| [2025-03-09 17:37:56,979] INFO: [0;35mWill use single-gpu: NVIDIA A100 80GB PCIe[0m | |
| [2025-03-09 17:37:56,979] INFO: configured dtype=torch.bfloat16 for autocast | |
| [2025-03-09 17:37:56,979] INFO: configured dtype=torch.bfloat16 for autocast | |
| [2025-03-09 17:37:57,065] INFO: layer conv_id_0 using attention_type=flash | |
| [2025-03-09 17:37:57,065] INFO: layer conv_id_0 using attention_type=flash | |
| [2025-03-09 17:37:57,096] INFO: layer conv_reg_0 using attention_type=flash | |
| [2025-03-09 17:37:57,096] INFO: layer conv_reg_0 using attention_type=flash | |
| [2025-03-09 17:37:57,128] INFO: layer conv_id_1 using attention_type=flash | |
| [2025-03-09 17:37:57,128] INFO: layer conv_id_1 using attention_type=flash | |
| [2025-03-09 17:37:57,160] INFO: layer conv_reg_1 using attention_type=flash | |
| [2025-03-09 17:37:57,160] INFO: layer conv_reg_1 using attention_type=flash | |
| [2025-03-09 17:37:57,192] INFO: layer conv_id_2 using attention_type=flash | |
| [2025-03-09 17:37:57,192] INFO: layer conv_id_2 using attention_type=flash | |
| [2025-03-09 17:37:57,223] INFO: layer conv_reg_2 using attention_type=flash | |
| [2025-03-09 17:37:57,223] INFO: layer conv_reg_2 using attention_type=flash | |
| [2025-03-09 17:37:57,522] INFO: MLPF( | |
| (nn0_id): ModuleList( | |
| (0-1): 2 x Sequential( | |
| (0): Linear(in_features=17, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1024, bias=True) | |
| ) | |
| ) | |
| (nn0_reg): ModuleList( | |
| (0-1): 2 x Sequential( | |
| (0): Linear(in_features=17, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1024, bias=True) | |
| ) | |
| ) | |
| (conv_id): ModuleList( | |
| (0-2): 3 x PreLnSelfAttentionLayer( | |
| (mha): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) | |
| ) | |
| (norm0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (seq): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): Linear(in_features=1024, out_features=1024, bias=True) | |
| (3): ReLU() | |
| ) | |
| (dropout): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (conv_reg): ModuleList( | |
| (0-2): 3 x PreLnSelfAttentionLayer( | |
| (mha): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) | |
| ) | |
| (norm0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (seq): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): Linear(in_features=1024, out_features=1024, bias=True) | |
| (3): ReLU() | |
| ) | |
| (dropout): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (nn_binary_particle): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=2, bias=True) | |
| ) | |
| (nn_pid): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=6, bias=True) | |
| ) | |
| (nn_pu): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1, bias=True) | |
| ) | |
| (nn_pt): RegressionOutput( | |
| (nn): ModuleList( | |
| (0-1): 2 x Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1, bias=True) | |
| ) | |
| ) | |
| ) | |
| (nn_eta): RegressionOutput( | |
| (nn): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=2, bias=True) | |
| ) | |
| ) | |
| (nn_sin_phi): RegressionOutput( | |
| (nn): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=2, bias=True) | |
| ) | |
| ) | |
| (nn_cos_phi): RegressionOutput( | |
| (nn): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=2, bias=True) | |
| ) | |
| ) | |
| (nn_energy): RegressionOutput( | |
| (nn): ModuleList( | |
| (0-1): 2 x Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1, bias=True) | |
| ) | |
| ) | |
| ) | |
| (final_norm_id): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (final_norm_reg): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| ) | |
| [2025-03-09 17:37:57,522] INFO: MLPF( | |
| (nn0_id): ModuleList( | |
| (0-1): 2 x Sequential( | |
