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License: GNU General Public License v3.0
It seems CUDA11.8 only works with pytorch version > 2.1 & python > 3.8.
But in conda install , COMEBin only works with python2.7 & pytorch 1.0 ?
Hi,
COMEBin cannot read gzip compressed fasta files.
Could you implement this feature?
Thanks,
Florian
Hi
Thanks for the great tool. I am currently looking into plant metagenomic data - so host contamination/complexity - so I wasn wondering if COMEBin been assessed on how it perform with eukaryotic contamination? I noticed that COMEBin predict lot higher number of bins in comparison to tools like MetaBat2 or MaxBin2 (eg 105 vs 26/10 bins from metabat/maxbin) - so was curious as to what might be the cause? any thoughts would be much appreciated.
Hello,
I'm getting an error that was seen in a closed thread, but the fixes did not address my problem. I'm running v1.0.4 on an assembly that has >56,000 contigs. I've tried using a BAM file created through the "gen_cov_file.sh" script. The error is:
Traceback (most recent call last):
File "main.py", line 369, in
main()
File "main.py", line 289, in main
train_CLmodel(logger,args)
File "/projects/comp_genomics/sahl/tools/miniconda/envs/comebin_env/bin/COMEBin/train_CLmodel.py", line 236, in train_CLmodel
simclr.train_addpretrain(train_loader, dataset, namelist)
File "/projects/comp_genomics/sahl/tools/miniconda/envs/comebin_env/bin/COMEBin/simclr.py", line 300, in train_addpretrain
top1, top5 = accuracy(logits, labels, topk=(1, 5))
UnboundLocalError: local variable 'logits' referenced before assignment
Something went wrong with running training network. Exiting.
I have successfully run the pipeline on the sample data. Any help would be appreciated.
thanks
Hi this tool looks great, and am excited to give it a go. I'm just curious what set of marker genes are used in the final clustering step. I am interested in generating eukaryote MAGs, though. Are the markers prokaryote-specific and thus exclude any eukaryote bins from the final output?
Hi there - great tool - I'm really excited about using this, but what I've noticed is that regardless of the value of -t
, the training part of the algorithm always uses all available CPU cores (in the absence of a GPU). Is there a way to control this better? If this can be addressed I can continue with my testing of the tool :)
Hello Ziye:
I tried to run the latest version of COMEBin with CPU/GPU and there is something wrong happened:
100%|██████████| 1665/1665 [10:54<00:00, 2.54it/s]
100%|██████████| 1665/1665 [10:55<00:00, 2.54it/s]
100%|██████████| 1665/1665 [10:45<00:00, 2.58it/s]
100%|██████████| 1665/1665 [11:01<00:00, 2.52it/s]
66%|██████▌ | 1096/1665 [07:14<03:45, 2.52it/s]
Traceback (most recent call last):
File "main.py", line 369, in <module>
main()
File "main.py", line 289, in main
train_CLmodel(logger,args)
File "/public/home/hymeta/anaconda3/envs/comebin/bin/COMEBin/train_CLmodel.py", line 236, in train_CLmodel
simclr.train_addpretrain(train_loader, dataset, namelist)
File "/public/home/hymeta/anaconda3/envs/comebin/bin/COMEBin/simclr.py", line 283, in train_addpretrain
logits, labels = self.info_nce_loss(features)
File "/public/home/hymeta/anaconda3/envs/comebin/bin/COMEBin/simclr.py", line 42, in info_nce_loss
labels = (labels.unsqueeze(0) == labels.unsqueeze(1)).float()
RuntimeError: [enforce fail at CPUAllocator.cpp:68] . DefaultCPUAllocator: can't allocate memory: you tried to allocate 150994944 bytes. Error code 12 (Cannot allocate memory)
Something went wrong with running training network. Exiting.
By the way, the memory in our HPC node is 512GB(CPU) and 64GB(GPU)
I am confused. Thanks in advance
Best wishes
Joey
Hello, thank you very much for providing such an excellent binning tool.
I have noticed that Metabinner also originates from your team. Could you please share your recommendation on which one you would suggest using? Thank you very much!
Hi, any idea what this is? My data is single-read and things worked fine until this step.
Running getting representation.
2024-03-01 20:41:57,205 - train
2024-03-01 20:41:57,231 - Generate features for the data.
Traceback (most recent call last):
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/main.py", line 369, in
main()
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/main.py", line 289, in main
train_CLmodel(logger,args)
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/train_CLmodel.py", line 38, in train_CLmodel
dataset, namelist = get_ContrastiveLearningDataset(args.data, args.n_views,
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/get_augfeature.py", line 155, in get_ContrastiveLearningDataset
dataset, namelist = get_kmer_coverage(data_path, n_views, kmer_model_path, device, nokmer, cov_meannormalize, cov_minmaxnormalize, cov_standardization,addvars,vars_sqrt,kmer_l2_normalize,kmerMetric_notl2normalize)
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/get_augfeature.py", line 41, in get_kmer_coverage
covIdxArr = np.empty(len(mapObj), dtype=np.int)
File "/mnt/scratch/robinm/software/mambaforge/lib/python3.10/site-packages/numpy/init.py", line 324, in getattr
raise AttributeError(former_attrs[attr])
AttributeError: module 'numpy' has no attribute 'int'.
np.int
was a deprecated alias for the builtin int
. To avoid this error in existing code, use int
by itself. Doing this will not modify any behavior and is safe. When replacing np.int
, you may wish to use e.g. np.int64
or np.int32
to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations. Did you mean: 'inf'?
