jax._src.traceback_util.UnfilteredStackTrace: AttributeError: module 'jax.dtypes' has no attribute 'prng_key'

i am running alphafold v2.3.2 to predict a multimer on 16CPU,241G,4* V100
>3JA9_1|Chains A|Proliferating cell nuclear antigen|Homo sapiens (9606)
MFEARLVQGSILKKVLEALKDLINEACWDISSSGVNLQSMDSSHVSLVQLTLRSEGFDTYRCDRNLAMGVNLTSMSKILKCAGNEDIITLRAEDNADTLALVFEAPNQEKVSDYEMKLMDLDVEQLGIPEQEYSCVVKMPSGEFARICRDLSHIGDAVVISCAKDGVKFSASGELGNGNIKLSQTSNVDKEEEAVTIEMNEPVQLTFALRYLNFFTKATPLSSTVTLSMSADVPLVVEYKIADMGHLKYYLAPKIEDEEGS
>3JA9_2|Chains B|Proliferating cell nuclear antigen|Homo sapiens (9606)
MFEARLVQGSILKKVLEALKDLINEACWDISSSGVNLQSMDSSHVSLVQLTLRSEGFDTYRCDRNLAMGVNLTSMSKILKCAGNEDIITLRAEDNADTLALVFEAPNQEKVSDYEMKLMDLDVEQLGIPEQEYSCVVKMPSGEFARICRDLSHIGDAVVISCAKDGVKFSASGELGNGNIKLSQTSNVDKEEEAVTIEMNEPVQLTFALRYLNFFTKATPLSSTVTLSMSADVPLVVEYKIADMGHLKYYLAPKIEDEEGS
>3JA9_3|Chains C|Proliferating cell nuclear antigen|Homo sapiens (9606)
MFEARLVQGSILKKVLEALKDLINEACWDISSSGVNLQSMDSSHVSLVQLTLRSEGFDTYRCDRNLAMGVNLTSMSKILKCAGNEDIITLRAEDNADTLALVFEAPNQEKVSDYEMKLMDLDVEQLGIPEQEYSCVVKMPSGEFARICRDLSHIGDAVVISCAKDGVKFSASGELGNGNIKLSQTSNVDKEEEAVTIEMNEPVQLTFALRYLNFFTKATPLSSTVTLSMSADVPLVVEYKIADMGHLKYYLAPKIEDEEGS

command was :
python3 /shared/alphafold-main/docker/run_docker.py --data_dir=/fsx/soca/dataset --fasta_paths=/data/home/admin/alphafold2/input/rcsb_pdb_3JA9_multimer.fasta --max_template_date=2022-01-01 --model_preset=multimer --db_preset=full_dbs --output_dir=/data/home/admin/alphafold2/output/
and i got error, never see this before:
The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/app/alphafold/run_alphafold.py", line 570, in <module>
    app.run(main)
  File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 312, in run
    _run_main(main, args)
  File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "/app/alphafold/run_alphafold.py", line 543, in main
    predict_structure(
  File "/app/alphafold/run_alphafold.py", line 284, in predict_structure
    prediction_result = model_runner.predict(processed_feature_dict,
  File "/app/alphafold/alphafold/model/model.py", line 167, in predict
    result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/transform.py", line 187, in apply_fn
    out, state = f.apply(params, None, *args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/transform.py", line 457, in apply_fn
    out = f(*args, **kwargs)
  File "/app/alphafold/alphafold/model/model.py", line 77, in _forward_fn
    return model(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 508, in __call__
    num_recycles, _, prev, safe_key = hk.while_loop(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 910, in while_loop
    val, state = jax.lax.while_loop(pure_cond_fun, pure_body_fun, init_val)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 903, in pure_body_fun
    val = body_fun(val)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 486, in recycle_body
    ret = apply_network(prev=prev, safe_key=safe_key2)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 449, in apply_network
    return impl(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 321, in __call__
    repr_shape = hk.eval_shape(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 930, in eval_shape
    out_shape = jax.eval_shape(stateless_fun, internal_state(), *args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 926, in stateless_fun
    out = fun(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 322, in <lambda>
    lambda: embedding_module(batch, is_training))
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 633, in __call__
    batch = sample_msa(sample_key, batch, c.num_msa)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 274, in sample_msa
    index_order = gumbel_argsort_sample_idx(key.get(), logits)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 110, in gumbel_argsort_sample_idx
    z = gumbel_noise(key, logits.shape)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 67, in gumbel_noise
    uniform_noise = uniform(
  File "/app/alphafold/alphafold/model/utils.py", line 166, in inner
    if jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)
AttributeError: module 'jax.dtypes' has no attribute 'prng_key'

full logs :
[admin@ip-10-247-132-167 ~]$ docker logs -f 39
I1031 11:58:49.800981<PHONE_NUMBER>11936 templates.py:858] Using precomputed obsolete pdbs /mnt/obsolete_pdbs_path/obsolete.dat.
I1031 11:58:50.871939<PHONE_NUMBER>11936 xla_bridge.py:353] Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker:
I1031 11:58:51.903923<PHONE_NUMBER>11936 xla_bridge.py:353] Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: "rocm". Available platform names are: Interpreter Host CUDA
I1031 11:58:51.904496<PHONE_NUMBER>11936 xla_bridge.py:353] Unable to initialize backend 'tpu': module 'jaxlib.xla_extension' has no attribute 'get_tpu_client'
I1031 11:58:51.904650<PHONE_NUMBER>11936 xla_bridge.py:353] Unable to initialize backend 'plugin': xla_extension has no attributes named get_plugin_device_client. Compile TensorFlow with //tensorflow/compiler/xla/python:enable_plugin_device set to true (defaults to false) to enable this.
I1031 11:58:58.515359<PHONE_NUMBER>11936 run_alphafold.py:524] Have 25 models: ['model_1_multimer_v3_pred_0', 'model_1_multimer_v3_pred_1', 'model_1_multimer_v3_pred_2', 'model_1_multimer_v3_pred_3', 'model_1_multimer_v3_pred_4', 'model_2_multimer_v3_pred_0', 'model_2_multimer_v3_pred_1', 'model_2_multimer_v3_pred_2', 'model_2_multimer_v3_pred_3', 'model_2_multimer_v3_pred_4', 'model_3_multimer_v3_pred_0', 'model_3_multimer_v3_pred_1', 'model_3_multimer_v3_pred_2', 'model_3_multimer_v3_pred_3', 'model_3_multimer_v3_pred_4', 'model_4_multimer_v3_pred_0', 'model_4_multimer_v3_pred_1', 'model_4_multimer_v3_pred_2', 'model_4_multimer_v3_pred_3', 'model_4_multimer_v3_pred_4', 'model_5_multimer_v3_pred_0', 'model_5_multimer_v3_pred_1', 'model_5_multimer_v3_pred_2', 'model_5_multimer_v3_pred_3', 'model_5_multimer_v3_pred_4']
I1031 11:58:58.515562<PHONE_NUMBER>11936 run_alphafold.py:538] Using random seed<PHONE_NUMBER>22164255 for the data pipeline
I1031 11:58:58.515847<PHONE_NUMBER>11936 run_alphafold.py:245] Predicting rcsb_pdb_3JA9_multimer
I1031 11:58:58.538447<PHONE_NUMBER>11936 pipeline_multimer.py:210] Running monomer pipeline on chain A: 3JA9_1|Chains A|Proliferating cell nuclear antigen|Homo sapiens (9606)
I1031 11:58:58.538740<PHONE_NUMBER>11936 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmpe3_s_gj6/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /tmp/tmp0isaglgt.fasta /mnt/uniref90_database_path/uniref90.fasta"
I1031 11:58:58.603930<PHONE_NUMBER>11936 utils.py:36] Started Jackhmmer (uniref90.fasta) query
I1031 12:07:14.808301<PHONE_NUMBER>11936 utils.py:40] Finished Jackhmmer (uniref90.fasta) query in 496.204 seconds
I1031 12:07:15.079364<PHONE_NUMBER>11936 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmpcr0k1q7t/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /tmp/tmp0isaglgt.fasta /mnt/mgnify_database_path/mgy_clusters_2022_05.fa"
I1031 12:07:15.080246<PHONE_NUMBER>11936 utils.py:36] Started Jackhmmer (mgy_clusters_2022_05.fa) query
I1031 12:20:35.566447<PHONE_NUMBER>11936 utils.py:40] Finished Jackhmmer (mgy_clusters_2022_05.fa) query in 800.486 seconds
I1031 12:20:36.874873<PHONE_NUMBER>11936 hmmbuild.py:121] Launching subprocess ['/usr/bin/hmmbuild', '--hand', '--amino', '/tmp/tmp41ukra03/output.hmm', '/tmp/tmp41ukra03/query.msa']
I1031 12:20:36.941542<PHONE_NUMBER>11936 utils.py:36] Started hmmbuild query
I1031 12:20:37.812366<PHONE_NUMBER>11936 hmmbuild.py:128] hmmbuild stdout:
# hmmbuild :: profile HMM construction from multiple sequence alignments
# HMMER 3.1b2 (February 2015); http://hmmer.org/
# Copyright (C) 2015 Howard Hughes Medical Institute.

