Monthly Archives: June 2023

Data scientists need a consistent and reproducible environment for machine learning (ML) and data science workloads that enables managing dependencies and is secure. AWS Deep Learning Containers already provides pre-built Docker images for training and serving models in common frameworks such as TensorFlow, PyTorch, and MXNet. To improve this experience, we announced a public beta of the SageMaker open-source distribution at 2023 JupyterCon. This provides a unified end-to-end ML experience across ML developers of varying levels of expertise. Developers no longer need to switch between different framework containers for experimentation, or as they move from local JupyterLab environments and SageMaker notebooks to production jobs on SageMaker. The open-source SageMaker Distribution supports the most common packages and libraries for data science, ML, and visualization, such as TensorFlow, PyTorch, Scikit-learn, Pandas, and Matplotlib. You can start using the container from the Amazon ECR Public Gallery starting today.

In this post, we show you how you can use the SageMaker open-source distribution to quickly experiment on your local environment and easily promote them to jobs on SageMaker.

Solution overview

For our example, we showcase training an image classification model using PyTorch. We use the KMNIST dataset available publicly on PyTorch. We train a neural network model, test the model’s performance, and finally print the training and test loss. The full notebook for this example is available in the SageMaker Studio Lab examples repository. We start experimentation on a local laptop using the open-source distribution, move it to Amazon SageMaker Studio for using a larger instance, and then schedule the notebook as a notebook job.

Prerequisites

You need the following prerequisites:

• Docker installed.
• An active AWS account with administrator permissions.
• An environment with the AWS Command Line Interface (AWS CLI) and Docker installed.
• An existing SageMaker domain. To create a domain, refer to Onboard to Amazon SageMaker Domain.

Set up your local environment

You can directly start using the open-source distribution on your local laptop. To start JupyterLab, run the following commands on your terminal:

export ECR_IMAGE_ID='public.ecr.aws/sagemaker/sagemaker-distribution:latest-cpu'
docker run -it 
    -p 8888:8888 
    --user `id -u`:`id -g` 
    -v `pwd`/sample-notebooks:/home/sagemaker-user/sample-notebooks 
    $ECR_IMAGE_ID jupyter-lab --no-browser --ip=0.0.0.0

You can replace ECR_IMAGE_ID with any of the image tags available in the Amazon ECR Public Gallery, or choose the latest-gpu tag if you are using a machine that supports GPU.

This command will start JupyterLab and provide a URL on the terminal, like http://127.0.0.1:8888/lab?token=<token>. Copy the link and enter it in your preferred browser to start JupyterLab.

Set up Studio

Studio is an end-to-end integrated development environment (IDE) for ML that lets developers and data scientists build, train, deploy, and monitor ML models at scale. Studio provides an extensive list of first-party images with common frameworks and packages, such as Data Science, TensorFlow, PyTorch, and Spark. These images make it simple for data scientists to get started with ML by simply choosing a framework and instance type of their choice for compute.

You can now use the SageMaker open-source distribution on Studio using Studio’s bring your own image feature. To add the open-source distribution to your SageMaker domain, complete the following steps:

1. Add the open-source distribution to your account’s Amazon Elastic Container Registry (Amazon ECR) repository by running the following commands on your terminal:

   # Use the latest-cpu or latest-gpu tag based on your requirements
   export ECR_GALLERY_IMAGE_ID='sagemaker-distribution:latest-cpu'
   export SAGEMAKER_IMAGE_NAME='sagemaker-runtime'
   export SAGEMAKER_STUDIO_DOMAIN_ID='d-xxxx'
   export SAGEMAKER_STUDIO_IAM_ROLE_ARN='<studio-default-execution-role-arn>'
   
   docker pull public.ecr.aws/sagemaker/$ECR_GALLERY_IMAGE_ID
   
   export ECR_PRIVATE_REPOSITORY_NAME='sm-distribution'
   export ECR_IMAGE_TAG='sagemaker-runtime-cpu'
   export AWS_ACCOUNT_ID='0123456789'
   export AWS_ECR_REPOSITORY_REGION='us-east-1'
   
   # create repository
   aws --region ${AWS_ECR_REPOSITORY_REGION} ecr create-repository --repository-name $ECR_PRIVATE_REPOSITORY_NAME
   aws --region ${AWS_ECR_REPOSITORY_REGION} ecr get-login-password | docker login --username AWS --password-stdin ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_ECR_REPOSITORY_REGION}.amazonaws.com
   export ECR_IMAGE_URI=$AWS_ACCOUNT_ID.dkr.ecr.$AWS_ECR_REPOSITORY_REGION.amazonaws.com/$ECR_PRIVATE_REPOSITORY_NAME:$ECR_IMAGE_TAG
   