Something went wrong with running training network. Exiting.
Hi,
First of all, thanks for developing ComeBin.
I was wondering if you had considered switching to checkm2 instead of checkm to determine the best settings for graph clustering?
Checkm2 is much faster than checkm (especially for low-completeness bins) and provide betters estimates.
Best,
Florian
Hi again! This time I have a new error ;)
I copy here the comebin.log. I dont understand why it says the files are not there, because they are there?
cat comebin.log
2024-02-07 14:39:07,115 - train
2024-02-07 14:39:07,287 - Generate features for the data.
2024-02-07 14:43:17,827 - Finish training.
2024-02-07 14:43:21,169 - bin
2024-02-07 14:43:21,170 - exec cmd: run_FragGeneScan.pl -genome=/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta -out=/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.frag -complete=0 -train=complete -thread=5 1>/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.frag.out 2>/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.frag.err
2024-02-07 14:43:24,136 - exec cmd: hmmsearch --domtblout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout --cut_tc --cpu 5 /ttmgr/COMEBin/COMEBin/../auxiliary/bacar_marker.hmm /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.frag.faa 1>/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout.out 2>/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout.err
2024-02-07 14:43:24,613 - exec cmd: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 14:43:24,648 - markerCmd failed! Not exist: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 14:43:24,648 - Start clustering.
2024-02-07 14:43:24,700 - N50: 7243
2024-02-07 14:43:27,335 - get_result
2024-02-07 14:43:28,662 - exec cmd: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 14:43:28,675 - markerCmd failed! Not exist: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 14:43:28,675 - Seed_num: 0
2024-02-07 14:43:28,675 - Run unitem profile: 0
2024-02-07 15:00:46,408 - bin
2024-02-07 15:00:46,418 - exec cmd: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 15:00:47,444 - markerCmd failed! Not exist: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 15:00:47,445 - Start clustering.
2024-02-07 15:00:48,023 - N50: 7243
2024-02-07 15:00:58,941 - get_result
2024-02-07 15:00:59,415 - exec cmd: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 15:00:59,430 - markerCmd failed! Not exist: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 15:00:59,431 - Seed_num: 0
2024-02-07 15:00:59,431 - Run unitem profile: 0
thanks in advance!
Hi,
I tried to run comebin like this:
run_comebin.sh -a /projects/0/prjs0956/assembly/megahit/binned/SB_DM_F5B_0-2_II_assembly/final.contigs.fasta \ -p /projects/0/prjs0956/mapping/ \ -o /projects/0/prjs0956/binning/comebin_out/ \ -t 64
but I got this error
`Traceback (most recent call last):
File "/gpfs/work4/0/prjs0956/tools/miniconda3/envs/comebin_env/bin/COMEBin/data_aug/gen_cov.py", line 24, in call
result = self.__callable(*args, **kwargs)
File "/gpfs/work4/0/prjs0956/tools/miniconda3/envs/comebin_env/bin/COMEBin/data_aug/gen_cov.py", line 129, in calculate_coverage_samplebyindex
start = aug_seq_info_dict[contig_name][0]
KeyError: 'NODE_1_length_355648_cov_13.388902'
Traceback (most recent call last):
File "main.py", line 369, in
main()
File "main.py", line 353, in main
run_gen_cov(logger, args)
File "/gpfs/work4/0/prjs0956/tools/miniconda3/envs/comebin_env/bin/COMEBin/data_aug/gen_cov.py", line 302, in run_gen_cov
gen_cov_from_bedout(logger, args.out_augdata_path, out, num_aug=args.n_views-1, contig_len=args.contig_len,num_process=args.num_threads)
File "/gpfs/work4/0/prjs0956/tools/miniconda3/envs/comebin_env/bin/COMEBin/data_aug/gen_cov.py", line 270, in gen_cov_from_bedout
res_mat = pd.read_csv(cov_file, sep='\t', header=0, index_col=0)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 586, in read_csv
return _read(filepath_or_buffer, kwds)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 482, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 811, in init
self._engine = self._make_engine(self.engine)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 1040, in _make_engine
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 51, in init
self._open_handles(src, kwds)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/base_parser.py", line 229, in _open_handles
errors=kwds.get("encoding_errors", "strict"),
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/common.py", line 707, in get_handle
newline="",
FileNotFoundError: [Errno 2] No such file or directory: '/gpfs/work4/0/prjs0956/binning/comebin_out/data_augmentation/depth/SB_DM_F5B_0-2_II.bam_0_depth.txt_aug1_data_cov.csv'
`
Any ideas why?
thanks!
Menia
Hello!
I wanted to use your tool on my sequencing data, but when I run run_comebin.sh I get this error message:
N50: 3523
Tau(temperature): 0.15
./processing/combined/data_augmentation does not exist.
Running data augmentation.
Something went wrong with running generating augmentation data. Exiting.
