# cnvrgv2 CLI

As data scientists, we use the terminal a lot to run experiments, write code, change parameters, and more.

The cnvrgv2 CLI tool was built to be simple and easy-to-use, so you can do what you love the way you know it, but faster and in a more organized fashion.

The cnvrgv2 CLI allows you to create and manage projects, run experiments, and more from the comfort of your terminal.

In this guide we will learn how to setup the cnvrgv2 CLI and explore the different commands it supports.

Guide content:

Install cnvrgv2 CLI
- Install options
Authenticate with cnvrgv2 CLI
- Login
- Logout
Project commands
Dataset commands
Workspace commands
Experiment commands
Flow commands
- Create new flow
Endpoint commands
Advanced Volumes commands
- Storage Class Management
Registry commands
Image commands
Members commands

# Install cnvrgv2 CLI

The cnvrgv2 CLI is built with python.

Prerequisits:

python version 3.6 or higher
pip

Linux
Windows

To install, open up your terminal/command prompt and run the following command:

pip3 install cnvrgv2
 
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# Install options

When on a self-hosted cnvrg environment, options need to be specified for the cnvrgv2 installation depending on the type of storage connected to the cnvrg environment, or if python3.6 is installed on your device.

For Metacloud, no options need to be specified.

Multiple options can be specified for the installation by adding options serperated by commas, options can be added to the installation command as follows:

pip3 install "cnvrgv2[options]"
 
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Available options are:

azure - Install packages relevant for Azure storage client
google - Install packages relevant for GCP storage client
python3.6 - Install specific dependencies for python version 3.6

# Authenticate with cnvrgv2 CLI

To authenticate with cnvrg use the following command:

cnvrgv2 login
 
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Login command options

Option	Short option	Value Type	Description
--domain	-d	Text	cnvrg domain/url starting from http/s, excluding the organization slug e.g. https://app.cnvrgdomain.com/
--email	-e	Text	User email
--password	-p	Text	authenticate using password
--auth-token	-t	Text	authenticate using the API Token
--organization	-o	Text	cnvrg organization, this can be used when the user is a memner of multiple organizations

For Metacloud and self-hosted cnvrg environments with SSO authentication, The user's API Token must be used instead of the Password, with the flag --auth-token. the token can be retrieved from the user settings page, under API Token field:

API Token

Verify you are logged in

You can verify you are logged in to your organization using following command:

cnvrgv2 me
 
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Switch to a different organization

In order to switch to another organization, use following command:

cnvrgv2 config --organization=ORGANIZATION_NAME
 
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# Logout

To deauthenticate with cnvrg use the following command:

cnvrgv2 logout
 
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# Project commands

# Create an empty project

To create an empty cnvrg project, use the following command:

cnvrgv2 project create --name=PROJECT_NAME
 
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NOTE

'create' will associates the current working directory with the cnvrg project

# Link a local directory to a new project

To create new cnvrg project, associate it with the current working directory, and upload the content of the current working directory to it, use the following command:

cnvrgv2 project link
 
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WARNING

Folder/file names must not contain special characters, which are - +?!@#$%&^*(){}[]

# Link an existing project to a git directory

To link or associate a local git directory with a git integrated cnvrg project, use the following command:

cnvrgv2 project link-git
 
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Project link-git command options

Option	Short option	Value Type	Description
--name	-n	Text	Project name
--soft	-s	Boolean	Don't link project if it's already linked

# List projects

To retrieve a list of the projects you have access to, use the following command:

cnvrgv2 project list
 
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# Clone a project

To clone a cnvrg project, use the following command:

cnvrgv2 project clone
 
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NOTE

By default, the project will be cloned into a directory with its name, and the directory will be associated with the cnvrg project.

Project clone command options

Option	Short option	Value Type	Description
--name	-n	Text	Project name
--commit	-c	Text	Project commit sha1
--override	-o	Boolean	Force clone the project even if is already exists
--current_dir	-d	Boolean	Clone into current directory

# Sync a project with a local directory

To synchronize the file changes of a local project with its corresponding cnvrg project, use the following command:

cnvrgv2 project sync
 
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This command will download updated and new files from the cnvrg project, and then upload local file changes.