| (0): Linear(in_features=17, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1024, bias=True) | |
| ) | |
| ) | |
| (nn0_reg): ModuleList( | |
| (0-1): 2 x Sequential( | |
| (0): Linear(in_features=17, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1024, bias=True) | |
| ) | |
| ) | |
| (conv_id): ModuleList( | |
| (0-2): 3 x PreLnSelfAttentionLayer( | |
| (mha): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) | |
| ) | |
| (norm0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (seq): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): Linear(in_features=1024, out_features=1024, bias=True) | |
| (3): ReLU() | |
| ) | |
| (dropout): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (conv_reg): ModuleList( | |
| (0-2): 3 x PreLnSelfAttentionLayer( | |
| (mha): MultiheadAttention( | |
| (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) | |
| ) | |
| (norm0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (seq): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): Linear(in_features=1024, out_features=1024, bias=True) | |
| (3): ReLU() | |
| ) | |
| (dropout): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (nn_binary_particle): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=2, bias=True) | |
| ) | |
| (nn_pid): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=6, bias=True) | |
| ) | |
| (nn_pu): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1, bias=True) | |
| ) | |
| (nn_pt): RegressionOutput( | |
| (nn): ModuleList( | |
| (0-1): 2 x Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1, bias=True) | |
| ) | |
| ) | |
| ) | |
| (nn_eta): RegressionOutput( | |
| (nn): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=2, bias=True) | |
| ) | |
| ) | |
| (nn_sin_phi): RegressionOutput( | |
| (nn): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=2, bias=True) | |
| ) | |
| ) | |
| (nn_cos_phi): RegressionOutput( | |
| (nn): Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=2, bias=True) | |
| ) | |
| ) | |
| (nn_energy): RegressionOutput( | |
| (nn): ModuleList( | |
| (0-1): 2 x Sequential( | |
| (0): Linear(in_features=1024, out_features=1024, bias=True) | |
| (1): ReLU() | |
| (2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (3): Dropout(p=0.0, inplace=False) | |
| (4): Linear(in_features=1024, out_features=1, bias=True) | |
| ) | |
| ) | |
| ) | |
| (final_norm_id): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| (final_norm_reg): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) | |
| ) | |
| [2025-03-09 17:37:57,523] INFO: Trainable parameters: 52630547 | |
| [2025-03-09 17:37:57,523] INFO: Trainable parameters: 52630547 | |
| [2025-03-09 17:37:57,523] INFO: Non-trainable parameters: 0 | |
| [2025-03-09 17:37:57,523] INFO: Non-trainable parameters: 0 | |
| [2025-03-09 17:37:57,523] INFO: Total parameters: 52630547 | |
| [2025-03-09 17:37:57,523] INFO: Total parameters: 52630547 | |
| [2025-03-09 17:37:57,525] INFO: Modules Trainable parameters Non-trainable parameters | |
| nn0_id.0.0.weight 17408 0 | |
| nn0_id.0.0.bias 1024 0 | |
| nn0_id.0.2.weight 1024 0 | |
| nn0_id.0.2.bias 1024 0 | |
| nn0_id.0.4.weight 1048576 0 | |
| nn0_id.0.4.bias 1024 0 | |
| nn0_id.1.0.weight 17408 0 | |
| nn0_id.1.0.bias 1024 0 | |
| nn0_id.1.2.weight 1024 0 | |
| nn0_id.1.2.bias 1024 0 | |
| nn0_id.1.4.weight 1048576 0 | |
| nn0_id.1.4.bias 1024 0 | |
| nn0_reg.0.0.weight 17408 0 | |
| nn0_reg.0.0.bias 1024 0 | |
| nn0_reg.0.2.weight 1024 0 | |
| nn0_reg.0.2.bias 1024 0 | |
| nn0_reg.0.4.weight 1048576 0 | |
| nn0_reg.0.4.bias 1024 0 | |
| nn0_reg.1.0.weight 17408 0 | |
| nn0_reg.1.0.bias 1024 0 | |
| nn0_reg.1.2.weight 1024 0 | |
| nn0_reg.1.2.bias 1024 0 | |
| nn0_reg.1.4.weight 1048576 0 | |
