Introduction
During the early 1980s, while first living in Franklin County, Massachusetts, I became active with other Vietnam veterans in response to the myriad physical, psychological, and social problems we seemed to be experiencing. I was active in a local Vietnam Veterans of America (VVA) chapter, and subsequently became director of the state-funded Western Massachusetts Agent Orange Information Project, and later, executive director of a veterans outreach center.
Through numerous conversations and formal interviews with hundreds of veterans I began to establish an empirical substantiation of the syndrome of problems that certainly is one of the tragic legacies of the Vietnam War (i.e., the war against the Vietnamese which the Vietnamese call "the American War"). Grasping the depth of the prevailing sense of shame, malaise, and deteriorating physical and mental health, I began to understand more deeply both the burden and incredible potential wisdom of the war, not just for veterans, but for the entire American society. Both the syndrome known as PTSD (Post Traumatic Stress Disorder), often delayed for a decade or more enabling the psyche time to integrate the horrible realities that the Vietnam experience possesses for many, and exposure to the most intensive application of chemical warfare in history by the Pentagon (in cahoots with seven chemical companies, including Monsanto and Dow), directly contributed to a myriad of symptoms, physical, emotional, and psychic. A pattern of extraordinary sickness and depression for this age group of young males (age 30-45 in 1984-85) is believed unprecedented in the United States.
Nineteen million gallons of Agents Orange, Blue, White, and Purple were sprayed over an area the size of Rhode Island and Massachusetts combined (over 6 million acres), applied up to 14 times the recommended domestic agricultural application rate. Subsequently, these chemicals have been banned in the U.S. due to their intense toxicity, being considered, perhaps, the most potent cancer-causing substance ever studied by the Environmental Protection Agency. It should be noted that the U.S. Department of Veterans Affairs, based on studies conducted by the National Academy of Sciences ‘ Institute of Medicine, now presumes the following conditions as service-connected for Vietnam veterans who were exposed to Agent Orange or other herbicides:
- Chloracne
- Non-Hodgkin’s lymphoma
- Soft tissue sarcoma
- Hodgkin’s disease
- Porphyria cutanea tarda
- Multiple myeloma
- Respiratory cancers (including cancers of the lung, larynx, trachea and bronchus)
- Prostate cancer
- Peripheral neuropathy (acute or subacute)
- Spina bifida in children of Vietnam veterans
Mortality Rates for Vietnam Veterans
There have been a number of representations and claims over the years that more Vietnam veterans have died from suicide since returning from the war than the 58,000-plus who died in the war. There is no certain way for determining precise data on veterans’ suicides. My involvement with many physically and mentally troubled veterans, and search of "scientific" data about the subject of mortality rates of Vietnam veterans, produced the following information by mid-1986.
When first conversing with local veterans in rural Franklin County, Massachusetts in mid-1983, they informed me that there had been four suicides of local Vietnam veterans between 1981 and June 1983.
Phil Girard, in 1982 the Senior Vice President, Agent Orange Victims International, reported at a public meeting at Greenfield, Massachusetts Community College (April 17) that their organizational research indicated that "from the end of the war to 1981 there have been 109,000 veterans who have died."
An unpublished manuscript, Vietnam Veterans, by Tom Williams, University of Denver School of Professional Psychology, April 1979, concluded that "More Vietnam veterans have died since the war by their own hand than were actually killed in Vietnam."
Testimony presented to the Massachusetts Commission on the Concerns of Vietnam veterans in Greenfield, Massachusetts on May 4, 1982, declared that "Vietnam veterans have nationally averaged 28 suicides a day since 1975, amounting to over 70,000."
"Suicide rates 33% higher than the national average rate" were reported in The Forgotten Warrior Project by John P. Wilson, Cleveland State University, 1978. This definitive study was originally titled, Identity, Ideology and Crisis: The Vietnam Veteran in Transition.
