Add CUDA ONNX variant

.circleci/config.yml

@@ -98,7 +98,7 @@ workflows:
       - deploy:
           matrix:
             parameters:
-              docker-variant: ['', '-cuda', '-cuda-tf2', '-cuda-torch']
+              docker-variant: ['', '-cuda', '-cuda-tf2', '-cuda-torch', '-cuda-onnx']
           filters:
             branches:
               only: master

.github/workflows/docker-image.yml

@@ -54,4 +54,4 @@ jobs:
         username: ${{ secrets.DOCKERIO_USERNAME }}
         password: ${{ secrets.DOCKERIO_PASSWORD }}
     - name: Build the Docker images
-      run: make docker docker-cuda docker-cuda-tf2 docker-cuda-torch
+      run: make docker docker-cuda docker-cuda-tf2 docker-cuda-torch docker-cuda-onnx

Dockerfile.cuda-onnx

@@ -0,0 +1,15 @@
+ARG BASE_IMAGE=docker.io/ocrd/core-cuda
+FROM $BASE_IMAGE AS ocrd_core_base
+
+WORKDIR /build/core
+
+COPY Makefile .
+
+RUN make deps-onnx
+# Smoke Test
+RUN ocrd --version
+
+WORKDIR /data
+
+CMD ["/usr/local/bin/ocrd", "--help"]
+

Makefile

@@ -84,29 +84,40 @@ get-conda: ;
 endif
 
 # Dependencies for CUDA installation via Conda
-deps-cuda: PYTHON_PREFIX != $(PYTHON) -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])'
 deps-cuda: get-conda
 # Get CUDA toolkit, including compiler and libraries with dev from NVIDIA channels
-# Get CUDNN (needed for Torch, TF etc) from conda-forge.
-# CUDA runtime libs will be pulled by `pip` for TF and Torch differently anyway,
+# CUDA runtime libs will be pulled by `pip` for ONNX, TF and Torch differently anyway,
 # so do _not_ install them here to avoid wasting space.
 	conda install -c nvidia/label/cuda-12.4.0 cuda-minimal-build \
 	&& conda clean -a && ldconfig
 
+deps-tf2: CUDA_RPATH := '$$ORIGIN:$$ORIGIN/../nvidia/cufft/lib:$$ORIGIN/../nvidia/cudnn/lib:$$ORIGIN/../nvidia/cusolver/lib:$$ORIGIN/../nvidia/cublas/lib:$$ORIGIN/../nvidia/cusparse/lib:$$ORIGIN/../nvidia/cuda_cupti/lib:$$ORIGIN/../nvidia/cuda_runtime/lib:$$ORIGIN/../tensorrt_libs'
 deps-tf2:
 	$(PIP) install "tensorflow[and-cuda]"  -r requirements.txt
+	# fix missing search paths for CUDA runtime libs
+	cd $$(dirname `$(PYTHON) -c 'print(__import__("tensorflow").__file__)'`) && \
+	patchelf --set-rpath $(CUDA_RPATH) libtensorflow_framework.so.2 && \
+	patchelf --set-rpath $(CUDA_RPATH) libtensorflow_cc.so.2
 
 deps-torch:
 	$(PIP) install torch==2.5.1 torchvision==0.20.1 --extra-index-url https://download.pytorch.org/whl/cu124 -r requirements.txt
 
+deps-onnx: CUDA_RPATH := '$$ORIGIN/../../nvidia/cudnn/lib:$$ORIGIN/../../nvidia/cusolver/lib:$$ORIGIN/../../nvidia/cublas/lib:$$ORIGIN/../../nvidia/cufft/lib:$$ORIGIN/../../nvidia/curand/lib:$$ORIGIN/../../nvidia/cusparse/lib:$$ORIGIN/../../nvidia/cuda_cupti/lib:$$ORIGIN/../../nvidia/cuda_runtime/lib:$$ORIGIN/../../tensorrt_libs'
+deps-onnx:
+	$(PIP) install "onnxruntime-gpu[cuda,cudnn]" "tensorrt_cu12<11" -r requirements.txt
+	# fix missing search paths for CUDA runtime libs
+	cd $$(dirname `$(PYTHON) -c 'print(__import__("onnxruntime").__file__)'`)/capi && \
+	patchelf --set-rpath $(CUDA_RPATH) libonnxruntime_providers_cuda.so && \
+	patchelf --set-rpath $(CUDA_RPATH) libonnxruntime_providers_tensorrt.so
+
 # deps-*: always mix core's requirements.txt with additional deps,
 # so pip does not ignore the older version reqs,
 # but instead tries to find a mutually compatible set.
 
