Errors when running in end_to_end mode

[ErnieHsieh]

Hi there,

Thanks for developing this tool. I tried to download the latest version v2.1.11 via conda and tried to install it on our HPC cluster and my local desktop (M2 pro arm64, with conda create --platform 'osx-64' -n phabox for installation). However, I encountered some issues when running a test using example_contigs.fa provided and my viral contigs.fasta.

On my local desktop, my command is: phabox2 --task end_to_end --skip Y --contigs merged_uvigs.fasta -o ./test_out -d /Users/Databases/phabox_db_v2 --len 500 --threads 8

And an error was as follows:

PhaBOX2 is running with: 8 threads!
Running program: Data preprocessing
[1/7] filtering the length of contigs...
[2/7] calling genes with prodigal...
[3/7] running all-against-all alignment...
[4/7] converting sequences to sentences for language model...
[5/7] Analyzing the seuqences...
  0%|                                                                                                                           | 0/4 [00:00<?, ?it/s]Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 16.77it/s]
[6/7] summarizing the results...
[7/7] writing the results...
Command 'sed -i '1i\qseqid	sseqid	pident	length	mismatch	gapopen	qstart	qend	sstart	send	evalue	bitscore' ./test_out/final_prediction//preprocessing_supplementary/alignment_results.tab' failed with exit code 1
sed: 1: "./test_out/final_predic ...": invalid command code .

please run the command manually to check the error

Then, I also ran it on HPC using the example_contigs.fa for a test: (phabox2) bash-4.4$ phabox2 --task end_to_end --contigs example_contigs.fa --outpth phabox_test_out --threads 16 --dbdir /mnt/scratch2/users/3058727/databases/phabox_db_v2

And an error was like below:

PhaBOX2 is running with: 16 threads!
Running program: PhaMer (virus identification)
[1/7] filtering the length of contigs...
[2/7] calling genes with prodigal...
[3/7] running all-against-all alignment...
[4/7] converting sequences to sentences for language model...
[5/7] Predicting the viruses...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 351/351 [00:02<00:00, 139.19it/s]
[6/7] summarizing the results...
[7/7] writing the results...
Run time: 13.17 seconds

PhaMer finished! please check the results in phabox_test_out/final_prediction
Running program: PhaGCN (taxonomy classification)
[1/8] reusing existing filtered contigs...
[2/8] reusing existing protein file...
[3/8] using existing all-against-all alignment results...
[4/8] generating phagcn networks...
[5/8] predicting the taxonomy...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 292/292 [00:00<00:00, 7168.11it/s]
[6/8] summarizing the results...
[7/8] generating genus level clusters...
[8/8] writing the results...
Run time: 36.82 seconds

PhaGCN finished! please check the results in phabox_test_out/final_prediction
Running program: CHERRY (Host prediction)
[1/10] reusing existing filtered contigs...
[2/10] no MAGs provided, skipping MAG CRISPRs prediction...
[3/10] will continue running CHERRY with database bersion...
[4/10] predicting DB CRISPRs...
[5/10] reusing existing protein file...
[6/10] reusing all-against-all alignment from PhaGCN...
[7/10] generating cherry networks...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 292/292 [00:00<00:00, 8159.69it/s]
[8/10] predicting the host...
[9/10] summarizing the results...
[10/10] writing the results...
Run time: 67.05 seconds