I1031 12:41:38.367300<PHONE_NUMBER>11936 pipeline.py:234] Uniref90 MSA size: 4473 sequences.
I1031 12:41:38.367510<PHONE_NUMBER>11936 pipeline.py:235] BFD MSA size: 1795 sequences.
I1031 12:41:38.367598<PHONE_NUMBER>11936 pipeline.py:236] MGnify MSA size: 501 sequences.
I1031 12:41:38.367679<PHONE_NUMBER>11936 pipeline.py:237] Final (deduplicated) MSA size: 6644 sequences.
I1031 12:41:38.367957<PHONE_NUMBER>11936 pipeline.py:239] Total number of templates (NB: this can include bad templates and is later filtered to top 4): 20.
I1031 12:41:38.672444<PHONE_NUMBER>11936 run_alphafold.py:276] Running model model_1_multimer_v3_pred_0 on rcsb_pdb_3JA9_multimer
I1031 12:41:38.673051<PHONE_NUMBER>11936 model.py:165] Running predict with shape(feat) = {'aatype': (783,), 'residue_index': (783,), 'seq_length': (), 'msa': (2048, 783), 'num_alignments': (), 'template_aatype': (4, 783), 'template_all_atom_mask': (4, 783, 37), 'template_all_atom_positions': (4, 783, 37, 3), 'asym_id': (783,), 'sym_id': (783,), 'entity_id': (783,), 'deletion_matrix': (2048, 783), 'deletion_mean': (783,), 'all_atom_mask': (783, 37), 'all_atom_positions': (783, 37, 3), 'assembly_num_chains': (), 'entity_mask': (783,), 'num_templates': (), 'cluster_bias_mask': (2048,), 'bert_mask': (2048, 783), 'seq_mask': (783,), 'msa_mask': (2048, 783)}
Traceback (most recent call last):
  File "/app/alphafold/run_alphafold.py", line 570, in <module>
    app.run(main)
  File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 312, in run
    _run_main(main, args)
  File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "/app/alphafold/run_alphafold.py", line 543, in main
    predict_structure(
  File "/app/alphafold/run_alphafold.py", line 284, in predict_structure
    prediction_result = model_runner.predict(processed_feature_dict,
  File "/app/alphafold/alphafold/model/model.py", line 167, in predict
    result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/traceback_util.py", line 162, in reraise_with_filtered_traceback
    return fun(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/api.py", line 622, in cache_miss
    execute = dispatch._xla_call_impl_lazy(fun_, *tracers, **params)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/dispatch.py", line 236, in _xla_call_impl_lazy
    return xla_callable(fun, device, backend, name, donated_invars, keep_unused,
  File "/opt/conda/lib/python3.10/site-packages/jax/linear_util.py", line 303, in memoized_fun
    ans = call(fun, *args)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/dispatch.py", line 359, in _xla_callable_uncached
    return lower_xla_callable(fun, device, backend, name, donated_invars, False,
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/profiler.py", line 314, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/dispatch.py", line 445, in lower_xla_callable
    jaxpr, out_type, consts = pe.trace_to_jaxpr_final2(
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/profiler.py", line 314, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/jax/interpreters/partial_eval.py", line 2077, in trace_to_jaxpr_final2
    jaxpr, out_type, consts = trace_to_subjaxpr_dynamic2(fun, main, debug_info)
  File "/opt/conda/lib/python3.10/site-packages/jax/interpreters/partial_eval.py", line 2027, in trace_to_subjaxpr_dynamic2
    ans = fun.call_wrapped(*in_tracers_)
  File "/opt/conda/lib/python3.10/site-packages/jax/linear_util.py", line 167, in call_wrapped
    ans = self.f(*args, **dict(self.params, **kwargs))
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/transform.py", line 187, in apply_fn
    out, state = f.apply(params, None, *args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/transform.py", line 457, in apply_fn
    out = f(*args, **kwargs)
  File "/app/alphafold/alphafold/model/model.py", line 77, in _forward_fn
    return model(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 508, in __call__
    num_recycles, _, prev, safe_key = hk.while_loop(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 910, in while_loop
    val, state = jax.lax.while_loop(pure_cond_fun, pure_body_fun, init_val)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/traceback_util.py", line 162, in reraise_with_filtered_traceback
    return fun(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/lax/control_flow/loops.py", line 1111, in while_loop
    init_vals, init_avals, body_jaxpr, in_tree, *rest = _create_jaxpr(init_val)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/lax/control_flow/loops.py", line 1094, in _create_jaxpr
    body_jaxpr, body_consts, body_tree = _initial_style_jaxpr(
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/lax/control_flow/common.py", line 60, in _initial_style_jaxpr
    jaxpr, consts, out_tree = _initial_style_open_jaxpr(
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/lax/control_flow/common.py", line 54, in _initial_style_open_jaxpr
    jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(wrapped_fun, in_avals, debug)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/profiler.py", line 314, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/jax/interpreters/partial_eval.py", line 1981, in trace_to_jaxpr_dynamic
    jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(
  File "/opt/conda/lib/python3.10/site-packages/jax/interpreters/partial_eval.py", line 1998, in trace_to_subjaxpr_dynamic
    ans = fun.call_wrapped(*in_tracers_)
  File "/opt/conda/lib/python3.10/site-packages/jax/linear_util.py", line 167, in call_wrapped
    ans = self.f(*args, **dict(self.params, **kwargs))
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 903, in pure_body_fun
    val = body_fun(val)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 486, in recycle_body
    ret = apply_network(prev=prev, safe_key=safe_key2)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 449, in apply_network
    return impl(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 321, in __call__
    repr_shape = hk.eval_shape(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 930, in eval_shape
    out_shape = jax.eval_shape(stateless_fun, internal_state(), *args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/api.py", line 3201, in eval_shape
    out = pe.abstract_eval_fun(wrapped_fun.call_wrapped,
  File "/opt/conda/lib/python3.10/site-packages/jax/interpreters/partial_eval.py", line 660, in abstract_eval_fun
    _, avals_out, _ = trace_to_jaxpr_dynamic(
  File "/opt/conda/lib/python3.10/site-packages/jax/_src/profiler.py", line 314, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/jax/interpreters/partial_eval.py", line 1981, in trace_to_jaxpr_dynamic
    jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(
  File "/opt/conda/lib/python3.10/site-packages/jax/interpreters/partial_eval.py", line 1998, in trace_to_subjaxpr_dynamic
    ans = fun.call_wrapped(*in_tracers_)
  File "/opt/conda/lib/python3.10/site-packages/jax/linear_util.py", line 167, in call_wrapped
    ans = self.f(*args, **dict(self.params, **kwargs))
  File "/opt/conda/lib/python3.10/site-packages/jax/linear_util.py", line 167, in call_wrapped
    ans = self.f(*args, **dict(self.params, **kwargs))
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 926, in stateless_fun
    out = fun(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 322, in <lambda>
    lambda: embedding_module(batch, is_training))
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 633, in __call__
    batch = sample_msa(sample_key, batch, c.num_msa)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 274, in sample_msa
    index_order = gumbel_argsort_sample_idx(key.get(), logits)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 110, in gumbel_argsort_sample_idx
    z = gumbel_noise(key, logits.shape)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 67, in gumbel_noise
    uniform_noise = uniform(
  File "/app/alphafold/alphafold/model/utils.py", line 166, in inner
    if jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)
jax._src.traceback_util.UnfilteredStackTrace: AttributeError: module 'jax.dtypes' has no attribute 'prng_key'

The stack trace below excludes JAX-internal frames.
The preceding is the original exception that occurred, unmodified.

--------------------

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/app/alphafold/run_alphafold.py", line 570, in <module>
    app.run(main)
  File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 312, in run
    _run_main(main, args)
  File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "/app/alphafold/run_alphafold.py", line 543, in main
    predict_structure(
  File "/app/alphafold/run_alphafold.py", line 284, in predict_structure
    prediction_result = model_runner.predict(processed_feature_dict,
  File "/app/alphafold/alphafold/model/model.py", line 167, in predict
    result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/transform.py", line 187, in apply_fn
    out, state = f.apply(params, None, *args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/transform.py", line 457, in apply_fn
    out = f(*args, **kwargs)
  File "/app/alphafold/alphafold/model/model.py", line 77, in _forward_fn
    return model(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 508, in __call__
    num_recycles, _, prev, safe_key = hk.while_loop(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 910, in while_loop
    val, state = jax.lax.while_loop(pure_cond_fun, pure_body_fun, init_val)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 903, in pure_body_fun
    val = body_fun(val)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 486, in recycle_body
    ret = apply_network(prev=prev, safe_key=safe_key2)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 449, in apply_network
    return impl(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 321, in __call__
    repr_shape = hk.eval_shape(
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 930, in eval_shape
    out_shape = jax.eval_shape(stateless_fun, internal_state(), *args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 926, in stateless_fun
    out = fun(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 322, in <lambda>
    lambda: embedding_module(batch, is_training))
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
    out = f(*args, **kwargs)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
    return bound_method(*args, **kwargs)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 633, in __call__
    batch = sample_msa(sample_key, batch, c.num_msa)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 274, in sample_msa
    index_order = gumbel_argsort_sample_idx(key.get(), logits)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 110, in gumbel_argsort_sample_idx
    z = gumbel_noise(key, logits.shape)
  File "/app/alphafold/alphafold/model/modules_multimer.py", line 67, in gumbel_noise
    uniform_noise = uniform(
  File "/app/alphafold/alphafold/model/utils.py", line 166, in inner
    if jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)
AttributeError: module 'jax.dtypes' has no attribute 'prng_key'

I find a similar case :https://stackoverflow.com/questions/77221932/jax-prng-key-error-with-tfp-normal-distribution
it said:
jax.dtypes.prng_key was added in JAX version 0.4.14. You should update JAX to a newer version (0.4.14 or later), or if that is not possible, downgrade tensorflow_probability to an older version (0.21.0 or older should be sufficient for this issue).
as i check in alphafold dockerfile/ inside docker, the jax version is 0.3.25 and
ba07328202ae:/app/alphafold$ pip list | grep jax
WARNING: The directory '/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
jax                          0.3.25
jaxlib                       0.3.25+cuda11.cudnn805

the tensorflow version:2.13.0
ba07328202ae:/app/alphafold$ pip list | grep tensor
WARNING: The directory '/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
tensorboard                  2.13.0
tensorboard-data-server      0.7.2
tensorflow-cpu               2.13.0
tensorflow-estimator         2.13.0
tensorflow-io-gcs-filesystem 0.34.0

but tensorflow is not listed in dockerfile, where did alphafold installed it via dockefile?
it should be a common issus if all the docker images contains such version of jax+tensorflow