   # Tag
   docker tag public.ecr.aws/sagemaker/$ECR_GALLERY_IMAGE_ID $ECR_IMAGE_URI
   # Push the image to your private repository
   docker push $ECR_IMAGE_URI

2. Create a SageMaker image and attach the image to the Studio domain:

   # Create a SageMaker image
   aws sagemaker create-image 
       --image-name $SAGEMAKER_IMAGE_NAME 
       --role-arn $SAGEMAKER_STUDIO_IAM_ROLE_ARN
   # Create a SageMaker Image Version.
   aws sagemaker create-image-version 
       --image-name $SAGEMAKER_IMAGE_NAME 
       --base-image $ECR_IMAGE_URI
   
   # Optionally, describe the image version to ensure it's succesfully created
   aws sagemaker describe-image-version 
       --image-name $SAGEMAKER_IMAGE_NAME 
       --version-number 1
       
   # Create the app image configuration file
   cat > /tmp/app-config.json << EOF
   {
      "AppImageConfigName": "app-image-config-$SAGEMAKER_IMAGE_NAME",
      "KernelGatewayImageConfig": { 
         "FileSystemConfig": { 
            "DefaultGid": 100,
            "DefaultUid": 1000,
            "MountPath": "/home/sagemaker-user"
         },
         "KernelSpecs": [ 
            { 
               "DisplayName": "Python 3 (ipykernel)",
               "Name": "python3"
            }
         ]
      }
   }
   EOF
   
   # Create an Amazon SageMaker App Image Config.
   aws sagemaker create-app-image-config 
       --cli-input-json file:///tmp/app-config.json
       
   # Create a default user settings file
   # Update the file with your existing settings if you have additional custom images
   cat > /tmp/default-user-settings.json << EOF
   {
       "DefaultUserSettings": {
           "KernelGatewayAppSettings": {
               "CustomImages": [
                   {
                       "ImageName": "$SAGEMAKER_IMAGE_NAME",
                       "AppImageConfigName": "app-image-config-$SAGEMAKER_IMAGE_NAME",
                       "ImageVersionNumber": 1
                   }
               ]
           }
       }
   }
   EOF
   
   # Update Amazon SageMaker Domain with the new default User Settings.
   aws sagemaker update-domain 
       --domain-id $SAGEMAKER_STUDIO_DOMAIN_ID 
       --cli-input-json file:///tmp/default-user-settings.json
   

3. On the SageMaker console, launch Studio by choosing your domain and existing user profile.
4. Optionally, restart Studio by following the steps in Shut down and update SageMaker Studio.

Download the notebook

Download the sample notebook locally from the GitHub repo.

Open the notebook in your choice of IDE and add a cell to the beginning of the notebook to install torchsummary. The torchsummary package is not part of the distribution, and installing this on the notebook will ensure the notebook runs end to end. We recommend using conda or micromamba to manage environments and dependencies. Add the following cell to the notebook and save the notebook:

%pip install torchsummary

Experiment on the local notebook

Upload the notebook to the JupyterLab UI you launched by choosing the upload icon as shown in the following screenshot.

When it’s uploaded, launch the cv-kmnist.ipynb notebook. You can start running the cells immediately, without having to install any dependencies such as torch, matplotlib, or ipywidgets.

If you followed the preceding steps, you can see that you can use the distribution locally from your laptop. In the next step, we use the same distribution on Studio to take advantage of Studio’s features.

Move the experimentation to Studio (optional)

Optionally, let’s promote the experimentation to Studio. One of the advantages of Studio is that the underlying compute resources are fully elastic, so you can easily dial the available resources up or down, and the changes take place automatically in the background without interrupting your work. If you wanted to run the same notebook from earlier on a larger dataset and compute instance, you can migrate to Studio.

Navigate to the Studio UI you launched earlier and choose the upload icon to upload the notebook.

After you launch the notebook, you will be prompted to choose the image and instance type. On the kernel launcher, choose sagemaker-runtime as the image and an ml.t3.medium instance, then choose Select.

You can now run the notebook end to end without needing any changes on the notebook from your local development environment to Studio notebooks!

Schedule the notebook as a job

When you’re done with your experimentation, SageMaker provides multiple options to productionalize your notebook, such as training jobs and SageMaker pipelines. One such option is to directly run the notebook itself as a non-interactive, scheduled notebook job using SageMaker notebook jobs. For example, you might want to retrain your model periodically, or get inferences on incoming data periodically and generate reports for consumption by your stakeholders.

From Studio, choose the notebook job icon to launch the notebook job. If you have installed the notebook jobs extension locally on your laptop, you can also schedule the notebook directly from your laptop. See Installation Guide to set up the notebook jobs extension locally.

The notebook job automatically uses the ECR image URI of the open-source distribution, so you can directly schedule the notebook job.

Choose Run on schedule, choose a schedule, for example every week on Saturday, and choose Create. You can also choose Run now if you’d like to view the results immediately.

