous error message "or provide a meta_file_val for this dataset".
If you agree I can write this change.
**Alternative Solutions**
<!-- A clear and concise description of any alternative solutions or features you've considered. -->
**Additional context**
<!-- Add any other context or screenshots about the feature request here. -->
|
closed
|
2023-03-06T04:26:10Z
|
2023-04-19T14:12:37Z
|
https://github.com/coqui-ai/TTS/issues/2386
|
[
"help wanted",
"wontfix",
"feature request"
] |
Ca-ressemble-a-du-fake
| 3
|
sanic-org/sanic
|
asyncio
| 2,918
|
Unexpected behavior when using blueprint groups and app.url_for
|
### Is there an existing issue for this?
- [X] I have searched the existing issues
### Describe the bug
When using `request.app.url_for` in combination with blueprint groups, the expected name cannot be used:
```
Traceback (most recent call last):
File "handle_request", line 97, in handle_request
File "###/bpgtest.py", line 64, in index
build_urls.append(f'{url}: {request.app.url_for(url)}')
^^^^^^^^^^^^^^^^^^^^^^^^
File "###/env/lib/python3.11/site-packages/sanic/app.py", line 753, in url_for
raise URLBuildError(
sanic.exceptions.URLBuildError: Endpoint with name `nested.child.index` was not found
```
Instead, underscores must be used, like so: `nested_child.index`.
Deeper nesting of blueprints causes different issues, such as missing segments of the endpoint name (see the snippet for a good example of this)
### Code snippet
```python
import sanic
# An un-nested example
unnested_bp = sanic.Blueprint("unnested", url_prefix="/unnested")
@unnested_bp.route("/", methods=["GET"], name="index")
async def unnested_index(request):
return sanic.response.text("it is okay")
# A nested blueprint example
child_bp = sanic.Blueprint("child", url_prefix="/child")
@child_bp.route("/", methods=["GET"], name="index")
async def nested_child_index(request):
return sanic.response.text("all good")
nested_bpg = sanic.Blueprint.group(
child_bp,
url_prefix="/nested",
name_prefix="nested"
)
# A deeply nested blueprint example
deeply_nested_bp = sanic.Blueprint("deepest", url_prefix="/deepest")
@deeply_nested_bp.route("/", methods=["GET"], name="index")
async def deeply_nested_index(request):
return sanic.response.text("deeply satisfied")
deeper_bpg = sanic.Blueprint.group(
deeply_nested_bp,
url_prefix="/deeper",
name_prefix="deeper",
)
deep_bpg = sanic.Blueprint.group(
deeper_bpg,
url_prefix="/deep",
name_prefix="deep",
)
# Build the sanic app
app = sanic.Sanic("testapp")
app.blueprint(unnested_bp)
app.blueprint(nested_bpg)
app.blueprint(deep_bpg)
@app.route("/", methods=["GET"], name="index")
async def index(request):
# Try to build each url
urls = [
'unnested.index',
'nested.child.index',
'nested_child.index',
'deep.deeper.deepest.index',
'deep_deeper_deepest.index',
'deep_deepest.index',
]
build_urls = []
for url in urls:
try:
build_urls.append(f'{url}: {request.app.url_for(url)}')
except:
build_urls.append(f'-- {url} could not be built')
return sanic.response.text('\n'.join(build_urls))
if __name__ == "__main__":
app.run()
```
### Expected Behavior
```
unnested.index: /unnested
-- nested.child.index could not be built
nested_child.index: /nested/child
-- deep.deeper.deepest.index could not be built
-- deep_deeper_deepest.index could not be built
deep_deepest.index: /deep/deeper/deepest ```
### How do you run Sanic?
Sanic CLI
### Operating System
Linux
### Sanic Version
23.6.0
### Additional context
_No response_
|
closed
|
2024-02-17T12:03:58Z
|
2024-04-01T13:22:39Z
|
https://github.com/sanic-org/sanic/issues/2918
|
[
"bug"
] |
asyte
| 1
|
eamigo86/graphene-django-extras
|
graphql
| 41
|
How can I use the decorators in django-graphql-jwt ?
|
I am using [django-graphql-jwt](https://github.com/flavors/django-graphql-jwt) and it exposes a couple of decorators such as `@login_required` that are used to decorate the resolver functions to restrict their access.
How can I use such decorators with `DjangoSerializerType`, `.ListField()`, `RetrieveField()`, etc. I only want to restrict access for certain fields such as:
```
import graphene
from .types import UserType
class Query(graphene.ObjectType):
users = UserType.ListField()
user = UserType.RetrieveField()
class Mutation(graphene.ObjectType):
update_user = UserType.UpdateField()
```
Resolvers are not defined in these cases. Any suggestions on how to make it work with `django-graphql-jwt` ?
