AI Training - Tutorial - Train your first ML model
How to train your first machine learning model in AI Training
How to train your first machine learning model in AI Training
Last updated 26th January, 2023.
This tutorial will allow you to train your first Model in AI Training.
AI Training allows you to train your models easily, with just a few clicks or commands. This solution runs your training job on the computational cloud resources you have chosen (CPU/GPU). As soon as your training job is finished, the status of the AI Training job will change from Run to Done, which means that the billing will be stopped immediately. Thus, you will save time and increase the productivity of your team, while respecting the integrity of your sensitive data (GDPR).
AI Training is compatible with leading applications and frameworks such as Pytorch, Scikit-learn, TensorFlow, Transformers and others!
More information about AI Training can be found here.
At the end of this tutorial, you will have learned to master OVHcloud AI Training.
We will show you how you can:
Each step will be accompanied by an example to guide you.
We will train an image classifier on the Fashion MNIST dataset.
This dataset contains 70,000 examples of Zalando's article images. Each one is a 28x28 grayscale image, associated with a label from 10 classes.
This dataset is available on their GitHub repository, but you can directly download it from Kaggle. It is a
.zip file of 72MB size.
You will follow different steps to export your data, train your model and save it.
This step is optional because you may load some open datasets through libraries, commands, etc., so you will not need to upload your own data to the cloud.
On the other hand, you can upload your data (dataset, python and requirements files, etc.) to the cloud, in the Object Storage. This can be done in two ways: either from the OVHcloud Control Panel or via the ovhai CLI.
This data can be deleted at any time.
If you do not feel comfortable with commands, this way will be more intuitive for you.
First, go to the OVHcloud Public Cloud section.
Then, select the Object Storage section (in the Storage category) and create a new object container by clicking
Object Storage >
Create an object container.
Here you can create the object container that will store your datas. Several
regions are available, choose the best parameters for you.
Once your object container is created, you will see it in the Object Storage list. Click the one you just created. Here, you will be able to click the
Add Objects button, which will allow you to store your data in the cloud.
Using the manager to upload your data can be very long. We recommend to use the OVHcloud AI CLI.
In the OVHcloud Control Panel, you can upload files but not folders. For instance, you can upload a
.zip file to optimize the bandwidth, then unzip it in your code. But if your dataset is already split in several folders, you must use the AI CLI to upload them.
To follow this part, make sure you have installed the ovhai CLI on your computer or on an instance.
As in the Control Panel, you will have to specify the
name of your container and the
path where your data will be located. The creation of your object container can be done by the following command:
ovhai data upload <region> <container> <paths>
Assuming a file named
my-dataset.zip exists in your current working directory, you can use the following command to create an object container named
fashion_MNIST_dataset, located in the
GRA region that will contain your
ovhai data upload GRA fashion_MNIST_dataset my-dataset.zip
.zip file can now be accessed from all OVHcloud AI products, either with read-only (RO) or read-write (RW) permissions.
To launch your training job, you can also use the OVHcloud Control Panel or the ovhai CLI.
Now, select the
AI Training section (in the
AI & Machine Learning category) and create your job by clicking
Launch a new job.
Here you will have to specify a
region and a
Docker image. Depending on the framework you are using in your project, you will have to select the Docker image that suits you best. This will reduce the number of library installations you need to do.
Make sure to mention in a
requirements.txt file all the libraries that are not included in the image. For example, the Pytorch image does not contain the Pandas library. Since our code uses it, we will have to mention it in our
requirements.txt file and install it, otherwise the job will not run correctly.
Then, you can toggle the
I want to link volumes of data to the job switch, to attach your Object Storage container(s) to your job environment and access the data you have stored in the cloud (dataset, python files, requirements.txt, etc.).
If you have not created an Object Storage, don't worry, this step is optional.
If you want to add yours, just select the container that contains your data and specify the mount directory you have mentioned in your Python code.
For example, we load our data by calling the
/workspace/my_data/my-dataset.zip folder in our Python code. This is why we choose
/workspace/my_data as the mount directory.
Depending on your needs, you can enable or disable the cache and select the permission you want. Since we want to both read our data but also be able to extract the zip file (which means write new files) and save our model once dragged into this object container, we will opt for the Read & Write permission on this container.
A good practice is to attach a container with your input data, and attach a second container to save the output data in.
But since we want to keep this tutorial fairly simple, we will use only one container for our data.
Then, in the
Enter the Docker command step, you can specify the command that allows you to install your librairies and run your Python script, which are both contained in your object container.
