I’m receiving an error training on CUDA that doesn’t occur when I use a CPU.
First things first, I’m pretty sure it is due to memory. I am running tensors of length 5020 into a linear layer that outputs tensors of length 5000 (I am not sure whether this is too intensive or not).
I’m running on  NVIDIA-SMI 450.51.05    Driver Version: 450.51.05    CUDA Version: 11.0
PyTorch version: 1.6.0
I get the following error:
Traceback (most recent call last):
File "main.py", line 457, in <module>
  in()
File "main.py", line 373, in main
  ain_worker(args.gpu, ngpus_per_node, args, timers=Timers)
  Fi "/oak/stanford/groups/satpathy/users/hkitano/AtacWorks/scripts/worker.py", line 222, in train_worker
  ansform=args.transform)
  Fi "/oak/stanford/groups/satpathy/users/hkitano/AtacWorks/claragenomics/dl4atac/train.py", line 96, in train
  ed = model(x, l)
  Fi "/home/users/hkitano/miniconda3/envs/atacworks/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
  sult = self.forward(*input, **kwargs)
  Fi "/oak/stanford/groups/satpathy/users/hkitano/AtacWorks/claragenomics/dl4atac/models/models.py", line 378, in forward
  = linear_layer(x) #[64, 6000]
  Fi "/home/users/hkitano/miniconda3/envs/atacworks/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
  sult = self.forward(*input, **kwargs)
  Fi "/oak/stanford/groups/satpathy/users/hkitano/AtacWorks/claragenomics/dl4atac/layers.py", line 154, in forward
  = self.layer(x)
  Fi "/home/users/hkitano/miniconda3/envs/atacworks/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
  sult = self.forward(*input, **kwargs)
  Fi "/home/users/hkitano/miniconda3/envs/atacworks/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 91, in forward
  turn F.linear(input, self.weight, self.bias)
  Fi "/home/users/hkitano/miniconda3/envs/atacworks/lib/python3.6/site-packages/torch/nn/functional.py", line 1674, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: CUDA error: CUBLAS_STATUS_INTERNAL_ERROR when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`

If i run it on a separate screen and monitor GPU usage with nvidia-smi -l 1, I get the following:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.05    Driver Version: 450.51.05    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla V100-PCIE...  On   | 00000000:3F:00.0 Off |                    0 |
| N/A   29C    P0    25W / 250W |      4MiB / 32510MiB |      0%   E. Process |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
Sun Sep 13 17:32:30 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.05    Driver Version: 450.51.05    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla V100-PCIE...  On   | 00000000:3F:00.0 Off |                    0 |
| N/A   30C    P0    39W / 250W |    549MiB / 32510MiB |      4%   E. Process |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A    379509      C   python                            545MiB |
+-----------------------------------------------------------------------------+
Sun Sep 13 17:32:31 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.05    Driver Version: 450.51.05    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla V100-PCIE...  On   | 00000000:3F:00.0 Off |                    0 |
| N/A   30C    P0    39W / 250W |    839MiB / 32510MiB |      4%   E. Process |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A    379509      C   python                            837MiB |
+-----------------------------------------------------------------------------+
Sun Sep 13 17:32:32 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.05    Driver Version: 450.51.05    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla V100-PCIE...  On   | 00000000:3F:00.0 Off |                    0 |
| N/A   30C    P0    39W / 250W |   1351MiB / 32510MiB |      1%   E. Process |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A    379509      C   python                           1347MiB |
+-----------------------------------------------------------------------------+
**GPU 00000000:3F:00.0: Detected Critical Xid Error**
Sun Sep 13 17:32:33 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.05    Driver Version: 450.51.05    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla V100-PCIE...  On   | 00000000:3F:00.0 Off |                    0 |
| N/A   30C    P0    39W / 250W |      0MiB / 32510MiB |      0%   E. Process |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
Sun Sep 13 17:32:34 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.05    Driver Version: 450.51.05    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla V100-PCIE...  On   | 00000000:3F:00.0 Off |                    0 |
| N/A   30C    P0    39W / 250W |      0MiB / 32510MiB |      0%   E. Process |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

It seems to me like the amount of memory used is not near the limit.
If it helps, here is my Model:
class DenoisingResNetWithLocation(nn.Module):
    """Resnet model."""

    def __init__(self, interval_size, in_channels=1, out_channels=15,
                 num_blocks=5,
                 kernel_size=50, dilation=8, bn=False, afunc='relu',
                 num_blocks_class=2,
                 kernel_size_class=50, dilation_class=8,
                 out_channels_class=15,
                 num_layers_location=3, num_layers_joint=3,
                 in_channels_location=20, out_channels_location=20):
        """Initialize the class.

