katharina96f01/ideezy
1
0
Code Issues Actions Packages Projects Wiki

Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Katharina Le Hunte 2025-02-09 03:31:32 +00:00
commit a7e34d81f3
1 changed files with 93 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

93
DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md Normal file

@ -0,0 +1,93 @@
<br>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](http://gitlab.y-droid.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://git.clubcyberia.co) concepts on AWS.<br>
<br>In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled versions of the designs too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](http://code.exploring.cn) that uses reinforcement finding out to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying function is its support learning (RL) action, which was used to refine the [design's reactions](https://rsh-recruitment.nl) beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's equipped to break down complicated queries and reason through them in a detailed way. This directed reasoning procedure allows the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation design that can be [incorporated](https://saathiyo.com) into different workflows such as representatives, logical reasoning and data interpretation tasks.<br>
<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, allowing effective reasoning by routing queries to the most relevant specialist "clusters." This method permits the model to specialize in different issue domains while [maintaining](http://182.92.196.181) total effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 [xlarge circumstances](https://cagit.cacode.net) to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the thinking [abilities](https://localglobal.in) 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 describes a procedure of training smaller sized, more effective models to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher model.<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate designs against key safety requirements. 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 produce several [guardrails tailored](https://huconnect.org) to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://afacericrestine.ro) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. 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 increase, create a limit boost demand and connect to your account team.<br>
<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to introduce safeguards, avoid hazardous content, and examine designs against key [security criteria](https://git.clicknpush.ca). You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model actions released on [Amazon Bedrock](https://epspatrolscv.com) Marketplace 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.<br>
<br>The general circulation includes the following actions: First, the system receives an input for the design. This input is then [processed](https://revinr.site) through the ApplyGuardrail API. If the input passes the [guardrail](https://plamosoku.com) check, it's sent to the design for reasoning. After receiving the design'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 took place at the input or output phase. The examples showcased in the following areas show reasoning using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>[Amazon Bedrock](http://git.1473.cn) Marketplace provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br>
<br>1. On the [Amazon Bedrock](https://www.highpriceddatinguk.com) console, select Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can [utilize](https://gitea.freshbrewed.science) the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.<br>
<br>The model detail page offers essential details about the model's abilities, pricing structure, and implementation standards. You can find detailed usage guidelines, consisting of sample API calls and code bits for combination. The design supports various [text generation](https://wikibase.imfd.cl) tasks, including material creation, code generation, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:MadelaineLahey3) and question answering, [utilizing](http://www.s-golflex.kr) its reinforcement learning optimization and CoT reasoning capabilities.
The page likewise consists of release choices and licensing details to help you get begun with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.<br>
<br>You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, go into a variety of instances (in between 1-100).
6. For Instance type, select your [instance type](http://demo.qkseo.in). For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you may wish to examine these settings to align with your company's security and compliance requirements.
7. Choose Deploy to start utilizing the design.<br>
<br>When the deployment is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive interface where you can experiment with various prompts and adjust model parameters like temperature and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, content for reasoning.<br>
<br>This is an excellent way to check out the design's thinking and text generation abilities before integrating it into your applications. The playground supplies immediate feedback, [helping](https://bug-bounty.firwal.com) you understand how the model reacts to various inputs and letting you fine-tune your prompts for ideal results.<br>
<br>You can rapidly test the model in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you [require](https://gitea.ymyd.site) to get the endpoint ARN.<br>
<br>Run reasoning [utilizing guardrails](https://job.duttainnovations.com) with the [deployed](https://b52cum.com) DeepSeek-R1 endpoint<br>
<br>The following code example shows how to carry out reasoning using a [released](https://groups.chat) 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 execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends a request to create text based on a user prompt.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DannielleDixson) built-in algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](http://repo.fusi24.com3000) models to your usage case, with your information, and release them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the approach that finest matches your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The design web browser shows available designs, with details like the company name and [model capabilities](https://videoflixr.com).<br>
<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 [design card](https://gitea.mrc-europe.com).
Each model card shows crucial details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the design card to view the design details page.<br>
<br>The design details page includes the following details:<br>
<br>- The design name and supplier details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes essential details, such as:<br>
<br>- Model description.
- License details.
- Technical requirements.
- Usage standards<br>
<br>Before you deploy the design, it's recommended to examine the design details and license terms to verify compatibility with your usage case.<br>
<br>6. Choose Deploy to proceed with deployment.<br>
<br>7. For Endpoint name, use the instantly created name or [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:AthenaLucas) create a custom one.
8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of circumstances (default: 1).
Selecting appropriate instance types and counts is crucial for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that remains in place.
11. Choose Deploy to deploy the design.<br>
<br>The implementation procedure can take numerous minutes to finish.<br>
<br>When implementation is total, your endpoint status will change to [InService](https://git.augustogunsch.com). At this point, the design is prepared to accept inference requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can invoke the design using a SageMaker runtime client and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>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 approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run extra requests against the predictor:<br>
<br>Implement guardrails and run inference with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
<br>Tidy up<br>
<br>To avoid unwanted charges, complete the steps in this area to tidy up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, [wavedream.wiki](https://wavedream.wiki/index.php/User:VeroniqueBernhar) select Marketplace deployments.
2. In the Managed deployments section, locate the [endpoint](https://gitlab.rail-holding.lt) you wish to delete.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the [endpoint details](https://medicalrecruitersusa.com) to make certain you're deleting the correct deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart model you released 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.<br>
<br>Conclusion<br>
<br>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 in SageMaker Studio or [Amazon Bedrock](http://38.12.46.843333) Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](https://git.gilgoldman.com) models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://gitlab.together.social) companies develop ingenious solutions utilizing AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and [optimizing](http://git.dgtis.com) the reasoning performance of large language designs. In his spare time, Vivek delights in hiking, viewing films, and trying different cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://applykar.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://video.etowns.ir) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](http://fcgit.scitech.co.kr) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://2flab.com) hub. She is enthusiastic about constructing solutions that help customers accelerate their [AI](https://code.thintz.com) journey and unlock organization worth.<br>