@wttat Im having the identical issue today, I upgrade the jax/jaxlib to 0.4.14 (since they changed the attribute after 0.4.16) and rebuilt the docker image, and I'm currently running to test it out. will update the outcome soon

i tried another fasta, and got the same error
>7ALC_1|Chains A, B, C, D, E|GTP cyclohydrolase 1|Homo sapiens (9606)
PEAKSAQPADGWKGERPRSEEDNELNLPNLAAAYSSILSSLGENPQRQGLLKTPWRAASAMQFFTKGYQETISDVLNDAIFDEDHDEMVIVKDIDMFSMCEHHLVPFVGKVHIGYLPNKQVLGLSKLARIVEIYSRRLQVQERLTKQIAVAITEALRPAGVGVVVEATHMCMVMRGVQKMNSKTVTSTMLGVFREDPKTREEFLTLIRS
>7ALC_2|Chains F, G, H, I, J|GTP cyclohydrolase 1 feedback regulatory protein|Homo sapiens (9606)
GSHMPYLLISTQIRMEVGPTMVGDEQSDPELMQHLGASKRRALGNNFYEYYVDDPPRIVLDKLERRGFRVLSMTGVGQTLVWCLHKE

I1031 15:36:33.653162<PHONE_NUMBER>8848 run_docker.py:258] The above exception was the direct cause of the following exception:
I1031 15:36:33.653240<PHONE_NUMBER>8848 run_docker.py:258]
I1031 15:36:33.653314<PHONE_NUMBER>8848 run_docker.py:258] Traceback (most recent call last):
I1031 15:36:33.653389<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 570, in <module>
I1031 15:36:33.653509<PHONE_NUMBER>8848 run_docker.py:258] app.run(main)
I1031 15:36:33.653592<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 312, in run
I1031 15:36:33.653668<PHONE_NUMBER>8848 run_docker.py:258] _run_main(main, args)
I1031 15:36:33.653748<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 258, in _run_main
I1031 15:36:33.653822<PHONE_NUMBER>8848 run_docker.py:258] sys.exit(main(argv))
I1031 15:36:33.653898<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 543, in main
I1031 15:36:33.654003<PHONE_NUMBER>8848 run_docker.py:258] predict_structure(
I1031 15:36:33.654082<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 284, in predict_structure
I1031 15:36:33.654160<PHONE_NUMBER>8848 run_docker.py:258] prediction_result = model_runner.predict(processed_feature_dict,
I1031 15:36:33.654242<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/model.py", line 167, in predict
I1031 15:36:33.654320<PHONE_NUMBER>8848 run_docker.py:258] result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
I1031 15:36:33.654395<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/transform.py", line 187, in apply_fn
I1031 15:36:33.654470<PHONE_NUMBER>8848 run_docker.py:258] out, state = f.apply(params, None, *args, **kwargs)
I1031 15:36:33.654546<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/transform.py", line 457, in apply_fn
I1031 15:36:33.654622<PHONE_NUMBER>8848 run_docker.py:258] out = f(*args, **kwargs)
I1031 15:36:33.654698<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/model.py", line 77, in _forward_fn
I1031 15:36:33.654774<PHONE_NUMBER>8848 run_docker.py:258] return model(
I1031 15:36:33.654851<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
I1031 15:36:33.654928<PHONE_NUMBER>8848 run_docker.py:258] out = f(*args, **kwargs)
I1031 15:36:33.655004<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
I1031 15:36:33.655081<PHONE_NUMBER>8848 run_docker.py:258] return func(*args, **kwds)
I1031 15:36:33.655157<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
I1031 15:36:33.655234<PHONE_NUMBER>8848 run_docker.py:258] return bound_method(*args, **kwargs)
I1031 15:36:33.655307<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 508, in __call__
I1031 15:36:33.655385<PHONE_NUMBER>8848 run_docker.py:258] num_recycles, _, prev, safe_key = hk.while_loop(
I1031 15:36:33.655459<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 910, in while_loop
I1031 15:36:33.655536<PHONE_NUMBER>8848 run_docker.py:258] val, state = jax.lax.while_loop(pure_cond_fun, pure_body_fun, init_val)
I1031 15:36:33.655613<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 903, in pure_body_fun
I1031 15:36:33.655740<PHONE_NUMBER>8848 run_docker.py:258] val = body_fun(val)
I1031 15:36:33.655821<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 486, in recycle_body
I1031 15:36:33.655898<PHONE_NUMBER>8848 run_docker.py:258] ret = apply_network(prev=prev, safe_key=safe_key2)
I1031 15:36:33.655974<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 449, in apply_network
I1031 15:36:33.656050<PHONE_NUMBER>8848 run_docker.py:258] return impl(
I1031 15:36:33.656126<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
I1031 15:36:33.656202<PHONE_NUMBER>8848 run_docker.py:258] out = f(*args, **kwargs)
I1031 15:36:33.656279<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
I1031 15:36:33.656355<PHONE_NUMBER>8848 run_docker.py:258] return func(*args, **kwds)
I1031 15:36:33.656432<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
I1031 15:36:33.656507<PHONE_NUMBER>8848 run_docker.py:258] return bound_method(*args, **kwargs)
I1031 15:36:33.656583<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 321, in __call__
I1031 15:36:33.656660<PHONE_NUMBER>8848 run_docker.py:258] repr_shape = hk.eval_shape(
I1031 15:36:33.656736<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 930, in eval_shape
I1031 15:36:33.656813<PHONE_NUMBER>8848 run_docker.py:258] out_shape = jax.eval_shape(stateless_fun, internal_state(), *args, **kwargs)
I1031 15:36:33.656889<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/stateful.py", line 926, in stateless_fun
I1031 15:36:33.656971<PHONE_NUMBER>8848 run_docker.py:258] out = fun(*args, **kwargs)
I1031 15:36:33.657048<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 322, in <lambda>
I1031 15:36:33.657124<PHONE_NUMBER>8848 run_docker.py:258] lambda: embedding_module(batch, is_training))
I1031 15:36:33.657200<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 465, in wrapped
I1031 15:36:33.657277<PHONE_NUMBER>8848 run_docker.py:258] out = f(*args, **kwargs)
I1031 15:36:33.657353<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
I1031 15:36:33.657430<PHONE_NUMBER>8848 run_docker.py:258] return func(*args, **kwds)
I1031 15:36:33.657507<PHONE_NUMBER>8848 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
I1031 15:36:33.657582<PHONE_NUMBER>8848 run_docker.py:258] return bound_method(*args, **kwargs)
I1031 15:36:33.657659<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 633, in __call__
I1031 15:36:33.657734<PHONE_NUMBER>8848 run_docker.py:258] batch = sample_msa(sample_key, batch, c.num_msa)
I1031 15:36:33.657810<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 274, in sample_msa
I1031 15:36:33.657887<PHONE_NUMBER>8848 run_docker.py:258] index_order = gumbel_argsort_sample_idx(key.get(), logits)
I1031 15:36:33.657987<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 110, in gumbel_argsort_sample_idx
I1031 15:36:33.658070<PHONE_NUMBER>8848 run_docker.py:258] z = gumbel_noise(key, logits.shape)
I1031 15:36:33.658148<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/modules_multimer.py", line 67, in gumbel_noise
I1031 15:36:33.658225<PHONE_NUMBER>8848 run_docker.py:258] uniform_noise = uniform(
I1031 15:36:33.658302<PHONE_NUMBER>8848 run_docker.py:258] File "/app/alphafold/alphafold/model/utils.py", line 166, in inner
I1031 15:36:33.658379<PHONE_NUMBER>8848 run_docker.py:258] if jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)
I1031 15:36:33.658456<PHONE_NUMBER>8848 run_docker.py:258] AttributeError: module 'jax.dtypes' has no attribute 'prng_key'

@wttat Im having the identical issue today, I upgrade the jax/jaxlib to 0.4.14 (since they changed the attribute after 0.4.16) and rebuilt the docker image, and I'm currently running to test it out. will update the outcome soon

@Pipiiido hi, thanks for the reply.
Did you modified the dockerfile from
    && pip3 install --upgrade --no-cache-dir \
      jax==0.3.25 \
      jaxlib==0.3.25+cuda11.cudnn805 \

to

    && pip3 install --upgrade --no-cache-dir \
      jax==0.4.14 \
      jaxlib==0.4.14+cuda11.cudnn86 \

how did it go ? did it fixed ? really looking forward to your reply.

