GPULab CLI

Command-line interface for GPULab — deploy, manage, and interact with GPU containers from your terminal.

Install

# macOS / Linux
curl -fsSL https://gpulab.ai/cli.sh | sh

# Or build from source
go install github.com/GPULab-AI/gpulab-cli/cmd/gpulab@latest

# Or clone and build
git clone https://github.com/GPULab-AI/gpulab-cli.git
cd gpulab-cli
make install

Quick Start

# Authenticate
gpulab auth login --api-key gpulab_xxx

# List containers
gpulab ls

# Deploy a container
gpulab deploy --name my-project --template pytorch --gpu-type "RTX 4090" --wait

# Execute commands
gpulab exec <uuid> -- nvidia-smi
gpulab exec <uuid> -- python train.py

# View logs
gpulab logs <uuid> --follow

# SSH into container
gpulab ssh <uuid>

# Stop and delete
gpulab stop <uuid>
gpulab rm <uuid> --force

Deploy

The deploy command supports Docker-style syntax for ports, environment variables, and commands.

Ports

# Comma-separated list
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  --ports "8080,3000,6006"

# Docker-style repeatable flag
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  -p 8080 -p 3000 -p 6006

# host:container syntax (host port is auto-assigned by GPULab)
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  -p 8080:80 -p 3000:3000

# Both styles can be combined
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  --ports "8080" -p 3000 -p 6006

Startup Command

# Using --command flag
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  --command "python train.py"

# Docker-style — everything after -- becomes the command
# Useful when the command has its own flags
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  --wait -- python train.py --epochs 100 --lr 0.001

gpulab deploy --name sglang --template pytorch --gpu-type "RTX 4090" \
  -- sglang.deploy --model meta-llama/Llama-3-8B --tp 4

Environment Variables

# Set variables explicitly (Docker-style)
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  -e HF_TOKEN=hf_xxx \
  -e WANDB_API_KEY=abc123 \
  -e MODEL_NAME=meta-llama/Llama-3-8B

# Inherit from host environment (just pass the key name)
export HF_TOKEN=hf_xxx
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  -e HF_TOKEN -e WANDB_API_KEY

# Load from an env file
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  --env-file .env

# Combine all three — -e flags override --env-file values
gpulab deploy --name test --template pytorch --gpu-type "RTX 4090" \
  --env-file .env \
  -e HF_TOKEN=hf_override \
  -e WANDB_API_KEY

The --env-file format supports standard .env syntax:

# .env
HF_TOKEN=hf_xxx
WANDB_API_KEY=abc123
MODEL_NAME="meta-llama/Llama-3-8B"
export CUDA_VISIBLE_DEVICES=0,1
# Comments and blank lines are ignored

Full Deploy Example

gpulab deploy \
  --name training-run \
  --template pytorch \
  --gpu-type "RTX 4090" \
  --memory 32 \
  --ports "8080,6006" \
  --env-file .env \
  -e HF_TOKEN \
  -e RUN_ID=experiment-42 \
  --volume my-volume-uuid \
  --wait \
  -- python train.py --epochs 100 --lr 0.001 --batch-size 32

Serverless GPUs

Serverless endpoints use the same API key auth as containers.

# See available serverless templates, GPU types, regions, volumes, and policy templates
gpulab serverless options

# Create an endpoint
gpulab serverless create \
  --name llama-api \
  --template pytorch \
  --gpu-type "RTX 4090" \
  --memory 32 \
  --port 8000 \
  --min-replicas 0 \
  --max-replicas 2 \
  --concurrency 1 \
  -e HF_TOKEN \
  --command "python app.py"

# Inspect, invoke, and read logs/history
gpulab serverless inspect llama-api
gpulab serverless invoke llama-api /v1/chat/completions -d '{"prompt":"hello"}' --wait
gpulab serverless requests llama-api
gpulab serverless autoscaling-logs llama-api
gpulab serverless logs llama-api --replica all
gpulab serverless logs llama-api --deploy

# Update or delete
gpulab serverless update llama-api --max-replicas 4 --autoscaling-template pending_requests_linear
gpulab serverless delete llama-api --force

Deploying code & debugging

The serverless commands are built for closed-loop debugging. Logs are cleaned of Docker stream framing automatically (no more piping through strings), and all timestamps render in UTC by default (override with --tz local or --tz Asia/Kolkata).

