frozenleaves/alphafold3
1
0
Fork 0
mirror of https://github.com/google-deepmind/alphafold3.git synced 2025-10-20 21:33:47 +08:00
Code Issues Packages Projects Releases Wiki Activity

Improve documentation on running data pipeline and inference separately

Browse Source 
Prompted by https://github.com/google-deepmind/alphafold3/issues/485.

PiperOrigin-RevId: 789700619
Change-Id: I0e876a1130cc537421e8916fcad1230302196b64
This commit is contained in:
Augustin Zidek
2025-08-01 03:56:12 -07:00
committed by Copybara-Service
parent 3c4de6bb9b
commit 903a931d36
1 changed files with 20 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
docs/performance.md
View File

@ -4,12 +4,16 @@
The runtime of the data pipeline (i.e. genetic sequence search and template
search) can vary significantly depending on the size of the input and the number
of homologous sequences found, as well as the available hardware (disk speed can
influence genetic search speed in particular). If you would like to improve
performance, it’s recommended to increase the disk speed (e.g. by leveraging a
RAM-backed filesystem), or increase the available CPU cores and add more
parallelisation. Also note that for sequences with deep MSAs, Jackhmmer or
Nhmmer may need a substantial amount of RAM beyond the recommended 64 GB of RAM.
of homologous sequences found, as well as the available hardware – the disk
speed can influence genetic search speed in particular.
If you would like to improve performance, it's recommended to increase the disk
speed (e.g. by leveraging a RAM-backed filesystem), or increase the available
CPU cores and add more parallelisation. This can help because AlphaFold 3 runs
genetic search against 4 databases in parallel, so the optimal number of cores
is the number of cores used for each Jackhmmer process times 4. Also note that
for sequences with deep MSAs, Jackhmmer or Nhmmer may need a substantial amount
of RAM beyond the recommended 64 GB of RAM.
## Model Inference
@ -40,9 +44,14 @@ utilisation. This can be useful for:
1. Splitting the CPU-only data pipeline from model inference (which requires a
GPU), to optimise cost and resource usage.
1. Caching the results of MSA/template search, then reusing the augmented JSON
for multiple different inferences across seeds or across variations of other
features (e.g. a ligand).
1. Generating the JSON output file from the data pipeline only run and then
using it for multiple different inference only runs across seeds or across
variations of other features (e.g. a ligand or a partner chain).
1. Generating the JSON output for multiple individual monomer chains (e.g. for
chains A, B, C, D), then running the inference on all possible chain pairs
(AB, AC, AD, BC, BD, CD) by creating dimer JSONs by merging the monomer
JSONs. By doing this, the MSA and template search need to be run just 4
times (once for each chain), instead of 12 times.
### Data Pipeline Only
@ -56,7 +65,8 @@ directly used as input for running inference.
Launch `run_alphafold.py` with `--norun_data_pipeline` to skip the data pipeline
and run only featurisation and model inference. This stage requires the input
JSON file to contain pre-computed MSAs and templates.
JSON file to contain pre-computed MSAs and templates (or they must be explicitly
set to empty if you want to run MSA and template free).
## Accelerator Hardware Requirements