CLI - Launch an AI notebook
Learn how to run an AI Notebook using the CLI
Last updated 1st September, 2022.
Objective
This guide covers the submission of notebooks through the ovhai CLI.
Requirements
- a working
ovhaiCLI (how to install ovhai CLI)
Instructions
Run a new notebook
First, you need to select one of the machine learning frameworks and an editor among those available. You can
get a list of them using the ovhai capabilities framework list and ovhai capabilities editor list commands:
$ ovhai capabilities framework list
ID NAME DOC_URL VERSIONS
fastai fastai Course https://docs.fast.ai/ 2021-08-04-ovh.beta.1
pytorch PyTorch https://pytorch.org/docs/stable/index.html pytorch1.10.1-py39-cuda10.2-v22-4,pytorch1.9.0-py39-cuda10.2-v22-4,1.8.1-ovh.beta.1
mxnet MXNet https://mxnet.apache.org/versions/1.5.0/ 1.5.0-ovh.beta.1
conda Miniconda https://docs.conda.io/en/latest/miniconda.html conda-py39-cuda11.2-v22-4,conda-py39-cpu-v22-4,conda-py39-cuda11.3-v22-4,conda-py39-cuda11.0-v22-4,conda-py39-cuda10.2-v22-4,conda-py39-cuda10.1-v22-4,conda-py39-cuda10.0-v22-4,conda-py38-cuda11.3-v22-4,conda-py38-cuda11.2-v22-4,conda-py38-cuda11.0-v22-4,conda-py38-cuda10.2-v22-4,conda-py38-cuda10.1-v22-4,conda-py38-cuda10.0-v22-4,conda-py38-cpu-v22-4,conda-py39-cudaDevel11.3-v22-4,conda-py39-cudaDevel11.2-v22-4,conda-py39-cudaDevel11.0-v22-4,conda-py39-cudaDevel10.2-v22-4,conda-py39-cudaDevel10.1-v22-4,conda-py39-cudaDevel10.0-v22-4,conda-py38-cudaDevel11.3-v22-4,conda-py38-cudaDevel11.2-v22-4,conda-py38-cudaDevel11.0-v22-4,conda-py38-cudaDevel10.2-v22-4,conda-py38-cudaDevel10.1-v22-4,conda-py38-cudaDevel10.0-v22-4,conda-py39-cuda11.2-v0.5-beta,conda-py39-cuda11.0-v0.5-beta,conda-py39-cuda10.2-v0.5-beta,conda-py39-cuda10.1-v0.5-beta,conda-py39-cuda10.0-v0.5-beta,conda-py38-cuda11.2-v0.5-beta,conda-py38-cuda11.0-v0.5-beta,conda-py38-cuda10.2-v0.5-beta,conda-py38-cuda10.1-v0.5-beta,conda-py38-cuda10.0-v0.5-beta,conda-py37-cuda11.2-v0.5-beta,conda-py37-cuda11.0-v0.5-beta,conda-py37-cuda10.2-v0.5-beta,conda-py37-cuda10.1-v0.5-beta,conda-py37-cuda10.0-v0.5-beta
sklearn Scikit Learn https://scikit-learn.org/stable/user_guide.html sklearn1.0.2-py39-cpu-v22-4
perceval Quandela Perceval https://perceval.quandela.net/docs/index.html perceval0.6.1-py39-cpu-v22-4,perceval0.5.2-py39-cpu-v22-4
tensorflow Tensorflow https://www.tensorflow.org/api_docs tf2.8-py39-cuda11.2-v22-4,tf2.7-py39-cuda11.2-v22-4,tf2.6-py39-cuda11.2-v22-4,tf2.5-py39-cuda11.2-v22-4,tf2.4-py38-cuda11.0-v22-4,tf2.3-py38-cuda10.1-v22-4,tf2.2-py38-cuda10.1-v22-4,tf2.6-py39-cuda11.2-v0.5-beta,tf2.6-py38-cuda11.2-v0.5-beta,tf2.6-py37-cuda11.2-v0.5-beta,tf2.5-py39-cuda11.2-v0.5-beta,tf2.5-py38-cuda11.2-v0.5-beta,tf2.5-py37-cuda11.2-v0.5-beta,tf2.4-py38-cuda11.0-v0.5-beta,tf2.4-py37-cuda11.0-v0.5-beta,tf2.3-py38-cuda10.1-v0.5-beta,tf2.3-py37-cuda10.1-v0.5-beta,tf2.2-py38-cuda10.1-v0.5-beta,tf2.2-py37-cuda10.1-v0.5-beta,2.4.1-ovh.beta.1
huggingface-transformers Hugging Face Transformers https://huggingface.co/transformers/ 4.5.0-ovh.beta.1
one-for-all One image to rule them all v98-ovh.beta.1
autogluon-mxnet AutoGluon + MXNet 0.1.0-ovh.beta.1
$ ovhai capabilities editor list
ID NAME DOC_URL
jupyterlab JupyterLab https://jupyterlab.readthedocs.io/en/stable/
vscode VSCode https://code.visualstudio.com/docs
In our example, we will create a new notebook using PyTorch and JupyterLab, with 1 GPU:
$ ovhai notebook run --gpu 1 pytorch jupyterlab
id: fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e
createdAt: "2021-05-27T12:37:14.752980089Z"
updatedAt: "2021-05-27T12:37:14.752980089Z"
user: user-6quQBpkve6AT
spec:
name: ""
labels: {}
resources:
gpu: 1
gpuModel: Tesla-V100S
cpu: 13
volumes: []
unsecureHttp: false
env:
