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feat: upload jingliu_sdxl_20_ep dataset and logs

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  1. logs/20230805111330/wandb/debug-internal.log +181 -0
  2. logs/20230805111330/wandb/debug.log +29 -0
  3. logs/20230805111330/wandb/run-20230805_111355-hdfosgf0/files/config.yaml +36 -0
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  11. logs/20230805111449/wandb/debug-internal.log +185 -0
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  21. logs/20230805111959/wandb/debug-internal.log +0 -0
  22. logs/20230805111959/wandb/debug.log +35 -0
  23. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/files/config.yaml +37 -0
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  29. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/files/media/images/sample_1_1213_2c6cfb92b3db81319c13.png +3 -0
  30. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/files/media/images/sample_1_1617_630bcbcda82ac2af8072.png +3 -0
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  34. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/files/output.log +126 -0
  35. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/files/requirements.txt +323 -0
  36. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/files/wandb-metadata.json +209 -0
  37. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/files/wandb-summary.json +1 -0
  38. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/logs/debug-internal.log +0 -0
  39. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/logs/debug.log +35 -0
  40. logs/20230805111959/wandb/run-20230805_112032-qho88p4v/run-qho88p4v.wandb +0 -0
logs/20230805111330/wandb/debug-internal.log ADDED
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1
+
2
+ epoch 1/20
3
+ Traceback (most recent call last):
4
+ File "/workspace/kohya-trainer/sdxl_train.py", line 649, in <module>
5
+ train(args)
6
+ File "/workspace/kohya-trainer/sdxl_train.py", line 475, in train
7
+ accelerator.backward(loss)
8
+ File "/workspace/venv/lib/python3.10/site-packages/accelerate/accelerator.py", line 1743, in backward
9
+ self.scaler.scale(loss).backward(**kwargs)
10
+ File "/workspace/venv/lib/python3.10/site-packages/torch/_tensor.py", line 487, in backward
11
+ torch.autograd.backward(
12
+ File "/workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py", line 200, in backward
13
+ Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
14
+ File "/workspace/venv/lib/python3.10/site-packages/torch/autograd/function.py", line 274, in apply
15
+ return user_fn(self, *args)
16
+ File "/workspace/venv/lib/python3.10/site-packages/torch/utils/checkpoint.py", line 157, in backward
17
+ torch.autograd.backward(outputs_with_grad, args_with_grad)
18
+ File "/workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py", line 200, in backward
19
+ Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
20
+ torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 23.65 GiB total capacity; 18.73 GiB already allocated; 25.69 MiB free; 19.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
21
+ ╭───────────────────── Traceback (most recent call last) ──────────────────────╮
22
+ │ /workspace/kohya-trainer/sdxl_train.py:649 in <module> │
23
+ │ │
24
+ │ 646 │ args = parser.parse_args() │
25
+ │ 647 │ args = train_util.read_config_from_file(args, parser) │
26
+ │ 648 │ │
27
+ │ ❱ 649 │ train(args) │
28
+ │ 650 │
29
+ │ │
30
+ │ /workspace/kohya-trainer/sdxl_train.py:475 in train │
31
+ │ │
32
+ │ 472 │ │ │ │ else: │
33
+ │ 473 │ │ │ │ │ loss = torch.nn.functional.mse_loss(noise_pred.flo │
34
+ │ 474 │ │ │ │ │
35
+ │ ❱ 475 │ │ │ │ accelerator.backward(loss) │
36
+ │ 476 │ │ │ │ if accelerator.sync_gradients and args.max_grad_norm ! │
37
+ │ 477 │ │ │ │ │ params_to_clip = [] │
38
+ │ 478 │ │ │ │ │ for m in training_models: │
39
+ │ │
40
+ │ /workspace/venv/lib/python3.10/site-packages/accelerate/accelerator.py:1743 │
41
+ │ in backward │
42
+ │ │
43
+ │ 1740 │ │ elif self.distributed_type == DistributedType.MEGATRON_LM: │
44
+ │ 1741 │ │ │ return │
45
+ │ 1742 │ │ elif self.scaler is not None: │
46
+ │ ❱ 1743 │ │ │ self.scaler.scale(loss).backward(**kwargs) │
47
+ │ 1744 │ │ else: │
48
+ │ 1745 │ │ │ loss.backward(**kwargs) │