When the first notebook job is complete, you can view the notebook outputs directly from the Studio UI by choosing Notebook under Output files.

Additional considerations

In addition to using the publicly available ECR image directly for ML workloads, the open-source distribution offers the following advantages:

• The Dockerfile used to build the image is available publicly for developers to explore and build their own images. You can also inherit this image as the base image and install your custom libraries to have a reproducible environment.
• If you’re not used to Docker and prefer to use Conda environments on your JupyterLab environment, we provide an env.out file for each of the published versions. You can use the instructions in the file to create your own Conda environment that will mimic the same environment. For example, see the CPU environment file cpu.env.out.
• You can use the GPU versions of the image to run GPU-compatible workloads such as deep learning and image processing.

Clean up

Complete the following steps to clean up your resources:

1. If you have scheduled your notebook to run on a schedule, pause or delete the schedule on the Notebook Job Definitions tab to avoid paying for future jobs.
   
2. Shut down all Studio apps to avoid paying for unused compute usage. See Shut down and Update Studio Apps for instructions.
3. Optionally, delete the Studio domain if you created one.

Conclusion

Maintaining a reproducible environment across different stages of the ML lifecycle is one of the biggest challenges for data scientists and developers. With the SageMaker open-source distribution, we provide an image with mutually compatible versions of the most common ML frameworks and packages. The distribution is also open source, providing developers with transparency into the packages and build processes, making it easier to customize their own distribution.

In this post, we showed you how to use the distribution on your local environment, on Studio, and as the container for your training jobs. This feature is currently in public beta. We encourage you to try this out and share your feedback and issues on the public GitHub repository!

About the authors

Durga Sury is an ML Solutions Architect on the Amazon SageMaker Service SA team. She is passionate about making machine learning accessible to everyone. In her 4 years at AWS, she has helped set up AI/ML platforms for enterprise customers. When she isn’t working, she loves motorcycle rides, mystery novels, and long walks with her 5-year-old husky.

Ketan Vijayvargiya is a Senior Software Development Engineer in Amazon Web Services (AWS). His focus areas are machine learning, distributed systems and open source. Outside work, he likes to spend his time self-hosting and enjoying nature.

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Customers expect quick and efficient service from businesses in today’s fast-paced world. But providing excellent customer service can be significantly challenging when the volume of inquiries outpaces the human resources employed to address them. However, businesses can meet this challenge while providing personalized and efficient customer service with the advancements in generative artificial intelligence (generative AI) powered by large language models (LLMs).

Generative AI chatbots have gained notoriety for their ability to imitate human intellect. However, unlike task-oriented bots, these bots use LLMs for text analysis and content generation. LLMs are based on the Transformer architecture, a deep learning neural network introduced in June 2017 that can be trained on a massive corpus of unlabeled text. This approach creates a more human-like conversation experience and accommodates several topics.

As of this writing, companies of all sizes want to use this technology but need help figuring out where to start. If you are looking to get started with generative AI and the use of LLMs in conversational AI, this post is for you. We have included a sample project to quickly deploy an Amazon Lex bot that consumes a pre-trained open-source LLM. The code also includes the starting point to implement a custom memory manager. This mechanism allows an LLM to recall previous interactions to keep the conversation’s context and pace. Finally, it’s essential to highlight the importance of experimenting with fine-tuning prompts and LLM randomness and determinism parameters to obtain consistent results.

Solution overview

The solution integrates an Amazon Lex bot with a popular open-source LLM from Amazon SageMaker JumpStart, accessible through an Amazon SageMaker endpoint. We also use LangChain, a popular framework that simplifies LLM-powered applications. Finally, we use a QnABot to provide a user interface for our chatbot.

First, we start by describing each component in the preceding diagram:

• JumpStart offers pre-trained open-source models for various problem types. This enables you to begin machine learning (ML) quickly. It includes the FLAN-T5-XL model, an LLM deployed into a deep learning container. It performs well on various natural language processing (NLP) tasks, including text generation.
• A SageMaker real-time inference endpoint enables fast, scalable deployment of ML models for predicting events. With the ability to integrate with Lambda functions, the endpoint allows for building custom applications.
• The AWS Lambda function uses the requests from the Amazon Lex bot or the QnABot to prepare the payload to invoke the SageMaker endpoint using LangChain. LangChain is a framework that lets developers create applications powered by LLMs.
• The Amazon Lex V2 bot has the built-in AMAZON.FallbackIntent intent type. It is triggered when a user’s input doesn’t match any intents in the bot.
• The QnABot is an open-source AWS solution to provide a user interface to Amazon Lex bots. We configured it with a Lambda hook function for a CustomNoMatches item, and it triggers the Lambda function when QnABot can’t find an answer. We assume you have already deployed it and included the steps to configure it in the following sections.