A classified VA memo dated 6/30/82 identified a total of approximately 300,000 deaths occurring among Vietnam-era veterans from 1965-1981 calculated by adding together deaths in-service with an actuarial estimate of the number of Vietnam-era veterans who have died since returning to civilian life, a much higher figure than estimated by the VA in previous reports. Research conducted by the U.S. Center for Disease Control in the early 1980s had found a number of illnesses and suicides contributing to elevated death rates for Vietnam veterans than for non-veterans in the same age group.
An alarming disparity in official VA figures reporting a dramatic decrease in the estimated number of Vietnam Era Veterans in Civilian Life from September 30, 1981 to March 31, 1983, reveals a loss of 793,000 Vietnam-era veterans in that 18-month period. The disparity was never explained. I suggest four possible explanations: (1) changed, inconsistent, and/or mistaken reporting and estimation procedures; (2) a large emigration of Vietnam-era veterans out of the U.S.; (3) a high mortality rate for Vietnam-era veterans; or (4) a combination of any and/or all of the above explanations.
On Monday, January 28, 1985, the Massachusetts Agent Orange Program of the State Office of Commissioner of Veterans’ Services released results of its study, Mortality Among Vietnam Veterans in Massachusetts, 1972-1983. The one-year study revealed that deaths due to suicides and motor vehicle accidents, along with kidney cancer, were "significantly elevated" among Vietnam veterans compared to non-veteran Massachusetts males for the study period 1972-1983.
A comprehensive research study by the University of California at San Francisco published in the March 6, 1986 issue of the New England Journal of Medicine, titled "Delayed Effects of the Military Draft on Mortality," disclosed that Vietnam veterans were 86% more likely than non-veterans to die of suicide in the years after the war, and 53% more likely to die in traffic accidents. The researchers claim that this study of California and Pennsylvania men is the first to show a cause-and-effect relationship between military service during the Vietnam War and an unusual risk of suicide.
From Summer 1983 through Summer 1985 there were seven known additional suicides of Vietnam veterans in the Franklin-Hampshire County area of Massachusetts. Because one’s veteran status is often not known at time of death, whether by suicide or other cause, and because suicides are often masked under causes listed as single-car accidents, drug or alcohol overdose, etc., actual deaths by suicide remain unknown. Other veterans whom I knew in the 1982-1985 time period died of alcohol and drug abuse.
A November 1, 1984 U.S. House Report 98-1167, Diversion of Funds from Vietnam Veterans Readjustment Counseling Program, by the Committee on Government Operations, concluded that "the suicide rate among Vietnam veterans suffering from PTSD is high
. . . not because of massive underlying neuroses, but as a result of the harsh treatment they received in Vietnam, and experiences upon returning to the U.S." Dr. Arnold, Chief of Psychiatry at the VA Medical Center in Phoenix, Arizona at the time, and an acknowledged expert on PTSD, explained to the Committee that the VA’s most rece
nt statistics indicate that while Vietnam veterans make up only about 14% of the veterans they treat, Vietnam veterans constitute 30% of the suicides of all veterans treated by the VA, over-contributing substantially to the total number of suicides of patients who are treated by the VA.
Conclusion
There is no certain way of knowing how many Vietnam veterans have died through suicide, motor vehicle "accidents," or illnesses. The available evidence, both anecdotal and scientific, however, suggests elevated mortality rates from suicides, motor vehicle accidents, and certain cancers for Vietnam veterans. In some cases the data suggests mortality rates are "significantly elevated."
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Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek AI’s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI ideas on AWS.
In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the models also.
Overview of DeepSeek-R1
DeepSeek-R1 is a big language model (LLM) established by DeepSeek AI that utilizes support learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support knowing (RL) action, which was utilized to refine the model’s responses beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it’s equipped to break down complex inquiries and reason through them in a detailed way. This directed thinking process permits the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually caught the industry’s attention as a flexible text-generation design that can be integrated into various workflows such as agents, rational thinking and data interpretation tasks.