@wttat Im having the identical issue today, I upgrade the jax/jaxlib to 0.4.14 (since they changed the attribute after 0.4.16) and rebuilt the docker image, and I'm currently running to test it out. will update the outcome soon

i tried upgrade the jax/jaxlib to 0.4.14 but it got another error, still tring to figure it out..
Final (deduplicated) MSA size: 6644 sequences.
I1101 04:25:57.667878<PHONE_NUMBER>9680 run_docker.py:258] I1101 04:25:57.667175<PHONE_NUMBER>79680 pipeline.py:239] Total number of templates (NB: this can include bad templates and is later filtered to top 4): 20.
I1101 04:25:58.127340<PHONE_NUMBER>9680 run_docker.py:258] I1101 04:25:58.126729<PHONE_NUMBER>79680 run_alphafold.py:276] Running model model_1_multimer_v3_pred_0 on rcsb_pdb_3JA9_multimer
I1101 04:25:58.127711<PHONE_NUMBER>9680 run_docker.py:258] I1101 04:25:58.127323<PHONE_NUMBER>79680 model.py:165] Running predict with shape(feat) = {'aatype': (783,), 'residue_index': (783,), 'seq_length': (), 'msa': (2048, 783), 'num_alignments': (), 'template_aatype': (4, 783), 'template_all_atom_mask': (4, 783, 37), 'template_all_atom_positions': (4, 783, 37, 3), 'asym_id': (783,), 'sym_id': (783,), 'entity_id': (783,), 'deletion_matrix': (2048, 783), 'deletion_mean': (783,), 'all_atom_mask': (783, 37), 'all_atom_positions': (783, 37, 3), 'assembly_num_chains': (), 'entity_mask': (783,), 'num_templates': (), 'cluster_bias_mask': (2048,), 'bert_mask': (2048, 783), 'seq_mask': (783,), 'msa_mask': (2048, 783)}
I1101 04:26:04.274579<PHONE_NUMBER>9680 run_docker.py:258] 2023-11-01 04:26:04.273989: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:433] Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.6.0.  CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library.  If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration.
I1101 04:26:04.532196<PHONE_NUMBER>9680 run_docker.py:258] Traceback (most recent call last):
I1101 04:26:04.532341<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 570, in <module>
I1101 04:26:04.532469<PHONE_NUMBER>9680 run_docker.py:258] app.run(main)
I1101 04:26:04.532579<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 312, in run
I1101 04:26:04.532684<PHONE_NUMBER>9680 run_docker.py:258] _run_main(main, args)
I1101 04:26:04.532772<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 258, in _run_main
I1101 04:26:04.532890<PHONE_NUMBER>9680 run_docker.py:258] sys.exit(main(argv))
I1101 04:26:04.532994<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 543, in main
I1101 04:26:04.533097<PHONE_NUMBER>9680 run_docker.py:258] predict_structure(
I1101 04:26:04.533184<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 284, in predict_structure
I1101 04:26:04.533286<PHONE_NUMBER>9680 run_docker.py:258] prediction_result = model_runner.predict(processed_feature_dict,
I1101 04:26:04.533401<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/alphafold/model/model.py", line 167, in predict
I1101 04:26:04.533504<PHONE_NUMBER>9680 run_docker.py:258] result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
I1101 04:26:04.533587<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/random.py", line 177, in PRNGKey
I1101 04:26:04.533718<PHONE_NUMBER>9680 run_docker.py:258] return _return_prng_keys(True, _key('PRNGKey', seed, impl))
I1101 04:26:04.533821<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/random.py", line 139, in _key
I1101 04:26:04.533907<PHONE_NUMBER>9680 run_docker.py:258] return prng.seed_with_impl(impl, seed)
I1101 04:26:04.534006<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 406, in seed_with_impl
I1101 04:26:04.534091<PHONE_NUMBER>9680 run_docker.py:258] return random_seed(seed, impl=impl)
I1101 04:26:04.534192<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 689, in random_seed
I1101 04:26:04.534278<PHONE_NUMBER>9680 run_docker.py:258] return random_seed_p.bind(seeds_arr, impl=impl)
I1101 04:26:04.534361<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 701, in random_seed_impl
I1101 04:26:04.534445<PHONE_NUMBER>9680 run_docker.py:258] base_arr = random_seed_impl_base(seeds, impl=impl)
I1101 04:26:04.534528<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 706, in random_seed_impl_base
I1101 04:26:04.534611<PHONE_NUMBER>9680 run_docker.py:258] return seed(seeds)
I1101 04:26:04.534694<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 935, in threefry_seed
I1101 04:26:04.534806<PHONE_NUMBER>9680 run_docker.py:258] return _threefry_seed(seed)
I1101 04:26:04.534895<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/traceback_util.py", line 166, in reraise_with_filtered_traceback
I1101 04:26:04.534981<PHONE_NUMBER>9680 run_docker.py:258] return fun(*args, **kwargs)
I1101 04:26:04.535064<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 253, in cache_miss
I1101 04:26:04.535184<PHONE_NUMBER>9680 run_docker.py:258] outs, out_flat, out_tree, args_flat, jaxpr = _python_pjit_helper(
I1101 04:26:04.535287<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 166, in _python_pjit_helper
I1101 04:26:04.535371<PHONE_NUMBER>9680 run_docker.py:258] out_flat = pjit_p.bind(*args_flat, **params)
I1101 04:26:04.535454<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 2596, in bind
I1101 04:26:04.535695<PHONE_NUMBER>9680 run_docker.py:258] return self.bind_with_trace(top_trace, args, params)
I1101 04:26:04.535816<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 389, in bind_with_trace
I1101 04:26:04.535930<PHONE_NUMBER>9680 run_docker.py:258] out = trace.process_primitive(self, map(trace.full_raise, args), params)
I1101 04:26:04.536033<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 821, in process_primitive
I1101 04:26:04.536143<PHONE_NUMBER>9680 run_docker.py:258] return primitive.impl(*tracers, **params)
I1101 04:26:04.536231<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 1209, in _pjit_call_impl
I1101 04:26:04.537082<PHONE_NUMBER>9680 run_docker.py:258] return xc._xla.pjit(name, f, call_impl_cache_miss, [], [], donated_argnums,
I1101 04:26:04.537188<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 1192, in call_impl_cache_miss
I1101 04:26:04.537353<PHONE_NUMBER>9680 run_docker.py:258] out_flat, compiled = _pjit_call_impl_python(
I1101 04:26:04.537478<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 1128, in _pjit_call_impl_python
I1101 04:26:04.537683<PHONE_NUMBER>9680 run_docker.py:258] always_lower=False, lowering_platform=None).compile()
I1101 04:26:04.537790<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py", line 2206, in compile
I1101 04:26:04.544268<PHONE_NUMBER>9680 run_docker.py:258] executable = UnloadedMeshExecutable.from_hlo(
I1101 04:26:04.544380<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py", line 2544, in from_hlo
I1101 04:26:04.544739<PHONE_NUMBER>9680 run_docker.py:258] xla_executable, compile_options = _cached_compilation(
I1101 04:26:04.544845<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py", line 2454, in _cached_compilation
I1101 04:26:04.545219<PHONE_NUMBER>9680 run_docker.py:258] xla_executable = dispatch.compile_or_get_cached(
I1101 04:26:04.545353<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/dispatch.py", line 496, in compile_or_get_cached
I1101 04:26:04.546956<PHONE_NUMBER>9680 run_docker.py:258] return backend_compile(backend, computation, compile_options,
I1101 04:26:04.547050<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/profiler.py", line 314, in wrapper
I1101 04:26:04.547768<PHONE_NUMBER>9680 run_docker.py:258] return func(*args, **kwargs)
I1101 04:26:04.547861<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/dispatch.py", line 464, in backend_compile
I1101 04:26:04.547982<PHONE_NUMBER>9680 run_docker.py:258] return backend.compile(built_c, compile_options=options)
I1101 04:26:04.548102<PHONE_NUMBER>9680 run_docker.py:258] jax._src.traceback_util.UnfilteredStackTrace: jaxlib.xla_extension.XlaRuntimeError: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.
I1101 04:26:04.548218<PHONE_NUMBER>9680 run_docker.py:258]
I1101 04:26:04.548307<PHONE_NUMBER>9680 run_docker.py:258] The stack trace below excludes JAX-internal frames.
I1101 04:26:04.548406<PHONE_NUMBER>9680 run_docker.py:258] The preceding is the original exception that occurred, unmodified.
I1101 04:26:04.548511<PHONE_NUMBER>9680 run_docker.py:258]
I1101 04:26:04.548613<PHONE_NUMBER>9680 run_docker.py:258] --------------------
I1101 04:26:04.548715<PHONE_NUMBER>9680 run_docker.py:258]
I1101 04:26:04.548830<PHONE_NUMBER>9680 run_docker.py:258] The above exception was the direct cause of the following exception:
I1101 04:26:04.548930<PHONE_NUMBER>9680 run_docker.py:258]
I1101 04:26:04.549014<PHONE_NUMBER>9680 run_docker.py:258] Traceback (most recent call last):
I1101 04:26:04.549113<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 570, in <module>
I1101 04:26:04.549245<PHONE_NUMBER>9680 run_docker.py:258] app.run(main)
I1101 04:26:04.549376<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 312, in run
I1101 04:26:04.549467<PHONE_NUMBER>9680 run_docker.py:258] _run_main(main, args)
I1101 04:26:04.549566<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 258, in _run_main
I1101 04:26:04.549654<PHONE_NUMBER>9680 run_docker.py:258] sys.exit(main(argv))
I1101 04:26:04.549754<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 543, in main
I1101 04:26:04.549839<PHONE_NUMBER>9680 run_docker.py:258] predict_structure(
I1101 04:26:04.549944<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 284, in predict_structure
I1101 04:26:04.550027<PHONE_NUMBER>9680 run_docker.py:258] prediction_result = model_runner.predict(processed_feature_dict,
I1101 04:26:04.550125<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/alphafold/model/model.py", line 167, in predict
I1101 04:26:04.550212<PHONE_NUMBER>9680 run_docker.py:258] result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
I1101 04:26:04.550295<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/random.py", line 177, in PRNGKey
I1101 04:26:04.550378<PHONE_NUMBER>9680 run_docker.py:258] return _return_prng_keys(True, _key('PRNGKey', seed, impl))
I1101 04:26:04.550461<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/random.py", line 139, in _key
I1101 04:26:04.550544<PHONE_NUMBER>9680 run_docker.py:258] return prng.seed_with_impl(impl, seed)
I1101 04:26:04.550626<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 406, in seed_with_impl
I1101 04:26:04.550709<PHONE_NUMBER>9680 run_docker.py:258] return random_seed(seed, impl=impl)
I1101 04:26:04.550791<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 689, in random_seed
I1101 04:26:04.550873<PHONE_NUMBER>9680 run_docker.py:258] return random_seed_p.bind(seeds_arr, impl=impl)
I1101 04:26:04.550955<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 386, in bind
I1101 04:26:04.551036<PHONE_NUMBER>9680 run_docker.py:258] return self.bind_with_trace(find_top_trace(args), args, params)
I1101 04:26:04.551118<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 389, in bind_with_trace
I1101 04:26:04.551198<PHONE_NUMBER>9680 run_docker.py:258] out = trace.process_primitive(self, map(trace.full_raise, args), params)
I1101 04:26:04.551313<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 821, in process_primitive
I1101 04:26:04.551399<PHONE_NUMBER>9680 run_docker.py:258] return primitive.impl(*tracers, **params)
I1101 04:26:04.551482<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 701, in random_seed_impl
I1101 04:26:04.551564<PHONE_NUMBER>9680 run_docker.py:258] base_arr = random_seed_impl_base(seeds, impl=impl)
I1101 04:26:04.551646<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 706, in random_seed_impl_base
I1101 04:26:04.551729<PHONE_NUMBER>9680 run_docker.py:258] return seed(seeds)
I1101 04:26:04.551811<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 935, in threefry_seed
I1101 04:26:04.551900<PHONE_NUMBER>9680 run_docker.py:258] return _threefry_seed(seed)
I1101 04:26:04.551984<PHONE_NUMBER>9680 run_docker.py:258] jaxlib.xla_extension.XlaRuntimeError: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.
/var/spool/pbs/mom_priv/jobs/30.ip-10-247-0-20.SC: line 45: fg: no job control

