danloran / mellodetect Goto Github PK
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License: GNU General Public License v2.0
download isic dataset along with classification associated with each image.
decide on best filetype and method to store images.
Good questions include:
Error when using workers
in DataLoader in beefyRunner and basicRunner. I think there might be some minimum requirements that I might not meet by running on docker.
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
0%| | 0/10 [00:01<?, ?it/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 761, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/usr/lib/python3.6/multiprocessing/queues.py", line 104, in get
if not self._poll(timeout):
File "/usr/lib/python3.6/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/usr/lib/python3.6/multiprocessing/connection.py", line 414, in _poll
r = wait([self], timeout)
File "/usr/lib/python3.6/multiprocessing/connection.py", line 911, in wait
ready = selector.select(timeout)
File "/usr/lib/python3.6/selectors.py", line 376, in select
fd_event_list = self._poll.poll(timeout)
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 124) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
When executing the files in the /test directory I get the error.
E fixture 'fs' not found
@sam0109 Do you know how can I solve this? Do I need any extra dependency?
Encode a few augmentations
Which augmentations should be prioritized?
Baseline against existing CV models
runtime, training time, size of model
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