Project sync command options

Option	Short option	Value Type	Description
--job-slug	-j	Text	Slug of the job
--git-diff	-g	Boolean	Upload git changed files, files included in the output of the `git diff` command
--message	-m	Text	Commit message
--output-dir	-o	Text	The directory that will be synced inside the project

WARNING

Folder/file names must not contain special characters, which are - +?!@#$%&^*(){}[]

# Upload files to a project

To upload files or directories to a cnvrg project, use the following command:

cnvrgv2 project put
 
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Project put command options

Option	Short option	Value Type	Description
--name	-n	Text	Project name
--files	-f	Text	comma separated list of file and directory paths to upload. use `.` to upload the entire current working directory
--dir	-d	Text	Upload the files to a specific subdirectory in the remote project. For example, --dir /images will upload the files to the images folder. It will create the directory if it does not exist
--force	-fc	Boolean	Enable creating a new commit that is not related to the parent commit
--override	-or	Boolean	Enable re-uploading files even if they already exist in the project
--git-diff	-gd	Boolean	Upload git changed files, the files included in the output of the `git diff` command

WARNING

Folder/file names must not contain special characters, which are - +?!@#$%&^*(){}[]

# Upload updated project files

To upload all file changes in a local project directory to the cnvrg project, use the following command:

cnvrgv2 project upload
 
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Project upload command options

Option	Short option	Value Type	Description
--git-diff	-g	Text	Upload git changed files, files included in the output of the `git diff` command

# Download updated project files

To fetch latest commit into local project directory, use the following command:

cnvrgv2 project download
 
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# Delete files from a project

To delete files and directories from a cnvrg project, use the following command:

cnvrgv2 project rm
 
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Project rm command options

Option	Short option	Value Type	Description
--name	-n	Text	Project name
--files	-f	Text	Comma seperated list of files or directories. for example: cnvrgv2 project rm --files=folder/,file.py
--message	-m	Text	Commit message

NOTE

When specifying a directory name to be deleted, / must be added to the end of the directory name.

# Delete a project

To delete a cnvrg project, use the following command:

cnvrgv2 project delete --name=NAME
 
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# Dataset commands

# Create a dataset

To create a new dataset, use the following command:

cnvrgv2 dataset create --name=NAME
 
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This command will associates the current working directory with the created dataset.

# List datasets

To retrieve the list of the datasets that you have access to, use the following command:

cnvrgv2 dataset list
 
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# Clone a dataset

To clone a cnvrg dataset, use the following command:

cnvrgv2 dataset clone
 
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The dataset will be cloned into a directory with its name, and the directory will be associated with the cnvrg dataset.

Dataset clone command options

Option	Short option	Value Type	Description
--name	-n	Text	Dataset name
--override	-o	Boolean	Clone the dataset even if it already exist, to get missing and deleted files
--cache-link	-cl	Boolean	Use cached commit
--commit	-c	Text	clone a specific dataset commit (default is latest)
--threads	-t	Integer	Number of thread that will perform the clone process
--query	-q	Text	Query slug or title

# Put dataset

To upload files or directories to a cnvrg dataset, use the following command:

cnvrgv2 dataset put
 
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Dataset put command options

Option	Short option	Value Type	Description
--name	-n	Text	Dataset name
--files	-f	Text	Comma separated list of file and directory paths to upload. use `.` to upload the entire current working directory
--dir	-d	Text	Upload the files to a specific subdirectory in the remote dataset. For example, --dir /images will upload the files to the images folder. It will create the directory if it does not exist
--force	-fc	Boolean	Create a new commit with only the files included in the current upload.
--override	-or	Boolean	Upload every file specified, even if it already exists in the latest commit
--git-diff	-gd	Boolean	Upload git changed files, the files included in the output of the `git diff` command

WARNING

Folder/file names must not contain special characters, which are - +?!@#$%&^*(){}[]

# Delete files from a dataset

To delete files and directories from a cnvrg dataset, use the following command:

cnvrgv2 dataset remove
 
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Dataset remove command options

Option	Short option	Value Type	Description
--name	-n	Text	Dataset name
--files	-f	Text	Comma seperated list of files or directories. for example: cnvrgv2 dataset rm --files=folder/,file.py
--message	-m	Text	Commit message

NOTE

When specifying a directory name to be deleted, a / must be added to the end of its name.