The solution is described at a high level in the following sequence diagram.

Major tasks performed by the solution

In this section, we look at the major tasks performed in our solution. This solution’s entire project source code is available for your reference in this GitHub repository.

Handling chatbot fallbacks

The Lambda function handles the “don’t know” answers via AMAZON.FallbackIntent in Amazon Lex V2 and the CustomNoMatches item in QnABot. When triggered, this function looks at the request for a session and the fallback intent. If there is a match, it hands off the request to a Lex V2 dispatcher; otherwise, the QnABot dispatcher uses the request. See the following code:

def dispatch_lexv2(request):
    """Summary
    Args:
        request (dict): Lambda event containing a user's input chat message and context (historical conversation)
        Uses the LexV2 sessions API to manage past inputs https://docs.aws.amazon.com/lexv2/latest/dg/using-sessions.html
    
    Returns:
        dict: Description
    """
    lexv2_dispatcher = LexV2SMLangchainDispatcher(request)
    return lexv2_dispatcher.dispatch_intent()

def dispatch_QnABot(request):
    """Summary
    
    Args:
        request (dict): Lambda event containing a user's input chat message and context (historical conversation)
    
    Returns:
        dict: Dict formatted as documented to be a lambda hook for a "don't know" answer for the QnABot on AWS Solution
        see https://docs.aws.amazon.com/solutions/latest/QnABot-on-aws/specifying-lambda-hook-functions.html
    """
    request['res']['message'] = "Hi! This is your Custom Python Hook speaking!"
    qna_intent_dispatcher = QnASMLangchainDispatcher(request)
    return qna_intent_dispatcher.dispatch_intent()

def lambda_handler(event, context):
    print(event)
    if 'sessionState' in event:
        if 'intent' in event['sessionState']:
            if 'name' in event['sessionState']['intent']:
                if event['sessionState']['intent']['name'] == 'FallbackIntent':
                    return dispatch_lexv2(event)
    else:
        return dispatch_QnABot(event)

Providing memory to our LLM

To preserve the LLM memory in a multi-turn conversation, the Lambda function includes a LangChain custom memory class mechanism that uses the Amazon Lex V2 Sessions API to keep track of the session attributes with the ongoing multi-turn conversation messages and to provide context to the conversational model via previous interactions. See the following code:

class LexConversationalMemory(BaseMemory, BaseModel):

    """Langchain Custom Memory class that uses Lex Conversation history
    
    Attributes:
        history (dict): Dict storing conversation history that acts as the Langchain memory
        lex_conv_context (str): LexV2 sessions API that serves as input for convo history
            Memory is loaded from here
        memory_key (str): key to for chat history Langchain memory variable - "history"
    """
    history = {}
    memory_key = "chat_history" #pass into prompt with key
    lex_conv_context = ""

    def clear(self):
        """Clear chat history
        """
        self.history = {}

    @property
    def memory_variables(self) -> List[str]:
        """Load memory variables
        
        Returns:
            List[str]: List of keys containing Langchain memory
        """
        return [self.memory_key]

    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        """Load memory from lex into current Langchain session memory
        
        Args:
            inputs (Dict[str, Any]): User input for current Langchain session
        
        Returns:
            Dict[str, str]: Langchain memory object
        """
        input_text = inputs[list(inputs.keys())[0]]

        ccontext = json.loads(self.lex_conv_context)
        memory = {
            self.memory_key: ccontext[self.memory_key] + input_text + "nAI: ",
        }
        return memory

The following is the sample code we created for introducing the custom memory class in a LangChain ConversationChain:

# Create a conversation chain using the prompt, 
# llm hosted in Sagemaker, and custom memory class
self.chain = ConversationChain(
    llm=sm_flant5_llm,
    prompt=prompt,
    memory=LexConversationalMemory(lex_conv_context=lex_conv_history),
    verbose=True
)

Prompt definition

A prompt for an LLM is a question or statement that sets the tone for the generated response. Prompts function as a form of context that helps direct the model toward generating relevant responses. See the following code:

# define prompt
prompt_template = """The following is a friendly conversation between a human and an AI. The AI is 
talkative and provides lots of specific details from its context. If the AI does not know 
the answer to a question, it truthfully says it does not know. You are provided with information
about entities the Human mentions, if relevant.

Chat History:
{chat_history}

Conversation:
Human: {input}
AI:"""

Using an Amazon Lex V2 session for LLM memory support

Amazon Lex V2 initiates a session when a user interacts to a bot. A session persists over time unless manually stopped or timed out. A session stores metadata and application-specific data known as session attributes. Amazon Lex updates client applications when the Lambda function adds or changes session attributes. The QnABot includes an interface to set and get session attributes on top of Amazon Lex V2.

In our code, we used this mechanism to build a custom memory class in LangChain to keep track of the conversation history and enable the LLM to recall short-term and long-term interactions. See the following code:

class LexV2SMLangchainDispatcher():

    def __init__(self, intent_request):
        # See lex bot input format to lambda https://docs.aws.amazon.com/lex/latest/dg/lambda-input-response-format.html
        self.intent_request = intent_request
        self.localeId = self.intent_request['bot']['localeId']
        self.input_transcript = self.intent_request['inputTranscript'] # user input
        self.session_attributes = utils.get_session_attributes(
            self.intent_request)
        self.fulfillment_state = "Fulfilled"
        self.text = "" # response from endpoint
        self.message = {'contentType': 'PlainText','content': self.text}

class QnABotSMLangchainDispatcher():
    def __init__(self, intent_request):
        # QnABot Session attributes
        self.intent_request = intent_request
        self.input_transcript = self.intent_request['req']['question']
        self.intent_name = self.intent_request['req']['intentname']
        self.session_attributes = self.intent_request['req']['session']

Prerequisites

To get started with the deployment, you need to fulfill the following prerequisites:

• Access to the AWS Management Console via a user who can launch AWS CloudFormation stacks
• Familiarity navigating the Lambda and Amazon Lex consoles

Deploy the solution

To deploy the solution, proceed with the following steps:

1. Choose Launch Stack to launch the solution in the us-east-1 Region:
   
2. For Stack name, enter a unique stack name.
3. For HFModel, we use the Hugging Face Flan-T5-XL model available on JumpStart.
4. For HFTask, enter text2text.
5. Keep S3BucketName as is.

These are used to find Amazon Simple Storage Service (Amazon S3) assets needed to deploy the solution and may change as updates to this post are published.

6. Acknowledge the capabilities.
7. Choose Create stack.

There should be four successfully created stacks.

Configure the Amazon Lex V2 bot

There is nothing to do with the Amazon Lex V2 bot. Our CloudFormation template already did the heavy lifting.

Configure the QnABot

We assume you already have an existing QnABot deployed in your environment. But if you need help, follow these instructions to deploy it.

1. On the AWS CloudFormation console, navigate to the main stack that you deployed.
2. On the Outputs tab, make a note of the LambdaHookFunctionArn because you need to insert it in the QnABot later.

3. Log in to the QnABot Designer User Interface (UI) as an administrator.
4. In the Questions UI, add a new question.

5. Enter the following values:
   • ID – CustomNoMatches
   • Question – no_hits
   • Answer – Any default answer for “don’t know”
6. Choose Advanced and go to the Lambda Hook section.
7. Enter the Amazon Resource Name (ARN) of the Lambda function you noted previously.

8. Scroll down to the bottom of the section and choose Create.

You get a window with a success message.

Your question is now visible on the Questions page.

Test the solution

Let’s proceed with testing the solution. First, it’s worth mentioning that we deployed the FLAN-T5-XL model provided by JumpStart without any fine-tuning. This may have some unpredictability, resulting in slight variations in responses.

Test with an Amazon Lex V2 bot

This section helps you test the Amazon Lex V2 bot integration with the Lambda function that calls the LLM deployed in the SageMaker endpoint.

1. On the Amazon Lex console, navigate to the bot entitled Sagemaker-Jumpstart-Flan-LLM-Fallback-Bot.
   This bot has been configured to call the Lambda function that invokes the SageMaker endpoint hosting the LLM as a fallback intent when no other intents are matched.
2. Choose Intents in the navigation pane.

On the top right, a message reads, “English (US) has not built changes.”

3. Choose Build.
4. Wait for it to complete.

Finally, you get a success message, as shown in the following screenshot.

5. Choose Test.

A chat window appears where you can interact with the model.

We recommend exploring the built-in integrations between Amazon Lex bots and Amazon Connect. And also, messaging platforms (Facebook, Slack, Twilio SMS) or third-party Contact Centers using Amazon Chime SDK and Genesys Cloud, for example.

Test with a QnABot instance

This section tests the QnABot on AWS integration with the Lambda function that calls the LLM deployed in the SageMaker endpoint.

1. Open the tools menu in the top left corner.

2. Choose QnABot Client.

3. Choose Sign In as Admin.

4. Enter any question in the user interface.
5. Evaluate the response.

Clean up

To avoid incurring future charges, delete the resources created by our solution by following these steps:

1. On the AWS CloudFormation console, select the stack named SagemakerFlanLLMStack (or the custom name you set to the stack).
2. Choose Delete.
3. If you deployed the QnABot instance for your tests, select the QnABot stack.
4. Choose Delete.

Conclusion

In this post, we explored the addition of open-domain capabilities to a task-oriented bot that routes the user requests to an open-source large language model.

