text:
Job was killed with `/app/run_alphafold.sh: line 3:     8 Killed                  python /app/alphafold/run_alphafold.py "$@"``

Hi, I got an issue here.
My job was killed with '/app/run_alphafold.sh: line 3: 8 Killed python /app/alphafold/run_alphafold.py "$@"`'
python3 /home/song/alphafold/docker/run_docker.py --fasta_paths=HM.fasta --max_template_date=2030-03-10 --model_preset=monomer --db_preset=reduced_dbs --data_dir=/home/song/harddrive/alphafold_dbs
I0407 10:01:52.360448 140644131185024 run_docker.py:113] Mounting /home/song/alphafold/fasta -> /mnt/fasta_path_0
I0407 10:01:52.360547 140644131185024 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/uniref90 -> /mnt/uniref90_database_path
I0407 10:01:52.360589 140644131185024 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/mgnify -> /mnt/mgnify_database_path
I0407 10:01:52.360618 140644131185024 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs -> /mnt/data_dir
I0407 10:01:52.360645 140644131185024 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/pdb_mmcif/mmcif_files -> /mnt/template_mmcif_dir
I0407 10:01:52.360675 140644131185024 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/pdb_mmcif -> /mnt/obsolete_pdbs_path
I0407 10:01:52.360708 140644131185024 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/pdb70 -> /mnt/pdb70_database_path
I0407 10:01:52.360738 140644131185024 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/small_bfd -> /mnt/small_bfd_database_path
I0407 10:01:54.346276 140644131185024 run_docker.py:255] I0407 15:01:54.345562 140657684973376 templates.py:857] Using precomputed obsolete pdbs /mnt/obsolete_pdbs_path/obsolete.dat.
I0407 10:01:56.070738 140644131185024 run_docker.py:255] I0407 15:01:56.069675 140657684973376 tpu_client.py:54] Starting the local TPU driver.
I0407 10:01:56.071013 140644131185024 run_docker.py:255] I0407 15:01:56.070167 140657684973376 xla_bridge.py:212] Unable to initialize backend 'tpu_driver': Not found: Unable to find driver in registry given worker: local://
I0407 10:01:56.190624 140644131185024 run_docker.py:255] I0407 15:01:56.190001 140657684973376 xla_bridge.py:212] Unable to initialize backend 'tpu': Invalid argument: TpuPlatform is not available.
I0407 10:02:04.227958 140644131185024 run_docker.py:255] I0407 15:02:04.227193 140657684973376 run_alphafold.py:377] Have 5 models: ['model_1_pred_0', 'model_2_pred_0', 'model_3_pred_0', 'model_4_pred_0', 'model_5_pred_0']
I0407 10:02:04.228071 140644131185024 run_docker.py:255] I0407 15:02:04.227301 140657684973376 run_alphafold.py:393] Using random seed 598696474148977303 for the data pipeline
I0407 10:02:04.228105 140644131185024 run_docker.py:255] I0407 15:02:04.227442 140657684973376 run_alphafold.py:161] Predicting HM
I0407 10:02:04.228142 140644131185024 run_docker.py:255] I0407 15:02:04.227701 140657684973376 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmpolo2mr4d/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /mnt/fasta_path_0/HM.fasta /mnt/uniref90_database_path/uniref90.fasta"
I0407 10:02:04.283227 140644131185024 run_docker.py:255] I0407 15:02:04.282486 140657684973376 utils.py:36] Started Jackhmmer (uniref90.fasta) query
I0407 10:06:29.987746 140644131185024 run_docker.py:255] I0407 15:06:29.987046 140657684973376 utils.py:40] Finished Jackhmmer (uniref90.fasta) query in 265.704 seconds
I0407 10:06:30.251781 140644131185024 run_docker.py:255] I0407 15:06:30.251073 140657684973376 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmpzqrgg89o/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /mnt/fasta_path_0/HM.fasta /mnt/mgnify_database_path/mgy_clusters_2018_12.fa"
I0407 10:06:30.303924 140644131185024 run_docker.py:255] I0407 15:06:30.303090 140657684973376 utils.py:36] Started Jackhmmer (mgy_clusters_2018_12.fa) query
I0407 10:10:59.346460 140644131185024 run_docker.py:255] I0407 15:10:59.345720 140657684973376 utils.py:40] Finished Jackhmmer (mgy_clusters_2018_12.fa) query in 269.042 seconds
I0407 10:11:02.534446 140644131185024 run_docker.py:255] I0407 15:11:02.533800 140657684973376 hhsearch.py:85] Launching subprocess "/usr/bin/hhsearch -i /tmp/tmpi7u_y8pt/query.a3m -o /tmp/tmpi7u_y8pt/output.hhr -maxseq 1000000 -d /mnt/pdb70_database_path/pdb70"
I0407 10:11:02.591353 140644131185024 run_docker.py:255] I0407 15:11:02.590603 140657684973376 utils.py:36] Started HHsearch query
I0407 10:12:32.815087 140644131185024 run_docker.py:255] I0407 15:12:32.814265 140657684973376 utils.py:40] Finished HHsearch query in 90.223 seconds
I0407 10:12:35.013796 140644131185024 run_docker.py:255] I0407 15:12:35.006426 140657684973376 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmp12i9k_rf/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /mnt/fasta_path_0/HM.fasta /mnt/small_bfd_database_path/bfd-first_non_consensus_sequences.fasta"
I0407 10:12:35.058195 140644131185024 run_docker.py:255] I0407 15:12:35.057407 140657684973376 utils.py:36] Started Jackhmmer (bfd-first_non_consensus_sequences.fasta) query
I0407 10:13:46.664667 140644131185024 run_docker.py:255] I0407 15:13:46.663670 140657684973376 utils.py:40] Finished Jackhmmer (bfd-first_non_consensus_sequences.fasta) query in 71.606 seconds
I0407 10:13:47.202137 140644131185024 run_docker.py:255] I0407 15:13:47.201551 140657684973376 templates.py:878] Searching for template for:
I0407 10:13:47.533718 140644131185024 run_docker.py:255] I0407 15:13:47.533015 140657684973376 templates.py:268] Found an exact template match 5dzt_A.
I0407 10:13:47.686305 140644131185024 run_docker.py:255] I0407 15:13:47.685786 140657684973376 templates.py:268] Found an exact template match 3t33_A.
I0407 10:13:48.182532 140644131185024 run_docker.py:255] I0407 15:13:48.181832 140657684973376 templates.py:268] Found an exact template match 3e6u_A.
I0407 10:13:48.406836 140644131185024 run_docker.py:255] I0407 15:13:48.406119 140657684973376 templates.py:268] Found an exact template match 3e73_A.
I0407 10:13:48.543771 140644131185024 run_docker.py:255] I0407 15:13:48.543182 140657684973376 templates.py:268] Found an exact template match 2g0d_A.
I0407 10:13:48.553284 140644131185024 run_docker.py:255] I0407 15:13:48.552886 140657684973376 templates.py:268] Found an exact template match 3e6u_A.
I0407 10:13:48.563110 140644131185024 run_docker.py:255] I0407 15:13:48.562769 140657684973376 templates.py:268] Found an exact template match 3e73_A.
I0407 10:13:48.897225 140644131185024 run_docker.py:255] I0407 15:13:48.896682 140657684973376 templates.py:268] Found an exact template match 4v1r_B.
I0407 10:13:49.276198 140644131185024 run_docker.py:255] I0407 15:13:49.275689 140657684973376 templates.py:268] Found an exact template match 4v1s_A.
I0407 10:13:49.507065 140644131185024 run_docker.py:255] I0407 15:13:49.506518 140657684973376 templates.py:268] Found an exact template match 4c1s_B.
I0407 10:13:49.516093 140644131185024 run_docker.py:255] I0407 15:13:49.515664 140657684973376 templates.py:268] Found an exact template match 4v1r_B.
I0407 10:13:49.525147 140644131185024 run_docker.py:255] I0407 15:13:49.524823 140657684973376 templates.py:268] Found an exact template match 4v1s_A.
I0407 10:13:49.534539 140644131185024 run_docker.py:255] I0407 15:13:49.534040 140657684973376 templates.py:268] Found an exact template match 4c1s_B.
I0407 10:13:49.543745 140644131185024 run_docker.py:255] I0407 15:13:49.543389 140657684973376 templates.py:268] Found an exact template match 3t33_A.
I0407 10:13:50.003878 140644131185024 run_docker.py:255] I0407 15:13:50.003276 140657684973376 templates.py:268] Found an exact template match 4mu9_A.
I0407 10:13:50.012879 140644131185024 run_docker.py:255] I0407 15:13:50.012417 140657684973376 templates.py:268] Found an exact template match 4mu9_B.
I0407 10:13:50.021894 140644131185024 run_docker.py:255] I0407 15:13:50.021365 140657684973376 templates.py:268] Found an exact template match 3e6u_A.
I0407 10:13:50.031929 140644131185024 run_docker.py:255] I0407 15:13:50.031504 140657684973376 templates.py:268] Found an exact template match 3e73_A.
I0407 10:13:50.041911 140644131185024 run_docker.py:255] I0407 15:13:50.041568 140657684973376 templates.py:268] Found an exact template match 2g0d_A.
I0407 10:13:50.557740 140644131185024 run_docker.py:255] I0407 15:13:50.557202 140657684973376 templates.py:268] Found an exact template match 4wu0_B.
I0407 10:13:51.248248 140644131185024 run_docker.py:255] I0407 15:13:51.247645 140657684973376 pipeline.py:234] Uniref90 MSA size: 4104 sequences.
I0407 10:13:51.248351 140644131185024 run_docker.py:255] I0407 15:13:51.247781 140657684973376 pipeline.py:235] BFD MSA size: 1366 sequences.
I0407 10:13:51.248383 140644131185024 run_docker.py:255] I0407 15:13:51.247805 140657684973376 pipeline.py:236] MGnify MSA size: 501 sequences.
I0407 10:13:51.248409 140644131185024 run_docker.py:255] I0407 15:13:51.247826 140657684973376 pipeline.py:238] Final (deduplicated) MSA size: 5641 sequences.
I0407 10:13:51.248434 140644131185024 run_docker.py:255] I0407 15:13:51.247973 140657684973376 pipeline.py:241] Total number of templates (NB: this can include bad templates and is later filtered to top 4): 20.
I0407 10:13:51.310783 140644131185024 run_docker.py:255] I0407 15:13:51.310043 140657684973376 run_alphafold.py:190] Running model model_1_pred_0 on HM
I0407 10:13:53.805705 140644131185024 run_docker.py:255] 2022-04-07 15:13:53.804967: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 46268640 exceeds 10% of free system memory.
I0407 10:13:53.816205 140644131185024 run_docker.py:255] 2022-04-07 15:13:53.815266: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 491443920 exceeds 10% of free system memory.
I0407 10:13:53.817507 140644131185024 run_docker.py:255] 2022-04-07 15:13:53.817032: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 467513640 exceeds 10% of free system memory.
I0407 10:13:53.818194 140644131185024 run_docker.py:255] 2022-04-07 15:13:53.817859: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 44256960 exceeds 10% of free system memory.
I0407 10:13:53.985317 140644131185024 run_docker.py:255] 2022-04-07 15:13:53.984431: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 46268640 exceeds 10% of free system memory.