# Scan datasets

To get the datasets located in the current working directory, use the following command:

cnvrgv2 dataset scan
 
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# Upload updated dataset files

To upload all changed and new files from a local dataset directory to the cnvrg dataset, use the following command:

cnvrgv2 dataset upload
 
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Dataset upload command options

Option	Short option	Value Type	Description
--git-diff	-g	Text	Upload git changed files, the files included in the output of the `git diff` command

# Download updated dataset files

To fetch latest commit into local dataset directory, use the following command:

cnvrgv2 dataset download
 
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# Cache a dataset

To cache a dataset commit, use the following command:

cnvrgv2 dataset cache
 
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Dataset cache command options

Option	Short option	Value Type	Description
--name	-n	Text	Dataset name
--commit	-c	Text	Dataset commit sha1
--external-disk-title	-d	Text	NFS name

# Uncache dataset

To uncache a dataset commit, use the following command:

cnvrgv2 dataset uncache
 
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Dataset uncache command options

Option	Short option	Value Type	Description
--name	-n	Text	Dataset name
--commit	-c	Text	Dataset commit sha1
--external-disk-title	-d	Text	NFS name

# Verify dataset

To verify that certain datasets in the current working directory have finished downloading, use the following command:

cnvrgv2 dataset verify
 
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Dataset verify command options

Option	Short option	Value Type	Description
--name	-n	Text	Datasets names seperated by commas
--time-out	-t	Text	Timeout in seconds

NOTE

'verify' command should be executed outside the dataset directory
command stays alive until all the datasets provided have finished downloading or until it times out.

# Delete a dataset

To delete a cnvrg dataset, use the following command:

cnvrgv2 dataset delete --name=DATASET_NAME
 
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# Workspace commands

NOTE

Workspace commands require the current working directory (or sub-directory) to be a linked to a cnvrg project

# Create new workspace

To create new workspace use following command:

cnvrgv2 workspace create
 
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Option	Short option	Value Type	Description
--title	-t	Text	Name of the workspace
--templates	-tm	Text	Comma separated list of compute templates. format is: resource_name.template_name, for example: --templates=ics.medium (default is taken from project's settings)
--commit	-c	Text	Project commit sha1 to be used, (default is latest)
--notebook_type	-nt	Text	Type of workspace to create. options are: jupyterlab, vscode, r_studio. (default is jupyterlab)
--datasets	-d	Text	List of comma separated datasets names to cloned to the workspace
--volume	-v	Text	Name of volume to attach to this workspace
--image	-i	Text	Image name and tag to create workspace with. format is - image_name:tag (default is taken from project's settings)
--git-branch	-gb	Text	The name of the git branch to pull files from, this option works only when the project is git integrated (default is taken from project's settings)
--git-commit	-gc	Text	The git commit to pull files from, this option works only when the project is git integrated (default is latest)
--local_folders	-lf	Text	Local folders to mount with workspace

# Sync workspace file changes

To sync file changes from workspace, use following command:

cnvrgv2 workspace sync-remote --slug=WORKSPACE_SLUG
 
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# Start an existing workspace

cnvrgv2 workspace start --slug=WORKSPACE_SLUG
 
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# Stop a running workspace

cnvrgv2 workspace stop --slug=WORKSPACE_SLUG
 
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# Start tensorboard session in a workspace

cnvrgv2 workspace start-tensorboard --slug=WORKSPACE_SLUG
 
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# Stop tensorboard in a workspace

cnvrgv2 workspace stop-tensorboard --slug=WORKSPACE_SLUG
 
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# SSH into a workspace

To establish an SSH connection to a workspace for access through your code editor or terminal, use the following command:

cnvrgv2 ssh start WORKSPACE_SLUG
 
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NOTE.

This command will require access to your cluster, with a kubeconfig file

SSH start command options

Option	Short option	Value Type	Description
--port	-p	Integer	SSH port number to bind on the host machine (user device)
--username	-u	Text	The username to ssh to inside the workspace
--password	-pw	Text	SSH password
--kubeconfig	-kc	Text	Full path to the kubeconfig file for the cluster hosting the workspace pod

NOTE

By default the ssh start command uses the default kubeconfig file (located at $HOME/.kube/config), or the file pointed at by the $KUBECONFIG environment variable.
default port value is 2222, and the default username value is root. If not specified, the password will be generated randomly.

# Experiment commands

NOTE

The experiment commands require the current working directory to be a local cnvrg project directory(or sub-directory)to work.