|
closed
|
2018-04-29T19:36:41Z
|
2020-06-18T12:36:19Z
|
https://github.com/eamigo86/graphene-django-extras/issues/41
|
[] |
cidylle
| 16
|
xinntao/Real-ESRGAN
|
pytorch
| 57
|
seal
|
it smudge
<img src="https://user-images.githubusercontent.com/16495490/131243595-c7ba962c-3c0e-493f-9df7-30db3438ffd0.png" width="500">
<img src="https://user-images.githubusercontent.com/16495490/131243629-0a2d1e1b-0d54-43ef-a942-7aa5894acd01.png" width="500">
|
open
|
2021-08-29T08:20:50Z
|
2021-09-08T13:57:23Z
|
https://github.com/xinntao/Real-ESRGAN/issues/57
|
[
"hard-samples reported"
] |
OverLordGoldDragon
| 3
|
zappa/Zappa
|
django
| 1,267
|
Provide configuration for reserved and provisioned concurrency
|
Lambda now provides a reserved and provisioned concurrency setting/configuration.
https://docs.aws.amazon.com/lambda/latest/dg/lambda-concurrency.html#reserved-and-provisioned
https://docs.aws.amazon.com/lambda/latest/dg/configuration-concurrency.html
zappa currently provides a `keep_warm` function to periodically keep a single lambda instance "warm".
However, the current `keep_warm` method is not expected to reduce cold starts for more than a single instance.
This issue requests that reserved and provisioned concurrency are properly integrated with zappa.
Potentially consider depreciating `keep_warm` in favor of these new concurrency settings.
|
closed
|
2023-08-17T05:05:03Z
|
2024-04-21T03:37:49Z
|
https://github.com/zappa/Zappa/issues/1267
|
[
"no-activity",
"auto-closed"
] |
monkut
| 3
|
plotly/dash
|
flask
| 2,399
|
Document that a maximum 1 Dash instance is supported in a process at a time
|
Before Dash 2.0, it was possible to attach multiple `dash.Dash` app instances to a single `flask.Flask` server instance by passing the same Flask server in to each Dash app using the `server` param. This pattern was for a time [documented in the Dash docs](https://github.com/plotly/dash-docs/blob/master/dash_docs/chapters/combining_flask_with_dash/index.py). Since then, a number of Dash features have been introduced which involve `dash.Dash` instances setting global state associated with the app in the `dash` module. This means that it is not longer safe to run multiple Dash instances within the same process (and hence has been removed from the docs).
Since the docs briefly officially supported this pattern, and there are a numerous places online of people sharing solutions of how to attach multiple Dash instances to a Flask server, it would be good for the Dash docs to explicitly call out that spinning up more than one Dash instance in a process is not supported, and is likely to break things in fun and unexpected behaviour.
## Links with now unsupported multi Dash-app solutions
* https://github.com/plotly/dash/pull/70
* https://towardsdatascience.com/embed-multiple-dash-apps-in-flask-with-microsoft-authenticatio-44b734f74532
* https://stackoverflow.com/a/59628371/77533
* https://stackoverflow.com/a/51855852/77533
* https://stackoverflow.com/a/51255720/77533
* https://community.plotly.com/t/how-to-add-your-dash-app-to-flask/51870
* https://community.plotly.com/t/embedding-more-than-one-multi-page-dash-apps-in-a-flask-app/70945
## Dash features relying on global state
* The `dash.callback` decorator
* Dash Pages
* `get_asset_url()`
|
open
|
2023-01-26T12:46:01Z
|
2024-08-13T19:25:29Z
|
https://github.com/plotly/dash/issues/2399
|
[
"documentation",
"feature",
"P3"
] |
ned2
| 0
|
JaidedAI/EasyOCR
|
machine-learning
| 1,316
|
Fa (Persian) mismatch
|
some fonts and some car plate codes in Iran has mismatch detection
please let me contribute in Fa Persian dataset
|
open
|
2024-10-12T09:49:21Z
|
2024-10-12T09:49:21Z
|
https://github.com/JaidedAI/EasyOCR/issues/1316
|
[] |
mohamadkenway
| 0
|
iperov/DeepFaceLab
|
deep-learning
| 793
|
The change of head is very fuzzy
|
I'm changing my head in the test. The effect is not good. After replacing the SRC image, it's very fuzzy. I'm using merge SAEHD.dat I set the parameter to: as shown in the following figure
|
closed
|
2020-06-18T12:08:45Z
|
2023-09-21T15:37:36Z
|
https://github.com/iperov/DeepFaceLab/issues/793
|
[] |
stone100010
| 1
|
ultralytics/ultralytics
|
pytorch
| 19,612
|
YOLOv11 instance segmentation performance
|
### 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
As shown below, when I am using yolov11 for instance segmentation of pedestrians, the segmentation result and the target frame are easily affected by their own shadow, how can this improve the model?
### Additional
_No response_
|
open
|
2025-03-10T07:27:10Z
|
2025-03-12T10:38:36Z
|
https://github.com/ultralytics/ultralytics/issues/19612
|
[
"question",
"segment"
] |
WYL-Projects
| 6
|
geopandas/geopandas
|
pandas
| 3,137
|
BUG: .to_parquet unable to write empty GeoDataFrame
|
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of geopandas.
- [ ] (optional) I have confirmed this bug exists on the main branch of geopandas.