# Atomic deploy: upload code → verify byte-identical → rolling restart → smoke test.
# Replaces the manual upload-then-restart dance and prevents the stale-replica trap.
gpulab serverless deploy llama-api ./tasks.py --to server/tasks.py --drain

# One health surface: error rate, 4xx/5xx, latency, queue depth, cold starts,
# scaling ping-pong, and (with --logs) detected tracebacks / CUDA OOM.
gpulab serverless health llama-api --logs

# Trace one request end to end: queued → started → completed, with the HTTP result.
gpulab serverless trace <request-uuid>

# Replicas with age, and a stale check: flags replicas started before the
# volume's newest code file (⚠ running old code).
gpulab serverless replicas llama-api --stale --code-path server

# Restart: a single replica, all replicas, or a zero-downtime rolling restart.
# --drain finishes in-flight requests before tearing each replica down.
gpulab serverless restart llama-api <replica>          # one replica
gpulab serverless restart llama-api --all --rolling    # rolling, no full outage
gpulab serverless restart llama-api --all --drain      # graceful

# Logs: fleet-wide, filtered, time-bounded, with traceback detection.
gpulab serverless logs llama-api --replica all --since 10m --grep Traceback --detect

# Which endpoints use a volume? (reverse lookup)
gpulab volumes endpoints my-volume
gpulab serverless list --volume my-volume

Note on reloading code. serverless restart / serverless deploy reload the latest volume code because they re-provision a fresh replica container. The container-level gpulab restart <uuid> (and the /restart HTTP route) do not reload code — the CLI detects when you point one at a serverless replica and redirects you to serverless restart. A precise per-worker code fingerprint (to catch a worker that kept old code in memory) needs the model image to report its loaded version; until then, use replicas --stale as the practical signal.

Syncing a directory to a volume

# rsync-style: uploads only new/changed files (compared by content hash, so
# equal-length edits are caught), hash-verifies each upload, previews the diff,
# and warns how many endpoints/replicas use the volume (they keep old code until
# restarted).
gpulab volumes sync ./server my-volume server --dry-run   # preview only
gpulab volumes sync ./server my-volume server             # upload changes
gpulab volumes sync ./server my-volume server --size-only # faster, size-only compare
gpulab volumes files upload my-volume ./local.py --to server/tasks.py  # single file, renamed

volumes sync --delete refuses to run against an empty local dir (would wipe the volume) unless you pass --allow-empty, and --json/non-interactive runs won't mutate the volume without --yes.

Templates (Docker images)

Create and manage reusable container templates. Only --name and --image are required; visibility defaults to private, container type to gpu, disk to 10GB.

# List, inspect, and discover categories
gpulab templates
gpulab templates info my-template
gpulab templates categories

# Create
gpulab templates create --name web --image nginx:latest --ports 80,443
gpulab templates create --name trainer --image pytorch/pytorch:latest \
  --type gpu --memory 24 --disk 50 --mount-path /workspace \
  -e WANDB_API_KEY=xxx --env-file ./train.env

# Edit (only the flags you pass change) and delete
gpulab templates edit my-template --image nginx:1.27 --visibility public
gpulab templates delete my-template --force

The target for info/edit/delete may be a full UUID, a UUID prefix, or the template name.

Private images

For images that need a registry login, attach Docker credentials. Set them inline when creating the template (the credential is created and linked in one call):

gpulab templates create --name private-trainer --image adhik/private:v1 \
  --registry-username adhik --registry-password dckr_pat_xxx
# --registry defaults to docker.io; pass it for ghcr.io, quay.io, etc.

Or manage credentials as reusable, named resources and reference them by ID:

# Store once (use --password-stdin to keep the token out of shell history)
echo "$DOCKER_TOKEN" | gpulab credentials add --username adhik --password-stdin
gpulab credentials                 # list — shows the ID
gpulab templates create --name app --image adhik/private:v1 --credentials 42
gpulab credentials rm 42           # delete when no longer needed

Passwords/tokens are write-only — they are never returned by credentials list.

