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The AI Toolchain — issue -438, May 24, 2019

THE AI TOOLCHAIN NO. -438
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THE DAILY RELEASE FIREHOSE
PUBLISHED MAY 24, 2019 · EVERY WEEKDAY
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The daily firehose — everything the toolchain shipped today, already filtered.

// HOW THIS ISSUE IS MADE

We read every release from the 174 tools on our watchlist at the source — GitHub and GitLab release notes, vendor release pages and changelogs, project blogs and feeds, vendor press releases, and the source code behind the tag. Bug-fix-only releases and non-product newsroom noise are dropped; what's left is summarized down to the new capability, how to try it, and any screenshots or videos the release itself published. Every entry links to the sources it was built from.

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$ tct list   # 3 tools matched
AI & LLM Tooling
◆  AI Model & Data Infrastructure

Microsoft ONNX Runtime

Sources Release notes → v0.4.0 NOTES

ONNX Runtime v0.4.0 adds Intel nGraph and NVIDIA TensorRT execution providers, ONNX 1.5/Opset 10 support, and quantization operators.

└──▷ GET THIS VERSION
$ git clone --branch v0.4.0 https://github.com/microsoft/onnxruntime.git
# already have the repo? check out this version:
$ git checkout v0.4.0
  • Adds Intel nGraph execution provider for accelerated inference on Intel hardware.
  • Adds NVIDIA TensorRT execution provider for GPU-accelerated inference.
  • Supports ONNX 1.5 and Opset 10 operators, enabling use of newly added ONNX Model Zoo object detection models (YOLO v3, SSD).
  • Adds quantization operators from ONNX 1.5.
  • Extends the C API for Custom Operators to support allocation of outputs during compute, includes a C++ wrapper to simplify implementation, and enables custom op DLLs when ONNX Runtime is compiled statically.
+3 moreshow less
  • Adds graph optimization via Constant Folding for improved inference performance.
  • Publishes additional NuGet binaries built with MKL-ML.
  • Reduces binary sizes: 700 KB+ on Windows, 65% on Linux, 45% on macOS.
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NVIDIA Triton Inference Server

Sources Release notes → v1.2.0 NOTES

Triton v1.2.0 adds model ensembling pipelines, Kubernetes Helm chart, and new perf_client latency flags

└──▷ GET THIS VERSION
$ git clone --branch v1.2.0 https://github.com/triton-inference-server/server.git
# already have the repo? check out this version:
$ git checkout v1.2.0
└──▷ TRY IT
Benchmark a model under realistic SLO conditions by reporting p99 latency instead of average latency.
$ perf_client --percentile=99 -m <model_name> -u <server_url>
Run a zero-input benchmark to isolate server-side compute latency from data-generation overhead.
$ perf_client -z -m <model_name> -u <server_url>
  • Adds --percentile flag to perf_client to report Nth-percentile latency (e.g. --percentile=99 for p99) instead of average latency.
  • Adds -z flag to perf_client to use zero-valued input tensors instead of random values during benchmarking.
  • Adds --allow-gpu-metrics option to enable or disable reporting of GPU metrics.
  • Introduces ensemble model support: a single inference request to an ensemble triggers execution of an entire pipeline of chained models with connected input/output tensors.
  • Adds a Helm chart for deploying the inference server into a Kubernetes cluster.
+1 moreshow less
  • Client library builds now support both Ubuntu 16.04 and Ubuntu 18.04, with Python wheels compatible with both Python 2 and Python 3.
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Other / Uncategorized
◆  VECTOR DB RAG

Weaviate

Sources Release notes → 0.13.0 NOTES

Weaviate 0.13.0: initial stable release with GraphQL traversal, pluggable backends, and OIDC auth support

└──▷ GET THIS VERSION
$ git clone --branch 0.13.0 https://github.com/weaviate/weaviate.git
# already have the repo? check out this version:
$ git checkout 0.13.0
  • Supports pluggable database backends ('connectors'), defaulting to janusgraph with cassandra for storage and elasticsearch as the indexing backend
  • Pluggable authentication and authorization providers, defaulting to anonymous_access with optional OIDC (Open ID Connect) configuration
  • Full REST API for CRUD operations on schema and concepts (Things and Actions)
  • Dynamic graph traversal via GraphQL, including context-based search through 'Fetch' GQL APIs
  • Optional asynchronous analytics jobs via Spark integration
+3 moreshow less
  • Horizontal scaling (HA) support and 12-factor compatible configuration management
  • Production-quality Helm charts available (separate release lifecycle) and Docker Compose 'Try Out' setups
  • Ships as Docker image semitechnologies/weaviate:0.13.0
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