---
#### Code Sample, a copy-pastable example
```python
import geopandas as gpd
gpd.GeoDataFrame().to_parquet('test.parquet')
```
### Generated output (short version):
raise ValueError("Writing to Parquet/Feather requires string column names")
ValueError: Writing to Parquet/Feather requires string column names
#### Problem description
Well, I think that this capacity is nice (or even needed) for generalization, as for example considering GeoJson, when an empty geodataframe can be easily serialized and read back. I know that parquet is column-oriented, and as the output suggests it must have at least one column, so writing like this, works:
```python
import geopandas as gpd
gpd.GeoDataFrame(columns=['geometry']).to_parquet('test.parquet')
print(gpd.read_parquet('test.parquet'))
```
But with a column sets (such as `['data1','data2']` or `['data']` that doest not includes "geometry, it throws this error at print() call:
ValueError: 'geo' metadata in Parquet/Feather file is missing required key: 'primary_column'
output:
Empty GeoDataFrame
Columns: [geometry]
Index: []
#### Expected Output
The generation of a file that will be read as `Empty GeoDataFrame` without the need to specify any column names.
#### Output of ``geopandas.show_versions()``
<details>
SYSTEM INFO
-----------
python : 3.11.7 (main, Dec 8 2023, 18:56:58) [GCC 11.4.0]
executable : /home/kaue/opensidewalkmap_beta/.venv/bin/python
machine : Linux-6.2.0-39-generic-x86_64-with-glibc2.35
GEOS, GDAL, PROJ INFO
---------------------
GEOS : 3.11.2
GEOS lib : None
GDAL : 3.6.4
GDAL data dir: /home/kaue/opensidewalkmap_beta/.venv/lib/python3.11/site-packages/fiona/gdal_data
PROJ : 9.3.0
PROJ data dir: /home/kaue/opensidewalkmap_beta/.venv/lib/python3.11/site-packages/pyproj/proj_dir/share/proj
PYTHON DEPENDENCIES
-------------------
geopandas : 0.14.2
numpy : 1.26.3
pandas : 2.1.4
pyproj : 3.6.1
shapely : 2.0.2
fiona : 1.9.5
geoalchemy2: None
geopy : None
matplotlib : None
mapclassify: None
pygeos : None
pyogrio : None
psycopg2 : None
pyarrow : 14.0.2
rtree : None
None
</details>
|
open
|
2024-01-10T15:05:33Z
|
2024-01-10T17:51:16Z
|
https://github.com/geopandas/geopandas/issues/3137
|
[
"bug"
] |
kauevestena
| 1
|
hbldh/bleak
|
asyncio
| 1,278
|
Disconnect causes "Event loop is closed"
|
* bleak version: 1.2.0
* Python version: 3.11.2
* Operating System: Windows 10
### Description
I am trying to use bleak without using the async context manager (I have found that with the bluetooth device I am using it causes a few issues with latency) as I will later use this to implement into a GUI.
While using the disconnect function in the BleakClient, while it does disconnect, it consistently gives this error:
`Traceback (most recent call last):
loop.call_soon_threadsafe(handle_session_status_changed, args)
self._check_closed()
File "C:\redacted\AppData\Local\Programs\Python\Python311\Lib\asyncio\base_events.py", line 519, in _check_closed
raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed`
### Code
Here is the program I ran to get this error
```python
from bleak import BleakScanner, BleakClient, backends
import asyncio
class bluetooth:
def __init__(self):
self.bleClient = None
async def callback(self, characteristic, d : bytearray):
data_array = d.decode().rstrip().split('\t')
print("Calling!")
async def disconnect_callback(self, client):
print("Diconnected")
async def find_and_connect(self, name : str):
devices = await BleakScanner.discover()
for d in devices:
if d.name == name:
self.bleClient = BleakClient(d, disconnected_callback= lambda : self.disconnect_callback(), timeout=5.0)
try:
await self.bleClient.connect()
except Exception as e:
print(e)
print("Connected")
return
raise Exception("No Device")
async def retrieve(self):
if self.bleClient != None:
if self.bleClient.is_connected:
print("is connected")
try:
await self.bleClient.start_notify("eb523251-6513-11ed-9c9d-0800200c9a66".format(0xFFE1), self.callback)
await asyncio.sleep(0.1)
await self.bleClient.stop_notify("eb523251-6513-11ed-9c9d-0800200c9a66".format(0xFFE1))
await self.bleClient.disconnect()
except Exception as e:
print(e)
else:
print("not connected")
ble = bluetooth()
try:
asyncio.run(ble.find_and_connect("FlexibleSensorBLE-100"))
except Exception as e:
print(e)
print("Closing")
exit
asyncio.run(ble.retrieve())
```
|
closed
|
2023-04-13T19:34:38Z
|
2023-04-13T20:03:21Z
|
https://github.com/hbldh/bleak/issues/1278
|
[] |
jacobbreen25
| 0
|
AirtestProject/Airtest
|
automation
| 391
|
Expand protected bundle identifier from iphone?
|
Can i know how to check iphone installed app bundle identifier?
Added star_app (bundle identifier).
|
closed
|
2019-05-09T06:29:27Z
|
2019-05-13T06:11:09Z
|
https://github.com/AirtestProject/Airtest/issues/391
|
[] |
JJunM
| 2
|
seleniumbase/SeleniumBase
|
web-scraping
| 3,027
|
RuntimeError: You cannot reuse the ChromeOptions object
|
Getting the following error when trying to use user_data_dir.
