JAX

XLA (Accelerated Linear Algebra) is an open-source machine-learning compiler for GPUs, CPUs, and other ML accelerators. It takes models from frameworks such as JAX, PyTorch, and TensorFlow, traces and compiles them into optimized executable code for the target hardware. The project is developed under OpenXLA and is primarily used through the documentation and integration of the corresponding ML framework; its repository is intended mainly for compiler contributors and frontend or hardware-backend integrators.

Flax NNX is a neural-network API for JAX developed by the Flax team in collaboration with the JAX team. Released in 2024, it uses Python reference semantics so models can be expressed as regular Python objects with reference sharing and mutability, supporting inspection, debugging, and analysis. Its API includes layers and modules such as Linear, convolution, normalization, attention, recurrent cells, and dropout, along with patterns for replicated training, serialization, and checkpointing. In distributed training, it can manage model state while JAX handles computation across a device mesh.

Orbax is a Google-developed collection of checkpointing and persistence utilities for JAX models and training state. Its checkpointing API saves and restores pytrees such as model weights and optimizer state, with support for asynchronous checkpointing, standard and custom types, and flexible storage formats. It is distributed through domain-specific PyPI packages including orbax-checkpoint and orbax-export, and is used by JAX-based machine-learning frameworks and model implementations.

AX is Google's open agentic orchestration runtime for declaring and running autonomous agent workloads in Kubernetes clusters. It defines Tasks, Workspaces, Gateways, and Models as ax.io/v1alpha1 manifests: Tasks run agent code in isolated sandboxes, Workspaces preconfigure Git repositories, MCP servers, and skill packages, Gateways restrict outbound traffic with host allowlists, and Models configure the platform's LLM access through Kubernetes secrets. The control plane runs on Agent Substrate and is operated through a kubectl-shaped CLI that applies manifests, watches task status, opens SSH sessions into sandboxes, and suspends or resumes checkpointed agents. AX is described as being designed for high-throughput cluster operation and is still subject to breaking changes before a stable release. It is distributed under the Apache License 2.0.

DeepJIT is a lightweight, header-only C++20 library for just-in-time compilation, caching, loading, and launching of device kernels on NVIDIA CUDA GPUs and Huawei Ascend NPUs. It provides a shared runtime interface for both backends while keeping kernel source and compiler options backend-specific. The runtime hashes kernel source, tracked includes, compiler information, effective options, and application-provided dependency signatures to reuse compiled artifacts through in-memory and on-disk caches. Cache directories can be shared across users, processes, and nodes on local or distributed filesystems, with completed entries published through atomic directory renames. Lazy initialization defers device and compiler discovery until first use. DeepJIT integrates with PyTorch CUDA and torch_npu streams and can expose a runtime through pybind11. The CUDA backend uses NVCC and the CUDA Driver API to produce, load, and launch CUBIN files; the Ascend backend uses Bisheng, ld.lld, and ACL to compile, link, load, and launch kernels. It supports per-kernel compiler and launch options, compilation diagnostics, and optional PTX, SASS, or Ascend assembly dumps. The host environment requires Linux, a C++20 compiler with std::format support, Python, pybind11, and the dependencies for the selected backend; it is intended to be embedded in another C++ or Python extension as a header-only dependency.