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Apply for community grant: Academic project (gpu)
#1
by
robinwitch
- opened
My project is an academic and opensource project. Focus on speech-to-gesture.
Here is my github link: https://github.com/RobinWitch/SynTalker.
Here is my project websit link: https://robinwitch.github.io/SynTalker-Page/
T4 gpu is enough for me.
Thank your a lot!
Hi @robinwitch , we've assigned ZeroGPU to this Space. Please check the compatibility and usage sections of this page so your Space can run on ZeroGPU.
@hysts Thank you a lot!
I make a mistake to change hardware to CPU basic, and I can't change it to ZeroGPU, it shows that "Only Hugging Face team members can update the sleep time of a Space with a community grant.".
Can you help me change it?
Done!
@hysts
I meet a strange problem while using zerogpu.
Beacuse I find I have a gpu use time limitation, so I just using @spaces.GPU(duration=80) on only one function _warp at row 337 in app.py.
debug4:finish it!
debug5:finish it!
2024-10-10 11:13:11.582 | ERROR | gradio.utils:wrapped:1325 - An error has been caught in function 'wrapped', process 'MainProcess' (1), thread 'AnyIO worker thread' (139876703913664):
Traceback (most recent call last):
File "/usr/local/lib/python3.10/threading.py", line 973, in _bootstrap
self._bootstrap_inner()
│ └ <function Thread._bootstrap_inner at 0x7f379fe7ed40>
└ <WorkerThread(AnyIO worker thread, started daemon 139876703913664)>
File "/usr/local/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
self.run()
│ └ <function WorkerThread.run at 0x7f3799d17f40>
└ <WorkerThread(AnyIO worker thread, started daemon 139876703913664)>
File "/usr/local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 914, in run
result = context.run(func, *args)
│ │ │ └ (<generator object Examples.sync_lazy_cache at 0x7f379a8a63b0>,)
│ │ └ <function run_sync_iterator_async at 0x7f36f446feb0>
│ └ <method 'run' of '_contextvars.Context' objects>
└ <_contextvars.Context object at 0x7f379a964cc0>
File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 647, in run_sync_iterator_async
return next(iterator)
└ <generator object Examples.sync_lazy_cache at 0x7f379a8a63b0>
File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 809, in gen_wrapper
response = next(iterator)
└ <generator object Examples.sync_lazy_cache at 0x7f3799d48c10>
File "/usr/local/lib/python3.10/site-packages/gradio/helpers.py", line 451, in sync_lazy_cache
for output in fn(*input_values):
│ │ └ ((16000, array([ 20, 20, 32, ..., -44, 8, -11], dtype=int16)), 0)
│ └ <function sync_fn_to_generator.<locals>.wrapped at 0x7f3799f3f400>
└ [None, None]
> File "/usr/local/lib/python3.10/site-packages/gradio/utils.py", line 1325, in wrapped
yield fn(*args, **kwargs)
│ │ └ {}
│ └ ((16000, array([ 20, 20, 32, ..., -44, 8, -11], dtype=int16)), 0)
└ <function syntalker at 0x7f3799d84c10>
File "/home/user/app/app.py", line 755, in syntalker
result = trainer.test_demo(999)
│ └ <function BaseTrainer.test_demo at 0x7f379a8badd0>
└ <__main__.BaseTrainer object at 0x7f3799d26fb0>
File "/home/user/app/app.py", line 270, in test_demo
net_out = _warp(self.args,self.model, batch_data,self.joints,self.joint_mask_upper,self.joint_mask_hands,self.joint_mask_lower,self.use_trans,self.mean_upper,self.mean_hands,self.mean_lower,self.std_upper,self.std_hands,self.std_lower,self.trans_mean,self.trans_std)
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ array([0.00434298, 0.01658506, 0.00604226])
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ array([-1.03093567e-05, 1.30673285e+00, 3.21644151e-05])
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ array([0.15210988, 0.09043588, 0.32370892, 0.04405705, 0.02246166,
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ 0.08144826, 0.02109734, 0.10316863, 0.06898032, 0.0...
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ array([0.06018772, 0.23905125, 0.11087885, 0.24111511, 0.0542646 ,
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ 0.05752097, 0.125199 , 0.22886859, 0.06037393, 0.2...
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ array([0.00276254, 0.04254209, 0.04602624, 0.04832461, 0.02070488,
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ 0.07023287, 0.0021067 , 0.03852088, 0.03396116, 0.0...
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ array([ 0.9268511 , 0.06455171, 0.0322784 , -0.06788209, 0.9549398 ,
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ 0.27286723, 0.9663208 , -0.09199008, 0.2048...
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ array([ 0.93831116, 0.07900303, 0.20092662, -0.06968854, 0.96385115,
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ -0.04870924, 0.89996815, 0.31663465, -0.1351...
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ array([ 0.998115 , 0.00287484, 0.04198178, 0.00790095, 0.96294385,
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ -0.25487295, 0.9989194 , 0.01088245, -0.0377...
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ └ True
│ │ │ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ │ │ └ array([1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1.,
│ │ │ │ │ │ │ │ │ │ │ │ │ 1., 0., 0., 0., 1., 1., 1., 1., 1., 1., 0.,...
│ │ │ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ │ │ └ array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
│ │ │ │ │ │ │ │ │ │ │ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,...
│ │ │ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ │ │ └ array([0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0.,
│ │ │ │ │ │ │ │ │ 0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 1.,...
│ │ │ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ │ │ └ 55
│ │ │ │ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ │ │ │ └ {'pose': tensor([[[-0.1800, -0.0463, -0.0809, ..., 1.0000, 1.0000, 1.0000],
│ │ │ │ │ [-0.1806, -0.0473, -0.0812, ..., 1...
│ │ │ │ └ MDM(
│ │ │ │ (WavEncoder): WavEncoder(
│ │ │ │ (feat_extractor): Sequential(
│ │ │ │ (0): BasicBlock(
│ │ │ │ (conv1): Conv1d(2, 64, kern...
│ │ │ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
│ │ └ Namespace(config='configs/diffusion_rvqvae_128_hf.yaml', project='s2g', stat='ts', csv_name='a2g_0', notes='', trainer='diffu...
│ └ <__main__.BaseTrainer object at 0x7f3799d26fb0>
└ <function _warp at 0x7f379a8bae60>
File "/usr/local/lib/python3.10/site-packages/spaces/zero/wrappers.py", line 211, in gradio_handler
raise gr.Error("GPU task aborted")
│ └ <class 'gradio.exceptions.Error'>
└ <module 'gradio' from '/usr/local/lib/python3.10/site-packages/gradio/__init__.py'>
gradio.exceptions.Error: 'GPU task aborted'
Note that my _warp function has complete run, because I add debug5:finish it! in the end of this function.
When completed this decorator function _warp, my process seems like been kill by the error GPU task aborted.
Hum, can you give me some advice? You can simply try my app by click any examples.
By the way, you may meet error "You have exceeded your GPU quota (80s requested vs. 80s left).", just try another times!
cc @cbensimon
I have solved it!
Awesome!