The first run of the code (before user_data_dir is created) works, but then once its created and we try to use it, I get the error
```
Traceback (most recent call last):
File "//main.py", line 67, in <module>
Scrape().run()
File "/bots/scrape.py", line 17, in run
with SB(uc=True, user_data_dir=self.user_data_dir, incognito=True, test=True, slow=True, demo_sleep=3) as sb:
File "/usr/lib/python3.10/contextlib.py", line 135, in __enter__
return next(self.gen)
File "/usr/local/lib/python3.10/dist-packages/seleniumbase/plugins/sb_manager.py", line 986, in SB
sb.setUp()
File "/usr/local/lib/python3.10/dist-packages/seleniumbase/fixtures/base_case.py", line 14916, in setUp
self.driver = self.get_new_driver(
File "/usr/local/lib/python3.10/dist-packages/seleniumbase/fixtures/base_case.py", line 4101, in get_new_driver
new_driver = browser_launcher.get_driver(
File "/usr/local/lib/python3.10/dist-packages/seleniumbase/core/browser_launcher.py", line 2339, in get_driver
return get_local_driver(
File "/usr/local/lib/python3.10/dist-packages/seleniumbase/core/browser_launcher.py", line 4304, in get_local_driver
driver = undetected.Chrome(
File "/usr/local/lib/python3.10/dist-packages/seleniumbase/undetected/__init__.py", line 136, in __init__
raise RuntimeError("You cannot reuse the ChromeOptions object")
RuntimeError: You cannot reuse the ChromeOptions object
```
where `self.user_data_dir = "user_data_dir/scrape"` and `user_data_dir` is a blank folder I created, which I can see being written to successfully on the first run
Code is simply:
```Python
with SB(uc=True, incognito=True, user_data_dir=self.user_data_dir, test=True, slow=True, demo_sleep=3) as sb:
self.login(sb)
```
Interestingly enough when I run the code locally (virtualenv on MacBook) I do not get this error, however when running the code in docker I do. Pems on docker are all 755 and the path is mounted to host (where I see files being written on the first execution of code)
```yml
volumes:
- ${APPDATA_DIR}/Scraper/saved_cookies:/saved_cookies
- ${APPDATA_DIR}/Scraper/user_data_dir:/user_data_dir
```
|
closed
|
2024-08-15T16:34:35Z
|
2024-10-03T13:45:08Z
|
https://github.com/seleniumbase/SeleniumBase/issues/3027
|
[
"duplicate",
"invalid usage",
"UC Mode / CDP Mode"
] |
ttraxxrepo
| 6
|
modin-project/modin
|
pandas
| 7,217
|
Update docs as to when Modin operators work best
|
closed
|
2024-04-25T11:33:56Z
|
2024-04-26T13:32:37Z
|
https://github.com/modin-project/modin/issues/7217
|
[
"documentation 📜"
] |
YarShev
| 4
|
|
awesto/django-shop
|
django
| 431
|
Demo store not building or running
|
I am trying to build a new django-shop app, as per the instructions at http://django-shop.readthedocs.io/en/latest/tutorial/intro.html and http://django-shop.readthedocs.io/en/latest/tutorial/quickstart.html#tutorial-quickstart
Here is my initial installation log.
```
(heroku) vagrant@vagrant:/vagrant/projects$ mkvirtualenv django-shop
Using base prefix '/usr'
New python executable in /home/vagrant/virtualenvs/django-shop/bin/python3.4
Also creating executable in /home/vagrant/virtualenvs/django-shop/bin/python
Installing setuptools, pip, wheel...done.
(django-shop) vagrant@vagrant:/vagrant/projects$ mkdir shop
(django-shop) vagrant@vagrant:/vagrant/projects$ cd shop
(django-shop) vagrant@vagrant:/vagrant/projects/shop$ git clone --depth 1 https://github.com/awesto/django-shop
Cloning into 'django-shop'...
remote: Counting objects: 521, done.
remote: Compressing objects: 100% (461/461), done.
remote: Total 521 (delta 52), reused 208 (delta 25), pack-reused 0
Receiving objects: 100% (521/521), 1.82 MiB | 87.00 KiB/s, done.
Resolving deltas: 100% (52/52), done.
Checking connectivity... done.
(django-shop) vagrant@vagrant:/vagrant/projects/shop$ cd django-shop
(django-shop) vagrant@vagrant:/vagrant/projects/shop/django-shop$ pip install -e .
