idrblab / annopro Goto Github PK

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

Hi,
I have installed the annopro model and used it for predicting function of an amino acid sequence. It generated many files including the diamond_scores.txt file. This files do not seem to have a header. So I was wondering what does each column indicate here. I understood that the first column is the name of the sequence provided and the second column is probably showing uniprot id. But what about the rest?

Please have a look at the first five rows of the diamond_scores.txt file:
Secreted_Seq1 P32261 28.9 360 244 5 62 409 103 462 2.88e-34 134
Secreted_Seq1 Q60854 30.9 363 229 9 63 409 22 378 7.08e-34 131
Secreted_Seq1 P80229 32.3 372 227 10 56 409 14 378 3.59e-33 129
Secreted_Seq1 O08800 30.7 374 240 8 49 409 7 374 6.39e-33 128
Secreted_Seq1 Q9S7T8 32.2 369 215 15 67 409 30 389 8.55e-33 128

What does column 3 to 12 indicate? What are these numbers? How to interpret them?

Many thanks for your time and I look forward to hearing from you.

Best,
Sourav

Hello,

I've attempted to use your program but it is practically stuck on downloading cafa4.dmnd. The download speed is less than 1kb/sec. It seems that your server needs a restart? In case there is a fault, is there a way to download it from somewhere else and use it with AnnoPro?

Thanks,
Maxim

Bug Description

I tried to run it on the following fasta file it gives me this error:

>seq-2
MKKKKKKKLKKLKKKLKKKLKKKKKLLLLLLLLKKKKKKK
>seq-9
MKKKIKKIKKKIEKKKKKKLKKLKKKKKKKKLLLLLLLLL
>seq-10
MSEKFSEIAEKYDEERILSRSAGELAELTRELGLKPGDRVLDVGCGTGYLTLPLAERVGPEGTVIGIDRSEEMLARARERAAAAGLSNVEFQVADAEALPFPDESFDLVTCRLVLHHLPDPAKALREMRRVLKPGGRFVVSDWDASSMAFPDEEAELAERLRRYAEARAAAGGERDALRRALEAAGFRDVTVRSLTAWRRRAGEAAAAAL
>seq-13
MKKKKKLKKKLKKKKKKKK

