Comments (7)
cool. many thanks. works
from super-gradients.
Join the discussion on DagsHub!
from super-gradients.
Hi @ngocnd2402.
This is a general error of Python on Windows.
We do not support Windows officially, but if you provide some more information, perhaps we could help.
Could you send information on the OS, relevant HW (GPU + CUDA), and Python environment?
Can you share the full error stack trace?
from super-gradients.
I have the same problem:
Windows Server 2022
Torch 1.13.0
Quadro P4000 GPU
Python 3.8
CUDA 12.0
AttributeError Traceback (most recent call last)
Cell In[1], line 1
----> 1 from super_gradients.training import models
3 yolo_nas_l = models.get("yolo_nas_l", pretrained_weights="coco")
File ~\anaconda3\envs\yolo-nas\lib\site-packages\super_gradients_init_.py:2
1 from super_gradients.common import init_trainer, is_distributed, object_names
----> 2 from super_gradients.training import losses, utils, datasets_utils, DataAugmentation, Trainer, KDTrainer, QATTrainer
3 from super_gradients.common.registry.registry import ARCHITECTURES
4 from super_gradients.sanity_check import env_sanity_check
File ~\anaconda3\envs\yolo-nas\lib\site-packages\super_gradients\training_init_.py:2
1 # PACKAGE IMPORTS FOR EXTERNAL USAGE
----> 2 import super_gradients.training.utils.distributed_training_utils as distributed_training_utils
3 from super_gradients.training.datasets import datasets_utils, DataAugmentation
4 from super_gradients.training.sg_trainer import Trainer
File ~\anaconda3\envs\yolo-nas\lib\site-packages\super_gradients\training\utils\distributed_training_utils.py:13
11 from torch.distributed.elastic.multiprocessing import Std
12 from torch.distributed.elastic.multiprocessing.errors import record
---> 13 from torch.distributed.launcher.api import LaunchConfig, elastic_launch
15 from super_gradients.common.environment.ddp_utils import init_trainer
16 from super_gradients.common.data_types.enum import MultiGPUMode
File ~\anaconda3\envs\yolo-nas\lib\site-packages\torch\distributed\launcher_init_.py:10
1 #!/usr/bin/env/python3
2
3 # Copyright (c) Facebook, Inc. and its affiliates.
(...)
6 # This source code is licensed under the BSD-style license found in the
7 # LICENSE file in the root directory of this source tree.
---> 10 from torch.distributed.launcher.api import ( # noqa: F401
11 LaunchConfig,
12 elastic_launch,
13 launch_agent,
14 )
File ~\anaconda3\envs\yolo-nas\lib\site-packages\torch\distributed\launcher\api.py:15
13 import torch.distributed.elastic.rendezvous.registry as rdzv_registry
14 from torch.distributed.elastic import events, metrics
---> 15 from torch.distributed.elastic.agent.server.api import WorkerSpec
16 from torch.distributed.elastic.agent.server.local_elastic_agent import LocalElasticAgent
17 from torch.distributed.elastic.multiprocessing import SignalException, Std
File ~\anaconda3\envs\yolo-nas\lib\site-packages\torch\distributed\elastic\agent\server_init_.py:40
9 """
10 The elastic agent is the control plane of torchelastic. It is a process
11 that launches and manages underlying worker processes. The agent is
(...)
28 in the same job) to make a collective decision.
29 """
31 from .api import ( # noqa: F401
32 ElasticAgent,
33 RunResult,
(...)
38 WorkerState,
39 )
---> 40 from .local_elastic_agent import TORCHELASTIC_ENABLE_FILE_TIMER, TORCHELASTIC_TIMER_FILE
File ~\anaconda3\envs\yolo-nas\lib\site-packages\torch\distributed\elastic\agent\server\local_elastic_agent.py:19
16 import uuid
17 from typing import Any, Dict, Optional, Tuple
---> 19 import torch.distributed.elastic.timer as timer
20 from torch.distributed.elastic import events
22 from torch.distributed.elastic.agent.server.api import (
23 RunResult,
24 SimpleElasticAgent,
(...)
27 WorkerState,
28 )
File ~\anaconda3\envs\yolo-nas\lib\site-packages\torch\distributed\elastic\timer_init_.py:44
42 from .api import TimerClient, TimerRequest, TimerServer, configure, expires # noqa: F401
43 from .local_timer import LocalTimerClient, LocalTimerServer # noqa: F401
---> 44 from .file_based_local_timer import FileTimerClient, FileTimerServer, FileTimerRequest
File ~\anaconda3\envs\yolo-nas\lib\site-packages\torch\distributed\elastic\timer\file_based_local_timer.py:63
51 def to_json(self) -> str:
52 return json.dumps(
53 {
54 "version": self.version,
(...)
59 },
60 )
---> 63 class FileTimerClient(TimerClient):
64 """
65 Client side of FileTimerServer
. This client is meant to be used
66 on the same host that the FileTimerServer
is running on and uses
(...)
79 negative or zero signal will not kill the process.
80 """
81 def init(self, file_path: str, signal=signal.SIGKILL) -> None:
File ~\anaconda3\envs\yolo-nas\lib\site-packages\torch\distributed\elastic\timer\file_based_local_timer.py:81, in FileTimerClient()
63 class FileTimerClient(TimerClient):
64 """
65 Client side of FileTimerServer
. This client is meant to be used
66 on the same host that the FileTimerServer
is running on and uses
(...)
79 negative or zero signal will not kill the process.
80 """
---> 81 def init(self, file_path: str, signal=signal.SIGKILL) -> None:
82 super().init()
83 self._file_path = file_path
AttributeError: module 'signal' has no attribute 'SIGKILL'
from super-gradients.
I solve it by go to the file of the error and replace SIGKILL into SIGILL
from super-gradients.
@ngocnd2402 , does this solve your issue ?
from super-gradients.
yeah, it works. Thanks <3
from super-gradients.
Related Issues (20)
- Work with keypoints for recognize some poses HOT 1
- Custom metrics that depends on image_path?
- DetectionRandomAffine target-size is in wrong format HOT 2
- COCO Recipe reporting low precision HOT 1
- ImportError: cannot import name 'utils' from partially initialized module 'super_gradients.training' (most likely due to a circular import HOT 4
- yolo-nas-sat model availability
- AttributeError: 'RegSeg48' object has no attribute 'set_dataset_processing_params' HOT 1
- How to set different weight decay values for different modules of the model
- yolo nas pose demo/colab is broken HOT 1
- How to get edge_links, edge_colors, keypoint_colors when using yolo nas pose onnx?
- Validation metrics = 0.0 during training yolo-nas
- YOLO NAS'S Precision is significantly lower compare to other later YOLO model even when using same dataset ? HOT 4
- BaseSGLogger storage_location parameter is systematically overriden, why?
- Access Joints Coordinate
- Ground tensor shape issue when training YOLO_NAS_S model on a custom dataset HOT 1
- Issue when training and predicting with a custom dataset and the YOLO_NAS_S model HOT 2
- Model training process halted for small dataset HOT 4
- Inquiry About Official Release Date of OBB Detection Models for YOLO-NAS and Training HOT 1
- ONNX Export Output has Incorrect Class Labels but Correct Box and Confidence HOT 1
- Procuring license for commerical application of YOLO - NAS (with pre-trained weights)
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from super-gradients.