I0407 10:13:55.190978 140644131185024 run_docker.py:255] I0407 15:13:55.189868 140657684973376 model.py:166] Running predict with shape(feat) = {'aatype': (4, 990), 'residue_index': (4, 990), 'seq_length': (4,), 'template_aatype': (4, 4, 990), 'template_all_atom_masks': (4, 4, 990, 37), 'template_all_atom_positions': (4, 4, 990, 37, 3), 'template_sum_probs': (4, 4, 1), 'is_distillation': (4,), 'seq_mask': (4, 990), 'msa_mask': (4, 508, 990), 'msa_row_mask': (4, 508), 'random_crop_to_size_seed': (4, 2), 'template_mask': (4, 4), 'template_pseudo_beta': (4, 4, 990, 3), 'template_pseudo_beta_mask': (4, 4, 990), 'atom14_atom_exists': (4, 990, 14), 'residx_atom14_to_atom37': (4, 990, 14), 'residx_atom37_to_atom14': (4, 990, 37), 'atom37_atom_exists': (4, 990, 37), 'extra_msa': (4, 5120, 990), 'extra_msa_mask': (4, 5120, 990), 'extra_msa_row_mask': (4, 5120), 'bert_mask': (4, 508, 990), 'true_msa': (4, 508, 990), 'extra_has_deletion': (4, 5120, 990), 'extra_deletion_value': (4, 5120, 990), 'msa_feat': (4, 508, 990, 49), 'target_feat': (4, 990, 22)}
I0407 10:22:21.857693 140644131185024 run_docker.py:255] I0407 15:22:21.856382 140657684973376 model.py:176] Output shape was {'distogram': {'bin_edges': (63,), 'logits': (990, 990, 64)}, 'experimentally_resolved': {'logits': (990, 37)}, 'masked_msa': {'logits': (508, 990, 23)}, 'predicted_lddt': {'logits': (990, 50)}, 'structure_module': {'final_atom_mask': (990, 37), 'final_atom_positions': (990, 37, 3)}, 'plddt': (990,), 'ranking_confidence': ()}
I0407 10:22:21.858033 140644131185024 run_docker.py:255] I0407 15:22:21.856493 140657684973376 run_alphafold.py:204] Total JAX model model_1_pred_0 on HM predict time (includes compilation time, see --benchmark): 506.7s
I0407 10:22:40.344387 140644131185024 run_docker.py:255] I0407 15:22:40.343476 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {<Residue 989 (ARG) of chain 0>: ['OXT']}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:22:41.369135 140644131185024 run_docker.py:255] I0407 15:22:41.368305 140657684973376 amber_minimize.py:408] Minimizing protein, attempt 1 of 100.
I0407 10:22:42.601895 140644131185024 run_docker.py:255] I0407 15:22:42.601060 140657684973376 amber_minimize.py:69] Restraining 7934 / 15723 particles.
I0407 10:22:56.900804 140644131185024 run_docker.py:255] I0407 15:22:56.899905 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:23:07.534636 140644131185024 run_docker.py:255] I0407 15:23:07.533953 140657684973376 amber_minimize.py:500] Iteration completed: Einit 4763421.61 Efinal -18665.63 Time 3.53 s num residue violations 0 num residue exclusions 0
I0407 10:23:21.027155 140644131185024 run_docker.py:255] I0407 15:23:21.026266 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {<Residue 989 (ARG) of chain 0>: ['OXT']}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:23:22.952107 140644131185024 run_docker.py:255] I0407 15:23:22.951289 140657684973376 run_alphafold.py:190] Running model model_2_pred_0 on HM
I0407 10:23:25.586225 140644131185024 run_docker.py:255] I0407 15:23:25.585409 140657684973376 model.py:166] Running predict with shape(feat) = {'aatype': (4, 990), 'residue_index': (4, 990), 'seq_length': (4,), 'template_aatype': (4, 4, 990), 'template_all_atom_masks': (4, 4, 990, 37), 'template_all_atom_positions': (4, 4, 990, 37, 3), 'template_sum_probs': (4, 4, 1), 'is_distillation': (4,), 'seq_mask': (4, 990), 'msa_mask': (4, 508, 990), 'msa_row_mask': (4, 508), 'random_crop_to_size_seed': (4, 2), 'template_mask': (4, 4), 'template_pseudo_beta': (4, 4, 990, 3), 'template_pseudo_beta_mask': (4, 4, 990), 'atom14_atom_exists': (4, 990, 14), 'residx_atom14_to_atom37': (4, 990, 14), 'residx_atom37_to_atom14': (4, 990, 37), 'atom37_atom_exists': (4, 990, 37), 'extra_msa': (4, 1024, 990), 'extra_msa_mask': (4, 1024, 990), 'extra_msa_row_mask': (4, 1024), 'bert_mask': (4, 508, 990), 'true_msa': (4, 508, 990), 'extra_has_deletion': (4, 1024, 990), 'extra_deletion_value': (4, 1024, 990), 'msa_feat': (4, 508, 990, 49), 'target_feat': (4, 990, 22)}
I0407 10:30:00.731788 140644131185024 run_docker.py:255] I0407 15:30:00.724386 140657684973376 model.py:176] Output shape was {'distogram': {'bin_edges': (63,), 'logits': (990, 990, 64)}, 'experimentally_resolved': {'logits': (990, 37)}, 'masked_msa': {'logits': (508, 990, 23)}, 'predicted_lddt': {'logits': (990, 50)}, 'structure_module': {'final_atom_mask': (990, 37), 'final_atom_positions': (990, 37, 3)}, 'plddt': (990,), 'ranking_confidence': ()}
I0407 10:30:00.733442 140644131185024 run_docker.py:255] I0407 15:30:00.732738 140657684973376 run_alphafold.py:204] Total JAX model model_2_pred_0 on HM predict time (includes compilation time, see --benchmark): 395.1s
I0407 10:30:16.300615 140644131185024 run_docker.py:255] I0407 15:30:16.299608 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {<Residue 989 (ARG) of chain 0>: ['OXT']}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:30:16.988835 140644131185024 run_docker.py:255] I0407 15:30:16.987960 140657684973376 amber_minimize.py:408] Minimizing protein, attempt 1 of 100.
I0407 10:30:18.402831 140644131185024 run_docker.py:255] I0407 15:30:18.401961 140657684973376 amber_minimize.py:69] Restraining 7934 / 15723 particles.
I0407 10:30:32.590772 140644131185024 run_docker.py:255] I0407 15:30:32.583527 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:30:57.097590 140644131185024 run_docker.py:255] I0407 15:30:57.096977 140657684973376 amber_minimize.py:500] Iteration completed: Einit 11861470.36 Efinal -18421.26 Time 3.81 s num residue violations 0 num residue exclusions 0
I0407 10:31:13.040065 140644131185024 run_docker.py:255] I0407 15:31:13.039160 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {<Residue 989 (ARG) of chain 0>: ['OXT']}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:31:14.855728 140644131185024 run_docker.py:255] I0407 15:31:14.854954 140657684973376 run_alphafold.py:190] Running model model_3_pred_0 on HM
I0407 10:31:17.374645 140644131185024 run_docker.py:255] I0407 15:31:17.371672 140657684973376 model.py:166] Running predict with shape(feat) = {'aatype': (4, 990), 'residue_index': (4, 990), 'seq_length': (4,), 'is_distillation': (4,), 'seq_mask': (4, 990), 'msa_mask': (4, 512, 990), 'msa_row_mask': (4, 512), 'random_crop_to_size_seed': (4, 2), 'atom14_atom_exists': (4, 990, 14), 'residx_atom14_to_atom37': (4, 990, 14), 'residx_atom37_to_atom14': (4, 990, 37), 'atom37_atom_exists': (4, 990, 37), 'extra_msa': (4, 5120, 990), 'extra_msa_mask': (4, 5120, 990), 'extra_msa_row_mask': (4, 5120), 'bert_mask': (4, 512, 990), 'true_msa': (4, 512, 990), 'extra_has_deletion': (4, 5120, 990), 'extra_deletion_value': (4, 5120, 990), 'msa_feat': (4, 512, 990, 49), 'target_feat': (4, 990, 22)}
I0407 10:37:45.255258 140644131185024 run_docker.py:255] I0407 15:37:45.254414 140657684973376 model.py:176] Output shape was {'distogram': {'bin_edges': (63,), 'logits': (990, 990, 64)}, 'experimentally_resolved': {'logits': (990, 37)}, 'masked_msa': {'logits': (512, 990, 23)}, 'predicted_lddt': {'logits': (990, 50)}, 'structure_module': {'final_atom_mask': (990, 37), 'final_atom_positions': (990, 37, 3)}, 'plddt': (990,), 'ranking_confidence': ()}
I0407 10:37:45.264786 140644131185024 run_docker.py:255] I0407 15:37:45.263966 140657684973376 run_alphafold.py:204] Total JAX model model_3_pred_0 on HM predict time (includes compilation time, see --benchmark): 387.9s
I0407 10:38:12.306217 140644131185024 run_docker.py:255] I0407 15:38:12.305210 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {<Residue 989 (ARG) of chain 0>: ['OXT']}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:38:13.627728 140644131185024 run_docker.py:255] I0407 15:38:13.626933 140657684973376 amber_minimize.py:408] Minimizing protein, attempt 1 of 100.
I0407 10:38:15.155689 140644131185024 run_docker.py:255] I0407 15:38:15.154838 140657684973376 amber_minimize.py:69] Restraining 7934 / 15723 particles.
I0407 10:38:29.867942 140644131185024 run_docker.py:255] I0407 15:38:29.867018 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:38:48.780642 140644131185024 run_docker.py:255] I0407 15:38:48.779655 140657684973376 amber_minimize.py:500] Iteration completed: Einit 1463943.83 Efinal -18591.48 Time 4.98 s num residue violations 0 num residue exclusions 0
I0407 10:39:02.920470 140644131185024 run_docker.py:255] I0407 15:39:02.919537 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {<Residue 989 (ARG) of chain 0>: ['OXT']}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:39:04.851090 140644131185024 run_docker.py:255] I0407 15:39:04.849699 140657684973376 run_alphafold.py:190] Running model model_4_pred_0 on HM
I0407 10:39:07.390420 140644131185024 run_docker.py:255] I0407 15:39:07.388259 140657684973376 model.py:166] Running predict with shape(feat) = {'aatype': (4, 990), 'residue_index': (4, 990), 'seq_length': (4,), 'is_distillation': (4,), 'seq_mask': (4, 990), 'msa_mask': (4, 512, 990), 'msa_row_mask': (4, 512), 'random_crop_to_size_seed': (4, 2), 'atom14_atom_exists': (4, 990, 14), 'residx_atom14_to_atom37': (4, 990, 14), 'residx_atom37_to_atom14': (4, 990, 37), 'atom37_atom_exists': (4, 990, 37), 'extra_msa': (4, 5120, 990), 'extra_msa_mask': (4, 5120, 990), 'extra_msa_row_mask': (4, 5120), 'bert_mask': (4, 512, 990), 'true_msa': (4, 512, 990), 'extra_has_deletion': (4, 5120, 990), 'extra_deletion_value': (4, 5120, 990), 'msa_feat': (4, 512, 990, 49), 'target_feat': (4, 990, 22)}
I0407 10:45:44.214986 140644131185024 run_docker.py:255] I0407 15:45:44.207410 140657684973376 model.py:176] Output shape was {'distogram': {'bin_edges': (63,), 'logits': (990, 990, 64)}, 'experimentally_resolved': {'logits': (990, 37)}, 'masked_msa': {'logits': (512, 990, 23)}, 'predicted_lddt': {'logits': (990, 50)}, 'structure_module': {'final_atom_mask': (990, 37), 'final_atom_positions': (990, 37, 3)}, 'plddt': (990,), 'ranking_confidence': ()}
I0407 10:45:44.218248 140644131185024 run_docker.py:255] I0407 15:45:44.217665 140657684973376 run_alphafold.py:204] Total JAX model model_4_pred_0 on HM predict time (includes compilation time, see --benchmark): 396.8s
I0407 10:46:03.766776 140644131185024 run_docker.py:255] I0407 15:46:03.766033 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {<Residue 989 (ARG) of chain 0>: ['OXT']}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:46:04.454188 140644131185024 run_docker.py:255] I0407 15:46:04.453492 140657684973376 amber_minimize.py:408] Minimizing protein, attempt 1 of 100.
I0407 10:46:06.094631 140644131185024 run_docker.py:255] I0407 15:46:06.093979 140657684973376 amber_minimize.py:69] Restraining 7934 / 15723 particles.
I0407 10:46:18.494993 140644131185024 run_docker.py:255] I0407 15:46:18.494155 140657684973376 amber_minimize.py:177] alterations info: {'nonstandard_residues': [], 'removed_heterogens': set(), 'missing_residues': {}, 'missing_heavy_atoms': {}, 'missing_terminals': {}, 'Se_in_MET': [], 'removed_chains': {0: []}}
I0407 10:47:47.292335 140644131185024 run_docker.py:255] /app/run_alphafold.sh: line 3:     8 Killed                  python /app/alphafold/run_alphafold.py "$@"