| nn0_reg.1.4.bias 1024 0 | |
| conv_id.0.mha.in_proj_weight 3145728 0 | |
| conv_id.0.mha.in_proj_bias 3072 0 | |
| conv_id.0.mha.out_proj.weight 1048576 0 | |
| conv_id.0.mha.out_proj.bias 1024 0 | |
| conv_id.0.norm0.weight 1024 0 | |
| conv_id.0.norm0.bias 1024 0 | |
| conv_id.0.norm1.weight 1024 0 | |
| conv_id.0.norm1.bias 1024 0 | |
| conv_id.0.seq.0.weight 1048576 0 | |
| conv_id.0.seq.0.bias 1024 0 | |
| conv_id.0.seq.2.weight 1048576 0 | |
| conv_id.0.seq.2.bias 1024 0 | |
| conv_id.1.mha.in_proj_weight 3145728 0 | |
| conv_id.1.mha.in_proj_bias 3072 0 | |
| conv_id.1.mha.out_proj.weight 1048576 0 | |
| conv_id.1.mha.out_proj.bias 1024 0 | |
| conv_id.1.norm0.weight 1024 0 | |
| conv_id.1.norm0.bias 1024 0 | |
| conv_id.1.norm1.weight 1024 0 | |
| conv_id.1.norm1.bias 1024 0 | |
| conv_id.1.seq.0.weight 1048576 0 | |
| conv_id.1.seq.0.bias 1024 0 | |
| conv_id.1.seq.2.weight 1048576 0 | |
| conv_id.1.seq.2.bias 1024 0 | |
| conv_id.2.mha.in_proj_weight 3145728 0 | |
| conv_id.2.mha.in_proj_bias 3072 0 | |
| conv_id.2.mha.out_proj.weight 1048576 0 | |
| conv_id.2.mha.out_proj.bias 1024 0 | |
| conv_id.2.norm0.weight 1024 0 | |
| conv_id.2.norm0.bias 1024 0 | |
| conv_id.2.norm1.weight 1024 0 | |
| conv_id.2.norm1.bias 1024 0 | |
| conv_id.2.seq.0.weight 1048576 0 | |
| conv_id.2.seq.0.bias 1024 0 | |
| conv_id.2.seq.2.weight 1048576 0 | |
| conv_id.2.seq.2.bias 1024 0 | |
| conv_reg.0.mha.in_proj_weight 3145728 0 | |
| conv_reg.0.mha.in_proj_bias 3072 0 | |
| conv_reg.0.mha.out_proj.weight 1048576 0 | |
| conv_reg.0.mha.out_proj.bias 1024 0 | |
| conv_reg.0.norm0.weight 1024 0 | |
| conv_reg.0.norm0.bias 1024 0 | |
| conv_reg.0.norm1.weight 1024 0 | |
| conv_reg.0.norm1.bias 1024 0 | |
| conv_reg.0.seq.0.weight 1048576 0 | |
| conv_reg.0.seq.0.bias 1024 0 | |
| conv_reg.0.seq.2.weight 1048576 0 | |
| conv_reg.0.seq.2.bias 1024 0 | |
| conv_reg.1.mha.in_proj_weight 3145728 0 | |
| conv_reg.1.mha.in_proj_bias 3072 0 | |
| conv_reg.1.mha.out_proj.weight 1048576 0 | |
| conv_reg.1.mha.out_proj.bias 1024 0 | |
| conv_reg.1.norm0.weight 1024 0 | |
| conv_reg.1.norm0.bias 1024 0 | |
| conv_reg.1.norm1.weight 1024 0 | |
| conv_reg.1.norm1.bias 1024 0 | |
| conv_reg.1.seq.0.weight 1048576 0 | |
| conv_reg.1.seq.0.bias 1024 0 | |
| conv_reg.1.seq.2.weight 1048576 0 | |
| conv_reg.1.seq.2.bias 1024 0 | |
| conv_reg.2.mha.in_proj_weight 3145728 0 | |
| conv_reg.2.mha.in_proj_bias 3072 0 | |
| conv_reg.2.mha.out_proj.weight 1048576 0 | |
| conv_reg.2.mha.out_proj.bias 1024 0 | |
| conv_reg.2.norm0.weight 1024 0 | |
| conv_reg.2.norm0.bias 1024 0 | |
| conv_reg.2.norm1.weight 1024 0 | |
| conv_reg.2.norm1.bias 1024 0 | |
| conv_reg.2.seq.0.weight 1048576 0 | |
| conv_reg.2.seq.0.bias 1024 0 | |
| conv_reg.2.seq.2.weight 1048576 0 | |
| conv_reg.2.seq.2.bias 1024 0 | |
| nn_binary_particle.0.weight 1048576 0 | |
| nn_binary_particle.0.bias 1024 0 | |
| nn_binary_particle.2.weight 1024 0 | |
| nn_binary_particle.2.bias 1024 0 | |
| nn_binary_particle.4.weight 2048 0 | |
| nn_binary_particle.4.bias 2 0 | |
| nn_pid.0.weight 1048576 0 | |
| nn_pid.0.bias 1024 0 | |
| nn_pid.2.weight 1024 0 | |
| nn_pid.2.bias 1024 0 | |
| nn_pid.4.weight 6144 0 | |
| nn_pid.4.bias 6 0 | |
| nn_pu.0.weight 1048576 0 | |
| nn_pu.0.bias 1024 0 | |
| nn_pu.2.weight 1024 0 | |
| nn_pu.2.bias 1024 0 | |
| nn_pu.4.weight 1024 0 | |
| nn_pu.4.bias 1 0 | |
| nn_pt.nn.0.0.weight 1048576 0 | |
| nn_pt.nn.0.0.bias 1024 0 | |
| nn_pt.nn.0.2.weight 1024 0 | |
| nn_pt.nn.0.2.bias 1024 0 | |
| nn_pt.nn.0.4.weight 1024 0 | |
| nn_pt.nn.0.4.bias 1 0 | |
| nn_pt.nn.1.0.weight 1048576 0 | |
| nn_pt.nn.1.0.bias 1024 0 | |
| nn_pt.nn.1.2.weight 1024 0 | |
| nn_pt.nn.1.2.bias 1024 0 | |
| nn_pt.nn.1.4.weight 1024 0 | |
| nn_pt.nn.1.4.bias 1 0 | |
| nn_eta.nn.0.weight 1048576 0 | |
| nn_eta.nn.0.bias 1024 0 | |
| nn_eta.nn.2.weight 1024 0 | |
| nn_eta.nn.2.bias 1024 0 | |
| nn_eta.nn.4.weight 2048 0 | |
| nn_eta.nn.4.bias 2 0 | |
| nn_sin_phi.nn.0.weight 1048576 0 | |
| nn_sin_phi.nn.0.bias 1024 0 | |
| nn_sin_phi.nn.2.weight 1024 0 | |
| nn_sin_phi.nn.2.bias 1024 0 | |
| nn_sin_phi.nn.4.weight 2048 0 | |
| nn_sin_phi.nn.4.bias 2 0 | |
| nn_cos_phi.nn.0.weight 1048576 0 | |
| nn_cos_phi.nn.0.bias 1024 0 | |
| nn_cos_phi.nn.2.weight 1024 0 | |
| nn_cos_phi.nn.2.bias 1024 0 | |
| nn_cos_phi.nn.4.weight 2048 0 | |
| nn_cos_phi.nn.4.bias 2 0 | |
| nn_energy.nn.0.0.weight 1048576 0 | |
| nn_energy.nn.0.0.bias 1024 0 | |
| nn_energy.nn.0.2.weight 1024 0 | |
| nn_energy.nn.0.2.bias 1024 0 | |
| nn_energy.nn.0.4.weight 1024 0 | |
| nn_energy.nn.0.4.bias 1 0 | |
| nn_energy.nn.1.0.weight 1048576 0 | |
| nn_energy.nn.1.0.bias 1024 0 | |
| nn_energy.nn.1.2.weight 1024 0 | |
| nn_energy.nn.1.2.bias 1024 0 | |
| nn_energy.nn.1.4.weight 1024 0 | |
| nn_energy.nn.1.4.bias 1 0 | |
| final_norm_id.weight 1024 0 | |
| final_norm_id.bias 1024 0 | |
| final_norm_reg.weight 1024 0 | |
| final_norm_reg.bias 1024 0 | |
| [2025-03-09 17:37:57,525] INFO: Modules Trainable parameters Non-trainable parameters | |
| nn0_id.0.0.weight 17408 0 | |
| nn0_id.0.0.bias 1024 0 | |
| nn0_id.0.2.weight 1024 0 | |
| nn0_id.0.2.bias 1024 0 | |
| nn0_id.0.4.weight 1048576 0 | |
| nn0_id.0.4.bias 1024 0 | |
| nn0_id.1.0.weight 17408 0 | |
| nn0_id.1.0.bias 1024 0 | |
| nn0_id.1.2.weight 1024 0 | |
| nn0_id.1.2.bias 1024 0 | |
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| [2025-03-09 17:37:57,526] INFO: Creating experiment dir experiments/pyg-clic_20250309_173756_957486 | |
| [2025-03-09 17:37:57,526] INFO: Creating experiment dir experiments/pyg-clic_20250309_173756_957486 | |
| [2025-03-09 17:37:57,526] INFO: [1mModel directory experiments/pyg-clic_20250309_173756_957486[0m | |
| [2025-03-09 17:37:57,526] INFO: [1mModel directory experiments/pyg-clic_20250309_173756_957486[0m | |
| [2025-03-09 17:38:02,881] INFO: [0;34mtrain_dataset: clic_edm_qq_pf, 719492[0m | |
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| [2025-03-09 17:38:04,840] INFO: [0;34mvalid_dataset: clic_edm_qq_pf, 79948[0m | |
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| [2025-03-12 22:04:55,820] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_3.parquet | |
| [2025-03-12 22:04:55,820] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_3.parquet | |
| [2025-03-12 22:04:56,259] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_4.parquet | |
| [2025-03-12 22:04:56,259] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_4.parquet | |
| [2025-03-12 22:04:56,694] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_5.parquet | |
| [2025-03-12 22:04:56,694] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_5.parquet | |
| [2025-03-12 22:04:57,128] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_6.parquet | |
| [2025-03-12 22:04:57,128] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_6.parquet | |
| [2025-03-12 22:04:57,537] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_7.parquet | |
| [2025-03-12 22:04:57,537] INFO: Saved predictions at experiments/pyg-clic_20250309_173756_957486/preds_epoch_10/clic_edm_qq_pf/pred_0_7.parquet | |
| [2025-03-12 22:04:57,681] INFO: Time taken to make predictions on device 0 is: 0.07 min | |
| [2025-03-12 22:04:57,681] INFO: Time taken to make predictions on device 0 is: 0.07 min | |
| [2025-03-12 22:05:38,872] INFO: Training completed. Total time on device 0: 4587.548min | |
| [2025-03-12 22:05:38,872] INFO: Training completed. Total time on device 0: 4587.548min | |