Obtaining file:///vagrant/projects/shop/django-shop
Collecting Django>=1.9 (from django-shop==0.9.2)
Using cached Django-1.10.1-py2.py3-none-any.whl
Collecting djangocms-cascade>=0.10.2 (from django-shop==0.9.2)
Collecting beautifulsoup4>=4.4.0 (from django-shop==0.9.2)
Using cached beautifulsoup4-4.5.1-py3-none-any.whl
Collecting django-cms>=3.3.2 (from django-shop==0.9.2)
Using cached django_cms-3.4.0-py2.py3-none-any.whl
Collecting django-post-office>=2.0.8 (from django-shop==0.9.2)
Using cached django_post_office-2.0.8-py2.py3-none-any.whl
Collecting django-filer>=1.2.5 (from django-shop==0.9.2)
Using cached django_filer-1.2.5-py2.py3-none-any.whl
Collecting django-ipware>=1.1.1 (from django-shop==0.9.2)
Collecting django-fsm>=2.4.0 (from django-shop==0.9.2)
Collecting django-fsm-admin>=1.2.3 (from django-shop==0.9.2)
Collecting djangorestframework>=3.2.5 (from django-shop==0.9.2)
Using cached djangorestframework-3.4.7-py2.py3-none-any.whl
Collecting django-angular>=0.8.4 (from django-shop==0.9.2)
Collecting Django-Select2>=5.8.9 (from django-shop==0.9.2)
Collecting django-rest-auth>=0.5.0 (from django-shop==0.9.2)
Collecting django-admin-sortable2>=0.6.4 (from django-shop==0.9.2)
Collecting jsonfield (from djangocms-cascade>=0.10.2->django-shop==0.9.2)
Collecting django-formtools>=1.0 (from django-cms>=3.3.2->django-shop==0.9.2)
Using cached django_formtools-1.0-py2.py3-none-any.whl
Collecting django-sekizai>=0.7 (from django-cms>=3.3.2->django-shop==0.9.2)
Using cached django_sekizai-0.10.0-py2.py3-none-any.whl
Collecting django-treebeard>=4.0.1 (from django-cms>=3.3.2->django-shop==0.9.2)
Collecting djangocms-admin-style>=1.0 (from django-cms>=3.3.2->django-shop==0.9.2)
Collecting django-classy-tags>=0.7.2 (from django-cms>=3.3.2->django-shop==0.9.2)
Using cached django_classy_tags-0.8.0-py2.py3-none-any.whl
Collecting Unidecode<0.05,>=0.04 (from django-filer>=1.2.5->django-shop==0.9.2)
Collecting django-mptt<0.9,>=0.6 (from django-filer>=1.2.5->django-shop==0.9.2)
Using cached django_mptt-0.8.6-py2.py3-none-any.whl
Collecting django-polymorphic<0.9,>=0.7 (from django-filer>=1.2.5->django-shop==0.9.2)
Using cached django_polymorphic-0.8.1-py2.py3-none-any.whl
Collecting easy-thumbnails<2.4,>=1.0 (from django-filer>=1.2.5->django-shop==0.9.2)
Using cached easy_thumbnails-2.3-py2.py3-none-any.whl
Collecting django-appconf>=0.6.0 (from Django-Select2>=5.8.9->django-shop==0.9.2)
Using cached django_appconf-1.0.2-py2.py3-none-any.whl
Collecting six>=1.9.0 (from django-rest-auth>=0.5.0->django-shop==0.9.2)
Using cached six-1.10.0-py2.py3-none-any.whl
Requirement already satisfied (use --upgrade to upgrade): setuptools in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from django-polymorphic<0.9,>=0.7->django-filer>=1.2.5->django-shop==0.9.2)
Collecting pillow (from easy-thumbnails<2.4,>=1.0->django-filer>=1.2.5->django-shop==0.9.2)
Using cached Pillow-3.3.1-cp34-cp34m-manylinux1_x86_64.whl
Installing collected packages: Django, jsonfield, djangocms-cascade, beautifulsoup4, django-formtools, django-classy-tags, django-sekizai, django-treebeard, djangocms-admin-style, django-cms, django-post-office, Unidecode, django-mptt, django-polymorphic, pillow, easy-thumbnails, django-filer, django-ipware, django-fsm, django-fsm-admin, djangorestframework, django-angular, django-appconf, Django-Select2, six, django-rest-auth, django-admin-sortable2, django-shop
Running setup.py develop for django-shop
Successfully installed Django-1.10.1 Django-Select2-5.8.9 Unidecode-0.4.19 beautifulsoup4-4.5.1 django-admin-sortable2-0.6.4 django-angular-0.8.4 django-appconf-1.0.2 django-classy-tags-0.8.0 django-cms-3.4.0 django-filer-1.2.5 django-formtools-1.0 django-fsm-2.4.0 django-fsm-admin-1.2.3 django-ipware-1.1.6 django-mptt-0.8.6 django-polymorphic-0.8.1 django-post-office-2.0.8 django-rest-auth-0.8.1 django-sekizai-0.10.0 django-shop django-treebeard-4.0.1 djangocms-admin-style-1.2.4 djangocms-cascade-0.10.2 djangorestframework-3.4.7 easy-thumbnails-2.3 jsonfield-1.0.3 pillow-3.3.1 six-1.10.0
(django-shop) vagrant@vagrant:/vagrant/projects/shop/django-shop$ pip install -r requirements/common.txt
Collecting beautifulsoup4==4.4.0 (from -r requirements/common.txt (line 1))
Using cached beautifulsoup4-4.4.0-py3-none-any.whl
Requirement already satisfied (use --upgrade to upgrade): django-admin-sortable2==0.6.4 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 2))
Collecting django-allauth==0.23.0 (from -r requirements/common.txt (line 3))
Requirement already satisfied (use --upgrade to upgrade): django-angular==0.8.4 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 4))
Collecting django-appconf==1.0.1 (from -r requirements/common.txt (line 5))
Using cached django_appconf-1.0.1-py2.py3-none-any.whl
Collecting django-classy-tags==0.7.2 (from -r requirements/common.txt (line 6))
Using cached django_classy_tags-0.7.2-py2.py3-none-any.whl
Collecting django-cms==3.3.2 (from -r requirements/common.txt (line 7))
Using cached django_cms-3.3.2-py2.py3-none-any.whl
Collecting django-compressor==1.6 (from -r requirements/common.txt (line 8))
Using cached django_compressor-1.6-py2.py3-none-any.whl
Requirement already satisfied (use --upgrade to upgrade): django-filer==1.2.5 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 9))