@wttat Im having the identical issue today, I upgrade the jax/jaxlib to 0.4.14 (since they changed the attribute after 0.4.16) and rebuilt the docker image, and I'm currently running to test it out. will update the outcome soon

i tried upgrade the jax/jaxlib to 0.4.14 but it got another error, still tring to figure it out..
Final (deduplicated) MSA size: 6644 sequences.
I1101 04:25:57.667878<PHONE_NUMBER>9680 run_docker.py:258] I1101 04:25:57.667175<PHONE_NUMBER>79680 pipeline.py:239] Total number of templates (NB: this can include bad templates and is later filtered to top 4): 20.
I1101 04:25:58.127340<PHONE_NUMBER>9680 run_docker.py:258] I1101 04:25:58.126729<PHONE_NUMBER>79680 run_alphafold.py:276] Running model model_1_multimer_v3_pred_0 on rcsb_pdb_3JA9_multimer
I1101 04:25:58.127711<PHONE_NUMBER>9680 run_docker.py:258] I1101 04:25:58.127323<PHONE_NUMBER>79680 model.py:165] Running predict with shape(feat) = {'aatype': (783,), 'residue_index': (783,), 'seq_length': (), 'msa': (2048, 783), 'num_alignments': (), 'template_aatype': (4, 783), 'template_all_atom_mask': (4, 783, 37), 'template_all_atom_positions': (4, 783, 37, 3), 'asym_id': (783,), 'sym_id': (783,), 'entity_id': (783,), 'deletion_matrix': (2048, 783), 'deletion_mean': (783,), 'all_atom_mask': (783, 37), 'all_atom_positions': (783, 37, 3), 'assembly_num_chains': (), 'entity_mask': (783,), 'num_templates': (), 'cluster_bias_mask': (2048,), 'bert_mask': (2048, 783), 'seq_mask': (783,), 'msa_mask': (2048, 783)}
I1101 04:26:04.274579<PHONE_NUMBER>9680 run_docker.py:258] 2023-11-01 04:26:04.273989: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:433] Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.6.0.  CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library.  If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration.
I1101 04:26:04.532196<PHONE_NUMBER>9680 run_docker.py:258] Traceback (most recent call last):
I1101 04:26:04.532341<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 570, in <module>
I1101 04:26:04.532469<PHONE_NUMBER>9680 run_docker.py:258] app.run(main)
I1101 04:26:04.532579<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 312, in run
I1101 04:26:04.532684<PHONE_NUMBER>9680 run_docker.py:258] _run_main(main, args)
I1101 04:26:04.532772<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 258, in _run_main
I1101 04:26:04.532890<PHONE_NUMBER>9680 run_docker.py:258] sys.exit(main(argv))
I1101 04:26:04.532994<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 543, in main
I1101 04:26:04.533097<PHONE_NUMBER>9680 run_docker.py:258] predict_structure(
I1101 04:26:04.533184<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 284, in predict_structure
I1101 04:26:04.533286<PHONE_NUMBER>9680 run_docker.py:258] prediction_result = model_runner.predict(processed_feature_dict,
I1101 04:26:04.533401<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/alphafold/model/model.py", line 167, in predict
I1101 04:26:04.533504<PHONE_NUMBER>9680 run_docker.py:258] result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
I1101 04:26:04.533587<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/random.py", line 177, in PRNGKey
I1101 04:26:04.533718<PHONE_NUMBER>9680 run_docker.py:258] return _return_prng_keys(True, _key('PRNGKey', seed, impl))
I1101 04:26:04.533821<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/random.py", line 139, in _key
I1101 04:26:04.533907<PHONE_NUMBER>9680 run_docker.py:258] return prng.seed_with_impl(impl, seed)
I1101 04:26:04.534006<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 406, in seed_with_impl
I1101 04:26:04.534091<PHONE_NUMBER>9680 run_docker.py:258] return random_seed(seed, impl=impl)
I1101 04:26:04.534192<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 689, in random_seed
I1101 04:26:04.534278<PHONE_NUMBER>9680 run_docker.py:258] return random_seed_p.bind(seeds_arr, impl=impl)
I1101 04:26:04.534361<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 701, in random_seed_impl
I1101 04:26:04.534445<PHONE_NUMBER>9680 run_docker.py:258] base_arr = random_seed_impl_base(seeds, impl=impl)
I1101 04:26:04.534528<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 706, in random_seed_impl_base
I1101 04:26:04.534611<PHONE_NUMBER>9680 run_docker.py:258] return seed(seeds)
I1101 04:26:04.534694<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 935, in threefry_seed
I1101 04:26:04.534806<PHONE_NUMBER>9680 run_docker.py:258] return _threefry_seed(seed)
I1101 04:26:04.534895<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/traceback_util.py", line 166, in reraise_with_filtered_traceback
I1101 04:26:04.534981<PHONE_NUMBER>9680 run_docker.py:258] return fun(*args, **kwargs)
I1101 04:26:04.535064<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 253, in cache_miss
I1101 04:26:04.535184<PHONE_NUMBER>9680 run_docker.py:258] outs, out_flat, out_tree, args_flat, jaxpr = _python_pjit_helper(
I1101 04:26:04.535287<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 166, in _python_pjit_helper
I1101 04:26:04.535371<PHONE_NUMBER>9680 run_docker.py:258] out_flat = pjit_p.bind(*args_flat, **params)
I1101 04:26:04.535454<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 2596, in bind
I1101 04:26:04.535695<PHONE_NUMBER>9680 run_docker.py:258] return self.bind_with_trace(top_trace, args, params)
I1101 04:26:04.535816<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 389, in bind_with_trace
I1101 04:26:04.535930<PHONE_NUMBER>9680 run_docker.py:258] out = trace.process_primitive(self, map(trace.full_raise, args), params)
I1101 04:26:04.536033<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 821, in process_primitive
I1101 04:26:04.536143<PHONE_NUMBER>9680 run_docker.py:258] return primitive.impl(*tracers, **params)
I1101 04:26:04.536231<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 1209, in _pjit_call_impl
I1101 04:26:04.537082<PHONE_NUMBER>9680 run_docker.py:258] return xc._xla.pjit(name, f, call_impl_cache_miss, [], [], donated_argnums,
I1101 04:26:04.537188<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 1192, in call_impl_cache_miss
I1101 04:26:04.537353<PHONE_NUMBER>9680 run_docker.py:258] out_flat, compiled = _pjit_call_impl_python(
I1101 04:26:04.537478<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/pjit.py", line 1128, in _pjit_call_impl_python
I1101 04:26:04.537683<PHONE_NUMBER>9680 run_docker.py:258] always_lower=False, lowering_platform=None).compile()
I1101 04:26:04.537790<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py", line 2206, in compile
I1101 04:26:04.544268<PHONE_NUMBER>9680 run_docker.py:258] executable = UnloadedMeshExecutable.from_hlo(
I1101 04:26:04.544380<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py", line 2544, in from_hlo
I1101 04:26:04.544739<PHONE_NUMBER>9680 run_docker.py:258] xla_executable, compile_options = _cached_compilation(
I1101 04:26:04.544845<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py", line 2454, in _cached_compilation
I1101 04:26:04.545219<PHONE_NUMBER>9680 run_docker.py:258] xla_executable = dispatch.compile_or_get_cached(
I1101 04:26:04.545353<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/dispatch.py", line 496, in compile_or_get_cached
I1101 04:26:04.546956<PHONE_NUMBER>9680 run_docker.py:258] return backend_compile(backend, computation, compile_options,
I1101 04:26:04.547050<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/profiler.py", line 314, in wrapper
I1101 04:26:04.547768<PHONE_NUMBER>9680 run_docker.py:258] return func(*args, **kwargs)
I1101 04:26:04.547861<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/dispatch.py", line 464, in backend_compile
I1101 04:26:04.547982<PHONE_NUMBER>9680 run_docker.py:258] return backend.compile(built_c, compile_options=options)
I1101 04:26:04.548102<PHONE_NUMBER>9680 run_docker.py:258] jax._src.traceback_util.UnfilteredStackTrace: jaxlib.xla_extension.XlaRuntimeError: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.
I1101 04:26:04.548218<PHONE_NUMBER>9680 run_docker.py:258]
I1101 04:26:04.548307<PHONE_NUMBER>9680 run_docker.py:258] The stack trace below excludes JAX-internal frames.
I1101 04:26:04.548406<PHONE_NUMBER>9680 run_docker.py:258] The preceding is the original exception that occurred, unmodified.
I1101 04:26:04.548511<PHONE_NUMBER>9680 run_docker.py:258]
I1101 04:26:04.548613<PHONE_NUMBER>9680 run_docker.py:258] --------------------
I1101 04:26:04.548715<PHONE_NUMBER>9680 run_docker.py:258]
I1101 04:26:04.548830<PHONE_NUMBER>9680 run_docker.py:258] The above exception was the direct cause of the following exception:
I1101 04:26:04.548930<PHONE_NUMBER>9680 run_docker.py:258]
I1101 04:26:04.549014<PHONE_NUMBER>9680 run_docker.py:258] Traceback (most recent call last):
I1101 04:26:04.549113<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 570, in <module>
I1101 04:26:04.549245<PHONE_NUMBER>9680 run_docker.py:258] app.run(main)
I1101 04:26:04.549376<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 312, in run
I1101 04:26:04.549467<PHONE_NUMBER>9680 run_docker.py:258] _run_main(main, args)
I1101 04:26:04.549566<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/absl/app.py", line 258, in _run_main
I1101 04:26:04.549654<PHONE_NUMBER>9680 run_docker.py:258] sys.exit(main(argv))
I1101 04:26:04.549754<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 543, in main
I1101 04:26:04.549839<PHONE_NUMBER>9680 run_docker.py:258] predict_structure(
I1101 04:26:04.549944<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/run_alphafold.py", line 284, in predict_structure
I1101 04:26:04.550027<PHONE_NUMBER>9680 run_docker.py:258] prediction_result = model_runner.predict(processed_feature_dict,
I1101 04:26:04.550125<PHONE_NUMBER>9680 run_docker.py:258] File "/app/alphafold/alphafold/model/model.py", line 167, in predict
I1101 04:26:04.550212<PHONE_NUMBER>9680 run_docker.py:258] result = self.apply(self.params, jax.random.PRNGKey(random_seed), feat)
I1101 04:26:04.550295<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/random.py", line 177, in PRNGKey
I1101 04:26:04.550378<PHONE_NUMBER>9680 run_docker.py:258] return _return_prng_keys(True, _key('PRNGKey', seed, impl))
I1101 04:26:04.550461<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/random.py", line 139, in _key
I1101 04:26:04.550544<PHONE_NUMBER>9680 run_docker.py:258] return prng.seed_with_impl(impl, seed)
I1101 04:26:04.550626<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 406, in seed_with_impl
I1101 04:26:04.550709<PHONE_NUMBER>9680 run_docker.py:258] return random_seed(seed, impl=impl)
I1101 04:26:04.550791<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 689, in random_seed
I1101 04:26:04.550873<PHONE_NUMBER>9680 run_docker.py:258] return random_seed_p.bind(seeds_arr, impl=impl)
I1101 04:26:04.550955<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 386, in bind
I1101 04:26:04.551036<PHONE_NUMBER>9680 run_docker.py:258] return self.bind_with_trace(find_top_trace(args), args, params)
I1101 04:26:04.551118<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 389, in bind_with_trace
I1101 04:26:04.551198<PHONE_NUMBER>9680 run_docker.py:258] out = trace.process_primitive(self, map(trace.full_raise, args), params)
I1101 04:26:04.551313<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/core.py", line 821, in process_primitive
I1101 04:26:04.551399<PHONE_NUMBER>9680 run_docker.py:258] return primitive.impl(*tracers, **params)
I1101 04:26:04.551482<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 701, in random_seed_impl
I1101 04:26:04.551564<PHONE_NUMBER>9680 run_docker.py:258] base_arr = random_seed_impl_base(seeds, impl=impl)
I1101 04:26:04.551646<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 706, in random_seed_impl_base
I1101 04:26:04.551729<PHONE_NUMBER>9680 run_docker.py:258] return seed(seeds)
I1101 04:26:04.551811<PHONE_NUMBER>9680 run_docker.py:258] File "/opt/conda/lib/python3.10/site-packages/jax/_src/prng.py", line 935, in threefry_seed
I1101 04:26:04.551900<PHONE_NUMBER>9680 run_docker.py:258] return _threefry_seed(seed)
I1101 04:26:04.551984<PHONE_NUMBER>9680 run_docker.py:258] jaxlib.xla_extension.XlaRuntimeError: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.
/var/spool/pbs/mom_priv/jobs/30.ip-10-247-0-20.SC: line 45: fg: no job control

