Merge branch 'TorchDSP:develop' into develop

.dockerignore

@@ -0,0 +1 @@
+examples/*

.gitignore

@@ -12,3 +12,5 @@ lightning_logs/
 *.jpg
 *.benchmarks/
 dist/
+examples/*.ipynb_checkpoints/
+docs/bin

.readthedocs.yaml

@@ -0,0 +1,26 @@
+# Read the Docs configuration file for Sphinx projects
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+  # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs
+  # builder: "dirhtml"
+  # Fail on all warnings to avoid broken references
+  # fail_on_warning: true
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+python:
+   install:
+   - requirements: docs/docs-requirements.txt

Dockerfile

@@ -3,6 +3,7 @@ FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get update && apt-get install -y \
+    build-essential \
     libgl1-mesa-glx && \
     rm -rf /var/lib/apt/lists/*
 
@@ -15,5 +16,3 @@ RUN pip3 install /build
 RUN pip3 install notebook jupyterlab
 
 WORKDIR /workspace/code
-
-ADD examples/ /workspace/code/examples

README.md

@@ -48,36 +48,36 @@ If you'd like to generate the named datasets without messing with your current P
 
 ```
 docker build -t torchsig -f Dockerfile .
-docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_sig53.py --root=/workspace/code/torchsig/data --all=True
+docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_sig53.py --root=/workspace/code/torchsig/examples/sig53
 ```
 
 For the wideband dataset, you can do:
 
 ```
 docker build -t torchsig -f Dockerfile .
-docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_wideband_sig53.py --root=/workspace/code/torchsig/data --all=True
+docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_wideband_sig53.py --root=/workspace/code/torchsig/examples/wideband_sig53
 ```
 
 If you do not need to use Docker, you can also just generate using the regular command-line interface
 
 ```
-python3 torchsig/scripts/generate_sig53.py --root=torchsig/data --all=True
+python3 torchsig/scripts/generate_sig53.py --root=torchsig/examples
 ```
 
 or for the wideband dataset:
 
 ```
-python3 torchsig/scripts/generate_wideband_sig53.py --root=torchsig/data --all=True
+python3 torchsig/scripts/generate_wideband_sig53.py --root=torchsig/examples
 ```
 
-Then, be sure to point scripts looking for ```root``` to ```torchsig/data```.
+Then, be sure to point scripts looking for ```root``` to ```torchsig/examples```.
 
 ## Using the Dockerfile
 If you have Docker installed along with compatible GPUs and drivers, you can try:
 
 ```
 docker build -t torchsig -f Dockerfile .
-docker run -d --rm --network=host --shm-size=32g --gpus all --name torchsig_workspace torchsig tail -f /dev/null
+docker run -d --rm --network=host --shm-size=32g --gpus all --name torchsig_workspace -v `pwd`/examples:/workspace/code/examples torchsig tail -f /dev/null
 docker exec torchsig_workspace jupyter notebook --allow-root --ip=0.0.0.0 --no-browser
 ```