Cherry finished! please check the results in phabox_test_out/final_prediction
Running program: PhaTYP (Lifestyle prediction)
running with cpu
[1/7] reusing existing filtered contigs...
[2/7] reusing existing protein file...
[3/7] using existing all-against-all alignment results...
[4/7] converting sequences to sentences for language model...
[5/7] Predicting the lifestyle...
Map: 100%|███████████████████████████████████████████████████████████████████████████████████████| 288/288 [00:00<00:00, 9472.04 examples/s]
Traceback (most recent call last):
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/bin/phabox2", line 10, in <module>
    sys.exit(main())
             ^^^^^^
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/phabox2/phabox2.py", line 385, in main
    phatyp.run(inputs)
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/phabox2/phatyp.py", line 166, in run
    trainer, tokenized_data = init_bert(rootpth, midfolder, parampth)
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/phabox2/scripts/ulity.py", line 217, in init_bert
    model = AutoModelForSequenceClassification.from_pretrained(f'{parampth}/bert', num_labels=2)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py", line 571, in from_pretrained
    return model_class.from_pretrained(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/transformers/modeling_utils.py", line 309, in _wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/transformers/modeling_utils.py", line 4574, in from_pretrained
    ) = cls._load_pretrained_model(
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/transformers/modeling_utils.py", line 4833, in _load_pretrained_model
    load_state_dict(checkpoint_files[0], map_location="meta", weights_only=weights_only).keys()
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/transformers/modeling_utils.py", line 554, in load_state_dict
    check_torch_load_is_safe()
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/transformers/utils/import_utils.py", line 1417, in check_torch_load_is_safe
    raise ValueError(
ValueError: Due to a serious vulnerability issue in `torch.load`, even with `weights_only=True`, we now require users to upgrade torch to at least v2.6 in order to use the function. This version restriction does not apply when loading files with safetensors.
See the vulnerability report here https://nvd.nist.gov/vuln/detail/CVE-2025-32434

Do you have and ideas and advice about how to resolve these issues? Many thanks in advance for looking into these problems!

Best

[KennthShang]

Hi there,

For the HPC (linux), please check issue #71 for reference. please try upgrade the transformer to version 4.43: conda install transformer=4.43

For the macOS, it could be a problem with the command "sed". We did not optimize the software on macOS, and it is not recommended to do so.

Best,
Jiayu

[ErnieHsieh]

Hi @KennthShang ,

Thanks for your prompt response.

On the HPC (linux), I tried to downgrade transformers from ver. 4.52.4 to ver. 4.43.4 via conda install transformers=4.43. Meanwhile, I noted that numpy would also be downgraded from 2.3.0 to 1.26.4, then it caused another issue as follows:

(phabox2) bash-4.4$ phabox2 -h

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.2.5 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/bin/phabox2", line 6, in <module>
    from phabox2.phabox2 import main
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/phabox2/__init__.py", line 1, in <module>
    from phabox2 import (phamer, phatyp, phagcn, cherry, contamination, votu, tree, phavip)
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/phabox2/phamer.py", line 4, in <module>
    import pandas as pd
  File "/users/3058727/.local/lib/python3.12/site-packages/pandas/__init__.py", line 26, in <module>
    from pandas.compat import (
  File "/users/3058727/.local/lib/python3.12/site-packages/pandas/compat/__init__.py", line 27, in <module>
    from pandas.compat.pyarrow import (
  File "/users/3058727/.local/lib/python3.12/site-packages/pandas/compat/pyarrow.py", line 8, in <module>
    import pyarrow as pa
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/pyarrow/__init__.py", line 65, in <module>
    import pyarrow.lib as _lib
Traceback (most recent call last):
  File "/users/3058727/.local/lib/python3.12/site-packages/numpy/core/_multiarray_umath.py", line 44, in __getattr__
    raise ImportError(msg)
ImportError: 
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.2.5 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/bin/phabox2", line 6, in <module>
    from phabox2.phabox2 import main
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/phabox2/__init__.py", line 1, in <module>
    from phabox2 import (phamer, phatyp, phagcn, cherry, contamination, votu, tree, phavip)
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/phabox2/phamer.py", line 7, in <module>
    from .scripts.ulity import *
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/phabox2/scripts/ulity.py", line 5, in <module>
    import datasets
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/datasets/__init__.py", line 17, in <module>
    from .arrow_dataset import Dataset
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 57, in <module>
    import pyarrow as pa
  File "/mnt/scratch2/users/3058727/conda/envs/phabox2/lib/python3.12/site-packages/pyarrow/__init__.py", line 65, in <module>
    import pyarrow.lib as _lib
  File "pyarrow/lib.pyx", line 36, in init pyarrow.lib
ImportError: numpy.core.multiarray failed to import

However, the version of numpy has actually been changed to 1.26.4. The conda packages of phabox 2.1.11 are:

# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       2_gnu    conda-forge
_python_abi3_support      1.0                  hd8ed1ab_2    conda-forge
accelerate                1.7.0              pyhe01879c_0    conda-forge
aiohappyeyeballs          2.6.1              pyhd8ed1ab_0    conda-forge
aiohttp                   3.12.13         py312h178313f_0    conda-forge
aiosignal                 1.3.2              pyhd8ed1ab_0    conda-forge
attrs                     25.3.0             pyh71513ae_0    conda-forge
aws-c-auth                0.7.22               h96bc93b_2    conda-forge
aws-c-cal                 0.6.14               h88a6e22_1    conda-forge
aws-c-common              0.9.19               h4ab18f5_0    conda-forge
aws-c-compression         0.2.18               h83b837d_6    conda-forge
aws-c-event-stream        0.4.2               ha47c788_12    conda-forge
aws-c-http                0.8.1               h29d6fba_17    conda-forge
aws-c-io                  0.14.8               h21d4f22_5    conda-forge
aws-c-mqtt                0.10.4               h759edc4_4    conda-forge
aws-c-s3                  0.5.9                h594631b_3    conda-forge
aws-c-sdkutils            0.1.16               h83b837d_2    conda-forge
aws-checksums             0.1.18               h83b837d_6    conda-forge
aws-crt-cpp               0.26.9               he3a8b3b_0    conda-forge
aws-sdk-cpp               1.11.329             hba8bd5f_3    conda-forge
biopython                 1.85            py312h66e93f0_1    conda-forge
blast                     2.16.0               hc155240_3    bioconda
brotli                    1.1.0                hb9d3cd8_3    conda-forge
brotli-bin                1.1.0                hb9d3cd8_3    conda-forge
brotli-python             1.1.0           py312h2ec8cdc_3    conda-forge
bzip2                     1.0.8                h4bc722e_7    conda-forge
c-ares                    1.34.5               hb9d3cd8_0    conda-forge
ca-certificates           2025.6.15            hbd8a1cb_0    conda-forge
certifi                   2025.6.15          pyhd8ed1ab_0    conda-forge
cffi                      1.17.1          py312h06ac9bb_0    conda-forge
charset-normalizer        3.4.2              pyhd8ed1ab_0    conda-forge
colorama                  0.4.6              pyhd8ed1ab_1    conda-forge
contourpy                 1.3.2           py312h68727a3_0    conda-forge
cpython                   3.12.11         py312hd8ed1ab_0    conda-forge
curl                      8.8.0                he654da7_1    conda-forge
cycler                    0.12.1             pyhd8ed1ab_1    conda-forge
datasets                  3.6.0              pyhd8ed1ab_0    conda-forge
diamond                   0.9.14               h2e03b76_4    bioconda
dill                      0.3.8              pyhd8ed1ab_0    conda-forge
entrez-direct             22.4                 he881be0_0    bioconda
fasttree                  2.1.11               h7b50bb2_5    bioconda
filelock                  3.18.0             pyhd8ed1ab_0    conda-forge
fonttools                 4.58.4          py312h178313f_0    conda-forge
freetype                  2.12.1               h267a509_2    conda-forge
frozenlist                1.6.0           py312hb9e946c_0    conda-forge
fsspec                    2025.3.0           pyhd8ed1ab_0    conda-forge
gawk                      5.3.1                hcd3d067_0    conda-forge
gflags                    2.2.2             h5888daf_1005    conda-forge
glog                      0.7.1                hbabe93e_0    conda-forge
gmp                       6.3.0                hac33072_2    conda-forge
gmpy2                     2.2.1           py312h7201bc8_0    conda-forge
h2                        4.2.0              pyhd8ed1ab_0    conda-forge
hf-xet                    1.1.3            py39h057ba11_0    conda-forge
hpack                     4.1.0              pyhd8ed1ab_0    conda-forge
huggingface_hub           0.33.0             pyhd8ed1ab_0    conda-forge
hyperframe                6.1.0              pyhd8ed1ab_0    conda-forge
idna                      3.10               pyhd8ed1ab_1    conda-forge
jinja2                    3.1.6              pyhd8ed1ab_0    conda-forge
kcounter                  0.1.1           py312h0fa9677_6    bioconda
keyutils                  1.6.1                h166bdaf_0    conda-forge
kiwisolver                1.4.8           py312h84d6215_0    conda-forge
krb5                      1.21.3               h659f571_0    conda-forge
lcms2                     2.16                 hb7c19ff_0    conda-forge
ld_impl_linux-64          2.43                 h1423503_5    conda-forge
lerc                      4.0.0                h0aef613_1    conda-forge
libabseil                 20240116.2      cxx17_he02047a_1    conda-forge
libarrow                  16.1.0           hcb6531f_6_cpu    conda-forge
libarrow-acero            16.1.0           hac33072_6_cpu    conda-forge
libarrow-dataset          16.1.0           hac33072_6_cpu    conda-forge
libarrow-substrait        16.1.0           h7e0c224_6_cpu    conda-forge
libasprintf               0.24.1               h8e693c7_0    conda-forge
libblas                   3.9.0           31_h59b9bed_openblas    conda-forge
libbrotlicommon           1.1.0                hb9d3cd8_3    conda-forge
