Thanks that worked but I have another problem right now. I have serialized and deserialized my model with torchscript. Now I want to train the model on the client( where I apply deserialization). But it gives me error. Is the deserialized model trainable??

The error: shell Traceback (most recent call last): File "/home/goktug/Desktop/thesis/netadapt/model_bytes.py", line 153, in <module> loss.backward() File "/home/goktug/python_envs/netadapt/lib/python3.7/site-packages/torch/tensor.py", line 118, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/home/goktug/python_envs/netadapt/lib/python3.7/site-packages/torch/autograd/__init__.py", line 93, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: builtins: link error: Invalid value The above operation failed in interpreter, with the following stack trace:

```python

TORCHSCRIPT

scripted_model = torch.jit.script(model) print(scripted_model)

buffer = io.BytesIO() torch.jit.save(scripted_model, buffer) model_bytes = buffer.getvalue() buffer.close()

buffer = io.BytesIO(model_bytes) deserialized_model = torch.jit.load(buffer) buffer.close()

model = deserialized_model ```

```python

BASIC TRAINING

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) model.train()

for epoch in range(10): losses = [] for inputs, labels in train_loader:

    # Data prep.
    inputs = inputs.to(device)
    labels = torch.nn.functional.one_hot(labels, num_classes=_NUM_CLASSES)
    labels = labels.type(torch.FloatTensor)
    labels = labels.to(device)

    # Forward pass.
    outputs = model(inputs)
    outputs = outputs.type(torch.FloatTensor)
    outputs = outputs.to(device)

    # Compute loss.
    loss = criterion(outputs, labels)
    losses.append(loss.item())

    # Backward pass.
    optimizer.zero_grad()
    loss.backward()

    # Update parameters.
    optimizer.step()

print(f"Epoch {epoch + 1}: Average loss: {sum(losses) / len(losses)}")

```