1.0 GA (v1.0.0)

This is the General Availability Release of RedisAI 1.0 (v1.0.0)!

Headlines:

• Data locality decreases the end-to-end inference time. RedisAI allows you to run your DL/ML models where the reference data for these models lives. RedisAI also allows you to persist intermediate states of a computation in-memory.
• Support for multiple backends which enables you to compose computations across backends but also to decouple model creation and model serving.
• Scale your AI serving infrastructure by scaling Redis.

Supported Backends:

• TensorFlow Lite 2.0
• TensorFlow 1.15.0
• PyTorch 1.5
• ONXXRuntime 1.2.0

Details:

• New Features:

  • #241, #270 auto-batching support. Requests from multiple clients can be automatically and transparently batched in a single request for increased CPU/GPU efficiency during serving.
  • #322 Add AI.DAGRUN. With the new AI.DAGRUN (DAG as in direct acycilc graph) command we support the prescription of combinations of other AI.* commands in a single execution pass, where intermediate keys are never materialised to Redis.
  • #334 Add AI.DAGRUN_RO command, a read-only variant of AI.DAGRUN
  • #338 AI.MODELSET Added the possibility to provide a model in chunks.
  • #332 Standardized GET methods (TENSORGET,MODELGET,SCRIPTGET) replies (breaking change for clients)
  • #331 Cache model blobs for faster serialization and thread-safety.

• Minor Enhancements:

  • #289 Memory access and leak fixes.
  • #319 Documentation improvements.

• Build Enhancements:

  • #299 Coverage info.
  • #273 Enable running valgrind/callgrind on test platform

• #277, #296 tests extension and refactoring per backend.

Notes:
The version inside Redis will be 10000 or 1.0.0 in semantic versioning.