python /software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold_test.py \
--db_dir /software/alphafold3.0-el8-x86_64/databases \
--output_dir /scratch/midway3/$USER/alphafold3_output \
--model_dir /software/alphafold3.0-el8-x86_64/params \
--run_data_pipeline \
--run_inference
[pnsinha@beagle3-0010 ~]$ nvidia-smi
Thu Jan 16 13:32:27 2025
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.216.03 Driver Version: 535.216.03 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA A100-PCIE-40GB On | 00000000:31:00.0 Off | 0 |
| N/A 29C P0 34W / 250W | 0MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
[pnsinha@beagle3-0010 ~]$ source /software/alphafold3.0-el8-x86_64/setup_env.sh
(alphafold_venv) [pnsinha@beagle3-0010 ~]$ python /software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold_test.py \--db_dir /software/alphafold3.0-el8-x86_64/databases \--output_dir /scratch/midway3/$USER/alphafold3_output \--model_dir /software/alphafold3.0-el8-x86_64/params \--run_data_pipeline \--run_inference
Running tests under Python 3.11.5: /software/alphafold3.0-el8-x86_64/alphafold_venv/bin/python
[ RUN ] InferenceTest.test_config
I0116 13:36:32.415333 139679608824192 xla_bridge.py:895] Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'
I0116 13:36:32.451882 139679608824192 xla_bridge.py:895] Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory
[ OK ] InferenceTest.test_config
[ RUN ] InferenceTest.test_featurisation
Processing chain A
I0116 13:36:32.469916 139679608824192 pipeline.py:40] Getting protein MSAs for sequence SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN
I0116 13:36:32.472579 139675289253632 jackhmmer.py:78] Query sequence: SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN
I0116 13:36:32.472659 139675507349248 jackhmmer.py:78] Query sequence: SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN
I0116 13:36:32.472831 139675280860928 jackhmmer.py:78] Query sequence: SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN
I0116 13:36:32.472923 139675272468224 jackhmmer.py:78] Query sequence: SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN
I0116 13:36:32.473177 139675507349248 subprocess_utils.py:68] Launching subprocess "/software/alphafold3.0-el8-x86_64/hmmer/bin/jackhmmer -o /dev/null -A /tmp/pnsinha/tmpdcb04zsu/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --cpu 8 -N 1 -E 0.0001 --incE 0.0001 /tmp/pnsinha/tmpdcb04zsu/query.fasta /software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/alphafold3/test_data/miniature_databases/uniref90__subsampled_1000.fasta"
I0116 13:36:32.473270 139675289253632 subprocess_utils.py:68] Launching subprocess "/software/alphafold3.0-el8-x86_64/hmmer/bin/jackhmmer -o /dev/null -A /tmp/pnsinha/tmpv68ocee6/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --cpu 8 -N 1 -E 0.0001 --incE 0.0001 /tmp/pnsinha/tmpv68ocee6/query.fasta /software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/alphafold3/test_data/miniature_databases/mgy_clusters__subsampled_1000.fa"
I0116 13:36:32.473341 139675280860928 subprocess_utils.py:68] Launching subprocess "/software/alphafold3.0-el8-x86_64/hmmer/bin/jackhmmer -o /dev/null -A /tmp/pnsinha/tmpso566b_v/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --cpu 8 -N 1 -E 0.0001 --incE 0.0001 /tmp/pnsinha/tmpso566b_v/query.fasta /software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/alphafold3/test_data/miniature_databases/bfd-first_non_consensus_sequences__subsampled_1000.fasta"
