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Merge pull request #3 from bespokelabsai/feature/preset-images
Add preset images with pinned tags and Tensorlake support
2 parents fafad9b + 9493c61 commit c4faae8

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‎.github/workflows/build-images.yml‎

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name: Build and push preset images
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# Two modes of operation:
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#
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# * Push to main -> build+push :main (mutable dev tag). Library code
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# never references :main; it exists for manual testing.
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#
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# * workflow_dispatch -> build+push a pinned, immutable tag (e.g. :v1)
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# matching PRESET_IMAGE_TAG in src/bespokelabs/sandbox/presets.py.
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# This is how you cut a new image release: bump the constant in
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# Python, then run this workflow with the same tag value.
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#
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# :sha-<short> is always emitted for audit/rollback.
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on:
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push:
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branches: [main]
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paths:
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- "images/**"
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workflow_dispatch:
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inputs:
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image:
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description: "Specific image to build (e.g. claude-sdk), or 'all'"
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required: false
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default: "all"
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tag:
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description: "Pinned tag to publish (e.g. v1). Must match PRESET_IMAGE_TAG."
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required: true
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default: "v1"
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env:
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REGISTRY: ghcr.io
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IMAGE_PREFIX: ghcr.io/bespokelabsai/sandbox
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jobs:
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detect-changes:
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runs-on: ubuntu-latest
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outputs:
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images: ${{ steps.set-matrix.outputs.images }}
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 2
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- id: set-matrix
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run: |
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if [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
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INPUT="${{ github.event.inputs.image }}"
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if [[ "$INPUT" == "all" || -z "$INPUT" ]]; then
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IMAGES=$(ls -d images/*/ | xargs -I{} basename {} | jq -R -s -c 'split("\n") | map(select(. != ""))')
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else
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IMAGES=$(echo "[\"$INPUT\"]")
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fi
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else
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# On push, only build images whose Dockerfile changed
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CHANGED=$(git diff --name-only HEAD~1 HEAD -- images/ | cut -d/ -f2 | sort -u | jq -R -s -c 'split("\n") | map(select(. != ""))')
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if [[ "$CHANGED" == "[]" || "$CHANGED" == '[""]' ]]; then
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IMAGES="[]"
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else
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IMAGES="$CHANGED"
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fi
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fi
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echo "images=$IMAGES" >> "$GITHUB_OUTPUT"
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build:
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needs: detect-changes
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if: needs.detect-changes.outputs.images != '[]'
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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strategy:
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fail-fast: false
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matrix:
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image: ${{ fromJson(needs.detect-changes.outputs.images) }}
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steps:
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- uses: actions/checkout@v4
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- uses: docker/login-action@v3
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- uses: docker/setup-buildx-action@v3
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- uses: docker/metadata-action@v5
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id: meta
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with:
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images: ${{ env.IMAGE_PREFIX }}/${{ matrix.image }}
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# On workflow_dispatch -> the pinned tag input (e.g. :v1).
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# On push to main -> :main (dev tag, never referenced by library).
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# Always emit :sha-<short> for auditability.
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tags: |
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type=raw,value=${{ github.event.inputs.tag }},enable=${{ github.event_name == 'workflow_dispatch' }}
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type=raw,value=main,enable=${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}
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type=sha,prefix=sha-
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- uses: docker/build-push-action@v5
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with:
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context: images/${{ matrix.image }}
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push: true
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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cache-from: type=gha
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cache-to: type=gha,mode=max

‎.gitignore‎

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*.pyo
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dist/
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examples/.sandbox_workdir/
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examples/__pycache__/
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examples/__pycache__/
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.claude/

‎images/claude-code/Dockerfile‎

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FROM python:3.12-slim
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RUN apt-get update && apt-get install -y --no-install-recommends \
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curl \
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&& curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \
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&& apt-get install -y --no-install-recommends nodejs \
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&& npm install -g @anthropic-ai/claude-code \
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&& apt-get clean && rm -rf /var/lib/apt/lists/*

‎images/claude-sdk/Dockerfile‎

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FROM python:3.12-slim
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RUN pip install --no-cache-dir claude-agent-sdk

‎images/codex/Dockerfile‎

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FROM python:3.12-slim
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RUN apt-get update && apt-get install -y --no-install-recommends \
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curl \
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&& curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \
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&& apt-get install -y --no-install-recommends nodejs \
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&& npm install -g @openai/codex \
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&& apt-get clean && rm -rf /var/lib/apt/lists/*