Assuming you have added your main .py file and your requirements.txt file to a container that you have linked to your job with
my_data as your mount directory, you can then use:
-- bash -c 'pip install -r /workspace/my_data/requirements.txt && python /workspace/my_data/cnn_classification_mode_dataset.py'
This feature is currently being updated. It is possible that you will encounter problems when using it. If this is the case, we advise you to use the AI CLI.
To finish, you just have to enter an SSH command in case you would like to access the job remotely via SSH, and you must specify the resources of your job (number of
When everything is done, you can launch the job by clicking the
If you prefer to launch your job with the CLI, the principle is identical to the UI method. Indeed, you will have to attach your data volumes to a
mount directory, specify your resources (number of
GPUs) and your
region, for the same reasons explained above.
A classic training job can be launched with the following command:
ovhai job run <docker_image_name> \ --gpu <nb_gpus> \ --volume <object_storage_name>@<region>/:/workspace/<mount_directory_name>:<permission_mode> \ -- bash -c 'pip install -r /workspace/mount_directory_name/requirements.txt && python /workspace/mount_directory_name/python_script.py'
<docker_image_name>: Choose the image you want to use to create your environment. You can get a list of the framework images hosted by OVHcloud here. You can also use another Docker image, but it may take longer to load.
<nb_gpus>: Number of GPUs your job will be run on. You can also use CPUs by replacing this line by
--volume: Volume you want to add to your job (object container or public GitHub repository).
You can add several volumes to your job by calling the
--volume argument several times. You can also add a GitHub repository as a volume.
-- bash -c: Allows you to provide commands through which you can for example install the libraries mentioned in your
requirements.txt file, and run a Python script.
If you have followed the optional part that shows you how to store your data (first step), you can load your volume with the
--volume parameter. You will have to specify the
region and its
Depending on your needs, you can select the
permission_mode you want (Read-Only with
RO, Read & Write with
RW, and Read, Write & Delete with
RWD). To finish, you can also enable or disable the cache on your volume, by adding
:cache just after the
Otherwise, you can remove the --volume line, since it will not bring anything to your app.
To give you a real example, here is the command we will use to launch our job, assuming this time that our
dataset.zip is contained in a
fashion_MNIST_dataset container, with a
my_data, and that our Python file and our
requirements.txt file are in the
ovh/ai-training-examples GitHub repository:
ovhai job run ovhcom/ai-training-pytorch \ --gpu 1 \ --volume fashion_to_delete@GRA/:/workspace/my_data:RW \ --volume https://github.com/ovh/ai-training-examples.git:/workspace/github_repo:RO \ -- bash -c 'pip install -r /workspace/github_repo/jobs/getting-started/train-first-model/requirements.txt && python /workspace/github_repo/jobs/getting-started/train-first-model/train-first-model.py'
Whatever method you use, make sure to specify one mount path per volume. They must be different in order not to conflict.
When your job is launched, you can follow its progress (loading of your volumes, evolution of the job status, follow your prints, etc.)
This can be done by clicking on your job's name, in the
AI & Machine Learning >
AI Training. You will find there two categories:
Job Information: There you will find the general progress of your job.
Logs: This is particularly useful to track your prints and to understand why your job could not be launched, in the case of errors. You will find a console that will display all this information.
Once your job is launched, you will get a lot of information via the CLI. Two main ones will be useful: the
Id of your AI Training job, given in the very first lines (not to be confused with the ID of the added volumes), and the
Info URL will allow you to visually follow your job live. You will see the progress of the loading of your volumes, the status of your job which is evolving and will go to
Running at the time of launch.
Warning: When the job is running, the
auto-refresh option of the Info URL page is automatically disabled. Remember to reactivate it if you want to see when your job will be finished and will switch to
As mentioned before, logs are useful to follow and understand the progress of your job. To take a look at it, use the following command:
ovhai job logs <job_id>
If you are not present when the job is finished and goes to
Done, don't worry, the billing stops automatically!
We can now download the trained model, again in two ways (UI/CLI).
Just click your
object container as if you still wanted to add new files to it. This will allow you to see all the files that are contained in it. You should find your trained model in it, that you can download by clicking the
If you prefer to use the AI CLI, you will need to use the following command:
ovhai data download [OPTIONS] <DATA_STORE> <CONTAINER> [OBJECTS]...
<DATA_STORE>: Data store of the container to download from. You can get a list of all available stores for the Object Storage by typing
ovhai data store list.
<CONTAINER>: Name of container to download from.
[OBJECTS]...: Name(s) of object(s) to download.
For more info about this command, you can use:
ovhai data download --help
For example, in our case we will use:
ovhai data download GRA fashion_MNIST_dataset model.net
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