Collecting django-filter==0.13.0 (from -r requirements/common.txt (line 10))
Using cached django_filter-0.13.0-py2.py3-none-any.whl
Requirement already satisfied (use --upgrade to upgrade): django-fsm==2.4.0 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 11))
Requirement already satisfied (use --upgrade to upgrade): django-fsm-admin==1.2.3 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 12))
Collecting django-haystack==2.5.0 (from -r requirements/common.txt (line 13))
Collecting django-ipware==1.1.1 (from -r requirements/common.txt (line 14))
Requirement already satisfied (use --upgrade to upgrade): django-mptt==0.8.6 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 15))
Collecting django-parler==1.6.4 (from -r requirements/common.txt (line 16))
Using cached django_parler-1.6.4-py2.py3-none-any.whl
Collecting django-polymorphic==1.0.1 (from -r requirements/common.txt (line 17))
Using cached django_polymorphic-1.0.1-py2.py3-none-any.whl
Requirement already satisfied (use --upgrade to upgrade): django-post-office==2.0.8 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 18))
Collecting django-rest-auth==0.5.0 (from -r requirements/common.txt (line 19))
Collecting django-sass-processor==0.5.1 (from -r requirements/common.txt (line 20))
Requirement already satisfied (use --upgrade to upgrade): django-sekizai==0.10.0 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 21))
Requirement already satisfied (use --upgrade to upgrade): Django-Select2==5.8.9 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 22))
Collecting djangoshop-stripe==0.2.0 (from -r requirements/common.txt (line 23))
Collecting django-treebeard==4.0 (from -r requirements/common.txt (line 24))
Collecting djangocms-bootstrap3==0.1.1 (from -r requirements/common.txt (line 25))
Collecting djangocms-cascade==0.10.1 (from -r requirements/common.txt (line 26))
Collecting djangocms-text-ckeditor==3.0.1 (from -r requirements/common.txt (line 27))
Using cached djangocms_text_ckeditor-3.0.1-py2.py3-none-any.whl
Collecting djangorestframework==3.2.5 (from -r requirements/common.txt (line 28))
Using cached djangorestframework-3.2.5-py2.py3-none-any.whl
Collecting drf-haystack==1.5.6 (from -r requirements/common.txt (line 29))
Requirement already satisfied (use --upgrade to upgrade): easy-thumbnails==2.3 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 30))
Collecting elasticsearch==1.7.0 (from -r requirements/common.txt (line 31))
Using cached elasticsearch-1.7.0-py2.py3-none-any.whl
Collecting html5lib==0.9999999 (from -r requirements/common.txt (line 32))
Requirement already satisfied (use --upgrade to upgrade): jsonfield==1.0.3 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from -r requirements/common.txt (line 33))
Collecting libsass==0.8.3 (from -r requirements/common.txt (line 34))
Collecting oauthlib==1.0.3 (from -r requirements/common.txt (line 35))
Collecting Pillow==3.3.0 (from -r requirements/common.txt (line 36))
Using cached Pillow-3.3.0-cp34-cp34m-manylinux1_x86_64.whl
Collecting pluggy==0.3.1 (from -r requirements/common.txt (line 37))
Using cached pluggy-0.3.1-py2.py3-none-any.whl
Collecting py==1.4.30 (from -r requirements/common.txt (line 38))
Using cached py-1.4.30-py2.py3-none-any.whl
Collecting Pygments==2.0.2 (from -r requirements/common.txt (line 39))
Using cached Pygments-2.0.2-py3-none-any.whl
Collecting python-openid==2.2.5 (from -r requirements/common.txt (line 40))
Collecting pytz==2015.7 (from -r requirements/common.txt (line 41))
Using cached pytz-2015.7-py2.py3-none-any.whl
Collecting requests==2.7.0 (from -r requirements/common.txt (line 42))
Using cached requests-2.7.0-py2.py3-none-any.whl
Collecting requests-oauthlib==0.5.0 (from -r requirements/common.txt (line 43))
Using cached requests_oauthlib-0.5.0-py2.py3-none-any.whl
Collecting six==1.9.0 (from -r requirements/common.txt (line 44))
Using cached six-1.9.0-py2.py3-none-any.whl
Collecting stripe==1.37.0 (from -r requirements/common.txt (line 45))
Using cached stripe-1.37.0-py3-none-any.whl
Collecting Unidecode==0.4.18 (from -r requirements/common.txt (line 46))
Collecting urllib3==1.12 (from -r requirements/common.txt (line 47))
Using cached urllib3-1.12-py2.py3-none-any.whl
Collecting virtualenv==13.1.2 (from -r requirements/common.txt (line 48))
Using cached virtualenv-13.1.2-py2.py3-none-any.whl
Collecting wheel==0.24.0 (from -r requirements/common.txt (line 49))
Using cached wheel-0.24.0-py2.py3-none-any.whl
Collecting python3-openid>=3.0.1 (from django-allauth==0.23.0->-r requirements/common.txt (line 3))
Requirement already satisfied (use --upgrade to upgrade): Django>=1.6 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from django-allauth==0.23.0->-r requirements/common.txt (line 3))
Requirement already satisfied (use --upgrade to upgrade): djangocms-admin-style>=1.0 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from django-cms==3.3.2->-r requirements/common.txt (line 7))
Requirement already satisfied (use --upgrade to upgrade): django-formtools>=1.0 in /home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages (from django-cms==3.3.2->-r requirements/common.txt (line 7))
