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Documentation updates - older GPUs and JAX installation

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PiperOrigin-RevId: 697634146
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Augustin Zidek
2024-11-18 16:21:14 +00:00
parent b618d2d576
commit 4b2b567103
3 changed files with 19 additions and 6 deletions
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6
docs/installation.md
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@@ -13,7 +13,11 @@ we recommend running with at least 64 GB of RAM.
We provide installation instructions for a machine with an NVIDIA A100 80 GB GPU
and a clean Ubuntu 22.04 LTS installation, and expect that these instructions
should aid others with different setups.
should aid others with different setups. If you are installing locally outside
of a Docker container, please ensure CUDA, cuDNN, and JAX are correctly
installed; the
[JAX installation documentation](https://jax.readthedocs.io/en/latest/installation.html#nvidia-gpu)
is a useful reference for this case.
The instructions provided below describe how to:

8
docs/known_issues.md
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@@ -1 +1,9 @@
# Known Issues
### Devices other than NVIDIA A100 or H100
There are currently known unresolved numerical issues with using devices other
than NVIDIA A100 and H100. For now, accuracy has only been validated for A100
and H100 GPU device types. See
[this Issue](https://github.com/google-deepmind/alphafold3/issues/59) for
tracking.

11
docs/performance.md
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@@ -87,12 +87,13 @@ AlphaFold 3 can run on inputs of size up to 4,352 tokens on a single NVIDIA A100
While numerically accurate, this configuration will have lower throughput
compared to the set up on the NVIDIA A100 (80 GB), due to less available memory.
#### NVIDIA V100 (16 GB)
#### Devices other than NVIDIA A100 or H100
While you can run AlphaFold 3 on sequences up to 1,280 tokens on a single NVIDIA
V100 using the flag `--flash_attention_implementation=xla` in
`run_alphafold.py`, this configuration has not been tested for numerical
accuracy or throughput efficiency, so please proceed with caution.
There are currently known unresolved numerical issues with using devices other
than NVIDIA A100 and H100. For now, accuracy has only been validated for A100
and H100 GPU device types. See
[this Issue](https://github.com/google-deepmind/alphafold3/issues/59) for
tracking.
## Compilation Buckets