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The idea of the code below is to run a custom version of SchNet on SMILES representations of molecules. Code:
print("Importing packages...")
importtorchimporttorch.nn.functionalasFfromtorch_geometric.loaderimportDataLoaderfromtorch_geometric.datasetsimportQM9fromtorch_geometric.nnimportSchNetfromtqdmimporttqdmimportpickleimportosprint("Defining functions...")
# Define a function to convert SMILES to PyG data objectsdefsmiles_to_pyg_graph(smiles):
fromrdkitimportChemfromrdkit.ChemimportAllChemfromtorch_geometric.dataimportDatatry:
mol=Chem.MolFromSmiles(smiles)
except:
returnNoneifmolisNone:
returnNone# Add Hydrogens to the moleculemol=Chem.AddHs(mol)
AllChem.EmbedMolecule(mol)
# Convert the molecule to a graphnode_features= []
foratominmol.GetAtoms():
node_features.append(atom_feature(atom))
# node_features = torch.tensor(node_features, dtype=torch.float)node_features=torch.tensor(node_features, dtype=torch.long)
edge_indices= []
edge_features= []
forbondinmol.GetBonds():
start, end=bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
edge_indices.append((start, end))
edge_indices.append((end, start))
edge_features.append(bond_feature(bond))
edge_features.append(bond_feature(bond))
edge_indices=torch.tensor(edge_indices).t().to(torch.long)
# edge_features = torch.tensor(edge_features, dtype=torch.float)edge_features=torch.tensor(edge_features, dtype=torch.long)
returnData(x=node_features, edge_index=edge_indices, edge_attr=edge_features)
# Helper functions for node and edge featuresdefatom_feature(atom):
return [atom.GetAtomicNum(), atom.GetFormalCharge()]
defbond_feature(bond):
return [int(bond.GetBondTypeAsDouble())]
# Load dataset and convert SMILES to PyG data objectsprint("Creating dataset...")
# if we have cached data, load itifos.path.exists('data/qm9_pyg_data.pkl'):
print("Loading data from cache...")
withopen('data/qm9_pyg_data.pkl', 'rb') asf:
data_list=pickle.load(f)
else:
print("Creating dataset from scratch...")
dataset=QM9(root='data')
data_list= []
# for i in tqdm(range(len(dataset))):foriintqdm(range(1000)):
smiles=dataset[i]['smiles']
data=smiles_to_pyg_graph(smiles)
ifdataisnotNone:
data_list.append(data)
# Save data_list to a pickle filewithopen('data/qm9_pyg_data.pkl', 'wb') asf:
pickle.dump(data_list, f)
print(f"Example data entry in the data_list: {data_list[0]}")
# Define a SchNet modelclassMySchNet(torch.nn.Module):
def__init__(self, num_features, hidden_channels, num_targets):
super(MySchNet, self).__init__()
self.schnet=SchNet(hidden_channels, num_features)
self.lin=torch.nn.Linear(hidden_channels, num_targets)
defforward(self, data):
print(f'pirnt from forward: data.x.shape: {data.x.shape}')
print(f'pirnt from forward: data.edge_index.shape: {data.edge_index.shape}')
print(f'pirnt from forward: data.edge_attr.shape: {data.edge_attr.shape}')
out=self.schnet(data.x, data.edge_index, data.edge_attr)
out=self.lin(out)
returnout# Instantiate the model and define other training parametersprint("Defining model...")
model=MySchNet(num_features=2, hidden_channels=64, num_targets=1)
optimizer=torch.optim.Adam(model.parameters(), lr=0.001)
criterion=torch.nn.MSELoss()
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In[5], [line 17](vscode-notebook-cell:?execution_count=5&line=17)
[15](vscode-notebook-cell:?execution_count=5&line=15) print(f'Batch edge_attr dtype: {batch.edge_attr.dtype}')
[16](vscode-notebook-cell:?execution_count=5&line=16) optimizer.zero_grad()
---> [17](vscode-notebook-cell:?execution_count=5&line=17) output = model(batch)
[18](vscode-notebook-cell:?execution_count=5&line=18) loss = criterion(output, batch.y.view(-1, 1)) # Assuming targets are stored in batch.y
[19](vscode-notebook-cell:?execution_count=5&line=19) loss.backward()
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
[1530](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1530) return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
[1531](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1532) return self._call_impl(*args, **kwargs)
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1541, in Module._call_impl(self, *args, **kwargs)
[1536](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
[1537](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
[1538](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
[1539](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1539) or _global_backward_pre_hooks or _global_backward_hooks
[1540](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1540) or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1541) return forward_call(*args, **kwargs)
[1543](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1543) try:
[1544](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1544) result = None
Cell In[4], [line 14](vscode-notebook-cell:?execution_count=4&line=14)
[12](vscode-notebook-cell:?execution_count=4&line=12) print(f'pirnt from forward: data.edge_index.shape: {data.edge_index.shape}')
[13](vscode-notebook-cell:?execution_count=4&line=13) print(f'pirnt from forward: data.edge_attr.shape: {data.edge_attr.shape}')
---> [14](vscode-notebook-cell:?execution_count=4&line=14) out = self.schnet(data.x, data.edge_index, data.edge_attr)
[15](vscode-notebook-cell:?execution_count=4&line=15) out = self.lin(out)
[16](vscode-notebook-cell:?execution_count=4&line=16) return out
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
[1530](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1530) return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
[1531](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1532) return self._call_impl(*args, **kwargs)
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1541, in Module._call_impl(self, *args, **kwargs)
[1536](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
[1537](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
[1538](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
[1539](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1539) or _global_backward_pre_hooks or _global_backward_hooks
[1540](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1540) or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1541) return forward_call(*args, **kwargs)
[1543](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1543) try:
[1544](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1544) result = None
