How long does it take on T1050 (779 residues) #6
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Hi Kui, this is hard to answer without more context, especially without knowing the speed of your CPU and your hard drive (whether it is an SSD or an HDD). But in general, you can expect the time to grow with the length of the protein and the MSA search taking up to a few hours with a slow disk / CPU. For the actual folding (i.e. running the AlphaFold model), the disk speed doesn't matter anymore, what matters is whether you are using a GPU and its performance. |
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* fix memory leaks various edits to fix memory leaks memory leak fix * v2.3.4 - fix memory leaks another attempt to fix memory leaks! * Update config.py * bugfix - num-ensemble
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* Update residue_constants.py * Lower log level for unimportant message * Apply config patch * Apply model patch * Apply patch * Apply protein patch * - Remove seen_sequence from make_msa_features to avoid paired sequences to be removed - change Protein.to_pdb back to default implementation * Change name for publishing * Comment _merge_homomers_dense_msa to improve monomer prediction * Turn on dense MSA again * Bump to 2.1.1 * Rebase fallout * Update repo url * Speed up param loading * Bump to 2.1.2 * Publish form github actions * Stop at score for recycles * Make stop_at_score float for google-deepmind#119 * Show num_recycles for multimer * Bump to 2.1.4 * Set max_subsequence_ratio to 1.0 * Update version to 2.1.6 * Update setup.py * Remove duplicate, template_sequence len and align_ratio check * Update setup.py to 2.1.8 * Try to improve compiling * Update setup.py * Update setup.py * Change folding_multimer to compile faster * Change return type in multimer * Update modules.py * Update modules.py * Update modules.py * adding recycling outside of the jax compiled code * fixing typo * fixing typos * fixing typos * typo * Update modules_multimer.py * Update modules.py * Update model.py * adding manual recycling support for multimers * fixing recycle count * Update modules_multimer.py * rm key subsampling * debugging * debugging * Update modules_multimer.py * Update model.py * cleaning up the code * typo * Cond fix for older jax version * Add recycle early-stopping to the model * Update repository to 2.1.10 * fixing the missing key in fold_iteration * Update version to 2.1.11 * bugfix: removing nans from alphafold-multimer+templates google-deepmind#513 * Update to 2.1.12 * Update model.py * Update model.py * Add 2.1.13 (fix ensemble) * Update setup.py * Workaround for breaking change in PDB.PDBIO v1.80 not working correctly with StringIO * Actually fix PDBIO issue * adding support for fused triangle attention to monomers (google-deepmind#2) * adding option to "unfuse" * Update utils.py * Update utils.py * Squashed commit of the following: commit a394167 Author: Augustin Zidek <augustinzidek@google.com> Date: Thu Jan 12 03:05:53 2023 -0800 Fix typo in a comment in residue constants. PiperOrigin-RevId: 501515486 Change-Id: I2a86a64ebbf0ab8222689268755ba3b7780878e5 commit d6d2fc0 Author: Hamish Tomlinson <htomlinson@google.com> Date: Wed Jan 11 09:05:42 2023 -0800 Bump version to 2.3.1 PiperOrigin-RevId: 501297397 Change-Id: Ic1bb166581047e0e8c46845f41f8c55c10f32ef9 commit e3231de Author: DeepMind <noreply@google.com> Date: Wed Jan 11 07:41:58 2023 -0800 Update of README.md PiperOrigin-RevId: 501279176 Change-Id: I9cf92212322b29691844973ded9e337e81b3a9fd commit 8f1ebd5 Author: Ali Cowen-Rivers <alexcr@google.com> Date: Tue Jan 10 15:08:44 2023 -0800 Fix GPU relax for longer chains by pinning large memory ops to cpu. PiperOrigin-RevId: 501105389 Change-Id: I6c981d1d3231e008ebae192edb4586479eb5eb34 commit 420fb08 Author: Ali Cowen-Rivers <alexcr@google.com> Date: Fri Dec 23 05:51:21 2022 -0800 Adding recycle information for timings. PiperOrigin-RevId: 497360470 Change-Id: I3a9f3ba608ac3ceeaaccfe281a82d43fae1d265e commit a9e5451 Author: Ali Cowen-Rivers <alexcr@google.com> Date: Wed Dec 21 09:14:48 2022 -0800 Adding prediction timings to README. PiperOrigin-RevId: 496937349 Change-Id: I9e9f447b3ce11d1b5a5c7433aeae7b03a3ed19e9 commit f96e254 Author: Josh Abramson <jabramson@google.com> Date: Wed Dec 21 04:02:33 2022 -0800 Add `eval_dropout` option for using dropout in trunk at eval time. PiperOrigin-RevId: 496885301 Change-Id: I42de2dd13784e2b358320349398a3fc88ee0d708 commit 4b726a2 Author: Augustin Zidek <augustinzidek@google.com> Date: Tue Dec 20 05:31:19 2022 -0800 Speed up Colab multimer MSA search: fetch each db chunk only once and run all queries against