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# What does this PR do? Adds a write worker queue for writes to inference store. This avoids overwhelming request processing with slow inference writes. ## Test Plan Benchmark: ``` cd /docs/source/distributions/k8s-benchmark # start mock server python openai-mock-server.py --port 8000 # start stack server uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml # run benchmark script uv run python3 benchmark.py --duration 120 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct ``` Before: ============================================================ BENCHMARK RESULTS Response Time Statistics: Mean: 1.111s Median: 0.982s Min: 0.466s Max: 15.190s Std Dev: 1.091s Percentiles: P50: 0.982s P90: 1.281s P95: 1.439s P99: 5.476s Time to First Token (TTFT) Statistics: Mean: 0.474s Median: 0.347s Min: 0.175s Max: 15.129s Std Dev: 0.819s TTFT Percentiles: P50: 0.347s P90: 0.661s P95: 0.762s P99: 2.788s Streaming Statistics: Mean chunks per response: 67.2 Total chunks received: 122154 ============================================================ Total time: 120.00s Concurrent users: 50 Total requests: 1919 Successful requests: 1819 Failed requests: 100 Success rate: 94.8% Requests per second: 15.16 Errors (showing first 5): Request error: Request error: Request error: Request error: Request error: Benchmark completed. Stopping server (PID: 679)... Server stopped. After: ============================================================ BENCHMARK RESULTS Response Time Statistics: Mean: 1.085s Median: 1.089s Min: 0.451s Max: 2.002s Std Dev: 0.212s Percentiles: P50: 1.089s P90: 1.343s P95: 1.409s P99: 1.617s Time to First Token (TTFT) Statistics: Mean: 0.407s Median: 0.361s Min: 0.182s Max: 1.178s Std Dev: 0.175s TTFT Percentiles: P50: 0.361s P90: 0.644s P95: 0.744s P99: 0.932s Streaming Statistics: Mean chunks per response: 66.8 Total chunks received: 367240 ============================================================ Total time: 120.00s Concurrent users: 50 Total requests: 5495 Successful requests: 5495 Failed requests: 0 Success rate: 100.0% Requests per second: 45.79 Benchmark completed. Stopping server (PID: 97169)... Server stopped. |
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| _static | ||
| notebooks | ||
| openapi_generator | ||
| resources | ||
| source | ||
| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
| make.bat | ||
| Makefile | ||
| original_rfc.md | ||
| quick_start.ipynb | ||
| README.md | ||
Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Render locally
From the llama-stack root directory, run the following command to render the docs locally:
uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
You can open up the docs in your browser at http://localhost:8000
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack