Experiments with Reinforcement learning.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Docker
docker-compose
- Install docker and docker-compose for your platfrom
- Clone this repo and run
./start.sh
in the project root folder. This starts a docker container with correct port configuration. - Run
docker ps
at command line. This prints the port forwarding information. - The output looks something like
- Go to https://localhost/port_number where port_number is where 8888 is mapped to. In the above example, it is 32784.
- You can access the notebooks from that url.
- For the curious, we also mapped tensorboard port (6006) to a port. (In the above example, it is mapped to 32785 on your local machine). So if tensorboard is running inside your container, you could access it at https://localhost/32785
- If you want to exec into the container and run code there, do
docker exec -it container_id /bin/bash
where container_id is from the output ofdocker ps
command
Project based on the cookiecutter data science project template. #cookiecutterdatascience