The first step, gen_cov_file.sh, gave me the following output:
assembly.fa
assembly.fa.bwt
filtered_barcode01_passed.bam
filtered_barcode11_passed.bam
filtered_barcode14_passed.bam
assembly.fa.amb
assembly.fa.pac
filtered_barcode06_passed.bam
filtered_barcode12_passed.bam
assembly.fa.ann assembly.fa.sa
filtered_barcode07_passed.bam
filtered_barcode13_passed.bam
I am a bit lost! Thanks in advance :)
Hi, I'm trying to run COMEBin but I'm encountering the following issue:
Traceback (most recent call last):
File "main.py", line 368, in <module>
main()
File "main.py", line 352, in main
run_gen_cov(logger, args)
File "/work/ese-alexp/software/python/anaconda3/2021.11/envs/comebin_v103/bin/COMEBin/data_aug/gen_cov.py", line 302, in run_gen_cov
gen_cov_from_bedout(logger, args.out_augdata_path, out, num_aug=args.n_views-1, contig_len=args.contig_len,num_process=args.num_threads)
File "/work/ese-alexp/software/python/anaconda3/2021.11/envs/comebin_v103/bin/COMEBin/data_aug/gen_cov.py", line 244, in gen_cov_from_bedout
joined.to_csv(outfile, sep='\t', header=True)
UnboundLocalError: local variable 'joined' referenced before assignment
This is the command I'm running:
run_comebin.sh -a contigs_1000.fasta -o comebin_bins -p /data/sort_bam_files -t 40
This is the log file:
2023-11-01 15:02:38,764 - generate_aug_data: fastafile
2023-11-01 15:05:53,818 - aug: 1
2023-11-01 15:06:55,554 - aug: 2
2023-11-01 15:08:01,244 - aug: 3
2023-11-01 15:09:04,357 - aug: 4
2023-11-01 15:10:05,453 - aug: 5
2023-11-01 15:11:10,134 - Generate coverage files from bam files.
Traceback (most recent call last):
File "main.py", line 368, in <module>
main()
File "main.py", line 352, in main
run_gen_cov(logger, args)
File "/work/ese-alexp/software/python/anaconda3/2021.11/envs/comebin_v103/bin/COMEBin/data_aug/gen_cov.py", line 302, in run_gen_cov
gen_cov_from_bedout(logger, args.out_augdata_path, out, num_aug=args.n_views-1, contig_len=args.contig_len,num_process=args.num_threads)
File "/work/ese-alexp/software/python/anaconda3/2021.11/envs/comebin_v103/bin/COMEBin/data_aug/gen_cov.py", line 244, in gen_cov_from_bedout
joined.to_csv(outfile, sep='\t', header=True)
UnboundLocalError: local variable 'joined' referenced before assignment
Hi,
I have used Comebin several times without any issues, but I encountered a problem when I tried to run it on a large dataset. I ran Comebin in CPU mode on a dataset containing more than 2.5 million contigs. I received the following message:
2024-06-12 14:45:48,897 - bin
2024-06-12 14:45:48,900 - Start clustering.
2024-06-12 14:47:19,684 - N50: 4304
2024-06-12 14:47:27,225 - Run weighted seed k-means for obtaining the SCG information of the contigs within a manageable time during the final step.
2024-06-12 14:47:27,225 - Bin_numbers: [2539]
2024-06-12 14:47:27,225 - 2539
/home/user/.conda/envs/comebin_env/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py:705: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
"timeout or by a memory leak.", UserWarning
It has been stuck at this step for 2 days now. I checked Comebin's status in htop and saw that it is still running:
/home/user/.conda/envs/comebin_env/bin/python -m joblib.externals.loky.backend.popen_loky_posix --process-name LokyProcess-1 --pipe 14
/home/user/.conda/envs/comebin_env/bin/python -m joblib.externals.loky.backend.popen_loky_posix --process-name LokyProcess-2 --pipe 15
/home/user/.conda/envs/comebin_env/bin/python -m joblib.externals.loky.backend.popen_loky_posix --process-name LokyProcess-3 --pipe 16
...
Is this normal? Has anyone else encountered this message before? I googled the message and found that it does not necessarily affect the results. Should I continue to wait for the results? The program has been running for about 20 days now.
I have 192 GB RAM and 47 threads. Command used: Comebin: -t 46
Conda version: 23.3.1
Best,
Roland
Hello,
Thank you for this new tool. I was wondering if we can use the coverage table generated by METABAT2 directly with comebin, instead of generating a new one?
Cheers, Adi
Hi! Sorry to bug you again.
I've tested COMEBin with the test data provided and a reduced dataset and everything ran fine.
Now I'm trying to bin contigs generated by the co-assembly of five samples, but I'm consistently getting this error after the Run unitem profile
step:
Traceback (most recent call last):
File "main.py", line 369, in <module>
main()
File "main.py", line 365, in main
run_get_final_result(logger, args, seed_num, num_threads, ignore_kmeans_res=True)
File "/stor/progetti/p1037/p1037u03/miniconda3/envs/comebin/bin/COMEBin/get_final_result.py", line 232, in run_get_final_result
best_method = estimate_bins_quality_nobins(bac_mg_table, ar_mg_table, args.output_path + '/cluster_res/',ignore_kmeans_res=ignore_kmeans_res)
File "/stor/progetti/p1037/p1037u03/miniconda3/envs/comebin/bin/COMEBin/get_final_result.py", line 190, in estimate_bins_quality_nobins
bin_quality_dict, best_method = get_bin_quality(orig_bins, methods_sorted, markers)
File "/stor/progetti/p1037/p1037u03/miniconda3/envs/comebin/bin/COMEBin/get_final_result.py", line 90, in get_bin_quality
sum_max = max(sum_list)
ValueError: max() arg is an empty sequence
Any idea on what might be causing it?
Thank you!
Riccardo
Here's the full log:
comebin_error_15Mar24.txt
Hello folks,
Great tool so far, I appreciate the ease of use/install compared to the complexity of metaWRAP and DAS_tool dependencies.
I had a question about how to best parse the output of bins. Running the tool through a single sample, it looks like two folders of bins are created - comebin_res_bins
and cluster_res/$(best model name)_bins
From what I can tell, the comebin_res_bins
is the initial split of every contig such that all contigs are placed into some bin, while the cluster_res
result only has some contigs placed into bins, based on your Leiden clustering. When I run the comebin_res_bins
fastas through CheckM1 - I get 94 bins meeting 50% completeness, 10% contamination. But, I can see the CheckM1 output provided by combin for the cluster_res
bins given in cluster_res/unitem_profile
, which has only 30 clustered bins that reach > 50% complete, < 10% contaminated. The number of higher quality (90% complete, 5% contamination) bins is also higher noticably before clustering (33 vs. 16).
Is the clustering performing a dereplication function similar to dRep, where only the best bin from a kmer-coverage-similarity group is kept? I often use % nucleotides binned in medium-quality MAGs as a metric for evaluating binning software, so this difference (26% nucleotides binned after clustering vs. 56% nucleotides binned before clustering) jumped out at me. I can appreciate the value in having fewer, consolidated bins per predicted taxa, but I just wanted to confirm what this tool is doing before I move forward with any post processing of the MAGs.
Thanks for your help!
COMEBin looks very interesting!
I would really like to use this with Apple silicon metal performance shaders ('mps') support instead of cuda.
I could be able to help add some of the changes but it looks like mps does not support autocast which is currently being worked on in this PR
$ conda create -y -n comebin-1.0.4 -c pytorch -c nvidia -c conda-forge -c bioconda comebin=1.0.4 pytorch pytorch-cuda=11.8
Channels:
- pytorch
- nvidia
- conda-forge
- bioconda
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /home/apps/conda/miniconda3/envs/comebin-1.0.4
added / updated specs:
- comebin=1.0.4
- pytorch
- pytorch-cuda=11.8
The following packages will be downloaded:
package | build
---------------------------|-----------------
_openmp_mutex-4.5 | 2_kmp_llvm 6 KB conda-forge
blas-2.121 | mkl 14 KB conda-forge
blas-devel-3.9.0 | 21_linux64_mkl 14 KB conda-forge
cached-property-1.5.2 | hd8ed1ab_1 4 KB conda-forge
comebin-1.0.4 | hdfd78af_0 2.6 MB bioconda
cuda-libraries-11.8.0 | 0 1 KB nvidia
cuda-runtime-11.8.0 | 0 1 KB nvidia
importlib_metadata-4.11.4 | hd8ed1ab_0 4 KB conda-forge