I noticed when it came to the 4th prediction and minimization, GPU memory was almost full, and then it was killed.
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.60.02    Driver Version: 510.60.02    CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------+
| 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 GeForce ...  Off  | 00000000:01:00.0  On |                  N/A |
| 81%   82C    P2   331W / 350W |  11992MiB / 12288MiB |    100%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1141      G   /usr/lib/xorg/Xorg                 96MiB |
|    0   N/A  N/A      1642      G   /usr/lib/xorg/Xorg                318MiB |
|    0   N/A  N/A      1772      G   /usr/bin/gnome-shell               63MiB |
|    0   N/A  N/A    230121      G   ...veSuggestionsOnlyOnDemand       62MiB |
|    0   N/A  N/A    377645      G   ...592484650516736366,131072      193MiB |
|    0   N/A  N/A    480339      C   python                            939MiB |

And I also try the method mentioned in this issue #197, I comment out the following lines in run_docker.py
‘TF_FORCE_UNIFIED_MEMORY’: ‘1’,
‘XLA_PYTHON_CLIENT_MEM_FRACTION’: ‘4.0’,

And then, it showed the same error @Ikajiro said in #197. Perhaps because my sequence is long (~900)
python3 /home/song/alphafold/docker/run_docker.py --fasta_paths=HM.fasta --max_template_date=2030-03-10 --model_preset=monomer --db_preset=reduced_dbs --data_dir=/home/song/harddrive/alphafold_dbs
I0407 10:56:48.221208 139844004888960 run_docker.py:113] Mounting /home/song/alphafold/fasta -> /mnt/fasta_path_0
I0407 10:56:49.324587 139844004888960 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/uniref90 -> /mnt/uniref90_database_path
I0407 10:56:49.337784 139844004888960 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/mgnify -> /mnt/mgnify_database_path
I0407 10:56:49.338311 139844004888960 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs -> /mnt/data_dir
I0407 10:56:49.338590 139844004888960 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/pdb_mmcif/mmcif_files -> /mnt/template_mmcif_dir
I0407 10:56:49.339505 139844004888960 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/pdb_mmcif -> /mnt/obsolete_pdbs_path
I0407 10:56:49.340553 139844004888960 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/pdb70 -> /mnt/pdb70_database_path
I0407 10:56:49.346934 139844004888960 run_docker.py:113] Mounting /home/song/harddrive/alphafold_dbs/small_bfd -> /mnt/small_bfd_database_path
I0407 10:56:54.050762 139844004888960 run_docker.py:255] I0407 15:56:54.049874 140462593042240 templates.py:857] Using precomputed obsolete pdbs /mnt/obsolete_pdbs_path/obsolete.dat.
I0407 10:56:55.568237 139844004888960 run_docker.py:255] I0407 15:56:55.567192 140462593042240 tpu_client.py:54] Starting the local TPU driver.
I0407 10:56:55.570917 139844004888960 run_docker.py:255] I0407 15:56:55.569945 140462593042240 xla_bridge.py:212] Unable to initialize backend 'tpu_driver': Not found: Unable to find driver in registry given worker: local://
I0407 10:56:55.677566 139844004888960 run_docker.py:255] I0407 15:56:55.677165 140462593042240 xla_bridge.py:212] Unable to initialize backend 'tpu': Invalid argument: TpuPlatform is not available.
I0407 10:57:03.503803 139844004888960 run_docker.py:255] I0407 15:57:03.503148 140462593042240 run_alphafold.py:377] Have 5 models: ['model_1_pred_0', 'model_2_pred_0', 'model_3_pred_0', 'model_4_pred_0', 'model_5_pred_0']
I0407 10:57:03.503915 139844004888960 run_docker.py:255] I0407 15:57:03.503263 140462593042240 run_alphafold.py:393] Using random seed 1268619124088369252 for the data pipeline
I0407 10:57:03.503951 139844004888960 run_docker.py:255] I0407 15:57:03.503389 140462593042240 run_alphafold.py:161] Predicting HM
I0407 10:57:03.507074 139844004888960 run_docker.py:255] I0407 15:57:03.506884 140462593042240 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmpcez7kft8/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /mnt/fasta_path_0/HM.fasta /mnt/uniref90_database_path/uniref90.fasta"
I0407 10:57:03.532036 139844004888960 run_docker.py:255] I0407 15:57:03.531312 140462593042240 utils.py:36] Started Jackhmmer (uniref90.fasta) query
I0407 11:01:29.486995 139844004888960 run_docker.py:255] I0407 16:01:29.485760 140462593042240 utils.py:40] Finished Jackhmmer (uniref90.fasta) query in 265.954 seconds
I0407 11:01:29.757087 139844004888960 run_docker.py:255] I0407 16:01:29.756328 140462593042240 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmp3tz2iwv8/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /mnt/fasta_path_0/HM.fasta /mnt/mgnify_database_path/mgy_clusters_2018_12.fa"
I0407 11:01:29.780973 139844004888960 run_docker.py:255] I0407 16:01:29.780000 140462593042240 utils.py:36] Started Jackhmmer (mgy_clusters_2018_12.fa) query
I0407 11:05:58.770235 139844004888960 run_docker.py:255] I0407 16:05:58.768591 140462593042240 utils.py:40] Finished Jackhmmer (mgy_clusters_2018_12.fa) query in 268.988 seconds
I0407 11:06:01.998098 139844004888960 run_docker.py:255] I0407 16:06:01.997191 140462593042240 hhsearch.py:85] Launching subprocess "/usr/bin/hhsearch -i /tmp/tmpmflo69re/query.a3m -o /tmp/tmpmflo69re/output.hhr -maxseq 1000000 -d /mnt/pdb70_database_path/pdb70"
I0407 11:06:02.025695 139844004888960 run_docker.py:255] I0407 16:06:02.024801 140462593042240 utils.py:36] Started HHsearch query
I0407 11:07:30.042175 139844004888960 run_docker.py:255] I0407 16:07:30.041380 140462593042240 utils.py:40] Finished HHsearch query in 88.016 seconds
I0407 11:07:32.267431 139844004888960 run_docker.py:255] I0407 16:07:32.266582 140462593042240 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmpmgl82w8p/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /mnt/fasta_path_0/HM.fasta /mnt/small_bfd_database_path/bfd-first_non_consensus_sequences.fasta"
I0407 11:07:32.294763 139844004888960 run_docker.py:255] I0407 16:07:32.293882 140462593042240 utils.py:36] Started Jackhmmer (bfd-first_non_consensus_sequences.fasta) query
I0407 11:08:43.829909 139844004888960 run_docker.py:255] I0407 16:08:43.828865 140462593042240 utils.py:40] Finished Jackhmmer (bfd-first_non_consensus_sequences.fasta) query in 71.535 seconds
I0407 11:08:44.381553 139844004888960 run_docker.py:255] I0407 16:08:44.380667 140462593042240 templates.py:878] Searching for template for:
I0407 11:08:44.724613 139844004888960 run_docker.py:255] I0407 16:08:44.723805 140462593042240 templates.py:268] Found an exact template match 5dzt_A.
I0407 11:08:44.878182 139844004888960 run_docker.py:255] I0407 16:08:44.877429 140462593042240 templates.py:268] Found an exact template match 3t33_A.
I0407 11:08:45.386653 139844004888960 run_docker.py:255] I0407 16:08:45.385856 140462593042240 templates.py:268] Found an exact template match 3e6u_A.
I0407 11:08:45.622028 139844004888960 run_docker.py:255] I0407 16:08:45.621242 140462593042240 templates.py:268] Found an exact template match 3e73_A.
I0407 11:08:45.781338 139844004888960 run_docker.py:255] I0407 16:08:45.780581 140462593042240 templates.py:268] Found an exact template match 2g0d_A.
I0407 11:08:45.790633 139844004888960 run_docker.py:255] I0407 16:08:45.790066 140462593042240 templates.py:268] Found an exact template match 3e6u_A.
I0407 11:08:45.800488 139844004888960 run_docker.py:255] I0407 16:08:45.799908 140462593042240 templates.py:268] Found an exact template match 3e73_A.
I0407 11:08:46.149326 139844004888960 run_docker.py:255] I0407 16:08:46.148638 140462593042240 templates.py:268] Found an exact template match 4v1r_B.
I0407 11:08:46.537471 139844004888960 run_docker.py:255] I0407 16:08:46.536653 140462593042240 templates.py:268] Found an exact template match 4v1s_A.
I0407 11:08:46.787966 139844004888960 run_docker.py:255] I0407 16:08:46.787169 140462593042240 templates.py:268] Found an exact template match 4c1s_B.
I0407 11:08:46.796962 139844004888960 run_docker.py:255] I0407 16:08:46.796271 140462593042240 templates.py:268] Found an exact template match 4v1r_B.
I0407 11:08:46.806015 139844004888960 run_docker.py:255] I0407 16:08:46.805377 140462593042240 templates.py:268] Found an exact template match 4v1s_A.
I0407 11:08:46.815184 139844004888960 run_docker.py:255] I0407 16:08:46.814651 140462593042240 templates.py:268] Found an exact template match 4c1s_B.
I0407 11:08:46.824412 139844004888960 run_docker.py:255] I0407 16:08:46.823835 140462593042240 templates.py:268] Found an exact template match 3t33_A.
I0407 11:08:47.299077 139844004888960 run_docker.py:255] I0407 16:08:47.298307 140462593042240 templates.py:268] Found an exact template match 4mu9_A.
I0407 11:08:47.307845 139844004888960 run_docker.py:255] I0407 16:08:47.307226 140462593042240 templates.py:268] Found an exact template match 4mu9_B.
I0407 11:08:47.316535 139844004888960 run_docker.py:255] I0407 16:08:47.316039 140462593042240 templates.py:268] Found an exact template match 3e6u_A.
I0407 11:08:47.326386 139844004888960 run_docker.py:255] I0407 16:08:47.325797 140462593042240 templates.py:268] Found an exact template match 3e73_A.
I0407 11:08:47.336363 139844004888960 run_docker.py:255] I0407 16:08:47.335795 140462593042240 templates.py:268] Found an exact template match 2g0d_A.
I0407 11:08:47.867216 139844004888960 run_docker.py:255] I0407 16:08:47.859856 140462593042240 templates.py:268] Found an exact template match 4wu0_B.
I0407 11:08:48.581511 139844004888960 run_docker.py:255] I0407 16:08:48.580777 140462593042240 pipeline.py:234] Uniref90 MSA size: 4104 sequences.
I0407 11:08:48.581789 139844004888960 run_docker.py:255] I0407 16:08:48.580890 140462593042240 pipeline.py:235] BFD MSA size: 1366 sequences.
I0407 11:08:48.581957 139844004888960 run_docker.py:255] I0407 16:08:48.580915 140462593042240 pipeline.py:236] MGnify MSA size: 501 sequences.
I0407 11:08:48.582135 139844004888960 run_docker.py:255] I0407 16:08:48.580936 140462593042240 pipeline.py:238] Final (deduplicated) MSA size: 5641 sequences.
I0407 11:08:48.582287 139844004888960 run_docker.py:255] I0407 16:08:48.581155 140462593042240 pipeline.py:241] Total number of templates (NB: this can include bad templates and is later filtered to top 4): 20.
I0407 11:08:48.647255 139844004888960 run_docker.py:255] I0407 16:08:48.646430 140462593042240 run_alphafold.py:190] Running model model_1_pred_0 on HM
I0407 11:08:51.183267 139844004888960 run_docker.py:255] 2022-04-07 16:08:51.182694: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 46268640 exceeds 10% of free system memory.
I0407 11:08:51.211383 139844004888960 run_docker.py:255] 2022-04-07 16:08:51.210625: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 491443920 exceeds 10% of free system memory.
I0407 11:08:51.212475 139844004888960 run_docker.py:255] 2022-04-07 16:08:51.211673: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 44256960 exceeds 10% of free system memory.