        Args:
            interval_size : length (in bp) of the genomic intervals supplied
            to the model.
            in_channels : Number of input channels.
            out_channels: Number of output channels for all residual blocks
            in the regression part of the model.
            num_blocks: Number of residual blocks in the regression part
            of the model.
            kernel_size: Size of the kernel filter for all
            convolutional layers in the regression part of the model.
            dilation: Dilation parameter for all convolutional layers
            in the regression part of the model.
            bn: Batch normalization.
            afunc: activation function.
            num_blocks_class: Number of residual blocks in the
            classification part of the model.
            kernel_size_class: Size of the kernel filter for all
            convolutional layers in the classification part of the model.
            dilation_class: Dilation parameter for all convolutional layers
            in the classification part of the model.
            out_channels_class: Number of output channels for all residual
            blocks in the classification part of the model

        """
        self.interval_size = interval_size
        super(DenoisingResNetWithLocation, self).__init__()

        self.res_blocks = nn.ModuleList()
        self.location_blocks = nn.ModuleList()
        self.joint_blocks = nn.ModuleList()
        self.res_blocks_class = nn.ModuleList()


        # Residual blocks for regression
        self.res_blocks.append(
            ResBlock(interval_size, in_channels, out_channels, kernel_size,
                     dilation=dilation, bn=bn, afunc=afunc, conv_input=True))
        for _ in range(num_blocks - 1):
            self.res_blocks.append(
                ResBlock(interval_size, out_channels, out_channels,
                         kernel_size,
                         dilation=dilation, bn=bn, afunc=afunc,
                         conv_input=False))

        self.location_blocks.append(
            Linear(in_channels_location, out_channels_location, afunc))

        for _ in range(num_layers_location - 1):
            self.location_blocks.append(
                Linear(out_channels_location, out_channels_location, afunc))

        self.regressor = ConvAct1d(interval_size, in_channels=out_channels,
                                   out_channels=1, kernel_size=1, dilation=1,
                                   bn=bn, afunc=afunc)

        joint_in = interval_size + out_channels_location
        self.joint_blocks.append(
            Linear(joint_in, interval_size, afunc))
        
        for _ in range(num_layers_joint - 1):
            self.joint_blocks.append(
                Linear(interval_size, interval_size, afunc))



        # Residual blocks for classification
        self.res_blocks_class.append(ResBlock(interval_size, in_channels=1,
                                              out_channels=out_channels_class,
                                              kernel_size=kernel_size_class,
                                              dilation=dilation_class, bn=bn,
                                              afunc=afunc, conv_input=True,
                                              bias=True))
        for _ in range(num_blocks_class - 1):
            self.res_blocks_class.append(
                ResBlock(interval_size, out_channels_class, out_channels_class,
                         kernel_size_class, dilation=dilation_class, bn=bn,
                         afunc=afunc, conv_input=False, bias=True))
        self.classifier = ConvAct1d(interval_size, in_channels=out_channels,
                                    out_channels=1, kernel_size=1, dilation=1,
                                    bn=bn, afunc=None, bias=True)

    def forward(self, x, l):
        """Get regression and classification forward propagated output.

        Args:
            x : Input.

        Return:
            out_reg: Regression output.
            out_cla: Classification output.

        """
        for res_block in self.res_blocks:
            x = res_block(x) #[64, 15, 6000]
        x = self.regressor(x) #[64, 1, 6000]
        for linear_layer in self.location_blocks:
            l = linear_layer(l) #[64, 20]

        x = x.view(x.size(0), -1) #[64, 6000]
        
        x = torch.cat((l, x), dim=1) #[64, 6020]

        for linear_layer in self.joint_blocks:
            x = linear_layer(x) #[64, 6000] ## fails here!
        out_reg = x #[64, 6000]
        x = x.unsqueeze(1) #[64, 1, 6000]
        for res_block in self.res_blocks_class:
            x = res_block(x) ##[64, 15, 60000]
        out_cla = torch.sigmoid(self.classifier(x).squeeze(1))# [64, 6000]

        return out_reg, out_cla

Any thoughts! Thank you!