We encourage you to:

• Save the conversation history to an external persistence mechanism. For example, you can save the conversation history to Amazon DynamoDB or an S3 bucket and retrieve it in the Lambda function hook. In this way, you don’t need to rely on the internal non-persistent session attributes management offered by Amazon Lex.
• Experiment with summarization – In multiturn conversations, it’s helpful to generate a summary that you can use in your prompts to add context and limit the usage of conversation history. This helps to prune the bot session size and keep the Lambda function memory consumption low.
• Experiment with prompt variations –  Modify the original prompt description that matches your experimentation purposes.
• Adapt the language model for optimal results – You can do this by fine-tuning the advanced LLM parameters such as randomness (temperature) and determinism (top_p) according to your applications. We demonstrated a sample integration using a pre-trained model with sample values, but have fun adjusting the values for your use cases.

In our next post, we plan to help you discover how to fine-tune pre-trained LLM-powered chatbots with your own data.

Are you experimenting with LLM chatbots on AWS? Tell us more in the comments!

Resources and references

• Companion source code for this post
• Amazon Lex V2 Developer Guide
• AWS Solutions Library: QnABot on AWS
• Text2Text Generation with FLAN T5 models
• LangChain – Building applications with LLMs
• Amazon SageMaker Examples with Jumpstart Foundation Models
• Amazon BedRock – The easiest way to build and scale generative AI applications with foundation models
• Quickly build high-accuracy Generative AI applications on enterprise data using Amazon Kendra, LangChain, and large language models

About the Authors

Marcelo Silva is an experienced tech professional who excels in designing, developing, and implementing cutting-edge products. Starting off his career at Cisco, Marcelo worked on various high-profile projects including deployments of the first ever carrier routing system and the successful rollout of ASR9000. His expertise extends to cloud technology, analytics, and product management, having served as senior manager for several companies like Cisco, Cape Networks, and AWS before joining GenAI. Currently working as a Conversational AI/GenAI Product Manager, Marcelo continues to excel in delivering innovative solutions across industries.

Victor Rojo is a highly experienced technologist who is passionate about the latest in AI, ML, and software development. With his expertise, he played a pivotal role in bringing Amazon Alexa to the US and Mexico markets while spearheading the successful launch of Amazon Textract and AWS Contact Center Intelligence (CCI) to AWS Partners. As the current Principal Tech Leader for the Conversational AI Competency Partners program, Victor is committed to driving innovation and bringing cutting-edge solutions to meet the evolving needs of the industry.

Justin Leto is a Sr. Solutions Architect at Amazon Web Services with a specialization in machine learning. His passion is helping customers harness the power of machine learning and AI to drive business growth. Justin has presented at global AI conferences, including AWS Summits, and lectured at universities. He leads the NYC machine learning and AI meetup. In his spare time, he enjoys offshore sailing and playing jazz. He lives in New York City with his wife and baby daughter.

Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice. He is passionate about helping customers achieve better outcomes through analytics and machine learning solutions in the cloud. Outside work, he enjoys fitness, cooking, and spending quality time with friends and family.

Mahesh Birardar is a Sr. Solutions Architect at Amazon Web Services with specialization in DevOps and Observability. He enjoys helping customers implement cost-effective architectures that scale. Outside work, he enjoys watching movies and hiking.

Kanjana Chandren is a Solutions Architect at Amazon Web Services (AWS) who is passionate about Machine Learning. She helps customers in designing, implementing and managing their AWS workloads. Outside of work she loves travelling, reading and spending time with family and friends.

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Amazon Personalize now enables popularity tuning for its Similar-Items recipe (aws-similar-items). Similar-Items generates recommendations that are similar to the item that a user selects, helping users discover new items in your catalog based on the previous behavior of all users and item metadata. Previously, this capability was only available for SIMS, the other Related_Items recipe within Amazon Personalize.

Every customer’s item catalog and the way that users interact with it are unique to their business. When recommending similar items, some customers may want to place more emphasis on popular items because they increase the likelihood of user interaction, while others may want to de-emphasize popular items to surface recommendations that are more similar to the selected item but are less widely known. This launch gives you more control over the degree to which popularity influences Similar-Items recommendations, so you can tune the model to meet your particular business needs.

In this post, we show you how to tune popularity for the Similar-Items recipe. We specify a value closer to zero to include more popular items, and specify a value closer to 1 to place less emphasis on popularity.

Example use cases

To explore the impact of this new feature in greater detail, let’s review two examples. [1]

First, we used the Similar-Items recipe to find recommendations similar to Disney’s 1994 movie The Lion King (IMDB record). When the popularity discount is set to 0, Amazon Personalize recommends movies that have a high frequency of occurrence (are popular). In this example, the movie Seven (a.k.a. Se7en), which occurred 19,295 times in the dataset, is recommended at rank 3.0.