Runtime Environment

Fresh install of requirements

Logs

annopro -i test_proteins.fasta -o output-test
Download cafa4.dmnd...
100% [........................................................................] 46988123 / 46988123
Validate md5sum of cafa4.dmnd...
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-test
#Target sequences to report alignments for: 25
Opening the database...  [0.042s]
Database: /home/ubuntu/.annopro/data/cafa4.dmnd (type: Diamond database, sequences: 87514, letters: 44798577)
Block size = 2000000000
Opening the input file...  [0s]
Opening the output file...  [0s]
Loading query sequences...  [0s]
Masking queries...  [0.001s]
Algorithm: Double-indexed
Building query histograms...  [0s]
Loading reference sequences...  [0.055s]
Masking reference...  [0.588s]
Initializing temporary storage...  [0s]
Building reference histograms...  [0.493s]
Allocating buffers...  [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 1/4.
Building reference seed array...  [0.163s]
Building query seed array...  [0s]
Computing hash join...  [0.004s]
Masking low complexity seeds...  [0s]
Searching alignments...  [0s]
Deallocating memory...  [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 2/4.
Building reference seed array...  [0.192s]
Building query seed array...  [0s]
Computing hash join...  [0.002s]
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...  [0.213s]
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...  [0.154s]
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...  [0.155s]
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...  [0.19s]
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...  [0.211s]
Building query seed array...  [0s]
Computing hash join...  [0.002s]
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...  [0.154s]
Building query seed array...  [0s]
Computing hash join...  [0.004s]
Masking low complexity seeds...  [0s]
Searching alignments...  [0s]
Deallocating memory...  [0s]
Deallocating buffers...  [0.004s]
Clearing query masking...  [0s]
Computing alignments... Loading trace points...  [0.001s]
Sorting trace points...  [0s]
Computing alignments...  [0s]
Deallocating buffers...  [0s]
Loading trace points...  [0s]
 [0.002s]
Deallocating reference...  [0.002s]
Loading reference sequences...  [0s]
Deallocating buffers...  [0s]
Deallocating queries...  [0s]
Loading query sequences...  [0s]
Closing the input file...  [0s]
Closing the output file...  [0s]
Closing the database...  [0.002s]
Cleaning up...  [0s]
Total time = 2.766s
Reported 21 pairwise alignments, 21 HSPs.
1 queries aligned.
Invalid feature 0.6934-309 for seq-13 at line 596
Invalid feature 0.6934-309 for seq-13 at line 596
Invalid feature 0.5127-315 for seq-13 at line 596
Invalid feature 0.5127-315 for seq-13 at line 596
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/annopro/bin/annopro", line 8, in <module>
    sys.exit(console_main())
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/annopro/__init__.py", line 27, in console_main
    main(
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/annopro/__init__.py", line 71, in main
    process(
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/annopro/data_procession/__init__.py", line 8, in process
    data = Data_process(protein_file=profeat_file,
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/annopro/data_procession/data_predict.py", line 36, in __init__
    self.__data__()
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/annopro/data_procession/data_predict.py", line 39, in __data__
    proteins_f = profeat_to_df(self.protein_file)
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/profeat/__init__.py", line 69, in profeat_to_df
    return pd.DataFrame(feature_list).T
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/pandas/core/frame.py", line 636, in __init__
    mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 502, in dict_to_mgr
    return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 120, in arrays_to_mgr
    index = _extract_index(arrays)
  File "/home/ubuntu/anaconda3/envs/annopro/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 674, in _extract_index
    raise ValueError("All arrays must be of the same length")
ValueError: All arrays must be of the same length

生成promap的代码（即使用序列生成类图像的过程）可否详解？我对此处有极其大的兴趣。此外如果要使用Uniprot上的数据集，所使用的数据文件是否都要进行改动？希望您能不吝赐教。祝好

Hello！ @swallow-design @GCS-ZHN
I have tried several times but each time I only get a result of 8001 sequences, but I submit 16000 sequences. Is there something wrong with my parameter settings, here is my code.