I0407 11:08:51.212612 139844004888960 run_docker.py:255] 2022-04-07 16:08:51.212198: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 467513640 exceeds 10% of free system memory.
I0407 11:08:51.377755 139844004888960 run_docker.py:255] 2022-04-07 16:08:51.377100: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 46268640 exceeds 10% of free system memory.
I0407 11:08:52.583285 139844004888960 run_docker.py:255] I0407 16:08:52.582467 140462593042240 model.py:166] Running predict with shape(feat) = {'aatype': (4, 990), 'residue_index': (4, 990), 'seq_length': (4,), 'template_aatype': (4, 4, 990), 'template_all_atom_masks': (4, 4, 990, 37), 'template_all_atom_positions': (4, 4, 990, 37, 3), 'template_sum_probs': (4, 4, 1), 'is_distillation': (4,), 'seq_mask': (4, 990), 'msa_mask': (4, 508, 990), 'msa_row_mask': (4, 508), 'random_crop_to_size_seed': (4, 2), 'template_mask': (4, 4), 'template_pseudo_beta': (4, 4, 990, 3), 'template_pseudo_beta_mask': (4, 4, 990), 'atom14_atom_exists': (4, 990, 14), 'residx_atom14_to_atom37': (4, 990, 14), 'residx_atom37_to_atom14': (4, 990, 37), 'atom37_atom_exists': (4, 990, 37), 'extra_msa': (4, 5120, 990), 'extra_msa_mask': (4, 5120, 990), 'extra_msa_row_mask': (4, 5120), 'bert_mask': (4, 508, 990), 'true_msa': (4, 508, 990), 'extra_has_deletion': (4, 5120, 990), 'extra_deletion_value': (4, 5120, 990), 'msa_feat': (4, 508, 990, 49), 'target_feat': (4, 990, 22)}
I0407 11:10:03.927810 139844004888960 run_docker.py:255] 2022-04-07 16:10:03.919715: W external/org_tensorflow/tensorflow/core/common_runtime/bfc_allocator.cc:457] Allocator (GPU_0_bfc) ran out of memory trying to allocate 8.30GiB (rounded to 8913849088)requested by op
I0407 11:10:03.932932 139844004888960 run_docker.py:255] 2022-04-07 16:10:03.931572: W external/org_tensorflow/tensorflow/core/common_runtime/bfc_allocator.cc:468] ****************************________________________________________________________________________
I0407 11:10:03.933348 139844004888960 run_docker.py:255] 2022-04-07 16:10:03.931752: E external/org_tensorflow/tensorflow/compiler/xla/pjrt/pjrt_stream_executor_client.cc:2040] Execution of replica 0 failed: Resource exhausted: Out of memory while trying to allocate 8913848992 bytes.
I0407 11:10:03.949940 139844004888960 run_docker.py:255] Traceback (most recent call last):
I0407 11:10:03.950385 139844004888960 run_docker.py:255] File "/app/alphafold/run_alphafold.py", line 422, in <module>
I0407 11:10:03.950693 139844004888960 run_docker.py:255] app.run(main)
I0407 11:10:03.951089 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/absl/app.py", line 312, in run
I0407 11:10:03.951364 139844004888960 run_docker.py:255] _run_main(main, args)
I0407 11:10:03.951620 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/absl/app.py", line 258, in _run_main
I0407 11:10:03.951720 139844004888960 run_docker.py:255] sys.exit(main(argv))
I0407 11:10:03.951772 139844004888960 run_docker.py:255] File "/app/alphafold/run_alphafold.py", line 406, in main
I0407 11:10:03.951828 139844004888960 run_docker.py:255] random_seed=random_seed)
I0407 11:10:03.951884 139844004888960 run_docker.py:255] File "/app/alphafold/run_alphafold.py", line 199, in predict_structure
I0407 11:10:03.951936 139844004888960 run_docker.py:255] random_seed=model_random_seed)
I0407 11:10:03.951988 139844004888960 run_docker.py:255] File "/app/alphafold/alphafold/model/model.py", line 167, in predict
I0407 11:10:03.952040 139844004888960 run_docker.py:255] result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
I0407 11:10:03.952091 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/_src/traceback_util.py", line 183, in reraise_with_filtered_traceback
I0407 11:10:03.952144 139844004888960 run_docker.py:255] return fun(*args, **kwargs)
I0407 11:10:03.952196 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/_src/api.py", line 427, in cache_miss
I0407 11:10:03.952247 139844004888960 run_docker.py:255] donated_invars=donated_invars, inline=inline)
I0407 11:10:03.952299 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/core.py", line 1560, in bind
I0407 11:10:03.952349 139844004888960 run_docker.py:255] return call_bind(self, fun, *args, **params)
I0407 11:10:03.952400 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/core.py", line 1551, in call_bind
I0407 11:10:03.952451 139844004888960 run_docker.py:255] outs = primitive.process(top_trace, fun, tracers, params)
I0407 11:10:03.952502 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/core.py", line 1563, in process
I0407 11:10:03.952553 139844004888960 run_docker.py:255] return trace.process_call(self, fun, tracers, params)
I0407 11:10:03.952604 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/core.py", line 606, in process_call
I0407 11:10:03.952850 139844004888960 run_docker.py:255] return primitive.impl(f, *tracers, **params)
I0407 11:10:03.953096 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/interpreters/xla.py", line 595, in _xla_call_impl
I0407 11:10:03.953342 139844004888960 run_docker.py:255] return compiled_fun(*args)
I0407 11:10:03.953590 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/interpreters/xla.py", line 893, in _execute_compiled
I0407 11:10:03.953839 139844004888960 run_docker.py:255] out_bufs = compiled.execute(input_bufs)
I0407 11:10:03.954083 139844004888960 run_docker.py:255] jax._src.traceback_util.UnfilteredStackTrace: RuntimeError: Resource exhausted: Out of memory while trying to allocate 8913848992 bytes.
I0407 11:10:03.954333 139844004888960 run_docker.py:255]
I0407 11:10:03.954578 139844004888960 run_docker.py:255] The stack trace below excludes JAX-internal frames.
I0407 11:10:03.954821 139844004888960 run_docker.py:255] The preceding is the original exception that occurred, unmodified.
I0407 11:10:03.955081 139844004888960 run_docker.py:255]
I0407 11:10:03.955332 139844004888960 run_docker.py:255] --------------------
I0407 11:10:03.955577 139844004888960 run_docker.py:255]
I0407 11:10:03.955823 139844004888960 run_docker.py:255] The above exception was the direct cause of the following exception:
I0407 11:10:03.956065 139844004888960 run_docker.py:255]
I0407 11:10:03.956306 139844004888960 run_docker.py:255] Traceback (most recent call last):
I0407 11:10:03.956411 139844004888960 run_docker.py:255] File "/app/alphafold/run_alphafold.py", line 422, in <module>
I0407 11:10:03.956462 139844004888960 run_docker.py:255] app.run(main)
I0407 11:10:03.956513 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/absl/app.py", line 312, in run
I0407 11:10:03.956564 139844004888960 run_docker.py:255] _run_main(main, args)
I0407 11:10:03.956614 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/absl/app.py", line 258, in _run_main
I0407 11:10:03.956665 139844004888960 run_docker.py:255] sys.exit(main(argv))
I0407 11:10:03.956715 139844004888960 run_docker.py:255] File "/app/alphafold/run_alphafold.py", line 406, in main
I0407 11:10:03.956765 139844004888960 run_docker.py:255] random_seed=random_seed)
I0407 11:10:03.956816 139844004888960 run_docker.py:255] File "/app/alphafold/run_alphafold.py", line 199, in predict_structure
I0407 11:10:03.956866 139844004888960 run_docker.py:255] random_seed=model_random_seed)
I0407 11:10:03.956917 139844004888960 run_docker.py:255] File "/app/alphafold/alphafold/model/model.py", line 167, in predict
I0407 11:10:03.956967 139844004888960 run_docker.py:255] result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
I0407 11:10:03.957017 139844004888960 run_docker.py:255] File "/opt/conda/lib/python3.7/site-packages/jax/interpreters/xla.py", line 893, in _execute_compiled
I0407 11:10:03.957067 139844004888960 run_docker.py:255] out_bufs = compiled.execute(input_bufs)
I0407 11:10:03.957118 139844004888960 run_docker.py:255] RuntimeError: Resource exhausted: Out of memory while trying to allocate 8913848992 bytes.

Same issue also happens here #130
Could anyone help me with this issue? Thanks.

Saw your issue because I had the same thing. Followed this tutorial to increase my swap size to 40GB (I have 32GB ram) and my runs work now. I'm very new to Linux, so I could be wrong.

Any luck?

I have the same questions. So how do you solve it?

I have the same questions. So how do you solve it?
so do I.Anybody knows how to deal with it?

u can try the solution given b @MMMJoey

authors: MMMJoey SONG-UIUC kbrunnerLXG lbw124765283 suzejie

repo: deepmind/alphafold


text:
400 Client Error

Hello,
I am trying to run AlphaFold using docker (command: python3 docker/run_docker.py --use_gpu=False --fasta_paths=seq.fa --max_template_date=2021-12-12) and I get this error:

Traceback (most recent call last):
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/docker/api/client.py", line 268, in _raise_for_status
response.raise_for_status()
File "/usr/lib/python3/dist-packages/requests/models.py", line 943, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http+docker://localhost/v1.41/containers/create
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "[HOME_dir_redacted]/Software/alphafold/docker/run_docker.py", line 201, in
app.run(main)
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/absl/app.py", line 312, in run
_run_main(main, args)
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/absl/app.py", line 258, in _run_main
sys.exit(main(argv))
File "[HOME_dir_redacted]/Software/alphafold/docker/run_docker.py", line 173, in main
container = client.containers.run(
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/docker/models/containers.py", line 811, in run
container = self.create(image=image, command=command,
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/docker/models/containers.py", line 870, in create
resp = self.client.api.create_container(**create_kwargs)
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/docker/api/container.py", line 430, in create_container
return self.create_container_from_config(config, name)
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/docker/api/container.py", line 441, in create_container_from_config
return self._result(res, True)
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/docker/api/client.py", line 274, in _result
self._raise_for_status(response)
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/docker/api/client.py", line 270, in _raise_for_status
raise create_api_error_from_http_exception(e)
File "[HOME_dir_redacted]/.local/lib/python3.9/site-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
raise cls(e, response=response, explanation=explanation)
docker.errors.APIError: 400 Client Error for http+docker://localhost/v1.41/containers/create: Bad Request ("invalid mount config for type "bind": bind source path does not exist: /tmp/alphafold")

I did not set up any keys for Docker. Is this related? And how to fix this? I tried removing the alphafold image and purging/reinstalling docker. Still, nothing helps.
Thanks,
Lukasz

Please modify docker/run_dockery.py  output_dir. the default is /tmp/alphafold cause this problem.