libbrotlidec              1.1.0                hb9d3cd8_3    conda-forge
libbrotlienc              1.1.0                hb9d3cd8_3    conda-forge
libcblas                  3.9.0           31_he106b2a_openblas    conda-forge
libcrc32c                 1.1.2                h9c3ff4c_0    conda-forge
libcurl                   8.8.0                hca28451_1    conda-forge
libdeflate                1.20                 hd590300_0    conda-forge
libedit                   3.1.20250104    pl5321h7949ede_0    conda-forge
libev                     4.33                 hd590300_2    conda-forge
libevent                  2.1.12               hf998b51_1    conda-forge
libexpat                  2.7.0                h5888daf_0    conda-forge
libffi                    3.4.6                h2dba641_1    conda-forge
libgcc                    15.1.0               h767d61c_2    conda-forge
libgcc-ng                 15.1.0               h69a702a_2    conda-forge
libgettextpo              0.24.1               h5888daf_0    conda-forge
libgfortran               15.1.0               h69a702a_2    conda-forge
libgfortran5              15.1.0               hcea5267_2    conda-forge
libgomp                   15.1.0               h767d61c_2    conda-forge
libgoogle-cloud           2.24.0               h2736e30_0    conda-forge
libgoogle-cloud-storage   2.24.0               h3d9a0c8_0    conda-forge
libgrpc                   1.62.2               h15f2491_0    conda-forge
libidn2                   2.3.8                ha4ef2c3_0    conda-forge
libjpeg-turbo             3.1.0                hb9d3cd8_0    conda-forge
liblapack                 3.9.0           31_h7ac8fdf_openblas    conda-forge
liblzma                   5.8.1                hb9d3cd8_2    conda-forge
liblzma-devel             5.8.1                hb9d3cd8_2    conda-forge
libnghttp2                1.58.0               h47da74e_1    conda-forge
libnsl                    2.0.1                hd590300_0    conda-forge
libopenblas               0.3.29          pthreads_h94d23a6_0    conda-forge
libparquet                16.1.0           h6a7eafb_6_cpu    conda-forge
libpng                    1.6.43               h2797004_0    conda-forge
libprotobuf               4.25.3               h08a7969_0    conda-forge
libre2-11                 2023.09.01           h5a48ba9_2    conda-forge
libsqlite                 3.46.0               hde9e2c9_0    conda-forge
libssh2                   1.11.0               h0841786_0    conda-forge
libstdcxx                 15.1.0               h8f9b012_2    conda-forge
libstdcxx-ng              15.1.0               h4852527_2    conda-forge
libthrift                 0.19.0               hb90f79a_1    conda-forge
libtiff                   4.6.0                h1dd3fc0_3    conda-forge
libtorch                  2.4.1           cpu_generic_hb3b73e9_0    conda-forge
libunistring              0.9.10               h7f98852_0    conda-forge
libutf8proc               2.8.0                hf23e847_1    conda-forge
libuuid                   2.38.1               h0b41bf4_0    conda-forge
libuv                     1.51.0               hb9d3cd8_0    conda-forge
libwebp-base              1.5.0                h851e524_0    conda-forge
libxcb                    1.15                 h0b41bf4_0    conda-forge
libxcrypt                 4.4.36               hd590300_1    conda-forge
libzlib                   1.2.13               h4ab18f5_6    conda-forge
lz4-c                     1.9.4                hcb278e6_0    conda-forge
mafft                     7.526                h4bc722e_0    conda-forge
markupsafe                3.0.2           py312h178313f_1    conda-forge
matplotlib-base           3.10.1          py312hd3ec401_0    conda-forge
mcl                       22.282          pl5321h7b50bb2_4    bioconda
mpc                       1.3.1                h24ddda3_1    conda-forge
mpfr                      4.2.1                h90cbb55_3    conda-forge
mpmath                    1.3.0              pyhd8ed1ab_1    conda-forge
multidict                 6.4.4           py312h178313f_0    conda-forge
multiprocess              0.70.16         py312h66e93f0_1    conda-forge
munkres                   1.1.4              pyhd8ed1ab_1    conda-forge
ncbi-vdb                  3.2.1                h9948957_0    bioconda
ncurses                   6.5                  h2d0b736_3    conda-forge
networkx                  3.5                pyhe01879c_0    conda-forge
nomkl                     1.0                  h5ca1d4c_0    conda-forge
numpy                     1.26.4          py312heda63a1_0    conda-forge
openjpeg                  2.5.2                h488ebb8_0    conda-forge
openssl                   3.5.0                h7b32b05_1    conda-forge
orc                       2.0.1                h17fec99_1    conda-forge
packaging                 25.0               pyh29332c3_1    conda-forge
pandas                    2.3.0           py312hf9745cd_0    conda-forge
perl                      5.32.1          7_hd590300_perl5    conda-forge
perl-archive-tar          3.04            pl5321hdfd78af_0    bioconda
perl-carp                 1.50            pl5321hd8ed1ab_0    conda-forge
perl-common-sense         3.75            pl5321hd8ed1ab_0    conda-forge
perl-compress-raw-bzip2   2.201           pl5321hbf60520_1    conda-forge
perl-compress-raw-zlib    2.202           pl5321hadc24fc_0    conda-forge