libcblas-3.9.0 | 21_linux64_mkl 14 KB conda-forge
liblapack-3.9.0 | 21_linux64_mkl 14 KB conda-forge
liblapacke-3.9.0 | 21_linux64_mkl 14 KB conda-forge
mkl-2024.0.0 | ha957f24_49657 120.2 MB conda-forge
mkl-devel-2024.0.0 | ha770c72_49657 31 KB conda-forge
mkl-include-2024.0.0 | ha957f24_49657 750 KB conda-forge
python_abi-3.7 | 4_cp37m 6 KB conda-forge
pytorch-cuda-11.8 | h7e8668a_5 3 KB pytorch
pytorch-mutex-1.0 | cuda 3 KB pytorch
seaborn-0.12.1 | hd8ed1ab_0 5 KB conda-forge
tbb-2021.11.0 | h00ab1b0_1 191 KB conda-forge
typing-extensions-4.7.1 | hd8ed1ab_0 10 KB conda-forge
------------------------------------------------------------
Total: 123.9 MB
The following NEW packages will be INSTALLED:
_libgcc_mutex conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge
_openmp_mutex conda-forge/linux-64::_openmp_mutex-4.5-2_kmp_llvm
absl-py conda-forge/noarch::absl-py-2.1.0-pyhd8ed1ab_0
anndata conda-forge/noarch::anndata-0.8.0-pyhd8ed1ab_1
arpack conda-forge/linux-64::arpack-3.7.0-hdefa2d7_2
atomicwrites conda-forge/noarch::atomicwrites-1.4.0-pyh9f0ad1d_0
bedtools bioconda/linux-64::bedtools-2.30.0-h468198e_3
biolib bioconda/noarch::biolib-0.1.6-py_0
biopython conda-forge/linux-64::biopython-1.76-py37h516909a_0
blas conda-forge/linux-64::blas-2.121-mkl
blas-devel conda-forge/linux-64::blas-devel-3.9.0-21_linux64_mkl
brotli conda-forge/linux-64::brotli-1.1.0-hd590300_1
brotli-bin conda-forge/linux-64::brotli-bin-1.1.0-hd590300_1
bwa bioconda/linux-64::bwa-0.7.17-he4a0461_11
bzip2 conda-forge/linux-64::bzip2-1.0.8-hd590300_5
c-ares conda-forge/linux-64::c-ares-1.26.0-hd590300_0
ca-certificates conda-forge/linux-64::ca-certificates-2024.2.2-hbcca054_0
cached-property conda-forge/noarch::cached-property-1.5.2-hd8ed1ab_1
cached_property conda-forge/noarch::cached_property-1.5.2-pyha770c72_1
certifi conda-forge/noarch::certifi-2023.11.17-pyhd8ed1ab_0
checkm-genome bioconda/noarch::checkm-genome-1.1.3-py_1
click conda-forge/linux-64::click-8.0.4-py37h89c1867_0
colorama conda-forge/noarch::colorama-0.4.6-pyhd8ed1ab_0
comebin bioconda/noarch::comebin-1.0.4-hdfd78af_0
cuda-cudart nvidia/linux-64::cuda-cudart-11.8.89-0
cuda-cupti nvidia/linux-64::cuda-cupti-11.8.87-0
cuda-libraries nvidia/linux-64::cuda-libraries-11.8.0-0
cuda-nvrtc nvidia/linux-64::cuda-nvrtc-11.8.89-0
cuda-nvtx nvidia/linux-64::cuda-nvtx-11.8.86-0
cuda-runtime nvidia/linux-64::cuda-runtime-11.8.0-0
cudatoolkit nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
cycler conda-forge/noarch::cycler-0.11.0-pyhd8ed1ab_0
dendropy bioconda/noarch::dendropy-4.6.1-pyhdfd78af_0
fonttools conda-forge/linux-64::fonttools-4.38.0-py37h540881e_0
fraggenescan bioconda/linux-64::fraggenescan-1.31-h031d066_6
freetype conda-forge/linux-64::freetype-2.12.1-h267a509_2
future conda-forge/linux-64::future-0.18.2-py37h89c1867_5
gettext conda-forge/linux-64::gettext-0.21.1-h27087fc_0
glpk conda-forge/linux-64::glpk-4.65-h9202a9a_1004
gmp conda-forge/linux-64::gmp-6.3.0-h59595ed_0
grpc-cpp conda-forge/linux-64::grpc-cpp-1.48.1-h05bd8bd_1
grpcio conda-forge/linux-64::grpcio-1.48.1-py37h3fae8ff_1
h5py conda-forge/linux-64::h5py-3.6.0-nompi_py37hd308b1e_100
hdf5 conda-forge/linux-64::hdf5-1.12.1-nompi_h2386368_104
hmmer bioconda/linux-64::hmmer-3.1b2-3
hnswlib conda-forge/linux-64::hnswlib-0.6.2-py37h48bf904_3
htslib bioconda/linux-64::htslib-1.17-h6bc39ce_1
icu conda-forge/linux-64::icu-73.2-h59595ed_0
igraph conda-forge/linux-64::igraph-0.9.9-h026ac8f_0
importlib-metadata conda-forge/linux-64::importlib-metadata-4.11.4-py37h89c1867_0
importlib_metadata conda-forge/noarch::importlib_metadata-4.11.4-hd8ed1ab_0
joblib conda-forge/noarch::joblib-1.1.0-pyhd8ed1ab_0
jpeg conda-forge/linux-64::jpeg-9e-h0b41bf4_3
keyutils conda-forge/linux-64::keyutils-1.6.1-h166bdaf_0
kiwisolver conda-forge/linux-64::kiwisolver-1.4.4-py37h7cecad7_0