49
+ │ 1746 │
50
+ │ │
51
+ │ /workspace/venv/lib/python3.10/site-packages/torch/_tensor.py:487 in │
52
+ │ backward │
53
+ │ │
54
+ │ 484 │ │ │ │ create_graph=create_graph, │
55
+ │ 485 │ │ │ │ inputs=inputs, │
56
+ │ 486 │ │ │ ) │
57
+ │ ❱  487 │ │ torch.autograd.backward( │
58
+ │ 488 │ │ │ self, gradient, retain_graph, create_graph, inputs=inputs │
59
+ │ 489 │ │ ) │
60
+ │ 490 │
61
+ │ │
62
+ │ /workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:200 │
63
+ │ in backward │
64
+ │ │
65
+ │ 197 │ # The reason we repeat same the comment below is that │
66
+ │ 198 │ # some Python versions print out the first line of a multi-line fu │
67
+ │ 199 │ # calls in the traceback and some print out the last line │
68
+ │ ❱ 200 │ Variable._execution_engine.run_backward( # Calls into the C++ eng │
69
+ │ 201 │ │ tensors, grad_tensors_, retain_graph, create_graph, inputs, │
70
+ │ 202 │ │ allow_unreachable=True, accumulate_grad=True) # Calls into th │
71
+ │ 203 │
72
+ │ │
73
+ │ /workspace/venv/lib/python3.10/site-packages/torch/autograd/function.py:274 │
74
+ │ in apply │
75
+ │ │
76
+ │ 271 │ │ │ │ │ │ │ "Function is not allowed. You should on │
77
+ │ 272 │ │ │ │ │ │ │ "of them.") │
78
+ │ 273 │ │ user_fn = vjp_fn if vjp_fn is not Function.vjp else backward_f │
79
+ │ ❱ 274 │ │ return user_fn(self, *args) │
80
+ │ 275 │ │
81
+ │ 276 │ def apply_jvp(self, *args): │
82
+ │ 277 │ │ # _forward_cls is defined by derived class │
83
+ │ │
84
+ │ /workspace/venv/lib/python3.10/site-packages/torch/utils/checkpoint.py:157 │
85
+ │ in backward │
86
+ │ │
87
+ │ 154 │ │ │ raise RuntimeError( │
88
+ │ 155 │ │ │ │ "none of output has requires_grad=True," │
89
+ │ 156 │ │ │ │ " this checkpoint() is not necessary") │
90
+ │ ❱ 157 │ │ torch.autograd.backward(outputs_with_grad, args_with_grad) │
91
+ │ 158 │ │ grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else N │
92
+ │ 159 │ │ │ │ │ for inp in detached_inputs) │
93
+ │ 160 │
94
+ │ │
95
+ │ /workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:200 │
96
+ │ in backward │
97
+ │ │
98
+ │ 197 │ # The reason we repeat same the comment below is that │
99
+ │ 198 │ # some Python versions print out the first line of a multi-line fu │
100
+ │ 199 │ # calls in the traceback and some print out the last line │
101
+ │ ❱ 200 │ Variable._execution_engine.run_backward( # Calls into the C++ eng │
102
+ │ 201 │ │ tensors, grad_tensors_, retain_graph, create_graph, inputs, │
103
+ │ 202 │ │ allow_unreachable=True, accumulate_grad=True) # Calls into th │
104
+ │ 203 │
105
+ ╰──────────────────────────────────────────────────────────────────────────────╯
106
+ OutOfMemoryError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 23.65
107
+ GiB total capacity; 18.73 GiB already allocated; 25.69 MiB free; 19.47 GiB
108
+ reserved in total by PyTorch) If reserved memory is >> allocated memory try
109
+ setting max_split_size_mb to avoid fragmentation. See documentation for Memory
110
+ Management and PYTORCH_CUDA_ALLOC_CONF
logs/20230805111330/wandb/run-20230805_111355-hdfosgf0/files/requirements.txt ADDED
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+ 8:
36
+ - 5
logs/20230805111449/wandb/run-20230805_111514-2uyktqe5/files/output.log ADDED
@@ -0,0 +1,110 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ epoch 1/20
3
+ Traceback (most recent call last):
4
+ File "/workspace/kohya-trainer/sdxl_train.py", line 649, in <module>
5
+ train(args)
6
+ File "/workspace/kohya-trainer/sdxl_train.py", line 475, in train
7
+ accelerator.backward(loss)
8
+ File "/workspace/venv/lib/python3.10/site-packages/accelerate/accelerator.py", line 1743, in backward
9
+ self.scaler.scale(loss).backward(**kwargs)
10
+ File "/workspace/venv/lib/python3.10/site-packages/torch/_tensor.py", line 487, in backward
11
+ torch.autograd.backward(
12
+ File "/workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py", line 200, in backward
13
+ Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
14
+ File "/workspace/venv/lib/python3.10/site-packages/torch/autograd/function.py", line 274, in apply
15
+ return user_fn(self, *args)
16
+ File "/workspace/venv/lib/python3.10/site-packages/torch/utils/checkpoint.py", line 157, in backward
17
