From notebook to production: Serving JAX at scale // TRAIN BRAIN

From notebook to production: Serving JAX at scale

Join the Google Cloud & NVIDIA community → https://g.dev/cloud/google-nvidia-community
Deploy models using robust JAX serving architectures. Watch along and learn how to achieve low latency and optimize setups specifically for web APIs.
* *Deploy AOT compilation:* Use ahead-of-time compilation to lock down input shapes and guarantee predictable inference latency.
* *Export native execution graphs:* Package model code and checkpoints using jax.export for portable runtime deployment.
* *Bridge JAX to TensorFlow serving:* Convert JAX graphs to standard TensorFlow SavedModels using jax2tf for corporate server integration.
This is part 4 of JAX on NVIDIA GPUs Crash Course.
Watch more JAX on NVIDIA GPUs Crash Course → https://g.dev/cloud/jax-nvidia-gpu
? Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
Speakers: Ivan Nardini, Ekaterina Sirazitdinova
Products Mentioned: Google Cloud, JAX, TensorFlow

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