krb5 conda-forge/linux-64::krb5-1.20.1-hf9c8cef_0
lcms2 conda-forge/linux-64::lcms2-2.14-h6ed2654_0
ld_impl_linux-64 conda-forge/linux-64::ld_impl_linux-64-2.40-h41732ed_0
leidenalg conda-forge/linux-64::leidenalg-0.8.10-py37hd23a5d3_0
lerc conda-forge/linux-64::lerc-4.0.0-h27087fc_0
libabseil conda-forge/linux-64::libabseil-20220623.0-cxx17_h05df665_6
libblas conda-forge/linux-64::libblas-3.9.0-21_linux64_mkl
libbrotlicommon conda-forge/linux-64::libbrotlicommon-1.1.0-hd590300_1
libbrotlidec conda-forge/linux-64::libbrotlidec-1.1.0-hd590300_1
libbrotlienc conda-forge/linux-64::libbrotlienc-1.1.0-hd590300_1
libcblas conda-forge/linux-64::libcblas-3.9.0-21_linux64_mkl
libcublas nvidia/linux-64::libcublas-11.11.3.6-0
libcufft nvidia/linux-64::libcufft-10.9.0.58-0
libcufile nvidia/linux-64::libcufile-1.8.1.2-0
libcurand nvidia/linux-64::libcurand-10.3.4.107-0
libcurl conda-forge/linux-64::libcurl-7.87.0-h6312ad2_0
libcusolver nvidia/linux-64::libcusolver-11.4.1.48-0
libcusparse nvidia/linux-64::libcusparse-11.7.5.86-0
libdeflate conda-forge/linux-64::libdeflate-1.13-h166bdaf_0
libedit conda-forge/linux-64::libedit-3.1.20191231-he28a2e2_2
libev conda-forge/linux-64::libev-4.33-hd590300_2
libffi conda-forge/linux-64::libffi-3.4.2-h7f98852_5
libgcc-ng conda-forge/linux-64::libgcc-ng-13.2.0-h807b86a_5
libgfortran-ng conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_5
libgfortran5 conda-forge/linux-64::libgfortran5-13.2.0-ha4646dd_5
libhwloc conda-forge/linux-64::libhwloc-2.9.3-default_h554bfaf_1009
libiconv conda-forge/linux-64::libiconv-1.17-hd590300_2
libidn2 conda-forge/linux-64::libidn2-2.3.7-hd590300_0
liblapack conda-forge/linux-64::liblapack-3.9.0-21_linux64_mkl
liblapacke conda-forge/linux-64::liblapacke-3.9.0-21_linux64_mkl
libllvm11 conda-forge/linux-64::libllvm11-11.1.0-he0ac6c6_5
libnghttp2 conda-forge/linux-64::libnghttp2-1.51.0-hdcd2b5c_0
libnpp nvidia/linux-64::libnpp-11.8.0.86-0
libnsl conda-forge/linux-64::libnsl-2.0.1-hd590300_0
libnvjpeg nvidia/linux-64::libnvjpeg-11.9.0.86-0
libpng conda-forge/linux-64::libpng-1.6.42-h2797004_0
libprotobuf conda-forge/linux-64::libprotobuf-3.21.8-h6239696_0
libsqlite conda-forge/linux-64::libsqlite-3.44.2-h2797004_0
libssh2 conda-forge/linux-64::libssh2-1.10.0-haa6b8db_3
libstdcxx-ng conda-forge/linux-64::libstdcxx-ng-13.2.0-h7e041cc_5
libtiff conda-forge/linux-64::libtiff-4.4.0-h0e0dad5_3
libunistring conda-forge/linux-64::libunistring-0.9.10-h7f98852_0
libuv conda-forge/linux-64::libuv-1.46.0-hd590300_0
libwebp-base conda-forge/linux-64::libwebp-base-1.3.2-hd590300_0
libxcb conda-forge/linux-64::libxcb-1.13-h7f98852_1004
libxcrypt conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1
libxml2 conda-forge/linux-64::libxml2-2.12.4-h232c23b_1
libzlib conda-forge/linux-64::libzlib-1.2.13-hd590300_5
llvm-openmp conda-forge/linux-64::llvm-openmp-17.0.6-h4dfa4b3_0
llvmlite conda-forge/linux-64::llvmlite-0.38.1-py37h0761922_0
markdown conda-forge/noarch::markdown-3.5.2-pyhd8ed1ab_0
markupsafe conda-forge/linux-64::markupsafe-2.1.1-py37h540881e_1
matplotlib-base conda-forge/linux-64::matplotlib-base-3.5.1-py37h1058ff1_0
matplotlib-inline conda-forge/noarch::matplotlib-inline-0.1.3-pyhd8ed1ab_0
metis conda-forge/linux-64::metis-5.1.0-h59595ed_1007
mkl conda-forge/linux-64::mkl-2024.0.0-ha957f24_49657
mkl-devel conda-forge/linux-64::mkl-devel-2024.0.0-ha770c72_49657
mkl-include conda-forge/linux-64::mkl-include-2024.0.0-ha957f24_49657
mpfr conda-forge/linux-64::mpfr-4.2.1-h9458935_0
munkres conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0
natsort conda-forge/noarch::natsort-8.4.0-pyhd8ed1ab_0
ncurses conda-forge/linux-64::ncurses-6.4-h59595ed_2
networkx conda-forge/noarch::networkx-2.6.3-pyhd8ed1ab_1
numba conda-forge/linux-64::numba-0.55.1-py37h2d894fd_0
numpy conda-forge/linux-64::numpy-1.19.0-py37h8960a57_0