I0116 13:36:32.473571 139675272468224 subprocess_utils.py:68] Launching subprocess "/software/alphafold3.0-el8-x86_64/hmmer/bin/jackhmmer -o /dev/null -A /tmp/pnsinha/tmp5bspawcv/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --cpu 8 -N 1 -E 0.0001 --incE 0.0001 /tmp/pnsinha/tmp5bspawcv/query.fasta /software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/alphafold3/test_data/miniature_databases/uniprot_all__subsampled_1000.fasta"
I0116 13:36:32.531080 139675289253632 subprocess_utils.py:97] Finished Jackhmmer in 0.057 seconds
I0116 13:36:32.533275 139675507349248 subprocess_utils.py:97] Finished Jackhmmer in 0.060 seconds
I0116 13:36:32.538449 139675280860928 subprocess_utils.py:97] Finished Jackhmmer in 0.045 seconds
I0116 13:36:32.540442 139675272468224 subprocess_utils.py:97] Finished Jackhmmer in 0.046 seconds
I0116 13:36:32.540806 139679608824192 pipeline.py:73] Getting protein MSAs took 0.07 seconds for sequence SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN
I0116 13:36:32.540859 139679608824192 pipeline.py:79] Deduplicating MSAs and getting protein templates for sequence SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN
I0116 13:36:32.542149 139675280860928 subprocess_utils.py:68] Launching subprocess "/software/alphafold3.0-el8-x86_64/hmmer/bin/hmmbuild --informat stockholm --hand --amino /tmp/pnsinha/tmp5ia28fhs/output.hmm /tmp/pnsinha/tmp5ia28fhs/query.msa"
I0116 13:36:32.612952 139675280860928 subprocess_utils.py:97] Finished Hmmbuild in 0.071 seconds
I0116 13:36:32.613351 139675280860928 subprocess_utils.py:68] Launching subprocess "/software/alphafold3.0-el8-x86_64/hmmer/bin/hmmsearch --noali --cpu 8 --F1 0.1 --F2 0.1 --F3 0.1 -E 100 --incE 100 --domE 100 --incdomE 100 -A /tmp/pnsinha/tmp72no47zc/output.sto /tmp/pnsinha/tmp72no47zc/query.hmm /software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/alphafold3/test_data/miniature_databases/pdb_seqres_2022_09_28__subsampled_1000.fasta"
I0116 13:36:32.696105 139675280860928 subprocess_utils.py:97] Finished Hmmsearch in 0.083 seconds
W0116 13:36:32.696930 139675280860928 templates.py:779] Failed to get mmCIF for 5y2e.
W0116 13:36:32.697025 139675280860928 templates.py:779] Failed to get mmCIF for 6ydw.
W0116 13:36:32.697076 139675280860928 templates.py:779] Failed to get mmCIF for 7rye.
W0116 13:36:32.697122 139675280860928 templates.py:779] Failed to get mmCIF for 6s61.
W0116 13:36:32.697165 139675280860928 templates.py:779] Failed to get mmCIF for 6s61.
W0116 13:36:32.697208 139675280860928 templates.py:779] Failed to get mmCIF for 7ck8.
W0116 13:36:32.697250 139675280860928 templates.py:779] Failed to get mmCIF for 7ck8.
W0116 13:36:32.697302 139675280860928 templates.py:779] Failed to get mmCIF for 5e18.
W0116 13:36:32.697346 139675280860928 templates.py:779] Failed to get mmCIF for 5e18.
W0116 13:36:32.697390 139675280860928 templates.py:779] Failed to get mmCIF for 7of6.
W0116 13:36:32.697431 139675280860928 templates.py:779] Failed to get mmCIF for 7of6.
W0116 13:36:32.697473 139675280860928 templates.py:779] Failed to get mmCIF for 5m9e.
W0116 13:36:32.697516 139675280860928 templates.py:779] Failed to get mmCIF for 5m9e.
W0116 13:36:32.697560 139675280860928 templates.py:779] Failed to get mmCIF for 6umr.
W0116 13:36:32.697602 139675280860928 templates.py:779] Failed to get mmCIF for 6umr.
W0116 13:36:32.697643 139675280860928 templates.py:779] Failed to get mmCIF for 4ji2.
W0116 13:36:32.697684 139675280860928 templates.py:779] Failed to get mmCIF for 4v8d.
W0116 13:36:32.697726 139675280860928 templates.py:779] Failed to get mmCIF for 3k7z.
