/qzcli

name: qzcli
description: Manage GPU compute jobs on the Qizhi (启智) platform using qzcli — a kubectl-style CLI tool. Use when user says "qzcli", "启智平台", "submit job", "stop job", "查计算组", "avail", "list jobs", "batch submit", or needs to manage distributed training jobs on a Qizhi instance.
argument-hint: "[login|avail|list|create|stop <job-id>|batch|status|watch]"
allowed-tools: Bash(*), Read, Write

qzcli — 启智平台任务管理

A kubectl/docker-style CLI for managing GPU compute jobs on the Qizhi (启智) platform.

**GitHub:** [tianyilt/qzcli_tool](https://github.com/tianyilt/qzcli_tool)

Environment contract

Qizhi is the scheduler-cluster shape of `../shared-references/compute-env-contract.md`: images are built OFF-platform and referenced at submit time, so the declarative env spec + `env:<name>@<specHash>` ledger (`.aris/compute/qizhi.md`) is what keeps "which image has which stack" answerable. Run the kernel witness inside a submitted job (not on the login side) before trusting an image for a long run.

Installation

pip install rich requests prompt_toolkit mcp
git clone https://github.com/tianyilt/qzcli_tool
cd qzcli_tool && pip install -e .

MCP Integration (optional)

To use qzcli as an MCP tool directly from Claude Code or Codex:

# Claude Code
claude mcp add qzcli -- qzcli-mcp

# Codex
codex mcp add qzcli -- qzcli-mcp

---

Configuration

Credentials are read in this priority order: `CLI args > --password-stdin > env vars > QZCLI_ENV_FILE (.env) > ~/.qzcli/config.json > interactive input`

# Option A: env file (recommended)
mkdir -p ~/.qzcli
cat > ~/.qzcli/.env <<'EOF'
QZCLI_USERNAME="your_username"
QZCLI_PASSWORD="your_password"
EOF

# Option B: environment variables
export QZCLI_USERNAME="your_username"
export QZCLI_PASSWORD="your_password"
export QZCLI_API_URL="https://qz.yourorg.edu.cn"

Config files are stored in `~/.qzcli/`: `config.json`, `.cookie`, `resources.json`, `jobs.json`.

---

Quick Start

# 1. Login
qzcli login

# 2. Discover and cache workspaces/compute groups (run once, re-run after joining new workspaces)
qzcli res -u

# 3. Check available nodes
qzcli avail

# 4. List running jobs
qzcli ls -c -r

---

Authentication

# Interactive login
qzcli login

# With credentials
qzcli login -u YOUR_USERNAME -p 'YOUR_PASSWORD'

# Read password from stdin (for scripts)
echo 'YOUR_PASSWORD' | qzcli login -u YOUR_USERNAME --password-stdin

# Check current cookie
qzcli cookie --show

# Clear cookie
qzcli cookie --clear

**Note:** `qzcli avail` auto-refreshes the cookie if it expires and credentials are configured.

---

Resource Discovery

# List cached workspaces
qzcli res --list

# Refresh all workspace resource cache (run this first!)
qzcli res -u

# Refresh a specific workspace
qzcli res -w MY_WORKSPACE -u

# Set a human-readable alias for a workspace
qzcli res -w ws-xxxxxxxx --name "My Workspace"

---

Check Available Nodes

# All workspaces
qzcli avail

# Including low-priority task nodes (slower but more accurate)
qzcli avail --lp

# Specific workspace
qzcli avail -w MY_WORKSPACE

# Find compute groups with N free nodes
qzcli avail -n 4

# Export IDs for scripting
qzcli avail -n 4 -e

# Show idle node names
qzcli avail -w MY_WORKSPACE -v

---

Job Submission

Interactive (recommended for first-time use)

# Full interactive selection: workspace → project → compute group → spec
qzcli create -i

# Interactive for a specific workspace only
qzcli create -i -w "My Workspace"

The TUI shows GPU type, availability, and spec status at each level. Press `Enter/→` to go deeper, `←` to go back.

Non-interactive

# Using names (resolved from qzcli res cache)
qzcli create \
  --name "my-training-job" \
  --command "bash /path/to/train.sh" \
  --workspace "My Workspace" \
  --compute-group "My Compute Group" \
  --image YOUR_REGISTRY/team/image:tag \
  --instances 4 \
  --priority 10

# Using IDs directly
qzcli create \
  --name "my-job" \
  --command "bash /path/to/train.sh" \
  --workspace ws-YOUR_WORKSPACE_ID \
  --compute-group lcg-YOUR_LCG_ID \
  --spec YOUR_SPEC_ID \
  --image YOUR_REGISTRY/team/image:tag \
  --instances 4

**Key parameters:**

| Parameter | Default | Description | |-----------|---------|-------------| | `--name` / `-n` | required | Job name | | `--command` / `-c` | required | Command to run | | `--workspace` / `-w` | | Workspace name or ID (`ws-...`) | | `--compute-group` / `-g` | auto | Compute group name or ID (`lcg-...`) | | `--spec` / `-s` | auto | Resource spec ID | | `--image` / `-m` | | Docker image | | `--instances` | 1 | Number of instances | | `--shm` | 1200 | Shared memory (GiB) | | `--priority` | 10 | Priority (1–10) | | `--dry-run` | | Preview only, don't submit | | `--json` | | JSON output for scripting |

# Preview before submitting
qzcli create --name test --command "echo hi" --workspace "My Workspace" \
  --image YOUR_IMAGE --dry-run

Env-var passthrough (for existing submission scripts)

# Pass vars directly — do NOT use "export VAR; bash script.sh"
WORKSPACE_ID="ws-YOUR_WORKSPACE_ID" \
LCG_ID="lcg-YOUR_LCG_ID" \
SPEC_ID="YOUR_SPEC_ID" \
CHECKPOINT_DIR="/path/to/checkpoint" \
bash YOUR_SUBMIT_SCRIPT.sh

HPC / CPU jobs (Slurm)

qzcli hpc \
  --name "my-cpu-job" \
  --workspace ws-YOUR_WORKSPACE_ID \
  --compute-group lcg-YOUR_LCG_ID \
  --predef-quota-id YOUR_QUOTA_ID \
  --cpu 55 --mem-gi 300 --instances 30 \
  --image YOUR_REGISTRY/team/cpu-image:tag \
  --entrypoint "cd /path/to/dir && bash run.sh"

---

Batch Submission

# Submit from config file
qzcli batch batch_config.json --delay 3

# Preview all jobs
qzcli batch batch_config.json --dry-run

# Continue on error
qzcli batch batch_config.json --continue-on-error

**Config format** (`batch_config.json`):

{
  "defaults": {
    "workspace": "ws-YOUR_WORKSPACE_ID",
    "compute_group":