# Training

{% code title="" %}

```python
model = SimpleMLP(hidden_size=args.hs)
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
criterion = torch.nn.CrossEntropyLoss()

# check if selected GPU is available or use CPU
assert args.cuda == -1 or args.cuda >= 0, "cuda must be -1 or >= 0."
if args.cuda >= 0:
    assert torch.cuda.device_count() > args.cuda,
    f"{args.cuda + 1} GPU needed. Found {torch.cuda.device_count()}."
device = 'cpu' if args.cuda == -1 else f'cuda:{args.cuda}'
print(f'Using device: {device}')

# create split scenario
scenario = SplitMNIST(n_experiences=5, return_task_id=False)

interactive_logger = InteractiveLogger()
eval_plugin = EvaluationPlugin(
    accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    ExperienceForgetting(),
    loggers=[interactive_logger])

# create strategy
assert len(args.lwf_alpha) == 1 or len(args.lwf_alpha) == 5,
'Alpha must be a non-empty list.'
lwf_alpha = args.lwf_alpha[0] if len(args.lwf_alpha) == 1 else args.lwf_alpha

strategy = LwF(model, optimizer, criterion, alpha=lwf_alpha,
               temperature=args.softmax_temperature,
               train_epochs=args.epochs, device=device,
               train_mb_size=args.minibatch_size, evaluator=eval_plugin)

# train on the selected scenario with the chosen strategy
print('Starting experiment...')
results = []
for train_batch_info in scenario.train_stream:
    print("Start training on experience ", train_batch_info.current_experience)

    strategy.train(train_batch_info, num_workers=4)
    print("End training on experience ", train_batch_info.current_experience)
    print('Computing accuracy on the test set')
    results.append(strategy.eval(scenario.test_stream[:]))
```

{% endcode %}

## 🤝 Run it on Google Colab

You can run *this chapter* and play with it on Google Colaboratory:

{% hint style="danger" %}
Notebook currently unavailable.
{% endhint %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://avalanche.continualai.org/avalanche-v0.1.0/examples/training.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.