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.
npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill qzcli
A kubectl/docker-style CLI for managing GPU compute jobs on the Qizhi (启智) platform.
GitHub: tianyilt/qzcli_tool
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.
pip install rich requests prompt_toolkit mcp
git clone https://github.com/tianyilt/qzcli_tool
cd qzcli_tool && pip install -e .
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
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.
# 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
# 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.
# 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"
# 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
# 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.
# 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
# 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
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"
# 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": "lcg-YOUR_LCG_ID",
"spec": "YOUR_SPEC_ID",
"image": "YOUR_REGISTRY/team/image:tag",
"instances": 4,
"priority": 10
},
"matrix": {
"checkpoint": ["/path/to/ckpt1", "/path/to/ckpt2"],
"step": [50000, 100000]
},
"name_template": "eval-{checkpoint_basename}-step{step}",
"command_template": "bash eval.sh --checkpoint {checkpoint} --step {step}"
}
Matrix keys are Cartesian-producted (2×2 = 4 jobs above). Use {key_basename} for path basenames.
for step in 040000 050000 060000; do
qzcli create \
--name "eval-step${step}" \
--command "bash eval.sh --step $step" \
--workspace "My Workspace" \
--compute-group "My Compute Group" \
--instances 4
sleep 3
done
# List jobs
qzcli ls -c -w MY_WORKSPACE # specific workspace
qzcli ls -c --all-ws # all workspaces
qzcli ls -c -w MY_WORKSPACE -r # running only
qzcli ls -c -w MY_WORKSPACE -n 50 # show 50
# Stop a job
qzcli stop JOB_ID
# Job status / details
qzcli status JOB_ID
# Watch all running jobs (refresh every 10s)
qzcli watch -i 10
# Workspace view with GPU utilization
qzcli ws
qzcli ws -a # all projects
qzcli ws -p "My Project"
| Problem | Cause | Fix |
|---------|-------|-----|
| Cookie expired | Session gap | Re-run qzcli login |
| 未找到名称为 'xxx' 的工作空间 | Stale cache | Run qzcli res -u |
| No resources in create -i | Cache empty | Run qzcli login && qzcli res -u |
| qzcli-mcp not found | Not installed | cd qzcli_tool && pip install -e . |
| Spec not in workspace | ID mismatch | Match spec ID to the correct workspace |
| Silent job failure | Script sys.exit(0) | Check job logs directly |
| zsh glob errors | Remote shell is zsh | Wrap commands in bash -c or use Python |
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Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
Take wanshuiyin/qzcli from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.