# Run an experiment

To run an experiment in a cnvrg project, use the following command:

cnvrgv2 experiment run --command=COMMAND
 
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If only the experiment run command was specified the project's default image and compute template will be used to run the experiment.

Experiment run command options

Option	Short option	Value Type	Description
--title	-t	Text	Experiment title
--templates	-tm	Text	A comma separated list of template names
--local	-l	Boolean	Run experiment locally
--command	-c	Text	The command that will run in the experiment
--image	-i	Text	The name and tag of the image that will be used to run the experiment. image value format: IMAGE_NAME:IMAGE_TAG
--datasets	-d	Text	A comma separated list of the slugs of dataset that will be used by the experiment (unique identifier of the dataset, can be taken for the ending of the dataset's url. for example: domain.com/organization/datasets/my-dataset). Example: `cnvrgv2 experiment run -c 'python3 mnist.py' -d '[{"slug": "my-dataset"},{"slug": "my-dataset2"}]'`
--volume	-v	Text	A volume name that will be attached to the experiment
--sync-before	-sb	Boolean	Enable Syncing the local project with the cnvrg project before running the experiment (default is true)
--no-sync-before	-nsb	Boolean	Disable Syncing the local project with the cnvrg project before running the experiment
--sync-after	-sa	Boolean	Enable Syncing the local project with the cnvrg project after running the experiment (default is true)
--no-sync-after	-nsa	Boolean	Disable Syncing the local project with the cnvrg project after running the experiment
--git-branch	-gb	Text	The name of the git branch to pull files from, this option works only when the project is git integrated
--git-commit	-gc	Text	The git commit to pull files from, this option works only when the project is git integrated
--output_dir	-od	Text	Output folder for cnvrg to track experiment artifacts
--grid	-g	Text	Path to yaml file outlining the parameters to be run as gridsearch (example bellow)
--local_folders	-lf	Text	Local folders to mount with experiment
--log	-log	Boolean	Display log of local experiment
--wait	-w	Boolean	Wait for local experiment to complete and return the exit status of the experiment
--project-slug	-ps	Text	Enables running an experiment without the need to perform a project clone before (must add flag `--no-sync-before` when used)

NOTE.

Options log and wait are only applicable for local experiments

Example YAML file for grid searches:

parameters:
    - key: "learning_rate"
      type: "discrete" # An array of numerical values
      values: [0.1, 0.01]

    - key: "kernel"
      type: "categorical" # An array of string values
      values: ["linear", "rbf"]

    - key: "epochs"
      type: "integer"
      min: 10 # inclusive
      max: 20 # not inclusive
      scale: "linear"
      steps: 2 # The number of linear steps to produce.
 
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# Download the output artifacts of an experiment

To download the output artifacts of an experiment, use the following command:

cnvrgv2 experiment pull-artifacts --slug EXPERIMENT_SLUG 
 
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Experiment pull-artifacts command options

Option	Short option	Value Type	Description
--slug	-s	Text	Experiment slug
--project-slug	-ps	Text	Enables running a command without the need to clone a project first
--commit	-c	Text	The sha1 of the commit that contains the artifacts (default is the end commit)

# Upload output artifacts to an experiment

To upload and save files and directories as output artifacts of an experiment, use the following command:

cnvrgv2 experiment log-artifacts --slug=EXPERIMENT_SLUG
 
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Experiment log-artifacts command options

Option	Short option	Value Type	Description
--slug	-s	Text	Experiment slug
--files	-f	Text	Comma separated list of file and directory paths to upload. use `.` to upload the entire current working directory
--git-diff	-g	Boolean	Upload git changed files, the files included in the output of the `git diff` command

# Upload image output artifacts to an experiment

To upload and save image files and directories as output artifacts of an experiment, use the following command:

cnvrgv2 experiment log-images --slug=EXPERIMENT_SLUG
 
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This command exclusivly uploads image files, and ignores other files types.