By tuning the popularity discount to a value of 0.4 for The Lion King recommendations, we see that the rank of the movie Seven drops to 4.0. We also see movies from the Children genre like Babe, Beauty and the Beast, Aladdin, and Snow White and the Seven Dwarfs get recommended at a higher rank despite their lower overall popularity in the dataset.

Let’s explore another example. We used the Similar-Items recipe to find recommendations similar to Disney and Pixar’s 1995 movie Toy Story (IMDB record). When the popularity discount is set to 0, Amazon Personalize recommends movies that have a high frequency occurrence in the dataset. In this example, we see that the movie Twelve Monkeys (a.k.a. 12 Monkeys), which occurred 6,678 times in the dataset, is recommended at rank 5.0.

By tuning the popularity discount to a value of 0.4 for Toy Story recommendations, we see that the rank of the Twelve Monkeys is no longer recommended in the top 10. We also see movies from the Children genre like Aladdin, Toy Story 2, and A Bug’s Life get recommended at a higher rank despite their lower overall popularity in the dataset.

Placing greater emphasis on more popular content can help increase likelihood that users will engage with item recommendations. Reducing emphasis on popularity may surface recommendations that seem more relevant to the queried item, but may be less popular with users. You can tune the degree of importance placed on popularity to meet your business needs for a specific personalization campaign.

Implement popularity tuning

To tune popularity for the Similar-Items recipe, configure the popularity_discount_factor hyperparameter via the AWS Management Console, the AWS SDKs, or the AWS Command Line Interface (AWS CLI).

The following is sample code setting the popularity discount factor to 0.5 via the AWS SDK:

{
	response = personalize.create_solution(
		name="movie_lens-with-popularity-discount-0_5".
		recipeARN="arn:aws:personalize:::recipe/aws-similar-items",
		datasetGroupArn=dsg_arn,
		solutionConfig={
			"algorithmHyperParameters" : {
				# set the preferred value of popularity discount here
				"popularity_discount_factor" : "0.50"
			}
		}
	]
}

The following screenshot shows setting the popularity discount factor to 0.3 on the Amazon Personalize console.

Conclusion

With popularity tuning, you can now further refine the Similar-Items recipe within Amazon Personalize to control the degree to which popularity influences item recommendations. This gives you greater control over defining the end-user experience and what is included or excluded in your Similar-Items recommendations.

For more details on how to implement popularity tuning for the Similar-Items recipe, refer to documentation.

References

About the Authors

Julia McCombs Clark is a  Sr. Technical Product Manager on the Amazon Personalize team.

Nihal Harish is a Software Development Engineer on the Amazon Personalize team.

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Training large language models (LLMs) with billions of parameters can be challenging. In addition to designing the model architecture, researchers need to set up state-of-the-art training techniques for distributed training like mixed precision support, gradient accumulation, and checkpointing. With large models, the training setup is even more challenging because the available memory in a single accelerator device bounds the size of models trained using only data parallelism, and using model parallel training requires additional level of modifications to the training code. Libraries such as DeepSpeed (an open-source deep learning optimization library for PyTorch) address some of these challenges, and can help accelerate model development and training.

In this post, we set up training on the Intel Habana Gaudi-based Amazon Elastic Compute Cloud (Amazon EC2) DL1 instances and quantify the benefits of using a scaling framework such as DeepSpeed. We present scaling results for an encoder-type transformer model (BERT with 340 million to 1.5 billion parameters). For the 1.5-billion-parameter model, we achieved a scaling efficiency of 82.7% across 128 accelerators (16 dl1.24xlarge instances) using DeepSpeed ZeRO stage 1 optimizations. The optimizer states were partitioned by DeepSpeed to train large models using the data parallel paradigm. This approach has been extended to train a 5-billion-parameter model using data parallelism. We also used Gaudi’s native support of the BF16 data type for reduced memory size and increased training performance compared to using the FP32 data type. As a result, we achieved pre-training (phase 1) model convergence within 16 hours (our target was to train a large model within a day) for the BERT 1.5-billion-parameter model using the wikicorpus-en dataset.

Training setup

We provisioned a managed compute cluster comprised of 16 dl1.24xlarge instances using AWS Batch. We developed an AWS Batch workshop that illustrates the steps to set up the distributed training cluster with AWS Batch. Each dl1.24xlarge instance has eight Habana Gaudi accelerators, each with 32 GB of memory and a full mesh RoCE network between cards with a total bi-directional interconnect bandwidth of 700 Gbps each (see Amazon EC2 DL1 instances Deep Dive for more information). The dl1.24xlarge cluster also used four AWS Elastic Fabric Adapters (EFA), with a total of 400 Gbps interconnect between nodes.

