



 Here is the Python code that performs the tasks you specified using the avalanche library:

```python
import argparse
import torch
from torch.optim import SGD
from torch.nn import CrossEntropyLoss
from avalanche.benchmarks.classic import MNIST
from avalanche.models import SimpleMLP
from avalanche.training.strategies import Naive
from avalanche.evaluation.metrics import accuracy_metrics, loss_metrics, forward_transfer_metrics, cpu_usage_metrics, disk_usage_metrics, gpu_usage_metrics, ram_usage_metrics, timing_metrics, class_accuracy_metrics
from avalanche.logging import TextLogger, InteractiveLogger, CSVLogger, TensorboardLogger
from avalanche.training.plugins import EvaluationPlugin

# Parse command line arguments to determine the device to use for computations.
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu', help='The device to use for computations.')
args = parser.parse_args()
device = args.device

# Define transformations for training and testing data.
train_transform = None
test_transform = None

# Create a benchmark using the MNIST dataset with the defined transformations.
benchmark = MNIST(train_transform=train_transform, test_transform=test_transform)

# Create a simple MLP model with the number of classes equal to the number of classes in the benchmark.
model = SimpleMLP(num_classes=benchmark.n_classes)

# Define various loggers including a text logger, an interactive logger, a CSV logger, and a Tensorboard logger.
text_logger = TextLogger(open('log.txt', 'w'))
interactive_logger = InteractiveLogger()
csv_logger = CSVLogger(open('log.csv', 'w'))
tensorboard_logger = TensorboardLogger()

# Define an evaluation plugin that computes a wide range of metrics including accuracy, loss, class accuracy, AMCA, forgetting, backward transfer, forward transfer, CPU usage, timing, RAM usage, GPU usage, disk usage, MAC, and labels repartition metrics. The plugin should log these metrics using the defined loggers.
evaluation_plugin = EvaluationPlugin(
    accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    forward_transfer_metrics(experience=True, stream=True),
    cpu_usage_metrics(experience=True),
    disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    gpu_usage_metrics(experience=True),
    ram_usage_metrics(experience=True),
    timing_metrics(epoch=True, experience=True),
    class_accuracy_metrics(experience=True, stream=True),
    loggers=[text_logger, interactive_logger, csv_logger, tensorboard_logger]
)

# Create a Naive continual learning strategy using the defined model, an SGD optimizer, a CrossEntropyLoss loss function, and the defined evaluation plugin.
strategy = Naive(model, SGD(model.parameters()), CrossEntropyLoss(), evaluator=evaluation_plugin)

# Train the model on the benchmark's training stream and evaluate it on the benchmark's test stream, printing the results after each experience.
for experience in benchmark.train_stream:
    strategy.train(experience)
    strategy.eval(benchmark.test_stream)

# After all experiences, print all the metrics stored by the evaluation plugin.
print(evaluation_plugin.get_last_metrics())
```

This code defines a simple MLP model, creates a benchmark using the MNIST dataset, and trains the model using a Naive strategy. It also defines various loggers and an evaluation plugin to compute and log various metrics during training and evaluation.

Please note that this code is just a starting point and may require additional modifications to fit your specific use case. Additionally, you may need to adjust the transformations, model architecture, and other parameters to achieve the best results for your specific task.

Please let me know if you have any further questions or if there's anything else I can help with. I'm here to assist you! 😊