--- title: "CLI - Commandes principales (EN)" description: "Découvrez les principales commandes de la CLI pour interagir sur les AI Tools" url: https://docs.ovhcloud.com/fr/guides/public-cloud/ai-machine-learning/ai-cli-overview lang: fr lastUpdated: 2023-11-27 --- # CLI - Commandes principales (EN) ## Objective This guide covers the usage of the **ovhai** CLI. ## Requirements - a working `ovhai` CLI [how to install ovhai CLI](/fr/guides/public-cloud/ai-machine-learning/ai-cli-install-client.md) ## 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 which executes a command `echo`: ```console ovhai job run ubuntu --gpu 1 -- echo "Hello from my first job" ``` :::info - Add the flag `--help` to get more information about a command. - You can increase the number of GPUs with the flag `--gpu ` or choose to use CPUs with the flag `--cpu `. ::: You can then list your current and old jobs with: ```console ovhai job list ``` :::info - Add the flag `--watch` to watch changes in live. - Most commands handle the flag `--output ` 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: ```console ovhai job get ``` To see its logs in live use the following command: ```console ovhai job logs --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. ```console ovhai bucket object upload ``@`` some-file.txt ``` :::info - 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`. ```console ovhai job run --volume @:/data:rw ``` :::info - 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: ```console ovhai bucket object list ``@`` ovhai bucket object download ``@`` --prefix /some-data ``` ### Synchronize data while a job is running You can synchronize data to the Object Storage while a job is running with: ```console ovhai job push-data `` ``` You can see the progress with: ```console ovhai job get `` ``` ### Execute commands inside a running job You can execute commands (like `bash`) while a job is running. ```console ovhai job exec -it -- bash ``` This way you can interact with a running job. ### Run a job with ssh access You can create a job with some ssh access: ```console 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: ```console ovhai job get `` --- id: 0d916855-1cd4-4b66-8803-b4782bc13902 createdAt: "2021-02-23T08:45:01.297780Z" updatedAt: "2021-02-23T08:45:19.823082Z" user: user-xxx spec: image: ovhcom/ai-training-fastai env: [] defaultHttpPort: 8080 resources: gpu: 1 gpuModel: Tesla-V100S cpu: 7 volumes: [] timeout: 0 name: ai-training-fastai-kind sshPublicKeys: - ssh-ed25519 AAAAC3NzaC1someKey status: state: RUNNING queuedAt: "2021-02-23T08:45:01.297318Z" startedAt: "2021-02-23T08:45:13Z" stoppedAt: ~ ip: 10.42.155.122 infos: ~ history: - state: QUEUED date: "2021-02-23T08:45:01.297012Z" - state: INITIALIZING date: "2021-02-23T08:45:04.356856Z" - state: PENDING date: "2021-02-23T08:45:10.163754Z" - state: RUNNING date: "2021-02-23T08:45:19.822354Z" duration: 6 jobUrl: "http://0d916855-1cd4-4b66-8803-b4782bc13902.job.gra.ai.cloud.ovh.net" sshUrl: "ssh://0d916855-1cd4-4b66-8803-b4782bc13902@gra.ai.cloud.ovh.net" monitoringUrl: "http://monitoring.gra.ai.cloud.ovh.net/d/job/job-monitoring?var-job=0d916855-1cd4-4b66-8803-b4782bc13902&from=1614069913000" ``` Then you can connect to it with a terminal: ```console ssh 0d916855-1cd4-4b66-8803-b4782bc13902@gra.ai.cloud.ovh.net -i ~/.ssh/id_ed25519 ``` ### Manage registries You can list available registries with: ```console ovhai registry list ``` By default, you have access to public registries, and a shared registry scoped to your project. :::warning 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: ```console ovhai registry add `` ``` You will be asked to type your credentials. More information about adding and managing public & private registries can be found [here](/fr/guides/public-cloud/ai-machine-learning/ai-manage-registries.md). ### Switch region in the CLI The regions available to install and use the `ovhai` CLI are `GRA` and `BHS`. If you want to change the region in the CLI, you can use the following commands. You can list the **Names** and **URLs** of the available regions with: ```console ovhai config list ``` :::info The region in **green** is the one you are currently in. ::: To change the region, use: ```console ovhai config set ``` :::info `` is the new region name (`GRA` or `BHS`), in which you want to be placed. ::: If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/fr/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project. ## Feedback Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud)