YAML

Field types

Metadata

name, description, and tags fields are the metadata of Run. They should be ideally represent the specific characteristics or purposes of your run for better identification.

Name	Type	Required	Description
`name`	string	Required	The name of the run.
`description`	string	Optional	The description of the run.
`tags`	list	Optional	The tags of the run.

Specify run metadata

name: stable-diffusion
description: This is the inference example of stable diffusion.
tags:
- "best"
- "A100-80g"
- "20epochs"

Resources

resources specifies the resource specs to use for run. Use preset provided by VESSL or request the desired resource with requests.

Common fields

Name	Type	Required	Description
`cluster`	string	Optional	The cluster to be used for the run. (default: VESSL-managed cluster)
`node_names`	list	Optional	Specify candidate nodes for workload assignment. If it’s not set, we’ll find any available node.

Using preset with common fields

Name	Type	Required	Description
`preset`	string	Required without `requests`	The name of resource spec preset that specified in VESSL. If the preset is not specified, we will offer the best option for you based on `reqeusts`.

resources:
  cluster: vessl-gcp-oregon
  preset: v1.l4-1.mem-42

Using requests with common fields (Upcomming feature)

Name	Type	Required	Description
`requests`	map	Required without `preset`	The desired resource specs.
`cpu`	string	Optional	The number of cpu cores.
`memory`	string	Optional	The memory size in GB.
`nvidia.com/gpu`	map	Optional	The `device_type` and `quanity` of the NVIDIA GPU to be used for the run.

resources:
  cluster: vessl-gcp-oregon
  requests:
    cpu: "4"
    memory: 12Gi
    nvidia.com/gpu:
      device_type: V100
      quantity: "2"

You can list available clusters or resource specs with the CLI command: vessl cluster list or vessl resource list.

pip install vessl
vessl cluster list

Container Image

The image field is a string that specifies the container image to be used in the run. This is typically a Docker image that includes all the necessary dependencies and environment for your machine learning model.

Name	Type	Required	Description
`image`	string or map	Requried	Container image url or map of `url` and `credential_name`.
`url`	string	Optional	Container image url.
`credential_name`	string	Optional	Registered credential name at VESSL for private image usage.

image: quay.io/vessl-ai/ngc-pytorch-kernel:22.10-py3-202306140422

You can list available VESSL-managed images with the CLI command: vessl image list.

List VESSL-manged images with the VESSL CLI

pip install vessl
vessl image list

Volumes

There are three type of volumes: import, mount, and export. Each field is a map that specifies a target path as a key and a source information as a value. The value is either a simple string with prefix or another map that holds more detailed information.

Import The import type signifies that the data will be downloaded from the source to a target path in the running container.

Prefix	Type	Required	Description
`git://`	string	Optional	Import a git repository. The repository will be cloned into the specified target path when container starts.
`vessl-dataset://`	string	Optional	Import a dataset stored in VESSL Dataset.
`vessl-model://`	string	Optional	Import a model stored in VESSL Model Registry.
`vessl-artifact://`	string	Optional	Import an artifact stored in VESSL Artifact.
`s3://`	string	Optional	Import an AWS S3 bucket.
`gs://`	string	Optional	Import a Google Cloud Storage.

import:
  /import/code: git://github.com/{accountName}/{repoName}
  /import/dataset: vessl-dataset://{organizationName}/{datasetName}
  /import/model: vessl-model://{organizationName}/{modelRepositoryName}/{modelNumber}
  /import/artifact: vessl-artifact://{organiztionName}/{projectName}/{artifactName}
  /import/s3: s3://{bucketName}/{path}
  /import/gs: gs://{buckeName}/{path}

Mount The mount type means that the data will be directly mounted to a target path in the run container, providing direct access to the user.

Prefix	Type	Required	Description
`vessl-dataset://`	string	Optional	Mount a dataset stored in VESSL Dataset.
`hostpath://`	string	Optional	Mount a file or directory from the host node’s filesystem.
`nfs://`	string	Optional	Mount a Network File System(NFS).
`readonly`	boolean	Optional	True if readonly mode. (default: True)

mount: 
  /mount/dataset: vessl-dataset://{organizationName}/{datasetName}
  /mount/hostpath: hostpath://{path}
  /mount/nfs: nfs://{server}/{path}

Export The export type is desgined for uploading data from a path in the run container to a target path after run execution.

Prefix	Type	Required	Description
`vessl-artifact://`	string	Optional	Export to VESSL Artifact.
`vessl-dataset://`	string	Optional	Export to VESSL Dataset.
`vessl-model://`	string	Optional	Export to VESSL Model.
`s3://`	string	Optional	Export to Amazon S3 bucket.
`gs://`	string	Optional	Export to Google Cloud Storage.

export:
  /export/output-artifact: vessl-artifact://  
  /export/backup-artifact: vessl-artifact://{organizationName}/{projectName}/{artifactName}
  /export/dataset: vessl-dataset://{organizationName}/{datasetName}
  /export/model: vessl-model://{organizationName}/{modelRepositoryName}
  /export/s3: s3://{buckeName}/{prefix}
  /export/gs: gs://{bucketName}/{prefix}

Run Command

The run field is a list that contains commands to be run in the container. Each item in the list is a map with the following keys. run could be empty if it’s an interactive run.

Name	Type	Required	Description
`workdir`	string	Optional	The working directory for the command.
`command`	string	Required	The command to be run.
`wait`	string	Optional	How long to wait after a command.

run:
  - command: |
      python train.py --learning_rate=$learning_rate --batch_size=$batch_size

Interactive

The interactive field is used to specify if the run allows interactive communication with the user. It provides multiple ways to interact with the container during the run, such as JupyterLab, SSH, or a custom service via specified ports.

Name	Type	Required	Description
`interactive`	map	Optional	Mark run as an interactive type that includes `max_runtime`, `jupyter`, and `idle_timeout`
`max_runtime`	string	Required	The amount of time to run. Set `0` for infintie use.
`jupyter`	map	Required	Jupyter configurations that includes `idle_timeout`
`idle_timeout`	string	Required	The amount of time a server can be inactive before it will be culled.

Maximum runtime 24h and idle_timeout 120m

interactive:
  max_runtime: 24h
  jupyter:
    idle_timeout: 120m

Ports

The ports field is a list of map that specifies the port information to expose.

Name	Type	Required	Description
`ports`	list	Optional	List of port numbers or port information that includes `name`, `type`, and `port` to expose.
`name`	string	Optional	The port name.
`type`	string	Optional	The protocol of port. (http or tcp)
`port`	int	Optional	The port number.

ports:
  - 3000

Environment Variables

The env field is a map that specifies the environment variables for the run. Each key-value pair in this map represents an environment variable and its value.

Name	Type	Required	Description
`env`	map	Optional	Key-value pairs for environment variables in the run container.
`value`	string	Optional	Value of environment variables.
`secret`	boolean	Optional	True if the variable is secret.

Set multiple environment variables

env:
  learning_rate: 0.001
  postgres_password:
    value: OUR_DB_PW
    secret: true

YAML Reference

CLI Reference

SDK Reference

Field types

Metadata

Resources

Container Image

Volumes

Run Command

Interactive

Ports

Environment Variables

YAML Reference

CLI Reference

SDK Reference

​Field types

​Metadata

​Resources

​Container Image

​Volumes

​Run Command

​Interactive

​Ports

​Environment Variables

Field types

Metadata

Resources

Container Image

Volumes

Run Command

Interactive

Ports

Environment Variables