Upgrade docker base image to nvcr.io/nvidia/pytorch:23.03-py3

Older pytorch/pytorch base images were broken due to stale PGP
keys.

This version will run PyTorch 2.0.

fixes #115
fixes #113

docker/Dockerfile

@@ -12,10 +12,9 @@
 #
 # This file defaults to pytorch/pytorch as it works on slightly older
 # driver versions.
-ARG BASE_IMAGE=pytorch/pytorch:1.10.0-cuda11.3-cudnn8-devel
-FROM $BASE_IMAGE
+FROM nvcr.io/nvidia/pytorch:23.03-py3
 
-RUN apt-get update && apt-get install -y --no-install-recommends \
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
     pkg-config \
     libglvnd0 \
     libgl1 \

run_sample.sh

@@ -34,7 +34,6 @@ rest=$@
 # Build the docker container
 if [ "$build_container" = "1" ]; then
     docker build --tag gltorch:latest -f docker/Dockerfile .
-    docker build --tag gltensorflow:latest --build-arg BASE_IMAGE=tensorflow/tensorflow:1.15.0-gpu-py3 -f docker/Dockerfile .
 fi
 
 if [ ! -f "$sample" ]; then
@@ -44,16 +43,10 @@ if [ ! -f "$sample" ]; then
 fi
 
 image="gltorch:latest"
-TENSORFLOW_CUDA_CACHE=""
-# Magically choose the tensorflow container if running a sample from the samples/tensorflow/ directory
-if [[ $sample == *"/tensorflow/"* ]]; then
-    image="gltensorflow:latest"
-    TENSORFLOW_CUDA_CACHE="-e NVDIFFRAST_CACHE_DIR=/app/tmp"
-fi
 
 echo "Using container image: $image"
 echo "Running command: $sample $rest"
 
 # Run a sample with docker
 docker run --rm -it --gpus all --user $(id -u):$(id -g) \
-    -v `pwd`:/app --workdir /app -e TORCH_EXTENSIONS_DIR=/app/tmp $TENSORFLOW_CUDA_CACHE $image python3 $sample $rest
+    -v `pwd`:/app --workdir /app -e TORCH_EXTENSIONS_DIR=/app/tmp $image python3 $sample $rest