python -m annopro -i  lh_over_30bp.fa -o lh-out  --overwrite

Here is my log file

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: lh-out
#Target sequences to report alignments for: 25
Opening the database...  [0.053s]
Database: /home/liuli/.annopro/data/cafa4.dmnd (type: Diamond database, sequences: 87514, letters: 44798577)
Block size = 2000000000
Opening the input file...  [0.01s]
Opening the output file...  [0s]
Loading query sequences...  [0.035s]
Masking queries...  [0.381s]
Algorithm: Double-indexed
Building query histograms...  [0.261s]
Loading reference sequences...  [0.064s]
Masking reference...  [2.06s]
Initializing temporary storage...  [0.025s]
Building reference histograms...  [1.422s]
Allocating buffers...  [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 1/4.
Building reference seed array...  [0.503s]
Building query seed array...  [0.096s]
Computing hash join...  [0.068s]
Masking low complexity seeds...  [0.006s]
Searching alignments...  [0.087s]
Deallocating memory...  [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 2/4.
Building reference seed array...  [0.53s]
Building query seed array...  [0.099s]
Computing hash join...  [0.067s]
Masking low complexity seeds...  [0.006s]
Searching alignments...  [0.074s]
Deallocating memory...  [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 3/4.
Building reference seed array...  [0.548s]
Building query seed array...  [0.103s]
Computing hash join...  [0.068s]
Masking low complexity seeds...  [0.006s]
Searching alignments...  [0.069s]
Deallocating memory...  [0s]
Processing query block 1, reference block 1/1, shape 1/2, index chunk 4/4.
Building reference seed array...  [0.485s]
Building query seed array...  [0.091s]
Computing hash join...  [0.068s]
Masking low complexity seeds...  [0.006s]
Searching alignments...  [0.066s]
Deallocating memory...  [0s]
Processing query block 1, reference block 1/1, shape 2/2, index chunk 1/4.
Building reference seed array...  [0.484s]
Building query seed array...  [0.091s]
Computing hash join...  [0.067s]
Masking low complexity seeds...  [0.006s]
Searching alignments...  [0.068s]
Deallocating memory...  [0s]
Processing query block 1, reference block 1/1, shape 2/2, index chunk 2/4.
Building reference seed array...  [0.526s]
Building query seed array...  [0.099s]
Computing hash join...  [0.067s]
Masking low complexity seeds...  [0.006s]
Searching alignments...  [0.061s]
Deallocating memory...  [0s]
Processing query block 1, reference block 1/1, shape 2/2, index chunk 3/4.
Building reference seed array...  [0.544s]
Building query seed array...  [0.102s]
Computing hash join...  [0.067s]
Masking low complexity seeds...  [0.006s]
Searching alignments...  [0.06s]
Deallocating memory...  [0s]
Processing query block 1, reference block 1/1, shape 2/2, index chunk 4/4.
Building reference seed array...  [0.481s]
Building query seed array...  [0.09s]
Computing hash join...  [0.066s]
Masking low complexity seeds...  [0.006s]
Searching alignments...  [0.058s]
Deallocating memory...  [0s]
Deallocating buffers...  [0.009s]
Clearing query masking...  [0s]
Computing alignments... Loading trace points...  [0.035s]
Sorting trace points...  [0.012s]
Computing alignments...  [10.428s]
Deallocating buffers...  [0s]
Loading trace points...  [0s]
 [10.48s]
Deallocating reference...  [0.003s]
Loading reference sequences...  [0s]
Deallocating buffers...  [0s]
Deallocating queries...  [0s]
Loading query sequences...  [0s]
Closing the input file...  [0s]
Closing the output file...  [0.016s]
Closing the database...  [0.003s]
Cleaning up...  [0s]
Total time = 20.936s
Reported 88062 pairwise alignments, 88062 HSPs.
8348 queries aligned.
2024-07-09 10:37:31.644732: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2024-07-09 10:37:31.654184: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: gpu03
2024-07-09 10:37:31.654200: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: gpu03
2024-07-09 10:37:31.654323: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 550.54.15
2024-07-09 10:37:31.654390: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 550.54.15
2024-07-09 10:37:31.654402: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 550.54.15
2024-07-09 10:37:31.669962: 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 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-07-09 10:37:31.808310: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 754647040 exceeds 10% of free system memory.
2024-07-09 10:37:32.854598: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 754647040 exceeds 10% of free system memory.
2024-07-09 10:37:33.089177: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 754647040 exceeds 10% of free system memory.
2024-07-09 10:37:34.509640: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 754647040 exceeds 10% of free system memory.
2024-07-09 10:37:36.304317: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
2024-07-09 10:48:23.850180: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 754647040 exceeds 10% of free system memory.

Any help would be appreciated！

I am running AnnoPro for the first time. However, it seems that there are something wrong while downloading related files:

...
Deallocating reference... [0.002s]
Loading reference sequences... [0s]
Deallocating buffers... [0s]
Deallocating queries... [0s]
Loading query sequences... [0s]
Closing the input file... [0s]
Closing the output file... [0s]
Closing the database... [0.003s]
Cleaning up... [0s]
Total time = 7.186s
Reported 0 pairwise alignments, 0 HSPs.
0 queries aligned.
Download cc.h5...
Traceback (most recent call last):
...

File "/home/user/miniforge3/envs/annopro/lib/python3.8/urllib/request.py", line 649, in http_error_default
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 404: Not Found

How can I deal with this?

Hello,

I've been unable to run AnnoPRO in GPU mode due to the requirement for old CUDA drivers; this is the error that I get:

tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory

Would you be able to update the code to accommodate the latest CUDA drivers?