I'd like to test HelixFold3, but I encountered this issue during the inference:
input_embedder:
atom_encoder:
atom_transformer:
diffusion_transformer:
a_channel_name: atom_channel
n_block: 3
n_head: 4
s_channel_name: atom_channel
z_channel_name: atom_pair_channel
n_key: 128
n_query: 32
in_token_channel_name: token_channel
out_token_channel_name: token_channel
use_dense_mode: true
relative_position_encoding:
relative_chain_max: 2
relative_token_max: 32
num_recycle: 3
resample_msa_in_recycling: true
W0908 01:42:33.273294 2256060 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 8.6, Driver API Version: 12.4, Runtime API Version: 12.0
W0908 01:42:33.324625 2256060 gpu_resources.cc:149] device: 0, cuDNN Version: 8.7.
Load pretrain model from /mnt/database/helixfold3/HelixFold3-params-240814/HelixFold3-240814.pdparams
============ Data Loading ============
Traceback (most recent call last):
File "/home/apps/PaddleHelix/apps/protein_folding/helixfold3/inference.py", line 637, in <module>
main(args)
File "/home/apps/PaddleHelix/apps/protein_folding/helixfold3/inference.py", line 496, in main
feature_dict = feature_processing_aa.process_input_json(
File "/home/apps/PaddleHelix/apps/protein_folding/helixfold3/infer_scripts/feature_processing_aa.py", line 398, in process_input_json
ccd_preprocessed_dict = load_ccd_dict(ccd_preprocessed_path)
File "/home/apps/PaddleHelix/apps/protein_folding/helixfold3/infer_scripts/feature_processing_aa.py", line 43, in load_ccd_dict
ccd_preprocessed_dict = pickle.load(fp)
_pickle.UnpicklingError: unpickling stack underflow
If anyone could solve this issue, I would be very grateful.
My environment:
OS: Rocky linux 8.9
GPU: RTX3090
CUDA: 11.8 and 12.4 tested
cudnn: 8.7 was installed using dnf -y install libcudnn8-8.7.0.84-1.cuda11.8.x86_64, and created symlink to /usr/lib64/libcudnn.so from /usr/lib64/libcudnn.so.8.
I'd like to test HelixFold3, but I encountered this issue during the inference:
If anyone could solve this issue, I would be very grateful.
My environment:
dnf -y install libcudnn8-8.7.0.84-1.cuda11.8.x86_64
, and created symlink to/usr/lib64/libcudnn.so
from/usr/lib64/libcudnn.so.8
.Installation procedure:
Script: