Skip to main content
Use tcr-workbench-setup to install TCR-Workbench on a laptop, Mac, workstation, or GPU machine and get it to the point where analyses can run. The Skill chooses the device, model size, and weight source for your machine, runs setup, and translates failures into fixes.
This Skill calls no Biohub tools. Your AI client runs the TCR-Workbench launcher in a local checkout of TCR-Workbench, so it needs a shell on your own computer, Python 3.9 or newer, and, for model setup, git and network access to GitHub and Hugging Face.

​
Before you begin

​
Run the Skill

Install TCR-Workbench on a Mac

​
What the Skill guides

  1. It starts read-only with python3 tcr.py doctor and stops when an install already fits the intended device and model.
  2. It chooses CPU, Apple Silicon, or NVIDIA GPU from the machine, and sends Windows users to WSL.
  3. It picks the weight source, Hugging Face by default or a shared registry when one is configured, and the DecoderTCR model size the hardware can hold.
  4. It runs python3 tcr.py setup, which installs DecoderTCR from a pinned commit and configures the chosen runtime and weight source.
  5. It diagnoses common failures, including missing git, Hugging Face authentication, and TLS-inspecting proxies, without disabling certificate verification.
  6. It verifies the install with python3 tcr.py doctor --deep and hands off to the tcr-workbench Skill.

​
Expected result

The result includes:
  • A managed .tcr/ environment in the checkout with the chosen device, model, precision, and weight source.
  • A doctor --deep report that shows a reachable weight source and healthy framework imports.
  • The next command to run, such as the synthetic example that python3 tcr.py example writes.
Model workflows require weight downloads, about 1.3 GB for the default 300M model. Review the selected device, model, and downloads before approving setup or scoring.

​
Skill files