Experiment log-images command options

Option	Short option	Value Type	Description
--slug	-s	Text	Experiment slug
--files	-f	Text	Comma separated list of file and directory paths to upload. use `.` to upload the entire current working directory

# Log a paramater to an experiment

To log a parameter as an experiment tag, use the following command:

cnvrgv2 experiment log-param --slug=EXPERIMENT_SLUG --key=KEY --value=VALUE
 
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# Merge artifacts into project

To merge commit created from an experiment into project master branch, use the following command:

cnvrgv2 experiment merge-to-master --slug=EXPERIMENT_SLUG
 
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Experiment merge-to-master command options

Option	Short option	Value Type	Description
--slug	-s	Text	Experiment slug
---commit	-c	Text	Commit sha1 to merge

# Start tensorboard in an experiment

To start the tensorboard in an experiment, use the following command:

cnvrgv2 experiment start-tensorboard --slug=EXPERIMENT_SLUG
 
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# List all experiments in the project

To get a list of all of the experiments in the project:

cnvrgv2 experiment list --project-slug PROJECT_SLUG
 
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Experiments list command options

Option	Short option	Value Type	Description
--project-slug	-ps	Text	The slug of the project
--chunk-size	-cs	Integer	Number of experiments to display at a time
--limit	-l	Integer	Maximum number of experiments to display

# Stop tensorboard in an experiment

To stop the tensorboard in an experiment, use the following command:

cnvrgv2 experiment stop-tensorboard --slug=EXPERIMENT_SLUG
 
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# Delete an experiment

To delete an experiment, use following command:

cnvrgv2 experiment delete --slug=EXPERIMENT_SLUG
 
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Experiment delete command options

Option	Short option	Value Type	Description
--slug	-s	Text	Experiment slug
--delete_artifacts	-d	Boolean	Delete related artifacts from the storage (default is false)

# Flow commands

NOTE

Flow commands require the current working directory (or sub-directory) to be a linked to a cnvrg project

# Create new flow

To create new flow use following command:

cnvrgv2 flow create --yaml-path=PATH_TO_FLOW_YAML
 
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Example YAML file for flow:

---
flow: Sentiment Analysis
recurring: 0 0 * * 1 Asia/Jerusalem
tasks:
- input: python3 train.py
  params:
  - key: epochs
    type: categorical
    min: 0
    max: 0
    scale: linear
    steps: 0
    values:
    - '5'
    - '10'
  - key: batch_size
    type: categorical
    min: 0
    max: 0
    scale: linear
    steps: 0
    values:
    - '128'
    - '256'
  computes:
  - large
  image: cnvrg:v5.0
  description: Train
  type: exec
  algorithm: GridSearch
  queue_slug: c9rlzkv5zazkdyxg7esg
  title: Train
  conditions: []
  commit: 5dd42b08460dae46456ca6e3cf9db621b59e67b6
- title: Sentiment API
  description: Sentiment API
  conditions:
  - target: 0.0
    task: 
    objective: max
    tag_key: test_accuracy
    value: ''
  type: deploy
  confirmation: false
  kind: webserver
  endpoint_title: sentiment-service
  function_name: predict
  file_name: predict.py
  env_setup: python_3
  min_replica: 1
  max_replica: 1
  local_folders: []
  accept_files: false
  computes:
  - medium
  image: cnvrg:v5.0
relations:
- from: Train
  to: Sentiment API
 
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# Endpoint commands

NOTE

Endpoint commands require the current working directory (or sub-directory) to be a linked to a cnvrg project

# Create new endpoint

To create new endpoint use following command:

cnvrgv2 endpoint create --title=TITLE --file_name=FILE_NAME --function_name=FUNCTION_NAME -stmOn
 
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Endpoint create command options

Option	Short option	Value Type	Description
--title	-t	Text	Name of the Endpoint (required)
--file_name	-f	Text	The file containing the endpoint's functions (required)
--function_name	-fn	Text	The name of the function the endpoint will route to (required)
--templates	-tm	Text	Comma separated list of compute templates. format is: resource_name.template_name, for example: --templates=ics.medium (default is taken from project's settings)
--kind	-k	Integer	Kind of endpoint to create. options are: Webserver = 0, Kafka = 1, Batch = 2, Triton = 3, Tensorflow = 4, RabbitMQ = 5 (default is 1)
--env_setup	-e	Text	The interpreter to use. options are: python_2,python_3,pyspark,r_endpoint
--kafka_brokers	-kb	Text	Comma separated list of kafka brokers
--kafka_input_topics	-kit	Text	Comma separated list of kafka input topics
None	-stmOn	No value	Does endpoint returns stream output
None	-stmOff	No value	The endpoint does't returns stream output
--args	-a	List	Additional arguments can be sent in a list. for example: --args=[image_slug=SLUG, commit=sha1]
			Options for args are: image_slug=string(slug of the image to be used), commit=string(sha1 of project commit), prep_file=string(preprocess file), prep_function=string(preprocess function), input_file=boolean(endpoint accepts files as input), prep_function=string(preprocess function), min_replica=integer(number of min pods), max_replica=integer(number of max pods), desired_percentage=integer(Canary deployment percentage),gunicorn_config=array(key:val),flask_config=array(key:val),nginx_config=array(key:val),models_config_file=string(Tensorflow serving config file path), models_folder=string(Triton server models folder path),rabbitmq_host=string(RabbitMQ host IP), rabbitmq_input_queues=array(RabbitMQ input queues), rabbitmq_output_queue=string(RabbitMQ output queue),rabbitmq_user=string(RabbitMQ login user), rabbitmq_password=string(RabbitMQ login password), rabbitmq_exchange=string(RabbitMQ exchange), rabbitmq_prefetch=integer(RabbitMQ prefetch count), model_id=string(name od the model), command_arguments=object(key value of parameters)

NOTE

These options, -stmOn and -stmOff, are flag options and should not be accompanied by any values. Please refer to the code example provided above for usage guidance. Stream output is supported only at web service endpoints.

# Get endpoint predictions:

Get last 50 predictions made by the endpoint:

cnvrgv2 endpoint get-predictions --slug=ENDPOINT_SLUG
 
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Endpoint get-predictions command options

Option	Short option	Value Type	Description
--slug	-s	Text	Endpoint slug
--start_time	-st	Text	The start time of the predictions to return
--end_time	-et	Text	The end time of the predictions to return
--offset	-o	Text	The offset of the predictions to return
--size	-si	Integer	Number of predictions to return (default is 20)
--model	-m	Text	The endpoint model number to query

# Add logs to endpoint:

Add logs to running endpoint using following command:

cnvrgv2 endpoint logs --slug=ENDPOINT_SLUG --logs="new log added"
 
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Endpoint logs command options

Option	Short option	Value Type	Description
--slug	-s	Text	Endpoint slug
--logs	-l	Text	Comma separated list of logs to write to endpoint
--log_level	-lv	Text	Level of the logs. options are: output, error, warning, info (default is output)

# Log metrics to endpoint:

Add metrics to running endpoint using following command:

cnvrgv2 endpoint log-metric --slug=ENDPOINT_SLUG --name=METRIC_NAME --x=X_VALUE --y=Y_VALUE
 
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Endpoint log-metric command options

Option	Short option	Value Type	Description
--slug	-s	Text	Endpoint slug
--name	-n	Text	The metric name
--x	-x	Text	Metric x value
--y	-y	Text	Metric y value

# update endpoint version

To update a running endpoint, use the following command:

cnvrgv2 endpoint update --slug=ENDPOINT_SLUG -stmOff
 
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Endpoint update command options

Option	Short option	Value Type	Description
--title	-t	Text	Name of the Endpoint (required)
--file_name	-f	Text	The file containing the endpoint's functions (required)
--function_name	-fn	Text	The name of the function the endpoint will route to (required)
--templates	-tm	Text	Comma separated list of compute templates. format is: resource_name.template_name, for example: --templates=ics.medium (default is taken from project's settings)
--kind	-k	Integer	Kind of endpoint to create. options are: Webserver = 0, Kafka = 1, Batch = 2, Triton = 3, Tensorflow = 4, RabbitMQ = 5 (default is 1)
--env_setup	-e	Text	The interpreter to use. options are: python_2,python_3,pyspark,r_endpoint
--kafka_brokers	-kb	Text	Comma separated list of kafka brokers
--kafka_input_topics	-kit	Text	Comma separated list of kafka input topics
None	-stmOn	No value	Does endpoint returns stream output
None	-stmOff	No value	The endpoint does't returns stream output
--args	-a	List	Additional arguments can be sent in a list. for example: --args=[image_slug=SLUG, commit=sha1]
			Options for args are: image_slug=string(slug of the image to be used), commit=string(sha1 of project commit), prep_file=string(preprocess file), prep_function=string(preprocess function), input_file=boolean(endpoint accepts files as input), prep_function=string(preprocess function), min_replica=integer(number of min pods), max_replica=integer(number of max pods), desired_percentage=integer(Canary deployment percentage),gunicorn_config=array(key:val),flask_config=array(key:val),nginx_config=array(key:val),models_config_file=string(Tensorflow serving config file path), models_folder=string(Triton server models folder path),rabbitmq_host=string(RabbitMQ host IP), rabbitmq_input_queues=array(RabbitMQ input queues), rabbitmq_output_queue=string(RabbitMQ output queue),rabbitmq_user=string(RabbitMQ login user), rabbitmq_password=string(RabbitMQ login password), rabbitmq_exchange=string(RabbitMQ exchange), rabbitmq_prefetch=integer(RabbitMQ prefetch count)

