Linux command
dss 命令
安全
权限或系统影响较大，执行前请核对目标。
常用示例
Initialize DSS
dss initialize --kubeconfig "$(sudo k8s config)"
Create
dss create [my-notebook] --image=pytorch
List
dss list
Show
dss status
Print logs
dss logs [my-notebook]
Print logs
dss logs --mlflow
Stop
dss stop [my-notebook]
Start
dss start [my-notebook]
Remove
dss remove [my-notebook]
Purge
dss purge
说明
dss is the command-line front end to Canonical's Data Science Stack, an opinionated bundle of Jupyter Notebook images, MLflow model tracking, and Kubernetes plumbing distributed as a snap. It targets local Canonical Kubernetes (k8s snap) clusters but works against any Kubernetes that can host the underlying components. After dss initialize, the cluster runs a shared MLflow server and is ready to host notebook pods. dss create spawns a notebook backed by a configurable container image, persistent storage, and a service account that can talk to MLflow. The remaining lifecycle commands (list, status, logs, start, stop, remove, purge) operate on those notebooks without requiring direct kubectl use. DSS aims to be the easiest way to get a reproducible local data-science environment with GPU support and experiment tracking, while still leaving the underlying Kubernetes resources accessible to power users.
参数
- initialize _--kubeconfig_ _file_
- Store cluster credentials, allocate persistent storage for notebooks, and deploy the MLflow model registry.
- create _name_ _--image_ _image_
- Create a new Jupyter notebook of the given _name_ wired to the shared MLflow instance. _image_ may be a shorthand (pytorch, tensorflow) or a fully qualified image like kubeflownotebookswg/jupyter-scipy:v1.8.0.
- list
- List every notebook tracked by DSS in the current cluster.
- status
- Display deployment status, MLflow URL, and detected GPU availability.
- logs _name_ _--kubeconfig_ _file_ _--all_ _--mlflow_
- Print logs for the named notebook, for --mlflow (the MLflow pod), or for everything with --all.
- start _name_
- Start a stopped notebook.
- stop _name_
- Stop a running notebook.
- remove _name_
- Delete a single notebook and its persistent volume claim.
- purge
- Tear down every DSS component (notebooks, MLflow, supporting resources). Irreversible - all stored data is lost.
- --kubeconfig _file_
- Path to a kubeconfig file. Used by initialize and logs when the user is not already authenticated to a cluster.
- --help
- Show usage for dss or for a specific subcommand.
FAQ
What is the dss command used for?
dss is the command-line front end to Canonical's Data Science Stack, an opinionated bundle of Jupyter Notebook images, MLflow model tracking, and Kubernetes plumbing distributed as a snap. It targets local Canonical Kubernetes (k8s snap) clusters but works against any Kubernetes that can host the underlying components. After dss initialize, the cluster runs a shared MLflow server and is ready to host notebook pods. dss create spawns a notebook backed by a configurable container image, persistent storage, and a service account that can talk to MLflow. The remaining lifecycle commands (list, status, logs, start, stop, remove, purge) operate on those notebooks without requiring direct kubectl use. DSS aims to be the easiest way to get a reproducible local data-science environment with GPU support and experiment tracking, while still leaving the underlying Kubernetes resources accessible to power users.
How do I run a basic dss example?
Run `dss initialize --kubeconfig "$(sudo k8s config)"` in a terminal, then adjust file names, paths, flags, or remote targets for your system.
What does initialize _--kubeconfig_ _file_ do in dss?
Store cluster credentials, allocate persistent storage for notebooks, and deploy the MLflow model registry.