Training not Starting?

[seishiruoParticipant]

Leaving it after several minutes I get this.

Error: 2 root error(s) found.
(0) Resource exhausted: failed to allocate memory
[[node mul_81 (defined at C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py:64) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

[[concat_4/concat/_463]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

(1) Resource exhausted: failed to allocate memory
[[node mul_81 (defined at C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py:64) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

0 successful operations.
0 derived errors ignored.

Errors may have originated from an input operation.
Input Source operations connected to node mul_81:
src_dst_opt/vs_inter/dense1/weight_0/read (defined at C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py:38)

Input Source operations connected to node mul_81:
src_dst_opt/vs_inter/dense1/weight_0/read (defined at C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py:38)

Original stack trace for ‘mul_81’:
File “threading.py”, line 884, in _bootstrap
File “threading.py”, line 916, in _bootstrap_inner
File “threading.py”, line 864, in run
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\mainscripts\Trainer.py”, line 58, in trainerThread
debug=debug)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\ModelBase.py”, line 193, in __init__
self.on_initialize()
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\Model_SAEHD\Model.py”, line 564, in on_initialize
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gvs))
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py”, line 64, in get_update_op
v_t = self.beta_2*vs + (1.0-self.beta_2) * tf.square(g-m_t)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\variables.py”, line 1076, in _run_op
return tensor_oper(a.value(), *args, **kwargs)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\math_ops.py”, line 1400, in r_binary_op_wrapper
return func(x, y, name=name)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\math_ops.py”, line 1710, in _mul_dispatch
return multiply(x, y, name=name)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\util\dispatch.py”, line 206, in wrapper
return target(*args, **kwargs)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\math_ops.py”, line 530, in multiply
return gen_math_ops.mul(x, y, name)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\gen_math_ops.py”, line 6245, in mul
“Mul”, x=x, y=y, name=name)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\op_def_library.py”, line 750, in _apply_op_helper
attrs=attr_protos, op_def=op_def)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py”, line 3569, in _create_op_internal
op_def=op_def)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py”, line 2045, in __init__
self._traceback = tf_stack.extract_stack_for_node(self._c_op)

Traceback (most recent call last):
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py”, line 1375, in _do_call
return fn(*args)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py”, line 1360, in _run_fn
target_list, run_metadata)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py”, line 1453, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
(0) Resource exhausted: failed to allocate memory
[[{{node mul_81}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

[[concat_4/concat/_463]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

(1) Resource exhausted: failed to allocate memory
[[{{node mul_81}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

0 successful operations.
0 derived errors ignored.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\mainscripts\Trainer.py”, line 129, in trainerThread
iter, iter_time = model.train_one_iter()
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\ModelBase.py”, line 474, in train_one_iter
losses = self.onTrainOneIter()
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\Model_SAEHD\Model.py”, line 774, in onTrainOneIter
src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\Model_SAEHD\Model.py”, line 584, in src_dst_train
self.target_dstm_em:target_dstm_em,
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py”, line 968, in run
run_metadata_ptr)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py”, line 1191, in _run
feed_dict_tensor, options, run_metadata)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py”, line 1369, in _do_run
run_metadata)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py”, line 1394, in _do_call
raise type(e)(node_def, op, message) # pylint: disable=no-value-for-parameter
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
(0) Resource exhausted: failed to allocate memory
[[node mul_81 (defined at C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py:64) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

[[concat_4/concat/_463]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

(1) Resource exhausted: failed to allocate memory
[[node mul_81 (defined at C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py:64) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn’t available when running in Eager mode.

0 successful operations.
0 derived errors ignored.

Errors may have originated from an input operation.
Input Source operations connected to node mul_81:
src_dst_opt/vs_inter/dense1/weight_0/read (defined at C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py:38)

Input Source operations connected to node mul_81:
src_dst_opt/vs_inter/dense1/weight_0/read (defined at C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py:38)

Original stack trace for ‘mul_81’:
File “threading.py”, line 884, in _bootstrap
File “threading.py”, line 916, in _bootstrap_inner
File “threading.py”, line 864, in run
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\mainscripts\Trainer.py”, line 58, in trainerThread
debug=debug)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\ModelBase.py”, line 193, in __init__
self.on_initialize()
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\Model_SAEHD\Model.py”, line 564, in on_initialize
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gvs))
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\optimizers\AdaBelief.py”, line 64, in get_update_op
v_t = self.beta_2*vs + (1.0-self.beta_2) * tf.square(g-m_t)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\variables.py”, line 1076, in _run_op
return tensor_oper(a.value(), *args, **kwargs)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\math_ops.py”, line 1400, in r_binary_op_wrapper
return func(x, y, name=name)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\math_ops.py”, line 1710, in _mul_dispatch
return multiply(x, y, name=name)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\util\dispatch.py”, line 206, in wrapper
return target(*args, **kwargs)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\math_ops.py”, line 530, in multiply
return gen_math_ops.mul(x, y, name)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\gen_math_ops.py”, line 6245, in mul
“Mul”, x=x, y=y, name=name)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\op_def_library.py”, line 750, in _apply_op_helper
attrs=attr_protos, op_def=op_def)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py”, line 3569, in _create_op_internal
op_def=op_def)
File “C:\Users\Ferros\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py”, line 2045, in __init__
self._traceback = tf_stack.extract_stack_for_node(self._c_op)

[defalafaParticipant]

dims are way to highin this model , try lower settings there

== ae_dims: 448== 256
== e_dims: 112 == 70
== d_dims: 112 == 70

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