W0116 13:36:32.697767 139675280860928 templates.py:779] Failed to get mmCIF for 5ez8.
W0116 13:36:32.697808 139675280860928 templates.py:779] Failed to get mmCIF for 5w93.
W0116 13:36:32.697848 139675280860928 templates.py:779] Failed to get mmCIF for 6gcv.
W0116 13:36:32.697889 139675280860928 templates.py:779] Failed to get mmCIF for 5xru.
W0116 13:36:32.697929 139675280860928 templates.py:779] Failed to get mmCIF for 7nal.
W0116 13:36:32.697974 139675280860928 templates.py:779] Failed to get mmCIF for 6epf.
W0116 13:36:32.698014 139675280860928 templates.py:779] Failed to get mmCIF for 3pi4.
W0116 13:36:32.698055 139675280860928 templates.py:779] Failed to get mmCIF for 1ixm.
W0116 13:36:32.698096 139675280860928 templates.py:779] Failed to get mmCIF for 1ixm.
W0116 13:36:32.698136 139675280860928 templates.py:779] Failed to get mmCIF for 1ixm.
W0116 13:36:32.698177 139675280860928 templates.py:779] Failed to get mmCIF for 6xhv.
W0116 13:36:32.698219 139675280860928 templates.py:779] Failed to get mmCIF for 4v79.
W0116 13:36:32.698259 139675280860928 templates.py:779] Failed to get mmCIF for 3is7.
W0116 13:36:32.698299 139675280860928 templates.py:779] Failed to get mmCIF for 3is7.
W0116 13:36:32.698340 139675280860928 templates.py:779] Failed to get mmCIF for 6nlj.
W0116 13:36:32.698381 139675280860928 templates.py:779] Failed to get mmCIF for 6nlj.
W0116 13:36:32.698421 139675280860928 templates.py:779] Failed to get mmCIF for 6bfq.
W0116 13:36:32.698462 139675280860928 templates.py:779] Failed to get mmCIF for 6onk.
W0116 13:36:32.698504 139675280860928 templates.py:779] Failed to get mmCIF for 7jor.
W0116 13:36:32.698545 139675280860928 templates.py:779] Failed to get mmCIF for 3kaq.
W0116 13:36:32.698586 139675280860928 templates.py:779] Failed to get mmCIF for 3gvt.
W0116 13:36:32.698627 139675280860928 templates.py:779] Failed to get mmCIF for 6z0l.
W0116 13:36:32.698668 139675280860928 templates.py:779] Failed to get mmCIF for 4fk2.
W0116 13:36:32.698708 139675280860928 templates.py:779] Failed to get mmCIF for 4fk2.
W0116 13:36:32.698748 139675280860928 templates.py:779] Failed to get mmCIF for 7oiz.
W0116 13:36:32.698788 139675280860928 templates.py:779] Failed to get mmCIF for 7e6c.
W0116 13:36:32.698829 139675280860928 templates.py:779] Failed to get mmCIF for 7e6c.
W0116 13:36:32.698869 139675280860928 templates.py:779] Failed to get mmCIF for 5lnn.
W0116 13:36:32.698909 139675280860928 templates.py:779] Failed to get mmCIF for 7b4v.
W0116 13:36:32.698953 139675280860928 templates.py:779] Failed to get mmCIF for 3hhq.
W0116 13:36:32.698995 139675280860928 templates.py:779] Failed to get mmCIF for 7vaq.
W0116 13:36:32.699035 139675280860928 templates.py:779] Failed to get mmCIF for 6v1q.
W0116 13:36:32.699076 139675280860928 templates.py:779] Failed to get mmCIF for 3gfj.
W0116 13:36:32.699116 139675280860928 templates.py:779] Failed to get mmCIF for 7v2c.
W0116 13:36:32.699156 139675280860928 templates.py:779] Failed to get mmCIF for 7ba7.
W0116 13:36:32.699195 139675280860928 templates.py:779] Failed to get mmCIF for 6htr.
