commit 380a77ebdb7b67aa9eea824813fb527e3531cb4d Author: margoeliott926 Date: Fri Feb 28 14:29:13 2025 +0000 Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' diff --git a/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md new file mode 100644 index 0000000..3dd86ab --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://exajob.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](https://projobfind.com) ideas on AWS.
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In this post, [wavedream.wiki](https://wavedream.wiki/index.php/User:QJEJesus8453118) we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://sharefriends.co.kr) that uses support discovering to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial differentiating function is its reinforcement knowing (RL) action, which was utilized to fine-tune the design's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually improving both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's geared up to break down complicated inquiries and reason through them in a detailed manner. This guided reasoning process allows the model to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, logical reasoning and data interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, allowing efficient reasoning by routing inquiries to the most appropriate specialist "clusters." This technique allows the design to concentrate on various problem domains while maintaining overall effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more effective designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and examine [designs](http://git.7doc.com.cn) against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and [standardizing security](http://dev.icrosswalk.ru46300) controls across your generative [AI](http://aiot7.com:3000) applications.
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Prerequisites
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To release the DeepSeek-R1 model, 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 validate you're using ml.p5e.48 xlarge for endpoint use. 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 limit boost demand and reach out to your account group.
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Because you will be deploying this model with [Amazon Bedrock](https://socialsnug.net) Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to use guardrails for content filtering.
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[Implementing guardrails](http://dev.shopraves.com) with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, [prevent harmful](https://complexityzoo.net) material, [it-viking.ch](http://it-viking.ch/index.php/User:JanetteGreig1) and examine designs against crucial [safety criteria](https://proputube.com). You can execute security procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design reactions released 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 produce the guardrail, see the GitHub repo.
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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 out to the model for reasoning. After receiving the model's output, another [guardrail check](https://www.sedatconsultlimited.com) is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections demonstrate inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation models 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 service provider and select the DeepSeek-R1 model.
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The design detail page offers essential details about the model's abilities, pricing structure, and implementation standards. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation tasks, including content creation, code generation, and question answering, utilizing its reinforcement learning optimization and [CoT reasoning](https://www.almanacar.com) capabilities. +The page also consists of release options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a number of instances (between 1-100). +6. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you might wish to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive interface where you can experiment with various prompts and change design specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, material for reasoning.
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This is an [exceptional](https://www.florevit.com) way to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, assisting you understand how the model reacts to various inputs and letting you fine-tune your triggers for optimal results.
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You can rapidly check the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the [Amazon Bedrock](https://www.atlantistechnical.com) [console](https://git.andreaswittke.de) or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends out a request to generate text based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two practical methods: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both [methods](https://ifin.gov.so) to assist you pick the [technique](https://music.elpaso.world) that finest fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following [actions](https://portalwe.net) to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model internet browser shows available designs, with details like the service provider name and model abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 [model card](http://42.194.159.649981). +Each model card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the design details page.
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The model details page consists of the following details:
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- The model name and supplier details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you release the model, it's advised to evaluate the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the automatically generated name or [develop](http://gitlab.fuxicarbon.com) a custom one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for cost and performance optimization. [Monitor](https://sujansadhu.com) your [deployment](https://wiki.lafabriquedelalogistique.fr) to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the design.
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The [release process](https://gitlab.buaanlsde.cn) can take a number of minutes to finish.
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When implementation is complete, your endpoint status will change to InService. At this point, the design is all set to accept inference demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can invoke the model using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and run from [SageMaker Studio](https://nodlik.com).
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You can run extra requests against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise 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 shown in the following code:
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Clean up
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To [prevent undesirable](https://selfyclub.com) charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. +2. In the Managed releases section, find the endpoint you wish to erase. +3. Select the endpoint, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MilesFellows9) and on the Actions menu, . +4. Verify the endpoint details to make certain you're erasing the right release: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we explored how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:HudsonFajardo7) SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](http://dgzyt.xyz3000) now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](https://git.healthathome.com.np) Marketplace, and Starting with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://nodlik.com) business build ingenious solutions using AWS services and accelerated compute. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference efficiency of large language models. In his leisure time, Vivek takes pleasure in hiking, viewing films, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://videoflixr.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://dasaram.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist [Solutions Architect](https://gitlab.edebe.com.br) working on generative [AI](https://feniciaett.com) with the Third-Party Model Science group at AWS.
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[Banu Nagasundaram](https://jobsdirect.lk) leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.fracturedcode.net) center. She is enthusiastic about building solutions that help consumers accelerate their [AI](http://www.boutique.maxisujets.net) journey and unlock organization worth.
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