perl-encode               3.21            pl5321hb9d3cd8_1    conda-forge
perl-exporter             5.74            pl5321hd8ed1ab_0    conda-forge
perl-exporter-tiny        1.002002        pl5321hd8ed1ab_0    conda-forge
perl-extutils-makemaker   7.70            pl5321hd8ed1ab_0    conda-forge
perl-io-compress          2.201           pl5321h503566f_5    bioconda
perl-io-zlib              1.15            pl5321hdfd78af_0    bioconda
perl-json                 4.10            pl5321hdfd78af_1    bioconda
perl-json-xs              4.03            pl5321h9948957_4    bioconda
perl-list-moreutils       0.430           pl5321hdfd78af_0    bioconda
perl-list-moreutils-xs    0.430           pl5321h7b50bb2_5    bioconda
perl-parent               0.243           pl5321hd8ed1ab_0    conda-forge
perl-pathtools            3.75            pl5321hb9d3cd8_2    conda-forge
perl-scalar-list-utils    1.69            pl5321hb9d3cd8_0    conda-forge
perl-storable             3.15            pl5321hb9d3cd8_2    conda-forge
perl-types-serialiser     1.01            pl5321hdfd78af_0    bioconda
phabox                    2.1.11             pyhdfd78af_1    bioconda
pillow                    10.3.0          py312hdcec9eb_0    conda-forge
pip                       25.1.1             pyh8b19718_0    conda-forge
prodigal-gv               2.11.0               he4a0461_4    bioconda
propcache                 0.3.1           py312h178313f_0    conda-forge
psutil                    7.0.0           py312h66e93f0_0    conda-forge
pthread-stubs             0.4               hb9d3cd8_1002    conda-forge
pyarrow                   16.1.0          py312h8da182e_1    conda-forge
pyarrow-core              16.1.0          py312h5429d62_1_cpu    conda-forge
pycparser                 2.22               pyh29332c3_1    conda-forge
pyparsing                 3.2.3              pyhd8ed1ab_1    conda-forge
pysocks                   1.7.1              pyha55dd90_7    conda-forge
python                    3.12.3          hab00c5b_0_cpython    conda-forge
python-dateutil           2.9.0.post0        pyhff2d567_1    conda-forge
python-gil                3.12.11              hd8ed1ab_0    conda-forge
python-tzdata             2025.2             pyhd8ed1ab_0    conda-forge
python-xxhash             3.5.0           py312h66e93f0_2    conda-forge
python_abi                3.12                    7_cp312    conda-forge
pytorch                   2.4.1           cpu_generic_py312h411db4e_0    conda-forge
pytz                      2025.2             pyhd8ed1ab_0    conda-forge
pyyaml                    6.0.2           py312h178313f_2    conda-forge
qhull                     2020.2               h434a139_5    conda-forge
re2                       2023.09.01           h7f4b329_2    conda-forge
readline                  8.2                  h8c095d6_2    conda-forge
regex                     2024.11.6       py312h66e93f0_0    conda-forge
requests                  2.32.4             pyhd8ed1ab_0    conda-forge
rpsbproc                  0.5.0                h6a68c12_1    bioconda
s2n                       1.4.15               he19d79f_0    conda-forge
safetensors               0.5.3           py312h12e396e_0    conda-forge
scipy                     1.15.2          py312ha707e6e_0    conda-forge
seaborn-base              0.13.2             pyhd8ed1ab_3    conda-forge
setuptools                80.9.0             pyhff2d567_0    conda-forge
six                       1.17.0             pyhd8ed1ab_0    conda-forge
sleef                     3.8                  h1b44611_0    conda-forge
snappy                    1.2.1                h8bd8927_1    conda-forge
sympy                     1.14.0           pyh2585a3b_105    conda-forge
tk                        8.6.13          noxft_h4845f30_101    conda-forge
tokenizers                0.19.1          py312hfef1a59_0    conda-forge
tqdm                      4.67.1             pyhd8ed1ab_1    conda-forge
transformers              4.43.4             pyhd8ed1ab_0    conda-forge
typing-extensions         4.14.0               h32cad80_0    conda-forge
typing_extensions         4.14.0             pyhe01879c_0    conda-forge
tzdata                    2025b                h78e105d_0    conda-forge
unicodedata2              16.0.0          py312h66e93f0_0    conda-forge
urllib3                   2.4.0              pyhd8ed1ab_0    conda-forge
wget                      1.21.4               hda4d442_0    conda-forge
wheel                     0.45.1             pyhd8ed1ab_1    conda-forge
xorg-libxau               1.0.12               hb9d3cd8_0    conda-forge
xorg-libxdmcp             1.1.5                hb9d3cd8_0    conda-forge
xxhash                    0.8.3                hb47aa4a_0    conda-forge
xz                        5.8.1                hbcc6ac9_2    conda-forge
xz-gpl-tools              5.8.1                hbcc6ac9_2    conda-forge
xz-tools                  5.8.1                hb9d3cd8_2    conda-forge
yaml                      0.2.5                h7f98852_2    conda-forge
yarl                      1.20.1          py312h178313f_0    conda-forge
zlib                      1.2.13               h4ab18f5_6    conda-forge
zstandard                 0.23.0          py312h66e93f0_2    conda-forge
zstd                      1.5.6                ha6fb4c9_0    conda-forge 