W0116 13:36:32.699235 139675280860928 templates.py:779] Failed to get mmCIF for 6hvu.
W0116 13:36:32.699275 139675280860928 templates.py:779] Failed to get mmCIF for 4otz.
W0116 13:36:32.699314 139675280860928 templates.py:779] Failed to get mmCIF for 4otz.
W0116 13:36:32.699354 139675280860928 templates.py:779] Failed to get mmCIF for 4dvi.
W0116 13:36:32.699393 139675280860928 templates.py:779] Failed to get mmCIF for 4dvi.
W0116 13:36:32.699431 139675280860928 templates.py:779] Failed to get mmCIF for 4v1a.
W0116 13:36:32.699472 139675280860928 templates.py:779] Failed to get mmCIF for 4nb5.
W0116 13:36:32.699512 139675280860928 templates.py:779] Failed to get mmCIF for 5sce.
W0116 13:36:32.699553 139675280860928 templates.py:779] Failed to get mmCIF for 2p1n.
W0116 13:36:32.699593 139675280860928 templates.py:779] Failed to get mmCIF for 6t7m.
W0116 13:36:32.699633 139675280860928 templates.py:779] Failed to get mmCIF for 6qbx.
W0116 13:36:32.699673 139675280860928 templates.py:779] Failed to get mmCIF for 7r4n.
W0116 13:36:32.699714 139675280860928 templates.py:779] Failed to get mmCIF for 5yfm.
W0116 13:36:32.699754 139675280860928 templates.py:779] Failed to get mmCIF for 5yfm.
W0116 13:36:32.699794 139675280860928 templates.py:779] Failed to get mmCIF for 2af7.
W0116 13:36:32.699833 139675280860928 templates.py:779] Failed to get mmCIF for 7z46.
W0116 13:36:32.699872 139675280860928 templates.py:779] Failed to get mmCIF for 7z4a.
W0116 13:36:32.699912 139675280860928 templates.py:779] Failed to get mmCIF for 4atq.
W0116 13:36:32.699955 139675280860928 templates.py:779] Failed to get mmCIF for 6gzq.
W0116 13:36:32.699995 139675280860928 templates.py:779] Failed to get mmCIF for 7ac8.
W0116 13:36:32.700034 139675280860928 templates.py:779] Failed to get mmCIF for 7ac8.
W0116 13:36:32.700074 139675280860928 templates.py:779] Failed to get mmCIF for 6r0z.
W0116 13:36:32.700115 139675280860928 templates.py:779] Failed to get mmCIF for 3mfe.
W0116 13:36:32.700154 139675280860928 templates.py:779] Failed to get mmCIF for 4v5l.
W0116 13:36:32.700194 139675280860928 templates.py:779] Failed to get mmCIF for 4w2f.
W0116 13:36:32.700233 139675280860928 templates.py:779] Failed to get mmCIF for 2zh3.
W0116 13:36:32.700274 139675280860928 templates.py:779] Failed to get mmCIF for 5ai4.
W0116 13:36:32.700315 139675280860928 templates.py:779] Failed to get mmCIF for 5k28.
W0116 13:36:32.700355 139675280860928 templates.py:779] Failed to get mmCIF for 5xhz.
W0116 13:36:32.700395 139675280860928 templates.py:779] Failed to get mmCIF for 5vbr.
W0116 13:36:32.700434 139675280860928 templates.py:779] Failed to get mmCIF for 2y0d.
W0116 13:36:32.700475 139675280860928 templates.py:779] Failed to get mmCIF for 6bx1.
W0116 13:36:32.700515 139675280860928 templates.py:779] Failed to get mmCIF for 7pe9.
W0116 13:36:32.700554 139675280860928 templates.py:779] Failed to get mmCIF for 7osz.
W0116 13:36:32.700594 139675280860928 templates.py:779] Failed to get mmCIF for 2f0x.
W0116 13:36:32.700633 139675280860928 templates.py:779] Failed to get mmCIF for 3kts.
W0116 13:36:32.700674 139675280860928 templates.py:779] Failed to get mmCIF for 1ztg.
W0116 13:36:32.700714 139675280860928 templates.py:779] Failed to get mmCIF for 1ztg.
W0116 13:36:32.700753 139675280860928 templates.py:779] Failed to get mmCIF for 5y6p.
W0116 13:36:32.700792 139675280860928 templates.py:779] Failed to get mmCIF for 5y6p.
W0116 13:36:32.700832 139675280860928 templates.py:779] Failed to get mmCIF for 7vnr.
W0116 13:36:32.700872 139675280860928 templates.py:779] Failed to get mmCIF for 7vnr.
W0116 13:36:32.700912 139675280860928 templates.py:779] Failed to get mmCIF for 3ab4.
W0116 13:36:32.700955 139675280860928 templates.py:779] Failed to get mmCIF for 3ab4.
W0116 13:36:32.700996 139675280860928 templates.py:779] Failed to get mmCIF for 6u42.
W0116 13:36:32.701036 139675280860928 templates.py:779] Failed to get mmCIF for 4o6c.
W0116 13:36:32.701076 139675280860928 templates.py:779] Failed to get mmCIF for 4r4j.
W0116 13:36:32.701117 139675280860928 templates.py:779] Failed to get mmCIF for 7eqd.
W0116 13:36:32.701156 139675280860928 templates.py:779] Failed to get mmCIF for 3fi2.
W0116 13:36:32.701196 139675280860928 templates.py:779] Failed to get mmCIF for 7o11.
W0116 13:36:32.701236 139675280860928 templates.py:779] Failed to get mmCIF for 5qn5.
W0116 13:36:32.701276 139675280860928 templates.py:779] Failed to get mmCIF for 5qob.
W0116 13:36:32.701315 139675280860928 templates.py:779] Failed to get mmCIF for 6pg2.
W0116 13:36:32.701358 139675280860928 templates.py:779] Failed to get mmCIF for 5den.
W0116 13:36:32.701398 139675280860928 templates.py:779] Failed to get mmCIF for 7jg2.
W0116 13:36:32.701438 139675280860928 templates.py:779] Failed to get mmCIF for 7eqg.
W0116 13:36:32.701478 139675280860928 templates.py:779] Failed to get mmCIF for 7eqg.
W0116 13:36:32.701517 139675280860928 templates.py:779] Failed to get mmCIF for 7eqg.
W0116 13:36:32.701558 139675280860928 templates.py:779] Failed to get mmCIF for 1svu.
W0116 13:36:32.701597 139675280860928 templates.py:779] Failed to get mmCIF for 6rxq.
W0116 13:36:32.701638 139675280860928 templates.py:779] Failed to get mmCIF for 1rx0.
W0116 13:36:32.701677 139675280860928 templates.py:779] Failed to get mmCIF for 6pee.
W0116 13:36:32.701718 139675280860928 templates.py:779] Failed to get mmCIF for 1oxy.
W0116 13:36:32.701758 139675280860928 templates.py:779] Failed to get mmCIF for 6fzd.
I0116 13:36:32.702001 139679608824192 pipeline.py:108] Deduplicating MSAs and getting protein templates took 0.16 seconds for sequence SEFEKLRQTGDELVQAFQRLREIFDKGDDDSLEQVLEEIEELIQKHRQLFDNRQEAADTEAAKQGDQWVQLFQRFREAIDKGDKDSLEQLLEELEQALQKIRELAEKKN
Processing chain A took 0.23 seconds
Processing chain B
Processing chain B took 0.00 seconds
I0116 13:36:36.242822 139679608824192 pipeline.py:165] processing 5tgy, random_seed=1234
I0116 13:36:36.253213 139679608824192 pipeline.py:258] Calculating bucket size for input with 150 tokens.
I0116 13:36:36.253341 139679608824192 pipeline.py:264] Got bucket size 150 for input with 150 tokens, resulting in 0 padded tokens.
