Issue when running the basic script

Hi,

I am fairly new to the Ubuntu world. I have installed RFdiffusion following all the steps indicated in the github repository for this package, and I did not get any errors.

However, when running:

./scripts/run_inference.py 'contigmap.contigs=[150-150]' inference.output_prefix=test_outputs/test inference.num_designs=10

I got the following error:

(SE3nv) apinedal@ubuntu:~/opt/RFdiffusion$ ./scripts/run_inference.py 'contigmap.contigs=[150-150]' inference.output_prefix=test_outputs/test inference.num_designs=10
DGL backend not selected or invalid. Assuming PyTorch for now.
Setting the default backend to "pytorch". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)
[2023-12-06 13:03:34,473][main][INFO] - ////////////////////////////////////////////////
[2023-12-06 13:03:34,473][main][INFO] - ///// NO GPU DETECTED! Falling back to CPU /////
[2023-12-06 13:03:34,473][main][INFO] - ////////////////////////////////////////////////
Reading models from /home/apinedal/opt/RFdiffusion/rfdiffusion/inference/../../models
[2023-12-06 13:03:34,474][rfdiffusion.inference.model_runners][INFO] - Reading checkpoint from /home/apinedal/opt/RFdiffusion/rfdiffusion/inference/../../models/Base_ckpt.pt
This is inf_conf.ckpt_path
/home/apinedal/opt/RFdiffusion/rfdiffusion/inference/../../models/Base_ckpt.pt
Assembling -model, -diffuser and -preprocess configs from checkpoint
USING MODEL CONFIG: self._conf[model][n_extra_block] = 4
USING MODEL CONFIG: self._conf[model][n_main_block] = 32
USING MODEL CONFIG: self._conf[model][n_ref_block] = 4
USING MODEL CONFIG: self._conf[model][d_msa] = 256
USING MODEL CONFIG: self._conf[model][d_msa_full] = 64
USING MODEL CONFIG: self._conf[model][d_pair] = 128
USING MODEL CONFIG: self._conf[model][d_templ] = 64
USING MODEL CONFIG: self._conf[model][n_head_msa] = 8
USING MODEL CONFIG: self._conf[model][n_head_pair] = 4
USING MODEL CONFIG: self._conf[model][n_head_templ] = 4
USING MODEL CONFIG: self._conf[model][d_hidden] = 32
USING MODEL CONFIG: self._conf[model][d_hidden_templ] = 32
USING MODEL CONFIG: self._conf[model][p_drop] = 0.15
USING MODEL CONFIG: self._conf[model][SE3_param_full] = {'num_layers': 1, 'num_channels': 32, 'num_degrees': 2, 'n_heads': 4, 'div': 4, 'l0_in_features': 8, 'l0_out_features': 8, 'l1_in_features': 3, 'l1_out_features': 2, 'num_edge_features': 32}
USING MODEL CONFIG: self._conf[model][SE3_param_topk] = {'num_layers': 1, 'num_channels': 32, 'num_degrees': 2, 'n_heads': 4, 'div': 4, 'l0_in_features': 64, 'l0_out_features': 64, 'l1_in_features': 3, 'l1_out_features': 2, 'num_edge_features': 64}
USING MODEL CONFIG: self._conf[model][freeze_track_motif] = False
USING MODEL CONFIG: self._conf[model][use_motif_timestep] = True
USING MODEL CONFIG: self._conf[diffuser][T] = 50
USING MODEL CONFIG: self._conf[diffuser][b_0] = 0.01
USING MODEL CONFIG: self._conf[diffuser][b_T] = 0.07
USING MODEL CONFIG: self._conf[diffuser][schedule_type] = linear
USING MODEL CONFIG: self._conf[diffuser][so3_type] = igso3
USING MODEL CONFIG: self._conf[diffuser][crd_scale] = 0.25
USING MODEL CONFIG: self._conf[diffuser][so3_schedule_type] = linear
USING MODEL CONFIG: self._conf[diffuser][min_b] = 1.5
USING MODEL CONFIG: self._conf[diffuser][max_b] = 2.5
USING MODEL CONFIG: self._conf[diffuser][min_sigma] = 0.02
USING MODEL CONFIG: self._conf[diffuser][max_sigma] = 1.5
USING MODEL CONFIG: self._conf[preprocess][sidechain_input] = False
USING MODEL CONFIG: self._conf[preprocess][motif_sidechain_input] = True
USING MODEL CONFIG: self._conf[preprocess][d_t1d] = 22
USING MODEL CONFIG: self._conf[preprocess][d_t2d] = 44
USING MODEL CONFIG: self._conf[preprocess][prob_self_cond] = 0.5
USING MODEL CONFIG: self._conf[preprocess][str_self_cond] = True
USING MODEL CONFIG: self._conf[preprocess][predict_previous] = False
[2023-12-06 13:03:35,240][rfdiffusion.inference.model_runners][INFO] - Loading checkpoint.
