Installation & Requirements
Tensorify has two parts: the web editor at app.tensorify.io (no install needed) and the CLI that runs workflows on your machine or server.
Using Managed (cloud) execution? You can skip this page entirely — just sign up at app.tensorify.io and start building. The CLI and Python are only needed if you want to run workflows on your own infrastructure or test locally from the canvas.
These apply only to machines running the CLI runner (your laptop for local testing, or a VPS/server for self-hosted production).
| Requirement | Version |
|---|---|
| Python | 3.9 or higher (for workflow execution) |
| Operating System | Linux, macOS, or Windows |
| Network | Outbound HTTPS to app.tensorify.io and triggers.tensorify.io |
Linux & macOS:
curl -fsSL https://cli.tensorify.io/install | sh
Windows (PowerShell):
irm https://cli.tensorify.io/install.ps1 | iex
Verify the install:
tensorify --version
You should see a version number printed. If you get command not found, ensure ~/.tensorify/bin is on your PATH.
Tensorify generates and runs Python code. The CLI manages runtime installation automatically — it creates isolated virtual environments per workflow and installs dependencies from a CDN-hosted wheel. You just need Python 3.9+ available on your system:
python3 --version
# Python 3.9.x or higher
The CLI detects python3 (or python) on your PATH. No manual venv setup is required.
On a machine with a browser, use tensorify login to authenticate via browser sign-in:
tensorify init
tensorify login
tensorify runner start
tensorify init creates the runner config (~/.tensorify/config.json). tensorify login opens your browser to sign in — your credentials and teamspace are saved automatically. Then tensorify runner start connects to Tensorify and waits for workflows.
On a server without a browser, pass your API key directly to tensorify init:
tensorify init --name my-vps --api-key tfk_your_key_here
tensorify runner start
Get an API key from Settings → API Keys in the web app. The --api-key flag stores it in config and resolves your teamspace — no browser needed.
For production, install the runner as a system service instead of keeping a terminal open:
tensorify init --name my-vps --api-key tfk_your_key_here
sudo tensorify runner install
This creates a systemd service that starts on boot and restarts on failure.
Your API key has full access to your workspace. Keep it out of source control.
Build a runner image for containerized deployment:
FROM python:3.11-slim
RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
RUN curl -fsSL https://cli.tensorify.io/install | sh
ENV PATH="/root/.tensorify/bin:$PATH"
ENV TENSORIFY_API_KEY=""
CMD ["sh", "-c", "tensorify init --name docker-runner --api-key $TENSORIFY_API_KEY && tensorify runner start"]
docker build -t my-runner .
docker run -e TENSORIFY_API_KEY=tfk_your_key my-runner
The CMD runs init --api-key on every start (resolves your teamspace fresh), then starts the runner. Pass the key via -e at runtime so it is not baked into the image.
Deploy individual workflows from the web UI after the container is running.
- Quick Start Guide — build and run your first workflow
- CLI Reference — all commands and flags