diff --git a/docs/modules/ROOT/pages/contributing/benchmarks.adoc b/docs/modules/ROOT/pages/contributing/benchmarks.adoc index ba5ccb55..01a6e501 100644 --- a/docs/modules/ROOT/pages/contributing/benchmarks.adoc +++ b/docs/modules/ROOT/pages/contributing/benchmarks.adoc @@ -1,5 +1,4 @@ = Official Engine Benchmarks -:url-prd: https://github.com/ainet-sk/SKaiNET/blob/main/bench-prd.md :url-pts: https://www.phoronix-test-suite.com/ :url-obo: https://openbenchmarking.org/ @@ -72,8 +71,7 @@ records should be excluded from public leaderboards. The full lane currently runs on a Linux x86 host with an AVX2-capable CPU. macOS Arm64 and Linux Arm64 lanes are tracked as -follow-ups in the -{url-prd}[engine benchmark PRD]. +follow-ups. == Reproducing a public run locally @@ -110,7 +108,7 @@ To register this machine as the official self-hosted runner: [source,bash] ---- GH_RUNNER_TOKEN= Actions -> Runners> \ - REPO=ainet-sk/SKaiNET \ + REPO=SKaiNET-developers/SKaiNET \ ./scripts/register_bench_runner.sh ---- @@ -136,7 +134,6 @@ comparisons don't silently break. == Related -* {url-prd}[Engine + Transformers benchmark PRD] * {url-pts}[Phoronix Test Suite] * {url-obo}[OpenBenchmarking.org] * xref:explanation/perf/jvm-cpu.adoc[JVM CPU backend notes] diff --git a/docs/modules/ROOT/pages/contributing/dtype-model.adoc b/docs/modules/ROOT/pages/contributing/dtype-model.adoc index 9a816dae..43e57e52 100644 --- a/docs/modules/ROOT/pages/contributing/dtype-model.adoc +++ b/docs/modules/ROOT/pages/contributing/dtype-model.adoc @@ -4,9 +4,8 @@ [NOTE] ==== **Audience: SKaiNET maintainers and contributors.** This page maps -the vocabulary used in the -https://github.com/SKaiNET-developers/SKaiNET/blob/develop/rfc.md[dtype-policy -RFC] (issue #615) onto the existing SKaiNET implementations. +the vocabulary used in the dtype-policy RFC (issue +https://github.com/SKaiNET-developers/SKaiNET/issues/615[#615]) onto the existing SKaiNET implementations. Library consumers don't need to read this — they call `tensor(ctx, FP32::class) { … }` and the engine does the rest. @@ -133,8 +132,7 @@ avoid; SKaiNET already prevents all three. == Related -* `rfc.md` (repo root) — the design document this page implements. -* Issue https://github.com/SKaiNET-developers/SKaiNET/issues/615[#615] — implementation tracker. +* Issue https://github.com/SKaiNET-developers/SKaiNET/issues/615[#615] — the dtype-policy design + implementation tracker this page describes. * xref:how-to/build-tensors.adoc[Build tensors with the data DSL] — the user-facing entry points that produce the `Tensor` values whose dtype this page describes. * xref:contributing/benchmarks.adoc[Engine benchmark program] — runtime numbers that the kernel SPI produces. * xref:contributing/matmul-kernels.adoc[Reading the matmul benchmark] — how the kernel SPI's dispatch actually shows up in measurements. diff --git a/docs/modules/ROOT/pages/how-to/io-readers.adoc b/docs/modules/ROOT/pages/how-to/io-readers.adoc index ef0ea83c..f83aef2a 100644 --- a/docs/modules/ROOT/pages/how-to/io-readers.adoc +++ b/docs/modules/ROOT/pages/how-to/io-readers.adoc @@ -20,7 +20,7 @@ Add the following dependencies to your `build.gradle.kts`: [source,kotlin] ---- dependencies { - implementation(platform("sk.ainet:skainet-bom:0.31.2")) + implementation(platform("sk.ainet:skainet-bom:0.32.1")) implementation("sk.ainet.core:skainet-io-gguf") implementation("org.jetbrains.kotlinx:kotlinx-io-core:0.8.2") @@ -32,7 +32,7 @@ dependencies { [source,kotlin] ---- dependencies { - implementation(platform("sk.ainet:skainet-bom:0.31.2")) + implementation(platform("sk.ainet:skainet-bom:0.32.1")) implementation("sk.ainet.core:skainet-io-onnx") implementation("org.jetbrains.kotlinx:kotlinx-io-core:0.8.2") diff --git a/docs/modules/ROOT/pages/how-to/java-model-training.adoc b/docs/modules/ROOT/pages/how-to/java-model-training.adoc index 4a673f7e..5b9476d2 100644 --- a/docs/modules/ROOT/pages/how-to/java-model-training.adoc +++ b/docs/modules/ROOT/pages/how-to/java-model-training.adoc @@ -12,7 +12,7 @@ This guide covers building neural networks, defining loss functions and optimize === Prerequisites * JDK 21{plus} (CI builds on JDK 25); Gradle passes `--enable-preview --add-modules jdk.incubator.vector` automatically -* See link:java-getting-started.md[Java Getting Started] for project setup +* See xref:tutorials/java-getting-started.adoc[Java Getting Started] for project setup ==== Maven Dependencies @@ -23,7 +23,7 @@ This guide covers building neural networks, defining loss functions and optimize sk.ainet skainet-bom - 0.27.0 + 0.32.1 pom import @@ -454,5 +454,5 @@ future.thenAccept(result -> { === Next Steps -* link:java-getting-started.md[Java Getting Started] -- tensor operations, project setup, and dependency management. -* link:java-llm-inference.md[LLM Inference Guide] -- load