openjpeg conda-forge/linux-64::openjpeg-2.5.0-h7d73246_1
openssl conda-forge/linux-64::openssl-1.1.1w-hd590300_0
packaging conda-forge/noarch::packaging-23.2-pyhd8ed1ab_0
pandas conda-forge/linux-64::pandas-1.3.5-py37he8f5f7f_0
patsy conda-forge/noarch::patsy-0.5.6-pyhd8ed1ab_0
perl conda-forge/linux-64::perl-5.32.1-7_hd590300_perl5
pillow conda-forge/linux-64::pillow-9.2.0-py37h850a105_2
pip conda-forge/noarch::pip-22.0.4-pyhd8ed1ab_0
pplacer bioconda/linux-64::pplacer-1.1.alpha19-h9ee0642_2
prodigal bioconda/linux-64::prodigal-2.6.3-h031d066_7
protobuf conda-forge/linux-64::protobuf-4.21.8-py37hd23a5d3_0
pthread-stubs conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001
pynndescent conda-forge/noarch::pynndescent-0.5.11-pyhca7485f_0
pyparsing conda-forge/noarch::pyparsing-3.1.1-pyhd8ed1ab_0
pysam bioconda/linux-64::pysam-0.21.0-py37hee149a5_0
python conda-forge/linux-64::python-3.7.12-hb7a2778_100_cpython
python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0
python-fastjsonsc~ conda-forge/noarch::python-fastjsonschema-2.15.3-pyhd8ed1ab_0
python-igraph conda-forge/linux-64::python-igraph-0.9.11-py37h87e8262_0
python_abi conda-forge/linux-64::python_abi-3.7-4_cp37m
pytorch pytorch/linux-64::pytorch-1.10.2-py3.7_cuda11.3_cudnn8.2.0_0
pytorch-cuda pytorch/linux-64::pytorch-cuda-11.8-h7e8668a_5
pytorch-mutex pytorch/noarch::pytorch-mutex-1.0-cuda
pytz conda-forge/noarch::pytz-2023.4-pyhd8ed1ab_0
pyyaml conda-forge/linux-64::pyyaml-6.0-py37h540881e_4
re2 conda-forge/linux-64::re2-2022.06.01-h27087fc_1
readline conda-forge/linux-64::readline-8.2-h8228510_1
samtools bioconda/linux-64::samtools-1.15.1-h6899075_1
scanpy conda-forge/noarch::scanpy-1.9.1-pyhd8ed1ab_0
scikit-learn conda-forge/linux-64::scikit-learn-0.22.1-py37hcdab131_1
scipy conda-forge/linux-64::scipy-1.7.3-py37hf2a6cf1_0
seaborn conda-forge/noarch::seaborn-0.12.1-hd8ed1ab_0
seaborn-base conda-forge/noarch::seaborn-base-0.12.1-pyhd8ed1ab_0
session-info conda-forge/noarch::session-info-1.0.0-pyhd8ed1ab_0
setuptools conda-forge/linux-64::setuptools-59.5.0-py37h89c1867_0
six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0
sqlite conda-forge/linux-64::sqlite-3.44.2-h2c6b66d_0
statsmodels conda-forge/linux-64::statsmodels-0.13.2-py37hb1e94ed_0
stdlib-list conda-forge/noarch::stdlib-list-0.10.0-pyhd8ed1ab_0
suitesparse conda-forge/linux-64::suitesparse-5.10.1-h5a4f163_3
tbb conda-forge/linux-64::tbb-2021.11.0-h00ab1b0_1
tensorboard conda-forge/linux-64::tensorboard-1.15.0-py37_0
texttable conda-forge/noarch::texttable-1.7.0-pyhd8ed1ab_0
tk conda-forge/linux-64::tk-8.6.13-noxft_h4845f30_101
tqdm conda-forge/noarch::tqdm-4.66.1-pyhd8ed1ab_0
traitlets conda-forge/noarch::traitlets-5.9.0-pyhd8ed1ab_0
typing-extensions conda-forge/noarch::typing-extensions-4.7.1-hd8ed1ab_0
typing_extensions conda-forge/noarch::typing_extensions-4.7.1-pyha770c72_0
umap-learn conda-forge/linux-64::umap-learn-0.5.3-py37h89c1867_0
unicodedata2 conda-forge/linux-64::unicodedata2-14.0.0-py37h540881e_1
werkzeug conda-forge/noarch::werkzeug-2.2.3-pyhd8ed1ab_0
wget conda-forge/linux-64::wget-1.20.3-ha56f1ee_1
wheel conda-forge/noarch::wheel-0.42.0-pyhd8ed1ab_0
xorg-libxau conda-forge/linux-64::xorg-libxau-1.0.11-hd590300_0
xorg-libxdmcp conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0
xz conda-forge/linux-64::xz-5.2.6-h166bdaf_0
yaml conda-forge/linux-64::yaml-0.2.5-h7f98852_2
zipp conda-forge/noarch::zipp-3.15.0-pyhd8ed1ab_0
zlib conda-forge/linux-64::zlib-1.2.13-hd590300_5
zstd conda-forge/linux-64::zstd-1.5.5-hfc55251_0
Downloading and Extracting Packages:
Preparing transaction: done
Verifying transaction: /
SafetyError: The package for checkm-genome located at /home/apps/conda/miniconda3/pkgs/checkm-genome-1.1.3-py_1
appears to be corrupted. The path 'site-packages/checkm/DATA_CONFIG'
has an incorrect size.
