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Operationalizing an AWS Image Classification Project

By improving and preparing an image classification project for production-grade deployment, you'll demonstrate that you have the skills to work on the most advanced ML pipelines in the world. You'll be prepared to do excellent work as an ML engineer with the ability to optimize ML pipelines for efficiency, speed, and security.

Link Write-up: https://docs.google.com/document/d/1HbFQ48U6uNV3GFZIxSJeFGaTAeVe7g3ZcDJo26vgWKo/edit?usp=sharing

Source code: vnk8071/operationalizing_aws_ml

Bucket S3​

Create bucket​

bucket_s3

Dataset​

The provided dataset is the dogbreed classification dataset which can be found in the classroom.

!wget https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip

Access​

Upload the data to an S3 bucket through the AWS Gateway so that SageMaker has access to the data.

!aws s3 cp dogImages s3://project4-khoivn/ --recursive

Setup EC2​

Create an EC2 instance with the following configuration:

  • AMI: Deep Learning AMI GPU Pytorch 1.13.1
  • Instance Type: t2.large
  • Storage: 45 GB
  • Security Group: Allow SSH and HTTP Setup EC2

Training model​

Model​

Choose Resnet18 as the pretrained model and replace the last layer with a new layer that has the number of classes as the output.

def get_model(self):
    '''
        Using a Resnet18 pretrained model
    '''
    model = models.resnet18(pretrained=True)

    for param in model.parameters():
        param.requires_grad = False

    model.fc = nn.Sequential(
        nn.Linear(in_features=512, out_features=128),
        nn.ReLU(inplace=True),
        nn.Linear(in_features=128, out_features=133)
    )
    return model

Hyperparameter Tuning Job​

  • Learning rate with a range of 0.001 to 0.1 and type of continuous (ContinuousParameter)
  • Batch size with a range of 32, 64, 128, 256, 512 and type of categorical (CategoricalParameter)
  • Metric definition for the objective metric of average test loss
hyperparameter_ranges = {
    "lr": ContinuousParameter(0.001, 0.1),
    "batch-size": CategoricalParameter([32, 64, 128, 256, 512]),
}

objective_metric_name = "average test loss"
objective_type = "Minimize"
metric_definitions = [{"Name": "average test loss", "Regex": "Test set: Average loss: ([0-9\\.]+)"}]

Best Hyperparameters​

hyperparameters = {
    "lr": 0.0033047276512977604,
    "batch-size": 512,
}

Hyperparameter Tuning Job

Training Job​

Training Job

Training Job Multiple Instances​

Training Job Multiple

Debugging and Profiling​

Use practice in the course to debug and profile the training job. Link: https://learn.udacity.com/nanodegrees/nd009t/parts/fb5cc8ed-69a8-4e9a-8d62-f034aa9f1994/lessons/e7ad71c4-e91e-46f1-a877-e37024fc2ebd/concepts/a7960dd1-1d8b-48ac-810b-ad34084dfba8

Follow the instructions:

rules = [
    Rule.sagemaker(rule_configs.loss_not_decreasing()),
    Rule.sagemaker(rule_configs.overfit()),
    Rule.sagemaker(rule_configs.overtraining()),
    Rule.sagemaker(rule_configs.poor_weight_initialization()),
    ProfilerRule.sagemaker(rule_configs.ProfilerReport()),
]

profiler_config = ProfilerConfig(
    system_monitor_interval_millis=500, framework_profile_params=FrameworkProfile(num_steps=10)
)

hook_config = DebuggerHookConfig(
    hook_parameters={"train.save_interval": "1", "eval.save_interval": "1"}
)

Training and saving on EC2​

Training and saving on EC2

Model Deployment​

Deploy model​

Deploy model

The inference script already handles the data transformation to tensors, so we only need to pass image in bytes format as the payload for the endpoint.

Endpoint​

Endpoint

Inference​

Inference

Setup Lambda Function​

Create Lambda Function​

Create Lambda Function

Deploy and Test Lambda Function​

Deploy and Test Lambda Function

Concurrency and auto-scaling​

Concurrency Lambda Function​

Lambda Concurrency

Auto-scaling Endpoint​

Endpoint Auto-scaling

Run shell​

i = 0
while (i < 100):
    response = predictor.predict(img_bytes, initial_args={"ContentType": "image/jpeg"})
    i += 1