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244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
90 lines
1.4 KiB
Markdown
90 lines
1.4 KiB
Markdown
# Installation
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## Requirements
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- Python ≥ 3.10
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- At least one model API key (Azure OpenAI, OpenAI, Anthropic, or local Qwen)
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## Quick Install
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```bash
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git clone https://github.com/microsoft/SkillOpt.git
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cd SkillOpt
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pip install -e .
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```
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## Optional Dependencies
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Install extras for specific benchmarks or backends:
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=== "ALFWorld"
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```bash
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pip install -e ".[alfworld]"
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```
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=== "Claude Backend"
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```bash
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pip install -e ".[claude]"
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```
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=== "Qwen (Local)"
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```bash
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pip install -e ".[qwen]"
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```
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=== "WebUI"
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```bash
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pip install -e ".[webui]"
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```
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=== "Development"
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```bash
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pip install -e ".[dev]"
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```
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=== "All"
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```bash
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pip install -e ".[alfworld,claude,qwen,webui,dev]"
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```
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## Environment Variables
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Copy the example `.env` file and fill in your credentials:
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```bash
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cp .env.example .env
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```
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Edit `.env` with your API keys:
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```ini
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# Azure OpenAI (default backend)
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AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
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AZURE_OPENAI_API_KEY=your-key
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# Or use OpenAI directly
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OPENAI_API_KEY=sk-...
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# Or Anthropic Claude
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ANTHROPIC_API_KEY=sk-ant-...
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```
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!!! tip
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You only need credentials for the backend you plan to use. Azure OpenAI is the default.
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## Verify Installation
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```bash
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python -c "import skillopt; print('SkillOpt ready!')"
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```
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## Next Steps
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→ [Run your first experiment](first-experiment.md)
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