GGUF/SafeTensors models, generate text, and build agents. +* xref:tutorials/java-getting-started.adoc[Java Getting Started] -- tensor operations, project setup, and dependency management. +* xref:how-to/java-llm-inference.adoc[LLM Inference Guide] -- load GGUF/SafeTensors models, generate text, and build agents. diff --git a/docs/modules/ROOT/pages/how-to/minerva-export.adoc b/docs/modules/ROOT/pages/how-to/minerva-export.adoc index 5c085625..59d4409a 100644 --- a/docs/modules/ROOT/pages/how-to/minerva-export.adoc +++ b/docs/modules/ROOT/pages/how-to/minerva-export.adoc @@ -38,7 +38,7 @@ For a published application, use the SKaiNET BOM and the Minerva artifact: [source,kotlin] ---- dependencies { - implementation(platform("sk.ainet:skainet-bom:0.31.2")) + implementation(platform("sk.ainet:skainet-bom:0.32.1")) implementation("sk.ainet.core:skainet-compile-minerva") } ---- diff --git a/docs/modules/ROOT/pages/tutorials/hlo-getting-started.adoc b/docs/modules/ROOT/pages/tutorials/hlo-getting-started.adoc index cad26ee6..846db38f 100644 --- a/docs/modules/ROOT/pages/tutorials/hlo-getting-started.adoc +++ b/docs/modules/ROOT/pages/tutorials/hlo-getting-started.adoc @@ -221,7 +221,7 @@ SKaiNET uses the MLIR/XLA compilation pipeline to target different hardware plat ==== Prerequisites for GPU Compilation [arabic] -. *XLA with GPU support*: https://www.tensorflow.org/xla/tutorials/compile[Installation guide] +. *XLA with GPU support*: https://openxla.org/xla[Installation guide] . *NVIDIA CUDA Toolkit* (for NVIDIA GPUs): https://developer.nvidia.com/cuda-downloads[Download here] . *ROCm* (for AMD GPUs): https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html[Installation guide] @@ -343,9 +343,9 @@ optimizer.dumpGraphviz("rgb2gray.dot") * https://github.com/openxla/stablehlo/blob/main/docs/spec.md[StableHLO Specification] * https://mlir.llvm.org/docs/[MLIR Documentation] -* https://www.tensorflow.org/xla[XLA Compilation Guide] +* https://openxla.org/xla[XLA Compilation Guide] * https://docs.nvidia.com/jetson/[NVIDIA Jetson Documentation] -* link:./examples/hlo/[SKaiNET HLO Examples] +* https://github.com/SKaiNET-developers/SKaiNET/tree/develop/skainet-compile/skainet-compile-hlo/src/commonMain/kotlin/sk/ainet/compile/hlo/examples[SKaiNET HLO Examples] === Next Steps @@ -355,4 +355,4 @@ optimizer.dumpGraphviz("rgb2gray.dot") . *Contribute*: Add new HLO converters for missing operations . *Optimize*: Profile and optimize your models using HLO tools -For more detailed information, see the link:./OPTIMIZATION.md[HLO Optimization Guide] and https://docs.skainet.sk/hlo/[API Documentation]. +For more detailed information, see the https://github.com/SKaiNET-developers/SKaiNET/blob/develop/skainet-compile/skainet-compile-hlo/OPTIMIZATION.md[HLO Optimization Guide] and xref:reference/api.adoc[API reference]. diff --git a/docs/modules/ROOT/pages/tutorials/image-data-getting-started.adoc b/docs/modules/ROOT/pages/tutorials/image-data-getting-started.adoc index 8d62a167..7c5d97cc 100644 --- a/docs/modules/ROOT/pages/tutorials/image-data-getting-started.adoc +++ b/docs/modules/ROOT/pages/tutorials/image-data-getting-started.adoc @@ -32,7 +32,7 @@ For a JVM project, add the image/data modules alongside the CPU backend: [source,kotlin] ---- dependencies { - implementation(platform("sk.ainet:skainet-bom:0.31.2")) + implementation(platform("sk.ainet:skainet-bom:0.32.1")) implementation("sk.ainet:skainet-backend-cpu-jvm") implementation("sk.ainet:skainet-io-image-jvm") diff --git a/docs/modules/ROOT/pages/tutorials/java-getting-started.adoc b/docs/modules/ROOT/pages/tutorials/java-getting-started.adoc index d72327e6..9ed9e9ed 100644 --- a/docs/modules/ROOT/pages/tutorials/java-getting-started.adoc +++ b/docs/modules/ROOT/pages/tutorials/java-getting-started.adoc @@ -46,7 +46,7 @@ The `skainet-bom` manages all SKaiNET module versions so you never have to keep ---- - 0.27.0 + 0.32.1 @@ -144,7 +144,7 @@ repositories { dependencies { // Import BOM for version alignment - implementation(platform("sk.ainet:skainet-bom:0.31.2")) + implementation(platform("sk.ainet:skainet-bom:0.32.1")) // Core tensor library implementation("sk.ainet:skainet-lang-core-jvm") @@ -325,5 +325,5 @@ var usq = TensorJavaOps.unsqueeze(a, 0); === Next Steps -* link:java-llm-inference.md[LLM Inference Guide] -- load GGUF/SafeTensors models, generate text, run BERT embeddings, and build tool-calling agents. -* link:java-model-training.md[Model Training Guide] -- build sequential models, train on MNIST, and run async training loops. +* xref:how-to/java-llm-inference.adoc[LLM Inference Guide] -- load GGUF/SafeTensors models, generate text, run BERT embeddings, and build tool-calling agents. +* xref:how-to/java-model-training.adoc[Model Training Guide] -- build sequential models, train on MNIST, and run async training loops.