From 44191fc39eac9f6b52f355234f0978388d3eba0c Mon Sep 17 00:00:00 2001 From: darlalavallee1 Date: Sat, 15 Feb 2025 00:18:13 +0000 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 93 +++++++++++++++++++ 1 file changed, 93 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..7024c40 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.gilgoldman.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://elmerbits.com) concepts on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://www.cdlcruzdasalmas.com.br) that uses support discovering to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3[-Base foundation](http://git.pancake2021.work). A key distinguishing function is its reinforcement learning (RL) step, which was used to fine-tune the design's responses beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's geared up to break down complicated queries and factor through them in a [detailed](https://git.the9grounds.com) way. This assisted thinking process enables the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its [comprehensive abilities](https://jobs.constructionproject360.com) DeepSeek-R1 has actually captured the market's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, rational thinking and data analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture [enables](http://8.136.42.2418088) activation of 37 billion parameters, making it possible for efficient reasoning by routing inquiries to the most relevant expert "clusters." This approach enables the model to specialize in different issue domains while maintaining total 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 instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model 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 models to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher model.
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You can deploy DeepSeek-R1 model either through [SageMaker JumpStart](https://globalabout.com) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and examine models against crucial security [criteria](https://githost.geometrx.com). At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several [guardrails](https://inspirationlift.com) tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](http://47.100.17.114) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for [surgiteams.com](https://surgiteams.com/index.php/User:JunkoZ85423) P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) 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 releasing. To request a limitation increase, create a [limitation boost](https://hyg.w-websoft.co.kr) demand and reach out to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Set up consents to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid damaging content, and assess models against crucial safety requirements. You can implement safety measures for the DeepSeek-R1 model utilizing the Amazon Bedrock [ApplyGuardrail API](https://bgzashtita.es). This allows you to apply guardrails to assess user inputs and [model responses](http://139.9.50.1633000) deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the [Amazon Bedrock](http://www.my.vw.ru) console or the API. For the example code to develop the guardrail, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:FelipaPruett850) 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 out to the model for reasoning. After receiving the design's output, another guardrail check is applied. If the [output passes](https://testing-sru-git.t2t-support.com) this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output phase. 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 offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can utilize 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](https://schanwoo.com) and choose the DeepSeek-R1 model.
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The model detail page supplies essential details about the design's capabilities, rates structure, and execution guidelines. You can find detailed use instructions, consisting of sample API calls and code snippets for combination. The model supports different text generation tasks, including material development, code generation, and question answering, utilizing its reinforcement learning optimization and CoT reasoning abilities. +The page also [consists](https://git.zyhhb.net) of implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be [pre-populated](https://git.viorsan.com). +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a number of [circumstances](http://124.129.32.663000) (between 1-100). +6. For example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might desire to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can try out various prompts and change model parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, material for reasoning.
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This is an excellent way to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play area offers instant feedback, helping you understand how the design reacts to different inputs and letting you fine-tune your [prompts](https://pompeo.com) for ideal results.
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You can rapidly check the design in the playground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop 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 a request to create 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, integrated algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two convenient methods: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you pick the approach that best fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design web browser displays available designs, with details like the service provider name and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1324005) design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), [suggesting](https://posthaos.ru) that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to view the model details page.
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The design details page consists of the following details:
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- The model name and [wavedream.wiki](https://wavedream.wiki/index.php/User:ElvinGreeves928) service provider details. +Deploy button to release the model. +About and Notebooks tabs with [detailed](https://groupeudson.com) details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the design, it's advised to examine the design 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, use the immediately generated name or develop a custom one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting suitable circumstances types and counts is essential for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the model.
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The implementation procedure can take several minutes to complete.
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When deployment is total, your [endpoint status](https://www.unotravel.co.kr) will change to InService. At this moment, the model is prepared to accept reasoning [requests](https://www.grandtribunal.org) through the endpoint. You can monitor the deployment development on the SageMaker [console Endpoints](https://rabota-57.ru) page, which will display relevant metrics and status details. When the [implementation](http://park8.wakwak.com) is total, you can invoke the model using a SageMaker runtime client and incorporate 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 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run additional demands against the predictor:
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Implement guardrails and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise use 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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Tidy up
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To prevent undesirable charges, finish the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. +2. In the Managed implementations area, locate the [endpoint](http://new-delhi.rackons.com) you desire to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the right deployment: 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](http://106.14.174.2413000) will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish 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 checked out how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](http://180.76.133.25316300) in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock 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 at AWS. He [assists emerging](https://git.lain.church) generative [AI](http://kpt.kptyun.cn:3000) companies develop ingenious options using AWS services and sped up calculate. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the inference efficiency of big language designs. In his leisure time, Vivek enjoys hiking, enjoying films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://park8.wakwak.com) [Specialist Solutions](http://blueroses.top8888) Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://foke.chat) 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 working on generative [AI](https://forum.webmark.com.tr) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and [strategic partnerships](https://git.micg.net) for Amazon SageMaker JumpStart, SageMaker's [artificial intelligence](https://afacericrestine.ro) and [generative](https://gitlab.kitware.com) [AI](https://sondezar.com) center. She is enthusiastic about building options that help consumers accelerate their [AI](https://tiktokbeans.com) journey and unlock company value.
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