feat: release of CV-CUDA v0.2.1-alpha

Author: milesp-nvidia

.gitattributes

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,3 +18,4 @@
 *.tiff filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
 *.bmp filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

.gitmodules

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -19,3 +19,6 @@
 [submodule "3rdparty/googletest"]
 	path = 3rdparty/googletest
 	url = https://github.com/google/googletest.git
+[submodule "3rdparty/dlpack"]
+	path = 3rdparty/dlpack
+	url = https://github.com/dmlc/dlpack.git

3rdparty/dlpack

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -23,7 +23,7 @@ endif()
 
 project(cvcuda
         LANGUAGES C CXX
-        VERSION 0.2.0
+        VERSION 0.2.1
         DESCRIPTION "CUDA-accelerated Computer Vision algorithms"
 )
 

CMakeLists.txt

@@ -1,6 +1,6 @@
 # Contributing to CV-CUDA
 
-**As of release v0.2.0-alpha, CV-CUDA is not accepting outside contribution.**
+**As of release v0.2.1-alpha, CV-CUDA is not accepting outside contribution.**
 
 Contributions to CV-CUDA fall into the following categories:
 
@@ -12,7 +12,7 @@ Contributions to CV-CUDA fall into the following categories:
 1. To propose a new feature, please file a new feature request
    [issue](https://github.com/CVCUDA/CV-CUDA/issues/new/choose). Describe the
    intended feature and discuss the design and implementation with the team and
-   community. NOTE: Currently, as of release v0.2.0-alpha, CV-CUDA is not accepting
+   community. NOTE: Currently, as of release v0.2.1-alpha, CV-CUDA is not accepting
    outside contribution.
 1. To ask a general question, please sumbit a question
    [issue](https://github.com/CVCUDA/CV-CUDA/issues/new/choose). If you need

CONTRIBUTING.md

@@ -2,7 +2,7 @@
 
 [![License](https://img.shields.io/badge/License-Apache_2.0-yellogreen.svg)](https://opensource.org/licenses/Apache-2.0)
 
-![Version](https://img.shields.io/badge/Version-v0.2.0--alpha-blue)
+![Version](https://img.shields.io/badge/Version-v0.2.1--alpha-blue)
 
 ![Platform](https://img.shields.io/badge/Platform-linux--64_%7C_win--64_wsl2-gray)
 
@@ -13,12 +13,12 @@
 
 CV-CUDA is an open-source project that enables building efficient cloud-scale
 Artificial Intelligence (AI) imaging and computer vision (CV) applications. It
-uses GPU acceleration to help developers build highly efficient pre- and post-
-processing pipelines. CV-CUDA originated as a collaborative effort between
-[NVIDIA][NVIDIA Develop] and [ByteDance][ByteDance].
+uses graphics processing unit (GPU) acceleration to help developers build highly
+efficient pre- and post-processing pipelines. CV-CUDA originated as a
+collaborative effort between [NVIDIA][NVIDIA Develop] and [ByteDance][ByteDance].
 
 Refer to our [Developer Guide](DEVELOPER_GUIDE.md) for more information on the
-operators avaliable as of release v0.2.0-alpha.
+operators available as of release v0.2.1-alpha.
 
 ## Getting Started
 
@@ -29,7 +29,10 @@ To get a local copy up and running follow these steps.
 - Linux distro:
   - Ubuntu x86_64 >= 18.04
   - WSL2 with Ubuntu >= 20.04 (tested with 20.04)
-- CUDA Driver >= 11.7 (Not tested on 12.0)
+- NVIDIA driver
+    - Linux: Driver version 520.56.06 or higher
+- CUDA Toolkit
+    - Version 11.7 or above. (12.0 is not yet tested.)
 - GCC >= 11.0
 - Python >= 3.7
 - cmake >= 3.22
@@ -41,88 +44,140 @@ packages. Choose the installation method that meets your environment needs.
 
 #### Tar File Installation
 
-```
-tar -xvf nvcv-lib-0.2.0-cuda11-x86_64-linux.tar.xz
+```shell
+tar -xvf nvcv-lib-0.2.1-cuda11-x86_64-linux.tar.xz
+tar -xvf nvcv-dev-0.2.1-cuda11-x86_64-linux.tar.xz
 ```
 
 #### DEB File Installation
 
-```
-sudo dpkg -i nvcv-lib-0.2.0-cuda11-x86_64-linux.deb
+```shell
+sudo apt-get install -y ./nvcv-lib-0.2.1-cuda11-x86_64-linux.deb ./nvcv-dev-0.2.1-cuda11-x86_64-linux.deb
 ```
 
 #### Python WHL File Installation
 
-```
-pip install nvcv_python-0.2.0-cp38-cp38-linux_x86_64.whl
+```shell
+pip install nvcv_python-0.2.1-cp38-cp38-linux_x86_64.whl
 ```
 
 ### Build from Source
 
-Follow these instruction to successfully build CV-CUDA from source:
+Follow these instruction to build CV-CUDA from source:
+
+1. Set up your local CV-CUDA repository
+
+    1. Install prerequisites needed to setup up the repository.
+
+       On Ubuntu 22.04, install the following packages:
+       - git-lfs: to retrieve binary files from remote repository
+
+       ```shell
+       sudo apt-get install -y git git-lfs
+       ```
+
+    2. After cloning the repository (assuming it was cloned in `~/cvcuda`),
+       it needs to be properly configured by running the `init_repo.sh` script only once.
+
+       ```shell
+       cd ~/cvcuda
+       ./init_repo.sh
+       ```
 
 1. Build CV-CUDA
 
