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14 May , 2017

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Amazon Web Services AI Blog

You’ve collected your datasets, designed your deep neural network architecture, and coded your training routines.

Spot-instance pricing makes high-performance GPUs much more affordable for deep learning researchers and developers who run training jobs that span several hours or days.

Spot instances are great for deep learning workflows, but there are a few challenges associated using spot instances versus on-demand instances.

Third, if you decide your application should not be interrupted after launching the spot instance, your only option is to stop the spot instance and re-launch as an on-demand or reserved instance.

To address these challenges, here is a step-by-step tutorial on how to set up spot instances for deep learning training workflows while minimizing training progress loss if a spot interruption occurs.

My goal is to implement a setup with the following characteristics: In this example, I use spot instances and the AWS Deep Learning AMI to train a ResNet50 model on the CIFAR10 dataset. I use TensorFlow 1.12 configured with CUDA 9 available on the AWS Deep Learning AMI version 21.

show you how to set up a spot fleet request for deep learning training jobs, which and you use as a starting point for your specific dataset and models.

If your dataset is small and you’re not going to be performing any pre-processing steps during preparation, then you could launch an instance with lesser memory and processing power that may cost less.

Later in step 3 you can see how each new spot instance will automatically self-mount the volume at launch so the datasets and checkpoints are available for training.

Here are all the AWS CLI commands you need to run at instance launch: In order for the instance to be able to perform these actions, I will need to grant the instance the permissions to do so on my behalf. This way I don’t grant the instance all the same permissions that I as a user have and risk potential abuse.

Next, I will create and attach a policy that grants the instance the following permissions: You can grant permissions to access other AWS services if you’re going to be using them in your application.

As discussed in step 2, Amazon EC2 allows you to pass user data shell scripts to an instance that gets executed at launch.

There are 4 key sections in the file:  Get instance ID and query volume In this section the script queries the instance metadata API to access to the ID instance on which this script is running.

It then uses this information to search for the datasets and checkpoints volume with the tag: DL-datasets-checkpoints Check if the volume and instance are in the same availability zone In this section the script checks with the volume and the instance are in the same Availability Zone.

Get training scripts: In this section, the script clones the training code git repository Initiate/resume training: The script activates the tensorflow_p36 Conda environment and runs the training script as the Ubuntu user.

Next, I will create a spot fleet configuration file that includes target capacity (1 instance in our example), launch specifications for the instance, and the maximum price that you are willing to pay.  Spot fleet places requests to meet the target capacity and automatically replenish any interrupted instances.

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