| Name | sequel-core JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | |
| Summary | A Continual Learning Framework for both Jax and PyTorch. |
| upload_time | 2023-04-18 15:50:11 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
| license | |
| keywords |
continual learning
pytorch
jax
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# Sequel: A Continual Learning Library in PyTorch and JAX
The goal of this library is to provide a simple and easy to use framework for continual learning. The library is written in PyTorch and JAX and provides a simple interface to run experiments. The library is still in development and we are working on adding more algorithms and datasets.
- Documetation: https://nik-dim.github.io/sequel-site/
- Reproducibility Board: https://nik-dim.github.io/sequel-site/reproducibility/
- Weights&Biases: https://wandb.ai/nikdim/SequeL
## Installation
The library can be installed via pip:
```bash
pip install sequel-core
```
Alternatively, you can install the library from source:
```bash
git clone https://github.com/nik-dim/sequel.git
python3 -m build
```
or use the library by cloning the repository. In order to use the library, you need to install the dependencies. This can be done via the `requirements.txt` file. We recommend to use a conda environment for this. The following commands will create a conda environment with the required packages and activate it:
```bash
# create the conda environment
conda create -n sequel -y python=3.10 cuda cudatoolkit cuda-nvcc -c nvidia -c anaconda
conda activate sequel
# install all required packages
pip install -r requirements.txt
# Optional: Depending on the machine, the next command might be needed to enable CUDA support for GPUs
pip install jax[cuda11_cudnn82] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
```
## Run an experiment
For some examples, you can modify the `example_pytorch.py` and `example_jax.py` files, or run:
```bash
# example experiment on PyTorch
python example_pytorch.py
# ...and in JAX
python example_jax.py
```
Experiments are located in the `examples/` directory in `configs`. In order to run an experiment you simply do the following:
```bash
python main.py +experiment=EXPERIMENT_DIR/EXPERRIMENT
# examples
python main.py +examples=ewc_rotatedmnist mode=pytorch # or mode=jax
python main.py +examples=mcsgd_rotatedmnist mode=pytorch # or mode=jax
```
In order to create your own experiment you follow the template of the experiments in `configs/examples/`. You override the defaults so that e.g. another algorithm is selected and you specify the training details. To run multiple experiments with different configs, the `--multirun` flag of [Hydra](https://hydra.cc/docs) can be used.
For instance:
```bash
python main.py --multirun +examples=ewc_rotatedmnist \
mode=pytorch optimizer.lr=0.01,0.001 \
benchmark.batch_size=128,256 \
training.epochs_per_task=1 # online setting
```
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"description": "# Sequel: A Continual Learning Library in PyTorch and JAX\n\nThe goal of this library is to provide a simple and easy to use framework for continual learning. The library is written in PyTorch and JAX and provides a simple interface to run experiments. The library is still in development and we are working on adding more algorithms and datasets.\n\n- Documetation: https://nik-dim.github.io/sequel-site/\n- Reproducibility Board: https://nik-dim.github.io/sequel-site/reproducibility/ \n- Weights&Biases: https://wandb.ai/nikdim/SequeL \n## Installation\n\nThe library can be installed via pip:\n```bash\npip install sequel-core\n```\n\nAlternatively, you can install the library from source:\n```bash\ngit clone https://github.com/nik-dim/sequel.git\npython3 -m build\n```\n\nor use the library by cloning the repository. In order to use the library, you need to install the dependencies. This can be done via the `requirements.txt` file. We recommend to use a conda environment for this. The following commands will create a conda environment with the required packages and activate it:\n```bash\n# create the conda environment\nconda create -n sequel -y python=3.10 cuda cudatoolkit cuda-nvcc -c nvidia -c anaconda\nconda activate sequel \n\n# install all required packages\npip install -r requirements.txt\n\n# Optional: Depending on the machine, the next command might be needed to enable CUDA support for GPUs\npip install jax[cuda11_cudnn82] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html\n```\n\n\n## Run an experiment\n\nFor some examples, you can modify the `example_pytorch.py` and `example_jax.py` files, or run:\n```bash\n# example experiment on PyTorch\npython example_pytorch.py\n\n# ...and in JAX\npython example_jax.py\n```\n\nExperiments are located in the `examples/` directory in `configs`. In order to run an experiment you simply do the following:\n\n```bash\npython main.py +experiment=EXPERIMENT_DIR/EXPERRIMENT\n\n# examples\npython main.py +examples=ewc_rotatedmnist mode=pytorch # or mode=jax\npython main.py +examples=mcsgd_rotatedmnist mode=pytorch # or mode=jax\n```\n\nIn order to create your own experiment you follow the template of the experiments in `configs/examples/`. You override the defaults so that e.g. another algorithm is selected and you specify the training details. To run multiple experiments with different configs, the `--multirun` flag of [Hydra](https://hydra.cc/docs) can be used. \nFor instance:\n```bash\npython main.py --multirun +examples=ewc_rotatedmnist \\\n mode=pytorch optimizer.lr=0.01,0.001 \\\n benchmark.batch_size=128,256 \\ \n training.epochs_per_task=1 # online setting\n```\n",
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