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:284, in SchNet.forward(self, z, pos, batch)
[271](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:271) r"""Forward pass. [272](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:272) [273](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:273) Args: (...) [280](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:280) (default: :obj:`None`) [281](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:281) """
[282](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:282) batch = torch.zeros_like(z) if batch is None else batch
--> [284](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:284) h = self.embedding(z)
[285](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:285) edge_index, edge_weight = self.interaction_graph(pos, batch)
[286](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch_geometric/nn/models/schnet.py:286) edge_attr = self.distance_expansion(edge_weight)
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
[1530](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1530) return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
[1531](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1532) return self._call_impl(*args, **kwargs)
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1541, in Module._call_impl(self, *args, **kwargs)
[1536](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
[1537](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
[1538](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
[1539](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1539) or _global_backward_pre_hooks or _global_backward_hooks
[1540](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1540) or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1541) return forward_call(*args, **kwargs)
[1543](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1543) try:
[1544](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/module.py:1544) result = None
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/sparse.py:163, in Embedding.forward(self, input)
[162](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/sparse.py:162) def forward(self, input: Tensor) -> Tensor:
--> [163](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/sparse.py:163) return F.embedding(
[164](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/sparse.py:164) input, self.weight, self.padding_idx, self.max_norm,
[165](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/modules/sparse.py:165) self.norm_type, self.scale_grad_by_freq, self.sparse)
File ~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/functional.py:2264, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
[2258](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/functional.py:2258) # Note [embedding_renorm set_grad_enabled]
[2259](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/functional.py:2259) # XXX: equivalent to
[2260](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/functional.py:2260) # with torch.no_grad():
[2261](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/functional.py:2261) # torch.embedding_renorm_
[2262](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/functional.py:2262) # remove once script supports set_grad_enabled
[2263](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/functional.py:2263) _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> [2264](/home/popova/Projects/citre-quantum-chemistry/nbs/~/.pyenv/versions/mambaforge/envs/pyg/lib/python3.12/site-packages/torch/nn/functional.py:2264) return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self
Thanks for reading! I appreciate any feedback regarding the issue.
Best regards,
Anton.
Versions
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31
Python version: 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-1058-aws-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 10.1.243
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla V100-SXM2-16GB
Nvidia driver version: 535.171.04
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 8
On-line CPU(s) list: 0-7
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
Stepping: 1
CPU MHz: 3000.000
CPU max MHz: 3000.0000
CPU min MHz: 1200.0000
BogoMIPS: 4600.02
Hypervisor vendor: Xen
Virtualization type: full
L1d cache: 128 KiB
L1i cache: 128 KiB
L2 cache: 1 MiB
L3 cache: 45 MiB
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single pti fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt
馃悰 Describe the bug
Hi!
The idea of the code below is to run a custom version of SchNet on SMILES representations of molecules. Code:
The correspondign output before the Exception:
And an Exception message:
Thanks for reading! I appreciate any feedback regarding the issue.
Best regards,
Anton.
Versions
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31
Python version: 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-1058-aws-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 10.1.243
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla V100-SXM2-16GB
Nvidia driver version: 535.171.04
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 8
On-line CPU(s) list: 0-7
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
Stepping: 1
CPU MHz: 3000.000
CPU max MHz: 3000.0000
CPU min MHz: 1200.0000
BogoMIPS: 4600.02
Hypervisor vendor: Xen
Virtualization type: full
L1d cache: 128 KiB
L1i cache: 128 KiB
L2 cache: 1 MiB
L3 cache: 45 MiB
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single pti fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==2.2.3
[pip3] torch==2.3.0
[pip3] torch_cluster==1.6.3+pt22cu121
[pip3] torch-ema==0.3
[pip3] torch_geometric==2.5.3
[pip3] torch_scatter==2.1.2+pt22cu121
[pip3] torch_sparse==0.6.18+pt22cu121
[pip3] torch_spline_conv==1.2.2+pt22cu121
[pip3] torchaudio==2.3.0
[pip3] torchmetrics==1.0.1
[pip3] torchvision==0.18.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] pytorch-lightning 2.2.3 pypi_0 pypi
[conda] torch 2.3.0 pypi_0 pypi
[conda] torch-cluster 1.6.3+pt22cu121 pypi_0 pypi
[conda] torch-ema 0.3 pypi_0 pypi
[conda] torch-geometric 2.5.3 pypi_0 pypi
[conda] torch-scatter 2.1.2+pt22cu121 pypi_0 pypi
[conda] torch-sparse 0.6.18+pt22cu121 pypi_0 pypi
[conda] torch-spline-conv 1.2.2+pt22cu121 pypi_0 pypi
[conda] torchaudio 2.3.0 pypi_0 pypi
[conda] torchmetrics 1.0.1 pypi_0 pypi
[conda] torchvision 0.18.0 pypi_0 pypi
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