[13:36:36] UFFTYPER: Unrecognized charge state for atom: 48
[13:36:36] UFFTYPER: Unrecognized atom type: Zn4+2 (48)
[13:36:37] UFFTYPER: Unrecognized charge state for atom: 48
[13:36:37] UFFTYPER: Unrecognized atom type: Zn4+2 (48)
[ OK ] InferenceTest.test_featurisation
[ RUN ] InferenceTest.test_inference_bucket_1024
Processing fold input 5tgy
Checking we can load the model parameters...
2025-01-16 13:36:38.912384: W external/xla/xla/service/gpu/nvptx_compiler.cc:930] The NVIDIA driver's CUDA version is 12.2 which is older than the PTX compiler version 12.6.85. Because the driver is older than the PTX compiler version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.
Running data pipeline...
Processing chain A
Processing chain A took 0.00 seconds
Processing chain B
Processing chain B took 0.00 seconds
Output directory: <absl.testing.absltest._TempDir object at 0x7f0789018a90>
Writing model input JSON to <absl.testing.absltest._TempDir object at 0x7f0789018a90>
Predicting 3D structure for 5tgy for seed(s) (1234,)...
Featurising data for seeds (1234,)...
Featurising 5tgy with rng_seed 1234.
I0116 13:36:45.817978 139679608824192 pipeline.py:165] processing 5tgy, random_seed=1234
I0116 13:36:45.827530 139679608824192 pipeline.py:258] Calculating bucket size for input with 150 tokens.
I0116 13:36:45.827627 139679608824192 pipeline.py:264] Got bucket size 1024 for input with 150 tokens, resulting in 874 padded tokens.
[13:36:46] UFFTYPER: Unrecognized charge state for atom: 48
[13:36:46] UFFTYPER: Unrecognized atom type: Zn4+2 (48)
[13:36:47] UFFTYPER: Unrecognized charge state for atom: 48
[13:36:47] UFFTYPER: Unrecognized atom type: Zn4+2 (48)
Featurising 5tgy with rng_seed 1234 took 1.53 seconds.
Featurising data for seeds (1234,) took 4.99 seconds.
Running model inference for seed 1234...
/software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/jax/_src/pjit.py:443: DeprecationWarning: backend and device argument on jit is deprecated. You can use `jax.device_put(..., jax.local_devices("cpu")[0])` on the inputs to the jitted function to get the same behavior.
warnings.warn(
/software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/jax/_src/linear_util.py:193: DeprecationWarning: Passing arguments 'a', 'a_min' or 'a_max' to jax.numpy.clip is deprecated. Please use 'arr', 'min' or 'max' respectively instead.
ans = self.f(*args, **dict(self.params, **kwargs))
E0116 13:37:18.135781 747668 pjrt_stream_executor_client.cc:3084] Execution of replica 0 failed: INTERNAL: CustomCall failed: ptxas exited with non-zero error code 65280, output: ptxas /tmp/pnsinha/tempfile-beagle3-0010.rcc.local-ea5255dc-747668-62bd7ec05f656, line 5; fatal : Unsupported .version 8.4; current version is '8.2'
ptxas fatal : Ptx assembly aborted due to errors
[ FAILED ] InferenceTest.test_inference_bucket_1024
[ RUN ] InferenceTest.test_inference_default_bucket
Processing fold input 5tgy
Checking we can load the model parameters...
Running data pipeline...
Processing chain A
Processing chain A took 0.00 seconds
Processing chain B
Processing chain B took 0.00 seconds
Output directory: <absl.testing.absltest._TempDir object at 0x7f07c74a4750>
Writing model input JSON to <absl.testing.absltest._TempDir object at 0x7f07c74a4750>
Predicting 3D structure for 5tgy for seed(s) (1234,)...
Featurising data for seeds (1234,)...
Featurising 5tgy with rng_seed 1234.
