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Running Inference on a Model stored on S3

Introduction

In this example, we will demonstrate how to run inference on a model stored on Amazon S3. We will use a PyTorch model trained on the MNIST dataset.

Running Locally

Prerequisites

Consider using the latest versions or use the docker method listed below in the article.

Python
PyTorch

Downloading the Datasets

Use the following commands to download the model and test image:

wget https://sagemaker-sample-files.s3.amazonaws.com/datasets/image/MNIST/model/pytorch-training-2020-11-21-22-02-56-203/model.tar.gz
wget https://raw.githubusercontent.com/js-ts/mnist-test/main/digit.png

Creating the Inference Script

This script is designed to load a pretrained PyTorch model for MNIST digit classification from a tar.gz file, extract it, and use the model to perform inference on a given input image. Ensure you have all required dependencies installed:

pip install Pillow torch torchvision

# content of the inference.py file
import torch
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
import argparse
import tarfile

class CustomModel(torch.nn.Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, 5)
        self.conv2 = torch.nn.Conv2d(10, 20, 5)
        self.fc1 = torch.nn.Linear(320, 50)
        self.fc2 = torch.nn.Linear(50, 10)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.max_pool2d(x, 2)
        x = torch.relu(self.conv2(x))
        x = torch.max_pool2d(x, 2)
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        output = torch.log_softmax(x, dim=1)
        return output

def extract_tar_gz(file_path, output_dir):
    with tarfile.open(file_path, 'r:gz') as tar:
        tar.extractall(path=output_dir)

# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--tar_gz_file_path', type=str, required=True, help='Path to the tar.gz file')
parser.add_argument('--output_directory', type=str, required=True, help='Output directory to extract the tar.gz file')
parser.add_argument('--image_path', type=str, required=True, help='Path to the input image file')
args = parser.parse_args()

# Extract the tar.gz file
tar_gz_file_path = args.tar_gz_file_path
output_directory = args.output_directory
extract_tar_gz(tar_gz_file_path, output_directory)

# Load the model
model_path = f"{output_directory}/model.pth"
model = CustomModel()
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
model.eval()

# Transformations for the MNIST dataset
transform = transforms.Compose([
    transforms.Resize((28, 28)),
    transforms.Grayscale(num_output_channels=1),
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,)),
])

# Function to run inference on an image
def run_inference(image, model):
    image_tensor = transform(image).unsqueeze(0)  # Apply transformations and add batch dimension
    input = Variable(image_tensor)

    # Perform inference
    output = model(input)
    _, predicted = torch.max(output.data, 1)
    return predicted.item()

# Example usage
image_path = args.image_path
image = Image.open(image_path)
predicted_class = run_inference(image, model)
print(f"Predicted class: {predicted_class}")

Running the Inference Script

To use this script, you need to provide the paths to the tar.gz file containing the pre-trained model, the output directory where the model will be extracted, and the input image file for which you want to perform inference. The script will output the predicted digit (class) for the given input image.

python3 inference.py --tar_gz_file_path ./model.tar.gz --output_directory ./model --image_path ./digit.png

Running Inference on Bacalhau

Prerequisite

To get started, you need to install the Bacalhau client, see more information here

Structure of the Command

export JOB_ID=$( ... ): Export results of a command execution as environment variable
-w /inputs Set the current working directory at /inputs in the container
-i src=s3://sagemaker-sample-files/datasets/image/MNIST/model/pytorch-training-2020-11-21-22-02-56-203/model.tar.gz,dst=/model/,opt=region=us-east-1: Mount the s3 bucket at the destination path provided - /model/ and specifying the region where the bucket is located opt=region=us-east-1
-i git://github.com/js-ts/mnist-test.git: Flag to mount the source code repo from GitHub. It would mount the repo at /inputs/js-ts/mnist-test in this case it also contains the test image
pytorch/pytorch: The name of the Docker image
-- python3 /inputs/js-ts/mnist-test/inference.py --tar_gz_file_path /model/model.tar.gz --output_directory /model-pth --image_path /inputs/js-ts/mnist-test/image.png: The command to run inference on the model. It consists of:
1. /model/model.tar.gz is the path to the model file
2. /model-pth is the output directory for the model
3. /inputs/js-ts/mnist-test/image.png is the path to the input image

export JOB_ID=$(bacalhau docker run \
--wait \
--id-only \
--timeout 3600 \
--wait-timeout-secs 3600 \
-w /inputs \
-i src=s3://sagemaker-sample-files/datasets/image/MNIST/model/pytorch-training-2020-11-21-22-02-56-203/model.tar.gz,dst=/model/,opt=region=us-east-1 \
-i git://github.com/js-ts/mnist-test.git \
pytorch/pytorch \
 -- python3 /inputs/js-ts/mnist-test/inference.py --tar_gz_file_path /model/model.tar.gz --output_directory /model-pth --image_path /inputs/js-ts/mnist-test/image.png)

When the job is submitted Bacalhau prints out the related job id. We store that in an environment variable JOB_ID so that we can reuse it later on.

Viewing the Output

Use the bacalhau job logs command to view the job output, since the script prints the result of execution to the stdout:

bacalhau job logs ${JOB_ID}

Predicted class: 0

You can also use bacalhau job get to download job results:

bacalhau job get ${JOB_ID}

Support

If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).

Running Inference on a Model stored on S3

Introduction

In this example, we will demonstrate how to run inference on a model stored on Amazon S3. We will use a PyTorch model trained on the MNIST dataset.

Running Locally

Prerequisites

Consider using the latest versions or use the docker method listed below in the article.

