Merge pull request #357 from ltindall/update

Big Update

Dockerfile

@@ -1,8 +1,8 @@
-FROM nvidia/cuda:12.2.2-base-ubuntu22.04
+FROM ubuntu:22.04
 LABEL maintainer="Lucas Tindall <[email protected]>"
 ENV PYTHONUNBUFFERED 1
 
-COPY  pyproject.toml .
+COPY pyproject.toml .
 COPY poetry.lock .
 
 RUN apt-get update && apt-get install -y libjpeg-dev python3 python3-pip python3-venv
@@ -18,5 +18,5 @@ RUN apt-get update && apt-get install -y --no-install-recommends curl gcc git g+
 COPY . /BirdsEye
 WORKDIR /BirdsEye
 EXPOSE 4999
-ENTRYPOINT ["python3"]
-CMD ["run_birdseye.py"]
+ENTRYPOINT ["python3", "geolocate.py"]
+CMD ["geolocate.ini"]

README.md

@@ -52,57 +52,49 @@ ___
 ## 1. Usage
 ### Installation
 ```
-pip install -r requirements.txt
+poetry config virtualenvs.create false 
+poetry install --no-root
 ```
 
 ### To run on command line
 ```
-$ python run_birdseye.py -h
-usage: run_birdseye.py [-h] -c FILE [-b]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -c FILE, --config FILE
-                        Specify a configuration file
-  -b, --batch           Perform batch run
+$ python geolocate.py -h
+usage: geolocate.py [-h] [--log LOG] config_path
+
+positional arguments:
+  config_path  Path to config file, geolocate.ini provided as example.
+
+options:
+  -h, --help   show this help message and exit
+  --log LOG    Log level
 ```
 
 ### To run using a Docker container
-First install Docker with GPU support. [Instructions here.](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
+First install Docker. [Instructions here.](https://docs.docker.com/engine/install/)
 
-A Docker file has also been provided for ease of use. To run with Docker, execute the following commands:
-```
-> docker build -t birds_eye .
-> docker run -it --gpus all birds_eye -c {config.yaml}
-```
-In order to streamline this process a `Makefile` has been provided as a shorthand.
+A [Dockerfile](Dockerfile) has been provided for ease of use. To run with Docker, execute the following commands:
 ```
-> make run_mcts
-> make run_dqn
-> make run_batch
+docker build -t birds_eye .
+docker run -it -p 4999:4999 birds_eye
 ```
-Accepted make values are: `run_mcts, run_dqn, run_batch, build`
 
-If you don't have or want to use GPUs you can preface the `make` command with `GPUS=` like so:
+A Docker compose file has also been provided. To run with docker compose:
 ```
-> GPUS= make run_mcts
+ORCHESTRATOR={INSERT ORCHESTRATOR IP} docker compose -f geolocate.yml up
 ```
 
+
 ## 2. Configurations
 Running experiments requires a set of configurations variables which specify settings for the envrionment and motion planning method.
 See [Configurations Documentation](docs/CONFIGS.md) for more information.
 
 
 ## 3. Examples
-### Run with Monte Carlo Tree Search policy
-```
-$ python run_birdseye.py -c configs/mcts.yaml
 ```
-### Run with Deep Q-Network policy
-```
-$ python run_birdseye.py -c configs/dqn.yaml
+$ python geolocate.py geolocate.ini
 ```
 
+
 ## 4. Results
 The results of the experiments will be stored in the `./runs` directory.
 Log files are created which store the configurations for each experiment along with metrics and state information per time step.