Train on the cloud (intermediate)¶
Audience: Anyone looking to train a model on the cloud in the background
What is background training?¶
Background training lets you train models in the background without you needing to interact with the machine. As the model trains you can monitor its progress via Tensorboard or an experiment manager of your choice.
0: Install lightning-grid¶
First Navigate to https://platform.grid.ai to create a free account.
Next, install lightning-grid and login
pip install lightning-grid grid login # Login successful. Welcome to Grid.
1: Create a dataset¶
Create a datastore which optimizes your datasets for training at scale on the cloud. Datastores can be created from all sorts of sources such as .zip and .tar links, local files/folders and even s3 buckets.
Let’s create a datastore from this .zip file
grid datastore create https://pl-flash-data.s3.amazonaws.com/tinycifar5.zip --name cifar5
Now your dataset is ready to be used for training on the cloud!
In some research workflows, your model script ALSO downloads the dataset. If the dataset is only a few GBs this is fine. Otherwise we recommend you create a Datastore.
2: Choose the model to run¶
You can run any python script in the background. For this example, we’ll use a simple classifier:
Clone the code to your machine:
git clone https://github.com/williamFalcon/cifar5-simple.git cd cifar5-simple
Code repositories can be as complicated as needed. This is just a simple demo.
3: Run on the cloud¶
To run this model on the cloud with the attached datastore, use the grid run command:
grid run --datastore_name cifar5 cifar5.py --data_dir /datastores/cifar5
The grid command has two parts the [run args] and the [file args]
grid run [run args] file.py [file args]
4: Monitor and manage¶
Now that your model is running in the background, monitor and manage it here.
You can also monitor its progress on the commandline:
If you want to run on your own AWS account and pay the cloud provider directly, please contact our onprem team: mailto:firstname.lastname@example.org
Here are the recommended next steps depending on your workflow.