Hello!
I followed the installation tutorial on the README.md.
I tried both
- installing using 'pip install annopro'
- install from source code.
The commands I used are:
- python -m annopro -i data.fasta -o output --used_gpu 0,1,2,3
- python -m annopro -i data.fasta -o output
- python run.py
After these attempts, I got the same error as indicated in the Logs.
Do you know how to fix this?
Thanks in advance!
Input
The data.fasta is like:
>sp|O14793|GDF8_HUMAN Growth/differentiation factor 8 OS=Homo sapiens OX=9606 GN=MSTN PE=1 SV=1
MQKLQLCVYIYLFMLIVAGPVDLNENSEQKENVEKEGLCNACTWRQNTKSSRIEAIKIQI
LSKLRLETAPNISKDVIRQLLPKAPPLRELIDQYDVQRDDSSDGSLEDDDYHATTETIIT
MPTESDFLMQVDGKPKCCFFKFSSKIQYNKVVKAQLWIYLRPVETPTTVFVQILRLIKPM
KDGTRYTGIRSLKLDMNPGTGIWQSIDVKTVLQNWLKQPESNLGIEIKALDENGHDLAVT
FPGPGEDGLNPFLEVKVTDTPKRSRRDFGLDCDEHSTESRCCRYPLTVDFEAFGWDWIIA
PKRYKANYCSGECEFVFLQKYPHTHLVHQANPRGSAGPCCTPTKMSPINMLYFNGKEQII
YGKIPAMVVDRCGCS
The run.py is:
from annopro import main
main("data.fasta", "output", "0,1,2,3")
or
from annopro import main
main("data.fasta", "output")
Runtime Environment
- Operation System: RedHat 8.6
- Python: Python 3.8
- Tensorflow: 2.6.5
- Cuda: 11.6.1
- CuDNN: 8.4.1.50
Logs
Running "module reset". Resetting modules to system default. The following $MODULEPATH directories have been removed: None
A conda environment has been detected CONDA_PREFIX=
CONDA_PATH/envs/annopro
anaconda3_gpu is loaded. Consider running conda deactivate and reloading it.
diamond v2.1.0.154 (C) Max Planck Society for the Advancement of Science
Documentation, support and updates available at http://www.diamondsearch.org/
Please cite: http://dx.doi.org/10.1038/s41592-021-01101-x Nature Methods (2021)
#CPU threads: 4
Scoring parameters: (Matrix=BLOSUM62 Lambda=0.267 K=0.041 Penalties=11/1)
Temporary directory: output
#Target sequences to report alignments for: 25
Opening the database... [0.044s]
Database: USER_DIR/.annopro/data/cafa4.dmnd (type: Diamond database, sequences: 87514, letters: 44798577)
Block size = 2000000000
Opening the input file... [0.001s]
Opening the output file... [0s]
Loading query sequences... [0s]
Masking queries... [0s]
Algorithm: Double-indexed
Building query histograms... [0s]
Loading reference sequences... [0.042s]
Masking reference... [0.583s]
Initializing temporary storage... [0.007s]
Building reference histograms... [0.402s]
Allocating buffers... [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 1/4.
Building reference seed array... [0.159s]
Building query seed array... [0s]
Computing hash join... [0.003s]
Masking low complexity seeds... [0s]
Searching alignments... [0.001s]
Deallocating memory... [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 2/4.
Building reference seed array... [0.186s]
Building query seed array... [0s]
Computing hash join... [0.003s]
Masking low complexity seeds... [0s]
Searching alignments... [0s]
Deallocating memory... [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 3/4.
Building reference seed array... [0.202s]
Building query seed array... [0s]
Computing hash join... [0.003s]
Masking low complexity seeds... [0s]
Searching alignments... [0s]
Deallocating memory... [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 4/4.
Building reference seed array... [0.16s]
Building query seed array... [0s]
Computing hash join... [0.003s]
Masking low complexity seeds... [0s]
Searching alignments... [0s]
Deallocating memory... [0s]
Processing query block 1, reference block 1/1, shape 2/2, index chunk 1/4.
Building reference seed array... [0.151s]
Building query seed array... [0s]
Computing hash join... [0.003s]
Masking low complexity seeds... [0s]
Searching alignments... [0s]
Deallocating memory... [0s]
Processing query block 1, reference block 1/1, shape 2/2, index chunk 2/4.
