From f78df23cb5304be5e5f8af86e6dcbc30406654d4 Mon Sep 17 00:00:00 2001 From: chaunceysimcha Date: Mon, 7 Apr 2025 19:11:02 +0000 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..e8295c7 --- /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](http://42.192.14.1353000) . With this launch, you can now deploy DeepSeek [AI](https://kibistudio.com:57183)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](http://1.12.255.88) 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. You can follow comparable actions to deploy the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://ivebo.co.uk) that uses [reinforcement discovering](https://wellandfitnessgn.co.kr) to improve thinking abilities through a multi-stage training process from a DeepSeek-V3[-Base foundation](https://localglobal.in). An essential differentiating function is its reinforcement learning (RL) action, which was utilized to fine-tune the model's responses beyond the basic pre-training and fine-tuning procedure. By [including](https://git.nothamor.com3000) RL, DeepSeek-R1 can adapt better to user [feedback](https://git.jordanbray.com) and goals, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate queries and factor through them in a detailed way. This assisted reasoning process allows the model to produce more accurate, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:BerylTazewell8) transparent, and detailed answers. This model [combines RL-based](https://securityjobs.africa) fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, sensible thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing queries to the most appropriate professional "clusters." This approach enables the model to focus on different issue domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of [HBM memory](https://photohub.b-social.co.uk) in FP8 format for [reasoning](http://124.70.149.1810880). In this post, we will use an ml.p5e.48 [xlarge instance](https://www.applynewjobz.com) to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs offering](https://hotjobsng.com) 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more [effective models](https://phoebe.roshka.com) to mimic 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 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with [guardrails](https://gitcq.cyberinner.com) in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and examine models against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://asw.alma.cl) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you're utilizing 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 limitation increase, create a limitation increase demand and connect to your [account team](https://www.youmanitarian.com).
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Set up [authorizations](https://laviesound.com) 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 introduce safeguards, avoid harmful material, and evaluate designs against key security criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model responses 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 create the guardrail, see the GitHub repo.
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The basic circulation involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the final result. However, if either the input or output is intervened 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 show reasoning 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 structure 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, select Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the [InvokeModel API](http://krzsyjtj.zlongame.co.kr9004) to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.
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The design detail page provides necessary details about the design's capabilities, rates structure, and execution guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for integration. The design supports numerous text generation jobs, consisting of material creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. +The page also includes implementation alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to configure the implementation details for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an [endpoint](https://git.andrewnw.xyz) name (in between 1-50 alphanumeric characters). +5. For [Variety](https://collegestudentjobboard.com) of circumstances, enter a variety of instances (between 1-100). +6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can [configure innovative](https://goodprice-tv.com) security and infrastructure settings, including virtual private cloud (VPC) networking, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Alfie04M080) service role authorizations, and file encryption settings. For many utilize cases, the default settings will work well. However, for [production](http://www.amrstudio.cn33000) implementations, you might want 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 release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with different triggers 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 results. For example, content for inference.
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This is an exceptional way to explore the [design's reasoning](http://sites-git.zx-tech.net) and text generation capabilities before incorporating it into your applications. The play ground offers immediate feedback, helping you comprehend how the design reacts to numerous inputs and letting you tweak your triggers for optimum results.
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You can quickly test the model in the [playground](https://geoffroy-berry.fr) through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning [utilizing](http://47.105.104.2043000) a deployed DeepSeek-R1 design through [Amazon Bedrock](http://123.60.103.973000) utilizing the invoke_model and ApplyGuardrail API. You can create 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 actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends out a request to create text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial [intelligence](https://suomalaistajalkapalloa.com) (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can release 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.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker [Python SDK](https://freelancejobsbd.com). Let's check out both methods to help you pick the technique that best suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, choose 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 model [web browser](http://briga-nega.com) displays available designs, with details like the provider name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card reveals crucial details, including:
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[- Model](https://lepostecanada.com) name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to [conjure](https://www.dataalafrica.com) up the design
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5. Choose the design card to view the design details page.
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The model details page includes the following details:
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- The model name and company details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you [release](https://forum.infinity-code.com) the design, it's suggested to review the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, use the automatically produced name or develop a customized one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, [89u89.com](https://www.89u89.com/author/ramonhaywoo/) get in the number of circumstances (default: 1). +Selecting appropriate instance types and counts is important for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to [release](https://git.spitkov.hu) the design.
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The deployment procedure can take a number of minutes to finish.
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When release is total, your endpoint status will alter to InService. At this point, the design is all set to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can conjure up the design using a SageMaker runtime customer and integrate it with your [applications](https://securityjobs.africa).
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get begun 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 consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run inference with your [SageMaker JumpStart](https://owow.chat) predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Tidy up
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To prevent unwanted charges, finish the [actions](http://soho.ooi.kr) in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments. +2. In the Managed implementations section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the correct deployment: 1. [Endpoint](https://career.ltu.bg) 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 costs if you leave it running. Use the following code to erase the endpoint if you want 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 release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with [Amazon SageMaker](https://www.findnaukri.pk) JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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 helps emerging generative [AI](https://git.aionnect.com) business construct innovative services using AWS services and sped up compute. Currently, he is concentrated on developing methods for [fine-tuning](https://saghurojobs.com) and optimizing the inference efficiency of big language designs. In his leisure time, Vivek takes pleasure in treking, seeing movies, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://www.becausetravis.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](http://hoteltechnovalley.com) of focus is AWS [AI](https://gitlab.vog.media) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://git.aimslab.cn:3000) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://sites-git.zx-tech.net) hub. She is enthusiastic about building options that assist customers accelerate their [AI](http://git.liuhung.com) journey and unlock business worth.
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