 # Dependencies for deployment in an ubuntu/debian linux
 deps-ubuntu:
 	apt-get update
-	apt-get install -y bzip2 python3 imagemagick libgeos-dev libxml2-dev libxslt-dev libssl-dev
+	apt-get install -y bzip2 python3 imagemagick libgeos-dev libxml2-dev libxslt-dev libssl-dev patchelf
 
 # Dependencies for deployment via Conda
 deps-conda: get-conda
@@ -343,9 +354,15 @@ docker-cuda-torch: DOCKER_FILE = Dockerfile.cuda-torch
 
 docker-cuda-torch: docker-cuda
 
+docker-cuda-onnx: DOCKER_BASE_IMAGE = $(DOCKER_BASE_TAG)/core-cuda
+docker-cuda-onnx: DOCKER_TAG = $(DOCKER_BASE_TAG:%=%/core-cuda-onnx)
+docker-cuda-onnx: DOCKER_FILE = Dockerfile.cuda-onnx
+
+docker-cuda-onnx: docker-cuda
+
 # if the current ref is a release, then use it as tag instead of :latest
-docker docker-cuda docker-cuda-tf2 docker-cuda-torch: GIT_TAG := $(strip $(shell git describe --tags | grep -x "v[0-9]\.[0-9][[0-9]\.[0-9]"))
-docker docker-cuda docker-cuda-tf2 docker-cuda-torch:
+docker docker-cuda docker-cuda-tf2 docker-cuda-torch docker-cuda-onnx: GIT_TAG := $(strip $(shell git describe --tags | grep -x "v[0-9]\.[0-9][[0-9]\.[0-9]"))
+docker docker-cuda docker-cuda-tf2 docker-cuda-torch docker-cuda-onnx:
 	$(DOCKER_BUILD) -f $(DOCKER_FILE) $(DOCKER_TAG:%=-t %) \
 	$(if $(GIT_TAG),$(DOCKER_TAG:%=-t %:$(GIT_TAG))) \
 	--target ocrd_core_base \

src/ocrd_utils/logging.py

@@ -83,10 +83,15 @@ def tf_disable_interactive_logs():
         from os import environ  # pylint: disable=import-outside-toplevel
         # This env variable must be set before importing from Keras
         environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+        environ['GLOG_minloglevel'] = '3'
+        environ['GRPC_VERBOSITY'] = 'ERROR'
+        environ['KMP_AFFINITY'] = 'noverbose'
         from tensorflow.keras.utils import disable_interactive_logging  # pylint: disable=import-outside-toplevel
         # Enabled interactive logging throws an exception
         # due to a call of sys.stdout.flush()
         disable_interactive_logging()
+        logging.getLogger('tensorflow').setLevel(LOGGING_DEFAULTS['tensorflow'])
+        logging.getLogger('absl').setLevel(LOGGING_DEFAULTS['tensorflow'])
     except ImportError:
         # Nothing should be handled here if TF is not available
         pass

src/ocrd_utils/os.py

@@ -83,7 +83,7 @@ def unzip_file_to_dir(path_to_zip : Union[str, PathLike], output_directory : str
 @lru_cache()
 def is_git_url(url: str) -> bool:
     try:
-        run(['git', 'ls-remote', '--exit-code', '-q', '-h', url], check=True)
+        run(['git', 'ls-remote', '--exit-code', '-q', '-h', url], check=True, capture_output=True)
     except CalledProcessError:
         return False
     return True