@wttat Same error here, im trying different ways to fix it, will update if get any positive outcome

@wttat Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.6.0.  CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library.  If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration. I1101 04:26:04.532196<PHONE_NUMBER>9680 run_docker.py:258] Traceback (most recent call last):   this explained your issue

Sorry about this, we are working on a fix.
A workaround is to change
jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)

to
isinstance(keys, jax.random.PRNGKeyArray)

in alphafold/model/utils.py.

Sorry about this, we are working on a fix.
A workaround is to change
jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)

to
isinstance(keys, jax.random.PRNGKeyArray)

in alphafold/model/utils.py.

Thanks for your help, but what causes this BUG?
Does this BUG also exists in older version?

Sorry about this, we are working on a fix.
A workaround is to change
jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)

to
isinstance(keys, jax.random.PRNGKeyArray)

in alphafold/model/utils.py.

@Augustin-Zidek Thanks for the reply, but the same error still exists after following your suggestion
def inner(key, shape, **kwargs):     keys = grid_keys(key, shape)     signature = (         '()->()'         if isinstance(keys, jax.random.PRNGKeyArray)         else '(2)->()'     )     return jnp.vectorize(         functools.partial(f, shape=(), **kwargs), signature=signature
after test run, i got if jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key) I1101 22:15:10.496742<PHONE_NUMBER>28320 run_docker.py:258] jax._src.traceback_util.UnfilteredStackTrace: AttributeError: module 'jax.dtypes' has no attribute 'prng_key' I1101 22:15:10.496822<PHONE_NUMBER>28320 run_docker.py:258] I1101 22:15:10.496895<PHONE_NUMBER>28320 run_docker.py:258] The stack trace below excludes JAX-internal frames. I1101 22:15:10.496971<PHONE_NUMBER>28320 run_docker.py:258] The preceding is the original exception that occurred, unmodified. I1101 22:15:10.497047<PHONE_NUMBER>28320 run_docker.py:258] I1101 22:15:10.497123<PHONE_NUMBER>28320 run_docker.py:258] -------------------- I1101 22:15:10.497187<PHONE_NUMBER>28320 run_docker.py:258] I1101 22:15:10.497249<PHONE_NUMBER>28320 run_docker.py:258] The above exception was the direct cause of the following exception:

@wttat Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.6.0. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration. I1101 04:26:04.532196<PHONE_NUMBER>9680 run_docker.py:258] Traceback (most recent call last): this explained your issue

i already update jaxlib to use cudnn8.6 and i did not find anywhere else to use 8.0.5

jaxlib==0.4.14+cuda11.cudnn86 \

Sorry about this, we are working on a fix.
A workaround is to change
jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)

to
isinstance(keys, jax.random.PRNGKeyArray)

in alphafold/model/utils.py.

Thanks for your help, but what causes this BUG? Does this BUG also exists in older version?

Regrettably, i also rerun the command after changing the code in alphafol/model/utils.py file
It came another error:
File "/home/AF2-2023/.local/lib/python3.10/site-packages/haiku/_src/base.py", line 602, in wrapped     return wrapped._current(*args, **kwargs)   File "/home/AF2-2023/.local/lib/python3.10/site-packages/haiku/_src/base.py", line 694, in get_parameter     raise param_missing_error jax._src.traceback_util.UnfilteredStackTrace: ValueError: Unable to retrieve parameter 'scale' for module 'alphafold/alphafold_iteration/evoformer/template_embedding/single_template_embedding/template_embedding_iteration/triangle_multiplication_outgoing/left_norm_input' All parameters must be created as part ofinit`.
The stack trace below excludes JAX-internal frames.
The preceding is the original exception that occurred, unmodified.

