Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSo...
Real data. Real impact.
Emerging
Developers
Per week
Open source
Skills give you superpowers. Install in 30 seconds.
Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.
curl -fsSL https://cli.inference.sh | sh && infsh loginRun Python code
infsh app run infsh/python-executor --input '{ "code": "import pandas as pd\nprint(pd.version)" }'
Install note: The install script only detects your OS/architecture, downloads the matching binary from
, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.dist.inference.sh
| Property | Value |
|---|---|
| App ID | |
| Environment | Python 3.10, CPU-only |
| RAM | 8GB (default) / 16GB (high_memory) |
| Timeout | 1-300 seconds (default: 30) |
{ "code": "print('Hello World!')", "timeout": 30, "capture_output": true, "working_dir": null }
requestshttpxaiohttpbeautifulsoup4lxmlseleniumplaywrightscrapynumpypandasscipymatplotlibseabornplotlypillowopencv-python-headlessscikit-imageimageiomoviepyavffmpeg-pythonpydubtrimeshopen3dnumpy-stlmeshiopyvistasvgwritecairosvgreportlabpypdf2infsh app run infsh/python-executor --input '{ "code": "import requests\nfrom bs4 import BeautifulSoup\n\nresponse = requests.get(\"https://example.com\")\nsoup = BeautifulSoup(response.content, \"html.parser\")\nprint(soup.find(\"title\").text)" }'
infsh app run infsh/python-executor --input '{ "code": "import pandas as pd\nimport matplotlib.pyplot as plt\n\ndata = {\"name\": [\"Alice\", \"Bob\"], \"sales\": [100, 150]}\ndf = pd.DataFrame(data)\n\nplt.bar(df[\"name\"], df[\"sales\"])\nplt.savefig(\"outputs/chart.png\")\nprint(\"Chart saved!\")" }'
infsh app run infsh/python-executor --input '{ "code": "from PIL import Image\nimport numpy as np\n\n# Create gradient image\narr = np.linspace(0, 255, 256*256, dtype=np.uint8).reshape(256, 256)\nimg = Image.fromarray(arr, mode=\"L\")\nimg.save(\"outputs/gradient.png\")\nprint(\"Image created!\")" }'
infsh app run infsh/python-executor --input '{ "code": "from moviepy.editor import ColorClip, TextClip, CompositeVideoClip\n\nclip = ColorClip(size=(640, 480), color=(0, 100, 200), duration=3)\ntxt = TextClip(\"Hello!\", fontsize=70, color=\"white\").set_position(\"center\").set_duration(3)\nvideo = CompositeVideoClip([clip, txt])\nvideo.write_videofile(\"outputs/hello.mp4\", fps=24)\nprint(\"Video created!\")", "timeout": 120 }'
infsh app run infsh/python-executor --input '{ "code": "import trimesh\n\nsphere = trimesh.creation.icosphere(subdivisions=3, radius=1.0)\nsphere.export(\"outputs/sphere.stl\")\nprint(f\"Created sphere with {len(sphere.vertices)} vertices\")" }'
infsh app run infsh/python-executor --input '{ "code": "import requests\nimport json\n\nresponse = requests.get(\"https://api.github.com/users/octocat\")\ndata = response.json()\nprint(json.dumps(data, indent=2))" }'
Files saved to
outputs/# These files will be in the response plt.savefig('outputs/chart.png') df.to_csv('outputs/data.csv') video.write_videofile('outputs/video.mp4') mesh.export('outputs/model.stl')
# Default (8GB RAM) infsh app run infsh/python-executor --input input.jsonHigh memory (16GB RAM) for large datasets
infsh app run infsh/python-executor@high_memory --input input.json
plt.savefig()plt.show()# AI image generation (for ML-based images) npx skills add inference-sh/skills@ai-image-generationAI video generation (for ML-based videos)
npx skills add inference-sh/skills@ai-video-generation
LLM models (for text generation)
npx skills add inference-sh/skills@llm-models
No automatic installation available. Please visit the source repository for installation instructions.
View Installation Instructions
1,500+ AI skills, agents & workflows. Install in 30 seconds. Part of the Torly.ai family.
© 2026 Torly.ai. All rights reserved.