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remote vllm distro
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13 changed files with 188 additions and 18 deletions
1
distributions/ollama-gpu/build.yaml
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distributions/ollama-gpu/build.yaml
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../../llama_stack/templates/ollama/build.yaml
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distributions/remote-vllm/build.yaml
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distributions/remote-vllm/build.yaml
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../../llama_stack/templates/ollama/build.yaml
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distributions/remote-vllm/cpu/compose.yaml
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distributions/remote-vllm/cpu/compose.yaml
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services:
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ollama:
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image: ollama/ollama:latest
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network_mode: "host"
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volumes:
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- ollama:/root/.ollama # this solution synchronizes with the docker volume and loads the model rocket fast
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ports:
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- "11434:11434"
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command: []
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llamastack:
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depends_on:
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- ollama
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image: llamastack/distribution-ollama
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network_mode: "host"
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volumes:
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- ~/.llama:/root/.llama
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# Link to ollama run.yaml file
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- ./run.yaml:/root/my-run.yaml
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ports:
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- "5000:5000"
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# Hack: wait for ollama server to start before starting docker
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entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/my-run.yaml"
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deploy:
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restart_policy:
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condition: on-failure
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delay: 3s
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max_attempts: 5
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window: 60s
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volumes:
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ollama:
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distributions/remote-vllm/cpu/run.yaml
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distributions/remote-vllm/cpu/run.yaml
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version: '2'
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built_at: '2024-10-08T17:40:45.325529'
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image_name: local
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docker_image: null
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conda_env: local
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apis:
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- shields
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- agents
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- models
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- memory
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- memory_banks
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- inference
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- safety
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providers:
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inference:
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- provider_id: ollama0
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provider_type: remote::ollama
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config:
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url: http://127.0.0.1:14343
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safety:
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- provider_id: meta0
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provider_type: meta-reference
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config:
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llama_guard_shield:
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model: Llama-Guard-3-1B
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excluded_categories: []
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disable_input_check: false
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disable_output_check: false
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prompt_guard_shield:
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model: Prompt-Guard-86M
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memory:
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- provider_id: meta0
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provider_type: meta-reference
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config: {}
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agents:
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- provider_id: meta0
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provider_type: meta-reference
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config:
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persistence_store:
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namespace: null
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type: sqlite
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db_path: ~/.llama/runtime/kvstore.db
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telemetry:
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- provider_id: meta0
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provider_type: meta-reference
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config: {}
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48
distributions/remote-vllm/gpu/compose.yaml
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distributions/remote-vllm/gpu/compose.yaml
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services:
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ollama:
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image: ollama/ollama:latest
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network_mode: "host"
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volumes:
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- ollama:/root/.ollama # this solution synchronizes with the docker volume and loads the model rocket fast
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ports:
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- "11434:11434"
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devices:
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- nvidia.com/gpu=all
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environment:
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- CUDA_VISIBLE_DEVICES=0
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command: []
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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# that's the closest analogue to --gpus; provide
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# an integer amount of devices or 'all'
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count: 1
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# Devices are reserved using a list of capabilities, making
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# capabilities the only required field. A device MUST
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# satisfy all the requested capabilities for a successful
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# reservation.
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capabilities: [gpu]
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runtime: nvidia
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llamastack:
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depends_on:
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- ollama
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image: llamastack/distribution-ollama
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network_mode: "host"
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volumes:
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- ~/.llama:/root/.llama
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# Link to ollama run.yaml file
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- ./run.yaml:/root/llamastack-run-ollama.yaml
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ports:
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- "5000:5000"
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# Hack: wait for ollama server to start before starting docker
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entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-ollama.yaml"
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deploy:
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restart_policy:
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condition: on-failure
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delay: 3s
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max_attempts: 5
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window: 60s
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volumes:
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ollama:
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distributions/remote-vllm/gpu/run.yaml
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distributions/remote-vllm/gpu/run.yaml
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version: '2'
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built_at: '2024-10-08T17:40:45.325529'
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image_name: local
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docker_image: null
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conda_env: local
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apis:
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- shields
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- agents
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- models
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- memory
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- memory_banks
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- inference
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- safety
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providers:
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inference:
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- provider_id: ollama0
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provider_type: remote::ollama
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config:
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url: http://127.0.0.1:14343
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safety:
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- provider_id: meta0
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provider_type: meta-reference
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config:
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llama_guard_shield:
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model: Llama-Guard-3-1B
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excluded_categories: []
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disable_input_check: false
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disable_output_check: false
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prompt_guard_shield:
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model: Prompt-Guard-86M
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memory:
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- provider_id: meta0
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provider_type: meta-reference
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config: {}
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agents:
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- provider_id: meta0
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provider_type: meta-reference
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config:
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persistence_store:
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namespace: null
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type: sqlite
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db_path: ~/.llama/runtime/kvstore.db
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telemetry:
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- provider_id: meta0
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provider_type: meta-reference
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config: {}
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@ -7,16 +7,22 @@ The `llamastack/distribution-ollama` distribution consists of the following prov
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| **Provider(s)** | remote::ollama | meta-reference | remote::pgvector, remote::chroma | remote::ollama | meta-reference |
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### Docker: Start a Distribution (Single Node GPU)
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### Docker: Start the Distribution (Single Node regular Desktop machine)
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> [!NOTE]
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> This will start an ollama server with CPU only, please see [Ollama Documentations](https://github.com/ollama/ollama) for serving models on CPU only.
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```
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$ cd distributions/ollama; docker compose up
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```
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### Docker: Start a Distribution (Single Node with nvidia GPUs)
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> [!NOTE]
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> This assumes you have access to GPU to start a Ollama server with access to your GPU.
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```
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$ cd distributions/ollama/gpu
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$ ls
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compose.yaml run.yaml
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$ docker compose up
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$ cd distributions/ollama-gpu; docker compose up
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```
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You will see outputs similar to following ---
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docker compose down
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```
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### Docker: Start the Distribution (Single Node CPU)
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> [!NOTE]
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> This will start an ollama server with CPU only, please see [Ollama Documentations](https://github.com/ollama/ollama) for serving models on CPU only.
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```
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$ cd distributions/ollama/cpu
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$ ls
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compose.yaml run.yaml
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$ docker compose up
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```
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### Conda: ollama run + llama stack run
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If you wish to separately spin up a Ollama server, and connect with Llama Stack, you may use the following commands.
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@ -144,7 +144,11 @@ docker compose down
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:::{tab-item} ollama
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```
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$ cd llama-stack/distributions/ollama/cpu && docker compose up
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$ cd llama-stack/distributions/ollama && docker compose up
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# OR
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$ cd llama-stack/distributions/ollama-gpu && docker compose up
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```
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You will see outputs similar to following ---
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