https://github.com/hugcis/benchmark_learning_efficiency

Code to reproduce results from "Benchmarking learning efficiency in deep reservoir computing"
https://github.com/hugcis/benchmark_learning_efficiency

learning research-paper reservoir-computing

Last synced: 7 days ago
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Code to reproduce results from "Benchmarking learning efficiency in deep reservoir computing"

• Host: GitHub
• URL: https://github.com/hugcis/benchmark_learning_efficiency
• Owner: hugcis
• License: mit
• Created: 2021-11-19T10:44:43.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-10-27T07:22:57.000Z (about 2 years ago)
• Last Synced: 2023-03-05T02:29:57.735Z (almost 2 years ago)
• Topics: learning, research-paper, reservoir-computing
• Language: Jupyter Notebook
• Homepage:
• Size: 2.03 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Benchmarking learning efficiency in deep reservoir computing

This is the code to reproduce results from the paper 

*Benchmarking Learning Efficiency in Deep Reservoir Computing. Cisneros, H.,

Mikolov, T., & Sivic, J. (2022). 1st Conference on Lifelong Learning Agents,

Montreal, Canada*.

## Re-run experiments

**WARNING**: Re-running all experiments might take a significant amount of time.

Experiments in the paper were done on a cluster using GPUs and a lot of

parallelism. The docker solution is particularly sub-optimal and will take a

long time to run experiments.

An alternative to running all the experiments is to download the data directly: 

``` sh

wget https://data.ciirc.cvut.cz/public/projects/2022BenchmarkingLearningEfficiency/experiment_2022-07-13T15:32:50.tar

tar -xvf "experiment_2022-07-13T15:32:50.tar"

```

### Running with poetry

The easiest way to run the experiments is to use

[poetry](https://python-poetry.org/). First, clone the repo 

 

```sh

git clone https://github.com/hugcis/benchmark_learning_efficiency.git

```

Then, run `poetry install` to create a virtual environment and install all

the dependencies.

Then run: 

```sh

./run_experiments.sh

```

### Running in Docker

If you don't have or don't want to install poetry, you can build and install

everything within a docker container. Just run the following from inside the

repo:

``` sh

docker build -t pypoetry_bledrc .

docker run -it --entrypoint=/bin/bash pypoetry_bledrc -i

```

This will open a bash tty within the docker container where you can run 

```sh

./run_experiments.sh

```

## Generate figures and tables

Once the data is generated or downloaded (make sure that you have the

experiment_gpu and experiment_sgd folders), you can run jupyter notebooks in

order to re-generate the figures and tables from the paper.

Just run

``` sh

poetry run jupyter notebook

```

and open the two jupyter notebooks in the folder `notebooks`.