# Rollback to previous endpoint version:

To rollback to a previous endpoint model,use following command

cnvrgv2 endpoint rollback --slug=ENDPOINT_SLUG --version_slug=VERSION_TO_ROLLBACK_TO
 
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# Start an existing endpoint

To start an existing endpoint which is currently offline, use following command:

cnvrgv2 endpoint start --slug=ENDPOINT_SLUG
 
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# Stop a running endpoint

To stop a running endpoint, use following command:

cnvrgv2 endpoint stop --slug=ENDPOINT_SLUG
 
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# Advanced Volumes commands

# Storage Class Management

# Create a New NFS Storage Class

To add a New NFS Storage Class, use the following command:

cnvrgv2 storage-class create --title ... --host-ip ... --host-path ... --wait   
 
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New NFS Storage Class create command options

Option	Short option	Value Type	Description
--title	-t	Text	Title of the storage class to create
--host-ip	-i	Text	Storage class host ip
--host-path	-p	Text	Storage class host path
--cluster-slug	-c	Text	Cluster slug to create storage class on. If not sent, will use the default cluster.
--wait	-w	Boolean	Will cause the command to wait until the storage class is ready to use

Mandatory attributes: title, host_ip, host_path.

# Connect Exists NFS Storage Class

To Connect Exists NFS Storage Class, use the following command:

cnvrgv2 storage-class connect --cluster-slug ... --title ... 
 
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Option	Short option	Value Type	Description
--title	-t	Text	Title of the storage class to connect
--connect-volumes	-cv	Text	To connect all volumes that exist on the storage class
--cluster-slug	-c	Text	Cluster slug to connect storage class to. If not sent, will use the default cluster.
--wait	-w	Boolean	Will cause the command to wait until the storage class is ready to use

Mandatory attributes: title

# Registry commands

# Create a registry

To create a registry in the cnvrg environment, use the following command:

cnvrgv2 registry create
 
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Registry create command options

Option	Short option	Value Type	Description
--title	-t	Text	Registry title
--url	-u	Text	Registry url
--type	-rt	Text	Registry type. options are: cnvrg, dockerhub, gcr, acr, ecr, nvidia, other
--username	-us	Text	Registry username, required for private registries
--password	-ps	Text	Registry password, required for private registries

# Get a registry

To retrieve information about a registry, use the following command:

cnvrgv2 registry get --slug=REGISTRY_SLUG
 
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Output will include the registry slug, title, url, username, type, and whether it's private or not.

# List all registries

To retrieve all the registries in the cnvrg environment, use the following command:

cnvrgv2 registry list
 
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# Update a registry

To update or modify a registry, use the following command with the required combination of options corrosponding to the fields you want to modify:

cnvrgv2 registry update --slug=REGISTRY_SLUG --title=NEW_TITLE --url=NEW_URL
 
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Registry update command options

Option	Short option	Value Type	Description
--title	-t	Text	Registry title
--url	-u	Text	Registry url
--username	-us	Text	Registry username
--password	-ps	Text	Registry password

# Delete a registry

To delete a registry from the cnvrg environment, use the following command:

cnvrgv2 registry delete --slug=REGISTRY_SLUG
 
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# Image commands

# Create an image

To create an image, use the following command:

cnvrgv2 image create --registry=REGISTRY_SLUG --name=IMAGE_NAME --tag=IMAGE_TAG
 
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Image create command options

Option	Short option	Value Type	Description
--name	-n	Text	Image repository name
--tag	-t	Text	The image tag
--registry	-r	Text	The slug of the registry that the image will be added to
--logo	-l	Text	Logo name
--custom	-c		Udr custom image (requires using the --dockerfile option)
--readme	-rd	Text	Readme file path for the image
--dockerfile	-df	Text	Dockerfile path to build a custom image