-   ```
-   cd ~/cvcuda
-   ci/build.sh
-   ```
+    1. Install the dependencies required for building CV-CUDA
 
-   This will compile a x86 release build of CV-CUDA inside `build-rel` directory.
-   The library is in build-rel/lib, docs in build-rel/docs and executables
-   (tests, etc...) in build-rel/bin.
+       On Ubuntu 22.04, install the following packages:
+       - g++-11: compiler to be used
+       - cmake, ninja-build (optional): manage build rules
+       - python3-dev: for python bindings
+       - libssl-dev: needed by the testsuite (MD5 hashing utilities)
 
-   The script accepts some parameters to control the creation of the build tree:
+       ```shell
+       sudo apt-get install -y g++-11 cmake ninja-build python3-dev libssl-dev
+       ```
 
-   ```
-   ci/build.sh [release|debug] [output build tree path]
-   ```
+       For CUDA Toolkit, any version of the 11.x series should work.
+       CV-CUDA was tested with 11.7, thus it should be preferred.
+
+       ```shell
+       sudo apt-get install -y cuda-minimal-build-11-7
+       ```
+
+    2. Build the project
+
+       ```shell
+       ci/build.sh
+       ```
 
-   By default it builds for release.
+       This will compile a x86 release build of CV-CUDA inside `build-rel` directory.
+       The library is in build-rel/lib, docs in build-rel/docs and executables
+       (tests, etc...) are in build-rel/bin.
 
-   If output build tree path isn't specified, it'll be `build-rel` for release
-   builds, and build-deb for debug.
+       The script accepts some parameters to control the creation of the build tree:
+
+       ```shell
+       ci/build.sh [release|debug] [output build tree path]
+       ```
+
+       By default it builds for release.
+
+       If output build tree path isn't specified, it'll be `build-rel` for release
+       builds, and `build-deb` for debug.
 
 1. Build Documentation
 
-   ```
-   ci/build_docs.sh [build folder]
-   ```
+    1. Install the dependencies required for building the documentation
 
-   Example:
-   `ci/build_docs.sh build
+       On Ubuntu 22.04, install the following packages:
+       - doxygen: parse header files for reference documentation
+       - python3, python3-pip: to install some python packages needed
+       - sphinx, breathe, exhale, recommonmark, graphiviz: to render the documentation
+       - sphinx-rtd-theme: documenation theme used
 
-1. Build Samples
+       ```shell
+       sudo apt-get install -y doxygen graphviz python3 python3-pip
+       sudo python3 -m pip install sphinx==4.5.0 breathe exhale recommonmark graphviz sphinx-rtd-theme
+       ```
 
-   ```
-   ./ci/build_samples.sh [build folder]
-   ```
+    2. Build the documentation
+       ```shell
+       ci/build_docs.sh [build folder]
+       ```
+
+       Example:
+       `ci/build_docs.sh build_docs`
 
-   _(For instructions on how to compile samples outside of the CV-CUDA project,
-   see the [Samples](samples/README.md) documentation)_
+1. Build and run Samples
+
+   1. For instructions on how to build samples from source and run them, see the [Samples](samples/README.md) documentation.
 
 1. Run Tests
 
-   The tests are in `<buildtree>/bin`. You can run the script below to run all
-   tests at once. Here's an example when build tree is created in `build-rel`
+   1. Install the dependencies required for running the tests
 
-   ```
-   build-rel/bin/run_tests.sh
-   ```
+       On Ubuntu 22.04, install the following packages:
+       - python3, python3-pip: to run python bindings tests
+       - torch: dependencies needed by python bindings tests
 
-1. Run Samples
+       ```shell
+       sudo apt-get install -y python3 python3-pip
+       sudo python3 -m pip install pytest torch
+       ```
 
-   The samples are installed in `<buildtree>/bin`. You can run the script below
-   to download and serialize the model and run the sample with the test data
-   provided.
+   2. Run the tests
 
-   ```shell
-   ./ci/run_samples.sh
-   ```
+       The tests are in `<buildtree>/bin`. You can run the script below to run all
+       tests at once. Here's an example when build tree is created in `build-rel`
+
+       ```shell
+       build-rel/bin/run_tests.sh
+       ```
 
 1. Package installers
 
-   From a succesfully built project, installers can be generated using cpack:
+   Installers can be generated using the following cpack command once you have successfully built the project
 
    ```shell
    cd build-rel
@@ -134,8 +189,8 @@ Follow these instruction to successfully build CV-CUDA from source:
 
    For a fine-grained choice of what installers to generate, the full syntax is:
 
-   ```
-   cmake . -G [DEB|TXZ]
+   ```shell
+   cpack . -G [DEB|TXZ]
    ```
 
    - DEB for Debian packages
@@ -145,7 +200,7 @@ Follow these instruction to successfully build CV-CUDA from source:
 
 CV-CUDA is an open source project. As part of the Open Source Community, we are
 committed to the cycle of learning, improving, and updating that makes this
-community thrive. However, as of release v0.2.0-alpha, CV-CUDA is not yet ready
+community thrive. However, as of release v0.2.1-alpha, CV-CUDA is not yet ready
 for external contributions.
 
 To understand the process for contributing the CV-CUDA, see our

README.md

@@ -0,0 +1,35 @@
+#!/bin/bash -e
+
+# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# SDIR is the directory where this script is located
+SDIR=$(dirname "$(readlink -f "$0")")
+
+if [ $# != 1 ]; then
+    echo "Invalid arguments"
+    echo "Usage: $(basename "$0") <container tag id>"
+    exit 1
+fi
+
+tag_used=$1
+shift
+
+# shellcheck source=docker/config
+. $SDIR/../docker/config
+
+if [ "$TAG_IMAGE" != "$tag_used" ]; then
+    echo "Tag of docker image used, $IMAGE_URL_BASE:$tag_used, must be $TAG_IMAGE. Please update the .gitlab-ci.yml" && false
+fi