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JAX Example

In this section, we demonstrate an end-to-end example for using JAX in Python Backend.

Create a JAX AddSub model repository

We will use the files that come with this example to create the model repository.

First, download the client.py, config.pbtxt and model.py to your local machine.

Next, at the directory where the three files located, create the model repository with the following commands:

$ mkdir -p models/jax/1
$ mv model.py models/jax/1
$ mv config.pbtxt models/jax

Pull the Triton Docker images

We need to install Docker and NVIDIA Container Toolkit before proceeding, refer to the installation steps.

To pull the latest containers, run the following commands:

$ docker pull nvcr.io/nvidia/tritonserver:<yy.mm>-py3
$ docker pull nvcr.io/nvidia/tritonserver:<yy.mm>-py3-sdk

See the installation steps above for the <yy.mm> version.

At the time of writing, the latest version is 22.08, which translates to the following commands:

$ docker pull nvcr.io/nvidia/tritonserver:22.08-py3
$ docker pull nvcr.io/nvidia/tritonserver:22.08-py3-sdk

Be sure to replace the <yy.mm> with the version pulled for all the remaining parts of this example.

Start the Triton Server

At the directory where we created the JAX models (at where the "models" folder is located), run the following command:

$ docker run --gpus all -it --rm -p 8000:8000 -v `pwd`:/jax nvcr.io/nvidia/tritonserver:<yy.mm>-py3 /bin/bash

Inside the container, we need to install JAX to run this example.

We recommend using the pip method mentioned in the JAX documentation. Make sure that JAX is available in the same Python environment as other dependencies.

To install for this example, run the following command:

$ pip3 install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Finally, we need to start the Triton Server, run the following command:

$ tritonserver --model-repository=/jax/models

To leave the container for the next step, press: CTRL + P + Q.

Test inference

At the directory where the client.py is located, run the following command:

$ docker run --rm --net=host -v `pwd`:/jax nvcr.io/nvidia/tritonserver:<yy.mm>-py3-sdk python3 /jax/client.py

A successful inference will print the following at the end:

INPUT0 ([0.89262384 0.645457   0.18913145 0.17099917]) + INPUT1 ([0.5703733  0.21917151 0.22854741 0.97336507]) = OUTPUT0 ([1.4629972  0.86462855 0.41767886 1.1443642 ])
INPUT0 ([0.89262384 0.645457   0.18913145 0.17099917]) - INPUT1 ([0.5703733  0.21917151 0.22854741 0.97336507]) = OUTPUT0 ([ 0.32225055  0.4262855  -0.03941596 -0.8023659 ])
PASS: jax

Note: You inputs can be different from the above, but the outputs always correspond to its inputs.