reported size: 215 bytes
actual size: 247 bytes
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::cuda-cudart-11.8.89-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcudart.so.11.0'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::cuda-cupti-11.8.87-0, nvidia/linux-64::cuda-nvtx-11.8.86-0
path: 'LICENSE'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::cuda-cupti-11.8.87-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcupti.so'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::cuda-nvrtc-11.8.89-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnvrtc.so.11.2'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::cuda-nvtx-11.8.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnvToolsExt.so'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::cuda-nvtx-11.8.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnvToolsExt.so.1'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::cuda-nvtx-11.8.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnvToolsExt.so.1.0.0'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcublas-11.11.3.6-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcublas.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcublas-11.11.3.6-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcublasLt.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcublas-11.11.3.6-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnvblas.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcufft-10.9.0.58-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcufft.so.10'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcufft-10.9.0.58-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcufftw.so.10'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcurand-10.3.4.107-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcurand.so.10'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcusolver-11.4.1.48-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcusolver.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcusolver-11.4.1.48-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcusolverMg.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libcusparse-11.7.5.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libcusparse.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppc.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppial.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppicc.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppidei.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppif.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppig.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppim.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppist.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppisu.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnppitc.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnpps.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
packages: nvidia/linux-64::libnvjpeg-11.9.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
path: 'lib/libnvjpeg.so.11'
done
Executing transaction: - By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.
html
| checkm data setRoot /home/apps/conda/miniconda3/envs/comebin-1.0.4/checkm_data
done
#
# To activate this environment, use
#
# $ conda activate comebin-1.0.4
#
# To deactivate an active environment, use
#
# $ conda deactivate
Checking the last mentioned file as a test:
$ conda activate comebin-1.0.4
$ ls -l $CONDA_PREFIX/lib/libnvjpeg.so*
lrwxrwxrwx 1 appadmin admin 23 Feb 2 11:28 /home/apps/conda/miniconda3/envs/comebin-1.0.4/lib/libnvjpeg.so -> libnvjpeg.so.11.5.0.109
lrwxrwxrwx 1 appadmin admin 23 Feb 2 11:28 /home/apps/conda/miniconda3/envs/comebin-1.0.4/lib/libnvjpeg.so.11 -> libnvjpeg.so.11.5.0.109
-rwxr-xr-x 3 appadmin admin 5170312 Jul 8 2021 /home/apps/conda/miniconda3/envs/comebin-1.0.4/lib/libnvjpeg.so.11.5.0.109
-rwxr-xr-x 5 appadmin admin 5699080 Aug 26 2022 /home/apps/conda/miniconda3/envs/comebin-1.0.4/lib/libnvjpeg.so.11.9.0.86
It seems that these shared paths are links, but I was wondering if the error/warning messages could be circumvented or ignored.
Thanks for your input on this!
Hi again,
after fixing the path issue I am getting a different error by now:
Traceback (most recent call last):
File "main.py", line 368, in
main()
File "main.py", line 288, in main
train_CLmodel(logger,args)
File "/COMEBin/COMEBin/train_CLmodel.py", line 235, in train_CLmodel
simclr.train_addpretrain(train_loader, dataset, namelist)
File "/COMEBin/COMEBin/simclr.py", line 299, in train_addpretrain
top1, top5 = accuracy(logits, labels, topk=(1, 5))
UnboundLocalError: local variable 'logits' referenced before assignment
Something went wrong with running training network. Exiting.
Is there a way to fix this issue?
Best,
tim
(comebin_env) lizhihua@lizhihua-T640:/media/lizhihua/software/binning_method/COMEBin/COMEBin/scripts$ bash gen_cov_file.sh -a /media/lizhihua/data/LZLJ/GJ/GJ_all/final.contigs.fa -o /media/lizhihua/data/LZLJ/GJ/GJ_all/bamfiles /media/lizhihua/data/LZLJ/GJ/GJ_all/*.fastq -t 20
The thread is 20 but the result is -t 1 as below
[main] CMD: bwa mem -v 1 -t 1 /media/lizhihua/data/LZLJ/GJ/GJ_all/bamfiles/work_files/assembly.fa /media/lizhihua/data/LZLJ/GJ/GJ_all/GJall_1.fastq /media/lizhihua/data/LZLJ/GJ/GJ_all/GJall_2.fastq
[main] Real time: 108706.147 sec; CPU: 109901.352 sec
[bam_sort_core] merging from 263 files and 1 in-memory blocks...
########################################################################################################################
########################################################################################################################
Hi,
I am using ComeBin to recover MAGs from cow rumen metagenomic data.
In a few samples ComeBin is stuck in the clustering step for more than 5 days now.
It generated quickly a few Leiden_*.tsv files but nothing else since then.
Could you investigate this? Your help would be very appreciated.
I can send you all data.
Best Florian.
Hi Ziye,
I am going to try the COMEBin v1.0.3 to bin my contigs, but is it for the individual-or co- assembly?
Looking forward your reply.
Thank you very much.
Best,
Bing
Regardless of the threads specified, the augmentation step only runs on a single thread. Is their any way to alter this? Since, it can take multiple days when dealing with sample sets with large .bam files.
Hello! I'm having an issue in the clustering step of version v1.0.4:
Regardless of the value of -t, cluster.py always uses all available CPUs.
We may have found the problem:
In cluster.py:
line 66:
int_labels = p.add_items(features, labels_index)
and line 398:
ann_neighbor_indices, ann_distances = p.knn_query(norm_embeddings, max_edges+1)
both functions lack the argument num_threads, causing them to default to -1 (all available cores)
Hope this helps and thank you for the tool, I'm getting promising results!
Riccardo
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