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chore: rename dynamo (ai-dynamo#44)
Co-authored-by: Biswa Panda <[email protected]>
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‎.dockerignore‎

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**/*.plan
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**/.cache/*
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**/*onnx*
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# Engine must be allowed because code contains dynemo_engine.py
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# Engine must be allowed because code contains dynamo_engine.py
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**/*tensorrtllm_engines*
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**/*tensorrtllm_models*
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**/*tensorrtllm_checkpoints*

‎.github/workflows/copyright-checks.yml‎

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env:
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NVBUILD_VERBOSITY: DETAILED
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timeout-minutes: 2
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working-directory: /workspace
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working-directory: /workspace

‎.github/workflows/pre-merge-rust.yml‎

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pre-merge-rust:
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runs-on: ubuntu-latest
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strategy:
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matrix: { dir: ['lib/runtime', 'lib/llm', 'lib/bindings/c', 'lib/bindings/python', 'launch/dynemo-run', 'components/metrics', 'examples/rust'] }
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matrix: { dir: ['lib/runtime', 'lib/llm', 'lib/bindings/c', 'lib/bindings/python', 'launch/dynamo-run', 'components/metrics', 'examples/rust'] }
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permissions:
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contents: read
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steps:

‎ATTRIBUTIONS.md‎

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# Open Source License Attribution
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Dynemo uses Open Source components. You can find the details of these open-source projects along with license information below.
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Dynamo uses Open Source components. You can find the details of these open-source projects along with license information below.
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We are grateful to the developers for their contributions to open source and acknowledge these below.
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## nats-py - [Apache License 2.0](https://github.com/nats-io/nats.py/blob/main/LICENSE)

‎CODEOWNERS‎

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# CODEOWNERS file for Dynemo
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# CODEOWNERS file for Dynamo
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#
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# For more information about CODEOWNERS files, see:
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# https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners

‎README.md‎

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limitations under the License.
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-->
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# Dynemo
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# Dynamo
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<h4> A Datacenter Scale Distributed Inference Serving Framework </h4>
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[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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[![GitHub Release](https://img.shields.io/github/v/release/dynemo-ai/dynemo)](https://github.com/dynemo-ai/dynemo/releases/latest)
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[![GitHub Release](https://img.shields.io/github/v/release/dynemo-ai/dynamo)](https://github.com/dynemo-ai/dynemo/releases/latest)
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Dynemo is a flexible, component based, data center scale
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Dynamo is a flexible, component based, data center scale
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inference serving framework designed to leverage the strengths of the
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standalone Dynemo Inference Server while expanding its capabilities
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standalone Dynamo Inference Server while expanding its capabilities
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to meet the demands of complex use cases including those of Generative
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AI. It is designed to enable developers to implement and customize
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routing, load balancing, scaling and workflow definitions at the data
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> rapid-prototyping stage and we are actively looking for feedback and
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> collaborators.
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## Building Dynemo
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## Building Dynamo
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### Requirements
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Dynemo development and examples are container based.
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Dynamo development and examples are container based.
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* [Docker](https://docs.docker.com/get-started/get-docker/)
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* [buildx](https://github.com/docker/buildx)
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### Development
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You can build the Dynemo container using the build scripts
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You can build the Dynamo container using the build scripts
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in `container/` (or directly with `docker build`).
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We provide 3 types of builds:
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Please see the instructions in the corresponding example for specific build instructions.
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## Running Dynemo for Local Testing and Development
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## Running Dynamo for Local Testing and Development
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You can run the Dynemo container using the run scripts in
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You can run the Dynamo container using the run scripts in
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`container/` (or directly with `docker run`).
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The run script offers a few common workflows:
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1. Running a command in a container and exiting.
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```
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./container/run.sh -- python3 -c "import dynemo.runtime; help(dynemo.runtime)"
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./container/run.sh -- python3 -c "import dynamo.runtime; help(dynamo.runtime)"
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```
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2. Starting an interactive shell.
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## Rust Based Runtime
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Dynemo has a new rust based distributed runtime with
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Dynamo has a new rust based distributed runtime with
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implementation under development. The rust based runtime enables
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serving arbitrary python code as well as native rust. Please note the
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APIs are subject to change.
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An intermediate example expanding further on the concepts introduced
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in the Hello World example. In this example, we demonstrate
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[Disaggregated Serving](https://arxiv.org/abs/2401.09670) as an
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application of the components defined in Dynemo.
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application of the components defined in Dynamo.
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# Disclaimers
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‎codespell.txt‎