+ torch.autograd.backward(outputs_with_grad, args_with_grad)
18
+ File "/workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py", line 200, in backward
19
+ Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
20
+ torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 23.65 GiB total capacity; 18.73 GiB already allocated; 25.69 MiB free; 19.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
21
+ ╭───────────────────── Traceback (most recent call last) ──────────────────────╮
22
+ │ /workspace/kohya-trainer/sdxl_train.py:649 in <module> │
23
+ │ │
24
+ │ 646 │ args = parser.parse_args() │
25
+ │ 647 │ args = train_util.read_config_from_file(args, parser) │
26
+ │ 648 │ │
27
+ │ ❱ 649 │ train(args) │
28
+ │ 650 │
29
+ │ │
30
+ │ /workspace/kohya-trainer/sdxl_train.py:475 in train │
31
+ │ │
32
+ │ 472 │ │ │ │ else: │
33
+ │ 473 │ │ │ │ │ loss = torch.nn.functional.mse_loss(noise_pred.flo │
34
+ │ 474 │ │ │ │ │
35
+ │ ❱ 475 │ │ │ │ accelerator.backward(loss) │
36
+ │ 476 │ │ │ │ if accelerator.sync_gradients and args.max_grad_norm ! │
37
+ │ 477 │ │ │ │ │ params_to_clip = [] │
38
+ │ 478 │ │ │ │ │ for m in training_models: │
39
+ │ │
40
+ │ /workspace/venv/lib/python3.10/site-packages/accelerate/accelerator.py:1743 │
41
+ │ in backward │
42
+ │ │
43
+ │ 1740 │ │ elif self.distributed_type == DistributedType.MEGATRON_LM: │
44
+ │ 1741 │ │ │ return │
45
+ │ 1742 │ │ elif self.scaler is not None: │
46
+ │ ❱ 1743 │ │ │ self.scaler.scale(loss).backward(**kwargs) │
47
+ │ 1744 │ │ else: │
48
+ │ 1745 │ │ │ loss.backward(**kwargs) │
49
+ │ 1746 │
50
+ │ │
51
+ │ /workspace/venv/lib/python3.10/site-packages/torch/_tensor.py:487 in │
52
+ │ backward │
53
+ │ │
54
+ │ 484 │ │ │ │ create_graph=create_graph, │
55
+ │ 485 │ │ │ │ inputs=inputs, │
56
+ │ 486 │ │ │ ) │
57
+ │ ❱  487 │ │ torch.autograd.backward( │
58
+ │ 488 │ │ │ self, gradient, retain_graph, create_graph, inputs=inputs │
59
+ │ 489 │ │ ) │
60
+ │ 490 │
61
+ │ │
62
+ │ /workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:200 │
63
+ │ in backward │
64
+ │ │
65
+ │ 197 │ # The reason we repeat same the comment below is that │
66
+ │ 198 │ # some Python versions print out the first line of a multi-line fu │
67
+ │ 199 │ # calls in the traceback and some print out the last line │
68
+ │ ❱ 200 │ Variable._execution_engine.run_backward( # Calls into the C++ eng │
69
+ │ 201 │ │ tensors, grad_tensors_, retain_graph, create_graph, inputs, │
70
+ │ 202 │ │ allow_unreachable=True, accumulate_grad=True) # Calls into th │
71
+ │ 203 │
72
+ │ │
73
+ │ /workspace/venv/lib/python3.10/site-packages/torch/autograd/function.py:274 │
74
+ │ in apply │
75
+ │ │
76
+ │ 271 │ │ │ │ │ │ │ "Function is not allowed. You should on │
77
+ │ 272 │ │ │ │ │ │ │ "of them.") │
78
+ │ 273 │ │ user_fn = vjp_fn if vjp_fn is not Function.vjp else backward_f │
79
+ │ ❱ 274 │ │ return user_fn(self, *args) │
80
+ │ 275 │ │
81
+ │ 276 │ def apply_jvp(self, *args): │
82
+ │ 277 │ │ # _forward_cls is defined by derived class │
83
+ │ │
84
+ │ /workspace/venv/lib/python3.10/site-packages/torch/utils/checkpoint.py:157 │
85
+ │ in backward │
86
+ │ │
87
+ │ 154 │ │ │ raise RuntimeError( │
88
+ │ 155 │ │ │ │ "none of output has requires_grad=True," │
89
+ │ 156 │ │ │ │ " this checkpoint() is not necessary") │
90
+ │ ❱ 157 │ │ torch.autograd.backward(outputs_with_grad, args_with_grad) │
91
+ │ 158 │ │ grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else N │
92
+ │ 159 │ │ │ │ │ for inp in detached_inputs) │
93
+ │ 160 │
94
+ │ │
95
+ │ /workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:200 │
96
+ │ in backward │
97
+ │ │
98
+ │ 197 │ # The reason we repeat same the comment below is that │
99
+ │ 198 │ # some Python versions print out the first line of a multi-line fu │
100
+ │ 199 │ # calls in the traceback and some print out the last line │
101
+ │ ❱ 200 │ Variable._execution_engine.run_backward( # Calls into the C++ eng │
102
+ │ 201 │ │ tensors, grad_tensors_, retain_graph, create_graph, inputs, │
103
+ │ 202 │ │ allow_unreachable=True, accumulate_grad=True) # Calls into th │
104
+ │ 203 │
105
+ ╰──────────────────────────────────────────────────────────────────────────────╯
106
+ OutOfMemoryError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 23.65