‎images/python-data-science/Dockerfile‎

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FROM python:3.12-slim
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RUN pip install --no-cache-dir \
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numpy \
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pandas \
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matplotlib \
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scikit-learn

‎images/python-ml/Dockerfile‎

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FROM python:3.12-slim
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RUN pip install --no-cache-dir \
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torch \
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transformers \
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datasets \
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accelerate

‎images/web-dev/Dockerfile‎

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FROM node:22-slim
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RUN npm install -g typescript ts-node prettier eslint

‎src/bespokelabs/sandbox/backends/tensorlake.py‎

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"timeout_secs": config.timeout_secs,
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"allow_internet_access": config.allow_internet,
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}
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if config.image:
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kwargs["image"] = config.image
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if config.snapshot_id:
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kwargs["snapshot_id"] = config.snapshot_id
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self._sandbox = self._client.create_and_connect(**kwargs)

‎src/bespokelabs/sandbox/presets.py‎

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Attributes:
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name: Human-readable preset name.
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description: What this preset provides.
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image: Pre-built OCI container image for docker/daytona/modal backends
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(skips setup_commands if the backend supports images).
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tensorlake_image: Tensorlake project-scoped image name. Tensorlake
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doesn't accept arbitrary OCI URLs, so this is a separate field.
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Users register a project image with `tl sbx image build` (our
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Dockerfiles under images/ can be used as-is) and then set this.
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When set, setup_commands are skipped on the Tensorlake backend.
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setup_commands: Shell commands run after sandbox creation (in order).
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cpu: Minimum recommended vCPUs.
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memory_mb: Minimum recommended RAM in MB.
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name: str
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description: str
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image: str | None = None
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tensorlake_image: str | None = None
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setup_commands: list[str] = field(default_factory=list)
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cpu: float = 1.0
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memory_mb: int = 1024
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return PRESETS[name]
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# -- Constants -------------------------------------------------------------
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IMAGE_REGISTRY = "ghcr.io/bespokelabsai/sandbox"
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# Pinned tag for preset images. Bump to cut a new immutable image set,
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# then run the build-images workflow_dispatch with the same tag value.
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# Never use ":latest" here — it's not reproducible.
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PRESET_IMAGE_TAG = "v1"
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# -- Built-in presets ------------------------------------------------------
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register_preset(SandboxPreset(
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name="claude-code",
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description="Sandbox with Claude Code (Anthropic CLI) installed",
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image=f"{IMAGE_REGISTRY}/claude-code:{PRESET_IMAGE_TAG}",
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setup_commands=[
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"npm install -g @anthropic-ai/claude-code",
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],
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register_preset(SandboxPreset(
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name="claude-sdk",
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description="Sandbox with Claude Agent SDK and bundled Claude Code CLI",
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image=f"{IMAGE_REGISTRY}/claude-sdk:{PRESET_IMAGE_TAG}",
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setup_commands=[
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"pip install claude-agent-sdk",
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],
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register_preset(SandboxPreset(
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name="codex",
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description="Sandbox with Codex CLI installed",
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image=f"{IMAGE_REGISTRY}/codex:{PRESET_IMAGE_TAG}",
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setup_commands=[
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"npm install -g @openai/codex",
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],
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register_preset(SandboxPreset(
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name="python-data-science",
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description="Python with numpy, pandas, matplotlib, scikit-learn",
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image=f"{IMAGE_REGISTRY}/python-data-science:{PRESET_IMAGE_TAG}",
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setup_commands=[
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"pip install numpy pandas matplotlib scikit-learn",
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],
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register_preset(SandboxPreset(
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name="web-dev",
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description="Node.js with common web development tools",
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image=f"{IMAGE_REGISTRY}/web-dev:{PRESET_IMAGE_TAG}",
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setup_commands=[
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"npm install -g typescript ts-node prettier eslint",
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],
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register_preset(SandboxPreset(
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name="python-ml",
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description="Python with PyTorch and common ML libraries",
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image=f"{IMAGE_REGISTRY}/python-ml:{PRESET_IMAGE_TAG}",
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setup_commands=[
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"pip install torch transformers datasets accelerate",
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],

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