Collecting python-dateutil (from drf-haystack==1.5.6->-r requirements/common.txt (line 29))
Using cached python_dateutil-2.5.3-py2.py3-none-any.whl
Collecting defusedxml (from python3-openid>=3.0.1->django-allauth==0.23.0->-r requirements/common.txt (line 3))
Installing collected packages: beautifulsoup4, defusedxml, python3-openid, oauthlib, requests, requests-oauthlib, django-allauth, six, django-appconf, django-classy-tags, django-treebeard, django-cms, django-compressor, django-filter, django-haystack, django-ipware, django-parler, django-polymorphic, djangorestframework, django-rest-auth, libsass, django-sass-processor, stripe, djangoshop-stripe, djangocms-bootstrap3, djangocms-cascade, Pillow, html5lib, djangocms-text-ckeditor, python-dateutil, drf-haystack, urllib3, elasticsearch, pluggy, py, Pygments, python-openid, pytz, Unidecode, virtualenv, wheel
Found existing installation: beautifulsoup4 4.5.1
Uninstalling beautifulsoup4-4.5.1:
Successfully uninstalled beautifulsoup4-4.5.1
Found existing installation: six 1.10.0
Uninstalling six-1.10.0:
Successfully uninstalled six-1.10.0
Found existing installation: django-appconf 1.0.2
Uninstalling django-appconf-1.0.2:
Successfully uninstalled django-appconf-1.0.2
Found existing installation: django-classy-tags 0.8.0
Uninstalling django-classy-tags-0.8.0:
Successfully uninstalled django-classy-tags-0.8.0
Found existing installation: django-treebeard 4.0.1
Uninstalling django-treebeard-4.0.1:
Successfully uninstalled django-treebeard-4.0.1
Found existing installation: django-cms 3.4.0
Uninstalling django-cms-3.4.0:
Successfully uninstalled django-cms-3.4.0
Found existing installation: django-ipware 1.1.6
Uninstalling django-ipware-1.1.6:
Successfully uninstalled django-ipware-1.1.6
Found existing installation: django-polymorphic 0.8.1
Uninstalling django-polymorphic-0.8.1:
Successfully uninstalled django-polymorphic-0.8.1
Found existing installation: djangorestframework 3.4.7
Uninstalling djangorestframework-3.4.7:
Successfully uninstalled djangorestframework-3.4.7
Found existing installation: django-rest-auth 0.8.1
Uninstalling django-rest-auth-0.8.1:
Successfully uninstalled django-rest-auth-0.8.1
Found existing installation: djangocms-cascade 0.10.2
Uninstalling djangocms-cascade-0.10.2:
Successfully uninstalled djangocms-cascade-0.10.2
Found existing installation: Pillow 3.3.1
Uninstalling Pillow-3.3.1:
Successfully uninstalled Pillow-3.3.1
Found existing installation: Unidecode 0.4.19
Uninstalling Unidecode-0.4.19:
Successfully uninstalled Unidecode-0.4.19
Found existing installation: wheel 0.30.0a0
Uninstalling wheel-0.30.0a0:
Successfully uninstalled wheel-0.30.0a0
Successfully installed Pillow-3.3.0 Pygments-2.0.2 Unidecode-0.4.18 beautifulsoup4-4.4.0 defusedxml-0.4.1 django-allauth-0.23.0 django-appconf-1.0.1 django-classy-tags-0.7.2 django-cms-3.3.2 django-compressor-1.6 django-filter-0.13.0 django-haystack-2.5.0 django-ipware-1.1.1 django-parler-1.6.4 django-polymorphic-1.0.1 django-rest-auth-0.5.0 django-sass-processor-0.5.1 django-treebeard-4.0 djangocms-bootstrap3-0.1.1 djangocms-cascade-0.10.1 djangocms-text-ckeditor-3.0.1 djangorestframework-3.2.5 djangoshop-stripe-0.2.0 drf-haystack-1.5.6 elasticsearch-1.7.0 html5lib-0.9999999 libsass-0.8.3 oauthlib-1.0.3 pluggy-0.3.1 py-1.4.30 python-dateutil-2.5.3 python-openid-2.2.5 python3-openid-3.0.10 pytz-2015.7 requests-2.7.0 requests-oauthlib-0.5.0 six-1.9.0 stripe-1.37.0 urllib3-1.12 virtualenv-13.1.2 wheel-0.24.0
(django-shop) vagrant@vagrant:/vagrant/projects/shop/django-shop$ npm install
django-shop@0.9.2 /vagrant/projects/shop/django-shop
├── angular@1.3.20
├── angular-animate@1.3.20
├── angular-bootstrap-plus@0.6.3
├── angular-i18n@1.3.20
├── angular-inview@1.5.7
├── angular-messages@1.3.20
├── angular-sanitize@1.3.20
├── angular-ui-bootstrap@0.13.4
├── bootstrap@3.3.7
├── bootstrap-sass@3.3.7
├── font-awesome@4.6.3
├── picturefill@3.0.2
└─┬ select2@4.0.3
├── almond@0.3.3
└── jquery-mousewheel@3.1.13
(django-shop) vagrant@vagrant:/vagrant/projects/shop/django-shop$ cd example/
(django-shop) vagrant@vagrant:/vagrant/projects/shop/django-shop/example$ export DJANGO_SHOP_TUTORIAL=commodity
```
After this I run the `./manage.py initialize_shop_demo` and get the following error:
```
(django-shop) vagrant@vagrant:/vagrant/projects/shop/django-shop/example$ ./manage.py initialize_shop_demo
Traceback (most recent call last):
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/cmsplugin_cascade/cms_plugins.py", line 11, in <module>
module_settings = import_module('{}.settings'.format(module))
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/importlib/__init__.py", line 109, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 2254, in _gcd_import
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2224, in _find_and_load_unlocked
ImportError: No module named 'cmsplugin_cascade.segmentation.settings'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/utils.py", line 65, in __getitem__
return self._engines[alias]
KeyError: 'django'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/backends/django.py", line 126, in get_package_libraries
module = import_module(entry[1])