frameworkId: pytorch
frameworkVersion: 1.8.1
editorId: jupyterlab
status:
lastTransitionDate: ~
infos: ~
state: STARTING
duration: 0
url: ~
monitoringUrl: ~
lastStartedAt: ~
lastStoppedAt: ~
dataSync: []
The first line in the output shows the ID of the notebook: fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e.
The "Url" corresponds to your JupyterLab server. Pasting this URL in your browser displays the following screen:
If you don't have the ID of the notebook you want to access, you can list all your notebooks using:
$ ovhai notebook ls
We need to wait a few seconds for the notebook to start. You can get the notebook information again using its ID:
$ ovhai notebook get fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e
Name:
ID: fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e
Created At: 27-05-21 12:37
Updated At: 27-05-21 12:37
User: user-6quQBpkve6AT
Spec:
Resources:
GPU: 1
GPU Model: Tesla-V100S
CPU: 13
Volumes:
Labels:
Unsecure: false
Env:
Framework ID: pytorch
Framework Version: 1.8.1
Editor ID: jupyterlab
Status:
State: RUNNING
Infos:
Transitioned At:
Duration: 67s
Url: https://fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e.notebook.gra.training.ai.cloud.ovh.net
Monitoring Url: https://monitoring.gra.training.ai.cloud.ovh.net/d/gpu?var-notebook=fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e&from=1622118974763
Transitioned At: 27-05-21 12:37
Now that the notebook is in the RUNNING state, a https address is defined in the "Url" field. This URL corresponds to your JupyterLab server. Pasting this URL in your browser displays the following screen:
You can now start writing code in your notebook. Since we used the PyTorch framework in our example, we will be able to use it without having to install anything ourselves.
Once you are done with your notebook, you can stop it with the command below:
$ ovhai notebook stop fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e
Stopping a notebook will make it unavailable from your browser, and start synchronising volumes mounted with RW permissions to your object storage.
Once the synchronisation is finished, and the notebook state is STOPPED, you can either start it again or delete it.
Being able to restart a notebook is one of the main differences compared to using jobs. Restarting a notebook will restore your notebook code as it was when you stopped it. However, you will need to re-run your code to reload your variables because the program state is not saved. To restart a notebook, run this command:
$ ovhai notebook start fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e
You are billed for RUNNING notebooks but not for STARTING, STOPPING and STOPPED notebooks.
However, to restore your code when you restart a STOPPED notebook, it needs to be stored in your object storage, which you are billed for.
This is useful when you work on a notebook for some time. If you know you will not use a notebook anymore, you can delete it:
$ ovhai notebook delete fa43cdad-97cc-46e7-ac3b-31dd1d7d5a1e
Note that a notebook can be deleted even if it is not stopped, and that deleted notebook cannot be restarted.
The notebook state stored in the Object Storage (including your notebook files) is not cleaned up automatically after notebook deletion.
You can find it and delete it in the notebooks_workspace container of your Object Storage, under the notebook ID directory.
Going further
Learn how to access your object storage data from your notebooks here.
Learn how to share your notebooks with other people here.
Feedback
Please send us your questions, feedback and suggestions to improve the service:
- On the OVHcloud Discord server
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