Hello,
May I ask you, do you fix this problem?
After I creat a new file place, and change my out_dir to new place, it also show the same problem.
And, what's meaning of  'clone this project'?
Wish you best,
Yuan licheng

Please modify docker/run_dockery.py line 37 output_dir. the default is /tmp/alphafold cause this problem.
or this project has updated , you can clone this project and run again.

Modifying the output_dir worked. Thank you, I am closing the issue.

source: gharchive/issue

authors: Newlcg gaorongchao lukaszsobala

license_type: permissive

license_source: bigquery


text:
BatchNorm with CNN

I have encoutered the following issue while applying BN in models (simple CNN for example).
    raise ValueError("If your transformed function uses `hk.{get,set}_state` "
ValueError: If your transformed function uses `hk.{get,set}_state` then use `hk.transform_with_state`.

My simple code snippet is
class CNN1(CNN):
    def __call__(self, x):
        batch_size = x.shape[0]

        x = hk.Conv2D(32, kernel_shape=3, stride=1, padding="VALID")(x)
        x = hk.BatchNorm(create_scale=True, create_offset=True, decay_rate=0.999)(x, True)
        x = activation(x)
        x = hk.MaxPool(window_shape=2, strides=2, padding="VALID")(x)

        x = hk.Conv2D(64, kernel_shape=3, stride=1)(x)
        x = hk.BatchNorm(create_scale=True, create_offset=True, decay_rate=0.999)(x, True)
        x = activation(x)
        x = hk.MaxPool(window_shape=2, strides=2, padding="VALID")(x)

I have tried calling hk.transform_with_state(CNN1) but it does not help. I wonder if I did anything wrong?

Hi @Nanboy-Ronan , Haiku is built around the idea of transforming functions rather than module objects. I've put an example of using the module you defined above in this gist, I hope that is usefu: https://colab.research.google.com/gist/tomhennigan/73835e426fa41f5a95ccc80302997aa3/cnn1-example.ipynb
The key difference is passing a function (that creates/uses modules and returns tensors) to transform_with_state rather than a constructor.

authors: Nanboy-Ronan tomhennigan

repo: deepmind/dm-haiku


text:
RenderWindow is None / Can't load EGL

System: Windows 10
While trying to run explore.py I encountered this error. It appears that an ImportError is being silently swallowed in dm_control.viewer.gui.init.py. The underlying error is that EGL cannot be loaded.
Is there some additional install / config step required to get EGL working?
Traceback (most recent call last):
File "dm_control\suite\explore.py", line 84, in
app.run(main)
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\absl\app.py", line 300, in run
_run_main(main, args)
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\absl\app.py", line 251, in run_main
sys.exit(main(argv))
File "dm_control\suite\explore.py", line 80, in main
viewer.launch(loader)
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\dm_control\viewer_init.py", line 42, in launch
app = application.Application(title=title, width=width, height=height)
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\dm_control\viewer\application.py", line 191, in init
self._window = gui.RenderWindow(width, height, title)
TypeError: 'NoneType' object is not callable

The swallowed error is
Traceback (most recent call last):
File "dm_control\suite\explore.py", line 27, in
from dm_control import viewer
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\dm_control\viewer_init_.py", line 22, in
from dm_control.viewer import application
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\dm_control\viewer\application.py", line 24, in
from dm_control.viewer import gui
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\dm_control\viewer\gui_init_.py", line 25, in
from dm_control.viewer.gui import glfw_gui
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\dm_control\viewer\gui\glfw_gui.py", line 25, in
from dm_control.viewer.gui import base
File "C:\Users\Ian\AppData\Roaming\Python\Python36\site-packages\dm_control\viewer\gui\base.py", line 28, in
from OpenGL import GL
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\GL_init_.py", line 3, in
from OpenGL import error as _error
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\error.py", line 12, in
from OpenGL import platform, configflags
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\platform_init.py", line 35, in
load()
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\platform_init.py", line 32, in _load
plugin.install(globals())
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\platform\baseplatform.py", line 92, in install
namespace[ name ] = getattr(self,name,None)
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\platform\baseplatform.py", line 14, in get
value = self.fget( obj )
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\platform\egl.py", line 93, in GetCurrentContext
return self.EGL.eglGetCurrentContext
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\platform\baseplatform.py", line 14, in get
value = self.fget( obj )
File "C:\Users\Ian\AppData\Local\Programs\Python\Python36\lib\site-packages\OpenGL\platform\egl.py", line 73, in EGL
raise ImportError("Unable to load EGL library", *err.args)
ImportError: ('Unable to load EGL library', 22, 'The specified module could not be found', None, 126, None, 'EGL', None)

Solved. Hadn't set the MUJOCO_GL env var and it was grumpy about this.
If I might suggest this might be improved by:

Moving the env var instructions out of the Linux section on the main Readme
Adding a helpful error in gui_init_.py rather than pass

or with more effort

If the env var isn't set, auto-detect if any of the backends will work and use the first that does.

I'm happy to do 1 and/or 2 if you're ok with me opening a PR.


text:
Huggingface models with NER support

Hello, with last release you uploaded pretrained models based on huggingface, but they don't support NER usage. Are there any plans to upload models with NER?

Hi!
Currently we don't have such plans, but we have multiple pretrained NER models based on BERT in DeepPavlov http://docs.deeppavlov.ai/en/master/features/models/ner.html

authors: max-frai yurakuratov

repo: deepmipt/DeepPavlov

license_source: github-api


text:
Missing sound

Would be perfect with some sound!
Nice as always, will gather feedback tomorrow :P

It really is meant to be a super small fun project for my kids, but that's right, sounds would be amazing here :)

authors: deepnight kLabz

repo: deepnight/fireworks

license: MIT


text:
Register @imported less files as dependencies on all require()d files

This allows watchify to reload when any of your less files change. Not
just the ones you required from Javascript.

I'm making https://github.com/pulls more useful to me today by closing my old PRs.

source: gharchive/pull-request

repo: deepsweet/lessbuildify

license: mit


text:
feat: style overrides

closes https://github.com/deer/fresh_blog/issues/12
@scottyg, what do you think of this? If you go into tests/styles_fixture you can run deno task start to start the test project. If you open the developer console (I'm assuming chrome -- otherwise you're on your own!) and then do "cmd shift p" followed by searching for "show rendering", you can eventually find a spot where it says "Emulate CSS media feature prefers-color-scheme". This lets you switch between the user's default, forced light, and forced dark.
In the sample project you can see that I've configured the markdown style (via the blog config) to be "auto", i.e. the user's default. Then I've also provided two separate styles in the twind config to demonstrate light and dark. Please let me know if you need more control over things. I guess I can always add more twind class names that can then be overridden in the user's twind config.

@deer I like where this is heading. I think the only thing that would be nice would be to allow you to change the class names for , use case would be if someone wanted to have a side bar or have a full width template. Eventually I'd love to embed the PostList into other page templates. But this change is a step in the right direction!

Great, thanks for the feedback. I've been busy learning more about tailwind, since I was just hacking things around without understanding the fundamentals. I have a much better sense of what's going on now, and I hope to do everything much better with the next commit. Hopefully that's tomorrow, but who knows.
Note: this is something of a moving target, since there's an effort underway to switch fresh from twind to uno: https://github.com/denoland/fresh/pull/1303.
Can you please log an issue with some thoughts on exporting PostList? In particular, please clarify your use case / motivation. And if you can imagine needing other things in the future, might as well include that for completeness as well. (Just to help me understand how people plan on using this.)

Additionally I should mention that the fresh documentation customizes the default https://github.com/denoland/deno-gfm. They do this by linking to a route that produces a stylesheet. This sheet is defined here. This is a nice approach that allows for further customization of the markdown body. I plan on doing this at some point in order to get the markdown to properly behave as well.

I feel like this is a good start but maybe we should hold off on styling until the proposed move to uno that you mentioned?

Agree, looks like they/re making good progress on the above mentioned PR.

Creating a new PR very soon, because this one is too outdated.

authors: deer scottyg


text:
Storybook story source code isn't being genreated correctly

Steps to reproduce

Go to e.g. https://storybook.design-system.digital.mod.uk/?path=/docs/button-experimental--default
Click on the code button

The source code isn't generated correctly:

However, this is supposed to work: https://github.com/storybookjs/storybook/pull/11332
It can also be seen working in this example here:
https://next--storybookjs.netlify.app/official-storybook/?path=/docs/docs-source--loading
We need to investigate why it's not working in this project.

It looks like it's been turned off here: https://github.com/defencedigital/mod-uk-design-system/blob/master/packages/react-component-library/.storybook/preview.js#L35
So we just need to check if there's any reason for that and if it can be turned on safely.

repo: defencedigital/mod-uk-design-system


text:
Zarf init fails on OpenShift

Environment
Device and OS: N/A
App version: v0.22.2
Kubernetes distro being used: K3S on OpenShift (using VCluster)
Other:
Steps to reproduce

Run zarf init on an OpenShift cluster without cluster-admin or any special SecurityContextConstraint (i.e. using only the default restricted SCC)
Notice that the Registry fails to schedule
Notice that the agent goes into CrashLoopBackoff

Expected result

Registry and Agent should be able to start without requiring excessive privileges

Actual Result

Registry sets a SecurityContext value that is not supported on OpenShift
Agent does not appropriately configure ephemeral storage and crashes

Visual Proof (screenshots, videos, text, etc)

Can't be easily provided as the OpenShift cluster in question is disconnected.

Severity/Priority
Additional Context
I'm running VCluster on OpenShift (https://www.vcluster.com/) to avoid needing to give Zarf cluster-level privileges. The issues I'm running into are not related to Cluster admin and are more to do with how the underlying pods are created.
Possible solutions
All of these issues can be easily fixed by a few changes to the init package. I'd be happy to open an MR if you'd like.
Agent

Mount emptyDir volumes to /.config and /etc/xdg. This fixes the crashlooping.

          volumeMounts:
            - name: tls-certs
              mountPath: /etc/certs
              readOnly: true
            - name: zarf-state
              mountPath: /etc/zarf-state
              readOnly: true
+           - name: config
+             mountPath: /.config
+           - name: xdg
+             mountPath: /etc/xdg
      volumes:
        - name: tls-certs
          secret:
            secretName: agent-hook-tls
        - name: zarf-state
          secret:
            secretName: zarf-state
+       - name: config
+         emptyDir: {}
+       - name: xdg
+         emptyDir: {}

Registry

Set securityContext.enabled: false in the Registry values.yaml (for both the seed and standard registry) so that OpenShift allows the pod to run.
Perhaps a followup could be adding a --openshift flag to zarf init

these are reasonable changes and good finds.  I believe /etc/xdg comes from our k9s vendoring, not super sure where /.config is coming from off the top of my head.  I'm working on a larger refactor of the registry package in #1039 and will try these changes out there.

