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CLI - Use your data in a notebook

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Learn how to access Object Storage data from your notebooks.

 

Requirements

  • A working ovhai CLI (see this guide to get started)

 

Instructions

Upload data to your Object Storage

First, we must push some data to the Object Storage before accessing it from the notebook.

Assuming a file named my-dataset.zip exists in your current working directory, you can use the following command to create a data container named my-dataset in the US-EAST-VA region that will contain your my-dataset.zip file.

$ ovhai bucket object upload my-dataset@US-EAST-VA my-dataset.zip

This file can now be accessed from your notebooks, either with read-only or read-write permissions.

 

Access with Read-Only permissions

To access your dataset, you can use the --volume option.

$ ovhai notebook run tensorflow jupyterlab --volume my-dataset@US-EAST-VA:/workspace/datasets:ro

This command can be read as "Load the container my-dataset from the US-EAST-VA region, in the /workspace/datasets directory, with ro (read-only) permissions".

Wait a few seconds for the notebook to start, then you should see its URL in the output that you can access from your browser. You can read the CLI - Launch an AI notebook guide to know how to find this URL.

You should get a page like this, showing your dataset in the file explorer:

PaaS AI CLI use your notebook - step 1

You will not be able to modify the dataset from this notebook because you loaded it with read-only permissions.

Read-only permissions are to ensure you don't modify your data by mistake. If you want to modify data from your notebooks, to store a trained neural network for example, you can use the read-write permission instead.

 

Access with Read-Write permissions

Similarly to the read-only mode, you can use the --volume option to load data with read-write permission. The only difference is that you specify rw instead of ro in the command:

$ ovhai notebook run tensorflow jupyterlab --volume my-neural-networks@US-EAST-VA:/workspace/neural-networks:rw

Once you have some data that you want to save (a trained neural network in this example), you can simply write it to the /workspace/neural-networks folder.

This folder will be uploaded to your Object Storage when you stop your notebook. As long as your notebook is in the STOPPING state, the upload is still in progress. Once the state changes to STOPPED, the data has been uploaded to your Object Storage.

With the RW permission, you can add or modify data but you cannot permanently delete it from your Object Storage.

Info

If you want to add, modify, or delete data, connect your volume with Read-write-delete specifying RWD instead of RW.

 

Attach a public Git repository

If Python code, notebooks, or other files are available on a public GitHub repository and you can attach them to your notebook with the --volume option. To be able to edit it and make changes easily, use the read-write permission (shorten by: rw).

The command is as follows:

$ ovhai notebook run tensorflow jupyterlab --volume https://github.com/ovh/ai-training-examples.git:/workspace/git-hub-repository:rw
Warning

NOTE: To make your command valid, don't forget to add a .git at the end of the GitHub repository URL.

 

Access multiple volumes

In many cases, you need at least one volume for your dataset, and another to store your results. You can load as many volumes as you want by chaining the --volume options:

$ ovhai notebook run tensorflow jupyterlab
    --volume my-dataset@US-EAST-VA:/workspace/datasets:ro \
    --volume my-neural-networks@US-EAST-VA:/workspace/neural-networks:rw \
    --volume https://github.com/ovh/ai-training-examples.git:/workspace/git-hub-repository:rw

In this case, we loaded my-dataset in read-only, and my-neural-networks and the GitHub repository in read-write mode.

 

Using cached volumes

When loading large files from the Object Storage, it can take some time to download to your notebooks. In these cases, you can cache the volumes so that it does not need to be downloaded again when you start new notebooks that use the same data.

To do so, you can append :cache after the permissions when specifying volumes:

$ ovhai notebook run tensorflow jupyterlab --volume my-dataset@US-EAST-VA:/workspace/datasets:ro:cache

Cached volumes will be deleted at least 72 hours after the last notebook using them has stopped. Note that the cache is shared with all users in your project. The main consequence you need to be careful about is the fact that if someone else modifies the data in your cached volume, you will also see the modifications on your side.

 

Update volume configuration

It's possible to update the volume's configuration of a notebook with the patch command. It can be convenient if you need a new object storage afterward and don't want to recreate the whole notebook.

Warning

NOTE: For the patch command to be successful, you must stop your notebook before running the command. Restart your notebook after the command has been run.

To do so, use the patch command with the --volume option:

$ ovhai notebook patch <notebook ID> -v my-dataset@US-EAST-VA:/workspace/datasets:rw
Info

This is the same option as the run command above. You can choose to mount the volume in read-only or read & write mode and mount multiple volumes.

 

Go further

Browse all the AI & Machine Learning guides.

Browse the rest of the OVHcloud US documentation for guides on our other products and services.

Learn more about AI & Machine Learning on the OVHcloud US website.

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