Ai Studio supports choosing one of the following logos for an image view:

Logo name	Logo name	Logo name
keras	tensorflow	sklearn
python	r	xgboost
bash	s3	spark
opencv	pytorch	vgg
cnvrg	mxnet	tensor_rt
rapids	nvidia

# Get an image

To retrieve information about an image using its slug, use the following command:

cnvrgv2 image get --slug=IMAGE_SLUG
 
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To retrieve information about an image using its name and tag, use the following command:

cnvrgv2 image get --name=IMAGE_NAME --tag=IMAGE_TAG
 
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# List all images

To retrieve all the images in the cnvrg environment, use the following command:

cnvrgv2 image list
 
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# Update an image

To update or modify an image, use the following command with the required combination of options corrosponding to the fields you want to modify:

cnvrgv2 image update --slug=IMAGE_SLUG 
 
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Image update command options

Option	Short option	Value Type	Description
--slug	-s	Text	Image slug
--logo	-l	Text	Logo name
--readme	-rd	Text	Readme file path for the image

# Delete an image

To delete an image using its slug, use the following command:

cnvrgv2 image delete --slug=IMAGE_SLUG
 
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To delete an image using its name and tag, use the following command:

cnvrgv2 image delete --name=IMAGE_NAME --tag=IMAGE_TAG
 
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# Members commands

# Add a new member

To add a member to the cnvrg environment, use the following command:

cnvrgv2 members add --email=MEMBER_EMAIL --role=MEMBER_ROLE
 
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Members add command options

Option	Short option	Value Type	Description
--email	-e	Text	Member email
--role	-r	Text	Member role, role values: admin, manager, member (corresponds to data scientist role), or reviewer

NOTE

Admin permission is required in order to add new members

# list all members

To retrieve all the members in the environment, use the following command:

cnvrgv2 members list
 
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# Revoke a member

To remove or revoke a user's membership to the organization, use the following command:

cnvrgv2 members revoke --email=MEMBER_EMAIL
 
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NOTE

Admin permission is required in order to revoke user membership

# Update the role of a member

To update the role of a member in the cnvrg environment, use the following command:

cnvrgv2 members update --email=MEMBER_EMAIL --role=NEW_ROLE
 
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NOTE

Admin permission is required in order to update user membership

← Python SDK v2 Python SDK v1 →

# cnvrgv2 CLI

# Install cnvrgv2 CLI

# Install options

# Authenticate with cnvrgv2 CLI

# Login

# Logout

# Project commands

# Create an empty project

# Link a local directory to a new project

# Link an existing project to a git directory

# List projects

# Clone a project

# Sync a project with a local directory

# Upload files to a project

# Upload updated project files

# Download updated project files

# Delete files from a project

# Delete a project

# Dataset commands

# Create a dataset

# List datasets

# Clone a dataset

# Put dataset

# Delete files from a dataset

# Scan datasets

# Upload updated dataset files

# Download updated dataset files

# Cache a dataset

# Uncache dataset

# Verify dataset

# Delete a dataset

# Workspace commands

# Create new workspace

# Sync workspace file changes

# Start an existing workspace

# Stop a running workspace

# Start tensorboard session in a workspace

# Stop tensorboard in a workspace

# SSH into a workspace

# Experiment commands

# Run an experiment

# Download the output artifacts of an experiment

# Upload output artifacts to an experiment

# Upload image output artifacts to an experiment

# Log a paramater to an experiment

# Merge artifacts into project

# Start tensorboard in an experiment

# List all experiments in the project

# Stop tensorboard in an experiment

# Delete an experiment

# Flow commands

# Create new flow

# Endpoint commands

# Create new endpoint

# Get endpoint predictions:

# Add logs to endpoint:

# Log metrics to endpoint:

# update endpoint version

# Rollback to previous endpoint version:

# Start an existing endpoint

# Stop a running endpoint

# Advanced Volumes commands

# Storage Class Management

# Create a New NFS Storage Class

# Connect Exists NFS Storage Class

# Registry commands

# Create a registry

# Get a registry

# List all registries

# Update a registry

# Delete a registry

# Image commands

# Create an image

# Get an image

# List all images

# Update an image

# Delete an image

# Members commands

# Add a new member

# list all members