Commands

Command	Description
`gpulab auth login`	Authenticate with API key
`gpulab auth whoami`	Show current user
`gpulab ls`	List all containers
`gpulab inspect <uuid>`	Show container details
`gpulab deploy`	Deploy a new container
`gpulab stop <uuid>`	Stop a container
`gpulab start <uuid>`	Start a stopped container
`gpulab restart <uuid>`	Restart a container
`gpulab redeploy <uuid>`	Redeploy a container
`gpulab rm <uuid>`	Delete a container
`gpulab logs <uuid>`	View container logs
`gpulab stats <uuid>`	View resource usage
`gpulab exec <uuid> -- <cmd>`	Execute a command
`gpulab ssh <uuid>`	Interactive terminal
`gpulab templates`	List templates (Docker images)
`gpulab templates info <uuid\|name>`	Show template details
`gpulab templates categories`	List template categories
`gpulab templates create --name <n> --image <img>`	Create a template
`gpulab templates edit <uuid\|name> [flags]`	Edit a template
`gpulab templates delete <uuid\|name>`	Delete a template
`gpulab credentials`	List Docker registry credentials
`gpulab credentials add --username <u> --password <p>`	Store a registry credential
`gpulab credentials rm <id>`	Delete a registry credential
`gpulab gpus types`	List GPU types
`gpulab volumes`	List volumes
`gpulab volumes sync <dir> <volume> [remote]`	rsync-style sync a local dir to a volume
`gpulab volumes endpoints <volume>`	List endpoints that use a volume (reverse lookup)
`gpulab volumes files upload <volume> <file> --to <path>`	Upload a file, renamed
`gpulab serverless`	Manage serverless GPU endpoints
`gpulab serverless deploy <endpoint> <file>`	Upload → verify → rolling restart → smoke test
`gpulab serverless health <endpoint>`	Error rate, latency, queue, crashes, cold starts
`gpulab serverless trace <request-uuid>`	Trace a request's lifecycle
`gpulab serverless restart <endpoint> [replica\|--all]`	Restart replicas (rolling/drain)
`gpulab serverless replicas <endpoint> --stale`	Replica ages + stale-code detection
`gpulab serverless logs <endpoint>`	Replica logs (`--since`, `--grep`, `--detect`, `--replica all`)
`gpulab serverless requests <endpoint>`	View serverless request logs
`gpulab serverless autoscaling-logs <endpoint>`	View autoscaling history + summary
`gpulab update`	Update the CLI from GitHub Releases

Global Flags

Flag	Description
`--json`	Output in JSON format (for scripting/AI agents)
`-q, --quiet`	Quiet output (UUIDs only)
`--api-key`	Override API key
`--debug`	Debug mode (show HTTP requests)
`--tz`	Timestamp timezone: `utc` (default), `local`, or an IANA name

Configuration

Config is stored at ~/.gpulab/config.json. API key priority:

--api-key flag
GPULAB_API_KEY environment variable
Config file

AI Agent Usage

The CLI is designed for AI agent integration (Claude Code, Cursor, etc.):

export GPULAB_API_KEY=gpulab_xxx

# Deploy with env vars and capture UUID
UUID=$(gpulab deploy \
  --name ai-test \
  --template pytorch \
  --gpu-type "RTX 4090" \
  -e HF_TOKEN -e WANDB_API_KEY \
  --wait --json | jq -r '.container_id')

# Run commands
gpulab exec $UUID -- nvidia-smi
gpulab exec $UUID -- python -c "print('hello')"

# Write and run a script
gpulab exec $UUID -- sh -c 'echo "print(42)" > /workspace/test.py'
gpulab exec $UUID -- python /workspace/test.py

# JSON output for parsing
gpulab ls --json
gpulab stats $UUID --json

# Cleanup
gpulab stop $UUID
gpulab rm $UUID --force

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
.github/workflows		.github/workflows
cmd/gpulab		cmd/gpulab
internal		internal
scripts		scripts
.gitattributes		.gitattributes
.gitignore		.gitignore
.goreleaser.yml		.goreleaser.yml
Makefile		Makefile
README.md		README.md
cli.md		cli.md
go.mod		go.mod
go.sum		go.sum

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GPULab CLI

Install

Quick Start

Deploy

Ports

Startup Command

Environment Variables

Full Deploy Example

Serverless GPUs

Deploying code & debugging

Syncing a directory to a volume

Templates (Docker images)

Private images

Commands

Global Flags

Configuration

AI Agent Usage

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GPULab CLI

Install

Quick Start

Deploy

Ports

Startup Command

Environment Variables

Full Deploy Example

Serverless GPUs

Deploying code & debugging

Syncing a directory to a volume

Templates (Docker images)

Private images

Commands

Global Flags

Configuration

AI Agent Usage

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