107
+ GiB total capacity; 18.73 GiB already allocated; 25.69 MiB free; 19.47 GiB
108
+ reserved in total by PyTorch) If reserved memory is >> allocated memory try
109
+ setting max_split_size_mb to avoid fragmentation. See documentation for Memory
110
+ Management and PYTORCH_CUDA_ALLOC_CONF
logs/20230805111449/wandb/run-20230805_111514-2uyktqe5/files/requirements.txt ADDED
@@ -0,0 +1,323 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ absl-py==1.4.0
2
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3
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4
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5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
+ asttokens==2.2.1
16
+ async-lru==2.0.3
17
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18
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20
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21
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22
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24
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25
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28
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29
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31
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32
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33
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34
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35
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39
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40
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41
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50
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51
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138
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139
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140
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141
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142
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146
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147
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148
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149
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151
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152
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153
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155
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156
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157
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158
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159
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164
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169
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181
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182
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183
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214
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215
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216
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217
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219
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231
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248
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258
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263
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264
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278
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279
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281
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283
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
+ webdataset==0.2.48
310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
+ zope.deprecation==5.0
323
+ zope.interface==6.0
logs/20230805111449/wandb/run-20230805_111514-2uyktqe5/files/wandb-metadata.json ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "os": "Linux-5.4.0-152-generic-x86_64-with-glibc2.35",
3
+ "python": "3.10.6",
4
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