Building reference seed array... [0.186s]
Building query seed array... [0s]
Computing hash join... [0.003s]
Masking low complexity seeds... [0s]
Searching alignments... [0s]
Deallocating memory... [0s]
Processing query block 1, reference block 1/1, shape 2/2, index chunk 3/4.
Building reference seed array... [0.201s]
Building query seed array... [0s]
Computing hash join... [0.003s]
Masking low complexity seeds... [0s]
Searching alignments... [0s]
Deallocating memory... [0s]
Processing query block 1, reference block 1/1, shape 2/2, index chunk 4/4.
Building reference seed array... [0.155s]
Building query seed array... [0s]
Computing hash join... [0.004s]
Masking low complexity seeds... [0s]
Searching alignments... [0s]
Deallocating memory... [0s]
Deallocating buffers... [0s]
Clearing query masking... [0s]
Computing alignments... Loading trace points... [0.014s]
Sorting trace points... [0s]
Computing alignments... [0.004s]
Deallocating buffers... [0s]
Loading trace points... [0s]
[0.022s]
Deallocating reference... [0s]
Loading reference sequences... [0s]
Deallocating buffers... [0s]
Deallocating queries... [0s]
Loading query sequences... [0s]
Closing the input file... [0s]
Closing the output file... [0.001s]
Closing the database... [0s]
Cleaning up... [0s]
Total time = 2.668s
Reported 5 pairwise alignments, 5 HSPs.
1 queries aligned.
2023-06-16 13:27:18.651280: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-06-16 13:27:27.991777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43511 MB memory: -> device: 0, name: NVIDIA A40, pci bus id: 0000:07:00.0, compute capability: 8.6
2023-06-16 13:27:28.072954: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 43511 MB memory: -> device: 1, name: NVIDIA A40, pci bus id: 0000:46:00.0, compute capability: 8.6
2023-06-16 13:27:28.074780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 43511 MB memory: -> device: 2, name: NVIDIA A40, pci bus id: 0000:85:00.0, compute capability: 8.6
2023-06-16 13:27:28.076816: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 43511 MB memory: -> device: 3, name: NVIDIA A40, pci bus id: 0000:c7:00.0, compute capability: 8.6
Traceback (most recent call last):
File "CONDA_PATH/envs/annopro/lib/python3.8/run.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "CONDA_PATH/envs/annopro/lib/python3.8/run.py", line 87, in _run_code
exec(code, run_globals)
File "PROJECT_PATH/AnnoPRO/annopro/main.py", line 4, in
console_main()
File "PROJECT_PATH/AnnoPRO/annopro/init.py", line 27, in console_main
main(
File "PROJECT_PATH/AnnoPRO/annopro/init.py", line 75, in main
predict(output_dir=output_dir,
File "PROJECT_PATH/AnnoPRO/annopro/prediction.py", line 19, in predict
init_evaluate(term_type=term_type,
File "PROJECT_PATH/AnnoPRO/annopro/prediction.py", line 160, in init_evaluate
preds = model.predict(data_generator, steps=data_steps)
File "CONDA_PATH/envs/annopro/lib/python3.8/site-packages/keras/engine/training.py", line 1720, in predict
data_handler = data_adapter.get_data_handler(
File "CONDA_PATH/envs/annopro/lib/python3.8/site-packages/keras/engine/data_adapter.py", line 1383, in get_data_handler
return DataHandler(*args, **kwargs)
File "CONDA_PATH/envs/annopro/lib/python3.8/site-packages/keras/engine/data_adapter.py", line 1138, in init
self._adapter = adapter_cls(
File "CONDA_PATH/envs/annopro/lib/python3.8/site-packages/keras/engine/data_adapter.py", line 917, in init
super(KerasSequenceAdapter, self).init(
File "CONDA_PATH/envs/annopro/lib/python3.8/site-packages/keras/engine/data_adapter.py", line 794, in init
peek, x = self._peek_and_restore(x)
File "CONDA_PATH/envs/annopro/lib/python3.8/site-packages/keras/engine/data_adapter.py", line 928, in _peek_and_restore
return x[0], x
File "PROJECT_PATH/AnnoPRO/annopro/prediction.py", line 50, in getitem
return ([data_onehot, data_si])
UnboundLocalError: local variable 'data_onehot' referenced before assignment