The distributed training workshop illustrates the steps to set up the distributed training cluster. The workshop shows the distributed training setup using AWS Batch and in particular, the multi-node parallel jobs feature to launch large-scale containerized training jobs on fully managed clusters. More specifically, a fully managed AWS Batch compute environment is created with DL1 instances. The containers are pulled from Amazon Elastic Container Registry (Amazon ECR) and launched automatically into the instances in the cluster based on the multi-node parallel job definition. The workshop concludes by running a multi-node, multi-HPU data parallel training of a BERT (340 million to 1.5 billion parameters) model using PyTorch and DeepSpeed.

BERT 1.5B pre-training with DeepSpeed

Habana SynapseAI v1.5 and v1.6 support DeepSpeed ZeRO1 optimizations. The Habana fork of the DeepSpeed GitHub repository includes the modifications necessary to support the Gaudi accelerators. There is full support of distributed data parallel (multi-card, multi-instance), ZeRO1 optimizations, and BF16 data types.

All these features are enabled on the BERT 1.5B model reference repository, which introduces a 48-layer, 1600-hidden dimension, and 25-head bi-directional encoder model, derived from a BERT implementation. The repository also contains the baseline BERT Large model implementation: a 24-layer, 1024-hidden, 16-head, 340-million-parameter neural network architecture. The pre-training modeling scripts are derived from the NVIDIA Deep Learning Examples repository to download the wikicorpus_en data, preprocess the raw data into tokens, and shard the data into smaller h5 datasets for distributed data parallel training. You can adopt this generic approach to train your custom PyTorch model architectures using your datasets using DL1 instances.

Pre-training (phase 1) scaling results

For pre-training large models at scale, we mainly focused on two aspects of the solution: training performance, as measured by the time to train, and cost-effectiveness of arriving at a fully converged solution. Next, we dive deeper into these two metrics with BERT 1.5B pre-training as an example.

Scaling performance and time to train

We start by measuring the performance of the BERT Large implementation as a baseline for scalability. The following table lists the measured throughput of sequences per second from 1-8 dl1.24xlarge instances (with eight accelerator devices per instance). Using the single-instance throughput as baseline, we measured the efficiency of scaling across multiple instances, which is an important lever to understand the price-performance training metric.

The following figure illustrates the scaling efficiency.

For BERT 1.5B, we modified the hyperparameters for the model in the reference repository to guarantee convergence. The effective batch size per accelerator was set to 384 (for maximum memory utilization), with micro-batches of 16 per step and 24 steps of gradient accumulation. Learning rates of 0.0015 and 0.003 were used for 8 and 16 nodes, respectively. With these configurations, we achieved convergence of the phase 1 pre-training of BERT 1.5B across 8 dl1.24xlarge instances (64 accelerators) in approximately 25 hours, and 15 hours across 16 dl1.24xlarge instances (128 accelerators). The following figure shows the average loss as a function of number of training epochs, as we scale up the number of accelerators.

With the configuration described earlier, we obtained 85% strong scaling efficiency with 64 accelerators and 83% with 128 accelerators, from a baseline of 8 accelerators in a single instance. The following table summarizes the parameters.

The following figure illustrates the scaling efficiency.

Conclusion

In this post, we evaluated support for DeepSpeed by Habana SynapseAI v1.5/v1.6 and how it helps scale LLM training on Habana Gaudi accelerators. Pre-training of a 1.5-billion-parameter BERT model took 16 hours to converge on a cluster of 128 Gaudi accelerators, with 85% strong scaling. We encourage you to take a look at the architecture demonstrated in the AWS workshop and consider adopting it to train custom PyTorch model architectures using DL1 instances.

About the authors

Mahadevan Balasubramaniam is a Principal Solutions Architect for Autonomous Computing with nearly 20 years of experience in the area of physics-infused deep learning, building, and deploying digital twins for industrial systems at scale. Mahadevan obtained his PhD in Mechanical Engineering from the Massachusetts Institute of Technology and has over 25 patents and publications to his credit.

RJ is an engineer in Search M5 team leading the efforts for building large scale deep learning systems for training and inference. Outside of work he explores different cuisines of food and plays racquet sports.

Sundar Ranganathan is the Head of Business Development, ML Frameworks on the Amazon EC2 team. He focuses on large-scale ML workloads across AWS services like Amazon EKS, Amazon ECS, Elastic Fabric Adapter, AWS Batch, and Amazon SageMaker. His experience includes leadership roles in product management and product development at NetApp, Micron Technology, Qualcomm, and Mentor Graphics.

Abhinandan Patni is a Senior Software Engineer at Amazon Search. He focuses on building systems and tooling for scalable distributed deep learning training and real time inference.

Pierre-Yves Aquilanti is Head of Frameworks ML Solutions at Amazon Web Services where he helps develop the industry’s best cloud based ML Frameworks solutions. His background is in High Performance Computing and prior to joining AWS, Pierre-Yves was working in the Oil & Gas industry. Pierre-Yves is originally from France and holds a Ph.D. in Computer Science from the University of Lille.

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