[2023-12-06 13:03:36,615][rfdiffusion.diffusion][INFO] - Calculating IGSO3.
Successful diffuser init
[2023-12-06 13:03:40,589][main][INFO] - Making design test_outputs/test_0
[2023-12-06 13:03:40,591][rfdiffusion.inference.model_runners][INFO] - Using contig: ['150-150']
With this beta schedule (linear schedule, beta_0 = 0.04, beta_T = 0.28), alpha_bar_T = 0.00013696048699785024
[2023-12-06 13:03:40,599][rfdiffusion.inference.model_runners][INFO] - Sequence init: ------------------------------------------------------------------------------------------------------------------------------------------------------
Error executing job with overrides: ['contigmap.contigs=[150-150]', 'inference.output_prefix=test_outputs/test', 'inference.num_designs=10']
Traceback (most recent call last):
File "/home/apinedal/opt/RFdiffusion/./scripts/run_inference.py", line 94, in main
px0, x_t, seq_t, plddt = sampler.sample_step(
File "/home/apinedal/opt/RFdiffusion/rfdiffusion/inference/model_runners.py", line 664, in sample_step
msa_prev, pair_prev, px0, state_prev, alpha, logits, plddt = self.model(msa_masked,
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/apinedal/opt/RFdiffusion/rfdiffusion/RoseTTAFoldModel.py", line 102, in forward
msa, pair, R, T, alpha_s, state = self.simulator(seq, msa_latent, msa_full, pair, xyz[:,:,:3],
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/apinedal/opt/RFdiffusion/rfdiffusion/Track_module.py", line 420, in forward
msa_full, pair, R_in, T_in, state, alpha = self.extra_block[i_m](msa_full,
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/apinedal/opt/RFdiffusion/rfdiffusion/Track_module.py", line 332, in forward
R, T, state, alpha = self.str2str(msa, pair, R_in, T_in, xyz, state, idx, motif_mask=motif_mask, top_k=0)
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/cuda/amp/autocast_mode.py", line 141, in decorate_autocast
return func(args, **kwargs)
File "/home/apinedal/opt/RFdiffusion/rfdiffusion/Track_module.py", line 266, in forward
shift = self.se3(G, node.reshape(BL, -1, 1), l1_feats, edge_feats)
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/apinedal/opt/RFdiffusion/rfdiffusion/SE3_network.py", line 83, in forward
return self.se3(G, node_features, edge_features)
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/se3_transformer-1.0.0-py3.9.egg/se3_transformer/model/transformer.py", line 140, in forward
basis = basis or get_basis(graph.edata['rel_pos'], max_degree=self.max_degree, compute_gradients=False,
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/se3_transformer-1.0.0-py3.9.egg/se3_transformer/model/basis.py", line 166, in get_basis
with nvtx_range('spherical harmonics'):
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/contextlib.py", line 119, in enter
return next(self.gen)
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/cuda/nvtx.py", line 59, in range
range_push(msg.format(*args, **kwargs))
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/cuda/nvtx.py", line 28, in range_push
return _nvtx.rangePushA(msg)
File "/home/apinedal/miniconda3/envs/SE3nv/lib/python3.9/site-packages/torch/cuda/nvtx.py", line 9, in _fail
raise RuntimeError("NVTX functions not installed. Are you sure you have a CUDA build?")
RuntimeError: NVTX functions not installed. Are you sure you have a CUDA build?

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

...

Somehow, it does not detect I have a gpu, and also I get these other errors.

Please, I would appreciate any suggestions as to how to solve these issues.

Best,

Antonio

Start with scripts/run_inference.py and trace the reported execution into rfdiffusion/Track_module.py and rfdiffusion/SE3_network.py. Reproduce the basic command while checking the GPU-detection path and the reported NVTX failure. Done means the command completes without the reported runtime error on the supported environment, with a regression test or documented reproduction if the payload supports one.