Python
PyTorch

Downloading the Datasets

Use the following commands to download the model and test image:

wget https://sagemaker-sample-files.s3.amazonaws.com/datasets/image/MNIST/model/pytorch-training-2020-11-21-22-02-56-203/model.tar.gz
wget https://raw.githubusercontent.com/js-ts/mnist-test/main/digit.png

Creating the Inference Script

pip install Pillow torch torchvision

# content of the inference.py file
import torch
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
import argparse
import tarfile

class CustomModel(torch.nn.Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, 5)
        self.conv2 = torch.nn.Conv2d(10, 20, 5)
        self.fc1 = torch.nn.Linear(320, 50)
        self.fc2 = torch.nn.Linear(50, 10)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.max_pool2d(x, 2)
        x = torch.relu(self.conv2(x))
        x = torch.max_pool2d(x, 2)
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        output = torch.log_softmax(x, dim=1)
        return output

def extract_tar_gz(file_path, output_dir):
    with tarfile.open(file_path, 'r:gz') as tar:
        tar.extractall(path=output_dir)

# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--tar_gz_file_path', type=str, required=True, help='Path to the tar.gz file')
parser.add_argument('--output_directory', type=str, required=True, help='Output directory to extract the tar.gz file')
parser.add_argument('--image_path', type=str, required=True, help='Path to the input image file')
args = parser.parse_args()

# Extract the tar.gz file
tar_gz_file_path = args.tar_gz_file_path
output_directory = args.output_directory
extract_tar_gz(tar_gz_file_path, output_directory)

# Load the model
model_path = f"{output_directory}/model.pth"
model = CustomModel()
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
model.eval()

# Transformations for the MNIST dataset
transform = transforms.Compose([
    transforms.Resize((28, 28)),
    transforms.Grayscale(num_output_channels=1),
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,)),
])

# Function to run inference on an image
def run_inference(image, model):
    image_tensor = transform(image).unsqueeze(0)  # Apply transformations and add batch dimension
    input = Variable(image_tensor)

    # Perform inference
    output = model(input)
    _, predicted = torch.max(output.data, 1)
    return predicted.item()

# Example usage
image_path = args.image_path
image = Image.open(image_path)
predicted_class = run_inference(image, model)
print(f"Predicted class: {predicted_class}")

Running the Inference Script

python3 inference.py --tar_gz_file_path ./model.tar.gz --output_directory ./model --image_path ./digit.png

Running Inference on Bacalhau

Prerequisite

To get started, you need to install the Bacalhau client, see more information here

Structure of the Command

export JOB_ID=$( ... ): Export results of a command execution as environment variable
-w /inputs Set the current working directory at /inputs in the container
-i src=s3://sagemaker-sample-files/datasets/image/MNIST/model/pytorch-training-2020-11-21-22-02-56-203/model.tar.gz,dst=/model/,opt=region=us-east-1: Mount the s3 bucket at the destination path provided - /model/ and specifying the region where the bucket is located opt=region=us-east-1
-i git://github.com/js-ts/mnist-test.git: Flag to mount the source code repo from GitHub. It would mount the repo at /inputs/js-ts/mnist-test in this case it also contains the test image
pytorch/pytorch: The name of the Docker image
-- python3 /inputs/js-ts/mnist-test/inference.py --tar_gz_file_path /model/model.tar.gz --output_directory /model-pth --image_path /inputs/js-ts/mnist-test/image.png: The command to run inference on the model. It consists of:
1. /model/model.tar.gz is the path to the model file
2. /model-pth is the output directory for the model
3. /inputs/js-ts/mnist-test/image.png is the path to the input image

export JOB_ID=$(bacalhau docker run \
--wait \
--id-only \
--timeout 3600 \
--wait-timeout-secs 3600 \
-w /inputs \
-i src=s3://sagemaker-sample-files/datasets/image/MNIST/model/pytorch-training-2020-11-21-22-02-56-203/model.tar.gz,dst=/model/,opt=region=us-east-1 \
-i git://github.com/js-ts/mnist-test.git \
pytorch/pytorch \
 -- python3 /inputs/js-ts/mnist-test/inference.py --tar_gz_file_path /model/model.tar.gz --output_directory /model-pth --image_path /inputs/js-ts/mnist-test/image.png)

When the job is submitted Bacalhau prints out the related job id. We store that in an environment variable JOB_ID so that we can reuse it later on.

Viewing the Output

Use the bacalhau job logs command to view the job output, since the script prints the result of execution to the stdout:

bacalhau job logs ${JOB_ID}

Predicted class: 0

You can also use bacalhau job get to download job results:

bacalhau job get ${JOB_ID}

Support

If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).

Running Inference on a Model stored on S3

Introduction​

Running Locally​

Prerequisites​

Downloading the Datasets​

Creating the Inference Script​

Running the Inference Script​

Running Inference on Bacalhau​

Prerequisite​

Structure of the Command​

Viewing the Output​

Support

Running Inference on a Model stored on S3

Introduction​

Running Locally​

Prerequisites​

Downloading the Datasets​

Creating the Inference Script​

Running the Inference Script​

Running Inference on Bacalhau​

Prerequisite​

Structure of the Command​

Viewing the Output​

Support

Introduction

Running Locally

Prerequisites

Downloading the Datasets

Creating the Inference Script

Running the Inference Script

Running Inference on Bacalhau

Prerequisite

Structure of the Command

Viewing the Output

Introduction

Running Locally

Prerequisites

Downloading the Datasets

Creating the Inference Script

Running the Inference Script

Running Inference on Bacalhau

Prerequisite

Structure of the Command

Viewing the Output