protobuf_cpp

protobuf_cpp

CONTAINERS IMAGES RUN BUILD

CONTAINERS

protobuf:cpp
Aliases	protobuf
Builds
Requires	L4T ['>=32.6']
Dependencies	build-essential python
Dependants	audiocraft l4t-ml l4t-tensorflow:tf1 l4t-tensorflow:tf2 tensorflow tensorflow2 voicecraft
Dockerfile	Dockerfile
Images	dustynv/protobuf:cpp-r32.7.1 (2023-12-06, 0.5GB) dustynv/protobuf:cpp-r35.2.1 (2023-12-06, 5.1GB) dustynv/protobuf:cpp-r35.3.1 (2023-08-29, 5.1GB) dustynv/protobuf:cpp-r35.4.1 (2023-10-07, 5.1GB)
Notes	build protobuf using cpp implementation (https://jkjung-avt.github.io/tf-trt-revisited/)

RUN CONTAINER

To start the container, you can use jetson-containers run and autotag, or manually put together a docker run command:

# automatically pull or build a compatible container image
jetson-containers run $(autotag protobuf_cpp)

# or if using 'docker run' (specify image and mounts/ect)
sudo docker run --runtime nvidia -it --rm --network=host protobuf_cpp:35.2.1

^{jetson-containers run forwards arguments to docker run with some defaults added (like --runtime nvidia, mounts a /data cache, and detects devices)
autotag finds a container image that's compatible with your version of JetPack/L4T - either locally, pulled from a registry, or by building it.}

To mount your own directories into the container, use the -v or --volume flags:

jetson-containers run -v /path/on/host:/path/in/container $(autotag protobuf_cpp)

To launch the container running a command, as opposed to an interactive shell:

jetson-containers run $(autotag protobuf_cpp) my_app --abc xyz

You can pass any options to it that you would to docker run, and it'll print out the full command that it constructs before executing it.

BUILD CONTAINER

If you use autotag as shown above, it'll ask to build the container for you if needed. To manually build it, first do the system setup, then run:

jetson-containers build protobuf_cpp

The dependencies from above will be built into the container, and it'll be tested during. Run it with --help for build options.