Was going to add these changes to that PR but it got a bit bigger, so recommend a separate PR now for this. I just to make sure we understand the changes fully and that they won't cause us headaches on other distros--but don't think that will be the case.

repo: defenseunicorns/zarf


text:
style(builder): add information around slow steps

The first time a builder pod lands on a node it needs to pull ~500MB of stuff. Laptop + wifi can mean a sad panda.
Let folks know that we are doing stuff! Refs deis/builder#60
example-ruby-sinatra [master]$ g push deis master
Warning: Permanently added the RSA host key for IP address '[52.88.164.253]:2222' to the list of known hosts.
Counting objects: 125, done.
Delta compression using up to 4 threads.
Compressing objects: 100% (75/75), done.
Writing objects: 100% (125/125), 25.84 KiB | 0 bytes/s, done.
Total 125 (delta 55), reused 95 (delta 42)
-----> Pushing slug to storage...
-----> Waiting for build pod 'gaslit-tailbone-git-e4f3679b' to start...
-----> Ruby app detected
-----> Compiling Ruby/Rack

Oops, didn't see your PR @arschles Refs #61

@slack ok if I close as a dupe of #61?

authors: arschles slack

repo: deis/builder


text:
chore(CHANGELOG): remove changelog

We now publish changelogs on github and no longer update the local changelog.

Just noticed that some of the Changelog items are not on the actual tags / releases, mostly pre-2.0 versions
Should we update those?

I think we can do that as part of deis/workflow#464, yes. I'll add a comment as a TODO.

Suspect we need to do it with all repos at this point

Current coverage is 87.78% (diff: 100%)

Merging #1091 into master will not change coverage

@@             master      #1091   diff @@
==========================================
  Files            42         42
  Lines          3603       3603
  Methods           0          0
  Messages          0          0
  Branches        610        610
==========================================
  Hits           3163       3163
  Misses          290        290
  Partials        150        150

Powered by Codecov. Last update dda3a95...fedff14

authors: bacongobbler codecov-io helgi

repo: deis/controller


text:
feat(contrib/ec2): loop until instances have passed health checks

The EC2 provisioning script requires the user to visit the EC2 control panel (or equivalent) and refresh until instances are available. This change makes provision-ec2-cluster.sh itself do the waiting, then prints out the instance IPs and the DNS name of the ELB:
$ ./provision-ec2-cluster.sh deis-foo
{
    "StackId": "arn:aws:cloudformation:us-west-2:693041077886:stack/deis-foo/16799670-c6a8-11e4-a859-5088487b0895"
}
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to be created...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Waiting for instances to pass initial health checks...
Instances are available:
i-67e1186a	52.11.209.224	m3.large	us-west-2b	running
i-60e1186d	52.11.169.227	m3.large	us-west-2b	running
i-244cab2b	52.11.227.108	m3.large	us-west-2a	running
Using ELB deis-foo-DeisWebEL-12W15A0NVP29L at deis-foo-DeisWebEL-12W15A0NVP29L-442218353.us-west-2.elb.amazonaws.com
Your Deis cluster has been successfully deployed to AWS CloudFormation and is started.
Please continue to follow the instructions in the documentation.

This is awesome!! I'd also love to see if we could output the DNS name of the ELB and one of the instance IPs so the user can get up and running without ever having to log into the AWS console. I really like the UX of the Azure script.

I'd also love to see if we could output the DNS name of the ELB and one of the instance IPs

Your wish is my command. I did both for my test-ec2 script; I'll migrate them to the provision script.

I did both for my test-ec2 script; I'll migrate them to the provision script.

:100:

Updated to print out the instances' public IPs and the name and public DNS of the ELB.

This commit has passed testing since it is the basis of my in-progress work on running our tests in AWS.

Code LGTM.

This commit has passed testing since it is now the basis of my in-progress work on running our tests in AWS.

awesome. Been waiting for a wait-loop in provision-ec2. :shipit:!

authors: bacongobbler carmstrong mboersma

repo: deis/deis


text:
feat(actions): add deisrel git tag command

This automates tagging all of the components at their latest SHAs. This can be overridden by
providing a --sha-filepath flag with a path to a file that has the component names and their
respective SHAs, separated by an equals sign.
closes #3

Note: do not run this command to manually test or you'll create git tags on the official deis repos, assuming your github token has the correct write permissions. It's best to modify actions/git.go, change all references of deis to a dummy org that you have read/write permissions (e.g. bacongobbler) and test that way.

can we make that parameterized then?

by default it prompts to make sure you don't accidentally run the command without giving confirmation (or use the --yes flag). Is that acceptable, or can you explain what you mean by "parameterized"?

We should just make the org a var that cna be overridden via an env var or a command line flag. that way people can run this against test orgs or heck in the event we ever move deis to another org. without having to make a code change

Yes, let's do that in a followup. This is hard-coded in more places than one so let's do that in another PR.

authors: bacongobbler jchauncey

repo: deis/deisrel


text:
Need more details on external object storage

I think this section requires more details.
I know @technosophos recently submitted and merged an awesome PR (https://github.com/deis/charts/pull/123) to hypothetically add a consolidated point of configuration for object storage platform-wide.  (In practice, I don't think all components are making use of this yet, but they should.)
I think no section on configuring object storage could be complete without covering this.

IIRC @kmala is working on consolidating the object storage configuration, so perhaps he would be the best person to tackle the docs :)

I had made changes to the docs apart from the database part which will be done using https://github.com/deis/workflow/pull/138

fixed in https://github.com/deis/deis/issues/4966

authors: bacongobbler kmala krancour

repo: deis/workflow


text:
Docker daemon access bug

The following issue was reported in cnab-spec, but is actually a bug in Duffle: https://github.com/deislabs/cnab-spec/issues/108

Closed in #661.

authors: radu-matei technosophos

repo: deislabs/duffle


text:
krustlet-wascc pods never enter the ready state

Using krustlet-wascc 0.5, the pods enter the running state, but never reports readiness.
 ><> k get po
NAME    READY   STATUS    RESTARTS   AGE
greet   0/1     Running   0          2m12s

krustlet-wascc logs:
Oct 28 21:57:52 krustlet krustlet-wascc[7888]: [2020-10-28T21:57:52Z INFO  wascc_provider::states::registered] Pod added: greet.
Oct 28 21:57:52 krustlet krustlet-wascc[7888]: [2020-10-28T21:57:52Z INFO  wascc_provider::states::starting] Starting containers for pod "greet"
Oct 28 21:57:52 krustlet krustlet-wascc[7888]: [2020-10-28T21:57:52Z INFO  wascc_provider] sending actor to wascc host
Oct 28 21:57:52 krustlet krustlet-wascc[7888]: [2020-10-28T21:57:52Z INFO  wascc_provider] configuring capability wascc:logging
Oct 28 21:57:52 krustlet krustlet-wascc[7888]: [2020-10-28T21:57:52Z INFO  wascc_provider] configuring capability wascc:http_server
Oct 28 21:57:52 krustlet krustlet-wascc[7888]: [2020-10-28T21:57:52Z INFO  wascc_provider] wascc actor executing
Oct 28 21:57:52 krustlet krustlet-wascc[7888]: [2020-10-28T21:57:52Z INFO  wascc_provider::states::starting] All containers started for pod "greet".

This should be closed by #458

authors: bacongobbler kflansburg

repo: deislabs/krustlet


text:
oras discover should return manifest collections

Similar to the /v2/_ext/oci-artifacts/v1/{repository}/manifests/{digest}/references?n=10&artifactType={artifactType} response, we should return a list of manifests, and the digest. See Artifact References API results
Output of --output-json should be consistent with the REST output

The raw API response from the distribution server should be transparent to the user.
The oras cli outputs the information in the same format of the API response but will never output the API response directly.

This work item depends on https://github.com/notaryproject/distribution/issues/4

Closed by db537ea3f63659f41738f5cfd24a7b47aab7959f

authors: SteveLasker shizhMSFT

repo: deislabs/oras


text:
Fix build failure on "ruby-head" x "CC=clang"

"clang: error: unknown argument: '-fexcess-precision=standard'"
occurs when "compiling ../../../../ext/byebug/breakpoint.c".
The reason why compile error occurs is that we use ruby build
by gcc and compile byebug with clang.
"mkmf" uses values of RbConfig module which were defined
when ruby is built.
This commit make "env CC" match build information of ruby.
ref: https://github.com/ruby/ruby/blob/v2_3_0/tool/mkconfig.rb

Or maybe we can remove
env:
  - CC=gcc
  - CC=clang

and add these versions to rvm

2.1.10-clang
2.2.5-clang
2.3.1-clang
ruby-head-clang

Yes, that looks like a good idea!

Which do you think is better

Do not remove env (now)
Remove env

I think if we use the clang rubies provided by travis we don't need to use env. So I'd say remove env.

@yui-knk Looks great now! Can you rebase so I can merge? :)

@deivid-rodriguez rebased!

Thanks a lot!

authors: deivid-rodriguez yui-knk

repo: deivid-rodriguez/byebug


text:
Fix typo in location.getLongitude() method

Thank you for creating this! It was very simple to implement.

Thank you so much for your good feedback and for spotting this mistake :)
Your fix has been merged here: https://github.com/delight-im/Android-SimpleLocation/commit/70b065b55a9ca90870a65e8c98582867f03728c5

authors: mwaclawek theholla

repo: delight-im/Android-SimpleLocation


text:
Update image tags for replication

Is this a new chart?
No
What this PR does / why we need it:
Updates image tag references for replication module
Which issue(s) is this PR associated with:

https://github.com/dell/csm/issues/1012

Special notes for your reviewer:
Checklist:
[Place an '[x]' (no spaces) in all applicable fields. Please remove unrelated fields.]

[ ] Chart Version bumped
[ ] Variables are documented in the chart README.md
[ ] Title of the PR starts with the chart name (e.g. [charts_dir/mychartname]) if applicable

charts/container-storage-modules/Chart.yaml and charts/csm-replication/Chart.yaml also need replication version updates.

Thanks @santhoshatdell , these files are updated

repo: dell/helm-charts


text:
appliance will not install due to no_log: true

Describe the bug
when installing the appliance using:  ansible-playbook appliance.yml -e  "ansible_python_interpreter=/usr/bin/python2"
we receive the following error:
TASK [common : Save input variables from file] ***********************************************************************************************
fatal: [localhost]: FAILED! => {"censored": "the output has been hidden due to the fact that 'no_log: true' was specified for this result"}

Expected behavior
should install the appliance

my mistake

repo: dellhpc/omnia


text:
Fix KeyError in client when response contains empty items

When the items field of the protobuf is empty, the resulting json won't contain the items field. This PR adds checks so that we won't have KeyErrors or NullPointerExceptions if we have empty responses for listSchema and listTables.
Added tests with empty shares and schemas.

I assume the test for this fix is covered by test_list_all_tables in test_delta_sharing.py. Is that right?

Yes, I added an empty share and a share with an empty schema in TestResource.scala, and the changes are implicitly tested by test_list_all_tables. Same for the scala client.

repo: delta-io/delta-sharing


text:
Multiple-table transaction

It will be nice to have the transaction working for multiple tables.
If the data entry in one of the tables fails - all the changes should be roll-back-ed.