I0116 13:37:20.282916 139679608824192 pipeline.py:165] processing 5tgy, random_seed=1234
I0116 13:37:20.293497 139679608824192 pipeline.py:258] Calculating bucket size for input with 150 tokens.
I0116 13:37:20.293570 139679608824192 pipeline.py:264] Got bucket size 150 for input with 150 tokens, resulting in 0 padded tokens.
[13:37:20] UFFTYPER: Unrecognized charge state for atom: 48
[13:37:20] UFFTYPER: Unrecognized atom type: Zn4+2 (48)
[13:37:21] UFFTYPER: Unrecognized charge state for atom: 48
[13:37:21] UFFTYPER: Unrecognized atom type: Zn4+2 (48)
Featurising 5tgy with rng_seed 1234 took 1.36 seconds.
Featurising data for seeds (1234,) took 1.36 seconds.
Running model inference for seed 1234...
/software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/jax/_src/pjit.py:443: DeprecationWarning: backend and device argument on jit is deprecated. You can use `jax.device_put(..., jax.local_devices("cpu")[0])` on the inputs to the jitted function to get the same behavior.
warnings.warn(
/software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/jax/_src/linear_util.py:193: DeprecationWarning: Passing arguments 'a', 'a_min' or 'a_max' to jax.numpy.clip is deprecated. Please use 'arr', 'min' or 'max' respectively instead.
ans = self.f(*args, **dict(self.params, **kwargs))
E0116 13:37:52.697293 747668 pjrt_stream_executor_client.cc:3084] Execution of replica 0 failed: INTERNAL: CustomCall failed: ptxas exited with non-zero error code 65280, output: ptxas /tmp/pnsinha/tempfile-beagle3-0010.rcc.local-ea5255dc-747668-62bd7ee16688d, line 5; fatal : Unsupported .version 8.4; current version is '8.2'
ptxas fatal : Ptx assembly aborted due to errors
[ FAILED ] InferenceTest.test_inference_default_bucket
[ RUN ] InferenceTest.test_model_inference
/software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/jax/_src/pjit.py:443: DeprecationWarning: backend and device argument on jit is deprecated. You can use `jax.device_put(..., jax.local_devices("cpu")[0])` on the inputs to the jitted function to get the same behavior.
warnings.warn(
E0116 13:38:25.645706 747668 pjrt_stream_executor_client.cc:3084] Execution of replica 0 failed: INTERNAL: CustomCall failed: ptxas exited with non-zero error code 65280, output: ptxas /tmp/pnsinha/tempfile-beagle3-0010.rcc.local-ea5255dc-747668-62bd7f00d2771, line 5; fatal : Unsupported .version 8.4; current version is '8.2'
ptxas fatal : Ptx assembly aborted due to errors
[ FAILED ] InferenceTest.test_model_inference
[ RUN ] InferenceTest.test_no_chains_in_input
Processing fold input empty
[ OK ] InferenceTest.test_no_chains_in_input
[ RUN ] InferenceTest.test_process_fold_input_runs_only_data_pipeline
Processing fold input 5tgy
Running data pipeline...
Processing chain A
Processing chain A took 0.00 seconds
Processing chain B
Processing chain B took 0.00 seconds
Output directory: <absl.testing.absltest._TempDir object at 0x7f07bc2a0410>
Writing model input JSON to <absl.testing.absltest._TempDir object at 0x7f07bc2a0410>
Skipping inference...
Done processing fold input 5tgy.
[ OK ] InferenceTest.test_process_fold_input_runs_only_data_pipeline
[ RUN ] InferenceTest.test_process_fold_input_runs_only_inference
Processing fold input 5tgy
Checking we can load the model parameters...
Skipping data pipeline...
Output directory: <absl.testing.absltest._TempDir object at 0x7f07bc272450>
Writing model input JSON to <absl.testing.absltest._TempDir object at 0x7f07bc272450>
Predicting 3D structure for 5tgy for seed(s) (1234,)...
Featurising data for seeds (1234,)...