The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/AF2-2023/alphafold/alphafold.py", line 572, in
app.run(main)
File "/home/AF2-2023/.local/lib/python3.10/site-packages/absl/app.py", line 312, in run
_run_main(main, args).........  return func(*args, **kwds)
File "/home/AF2-2023/.local/lib/python3.10/site-packages/haiku/_src/module.py", line 306, in run_interceptors
return bound_method(*args, **kwargs)
File "/home/AF2-2023/alphafold/alphafold/model/common_modules.py", line 176, in call
scale = hk.get_parameter(
File "/home/AF2-2023/.local/lib/python3.10/site-packages/haiku/_src/base.py", line 602, in wrapped
return wrapped._current(*args, **kwargs)
File "/home/AF2-2023/.local/lib/python3.10/site-packages/haiku/_src/base.py", line 694, in get_parameter
raise param_missing_error
ValueError: Unable to retrieve parameter 'scale' for module 'alphafold/alphafold_iteration/evoformer/template_embedding/single_template_embedding/template_embedding_iteration/triangle_multiplication_outgoing/left_norm_input' All parameters must be created as part of init.
`
I will rollback the code in utils.py and change my jax&jaxlib (now 0.3.25) version to 0.4.3, and then another error:
    return func(*args, **kwargs)   File "/home/AF2-2023/.local/lib/python3.10/site-packages/jax/_src/dispatch.py", line 1026, in backend_compile     return backend.compile(built_c, compile_options=options) jaxlib.xla_extension.XlaRuntimeError: INTERNAL: RET_CHECK failure (external/org_tensorflow/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc:626) dnn != nullptr
So weird  :(

i used my old fork of alphafold2, it was for running alphafold in aws batch, and I modified its dockerfile to make it works, i believe it was some commit after the fork caused this issue
https://github.com/wttat/alphafold
@@ -53,13 +53,14 @@ RUN wget -q -P /tmp \

 # Install conda packages.
 ENV PATH="/opt/conda/bin:$PATH"
-RUN conda install -qy conda==4.13.0 \
+ENV LD_LIBRARY_PATH="/opt/conda/lib:$LD_LIBRARY_PATH"
+RUN conda install -qy conda==23.5.2 \
     && conda install -y -c conda-forge \
       openmm=7.7.0 \
       cudatoolkit==${CUDA_VERSION} \
       pdbfixer \
       pip \
-      python=3.8 \
+      python=3.10 \
       && conda clean --all --force-pkgs-dirs --yes

 COPY . /app/alphafold
@@ -86,10 +87,8 @@ RUN chmod u+s /sbin/ldconfig.real
 # ENTRYPOINT does not support easily running multiple commands, so instead we
 # write a shell script to wrap them up.
 WORKDIR /app/alphafold
-# RUN echo $'#!/bin/bash\n\
-# ldconfig\n\
-# python /app/alphafold/run_alphafold.py "$@"' > /app/run_alphafold.sh \
-#   && chmod +x /app/run_alphafold.sh
-# ENTRYPOINT ["/app/run_alphafold.sh"]
-COPY run.sh /app/run.sh
-RUN chmod +x /app/run.sh
\ No newline at end of file
+RUN echo $'#!/bin/bash\n\
+ldconfig\n\

Sorry about this, we are working on a fix.
A workaround is to change
jax.dtypes.issubdtype(keys.dtype, jax.dtypes.prng_key)

to
isinstance(keys, jax.random.PRNGKeyArray)

in alphafold/model/utils.py.

Thanks for your help, but what causes this BUG? Does this BUG also exists in older version?

@wttat the cudnn version conflict may be due to the Tensorflow (8.0.5) that Alphafold is using, simply changing the docker file to 0.4.14 would lead to this upper stream version error before the Key attribution error. It works after I changed the docker file back to the default settings (0.3.25) and followed this walkaround to modify the utills.py

Fixed in https://github.com/google-deepmind/alphafold/commit/f715f016d875e2fd8515457af2b12277a716b9b0.
Note that you should not need to bump JAX or CuDNN version, just fork the repo and things should be fine.
@von-elfen

Thanks for your help, but what causes this BUG?

Incompatibility between the JAX version in the Docker container and this change. This change uses code that requires at least JAX 0.4.14, while AlphaFold Docker uses 0.3.25.

Does this BUG also exists in older version?

This change was added in https://github.com/google-deepmind/alphafold/commit/a8875a9c0d7c9e408662a7dcb9bbfa8b72affb1b, so impacts only v2.3.2 at or after this commit.


DocsArchive-<IP_ADDRESS>Auto archive content

Auto archive content from https://github.com/OPS-E2E-PPE/docs-archive-test

Docs Build status updates of commit d135807:
:warning: Validation status: warnings

File
Status
Preview URL
Details

archvieTargetFolder/content/someContent.md
:warning:Warning

Details

.openpublishing.redirection.json
:white_check_mark:Succeeded

archvieTargetFolder/TOC.yml
:white_check_mark:Succeeded

archvieTargetFolder/docfx.json
:white_check_mark:Succeeded

archvieTargetFolder/content/someContent.md

Line 10: [Warning] Invalid file link:(~/content/CoVer.jpg).
[Suggestion] Missing attribute: author. Add the current author's GitHub ID. NOTE: This Suggestion will be elevated to a Warning on 9/1/2019.

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


ditto with tauri

ditto : windows clipboard history management app
tauri : framework for cross-platform apps

there was an ditto with electron IIRC, reusing it might simplify the work.


Add a queryParser property to Property model

It will be used in query-building code to parse values for mlangs in a polymorphic way.

Similar to the way the Java parser works.

Revert this PR when in the process:
Fix bug with reference properties in queries #1886


ETCD Data Management

I was wondering if there is this kind of mechanism in kubernetes old state of data or something that is not in used anymore. So that the data in etcd will be manage.

@billyteves Seems like you've missed several words in your question but you meant "to find old state of data or something that is not in used anymore", right?
I'm also interested but unfortunately not aware of any solution.
Anyone?

Regardless of there are such solutions or not, basically, I won't try to find such "leaks" made by k8s in an etcd cluster until a sign of a leak is observed. I guess k8s is responsible to clean up all the unnecessary data from etcd.
@billyteves Would you mind syncing up with me about why you'd like to do so?


Access-Control-Allow_Origin not enabled for the JSON file, causing part 6 to fail

The JSON part of the tutorial is broken due to a security feature of XMLHttpRequest. I get this error when running part 6 of the tutorial:
XMLHttpRequest cannot load https://www.dartlang.org/codelabs/darrrt/files/piratenames.json. No 'Access-Control-Allow-Origin' header is present on the requested resource. Origin 'http://localhost:8080' is therefore not allowed access.
There's not anything we can necessarily do in this repo, dartlang.org has to be contacted about this so they can fix this.

Hi,
If you change "name_service.dart"
const _namesPath = 'https://www.dartlang.org/codelabs/darrrt/files/piratenames.json';
to
const _namesPath = '_https://www.dartlang.org/f/piratenames.json_';
everything works


bug? Rebindings on main layer ignored with active layout

I just noticed that any bindings in the main layer which are already bound by the layout (so alphanumerics and shift) seem to be ignored by keyd when that layout is active. As bindings of other keys (for example modifiers) are applied, I guess that that's a bug.

I just noticed that any bindings in the main layer which are already bound by the layout (so alphanumerics and shift) seem to be ignored by keyd when that layout is active. As bindings of other keys (for example modifiers) are applied, I guess that that's a bug.

This is a feature, not a bug. The main layer always takes precedence, so it can apply to all layouts.


Look into ember serve EHOSTUNREACH issue

Sometimes you seem to get the bug:
client   | Error: connect EHOSTUNREACH
client   |     at errnoException (net.js:904:11)
client   |     at connect (net.js:766:19)
client   |     at net.js:845:9
client   |     at asyncCallback (dns.js:68:16)
client   |     at Object.onanswer [as oncomplete] (dns.js:121:9)

Source of error has not been identified yet, seems to happen quite randomly so far.
web dev console shows:
WebSocket connection to 'ws://<IP_ADDRESS>:35729/livereload' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED

Seems like this got fixed by 38c1b45.


Set "data" in days

Hello,
is there a way to set some "content" for the single days from Java? I mean, if you have an appointment, to show it in the View as a marker or something? Like an adapter or such, in order to feed the data externally

You can use DayViewDecorators to modify appearances. Look at the BasicActivityDecorated class in the sample application.


Error when defining replit key

It works successfully on replit, but when I use visual studio code, it'll
'NoneType' object does not support item assignment

The REPLIT_DB_URL environment variable must be set to use the database. To test locally, you can copy it from a repl using the shell.

The REPLIT_DB_URL environment variable must be set to use the database. To test locally, you can copy it from a repl using the shell.

what do I input into the shell specifically?

You can echo $REPLIT_DB_URL on replit and then set that as an environment variable locally.

echo $REPLIT_DB_URL

Thanks, in advance.

It still isnt working, do I set replit.database.db_url to the received url?

No, you likely aren't setting the environment variable correctly.
On Sun, Mar 6, 2022, 11:37 AM k0vu @.***> wrote:

It still isnt working, do I set replit.database.db_url to the received
url?
—
Reply to this email directly, view it on GitHub
https://github.com/replit/replit-py/issues/122#issuecomment-1060025958,
or unsubscribe
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Fix auto-align of selected item when combo-box is opened

Fixes #2745
Additional description
This PR should fix the auto-align issue which affects sub-components like the date-picker. The item which is already selected should appear on the bottom of the combo-box view when it's opened.
CONTRIBUTOR checklist (do not remove)
Please complete for every pull request

[x] First-time contributors should sign the Contributor License Agreement.  If you haven't before, wait a few minutes and a bot will comment on this pull request with instructions.
[x] npm run lint:fix has been run and linting passes.
[ ] Mocha, Jest (Storyshots), and components/component-docs.json CI tests pass (npm test).
[ ] Tests have been added for new props to prevent regressions in the future. See readme.
[x] Review the appropriate Storybook stories. Open http://localhost:9001/.
[x] Review tests are passing in the browser. Open http://localhost:8001/.
[x] Review markup conforms to SLDS by looking at DOM snapshot strings.

REVIEWER checklist (do not remove)

[ ] TravisCI tests pass. This includes linting, Mocha, Jest, Storyshots, and components/component-docs.json tests.
[ ] Tests have been added for new props to prevent regressions in the future. See readme.
[ ] Review the appropriate Storybook stories. Open http://localhost:9001/.
[ ] The Accessibility panel of each Storybook story has 0 violations (aXe). Open http://localhost:9001/.
[ ] Review tests are passing in the browser. Open http://localhost:8001/.
[ ] Review markup conforms to SLDS by looking at DOM snapshot strings.

Required only if there are markup / UX changes

[ ] Add year-first date and commit SHA to last-slds-markup-review in package.json and push.
[ ] Request a review of the deployed Heroku app by the Salesforce UX Accessibility Team.
[ ] Add year-first review date, and commit SHA, last-accessibility-review, to package.json and push.
[ ] While the contributor's branch is checked out, run npm run local-update within locally cloned site repo to confirm the site will function correctly at the next release.

The focus part is looking good. Please update the activeIndex to the selection, so that the keyboard navigation works. Pressing down should start at the selected item and move the gray from there. Currently it starts at item 0 (at the top).

The focus part is looking good. Please update the activeIndex to the selection, so that the keyboard navigation works. Pressing down should start at the selected item and move the gray from there. Currently it starts at item 0 (at the top).

@interactivellama, Updated the code. Although it fixes the keyboard navigation, it causes two test-cases failures in the file components/expression/__tests__/expression.browser-test.jsx on lines 114 and 127. On exploring the cases, it seems they both are related to keyboard focus and down events. I think the ideal values should be equal to ResourceList[0] because of the behavior. Please correct me if I'm wrong.

@interactivellama, Updated the code. Although it fixes the keyboard navigation, it causes two test-cases failures in the file components/expression/__tests__/expression.browser-test.jsx on lines 114 and 127. On exploring the cases, it seems they both are related to keyboard focus and down events. I think the ideal values should be equal to ResourceList[0] because of the behavior. Please correct me if I'm wrong.

bump

bump

I'll take a look at this today.

I'll take a look at this today.

Excellent find! @chaitanyakaul Yes, Expression's tests should be updated to conform to the new behavior.

Excellent find! @chaitanyakaul Yes, Expression's tests should be updated to conform to the new behavior.


updates for FOSS Fund

This PR contains:

some updates to existing FOSS Fund information (mostly links)
Addition of FOSS Fund winners 18 - 23
logos for each

Closes #194
Thanks for the contributions on the original PR from:

balajik10
Aaron-Junker
aisgbnok

@JustinGOSSES :shipit: !


Node 4 Support and emit events

Codecov Report

Merging #1 into master will increase coverage by 6.16%.
The diff coverage is 59.48%.

@@            Coverage Diff             @@
##           master       #1      +/-   ##
==========================================
+ Coverage   53.33%   59.49%   +6.16%
==========================================
  Files          21       21
  Lines         405      437      +32
==========================================
+ Hits          216      260      +44
+ Misses        189      177      -12

Impacted Files
Coverage Δ

src/devices/data/base.js
100% <ø> (ø)

src/index.js
100% <ø> (ø)

src/devices/hs110.js
100% <ø> (ø)

src/devices/lb130.js
100% <ø> (ø)

src/devices/data/hs100.js
100% <ø> (ø)

src/devices/lb120.js
100% <ø> (ø)

src/devices/data/hs105.js
100% <ø> (ø)

src/devices/hs200.js
100% <ø> (ø)

src/devices/data/hs110.js
100% <ø> (ø)

src/utils.js
62.68% <ø> (ø)

... and 13 more

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Buttons: Text color on  elements

http://jsfiddle.net/x1hphsvb/4594/
The text color differs between <button> and <a> elements. Is this a bug or default behavior?
I know a workaround by specifying some extra css like:
a.btn-outline-danger {
  color:red !important;
}
a.btn-outline-danger:hover {
  color:white !important;
}

Hi @FabianKoestring,
The href attribute is missing from the links


ENH Add CUDA 11.1.1 support

Checklist

[x] Used a personal fork of the feedstock to propose changes
[ ] Bumped the build number (if the version is unchanged)
[x] Reset the build number to 0 (if the version changed)
[x] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[x] Ensured the license file is being packaged.

This follows AnacondaRecipes/cudatoolkit-feedstock#13 which was for 11.1.0 but has not been reviewed yet
The idea is to get 11.1 out ASAP through conda-forge for those who need it, and Anaconda can copy this package into the defaults channel later
Given this is a different recipe approach from the PR above, I welcome feedback to know how to best contribute new versions in the future
cc @jakirkham @scopatz @raydouglass @kkraus14

Hi! This is the friendly automated conda-forge-linting service.
I wanted to let you know that I linted all conda-recipes in your PR (recipe) and found some lint.
Here's what I've got...
For recipe:

The recipe license should not include the word "License".

For recipe:

License is not an SPDX identifier (or a custom LicenseRef) nor an SPDX license expression.

Documentation on acceptable licenses can be found here.

@conda-forge-admin, please rerender

This looks incomplete.
See CI logs,
WARNING (cudatoolkit,lib/libaccinj64.so.11.1.105): $RPATH/libcupti.so.11.1 not found in packages, sysroot(s) nor the missing_dso_whitelist.
2020-11-05T19:47:11.2526306Z .. is this binary repackaging?

I just checked the 11.0.3 conda pkg and it does not have libcupti.so either but I don't have the commit to reference to check the results to see if there is a warning there as well
libcupti.so is in a non-standard folder location so there will need to be a special copy command for it - I won't have time until tomorrow to try and extract this on a linux system to match the folders properly and to patch it. If anyone has a suggestion on the fix before then LMK

This looks incomplete.
See CI logs,
WARNING (cudatoolkit,lib/libaccinj64.so.11.1.105): $RPATH/libcupti.so.11.1 not found in packages, sysroot(s) nor the missing_dso_whitelist.
2020-11-05T19:47:11.2526306Z .. is this binary repackaging?

I just checked the 11.0.3 conda pkg and it does not have libcupti.so either but I don't have the commit to reference to check the results to see if there is a warning there as well

Just checked, it shows up in that build as well. Here's a CI build for reference and its log file (for posterity).

Should we merge this in and address the issue(s) in a follow up before moving off of the test label?

Closing this and will open a new PR once #31 merges


Add support for parallels provider if it exists.

Hi @rterbush, I'm sorry I've let this PR languish. I've come back to this many times. I think I'm going to close this PR rather than merge it. Basically, my reason is that I want to keep this example as simple as possible. While I really like the idea of having Parallels support in general, I think it will add a bit more complexity to the Vagrantfile than I'd like. Since this is a salt-vagrant-demo I think I'll keep it Vagrant-centric for now and keep things as simple as possible.
Thanks so much for your PR and the time you put into it. I'd be open to having another repo that includes more complicated examples if you think that would be helpful. Let me know what you think.

Dave,
Just to make a point here, using Parallels instead of Virtualbox for the VM
has nothing to do with Vagrant itself. Parallels is just another VM
provider for use with Vagrant so I don't think making the Vagrantfile
portable to support a common Mac VM software would deviate from the goal of
a "vagrant-salt" demo. The changes I made, determine what VM service you
have available on the computer you are running on. It uses Parallels if
available, otherwise it falls back to Virtualbox. The Vagrantfile does not
require any changes or configuration to for use on any operating system
with Vagrant installed.
If that really does overcomplicate it for folks, it would at least be worth
dropping it in a contrib folder for those of us that prefer to use
Parallels.
--
Randy
On Fri, Jun 10, 2016 at 3:04 PM, David Boucha<EMAIL_ADDRESS>wrote:

Hi @rterbush https://github.com/rterbush, I'm sorry I've let this PR
languish. I've come back to this many times. I think I'm going to close
this PR rather than merge it. Basically, my reason is that I want to keep
this example as simple as possible. While I really like the idea of having
Parallels support in general, I think it will add a bit more complexity to
the Vagrantfile than I'd like. Since this is a salt-vagrant-demo I think
I'll keep it Vagrant-centric for now and keep things as simple as possible.
Thanks so much for your PR and the time you put into it. I'd be open to
having another repo that includes more complicated examples if you think
that would be helpful. Let me know what you think.
—
You are receiving this because you were mentioned.
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Still isn't working after the update

The error message goes fast, the only thing I could read was some x64 string

Use print screen button to capture the error message....


Cask request: Mockuuups Studio

Cask details
Name: Mockuuups Studio
Homepage: https://mockuuups.studio/
Download URL: https://mockuuups.studio/download/mac
Description: Drag-and-drop tool for creating beautiful app previews and marketing materials.

https://github.com/caskroom/homebrew-cask/blob/master/Casks/mockuuups-studio.rb


Publish ground truth odometry from Isaac

MAVROS publishes a simulated noisy odometry. Let's also publish the ground truth for people to use.

requires articulation


added extra assertions and checks for MMFiles index consistency

Scope & Purpose
Added assertions and checks for MMFiles index consistency. These changes will have effect in maintainer mode only.
Additionally, add log messages in case MMFiles index operations go wrong.
Testing & Verification
This change is already covered by existing tests, such as (please describe tests).
http://<IP_ADDRESS>:8080/view/PR/job/arangodb-matrix-pr/8431/

http://<IP_ADDRESS>:8080/view/PR/job/arangodb-matrix-pr/8433/

http://<IP_ADDRESS>:8080/view/PR/job/arangodb-matrix-pr/8457/

http://jenkins.arangodb.biz:8080/job/arangodb-matrix-pr-linux-nonmaintainercompile/8198/

Tests blue.


[Workers for Platforms] Update note for unsupported outbound workers use cases

Outbound Workers do indeed intercept fetch requests from service bindings - and after the support for namespaced-script-implemented Durable Objects, a limitation is that, while you can invoke Durable Objects correctly, an Outbound Worker will not intercept outbound fetches made from that DO

@matthewdavidrodgers is it okay to merge this? 😄

good by me!