apherisfold-cli 0.1.11

Prototype ApherisFold CLI for Hub-backed customer demos.

ApherisFold CLI

Command-line interface for submitting and managing prediction, benchmark, and fine-tuning workflows on an Apheris Hub instance.

Requirements

• Python 3.10 or later
• Access to an Apheris Hub instance

Installation

conda

Recommended if you already use conda for scientific Python work. A dedicated environment avoids dependency conflicts.

Linux / macOS

conda create -n apherisfold python=3.11 -y
conda activate apherisfold
pip install apherisfold-cli

Windows (Anaconda Prompt or PowerShell)

conda create -n apherisfold python=3.11 -y
conda activate apherisfold
pip install apherisfold-cli

Activate the environment at the start of each session with conda activate apherisfold.

venv (standard Python)

Linux / macOS (venv)

python3 -m venv ~/.venvs/apherisfold
source ~/.venvs/apherisfold/bin/activate
pip install apherisfold-cli

Windows (PowerShell, venv)

python -m venv $env:USERPROFILE\.venvs\apherisfold
& $env:USERPROFILE\.venvs\apherisfold\Scripts\Activate.ps1
pip install apherisfold-cli

Activate the environment at the start of each session with the activate command above.

Verify

apherisfold --version
apherisfold --help

Upgrade

pip install --upgrade apherisfold-cli

Quick start

# Log in with your ApherisFold Hub URL
apherisfold login --url https://hub.example.com

# See available models and weights
apherisfold weights list

# Submit a prediction
apherisfold workflow predict run \
  --model openfold3 \
  --weight 3.0.0 \
  --input ./request.json

# By default, local artifacts are written under ./.apherisfold/runs/<workflow>/<id>
# relative to the directory where you run the command. Use --output to choose another directory.

# List submitted jobs
apherisfold workflow predict list

# Inspect a job
apherisfold workflow predict get --id job_NUlyMsnWq

Workflows

Workflow	Entry point
Structure prediction	apherisfold workflow predict run
Benchmarking	apherisfold workflow benchmark run
Fine-tuning	apherisfold workflow fine-tune prepare

Affinity prediction

When using weights that support affinity prediction, the CLI selects the ligand chain automatically if there is only one. For queries with multiple ligand chains, specify which one to score with --affinity-ligand:

apherisfold workflow predict run \
  --model openfold3 \
  --weight 3.0.0 \
  --input ./request.json \
  --affinity-ligand B

Use --help on any subcommand for the full option reference and examples:

apherisfold workflow predict run --help
apherisfold workflow benchmark run --help
apherisfold workflow fine-tune prepare --help

Support

Contact [e-mail] for access and deployment assistance.