# Training

*Avalanche* offers significant support for *training* (with *templates*, *strategies* and *plug-ins*). Here you can find a list of **examples** related to the training and some strategies available in Avalanche (each strategy reproduces original paper results in the [CL-Baselines](https://github.com/ContinualAI/continual-learning-baselines) repository: 

* [Joint-Training](https://github.com/ContinualAI/avalanche/blob/master/examples/joint_training.py): *this example shows how to take a stream of experiences and train simultaneously on all of them. This is useful to implement the "offline"  or "multi-task" upper bound.*
* [Replay strategy](https://github.com/ContinualAI/avalanche/blob/master/examples/replay.py)*: simple example on the usage of replay in Avalanche.*
* [AR1 strategy](https://github.com/ContinualAI/avalanche/blob/master/examples/ar1.py): t*his is a simple example on how to use the AR1 strategy.*
* [CoPE Strategy](https://github.com/ContinualAI/avalanche/blob/master/examples/cope.py): *this is a simple example on how to use the CoPE plugin. It's an example in the online data incremental setting, where both learning and evaluation is completely task-agnostic.*
* [Cumulative Strategy](https://github.com/ContinualAI/avalanche/blob/master/examples/dataloader.py): h*ow to define your own cumulative strategy based on the different Data Loaders made available in Avalanche.* 
* [Deep SLDA](https://github.com/ContinualAI/avalanche/blob/master/examples/deep_slda.py)*: this is a simple example on how to use the Deep SLDA strategy.*
* [Early Stopping](https://github.com/ContinualAI/avalanche/blob/master/examples/all_mnist_early_stopping.py): *this example shows how to use early stopping to dynamically stop the training procedure when the model converged instead of training for a fixed number of epochs.*
* [Object Detection](https://github.com/ContinualAI/avalanche/blob/master/examples/detection.py): *this example shows how to run object detection/segmentation tasks.*
* [Object Detection with Elvis](https://github.com/ContinualAI/avalanche/blob/master/examples/detection_lvis.py)*: this example shows how to run object detection/segmentation tasks with a* *toy benchmark based on the LVIS dataset.*
* [Object Detection Training](https://github.com/ContinualAI/avalanche/tree/master/examples/tvdetection): *set of examples showing how you can use Avalanche for distributed training of object detector.*
* [EWC on MNIST](https://github.com/ContinualAI/avalanche/blob/master/examples/ewc_mnist.py)*: this example tests EWC on Split MNIST and Permuted MNIST.*
* [LWF on MNIST](https://github.com/ContinualAI/avalanche/blob/master/examples/lfl_mnist.py)*: this example tests LWF on Permuted MNIST.*
* [GEM and A-GEM on MNIST](https://github.com/ContinualAI/avalanche/blob/master/examples/gem_agem_mnist.py)*: this example shows how to use GEM and A-GEM strategies on MNIST.*
* [Ex-Model Continual Learning](https://github.com/ContinualAI/avalanche/blob/master/examples/ex_model_cl.py)*: this example shows how to create a stream of pre-trained model from which to learn.*
* [Generative Replay](https://github.com/ContinualAI/avalanche/blob/master/examples/generative_replay_MNIST_generator.py)*: this is a simple example on how to implement generative replay in Avalanche.*
* [iCARL strategy](https://github.com/ContinualAI/avalanche/blob/master/examples/icarl.py): *simple example to show how to use the iCARL strategy.*
* [LaMAML strategy](https://github.com/ContinualAI/avalanche/blob/master/examples/lamaml_cifar100.py)*: example on how to use a meta continual learning in Avalanche.*
* [RWalk strategy](https://github.com/ContinualAI/avalanche/blob/master/examples/rwalk_mnist.py): *example of the RWalk strategy usage.*
* [Online Naive](https://github.com/ContinualAI/avalanche/blob/6dbabb2ab787a53b59b9cbcb245ad500e984f671/examples/online_naive.py): *example to run a naive strategy in an online setting.*
* [Synaptic Intelligence](https://github.com/ContinualAI/avalanche/blob/master/examples/synaptic_intelligence.py): *this is a simple example on how to use the Synaptic Intelligence Plugin.*
* [Continual Sequence Classification](https://github.com/ContinualAI/avalanche/blob/master/examples/continual_sequence_classification.py): *sequence classification example using torchaudio and Speech Commands.*


---

# 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-v0.3.0/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.