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/importlib/__init__.py", line 109, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 2254, in _gcd_import
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1471, in exec_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/treebeard/templatetags/admin_tree_list.py", line 7, in <module>
from treebeard.admin import TO_FIELD_VAR
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/treebeard/admin.py", line 6, in <module>
from django.conf.urls import patterns, url
ImportError: cannot import name 'patterns'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "./manage.py", line 10, in <module>
execute_from_command_line(sys.argv)
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/core/management/__init__.py", line 367, in execute_from_command_line
utility.execute()
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/core/management/__init__.py", line 341, in execute
django.setup()
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/__init__.py", line 27, in setup
apps.populate(settings.INSTALLED_APPS)
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/apps/registry.py", line 115, in populate
app_config.ready()
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/contrib/admin/apps.py", line 23, in ready
self.module.autodiscover()
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/contrib/admin/__init__.py", line 26, in autodiscover
autodiscover_modules('admin', register_to=site)
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/utils/module_loading.py", line 50, in autodiscover_modules
import_module('%s.%s' % (app_config.name, module_to_search))
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/importlib/__init__.py", line 109, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 2254, in _gcd_import
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1471, in exec_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/cms/admin/__init__.py", line 11, in <module>
plugin_pool.plugin_pool.discover_plugins()
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/cms/plugin_pool.py", line 32, in discover_plugins
load('cms_plugins')
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/cms/utils/django_load.py", line 57, in load
get_module(app, modname, verbose, failfast)
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/cms/utils/django_load.py", line 41, in get_module
module = import_module(module_name)
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/importlib/__init__.py", line 109, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 2254, in _gcd_import
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1471, in exec_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/cmsplugin_cascade/cms_plugins.py", line 22, in <module>
import_module('{}.cms_plugins'.format(module))
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/importlib/__init__.py", line 109, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 2254, in _gcd_import
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1471, in exec_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/cmsplugin_cascade/segmentation/cms_plugins.py", line 19, in <module>
class SegmentPlugin(TransparentMixin, CascadePluginBase):
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/cmsplugin_cascade/segmentation/cms_plugins.py", line 40, in SegmentPlugin
default_template = Template('{% load cms_tags %}{% for plugin in instance.child_plugin_instances %}{% render_plugin plugin %}{% endfor %}')
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/base.py", line 184, in __init__
engine = Engine.get_default()
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/functools.py", line 472, in wrapper
result = user_function(*args, **kwds)
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/engine.py", line 74, in get_default
django_engines = [engine for engine in engines.all()
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/utils.py", line 89, in all
return [self[alias] for alias in self]
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/utils.py", line 89, in <listcomp>
return [self[alias] for alias in self]
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/utils.py", line 80, in __getitem__
engine = engine_cls(params)
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/backends/django.py", line 30, in __init__
options['libraries'] = self.get_templatetag_libraries(libraries)
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/backends/django.py", line 48, in get_templatetag_libraries
libraries = get_installed_libraries()
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/backends/django.py", line 113, in get_installed_libraries
for name in get_package_libraries(pkg):
File "/home/vagrant/virtualenvs/django-shop/lib/python3.4/site-packages/django/template/backends/django.py", line 130, in get_package_libraries
"trying to load '%s': %s" % (entry[1], e)
django.template.library.InvalidTemplateLibrary: Invalid template library specified. ImportError raised when trying to load 'treebeard.templatetags.admin_tree_list': cannot import name 'patterns'
```
|
closed
|
2016-09-30T13:46:54Z
|
2018-01-13T08:36:06Z
|
https://github.com/awesto/django-shop/issues/431
|
[
"blocker",
"bug",
"accepted",
"documentation"
] |
petedermott
| 9
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.