Hey @marcin-workfellow thanks for making this issue. This feature isn't supported right now, but if this post gets enough upvotes and engagement over time, then we can think about implementing this feature.
In the meantime, how to overcome this problem was recently asked during our Delta Community Office Hours on Nov 11, 2021, and you can check out the question & answer here (17:50 mark)

The managed-commits effort is changing the atomicity point from object stores to an external commit-owner. This will open up the possibility to commit to multiple tables atomically using an external system. Extending the managed-commit API to support multi-table transactions will be the next step once managed-commits is ready.

repo: delta-io/delta


text:
Enable doclint on Circle CI

Looks like doclint is disabled on Circle CI. See https://github.com/Debian/openjdk-8/blob/master/debian/patches/disable-doclint-by-default.patch
This PR enables it manually to test our javadoc. In addition, it also includes the private package io.delta.sql.parser to fix javadoc build.

Thank you for figuring this out. :)

LGTM.

authors: tdas zsxwing


text:
[Kernel] Always read the checkpoint manifest for CPV2 if sidecar files are present

Which Delta project/connector is this regarding?

[ ] Spark
[ ] Standalone
[ ] Flink
[x] Kernel
[ ] Other (fill in here)

Description
The sidecar actions in the checkpoint manifest of a CPV2 table should always be read regardless of the checkpoint predicate, as actions satisfying the predicate may be found in the sidecar file.
How was this patch tested?
See added test.
Does this PR introduce any user-facing changes?
No.

See https://github.com/delta-io/delta/pull/3509


text:
Concurrent Tests

[ ] If we can give each example its own target dir just during testing...we could possibly run all tests concurrently.
[ ] The next dfx version let's us turn off the local update delay, but it seems there is some kind of rate limit locally
[ ] Locally I keep running into this problem: Canister rwlgt-iiaaa-aaaaa-aaaaa-cai is out of cycles: requested 3_100_000_000_000 cycles but the available balance is 798_702_996_718 cycles and the freezing threshold 175_839_480 cycles I assume there is some local canister, maybe the minter canister or something, that is running out of cycles...

Actually some of those issues don't seem to be issues...the single target dir might be working, and I was prematurely turning off the dfx script I don't think there's a rate limit

repo: demergent-labs/azle


text:
update

Pull Request
Short Summary
Include a short summary
Context
Add a more detailed explanation of changes
Is this a Breaking Change?
Is this a breaking change to the public interface?

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 88.66%. Comparing base (0bf881e) to head (b044331).

Additional details and impacted files
@@           Coverage Diff           @@
##             main       #9   +/-   ##
=======================================
  Coverage   88.66%   88.66%
=======================================
  Files           9        9
  Lines         344      344
=======================================
  Hits          305      305
  Misses         39       39

authors: codecov-commenter thorrester

repo: demml/scouter


text:
Better event queue

There are moments where the event queue can be starved with the same event and cause problems. This changes the implementation to only add events that are not currently in the queue.

Hmm to be honest this makes the operation of the event queue harder to understand for me. But I guess it achieves the same goal of not having the queue fill up with events that are not serviced in time. I would just worry this sets us in the future where we might have events that do happen in quick succession that should be buffered but instead will be dropped.

authors: democloid maks

repo: democloid/picoTracker


text:
Any update planed?

Hi,
I like your api. I tried it with Chartjs 2.7.1 but unfortunatly it does not seem to be compatible. I can see that you are using the 1.2 version of the library. The readme example gives this:
TypeError: (new Chart(...)).Bar is not a function

Do you plan to update and maintain this lib?

yeah what is going on with this?
And updates planned. It doesn't seem to work currently Even after using pip install it still appears to show as a missing module.

@JokerMartini : I started a little lib to do the job, as a subset of my main data analytics lib.
I could not find a maintained version of a python lib to generate Chartjs charts, so I started rolling my own. It is currently on a very primitive state and limited.
I would love some help or to collaborate on a serious project about python and Chartjs. Honestly I don't have much time and a big motivation for Chartjs support as i mostly work with Bokeh and Holoviews, this little lib is still just a  buggy workaround for now, no more. I wish some serious project to emerge or some old ones like this one here to revive!

authors: JokerMartini synw

repo: dendory/chartjs


text:
Schema Registry

Context
Should be possible for users to easily create, discover and use existing schemas
Proposal
Create a simple method (similar to pre-configured vaults) for accessing useful schemas.
Related issues

#712

I think the RFC for Schema Improvements #712 could be a valuable addition to this.

@flammehawk absolutely, I see them as related features (they both help each other).

Specially https://github.com/dendronhq/dendron-site/pull/89/files#diff-3956c364d18dd866588cd9d87abcaa45c5ea38d7d3af2b0d6f42a6938b83053d
that part of the RFC

authors: flammehawk kpathakota

repo: dendronhq/dendron


text:
Re-sequencing Fields in Data Role for Template

I've observed this while trying something out and don't have a full picture, but wanted to log while I remember: changing the order of fields in the Values pane will not update the template placeholders, and this could be handy for those who may want to change the sequence so that the inputs are more logical for end-users. This might be tricky to resolve, so marking it for exploration until I can have a look.

May also have something to do with the template re-mapping not working as well as it used to last two builds?

I'm not aware of any current problems with re-mapping fields. Please create a separate issue with steps to reproduce if something isn't working correctly and I can take a look. Ideally if you know which build you first noticed the issue, that would be extremely helpful too 👍

repo: deneb-viz/deneb


text:
2.22.1 fails to build due to fs_at crate

Describe the bug
2.22.1 fails to build on NetBSD.
Versions:

OS: NetBSD-current (development branch)
Rust-1.68.2

Additional context
Build fails due to fs_at as follows:
   Compiling fs_at v0.1.4
error[E0425]: cannot find value `O_PATH` in crate `libc`
   --> /usr/pkgsrc/wip/navi/work/vendor/fs_at-0.1.4/src/unix.rs:130:55
    |
130 |             libc::O_RDONLY | libc::O_NOFOLLOW | libc::O_PATH | libc::O_CLOEXEC | libc::O_NOCTTY;
    |                                                       ^^^^^^ not found in `libc`

For more information about this error, try `rustc --explain E0425`.
error: could not compile `fs_at` due to previous error
warning: build failed, waiting for other jobs to finish...
*** Error code 101

Stop.
make[1]: stopped in /usr/pkgsrc/wip/navi
*** Error code 1

Stop.

Could you please try to use the same versions as defined by the repo.

@denisidoro
I'm the NetBSD package maintainer and all our package builds are done in offline mode. The crate dependency versions used are the ones specified in Cargo.lock.
I don't see how downgrading Rust would help solving an unknown variable.
I understand that this is basically not a navi issue, rather a problem in fs_at but, our package is currently stuck at 2.20.2

@denisidoro This is worst than I thought, although I was afraid this would be the case :(
libc-0.2.141/src/unix/bsd/freebsdlike/freebsd/mod.rs:pub const O_PATH: ::c_int = 0x00400000;
libc-0.2.141/src/unix/linux_like/android/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/emscripten/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b32/mod.rs:        pub const O_PATH: ::c_int = 0x1000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b32/mod.rs:        pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b64/aarch64/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b64/loongarch64/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b64/mips64/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b64/powerpc64/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b64/riscv64/mod.rs:pub const O_PATH: ::c_int = 2097152;
libc-0.2.141/src/unix/linux_like/linux/gnu/b64/s390x.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b64/sparc64/mod.rs:pub const O_PATH: ::c_int = 0x1000000;
libc-0.2.141/src/unix/linux_like/linux/gnu/b64/x86_64/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/musl/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/uclibc/arm/mod.rs:pub const O_PATH: ::c_int = 0o10000000;
libc-0.2.141/src/unix/linux_like/linux/uclibc/mips/mod.rs:pub const O_PATH: ::c_int = 0o010000000;

When I find some time, I'll check cargo tree to understand why fs_at is being included.
If it's not being used by the core logic, in theory I could put the logic behind a cargo feature so that we can build a more stripped down version for NetBSD

Seria óptimo, se for possível. Obrigado!

