sophialayton96/epspatrolscv
1
0
Code Issues Actions Packages Projects Wiki

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

Sophia Layton 2025-02-20 07:42:14 +00:00
commit 22ab02365b
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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://www.goodbodyschool.co.kr)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](http://ecoreal.kr) concepts on AWS.<br>
<br>In this post, we demonstrate how to start with DeepSeek-R1 on [Amazon Bedrock](https://www.sexmasters.xyz) Marketplace and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:RefugiaOLeary3) SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://travel-friends.net) that uses support learning to improve reasoning abilities through a [multi-stage](https://kenyansocial.com) training procedure from a DeepSeek-V3-Base structure. A key distinguishing function is its support learning (RL) action, which was used to refine the design's actions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down [intricate questions](https://ratemywifey.com) and reason through them in a detailed way. This assisted reasoning process permits the model to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to [produce structured](https://tintinger.org) reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, logical thinking and data interpretation jobs.<br>
<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion in size. The [MoE architecture](http://195.58.37.180) enables activation of 37 billion criteria, allowing efficient reasoning by routing inquiries to the most relevant specialist "clusters." This technique enables the model to specialize in various issue domains while maintaining total performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for [reasoning](https://git.corp.xiangcms.net). In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open [designs](https://careerjunction.org.in) like Qwen (1.5 B, 7B, 14B, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1077911) and 32B) and Llama (8B and 70B). Distillation refers to a [process](http://git.iloomo.com) of [training](https://trademarketclassifieds.com) smaller sized, more efficient designs to simulate the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as an instructor design.<br>
<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, [kigalilife.co.rw](https://kigalilife.co.rw/author/maritzacate/) and evaluate designs against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://git.bubblesthebunny.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify 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 ask for a limit boost, develop a [limit increase](https://gigsonline.co.za) request and reach out to your account group.<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for material filtering.<br>
<br>[Implementing guardrails](http://121.36.62.315000) with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful material, and examine designs against essential security requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.<br>
<br>The basic circulation involves the following steps: First, the system gets an input for the design. 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 applied. If the output passes this final check, it's returned as the last result. 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 areas [demonstrate inference](https://jobs.careersingulf.com) using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock 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, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane.
At the time of [composing](http://ggzypz.org.cn8664) this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.<br>
<br>The design detail page supplies vital details about the design's abilities, prices structure, and execution guidelines. You can find detailed use directions, including sample API calls and code bits for integration. The design supports various text generation jobs, including material creation, code generation, and concern answering, using its [support finding](https://gitea.thuispc.dynu.net) out optimization and CoT reasoning abilities.
The page also [consists](https://git.randomstar.io) of deployment alternatives and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.<br>
<br>You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, enter a variety of instances (in 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 facilities settings, including virtual personal cloud (VPC) networking, service role permissions, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you may desire to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.<br>
<br>When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive interface where you can experiment with different prompts and adjust design criteria like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, material for reasoning.<br>
<br>This is an outstanding method to check out the design's reasoning and text generation capabilities before integrating it into your applications. The play area supplies immediate feedback, helping you understand how the design reacts to various inputs and letting you fine-tune your triggers for optimal outcomes.<br>
<br>You can rapidly test the model in the play ground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run inference using [guardrails](https://jollyday.club) with the released DeepSeek-R1 endpoint<br>
<br>The following code example shows how to carry out inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock [console](http://118.195.204.2528080) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a demand to generate 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, built-in algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://employmentabroad.com) models to your use case, with your information, and deploy them into production using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free methods: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both [techniques](https://catvcommunity.com.tr) to assist you select the [approach](http://saehanfood.co.kr) that finest suits your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:<br>
<br>1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br>
<br>The design internet browser displays available models, with details like the service provider name and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:RositaLiu5) model capabilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card reveals crucial details, including:<br>
<br>[- Model](http://candidacy.com.ng) name
[- Provider](http://47.100.81.115) name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if applicable), showing that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model<br>
<br>5. Choose the design card to view the model [details](http://dndplacement.com) page.<br>
<br>The design details page includes the following details:<br>
<br>- The design name and supplier details.
Deploy button to release the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of essential details, such as:<br>
<br>- Model description.
- License details.
- Technical [requirements](https://jmusic.me).
- Usage guidelines<br>
<br>Before you release the model, it's recommended to evaluate the model details and license terms to verify compatibility with your usage case.<br>
<br>6. Choose Deploy to proceed with implementation.<br>
<br>7. For Endpoint name, utilize the [instantly produced](http://chotaikhoan.me) name or create a custom-made one.
8. For example type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of circumstances (default: 1).
Selecting suitable circumstances types and counts is crucial for cost and efficiency optimization. Monitor your [implementation](https://nkaebang.com) to adjust these settings as needed.Under Inference type, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. For this design, we highly suggest adhering to SageMaker JumpStart [default](http://123.56.193.1823000) settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the design.<br>
<br>The implementation procedure can take numerous minutes to finish.<br>
<br>When release is total, your endpoint status will change to InService. At this point, the design is ready to accept inference requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can invoke the model using a SageMaker runtime client and integrate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is [offered](https://projob.co.il) in the Github here. You can clone the note pad and range from SageMaker Studio.<br>
<br>You can run extra demands against the predictor:<br>
<br>Implement guardrails and run [reasoning](https://imidco.org) 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 using the Amazon Bedrock console or the API, and execute it as revealed in the following code:<br>
<br>Clean up<br>
<br>To [prevent undesirable](https://wiki.uqm.stack.nl) charges, finish the actions in this area to tidy up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you [deployed](https://bpx.world) the model using Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
2. In the Managed implementations area, find the endpoint you desire to erase.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're erasing the correct deployment: 1. [Endpoint](https://git.slegeir.com) name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you deployed will sustain costs 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](https://manchesterunitedfansclub.com) out how you can access and deploy 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](https://jobz0.com) with Amazon SageMaker JumpStart designs, SageMaker JumpStart [pretrained](https://gitea.gai-co.com) 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://httelecom.com.cn:3000) companies develop innovative solutions utilizing AWS services and sped up compute. Currently, he is focused on [developing methods](https://catvcommunity.com.tr) for fine-tuning and optimizing the inference efficiency of big language models. In his leisure time, Vivek enjoys treking, viewing films, and trying different cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://www.jobzalerts.com) Specialist Solutions Architect with the Third-Party Model [Science](http://94.224.160.697990) group at AWS. His area of focus is AWS [AI](http://kiwoori.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions [Architect](http://metis.lti.cs.cmu.edu8023) working on generative [AI](https://adremcareers.com) with the Third-Party Model Science team at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://bgzashtita.es) hub. She is enthusiastic about constructing services that help clients accelerate their [AI](https://www.shopes.nl) [journey](https://mediascatter.com) and unlock organization worth.<br>