Backfill issue -411 — The AI Toolchain

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The daily firehose — everything the toolchain shipped today, already filtered.

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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   # 2 tools matched

AI & LLM Tooling

◆ AI Model & Data Infrastructure

NVIDIA Triton Inference Server

v2.13.0 NOTES

Triton v2.13.0 adds Business Logic Scripting beta, a Container Composition Utility, and Model Analyzer support for custom ops.

└──▷ GET THIS VERSION

$ git clone --branch v2.13.0 https://github.com/triton-inference-server/server.git
# already have the repo? check out this version:
$ git checkout v2.13.0

└──▷ TRY IT

Run Triton on Jetson with an explicit backend directory and TensorFlow 2.x selected instead of the default TF1.

$ tritonserver --model-repository=/path/to/model_repo --backend-directory=/path/to/tritonserver/backends --backend-config=tensorflow,version=2

›Adds --backend-directory flag and --backend-config=tensorflow,version=2 argument to tritonserver for explicit backend path and TensorFlow version selection on Jetson.
›Initial beta release of Business Logic Scripting — a new set of utility functions allowing a Python model to execute inference requests on other models being served by Triton.
›New Container Composition Utility (docs/compose.md) for building custom Triton containers with specific backends and repository agents.
›Publishes two new focused NGC containers: nvcr.io/nvidia/tritonserver:21.08-tf-python-py3 (TensorFlow 2.x + Python backends) and nvcr.io/nvidia/tritonserver:21.08-pyt-python-py3 (PyTorch + Python backends).
›Adds Model Analyzer support for models with custom operations.

└──▷ BREAKING ON UPGRADE

!In the Python client shared-memory support library, utils.serialize_byte_tensor() and utils.deserialize_byte_tensor() now return np.object_ numpy arrays instead of np.bytes_ arrays; code depending on np.bytes_ must be updated.

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Other / Uncategorized

◆ VECTOR DB RAG

Weaviate

v1.6.0 NOTES

Weaviate v1.6.0 adds zero-shot classification via "type": "zeroshot" in POST /v1/classficiations

└──▷ GET THIS VERSION

$ git clone --branch v1.6.0 https://github.com/weaviate/weaviate.git
# already have the repo? check out this version:
$ git checkout v1.6.0

└──▷ TRY IT

Run zero-shot classification to automatically label objects by vector proximity, filtering both source and target label objects inline.

$ curl -X POST http://localhost:8080/v1/classficiations \
  -H 'Content-Type: application/json' \
  -d '{
    "class": "Article",
    "type": "zeroshot",
    "classifyProperties": ["ofCategory"],
    "sourceWhere": { "operator": "IsNull", "path": ["ofCategory"], "valueBoolean": true },
    "targetWhere": { "operator": "Equal", "path": ["active"], "valueBoolean": true }
  }'

›Adds "type": "zeroshot" to the POST /v1/classficiations API, enabling zero-shot classification that works with any vectorizer or custom vectors — no training data required; use "classifyProperties", "sourceWhere", and "targetWhere" to control which objects and labels are classified.

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// END OF TRANSMISSION

The AI Toolchain · curated daily · August 28, 2021
Releases & features from the open-source and commercial AI tools that practitioners actually run.

The AI Toolchain — issue -411, August 28, 2021

NVIDIA Triton Inference Server

Weaviate