navi v2.22.1 (/home/pin/Downloads/navi)
├── anyhow v1.0.70
├── clap v4.2.1
│   ├── clap_builder v4.2.1
│   │   ├── anstream v0.2.6
│   │   │   ├── anstyle v0.3.5
│   │   │   ├── anstyle-parse v0.1.1
│   │   │   │   └── utf8parse v0.2.1
│   │   │   ├── concolor-override v1.0.0
│   │   │   ├── concolor-query v0.3.3
│   │   │   ├── is-terminal v0.4.6
│   │   │   │   ├── io-lifetimes v1.0.9
│   │   │   │   │   └── libc v0.2.140
│   │   │   │   └── rustix v0.37.7
│   │   │   │       ├── bitflags v1.3.2
│   │   │   │       ├── errno v0.3.0
│   │   │   │       │   └── libc v0.2.140
│   │   │   │       ├── io-lifetimes v1.0.9 (*)
│   │   │   │       └── libc v0.2.140
│   │   │   └── utf8parse v0.2.1
│   │   ├── anstyle v0.3.5
│   │   ├── bitflags v1.3.2
│   │   ├── clap_lex v0.4.1
│   │   ├── once_cell v1.17.1
│   │   └── strsim v0.10.0
│   ├── clap_derive v4.2.0 (proc-macro)
│   │   ├── heck v0.4.0
│   │   ├── proc-macro2 v1.0.56
│   │   │   └── unicode-ident v1.0.8
│   │   ├── quote v1.0.26
│   │   │   └── proc-macro2 v1.0.56 (*)
│   │   └── syn v2.0.13
│   │       ├── proc-macro2 v1.0.56 (*)
│   │       ├── quote v1.0.26 (*)
│   │       └── unicode-ident v1.0.8
│   └── once_cell v1.17.1
├── crossterm v0.26.1
│   ├── bitflags v1.3.2
│   ├── libc v0.2.140
│   ├── mio v0.8.3
│   │   ├── libc v0.2.140
│   │   └── log v0.4.14
│   │       └── cfg-if v1.0.0
│   ├── parking_lot v0.12.0
│   │   ├── lock_api v0.4.6
│   │   │   └── scopeguard v1.1.0
│   │   └── parking_lot_core v0.9.1
│   │       ├── cfg-if v1.0.0
│   │       ├── libc v0.2.140
│   │       └── smallvec v1.10.0
│   ├── signal-hook v0.3.13
│   │   ├── libc v0.2.140
│   │   └── signal-hook-registry v1.4.0
│   │       └── libc v0.2.140
│   └── signal-hook-mio v0.2.3
│       ├── libc v0.2.140
│       ├── mio v0.8.3 (*)
│       └── signal-hook v0.3.13 (*)
├── directories-next v2.0.0
│   ├── cfg-if v1.0.0
│   └── dirs-sys-next v0.1.2
│       └── libc v0.2.140
├── dns_common v0.2.1
│   ├── anyhow v1.0.70
│   ├── directories-next v2.0.0 (*)
│   ├── serde v1.0.159
│   │   └── serde_derive v1.0.159 (proc-macro)
│   │       ├── proc-macro2 v1.0.56 (*)
│   │       ├── quote v1.0.26 (*)
│   │       └── syn v2.0.13 (*)
│   ├── serde_json v1.0.95
│   │   ├── itoa v1.0.6
│   │   ├── ryu v1.0.9
│   │   └── serde v1.0.159 (*)
│   ├── serde_yaml v0.9.21
│   │   ├── indexmap v1.9.3
│   │   │   └── hashbrown v0.12.3
│   │   │   [build-dependencies]
│   │   │   └── autocfg v1.0.1
│   │   ├── itoa v1.0.6
│   │   ├── ryu v1.0.9
│   │   ├── serde v1.0.159 (*)
│   │   └── unsafe-libyaml v0.2.8
│   ├── tracing v0.1.37
│   │   ├── cfg-if v1.0.0
│   │   ├── pin-project-lite v0.2.9
│   │   ├── tracing-attributes v0.1.23 (proc-macro)
│   │   │   ├── proc-macro2 v1.0.56 (*)
│   │   │   ├── quote v1.0.26 (*)
│   │   │   └── syn v1.0.109
│   │   │       ├── proc-macro2 v1.0.56 (*)
│   │   │       ├── quote v1.0.26 (*)
│   │   │       └── unicode-ident v1.0.8
│   │   └── tracing-core v0.1.30
│   │       └── once_cell v1.17.1
│   └── tracing-subscriber v0.3.16
│       ├── matchers v0.1.0
│       │   └── regex-automata v0.1.10
│       │       └── regex-syntax v0.6.29
│       ├── nu-ansi-term v0.46.0
│       │   └── overload v0.1.1
│       ├── once_cell v1.17.1
│       ├── regex v1.7.3
│       │   ├── aho-corasick v0.7.18
│       │   │   └── memchr v2.4.0
│       │   ├── memchr v2.4.0
│       │   └── regex-syntax v0.6.29
│       ├── sharded-slab v0.1.4
│       │   └── lazy_static v1.4.0
│       ├── smallvec v1.10.0
│       ├── thread_local v1.1.7
│       │   ├── cfg-if v1.0.0
│       │   └── once_cell v1.17.1
│       ├── tracing v0.1.37 (*)
│       ├── tracing-core v0.1.30 (*)
│       └── tracing-log v0.1.3
│           ├── lazy_static v1.4.0
│           ├── log v0.4.14 (*)
│           └── tracing-core v0.1.30 (*)
├── dns_common_derive v0.2.1 (proc-macro)
│   ├── proc-macro2 v1.0.56 (*)
│   ├── quote v1.0.26 (*)
│   └── synstructure v0.12.6
│       ├── proc-macro2 v1.0.56 (*)
│       ├── quote v1.0.26 (*)
│       ├── syn v1.0.109 (*)
│       └── unicode-xid v0.2.2
├── edit v0.1.4
│   ├── tempfile v3.2.0
│   │   ├── cfg-if v1.0.0
│   │   ├── libc v0.2.140
│   │   ├── rand v0.8.3
│   │   │   ├── libc v0.2.140
│   │   │   ├── rand_chacha v0.3.1
│   │   │   │   ├── ppv-lite86 v0.2.10
│   │   │   │   └── rand_core v0.6.2
│   │   │   │       └── getrandom v0.2.3
│   │   │   │           ├── cfg-if v1.0.0
│   │   │   │           └── libc v0.2.140
│   │   │   └── rand_core v0.6.2 (*)
│   │   └── remove_dir_all v0.5.3
│   └── which v4.1.0
│       ├── either v1.6.1
│       └── libc v0.2.140
├── lazy_static v1.4.0
├── regex v1.7.3 (*)
├── remove_dir_all v0.8.2
│   ├── cfg-if v1.0.0
│   ├── cvt v0.1.2
│   │   └── cfg-if v1.0.0
│   ├── fs_at v0.1.4
│   │   ├── cfg-if v1.0.0
│   │   ├── cvt v0.1.2 (*)
│   │   ├── libc v0.2.140
│   │   └── nix v0.26.2
│   │       ├── bitflags v1.3.2
│   │       ├── cfg-if v1.0.0
│   │       ├── libc v0.2.140
│   │       └── static_assertions v1.1.0
│   ├── lazy_static v1.4.0
│   ├── libc v0.2.140
│   └── normpath v1.1.1
├── serde v1.0.159 (*)
├── serde_yaml v0.9.21 (*)
├── shellwords v1.1.0
│   ├── lazy_static v1.4.0
│   └── regex v1.7.3 (*)
├── strip-ansi-escapes v0.1.1
│   └── vte v0.10.1
│       ├── arrayvec v0.5.2
│       ├── utf8parse v0.2.1
│       └── vte_generate_state_changes v0.1.1 (proc-macro)
│           ├── proc-macro2 v1.0.56 (*)
│           └── quote v1.0.26 (*)
├── thiserror v1.0.40
│   └── thiserror-impl v1.0.40 (proc-macro)
│       ├── proc-macro2 v1.0.56 (*)
│       ├── quote v1.0.26 (*)
│       └── syn v2.0.13 (*)
├── unicode-width v0.1.10
└── walkdir v2.3.3
    └── same-file v1.0.6

Looks like remove_dir_all  v0.8.2 is pulling it in.

@denisidoro Workaround patch submitted, https://github.com/rbtcollins/fs_at/pull/74
...
   Compiling navi v2.22.1 (/usr/pkgsrc/wip/navi/work/navi-2.22.1)
    Finished release [optimized] target(s) in 3m 48s

repo: denisidoro/navi


text:
Dictionary of DateTime comparison. Am I doing something wrong, or is this an issue?

Two dateTimes put in identical Dictionaries, just 1 millisecond difference...
var date1 = new DateTime(2017, 3, 1);
var date2 = date1.AddMilliseconds(1);
var myDict1 = new Dictionary<string, DateTime>() { { "dateField", date1 } };
var myDict2 = new Dictionary<string, DateTime>() { { "dateField", date2 } };
myDict1.ShouldAllBeEquivalentTo(myDict2,
options => options.Using< DateTime >
(item =>   item.Subject.Should().BeCloseTo(item.Expectation, 1000))
.WhenTypeIs< DateTime >());
Why does the BeCloseTo rule not work? The test fails (Also tried with <KeyValuePair<sting, DateTime>> as the generic type? Confused. Can anybody help. thanks so much!

What's the failure that you're getting?  I just created a unit test using this code and the unit test passed.

Tried running this with v5.4.1
// Arrange
var date1 = new DateTime(2017, 3, 1);
var date2 = date1.AddMilliseconds(1);

var myDict1 = new Dictionary<string, DateTime>() { { "dateField", date1 } };
var myDict2 = new Dictionary<string, DateTime>() { { "dateField", date2 } };

// Act / Assert
myDict1.Should().BeEquivalentTo(myDict2,
    options => options.Using<DateTime>(item => item.Subject.Should().BeCloseTo(item.Expectation, 1.Seconds()))
        .WhenTypeIs<DateTime>());

and got

Expected pair[dateField] to be <2017-03-01 00:00:00.001>, but found <2017-03-01>

A description of what happens:
AssertEquality detects GenericDictionaryEquivalencyStep is able to handle Dictionary<string, DateTime>.
When AssertEquality tries to find an appropriate user handler for the context with two DateTimes as Subject and Expectation created by CreateForDictionaryItem, AssertionRuleEquivalencyStepAdapter.CanHandle returns false as context.SelectedMemberInfo is null.
If I force CanHandle to pass, the if (predicate(context)) in AssertionRule.AssertEquality is able to handle the context and the assertion passes as expected.
I'm not sure of the logic behind
public bool CanHandle(IEquivalencyValidationContext context, IEquivalencyAssertionOptions config)
{
    return (context.SelectedMemberInfo != null);
}

authors: ben-m-lucas jnyrup msonme

repo: dennisdoomen/FluentAssertions


text:
add more APIs to deprecation guide

Further work on top of #299. Apologies for the noisy diff - it's due to the re-sorting of sections in alphabetical order of their headings.

PTAL @kwhinnery

repo: denoland/deno-docs


text:
Add Fig as an autocomplete method

Fig provides autocomplete for 300+ CLI tools including Deno. It looks like this:

We think this will help users become more efficient with Deno CLI, so we would love to have Fig listed as an autocomplete method in your docs. Thanks so much and please let me know if you have any questions!

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

authors: CLAassistant brendanfalk

repo: denoland/manual


text:
[Bug]: Cant clone existing git repository

Describe the bug
My Vault sync stopped working, and then when i tried to create another vault and repository, the same quiet behaviour occurs.
When triing to clone an existing repo, it starts to clone (tooltip notification), restart the vault, but nothing happen.
No files, no config, no branch, nothing.
On mobile version I cannot see my current branch, or other git configs
If repo with .obsidian file, then all configs are missing and git config are lost, and the repo substitution appears not to be replaced.
Relevant errors (if available) from notifications or console (CTRL+SHIFT+I)
No error appears, only infinite history view refresh,

no console could be oppened on mobile version.
Notification: No upstream branch is set, pleas select one
I select origin and nothing happens

Steps to reproduce

Git clone nan existing remote repo
infinite fetch
vault auto restart
nothing happens

Expected Behavior

Git clone nan existing remote repo
fetch files
vault restart
files appears on explorer

Addition context
All operations are strange. Pull, push, fetch
Operating system
Android
Installation Method
None
Plugin version
2.22.0

On some additional test with few files on repository appeared work properly, so it can be related with large ammount of files or size, but i suspect on git internal files, because cleaning repo does not solve the problem

Same here

Same happened on my iOS device. After I request pull for a set up repo in a fresh vault, the pop-up notification goes like:
No upstream branch is set. Please select one -> (I selected origin) ->Fetching remote branches -> initializing fetch -> Fetching progress: Compressing objects: 227 of 227
Then Obsidian refreshed and nothing was pulled.
Moreover, if I keep trying to pull from the repo following the above process, it will eventually crash the whole Obsidian app, leaving it to be an unresponsive black screen. I have to kill Obsidian in iOS level.
The black screen problem would happen after 2~3 attempts to pull, as I tested for ~10 rounds.
The vault folder remains empty after these pulling attempts, as I checked in the iOS File app.
The git repo I was working with synced normally between my other macOS and Win devices. The vault is 34.3MB in size as I checked on my computer.

I just had the same issue with the exact same behavior as @theIroaia

authors: BasedDrainer lflage mteixeira theIroaia

repo: denolehov/obsidian-git


text:
Allow specification of environment and forceFetch in prepareData

PR for #50
Specifying environment allows injecting data already available locally. Specifying forceFetch allows primeCache to be used instead and a network request to not be needed if all data is present from preloading.

We need this capability.

Thank you @jakepusateri!

Note that I changed preloadedRequests to be a third argument of prepareData.
This is now available in v0.7.3 of isomorphic-relay, and I also added preloadedRequests as a third argument to prepareData of isomorphic-relay-router in v0.8.2.

authors: denvned jakepusateri jwb

repo: denvned/isomorphic-relay


text:
BUG: VA Profile Sync - Infra - Fix lambda timeout issue.

Check #719 for the original ticket. This is a follow-up to that effort.
When the trigger was added to execute the remove old opt outs lambda, it would time out whenever the lambda code tried to run. The issue may be tied to security group or other permissions not being set correctly.

Hey team! Please add your planning poker estimate with ZenHub @ianperera @jakehova @jessecanderson @k-macmillan @mjones-oddball

Verified all settings were in-line with equivalent lambdas. Dug through a bunch of suggestions and followed an AWS blog about timeouts when trying to connect to RDS. Opened up rules on the RDS security group for the lambda's security group, no dice. Modified the roles to have another policy and tinkered with the settings, no help. Created an AWS support ticket.

Please add your planning poker estimate with ZenHub @EvanParish

Issue ended up being infra-related. The port was not correct. It is possible that the port is incorrect for the other VA profile lambda. Used this task to add logging and ensure the connection gets closed. There is a PR up.

Merged. Tested master in Dev.

authors: EvanParish k-macmillan mjones-oddball zlaw