Thanks for your patience to look into this issue further.

Best,
Ernie

[KennthShang]

Hi Ernie,

Maybe you can try to install the PhaBOX in the second way: https://github.com/KennthShang/PhaBOX/wiki#-installing-phabox2-in-primitive-ways

Since I cannot reproduce the issues on our HPC, I will try another way to debug. Will get back to you soon.

Best,
Jiayu

[KennthShang]

Hi Ernie,

I just try another installation way, and it works on every HPCs I can access.

conda create -n phabox2 phabox=2.1.11 -c conda-forge -c bioconda -y
conda activate phabox2
pip install transformers==4.43

Hope it will help you. I will also update the BioConda channel for that.

Best,
Jiayu

[ErnieHsieh]

Hi Ernie,

I just try another installation way, and it works on every HPCs I can access.

conda create -n phabox2 phabox=2.1.11 -c conda-forge -c bioconda -y
conda activate phabox2
pip install transformers==4.43

Hope it will help you. I will also update the BioConda channel for that.

Best, Jiayu

Hi Jiayu,

Thanks for your solutions. The second way appears to work for me. Yesterday I tried to separately run each module side by side, then I found that the transformers issue would only affect the module of PhaTYP (Lifestyle), but now I think it has been resolved!

Cheers,
Ernie

[KennthShang]

Hi @ErnieHsieh ，

Good to know. I also updated the BioConda channel so the issue should be fixed in the future installation.

And if you like our tool, please give us a star on GitHub. This will provide power for our ongoing maintenance, thanks！

Best,
Jiayu