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dynamo->dynemo
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dynmo->dynemo
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dynmo->dynamo

‎components/metrics/Cargo.lock‎

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‎components/metrics/Cargo.toml‎

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[dependencies]
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# local
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dynemo-runtime = { path = "../../lib/runtime" }
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dynemo-llm = { path = "../../lib/llm" }
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dynamo-runtime = { path = "../../lib/runtime" }
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dynamo-llm = { path = "../../lib/llm" }
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# workspace - todo
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‎components/metrics/README.md‎

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For example:
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```bash
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# For more details, try DYN_LOG=debug
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DYN_LOG=info cargo run --bin metrics -- --namespace dynemo --component backend --endpoint generate
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DYN_LOG=info cargo run --bin metrics -- --namespace dynamo --component backend --endpoint generate
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# 2025-02-26T18:45:05.467026Z INFO metrics: Creating unique instance of Metrics at dynemo/components/metrics/instance
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# 2025-02-26T18:45:05.472146Z INFO metrics: Scraping service dynemo_backend_720278f8 and filtering on subject dynemo_backend_720278f8.generate
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# 2025-02-26T18:45:05.467026Z INFO metrics: Creating unique instance of Metrics at dynamo/components/metrics/instance
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# 2025-02-26T18:45:05.472146Z INFO metrics: Scraping service dynamo_backend_720278f8 and filtering on subject dynamo_backend_720278f8.generate
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# ...
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```
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With no matching endpoints running to collect stats from, you should see warnings in the logs:
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```bash
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2025-02-26T18:45:06.474161Z WARN metrics: No endpoints found matching subject dynemo_backend_720278f8.generate
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2025-02-26T18:45:06.474161Z WARN metrics: No endpoints found matching subject dynamo_backend_720278f8.generate
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```
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When stats are found from target endpoints, the metrics component will
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```
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2025-02-28T04:05:58.077901Z INFO metrics: Aggregated metrics: ProcessedEndpoints { endpoints: [Endpoint { name: "worker-7587884888253033398", subject: "dynemo_backend_720278f8.generate-694d951a80e06bb6", data: ForwardPassMetrics { request_active_slots: 58, request_total_slots: 100, kv_active_blocks: 77, kv_total_blocks: 100 } }, Endpoint { name: "worker-7587884888253033401", subject: "dynemo_backend_720278f8.generate-694d951a80e06bb9", data: ForwardPassMetrics { request_active_slots: 71, request_total_slots: 100, kv_active_blocks: 29, kv_total_blocks: 100 } }], worker_ids: [7587884888253033398, 7587884888253033401], load_avg: 53.0, load_std: 24.0 }
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2025-02-28T04:05:58.077901Z INFO metrics: Aggregated metrics: ProcessedEndpoints { endpoints: [Endpoint { name: "worker-7587884888253033398", subject: "dynamo_backend_720278f8.generate-694d951a80e06bb6", data: ForwardPassMetrics { request_active_slots: 58, request_total_slots: 100, kv_active_blocks: 77, kv_total_blocks: 100 } }, Endpoint { name: "worker-7587884888253033401", subject: "dynamo_backend_720278f8.generate-694d951a80e06bb9", data: ForwardPassMetrics { request_active_slots: 71, request_total_slots: 100, kv_active_blocks: 29, kv_total_blocks: 100 } }], worker_ids: [7587884888253033398, 7587884888253033401], load_avg: 53.0, load_std: 24.0 }
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```
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To see the metrics being published in prometheus format, you can run:

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