it. PiperOrigin-RevId: 496636186 Change-Id: I0a428b6269f8e1bcb1a6efb33cad2fc70b0d1f35 commit b21167b Author: Peter Hawkins <phawkins@google.com> Date: Mon Dec 19 07:41:04 2022 -0800 [NumPy] Remove references to deprecated NumPy type aliases. This change replaces references to a number of deprecated NumPy type aliases (np.bool, np.int, np.float, np.complex, np.object, np.str) with their recommended replacement (bool, int, float, complex, object, str). NumPy 1.24 drops the deprecated aliases, so we must remove uses before updating NumPy. PiperOrigin-RevId: 496396651 Change-Id: Ifbf86edb8c7ba3bf1a427c1b5276e8eb33041ab9 commit e80e252 Author: Andrew Cowie <cowie@google.com> Date: Thu Dec 15 08:43:30 2022 -0800 Restore the max sequence limit to 4000 now that unified memory is fixed. PiperOrigin-RevId: 495602713 Change-Id: I091644565b3bbb2e95ee434c6cd80d6d54d677dc commit ed3ecab Author: Augustin Zidek <augustinzidek@google.com> Date: Thu Dec 15 03:50:14 2022 -0800 Add query_multiple to Jackhmmer. This enables searching with multiple queries against a db chunk without having to re-download it each time. PiperOrigin-RevId: 495553498 Change-Id: I5e3df1cc31cdcef591a1516797f0372f171e413c commit dd643b1 Author: Peter Hawkins <phawkins@google.com> Date: Thu Dec 15 03:01:48 2022 -0800 [NumPy] Remove references to deprecated NumPy type aliases. This change replaces references to a number of deprecated NumPy type aliases (np.bool, np.int, np.float, np.complex, np.object, np.str) with their recommended replacement (bool, int, float, complex, object, str). NumPy 1.24 drops the deprecated aliases, so we must remove uses before updating NumPy. PiperOrigin-RevId: 495546406 Change-Id: Iaf1bfd2000fee1bffada5138ae16ec192916c076 commit a0b0cd9 Author: Andrew Cowie <cowie@google.com> Date: Tue Dec 13 08:26:52 2022 -0800 Set environment variables before any other code is executed. PiperOrigin-RevId: 495028896 Change-Id: I3b1a6ddceca1961bffdaba443e7db47bbfbc4fad * set eval_dropout when user specifies is_training * Update model.py * adjust config to revert back to old settings for multimer v1/v2 * enable fuse for all models by default * bugfix: typo in config (v1, v2 multimer settings) * add use_cluster_profile and recycle_early_stop_tolerance support * adding recycle_early_stop_tolerance support for monomers * add bfloat16 support for monomers * Update model.py * Create stereo_chemical_props.txt * Update setup.py * fix templates * Create MANIFEST.in * fix stereo_chemical_props.txt path * speedup attempt to speedup the function by removing ensembles when num_ensembles=1 * adding mask to ptm/iptm calculation important for batch compute that uses masking * adding iptm support for ptm models * Update modules.py * moving key splitting to model.py * keep raw outputs * Update model.py * Update modules.py * allow for no batch inputs * v2.3.3 - fixing memory leaks * fix memory leaks * use bfloat16 for representations * move confidence compute inside module.py * move multimer key splitting to model.py * v2.3.4 - fix memory leaks (attempt 2) (google-deepmind#6) * fix memory leaks various edits to fix memory leaks memory leak fix * v2.3.4 - fix memory leaks another attempt to fix memory leaks! * Update config.py * bugfix - num-ensemble * Update modules.py * add option to provide custom offset * Update config.py * Update config.py * Update OpenMM imports to work with new OpenMM API * patch for jax > 0.3.25 * fix single representation msa_activation is (N,L,256) in colabfold v1.5.2 we return msa_activation[0] as our single representation vector looks like there is one extra linear layer to convert msa_activations[0] to single_activation:  If anything the (L,256) representation might be better, as you might be losing some information by doing the extra transformation at the end. But since people are asking, I'm adding the transformation back so that the output is (L,386). typo * Update modules_multimer.py --------- Co-authored-by: konstin <konstin@mailbox.org> Co-authored-by: Martin Steinegger <themartinsteinegger@gmail.com> Co-authored-by: Martin Steinegger <martin.steinegger@mpibpc.mpg.de> Co-authored-by: Sergey O <krypton@uw.edu> Co-authored-by: Sergey O <so@fas.harvard.edu> Co-authored-by: Milot Mirdita <milot@mirdita.de> Co-authored-by: Sergey O <kingsergey@gmail.com>
dingquanyu
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Mar 27, 2024
…cles now allow monomeric modellign to run with num_recycle larger than 3
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It is still in extracting MSA.
Could anyone share the running time on T1050 (779 residues)? Thanks very much!
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