[ OK ] InferenceTest.test_process_fold_input_runs_only_inference
[ RUN ] InferenceTest.test_replace_db_dir0 (num_db_dirs=1)
[ OK ] InferenceTest.test_replace_db_dir0 (num_db_dirs=1)
[ RUN ] InferenceTest.test_replace_db_dir1 (num_db_dirs=2)
[ OK ] InferenceTest.test_replace_db_dir1 (num_db_dirs=2)
[ RUN ] InferenceTest.test_write_input_json
[ OK ] InferenceTest.test_write_input_json
======================================================================
ERROR: test_inference_bucket_1024 (__main__.InferenceTest)
InferenceTest.test_inference_bucket_1024
test_inference_bucket_1024(bucket=1024, exp_ranking_scores=[0.69, 0.71, 0.71, 0.69, 0.7])
----------------------------------------------------------------------
Traceback (most recent call last):
File "/software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/absl/testing/parameterized.py", line 319, in bound_param_test
return test_method(self, **testcase_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold_test.py", line 328, in test_inference
actual = run_alphafold.process_fold_input(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold.py", line 556, in process_fold_input
all_inference_results = predict_structure(
^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold.py", line 373, in predict_structure
result = model_runner.run_inference(example, rng_key)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold.py", line 311, in run_inference
result = self._model(rng_key, featurised_example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
jaxlib.xla_extension.XlaRuntimeError: INTERNAL: CustomCall failed: ptxas exited with non-zero error code 65280, output: ptxas /tmp/pnsinha/tempfile-beagle3-0010.rcc.local-ea5255dc-747668-62bd7ec05f656, line 5; fatal : Unsupported .version 8.4; current version is '8.2'
ptxas fatal : Ptx assembly aborted due to errors
--------------------
For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.
======================================================================
ERROR: test_inference_default_bucket (__main__.InferenceTest)
InferenceTest.test_inference_default_bucket
test_inference_default_bucket(bucket=None, exp_ranking_scores=[0.69, 0.69, 0.72, 0.75, 0.7])
----------------------------------------------------------------------
Traceback (most recent call last):
File "/software/alphafold3.0-el8-x86_64/alphafold_venv/lib/python3.11/site-packages/absl/testing/parameterized.py", line 319, in bound_param_test
return test_method(self, **testcase_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold_test.py", line 328, in test_inference
actual = run_alphafold.process_fold_input(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold.py", line 556, in process_fold_input
all_inference_results = predict_structure(
^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold.py", line 373, in predict_structure
result = model_runner.run_inference(example, rng_key)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold.py", line 311, in run_inference
result = self._model(rng_key, featurised_example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
jaxlib.xla_extension.XlaRuntimeError: INTERNAL: CustomCall failed: ptxas exited with non-zero error code 65280, output: ptxas /tmp/pnsinha/tempfile-beagle3-0010.rcc.local-ea5255dc-747668-62bd7ee16688d, line 5; fatal : Unsupported .version 8.4; current version is '8.2'
ptxas fatal : Ptx assembly aborted due to errors
--------------------
For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.
======================================================================
ERROR: test_model_inference (__main__.InferenceTest)
InferenceTest.test_model_inference
Run model inference and assert that the output is as expected.
----------------------------------------------------------------------
Traceback (most recent call last):
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold_test.py", line 243, in test_model_inference
inference_result = self._runner.run_inference(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/alphafold3.0-el8-x86_64/alphafold3/run_alphafold.py", line 311, in run_inference
result = self._model(rng_key, featurised_example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
jaxlib.xla_extension.XlaRuntimeError: INTERNAL: CustomCall failed: ptxas exited with non-zero error code 65280, output: ptxas /tmp/pnsinha/tempfile-beagle3-0010.rcc.local-ea5255dc-747668-62bd7f00d2771, line 5; fatal : Unsupported .version 8.4; current version is '8.2'
ptxas fatal : Ptx assembly aborted due to errors
--------------------
For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.
----------------------------------------------------------------------
Ran 11 tests in 116.769s
FAILED (errors=3)