Skip to main content

CLI - Commands reference

Stephanie R.
Updated

Learn how to use the ovhai CLI.

 

Requirements

  • a working ovhai CLI (see this guide for more information)

 

Instructions

Run a job

To submit a job, you need a docker image and (optionally) some data.

First, let's submit a simple job, a public Ubuntu image that executes a command echo:

ovhai job run ubuntu --gpu 1 -- echo "Hello from my first job"
  • Add the flag --help to get more information about a command.
  • You can increase the number of GPUs with the flag --gpu <x> or choose to use CPUs with the flag --cpu <x>.

You can then list your current and old jobs with:

ovhai job list
  • Add the flag --watch to watch changes live.
  • Most commands handle the flag --output <format> if you want a different output (JSON or YAML).

You should see the job you just ran; you need its ID to get more information about it.

To see information about a job, use the following command:

ovhai job get <job-id>

To see its logs live, use the following command:

ovhai job logs <job-id> --follow

You should see the output of the echo command:

Hello from my first job

 

Use data in your job

You can upload data to the Object Storage and mount that data when you run a job. If you mount that data with read/write it will be saved to the Object Storage when the job ends.

Let's upload some data and use it in a job that will produce more data.

ovhai bucket object upload <container>@<region> some-file.txt
  • If you already used the Swift CLI, the commands are similar.

Now that you have some data in a container, you can mount it when you run a job with the flag --volume.

ovhai job run <image> --volume <container>@<region>:/data:rw
  • You can use the flag --volume more than one time if you want to mount more containers.
  • Mounted containers with write access are only synchronized when the job is terminated.

You can list and download data you uploaded or generated by a job with:

ovhai bucket object list <container>@<region>
ovhai bucket object download <container>@<region> --prefix /some-file

 

Synchronize data while a job is running

You can synchronize data to the Object Storage while a job is running with:

ovhai job push-data <job-id>

You can see the progress with:

ovhai job get <job-id>

 

Execute commands inside a running job

You can execute commands (like bash) while a job is running.

ovhai job exec -it <id> -- bash

This way you can interact with a running job.

 

Run a job with ssh access

You can create a job with SSH access:

ovhai job run -s ~/.ssh/id_ed25519.pub ovhcom/ai-training-fastai

Multiple -s arguments can be used to provide multiple SSH public keys.

Once the job is in RUNNING state, you can retrieve the sshUrl with:

ovhai job get <job-id>

---
Id:         0d916855-1cd4-4b66-8803-b4782bc13902
Spec:
  Command:              
  Default Http Port:    8080
  Env Vars:             ~
  Image:                ovhcom/ai-training-fastai
  Labels:
    ovh/id:   0d916855-1cd4-4b66-8803-b4782bc13902
    ovh/type: job
  Name:                 AS-CLI-Test
  Resources:
    Cpu:               1
    Ephemeral Storage: 40.0 GiB
    Flavor:            ai1-1-cpu
    Gpu:               0
    Gpu Brand:         ~
    Gpu Memory:        ~
    Gpu Model:         ~
    Memory:            4.0 GiB
    Private Network:   0 bps
    Public Network:    500.0 Mbps
  Shutdown:             ~
  Ssh Public Keys:
  - ssh-ed25519 AAAAC3NzaC1someKey
  Timeout:              0
  Timeout Auto Restart: false
  Unsecure Http:        false
  Volumes:              ~
  Grpc Port:            0
Status:
  Data Sync:            ~
  Duration:             42s
  External Ip:          147.XXX.XXX.XXX
  History:
    DATE                  STATE
    26-02-25 20:23:55     QUEUED
    26-02-25 20:23:55     INITIALIZING
    26-02-25 20:23:55     PENDING
    26-02-25 20:23:56     RUNNING
  Info:
    Message:   Job is running
  Info Url:             https://ui.us-east-va.ai.cloud.ovh.us/job/0d916855-1cd4-4b66-8803-b4782bc13902
  Ip:                   10.42.155.122
  Monitoring Url:       https://monitoring.us-east-va.ai.cloud.ovh.us/d/job?var-job=0d916855-1cd4-4b66-8803-b4782bc13902&from=1740601375518
  Ssh Url:              ssh://0d916855-1cd4-4b66-8803-b4782bc13902@us-east-va.ai.cloud.ovh.us
  State:                RUNNING
  Url:                  https://0d916855-1cd4-4b66-8803-b4782bc13902.job.us-east-va.ai.cloud.ovh.us
  Volumes:              ~
  Grpc Address:         0d916855-1cd4-4b66-8803-b4782bc13902.job-grpc.us-east-va.ai.cloud.ovh.us:443
Updated At: 26-02-25 20:24:02
User:       user-xxx

Then you can connect to it with a terminal:

ssh 0d916855-1cd4-4b66-8803-b4782bc13902@us-east-va.ai.cloud.ovh.us -i ~/.ssh/id_ed25519

 

Manage registries

You can list available registries with:

ovhai registry list

By default, you have access to public registries and a shared registry scoped to your project.

NOTE: This shared registry can help you perform your tests, but should not be used in production, as we reserve the right to delete its content if deemed necessary. The images pushed to this registry are for AI Tools workloads only and will not be accessible for external uses.

This is why it can be interesting to add and manage other registries. To add a private registry, use:

ovhai registry add <url>

You will be asked to type your credentials.

More information about adding and managing public and private registries can be found here.

 

Go further

For more information and tutorials, please see our other AI & Machine Learning support guides or explore the guides for other OVHcloud products and services.

If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link to get a quote and ask our Professional Services experts for a custom analysis of your project.

Related articles