From 20ad212c3bd2867faae8dc2b51e2fc6a37204fd5 Mon Sep 17 00:00:00 2001 From: Aracely Barbosa Date: Mon, 17 Feb 2025 20:32:27 +0800 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md 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..3d1dd28 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,26 @@ +
Today, we are thrilled 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 release DeepSeek [AI](https://esunsolar.in)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://git.xxb.lttc.cn) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://improovajobs.co.za). You can follow comparable steps to release the distilled versions of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by [DeepSeek](http://118.89.58.193000) [AI](https://luckyway7.com) that utilizes reinforcement finding out to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying function is its [reinforcement knowing](https://zidra.ru) (RL) action, which was used to refine the design's reactions beyond the standard pre-training and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:YvonneBlubaugh) fine-tuning procedure. By including RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's equipped to break down intricate queries and factor through them in a detailed manner. This assisted thinking process allows the model to produce more precise, transparent, and detailed answers. This [design combines](http://gitlab.gavelinfo.com) RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, sensible thinking and information interpretation jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, enabling effective inference by routing questions to the most pertinent expert "clusters." This approach enables the model to concentrate on various issue domains while maintaining overall effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize 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 designs bring the [thinking capabilities](https://git.guildofwriters.org) of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.
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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 recommend deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and [wavedream.wiki](https://wavedream.wiki/index.php/User:DannieSalter0) examine designs against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on [SageMaker JumpStart](https://tuxpa.in) and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user [experiences](https://www.suntool.top) and standardizing safety controls across your generative [AI](http://www.grainfather.co.nz) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect 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 instance in the AWS Region you are releasing. To request a limit boost, produce a limitation boost request and connect to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid [hazardous](http://123.206.9.273000) content, and examine models against key safety criteria. You can implement safety procedures for the DeepSeek-R1 model utilizing the [Amazon Bedrock](https://deadreckoninggame.com) ApplyGuardrail API. This permits you to use guardrails to examine user inputs and model actions deployed on Amazon Bedrock [Marketplace](https://dongochan.id.vn) and SageMaker JumpStart. You can produce 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 involves 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 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 occurred at the input or output stage. The examples showcased in the following sections demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and [specialized foundation](http://120.26.108.2399188) designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 model.
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The design detail page supplies essential details about the model's abilities, rates structure, and execution standards. You can find detailed usage instructions, including [sample API](https://optimaplacement.com) calls and code snippets for integration. The design supports various text generation tasks, consisting of content creation, code generation, and question answering, using its reinforcement discovering optimization and [CoT reasoning](http://114.55.169.153000) abilities. +The page also consists of implementation options and [licensing details](http://121.40.194.1233000) to assist you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be [prompted](https://www.suntool.top) to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of circumstances (between 1-100). +6. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile \ No newline at end of file