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, making it possible for efficient inference by routing questions to the most relevant specialist “clusters.” This approach permits the model to concentrate on different problem domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective designs to mimic the habits and thinking patterns of the larger DeepSeek-R1 design, using it as an instructor model.
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate models against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative AI applications.
Prerequisites
To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you’re utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limitation boost, produce a limitation boost request and reach out to your account team.
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for material filtering.
Implementing guardrails with the ApplyGuardrail API
Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging material, and assess models against key security criteria. You can implement security procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
The general flow includes the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it’s sent to the design for inference. After getting the model’s output, another guardrail check is used. If the output passes this final check, it’s returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate reasoning using this API.
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.
The design detail page provides essential details about the model’s capabilities, rates structure, and application standards. You can discover detailed use instructions, including sample API calls and code snippets for combination. The design supports different text generation jobs, consisting of content development, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities.
The page likewise includes release options and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, pick Deploy.
You will be triggered to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, enter a number of circumstances (between 1-100).
6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you might wish to review these settings to line up with your organization’s security and compliance requirements.
7. Choose Deploy to begin utilizing the design.
When the implementation is complete, you can check DeepSeek-R1’s abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive user interface where you can try out various triggers and adjust design parameters like temperature and optimum length.
When using R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat design template for optimum outcomes. For instance, content for inference.
This is an exceptional way to check out the design’s thinking and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, assisting you understand how the model reacts to different inputs and letting you fine-tune your prompts for ideal results.
You can rapidly check the model in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
The following code example shows how to carry out inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a request to create text based on a user timely.
Deploy DeepSeek-R1 with SageMaker JumpStart
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.
Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free techniques: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s explore both techniques to help you choose the technique that finest fits your requirements.
Deploy DeepSeek-R1 through SageMaker JumpStart UI
Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be triggered to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.
The model web browser displays available designs, with details like the provider name and design capabilities.
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card shows crucial details, consisting of:
– Model name
– Provider name
– Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
5. Choose the model card to see the model details page.
The design details page includes the following details:
– The model name and provider details.
Deploy button to release the design.
About and Notebooks tabs with detailed details
The About tab consists of crucial details, such as:
– Model description.
– License details.
– Technical specifications.
– Usage standards
Before you release the model, it’s suggested to evaluate the model details and license terms to validate compatibility with your use case.
6. Choose Deploy to proceed with implementation.
7. For Endpoint name, utilize the immediately produced name or produce a custom-made one.
8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the variety of instances (default: 1).
Selecting suitable instance types and counts is essential for expense and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.
The deployment procedure can take several minutes to complete.
When deployment is total, your endpoint status will change to InService. At this point, the design is ready to accept reasoning requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can conjure up the design using a SageMaker runtime client and incorporate it with your applications.
Deploy DeepSeek-R1 using the SageMaker Python SDK
To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
You can run extra demands against the predictor:
Implement guardrails and run inference with your SageMaker JumpStart predictor
Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
Tidy up
To prevent undesirable charges, finish the steps in this section to clean up your resources.
Delete the Amazon Bedrock Marketplace implementation
If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
2. In the Managed deployments area, locate the endpoint you want to delete.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you’re deleting the proper release: 1. Endpoint name.
2. Model name.
3. Endpoint status
Delete the SageMaker JumpStart predictor
The SageMaker JumpStart model you released will sustain costs if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.
Conclusion
In this post, we checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
About the Authors
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative AI business develop innovative solutions using AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning efficiency of big language models. In his free time, Vivek delights in treking, enjoying motion pictures, and trying various foods.
Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor’s degree in Computer Science and Bioinformatics.
Jonathan Evans is a Specialist Solutions Architect working on generative AI with the Third-Party Model Science group at AWS.
Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about constructing options that assist clients accelerate their AI journey and unlock organization worth.
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