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Use docker image to run CI tests
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‎.github/actions/docker-daemon-run/action.yml‎

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name: Run Daemon Docker Container
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description: Pulls and runs LM Studio Daemon Docker image for testing
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inputs:
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docker-image:
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description: "Full Docker image name"
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required: true
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container-name:
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description: "Name for the container"
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required: false
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default: "llmster-test"
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port:
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description: "Port to expose (host:container)"
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required: false
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default: "1234:1234"
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outputs:
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container-id:
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description: "The ID of the running container"
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value: ${{ steps.run-container.outputs.container-id }}
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container-name:
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description: "The name of the running container"
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value: ${{ inputs.container-name }}
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runs:
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using: "composite"
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steps:
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- name: Pull Docker image
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shell: bash
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run: |
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echo "Pulling image: ${{ inputs.docker-image }}"
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docker pull ${{ inputs.docker-image }}
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- name: Run container
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id: run-container
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shell: bash
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run: |
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echo "Starting container: ${{ inputs.container-name }}"
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if [ "${{ inputs.use-local-image }}" = "true" ]; then
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echo "Using local image: ${{ inputs.docker-image }}"
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else
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echo "Using registry image: ${{ inputs.docker-image }}"
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fi
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CONTAINER_ID=$(docker run -d --name ${{ inputs.container-name }} -p ${{ inputs.port }} ${{ inputs.docker-image }})
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echo "Container ID: $CONTAINER_ID"
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echo "container-id=$CONTAINER_ID" >> $GITHUB_OUTPUT
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# Wait for container to become healthy
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TIMEOUT=120 # timeout in seconds (increased to account for start-period)
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START_TIME=$(date +%s)
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END_TIME=$((START_TIME + TIMEOUT))
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# Start with 1 second delay, then exponentially increase
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DELAY=1
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MAX_DELAY=16 # Cap maximum delay at 16 seconds
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while [ $(date +%s) -lt $END_TIME ]; do
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HEALTH_STATUS=$(docker inspect --format='{{.State.Health.Status}}' ${{ inputs.container-name }} 2>/dev/null || echo "unknown")
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if [ "$HEALTH_STATUS" = "healthy" ]; then
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echo "Container is running!"
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break
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elif [ "$HEALTH_STATUS" = "unhealthy" ]; then
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echo "Container is unhealthy - exiting"
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docker logs ${{ inputs.container-name }}
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exit 1
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fi
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ELAPSED=$(($(date +%s) - START_TIME))
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sleep $DELAY
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DELAY=$((DELAY * 2))
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if [ $DELAY -gt $MAX_DELAY ]; then
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DELAY=$MAX_DELAY
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fi
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done
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# Final check after waiting for the maximum timeout
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# Print logs and the health status
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if [ $(date +%s) -ge $END_TIME ]; then
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echo "Container health check timed out after ${TIMEOUT} seconds"
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echo "Final health status: $(docker inspect --format='{{.State.Health.Status}}' ${{ inputs.container-name }})"
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docker logs ${{ inputs.container-name }}
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exit 1
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fi

‎.github/workflows/test.yml‎

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restore-keys: |
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pip-${{ matrix.os }}-${{ matrix.python-version }}-v1-
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- name: Run the built image
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id: run
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uses: ./.github/actions/docker-daemon-run
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with:
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docker-image: lmstudio/llmster-preview:cpu
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# Use the same port as the always on API server
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port: "41343:1234"
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- name: Download models for tests (Ubuntu only)
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if: matrix.os == 'ubuntu-22.04'
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run: |
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echo "Downloading required models..."
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# Download text LLMs
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docker exec llmster lms get https://huggingface.co/hugging-quants/Llama-3.2-1B-Instruct-Q4_K_M-GGUF -y
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docker exec llmster lms get https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF -y
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docker exec llmster lms get https://huggingface.co/ZiangWu/MobileVLM_V2-1.7B-GGUF -y
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# Download vision LLM
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docker exec llmster lms get ZiangWu/MobileVLM_V2-1.7B-GGUF -y
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# Download additional model for speculative decoding examples
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docker exec llmster lms get qwen2.5-0.5b-instruct -y
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echo "Model downloads complete"
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- name: Load models into LM Studio (Ubuntu only)
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if: matrix.os == 'ubuntu-22.04'
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run: |
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echo "Loading models..."
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# Load embedding model
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docker exec llmster lms load nomic-embed-text-v1.5 --identifier text-embedding-nomic-embed-text-v1.5 -y
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# Load text LLMs
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docker exec llmster lms load llama-3.2-1b-instruct --identifier llama-3.2-1b-instruct -y
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docker exec llmster lms load qwen2.5-7b-instruct --identifier qwen2.5-7b-instruct-1m -y
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docker exec llmster lms load smollm2-135m-instruct --identifier smollm2-135m-instruct -y
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# Load vision LLM
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docker exec llmster lms load ZiangWu/MobileVLM_V2-1.7B-GGUF --identifier mobilevlm_v2-1.7b
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echo "Model loading complete"
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- name: Install PDM
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run: |
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# Ensure `pdm` uses the same version as specified in `pdm.lock`
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source "$VIRTUAL_ENV_BIN_DIR/activate"
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python -m tox -v -m static
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- name: CI-compatible tests
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- name: CI-compatible tests (Windows)
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if: matrix.os == 'windows-2022'
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run: |
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source "$VIRTUAL_ENV_BIN_DIR/activate"
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python -m tox -v -- -m 'not lmstudio'
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- name: All tests including LM Studio (Ubuntu)
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if: matrix.os == 'ubuntu-22.04'
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run: |
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source "$VIRTUAL_ENV_BIN_DIR/activate"
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python -m tox -v
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- name: Upload coverage data
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uses: actions/upload-artifact@v4
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with:
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include-hidden-files: true
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if-no-files-found: ignore
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- name: Stop LM Studio Docker container (Ubuntu only)
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if: matrix.os == 'ubuntu-22.04' && always()
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run: |
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docker stop llmster || true
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docker rm llmster || true
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# Coverage check based on https://hynek.me/articles/ditch-codecov-python/
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coverage:
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name: Combine & check coverage
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# Report again and fail if under 50%.
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# Highest historical coverage: 65%
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# Last noted local test coverage level: 94%
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# CI coverage percentage is low because many of the tests
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# aren't CI compatible (they need a local LM Studio instance).
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# It's only as high as it is because the generated data model
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# classes make up such a large portion of the total SDK code.
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# Accept anything over 50% until CI is set up to run LM Studio
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# in headless mode, and hence is able to run end-to-end tests.
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# Ubuntu runners now run LM Studio in Docker, so they achieve higher
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# coverage than Windows runners (which skip LM Studio tests).
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# The generated data model classes make up a large portion of the SDK code.
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# Accept anything over 50% as Windows runners still skip LM Studio tests.
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coverage report --fail-under=50
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- name: Upload HTML report if check failed

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